Childhood obesity: A review of current and future management options

Affiliations.

  • 1 Department of Paediatric Endocrinology, Alder Hey Children's Hospital, Liverpool, UK.
  • 2 Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University of London, London, UK.
  • 3 Department of Paediatric Dietetics, Alder Hey Children's Hospital, Liverpool, UK.
  • 4 Department of Paediatric Clinical Psychology, Alder Hey Children's Hospital, Liverpool, UK.
  • PMID: 34750858
  • DOI: 10.1111/cen.14625

Obesity is becoming increasingly prevalent in paediatric populations worldwide. In addition to increasing prevalence, the severity of obesity is also continuing to rise. Taken together, these findings demonstrate a worrying trend and highlight one of the most significant challenges to public health. Childhood obesity affects multiple organs in the body and is associated with both significant morbidity and ultimately premature mortality. The prevalence of complications associated with obesity, including dyslipidaemia, hypertension, fatty liver disease and psychosocial complications are becoming increasingly prevalent within the paediatric populations. Treatment guidelines currently focus on intervention with lifestyle and behavioural modifications, with pharmacotherapy and surgery reserved for patients who are refractory to such treatment. Research into adult obesity has established pharmacological novel therapies, which have been approved and established in clinical practice; however, the research and implementation of such therapies in paediatric populations have been lagging behind. Despite the relative lack of widespread research in comparison to the adult population, newer therapies are being trialled, which should allow a greater availability of treatment options for childhood obesity in the future. This review summarizes the current evidence for the management of obesity in terms of medical and surgical options. Both future therapeutic agents and those which cause weight loss but have an alternative indication are also included and discussed as part of the review. The review summarizes the most recent research for each intervention and demonstrates the potential efficacy and limitations of each treatment option.

Keywords: BMI; childhood obesity; lifestyle interventions; paediatrics; pharmacotherapy.

© 2021 John Wiley & Sons Ltd.

Publication types

  • Medical History Taking
  • Pediatric Obesity* / therapy
  • Weight Loss

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  • Published: 18 May 2023

Child and adolescent obesity

  • Natalie B. Lister   ORCID: orcid.org/0000-0002-9148-8632 1 , 2 ,
  • Louise A. Baur   ORCID: orcid.org/0000-0002-4521-9482 1 , 3 , 4 ,
  • Janine F. Felix 5 , 6 ,
  • Andrew J. Hill   ORCID: orcid.org/0000-0003-3192-0427 7 ,
  • Claude Marcus   ORCID: orcid.org/0000-0003-0890-2650 8 ,
  • Thomas Reinehr   ORCID: orcid.org/0000-0002-4351-1834 9 ,
  • Carolyn Summerbell 10 &
  • Martin Wabitsch   ORCID: orcid.org/0000-0001-6795-8430 11  

Nature Reviews Disease Primers volume  9 , Article number:  24 ( 2023 ) Cite this article

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  • Paediatric research

The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. Obesity arises when a mix of genetic and epigenetic factors, behavioural risk patterns and broader environmental and sociocultural influences affect the two body weight regulation systems: energy homeostasis, including leptin and gastrointestinal tract signals, operating predominantly at an unconscious level, and cognitive–emotional control that is regulated by higher brain centres, operating at a conscious level. Health-related quality of life is reduced in those with obesity. Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. Treatment incorporates a respectful, stigma-free and family-based approach involving multiple components, and addresses dietary, physical activity, sedentary and sleep behaviours. In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. Prevention of obesity requires a whole-system approach and joined-up policy initiatives across government departments. Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities.

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Introduction

The prevalence of child and adolescent obesity remains high and continues to rise in low-income and middle-income countries (LMICs) at a time when these regions are also contending with under-nutrition in its various forms 1 , 2 . In addition, during the COVID-19 pandemic, children and adolescents with obesity have been more likely to have severe COVID-19 requiring hospitalization and mechanical ventilation 3 . At the same time, the pandemic was associated with rising levels of childhood obesity in many countries. These developments are concerning, considering that recognition is also growing that paediatric obesity is associated with a range of immediate and long-term negative health outcomes, a decreased quality of life 4 , 5 , an increased presentation to health services 6 and increased economic costs to individuals and society 7 .

Body weight is regulated by a range of energy homeostatic and cognitive–emotional processes and a multifactorial interplay of complex regulatory circuits 8 . Paediatric obesity arises when multiple environmental factors — covering preconception and prenatal exposures, as well as broader changes in the food and physical activity environments — disturb these regulatory processes; these influences are now widespread in most countries 9 .

The treatment of obesity includes management of obesity-associated complications, a developmentally sensitive approach, family engagement, and support for long-term behaviour changes in diet, physical activity, sedentary behaviours and sleep 10 . New evidence highlights the role, in adolescents with more severe obesity, of bariatric surgery 11 and pharmacotherapy, particularly the potential for glucagon-like peptide 1 (GLP1) receptor agonists 12 .

Obesity prevention requires a whole-system approach, with policies across all government and community sectors systematically taking health into account, avoiding harmful health impacts and decreasing inequity. Programmatic prevention interventions operating ‘downstream’ at the level of the child and family, as well as ‘upstream’ interventions at the level of the community and broader society, are required if a step change in tackling childhood obesity is to be realized 13 , 14 .

In this Primer, we provide an overview of the epidemiology, causes, pathophysiology and consequences of child and adolescent obesity. We discuss diagnostic considerations, as well as approaches to its prevention and management. Furthermore, we summarize effects of paediatric obesity on quality of life, and open research questions.

Epidemiology

Definition and prevalence.

The World Health Organization (WHO) defines obesity as “abnormal or excessive fat accumulation that presents a risk to health” 15 . Paediatric obesity is defined epidemiologically using BMI, which is adjusted for age and sex because of the physiological changes in BMI during growth 16 . Global prevalence of paediatric obesity has risen markedly over the past four decades, initially in high-income countries (HICs), but now also in many LMICs 1 .

Despite attempts to standardize the epidemiological classification, several definitions of paediatric obesity are in use; hence, care is needed when comparing prevalence rates. The 2006 WHO Child Growth Standard, for children aged 0 to 5 years, is based on longitudinal observations of multiethnic populations of children with optimal infant feeding and child-rearing conditions 17 . The 2007 WHO Growth Reference is used for the age group 5–19 years 18 , and the 2000 US Centers for Disease Control and Prevention (CDC) Growth Charts for the age group 2–20 years 19 . The WHO and CDC definitions based on BMI-for-age charts are widely used, including in clinical practice. By contrast, the International Obesity Task Force (IOTF) definition, developed from nationally representative BMI data for the age group 2–18 years from six countries, is used exclusively for epidemiological studies 20 .

For the age group 5–19 years, between 1975 and 2016, the global prevalence of obesity (BMI >2 standard deviations (SD) above the median of the WHO growth reference) increased around eightfold to 5.6% in girls and 7.8% in boys 1 . Rates have plateaued at high levels in many HICs but have accelerated in other regions, particularly in parts of Asia. For the age group 2–4 years, between 1980 and 2015, obesity prevalence (IOTF definition, equivalent to an adult BMI of ≥30 kg/m 2 ) increased from 3.9% to 7.2% in boys and from 3.7% to 6.4% in girls 21 . Obesity prevalence is highest in Polynesia and Micronesia, the Middle East and North Africa, the Caribbean and the USA (Fig.  1 ). Variations in prevalence probably reflect different background levels of obesogenic environments, or the sum total of the physical, economic, policy, social and cultural factors that promote obesity 22 . Obesogenic environments include those with decreased active transport options, a ubiquity of food marketing directed towards children, and reduced costs and increased availability of nutrient-poor, energy-dense foods. Particularly in LMICs, the growth of urbanization, new forms of technology and global trade have led to reduced physical activity at work and leisure, a shift towards Western diets, and the expansion of transnational food and beverage companies to shape local food systems 23 .

figure 1

Maps showing the proportions of children and adolescents living with overweight or obesity (part  a , boys; part b , girls) according to latest available data from the Global Obesity Observatory . Data might not be comparable between countries owing to differences in survey methodology.

The reasons for varying sex differences in prevalence in different countries are unclear but may relate to cultural variations in parental feeding practices for boys and girls and societal ideals of body size 24 . In 2016, obesity in the age group 5–19 years was more prevalent in girls than in boys in sub-Saharan Africa, Oceania and some middle-income countries in other regions, whereas it was more prevalent in boys than in girls in all HICs, and in East and South-East Asia 21 . Ethnic and racial differences in obesity prevalence within countries are often assumed to mirror variations in social deprivation and other social determinants of obesity. However, an independent effect of ethnicity even after adjustment for socioeconomic status has been documented in the UK, with Black and Asian boys in primary school having higher prevalence of obesity than white boys 25 .

Among individuals with obesity, very high BMI values have become more common in the past 15 years. The prevalence of severe obesity (BMI ≥120% of the 95th percentile (CDC definition), or ≥35 kg/m 2 at any age 26 , 27 ) has increased in many HICs, accounting for one-quarter to one-third of those with obesity 28 , 29 . Future health risks of paediatric obesity in adulthood are well documented. For example, in a data linkage prospective study in Israel with 2.3 million participants who had BMI measured at age 17 years, those with obesity (≥95th percentile BMI for age) had a much higher risk of death from coronary heart disease (HR 4.9, 95% CI 3.9–6.1), stroke (HR 2.6, 95% CI 1.7–4.1) and sudden death (HR 2.1, 95% CI 1.5–2.9) compared with those whose BMI fell between the 5th and 24th percentiles 30 .

Causes and risk factors

Early life is a critical period for childhood obesity development 9 , 31 , 32 , 33 . According to the Developmental Origins of Health and Disease framework, the early life environment may affect organ structure and function and influence health in later life 34 , 35 . Meta-analyses have shown that preconception and prenatal environmental exposures, including high maternal pre-pregnancy BMI and, to a lesser extent, gestational weight gain, as well as gestational diabetes and maternal smoking, are associated with childhood obesity, potentially through effects on the in utero environment 33 , 36 , 37 , 38 . Paternal obesity is also associated with childhood obesity 33 . Birthweight, reflecting fetal growth, is a proxy for in utero exposures. Both low and high birthweights are associated with later adiposity, with high birthweight linked to increased BMI and low birthweight to central obesity 33 , 39 .

Growth trajectories in early life are important determinants of later adiposity. Rapid weight gain in early childhood is associated with obesity in adolescence 32 . Also, later age and higher BMI at adiposity peak (the usual peak in BMI around 9 months of age), as well as earlier age at adiposity rebound (the lowest BMI reached between 4 and 7 years of age), are associated with increased adolescent and adult BMI 40 , 41 . Specific early life nutritional factors, including a lower protein content in formula food, are consistently associated with a lower risk of childhood obesity 42 , 43 . These also include longer breastfeeding duration, which is generally associated with a lower risk of childhood obesity 42 . However, some controversy exists, as these effects are affected by multiple sociodemographic confounding factors and their underlying mechanisms remain uncertain 44 . Some studies comparing higher and lower infant formula protein content have reported that the higher protein group have a greater risk of subsequent obesity, especially in early childhood 41 , 42 ; however, one study with a follow-up period until age 11 years found no significant difference in the risk of obesity, but an increased risk of overweight in the high protein group was still observed 42 , 43 , 45 . A high intake of sugar-sweetened beverages is associated with childhood obesity 33 , 46 .

Many other behavioural factors are associated with an increased risk of childhood obesity, including increased screen time, short sleep duration and poor sleep quality 33 , 47 , reductions in physical activity 48 and increased intake of energy-dense micronutrient-poor foods 49 . These have been influenced by multiple changes in the past few decades in the broader social, economic, political and physical environments, including the widespread marketing of food and beverages to children, the loss of walkable green spaces in many urban environments, the rise in motorized transport, rapid changes in the use of technology, and the move away from traditional foods to ultraprocessed foods.

Obesity prevalence is inextricably linked to relative social inequality, with data suggesting a shift in prevalence over time towards those living with socioeconomic disadvantage, and thus contributes to social inequalities. In HICs, being in lower social strata is associated with a higher risk of obesity, even in infants and young children 50 , whereas the opposite relationship occurs in middle-income countries 51 . In low-income countries, the relationship is variable, and the obesity burden seems to be across socioeconomic groups 52 , 53 .

Overall, many environmental, lifestyle, behavioural and social factors in early life are associated with childhood obesity. These factors cannot be seen in isolation but are part of a complex interplay of exposures that jointly contribute to increased obesity risk. In addition to multiple prenatal and postnatal environmental factors, genetic variants also have a role in the development of childhood obesity (see section Mechanisms/pathophysiology).

Comorbidities and complications

Childhood obesity is associated with a wide range of short-term comorbidities (Fig.  2 ). In addition, childhood obesity tracks into adolescence and adulthood and is associated with complications across the life course 32 , 41 , 54 , 55 .

figure 2

Obesity in children and adolescents can be accompanied by various other pathologies. In addition, childhood obesity is associated with complications and disorders that manifest in adulthood (red box).

Increased BMI, especially in adolescence, is linked to a higher risk of many health outcomes, including metabolic disorders, such as raised fasting glucose, impaired glucose tolerance, type 2 diabetes mellitus (T2DM), metabolic syndrome and fatty liver disease 56 , 57 , 58 , 59 . Other well-recognized obesity-associated complications include coronary heart disease, asthma, obstructive sleep apnoea syndrome (itself associated with metabolic dysfunction and inflammation) 60 , orthopaedic complications and a range of mental health outcomes including depression and low self-esteem 27 , 55 , 57 , 61 , 62 , 63 .

A 2019 systematic review showed that children and adolescents with obesity are 1.4 times more likely to have prediabetes, 1.7 times more likely to have asthma, 4.4 times more likely to have high blood pressure and 26.1 times more likely to have fatty liver disease than those with a healthy weight 64 . In 2016, it was estimated that, at a global level by 2025, childhood obesity would lead to 12 million children aged 5–17 years with glucose intolerance, 4 million with T2DM, 27 million with hypertension and 38 million with fatty liver disease 65 . These high prevalence rates have implications for both paediatric and adult health services.

Mechanisms/pathophysiology

Body weight regulation.

Body weight is regulated within narrow limits by homeostatic and cognitive–emotional processes and a multifactorial interplay of hormones and messenger substances in complex regulatory circuits (Fig.  3 ). When these regulatory circuits are disturbed, an imbalance between energy intake and expenditure leads to obesity or to poor weight gain. As weight loss is much harder to achieve than weight gain in the long term due to the regulation circuits discussed below, the development of obesity is encouraged by modern living conditions, which enable underlying predispositions for obesity to become manifest 8 , 66 .

figure 3

Body weight is predominantly regulated by two systems: energy homeostasis and cognitive–emotional control. Both homeostatic and non-homeostatic signals are processed in the brain, involving multiple hormone and receptor cascades 217 , 218 , 219 . This overview depicts the best-known regulatory pathways. The homeostatic system, which is mainly regulated by brain centres in the hypothalamus and brainstem, operates on an unconscious level. Both long-term signals from the energy store in adipose tissue (for example, leptin) and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status. During gastric distension or after the release of gastrointestinal hormones (multiple receptors are involved) and insulin, a temporary feeling of fullness is induced. The non-homeostatic or hedonic system is regulated by higher-level brain centres and operates at the conscious level. After integration in the thalamus, homeostatic signals are combined with stimuli from the environment, experiences and emotions; emotional and cognitive impulses are then induced to control food intake. Regulation of energy homeostasis in the hypothalamus involves two neuron types of the arcuate nucleus: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). Leptin stimulates these neurons via specific leptin receptors (LEPR) inducing anabolic effects in case of decreasing leptin levels and catabolic effects in case of increasing leptin levels. Leptin inhibits the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production resulting in the feeling of hunger. Leptin directly stimulates POMC production in POMC neurons. POMC is cleaved into different hormone polypeptides including α-melanocyte-stimulating hormone which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. CART, cocaine and amphetamine responsive transcript; IR, insulin receptor.

In principle, there are two main systems in the brain which regulate body weight 8 , 66 (Fig.  3 ): energy homeostasis and cognitive–emotional control. Energy homeostasis is predominantly regulated by brain centres in the hypothalamus and brainstem and operates at an unconscious level. Both long-term signals from the adipose tissue energy stores and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status 8 , 66 . For example, negative energy balance leading to reduced fat mass results in reduced leptin levels, a permanently reduced urge to exercise and an increased feeling of hunger. During gastric distension or after the release of gastrointestinal hormones and insulin, a temporary feeling of fullness is induced 8 , 66 . Cognitive–emotional control is regulated by higher brain centres and operates at a conscious level. Here, the homeostatic signals are combined with stimuli from the environment (sight, smell and taste of food), experiences and emotions 8 , 66 . Disorders at the level of cognitive–emotional control mechanisms include emotional eating as well as eating disorders. For example, the reward areas in the brain of people with overweight are more strongly activated by high-calorie foods than those in the brain of people with normal weight 67 . Both systems interact with each other, and the cognitive–emotional system is strongly influenced by the homeostatic control circuits.

Disturbances in the regulatory circuits of energy homeostasis can be genetically determined, can result from disease or injury to the regulatory centres involved, or can be caused by prenatal programming 8 , 66 . If the target value of body weight has been shifted, the organism tries by all means (hunger, drive) to reach the desired higher weight. These disturbed signals of the homeostatic system can have an imperative, irresistible character, so that a conscious influence on food intake is no longer effectively possible 8 , 66 . The most important disturbances of energy homeostasis are listed in Table  1 .

The leptin pathway

The peptide hormone leptin is primarily produced by fat cells. Its production depends on the amount of adipose tissue and the energy balance. A negative energy balance during fasting results in a reduction of circulating leptin levels by 50% after 24 h (ref. 68 ). In a state of weight loss, leptin production is reduced 69 . In the brain, leptin stimulates two neuron types of the arcuate nucleus in the hypothalamus via specific leptin receptors: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). High leptin levels inhibit the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production. By contrast, leptin directly stimulates POMC production in POMC neurons (Fig.  3 ). POMC is a hormone precursor that is cleaved into different hormone polypeptides by specific enzymes, such as prohormone convertase 1 (PCSK1). This releases α-melanocyte-stimulating hormone (α-MSH) which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. Rare, functionally relevant mutations in the genes for leptin and leptin receptor, POMC , PCSK1/3 or MC4R lead to extreme obesity in early childhood. These forms of obesity are potential indications for specific pharmacological treatments, for example setmelanotide 70 , 71 . MC4R mutations are the most common cause of monogenic obesity, as heterozygous mutations can be symptomatic depending on the functional impairment and with variable penetrance and expression. Other genes have been identified, in which rare heterozygous pathological variants are also associated with early onset obesity (Table  1 ).

Pathological changes in adipose tissue

Adipose tissue can be classified into two types, white and brown adipose tissue. White adipose tissue comprises unilocular fat cells and brown adipose tissue contains multilocular fat cells, which are rich in mitochondria 72 . A third type of adipocyte, beige adipocytes, within the white adipose tissue are induced by prolonged exposure to cold or adrenergic signalling, and show a brown adipocyte-like morphology 72 . White adipose tissue has a large potential to change its volume to store energy and meet the metabolic demands of the body. The storage capacity and metabolic function of adipose tissue depend on the anatomical location of the adipose tissue depot. Predominant enlargement of white adipose tissue in the visceral, intra-abdominal area (central obesity) is associated with insulin resistance and an increased risk of metabolic disease development before puberty. Accumulation of adipose tissue in the hips and flanks has no adverse effect and may be protective against metabolic syndrome. In those with obesity, adipose tissue is characterized by an increased number of adipocytes (hyperplasia), which originate from tissue-resident mesenchymal stem cells, and by enlarged adipocytes (hypertrophy) 73 . Adipocytes with a very large diameter reach the limit of the maximal oxygen diffusion distance, resulting in hypoxia, the development of an inflammatory expression profile (characterized by, for example, leptin, TNF and IL-6) and adipocyte necrosis, triggering the recruitment of leukocytes. Resident macrophages switch from the anti-inflammatory M2 phenotype to a pro-inflammatory M1 phenotype, which is associated with insulin resistance, further promoting local sterile inflammation and the development of fibrotic adipose tissue. This process limits the expandability of the adipose tissue for further storage of triglycerides. In the patient, the increase in fat mass in obesity is associated with insulin resistance and systemic low-grade inflammation characterized by elevated serum levels of C-reactive protein and pro-inflammatory cytokines. The limitation of adipose tissue expandability results in storage of triglycerides in other organs, such as the liver, muscle and pancreas 74 .

Genetics and epigenetics in the general population

Twin studies have found heritability estimates for BMI of up to 70% 75 , 76 . In contrast to rare monogenic forms of obesity, which are often caused by a single genetic defect with a large effect, the genetic background of childhood obesity in the general population is shaped by the joint effects of many common genetic variants, each of which individually makes a small contribution to the phenotype. For adult BMI, genome-wide association studies, which examine associations of millions of such variants across the genome at the same time, have identified around 1,000 genetic loci 77 . The largest genome-wide association studies in children, which include much smaller sample sizes of up to 60,000 children, have identified 25 genetic loci for childhood BMI and 18 for childhood obesity, the majority of which overlap 78 , 79 . There is also a clear overlap with genetic loci identified in adults, for example for FTO , MC4R and TMEM18 , but this overlap is not complete, some loci are specific to early life BMI, or have a relatively larger contribution in childhood 78 , 79 , 80 . These findings suggest that biological mechanisms underlying obesity in childhood are mostly similar to those in adulthood, but the relative influence of these mechanisms may differ at different phases of life.

The role of epigenetic processes in childhood and adolescent obesity has gained increasing attention. In children, several studies found associations between DNA methylation and BMI 81 , 82 , 83 , 84 , but a meta-analysis including data from >4,000 children identified only minimal associations 85 . Most studies support the hypothesis that DNA methylation changes are predominantly a consequence rather than a cause of obesity, which may explain the lower number of identified (up to 12) associations in children, in whom duration of exposure to a higher BMI is shorter than in adults, in whom associations with DNA methylation at hundreds of sites have been identified 85 , 86 , 87 . In addition to DNA methylation, some specific circulating microRNAs have been found to be associated with obesity in childhood 84 .

The field of epigenetic studies in childhood obesity is relatively young and evolving quickly. Future studies will need to focus on defining robust associations in blood as well as other tissues and on identifying cause-and-effect relationships. In addition, other omics, such as metabolomics and proteomics, are promising areas that may contribute to an improved aetiological understanding or may provide biological signatures that can be used as predictive or prognostic markers of childhood obesity and its comorbidities.

Parental obesity and childhood obesity

There is an established link between increased parental BMI and increased childhood BMI 88 , 89 . This link may be due to shared genetics, shared environment, a direct intrauterine effect of maternal BMI or a combination of these factors. In the case of shared genetics, the child inherits BMI-increasing genetic variants from one or both parents. Shared environmental factors, such as diet or lifestyle, may also contribute to an increased BMI in both parents and child. In addition, maternal obesity might create an intrauterine environment that programmes metabolic processes in the fetus, which increases the risk of childhood obesity. Some studies show larger effects of maternal than paternal BMI, indicating a potential causal intrauterine mechanism of maternal obesity, but evidence showing similar maternal and paternal effects is increasing. The data may indicate that there is only a limited direct intrauterine effect of maternal obesity on childhood obesity; rather, genetic effects inherited from the mother or father, or both, and/or shared environmental factors may contribute to childhood obesity risk 90 , 91 , 92 , 93 , 94 , 95 .

Diagnosis, screening and prevention

Diagnostic work-up.

The extent of overweight in clinical practice is estimated using BMI based on national charts 96 , 97 , 98 , 99 , 100 . Of note, the clinical classification of overweight or obesity differ depending on the BMI charts used and national recommendations; hence, local guidelines should be referred to. For example, the US CDC Growth Charts and several others use the 85th and 95th centile cut-points to denote overweight and obesity, respectively 19 . The WHO Growth Reference for children aged 5–19 years defines cut-points for overweight and obesity as a BMI-for-age greater than +1 and +2 SDs for BMI for age, respectively 18 . For children <5 years of age, overweight and obesity are defined as weight-for-height greater than +2 and +3 SDs, respectively, above the WHO Child Growth Standards median 17 . The IOTF and many countries in Europe use cut-points of 85th, 90th and 97th to define overweight, obesity and extreme obesity 26 .

BMI as an indirect measurement of body fat has some limitations; for example, pronounced muscle tissue leads to an increase in BMI, and BMI is not independent of height. In addition, people of different ethnicities may have different cut-points for obesity risk; for example, cardiometabolic risk occurs at lower BMI values in individuals with south Asian than in those with European ancestry 101 . Thus, BMI is best seen as a convenient screening tool that is supplemented by clinical assessment and investigations.

Other measures of body fat may help differentiate between fat mass and other tissues. Some of these tools are prone to low reliability, such as body impedance analyses (high day-to-day variation and dependent on level of fluid consumption) or skinfold thickness (high inter-observer variation), or are more expensive or invasive, such as MRI, CT or dual-energy X-ray absorptiometry, than simpler measures of body composition or BMI assessment.

Primary diseases rarely cause obesity in children and adolescents (<2%) 102 . However, treatable diseases should be excluded in those with obesity. A suggested diagnostic work-up is summarized in Fig.  4 . Routine measurement of thyroid-stimulating hormone (TSH) is not recommended 96 . Moderately elevated TSH levels (usually <10 IU/l) are frequently observed in obesity and are a consequence, and not a cause, of obesity 103 . In a growing child with normal height velocity, a normal BMI at the age of 2 years and normal cognitive development, no further diagnostic steps are necessary to exclude primary diseases 96 , 104 .

figure 4

Concerning findings from a detailed medical history and physical examination will lead to further examinations. In individuals with early onset, extreme obesity (before age 3 years) and signs of hyperphagia, serum leptin level should be measured to rule out the extremely rare condition of congenital leptin deficiency. In individuals with normal or high leptin levels, genetic testing is indicated to search for monogenetic obesity. In individuals with intellectual disability, a syndromic disease may be present. Signs of impaired growth velocity or the history of central nervous system trauma or surgery will result in deeper endocrine evaluation and/or brain MRI. BDNF , brain-derived neurotropic factor; FT4, free thyroxin; KSR2 , kinase suppressor of ras 2; MC4R , melanocortin 4 receptor; POMC , pro-opiomelanocortin; SH2B1 , Src-homology 2 (SH2) B adapter protein 1; SIM1 , single-minded homologue 1; TSH, thyroid-stimulating hormone.

Clinical findings which need no further examination include pseudogynaecomastia (adipose tissue mimicking breast development; differentiated from breast tissue by ultrasonography), striae (caused by rapid weight increase) and a hidden penis in suprapubic adipose tissue (differentiated from micropenis by measurement of stretched penis length while pressing down on the suprapubic adipose tissue) 96 , 105 . Girls with obesity tend to have an earlier puberty onset (usually at around 8–9 years of age) and boys with severe obesity may have a delayed puberty onset (usually at around 13–14 years of age) 106 . Thus, if pubertal onset is slightly premature in girls or slightly delayed in boys, no further endocrine assessment is necessary.

Assessment of obesity-associated comorbidities

A waist to height ratio of >0.5 is a simple tool to identify central obesity 107 , 108 . Screening for cardiometabolic risk factors and fatty liver disease is recommended, especially in adolescents, and in those with more severe obesity or central adiposity, a strong family history of T2DM or premature heart disease, or relevant clinical symptoms, such as high blood pressure or acanthosis nigricans 96 , 97 , 98 , 99 , 109 . Investigations generally include fasting glucose levels, lipid profile, liver function and glycated haemoglobin, and might include an oral glucose tolerance test, polysomnography, and additional endocrine tests for polycystic ovary syndrome 96 , 97 , 98 , 99 .

T2DM in children and adolescents often occurs in the presence of a strong family history and may not be related to obesity severity 110 . T2DM onset usually occurs during puberty, a physiological state associated with increased insulin resistance 111 and, therefore, screening for T2DM should be considered in children and adolescents with obesity and at least one risk factor (family history of T2DM or features of metabolic syndrome) starting at pubertal onset 112 . As maturity-onset diabetes of the young (MODY) type II and type III are more frequent than T2DM in children and adolescents in many ethnicities, genetic screening for MODY may be appropriate 112 . Furthermore, type 1 diabetes mellitus (T1DM) should be excluded by measurement of autoantibodies in any individual with suspected diabetes with obesity. The differentiation of T2DM from MODY and T1DM is important as the diabetes treatment approaches differ 112 .

Several comorbidities of obesity should be considered if specific symptoms occur 96 , 109 . For polycystic ovary syndrome in hirsute adolescent girls with oligomenorrhoea or amenorrhoea, moderately increased testosterone levels and decreased sex hormone binding globulin levels are typical laboratory findings 113 . Obstructive sleep apnoea can occur in those with more severe obesity and who snore, have daytime somnolence or witnessed apnoeas. Diagnosis is made by polysomnography 114 . Minor orthopaedic disorders, such as flat feet and genu valgum, are frequent in children and adolescents with obesity and may cause pain. Major orthopaedic complications include slipped capital femoral epiphyses (acute and chronic), which manifest with hip and knee pain in young adolescents and are characterized by reduced range of hip rotation and waddling gait; and Blount disease (tibia vara), typically occurring in children aged 2–5 years 105 , 115 . In addition, children and adolescents with extreme obesity frequently have increased dyspnoea and decreased exercise capacity. A heightened demand for ventilation, elevated work of breathing, respiratory muscle inefficiency and diminished respiratory compliance are caused by increased truncal fat mass. This may result in a decreased functional residual capacity and expiratory reserve volume, ventilation to perfusion ratio abnormalities and hypoxaemia, especially when supine. However, conventional respiratory function tests are only mildly affected by obesity except in extreme cases 116 . Furthermore, gallstones should be suspected in the context of abdominal pain after rapid weight loss, which can be readily diagnosed via abdominal ultrasonography 105 . Finally, pseudotumor cerebri may present with chronic headache, and depression may present with flat affect, chronic fatigue and sleep problems 105 .

Obesity in adolescents can also be associated with disordered eating, eating disorders and other psychological disorders 117 , 118 . If suspected, assessment by a mental health professional is recommended.

A comprehensive approach

The 2016 report of the WHO Commission on Ending Childhood Obesity stated that progress in tackling childhood obesity has been slow and inconsistent, with obesity prevention requiring a whole-of-government approach in which policies across all sectors systematically take health into account, avoiding harmful health impacts and, therefore, improving population health and health equity 13 , 119 . The focus in developing and implementing interventions to prevent obesity in children should be on interventions that are feasible, effective and likely to reduce health inequalities 14 . Importantly, the voices of children and adolescents living with social disadvantage and those from minority groups must be heard if such interventions are to be effective and reduce inequalities 120 .

Figure  5 presents a system for the prevention of childhood obesity within different domains of the socioecological model 121 and highlights opportunities for interventions. These domains can be described on a continuum, from (most downstream) individual and interpersonal (including parents, peers and wider family) through to organizational (including health care and schools), community (including food, activity and environment), society (including media and finally cultural norms) and (most upstream) public policy (from local to national level). Interventions to prevent childhood obesity can be classified on the Nuffield intervention ladder 122 . This framework was proposed by the Nuffield Council on Bioethics in 2007 (ref. 122 ) and distributes interventions on the ladder steps depending on the degree of agency required by the individual to make the behavioural changes that are the aim of the intervention. The bottom step of the ladder includes interventions that provide information, which requires the highest agency and relies on a child, adolescent and/or family choosing (and their ability to choose) to act on that information and change behaviour. The next steps of the ladder are interventions that enable choice, guide choice through changing the default policy, guide choice through incentives, guide choice through disincentives, or restrict choice. On the top-most step of the ladder (lowest agency required) are interventions that eliminate choice.

figure 5

This schematic integrates interventions that were included in a Cochrane review 127 of 153 randomized controlled trials of interventions to prevent obesity in children and are high on the Nuffield intervention ladder 122 . The Nuffield intervention ladder distributes interventions depending on the degree of agency required for the behavioural changes that are the aim of the intervention. The socioecological model 121 comprises different domains (or levels) from the individual up to public policy. Interventions targeting the individual and interpersonal domains can be described as downstream interventions, and interventions within public policy can be described as the highest level of upstream interventions. Within each of these domains, arrow symbols with colours corresponding to the Nuffield intervention ladder category are used to show interventions that were both included in the Cochrane review 127 and that guide, restrict or eliminate choice as defined by the Nuffield intervention ladder 122 . Upstream interventions, and interventions on the top steps of the Nuffield ladder, are more likely to reduce inequalities. NGO, non-governmental organization.

Downstream and high-agency interventions (on the bottom steps of the Nuffield ladder) are more likely to result in intervention-generated inequalities 123 . This has been elegantly described and evidenced, with examples from the obesity prevention literature 124 , 125 . A particularly strong example is a systematic review of 38 interventions to promote healthy eating that showed that food price (an upstream and low-agency intervention) seemed to decrease inequalities, all interventions that combined taxes and subsidies consistently decreased inequalities, and downstream high-agency interventions, especially dietary counselling, seemed to increase inequalities 126 .

Effectiveness of prevention interventions

A 2019 Cochrane review of interventions to prevent obesity in children 127 included 153 randomized controlled trials (RCTs), mainly in HICs (12% were from middle-income countries). Of these RCTs, 56% tested interventions in children aged 6–12 years, 24% in children aged 0–5 years, and 20% in adolescents aged 13–18 years. The review showed that diet-only interventions to prevent obesity in children were generally ineffective across all ages. Interventions combining diet and physical activity resulted in modest benefits in children aged 0–12 years but not in adolescents. However, physical activity-only interventions to prevent obesity were effective in school-age children (aged 5–18 years). Whether the interventions were likely to work equitably in all children was investigated in 13 RCTs. These RCTs did not indicate that the strategies increased inequalities, although most of the 13 RCTs included relatively homogeneous groups of children from disadvantaged backgrounds.

The potential for negative unintended consequences of obesity prevention interventions has received much attention 128 . The Cochrane review 127 investigated whether children were harmed by any of the strategies; for example, by having injuries, losing too much weight or developing damaging views about themselves and their weight. Of the few RCTs that did monitor these outcomes, none found any harms in participants.

Intervention levels

Most interventions (58%) of RCTs in the Cochrane review aimed to change individual lifestyle factors via education-based approaches (that is, simply provide information) 129 . In relation to the socioecological model, only 11 RCTs were set in the food and physical activity environment domain, and child care, preschools and schools were the most common targets for interventions. Of note, no RCTs were conducted in a faith-based setting 130 . Table  2 highlights examples of upstream interventions that involve more than simply providing information and their classification on the Nuffield intervention ladder.

Different settings for interventions to prevent childhood obesity, including preschools and schools, primary health care, community settings and national policy, offer different opportunities for reach and effectiveness, and a reduction in inequalities.

Preschools and schools are key settings for public policy interventions for childhood obesity prevention, and mandatory and voluntary food standards and guidance on physical education are in place in many countries. Individual schools are tasked with translating and implementing these standards and guidance for their local context. Successful implementation of a whole-school approach, such as that used in the WHO Nutrition-Friendly Schools Initiative 131 , is a key factor in the effectiveness of interventions. Careful consideration should be given to how school culture can, and needs to, be shifted by working with schools to tailor the approach and manage possible staff capacity issues, and by building relationships within and outside the school gates to enhance sustainability 132 , 133 .

Primary health care offers opportunities for guidance for obesity prevention, especially from early childhood to puberty. Parent-targeted interventions conducted by clinicians in health-care or community settings have the strongest level of evidence for their effectiveness in reducing BMI z -score at age 2 years 134 . These interventions include group programmes, clinic nurse consultations, mobile phone text support or nurse home visiting, and focusing on healthy infant feeding, healthy childhood feeding behaviours and screen time.

A prospective individual participant data meta-analysis of four RCTs involving 2,196 mother–baby dyads, and involving nurse home visiting or group programmes, resulted in a small but significant reduction in BMI in infants in the intervention groups compared with control infants at age 18–24 months 134 . Improvements were also seen in television viewing time, breastfeeding duration and feeding practices. Interventions were more effective in settings with limited provision of maternal and child health services in the community. However, effectiveness diminished by age 5 years without further intervention, highlighting the need for ongoing interventions at each life stage 135 . Evidence exists that short-duration interventions targeting sleep in very early childhood may be more effective than nutrition-targeted interventions in influencing child BMI at age 5 years 136 .

Primary care clinicians can provide anticipatory guidance, as a form of primary prevention, to older children, adolescents and their families, aiming to support healthy weight and weight-related behaviours. Clinical guidelines recommend that clinicians monitor growth regularly, and provide guidance on healthy eating patterns, physical activity, sedentary behaviours and sleep patterns 97 , 100 . Very few paediatric trials have investigated whether this opportunistic screening and advice is effective in obesity prevention 100 . A 2021 review of registered RCTs for the prevention of obesity in infancy found 29 trials 137 , of which most were delivered, or were planned to be delivered, in community health-care settings, such as nurse-led clinics. At the time of publication, 11 trials had reported child weight-related outcomes, two of which showed a small but significant beneficial effect on BMI at age 2 years, and one found significant improvements in the prevalence of obesity but not BMI. Many of the trials showed improvements in practices, such as breastfeeding and screen time.

At the community level, local public policy should be mindful of the geography of the area (such as urban or rural) and population demographics. Adolescents usually have more freedom in food and beverage choices made outside the home than younger children. In addition, physical activity levels usually decline and sedentary behaviours rise during adolescence, particularly in girls 138 , 139 . These behavioural changes offer both opportunities and barriers for those developing community interventions. On a national societal level, public policies for interventions to prevent obesity in children include the control of advertising of foods and beverages high in fat, sugar and/or salt in some countries. Industry and the media, including social media, can have a considerable influence on the food and physical activity behaviours of children 13 , 119 .

Public policy may target interventions at all domains from the individual to the societal level. The main focus of interventions in most national public policies relies on the ability of individuals to make the behavioural changes that are the aim of the intervention (high-agency interventions) at the individual level (downstream interventions). An equal focus on low-agency and upstream interventions is required if a step change in tackling childhood obesity is to be realized 140 , 141 .

COVID-19 and obesity

Early indications in several countries show rising levels of childhood obesity, and an increase in inequalities in childhood obesity during the COVID-19 pandemic 142 . The substantial disruptions in nutrition and lifestyle habits of children during and since the pandemic include social isolation and addiction to screens 143 . Under-nutrition is expected to worsen in poor countries, but obesity rates could increase in middle-income countries and HICs, especially among vulnerable groups, widening the gap in health and social inequalities 143 . Public health approaches at national, regional and local levels should include strategies that not only prevent obesity and under-nutrition, but also reduce health inequalities.

In summary, although most trials of obesity prevention have occurred at the level of the individual, the immediate family, school or community, effective prevention of obesity will require greater investment in upstream, low-agency interventions.

Treatment goals

Treatment should be centred on the individual and stigma-free (Box  1 ) and may aim for a reduction in overweight and improvement in associated comorbidities and health behaviours. Clinical considerations when determining a treatment approach should include age, severity of overweight and the presence of associated complications 144 , 145 .

Box 1 Strategies for minimizing weight stigma in health care 220 , 221 , 222

Minimizing weight bias in the education of health-care professionals

Improved education of health professionals:

pay attention to the implicit and explicit communication of social norms

include coverage of the broader determinants of obesity

include discussion of harms caused by social and cultural norms and messages concerning body weight

provide opportunities to practise non-stigmatizing care throughout education

Provide causal information focusing on the genetic and/or socioenvironmental determinants of weight.

Provide empathy-invoking interventions, emphasizing size acceptance, respect and human dignity.

Provide a weight-inclusive approach, by emphasizing that all individuals, regardless of size, have the right to equal health care.

Addressing health facility infrastructure and processes

Provide appropriately sized chairs, blood pressure cuffs, weight scales, beds, toilets, showers and gowns.

Use non-stigmatizing language in signage, descriptions of clinical services and other documentation.

Providing clinical leadership and using appropriate language within health-care settings

Senior clinicians and managers should role-model supportive and non-biased behaviours towards people with obesity and indicate that they do not tolerate weight-based discrimination in any form.

Staff should identify the language that individuals prefer in referring to obesity.

Use person-first language, for example a ‘person with obesity’ rather than ‘an obese person’.

Treatment guidelines

Clinical guidelines advise that first-line management incorporates a family-based multicomponent approach that addresses dietary, physical activity, sedentary and sleep behaviours 97 , 99 , 109 , 146 . This approach is foundational, with adjunctive therapies, especially pharmacotherapy and bariatric surgery, indicated under specific circumstances, usually in adolescents with more severe obesity 144 , 145 . Guideline recommendations vary greatly among countries and are influenced by current evidence, and functionality and resourcing of local health systems. Hence, availability and feasibility of therapies differs internationally. In usual clinical practice, interventions may have poorer outcomes than is observed in original studies or anticipated in evidence-based guidelines 147 because implementation of guidelines is more challenging in resource-constrained environments 148 . In addition, clinical trials are less likely to include patients with specialized needs, such as children from culturally diverse populations, those living with social disadvantage, children with complex health problems, and those with severe obesity 149 , 150 .

Behavioural interventions

There are marked differences in individual responses to behavioural interventions, and overall weight change outcomes are often modest. In children aged 6–11 years, a 2017 Cochrane review 150 found that mean BMI z -scores were reduced in those involved in behaviour-changing interventions compared with those receiving usual care or no treatment by only 0.06 units (37 trials; 4,019 participants; low-quality evidence) at the latest follow-up (median 10 months after the end of active intervention). In adolescents aged 12–17 years, another 2017 Cochrane review 149 found that multicomponent behavioural interventions resulted in a mean reduction in weight of 3.67 kg (20 trials; 1,993 participants) and reduction in BMI of 1.18 kg/m 2 (28 trials; 2,774 participants). These effects were maintained at the 24-month follow-up. A 2012 systematic review found significant improvements in LDL cholesterol triglycerides and blood pressure up to 1 year from baseline following lifestyle interventions in children and adolescents 151 .

Family-based behavioural interventions are recommended in national level clinical practice guidelines 97 , 100 , 146 , 152 . They are an important element of intensive health behaviour and lifestyle treatments (IHBLTs) 109 . Family-based approaches use behavioural techniques, such as goal setting, parental monitoring or modelling, taught in family sessions or in individual sessions separately to children and care givers, depending on the child’s developmental level. The priority is to encourage the whole family to engage in healthier behaviours that result in dietary improvement, greater physical activity, and less sedentariness. This includes making changes to the family food environment and requires parental monitoring.

Family-based interventions differ in philosophy and implementation from those based on family systems theory and therapy 153 . All are intensive interventions that require multiple contact hours (26 or more) with trained specialists delivered over an extended period of time (6–12 months) 10 . Changing family lifestyle habits is challenging and expensive, and the therapeutic expertise is not widely available. Moving interventions to primary care settings, delivered by trained health coaches, and supplemented by remote contact (for example by phone), will improve access and equity 154 .

Very few interventions use single psychological approaches. Most effective IHBLTs are multicomponent and intensive (many sessions), and include face-to-face contact. There has been interest in motivational interviewing as an approach to delivery 155 . As client-centred counselling, this places the young person at the centre of their behaviour change. Fundamental to motivational interviewing is the practitioner partnership that helps the young person and/or parents to explore ambivalence to change, consolidate commitment to change, and develop a plan based on their own insights and expertise. Evidence reviews generally support the view that motivational interviewing reduces BMI. Longer interventions (>4 months), those that assess and report on intervention fidelity, and those that target both diet and physical activity are most effective 155 , 156 .

More intensive dietary interventions

Some individuals benefit from more intensive interventions 98 , 144 , 157 , 158 , which include very low-energy diets, very low-carbohydrate diets and intermittent energy restriction 159 . These interventions usually aim for weight loss and are only recommended for adolescents who have reached their final height. These diets are not recommended for long periods of time due to challenges in achieving nutritional adequacy 158 , 160 , and lack of long-term safety data 158 , 161 . However, intensive dietary interventions may be considered when conventional treatment is unsuccessful, or when adolescents with comorbidities or severe obesity require rapid or substantial weight loss 98 . A 2019 systematic review of very low-energy diets in children and adolescents found a mean reduction in body weight of −5.3 kg (seven studies) at the latest follow‐up, ranging from 5 to 14.5 months from baseline 161 .

Pharmacological treatment

Until the early 2020s the only drug approved in many jurisdictions for the treatment of obesity in adolescents was orlistat, a gastrointestinal lipase inhibitor resulting in reduced uptake of lipids and, thereby, a reduced total energy intake 162 . However, the modest effect on weight in combination with gastrointestinal adverse effects limit its usefulness overall 163 .

A new generation of drugs has been developed for the treatment of both T2DM and obesity. These drugs are based on gastrointestinal peptides with effects both locally and in the central nervous system. GLP1 is an incretin that reduces appetite and slows gastric motility. The GLP1 receptor agonist liraglutide is approved for the treatment of obesity in those aged 12 years and older both in the USA and Europe 164 , 165 . Liraglutide, delivered subcutaneously daily at a higher dose than used for T2DM resulted in a 5% better BMI reduction than placebo after 12 months 166 . A 2022 trial of semaglutide, another GLP1 receptor agonist, delivered subcutaneously weekly in adolescents demonstrated 16% weight loss after 68 weeks of treatment, with modest adverse events and a low drop-out rate 12 . Tirzepatide, an agonist of both GLP1 and glucose-dependent insulinotropic polypeptide (GIP), is approved by the FDA for the treatment of T2DM in adults 167 . Subcutaneous tirzepatide weekly in adults with obesity resulted in ~20% weight loss over 72 weeks 168 . Of note, GIP alone increases appetite, but the complex receptor–agonist interaction results in downregulation of the GIP receptors 169 , illustrating why slightly modified agonists exert different effects. A study of the use of tirzepatide in adolescents with T2DM has been initiated but results are not expected before 2027 (ref. 170 ). No trials of tirzepatide are currently underway in adolescents with obesity but without T2DM.

Hypothalamic obesity is difficult to treat. Setmelanotide is a MC4R agonist that reduces weight and improves quality of life in most people with LEPR and POMC mutations 71 . In trials of setmelanotide, 8 of 10 participants with POMC deficiency and 5 of 11 with LEPR deficiency had weight loss of at least 10% at ~1 year. The mean percentage change in most hunger score from baseline was −27.1% and −43.7% in those with POMC deficiency and leptin receptor deficiency, respectively 71 .

In the near future, effective new drugs with, hopefully, an acceptable safety profile will be available that will change the way we treat and set goals for paediatric obesity treatment 171 .

Bariatric surgery

Bariatric surgery is the most potent treatment for obesity in adolescents with severe obesity. The types of surgery most frequently used are sleeve gastrectomy and gastric bypass, both of which reduce appetite 172 . Mechanisms of action are complex, involving changes in gastrointestinal hormones, neural signalling, bile acid metabolism and gut microbiota 173 . Sleeve gastrectomy is a more straightforward procedure and the need for vitamin supplementation is lower than with gastric bypass. However, long-term weight loss may be greater after gastric bypass surgery 174 .

Prospective long-term studies demonstrate beneficial effects of both sleeve gastrectomy and gastric bypass on weight loss and comorbidities in adolescents with severe obesity 175 , 176 . In a 5-year follow-up period, in 161 participants in the US TEEN-LABS study who underwent gastric bypass, mean BMI declined from 50 to 37 kg/m 2 (ref. 11 ). In a Swedish prospective study in 81 adolescents who underwent gastric bypass, the mean decrease in BMI at 5 years was 13.1 kg/m 2 (baseline BMI 45.5 kg/m 2 ) compared with a BMI increase of 3.1 kg/m 2 in the control group 176 . Both studies showed marked inter-individual variations. Negative adverse effects, including gastrointestinal problems, vitamin deficits and reduction in lean body mass, are similar in adults and adolescents. Most surgical complications following bariatric surgery in the paediatric population are minor, occurring in the early postoperative time frame, but 8% of patients may have major perioperative complications 177 . Up to one-quarter of patients may require subsequent related procedures within 5 years 109 . However, many adolescents with severe obesity also have social and psychological problems, highlighting the need for routine and long-term monitoring 109 , 178 .

Recommendations for bariatric surgery in adolescents differ considerably among countries, with information on long-term outcomes emerging rapidly. In many countries, bariatric surgery is recommended only from Tanner pubertal stage 3–4 and beyond, and only in children with severe obesity and cardiometabolic comorbidities 177 . The 2023 American Academy of Pediatrics clinical practice guidelines recommend that bariatric surgery be considered in adolescents ≥13 years of age with a BMI of ≥35 kg/m 2 or 120% of the 95th percentile for age and sex, whichever is lower, as well as clinically significant disease, such as T2DM, non-alcoholic fatty liver disease, major orthopaedic complications, obstructive sleep apnoea, the presence of cardiometabolic risk, or depressed quality of life 109 . For those with a BMI of ≥40 kg/m 2 or 140% of the 95th percentile for age and sex, bariatric surgery is indicated regardless of the presence of comorbidities. Potential contraindications to surgery include correctable causes of obesity, pregnancy and ongoing substance use disorder. The guidelines comment that further evaluation, undertaken by multidisciplinary centres that offer bariatric surgery for adolescents, should determine the capacity of the patient and family to understand the risks and benefits of surgery and to adhere to the required lifestyle changes before and after surgery.

Long-term weight outcomes

Few paediatric studies have investigated long-term weight maintenance after the initial, more intensive, weight loss phase. A 2018 systematic review of 11 studies in children and adolescents showed that a diverse range of maintenance interventions, including support via face-to-face psychobehavioural therapies, individual physician consultations, or adjunctive therapeutic contact via newsletters, mobile phone text or e-mail, led to stabilization of BMI z -score compared with control participants, who had increases in BMI z -score 179 . Interventions that are web-based or use mobile devices may be particularly useful in young people 180 .

One concern is weight regain which occurs after bariatric surgery in general 181 but may be more prevalent in adolescents 176 . For example, in a Swedish prospective study, after 5 years, 25–30% of participants fulfilled the definitions of low surgical treatment effectiveness, which was associated with poorer metabolic outcomes 176 . As with adults, prevention of weight regain for most at-risk individuals might be possible with the combination of lifestyle support and pharmacological treatment 182 . Further weight maintenance strategies and long-term outcomes are discussed in the 2023 American Academy of Pediatrics clinical practice guidelines 109 . The appropriate role and timing of other therapies for long-term weight loss maintenance, such as anti-obesity medications, more intensive dietary interventions and bariatric surgery, are areas for future research.

In summary, management of obesity in childhood and adolescence requires intensive interventions. Emerging pharmacological therapies demonstrate greater short-term effectiveness than behavioural interventions; however, long-term outcomes at ≥2 years remain an important area for future research.

Quality of life

Weight bias describes the negative attitudes to, beliefs about and behaviour towards people with obesity 183 . It can lead to stigma causing exclusion, and discrimination in work, school and health care, and contributes to the inequities common in people with obesity 184 . Weight bias also affects social engagement and psychological well-being of children.

Children and adolescents with obesity score lower overall on health-related quality of life (HRQoL) 4 , 5 . In measures that assess domains of functioning, most score lower in physical functioning, physical/general health and psychosocial areas, such as appearance, and social acceptance and functioning. HRQoL is lowest in treatment-seeking children and in those with more extreme obesity 185 . Weight loss interventions generally increase HRQoL independent of the extent of weight loss 186 , especially in the domains most affected. However, changes in weight and HRQoL are often not strongly correlated. This may reflect a lag in the physical and/or psychosocial benefit from weight change, or the extent of change that is needed to drive change in a child’s self-perception.

Similar observations apply to the literature on self-esteem. Global self-worth is reduced in children and adolescents with obesity, as is satisfaction with physical appearance, athletic competence and social acceptance 187 . Data from intensive interventions suggest the psychological benefit of weight loss may be as dependent on some feature of the treatment environment or supportive social network as the weight loss itself 188 . This may include the daily company of others with obesity, making new friendships, and experienced improvements in newly prioritized competences.

There is a bidirectional relationship between HRQoL and obesity 189 , something also accepted in the relationship with mood disorder. Obesity increases the risk of depression and vice versa, albeit over a longer period of time and which may only become apparent in adulthood 190 . Obesity also presents an increased risk of anxiety 191 .

Structured and professionally delivered weight management interventions ameliorate mood disorder symptoms 192 and improve self-esteem 193 . Regular and extended support are important components beyond losing weight. Such interventions do not increase the risk of eating disorders 194 . This is despite a recognition that binge eating disorder is present in up to 5% of adolescents with overweight or obesity 195 . They are five times more likely to have binge eating symptoms than those with average weight. Importantly, adolescents who do not have access to professionally delivered weight management may be more likely to engage in self-directed dieting, which is implicated in eating disorder development 196 .

The literature linking childhood obesity with either attention deficit hyperactivity disorder or autism spectrum disorder is complex and the relationship is uncertain. The association seems to be clearer in adults but the mechanisms and their causal directions remain unclear 109 , 197 . Young children with obesity, especially boys, are more likely to be parent-rated as having behavioural problems 198 . This may be a response to the behaviour of others rather than reflect clinical diagnoses such as attention deficit hyperactivity disorder or autism spectrum disorder. Conduct and peer relationship problems co-occur in children, regardless of their weight.

Children with obesity experience more social rejection. They receive fewer friendship nominations and more peer rejections, most pronounced in those with severe obesity 199 . This continues through adolescence and beyond. Children with obesity are more likely to report being victimized 200 . Younger children may respond by being perpetrators themselves. While it is assumed that children are victimized because of their weight, very few studies have looked at the nature or reason behind victimization. A substantial proportion of children with obesity fail to identify themselves as being fat-teased 187 . Although the stigma associated with obesity should be anticipated in children, especially in those most overweight, it would be inappropriate to see all as victims. A better understanding of children’s resilience is needed.

Many gaps remain in basic, translational and clinical research in child and adolescent obesity. The mechanisms (genetic, epigenetic, environmental and social) behind the overwhelming association between parental obesity and child and adolescent obesity are still unclear given the paradoxically weak association in BMI between adopted children and their parents in combination with the modest effect size of known genetic loci associated with obesity 201 .

Early manifestation of extreme obesity in childhood suggests a strong biological basis for disturbances of homeostatic weight regulation. Deep genotyping (including next-generation sequencing) and epigenetic analyses in these patients will reveal new genetic causes and causal pathways as a basis for the development of mechanism-based treatments. Future work aiming to understand the mechanisms underlying the development of childhood obesity should consider the complex biopsychosocial interactions and take a systems approach to understanding causal pathways leading to childhood obesity to contribute to evidence-based prevention and treatment strategies.

Long-term outcome data to better determine the risks of eating disorders are required. Although symptoms improve during obesity treatment in most adolescents, screening and monitoring for disordered eating is recommended in those presenting for treatment 202 and effective tools for use in clinical practice are required. A limited number of tools are validated to identify binge eating disorder in youth with obesity 203 but further research is needed to screen appropriately for the full spectrum of eating disorder diagnoses in obesity treatment seeking youth 203 . Recent reviews provide additional detail regarding eating disorder risk in child and adolescent obesity 117 , 202 , 204 .

Most studies of paediatric obesity treatment have been undertaken in HICs and predominantly middle-class populations. However, research is needed to determine which strategies are best suited for those in LMICs and low-resource settings, for priority population groups including indigenous peoples, migrant populations and those living with social disadvantage, and for children with neurobehavioural and psychiatric disorders. We currently have a limited understanding of how best to target treatment pathways for different levels of genetic risk, age, developmental level, obesity severity, and cardiometabolic and psychological risk. Current outcomes for behavioural interventions are relatively modest and improved treatment outcomes are needed to address the potentially severe long-term health outcomes of paediatric obesity. Studies also need to include longer follow-up periods after an intervention, record all adverse events, incorporate cost-effectiveness analyses and have improved process evaluation.

Other areas in need of research include the role of new anti-obesity medications especially in adolescents, long-term outcomes following bariatric surgery and implementation of digital support systems to optimize outcomes and reduce costs of behavioural change interventions 205 . We must also better understand and tackle the barriers to implementation of treatment in real-life clinical settings, including the role of training of health professionals. Importantly, treatment studies of all kinds must engage people with lived experience — adolescents, parents and families — to understand what outcomes and elements of treatment are most valued.

Obesity prevention is challenging because it requires a multilevel, multisectoral approach that addresses inequity, involves many stakeholders and addresses both the upstream and the downstream factors influencing obesity risk. Some evidence exists of effectiveness of prevention interventions operating at the level of the child, family and school, but the very poor progress overall in modifying obesity prevalence globally highlights many areas in need of research and evidence implementation. Studies are needed especially in LMICs, particularly in the context of the nutrition transition and the double burden of malnutrition. A focus on intergenerational research, rather than the age-based focus of current work, is also needed. Systems research approaches should be used, addressing the broader food and physical activity environments, and links to climate change 206 . In all studies, strategies are needed that enable co-production with relevant communities, long-term follow-up, process evaluation and cost-effectiveness analyses. In the next few years, research and practice priorities must include a focus on intervention strategies in the earliest phases of life, including during pregnancy. The effects of COVID-19 and cost of living crises in many countries are leading to widening health inequalities 207 and this will further challenge obesity prevention interventions. Available resourcing for prevention interventions may become further constrained, requiring innovative solutions across agendas, with clear identification of co-benefits. For example, public health interventions for other diseases, such as dental caries or depression, or other societal concerns, such as urban congestion or climate change, may also act as obesity prevention strategies. Ultimately, to implement obesity prevention, societal changes are needed in terms of urban planning, social structures and health-care access.

Future high-quality paediatric obesity research can be enabled through strategies that support data sharing, which avoids research waste and bias, and enables new research questions to be addressed. Such approaches require leadership, careful engagement of multiple research teams, and resourcing. Four national or regional level paediatric weight registries exist 208 , 209 , 210 , 211 , which are all based in North America or Europe. Such registries should be established in other countries, especially in low-resource settings, even if challenging 208 . Another data-sharing approach is through individual participant data meta-analyses of intervention trials, which can include prospectively collected data 212 and are quite distinct from systematic reviews of aggregate data. Two recent examples are the Transforming Obesity Prevention in Childhood (TOPCHILD) Collaboration, which includes early interventions to prevent obesity in the first 2 years of life 213 , and the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration, which aims to identify characteristics of individuals or trials that increase or protect against eating disorder risk following obesity treatment 214 . Formal data linkage studies, especially those joining up routine administrative datasets, enable longer-term and broader outcome measures to be assessed than is possible with standard clinical or public health intervention studies.

Collaborative research will also be enhanced through the use of agreed core outcome sets, supporting data harmonization. The Edmonton Obesity Staging System – Paediatric 215 is one option for paediatric obesity treatment. A core outcome set for early intervention trials to prevent obesity in childhood (COS-EPOCH) has been recently established 216 . These efforts incorporate a balance between wanting and needing to share data and adhering to privacy protection regulations. Objective end points are ideal, including directly measured physical activity and body composition.

Collaborative efforts and a systems approach are paramount to understand, prevent and manage child and adolescent obesity. Research funding and health policies should focus on feasible, effective and equitable interventions.

NCD Risk Factor Collaboration. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet https://doi.org/10.1016/S0140-6736(17)32129-3 (2017).

Article   Google Scholar  

Popkin, B. M., Corvalan, C. & Grummer-Strawn, L. M. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet 395 , 65–74 (2020).

Article   PubMed   Google Scholar  

Kompaniyets, L. et al. Underlying medical conditions associated with severe COVID-19 illness among children. JAMA Netw. Open 4 , e2111182 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Griffiths, L. J., Parsons, T. J. & Hill, A. J. Self‐esteem and quality of life in obese children and adolescents: a systematic review. Int. J. Pediatr. Obes. 5 , 282–304 (2010).

Buttitta, M., Iliescu, C., Rousseau, A. & Guerrien, A. Quality of life in overweight and obese children and adolescents: a literature review. Qual. Life Res. 23 , 1117–1139 (2014).

Hayes, A. et al. Early childhood obesity: association with healthcare expenditure in Australia. Obesity 24 , 1752–1758 (2016).

Marcus, C., Danielsson, P. & Hagman, E. Pediatric obesity – long-term consequences and effect of weight loss. J. Intern. Med. 292 , 870–891 (2022).

Berthoud, H. R., Morrison, C. D. & Münzberg, H. The obesity epidemic in the face of homeostatic body weight regulation: what went wrong and how can it be fixed? Physiol. Behav. 222 , 112959 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

World Health Organization. Report of the commission on ending childhood obesity. WHO https://www.who.int/publications/i/item/9789241510066 (2016). This report from the WHO on approaches to childhood and adolescent obesity has six main recommendations for governments, covering food and physical activity, age-based settings and provision of weight management for those with obesity.

O’Connor, E. A. et al. Screening for obesity and intervention for weight management in children and adolescents: evidence report and systematic review for the US Preventive Services Task Force. JAMA 317 , 2427–2444 (2017).

Inge, T. H. et al. Five-year outcomes of gastric bypass in adolescents as compared with adults. N. Engl. J. Med. 380 , 2136–2145 (2019).

Weghuber, D. et al. Once-weekly semaglutide in adolescents with obesity. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa2208601 (2022). To our knowledge, the first RCT of semaglutide 2.4 mg, administered weekly by subcutaneous injection, in adolescents with obesity.

World Health Organization. Report of the Commission on Ending Childhood Obesity: Implementation Plan: Executive Summary (WHO, 2017).

Hillier-Brown, F. C. et al. A systematic review of the effectiveness of individual, community and societal level interventions at reducing socioeconomic inequalities in obesity amongst children. BMC Public Health 14 , 834 (2014).

World Health Organization. Obesity. WHO https://www.who.int/health-topics/obesity#tab=tab_1 (2023).

Mei, Z. et al. Validity of body mass index compared with other body-composition screening indexes for the assessment of body fatness in children and adolescents. Am. J. Clin. Nutr. 75 , 978–985 (2002).

Article   CAS   PubMed   Google Scholar  

World Health Organization. Child growth standards. WHO https://www.who.int/tools/child-growth-standards/standards (2006).

World Health Organization. Growth reference data for 5–19 years. WHO https://www.who.int/tools/growth-reference-data-for-5to19-years (2007).

National Center for Health Statistics. CDC growth charts. Centers for Disease Control and Prevention http://www.cdc.gov/growthcharts/ (2022).

Cole, T. J., Bellizzi, M. C., Flegal, K. M. & Dietz, W. H. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320 , 1240 (2000).

Di Cesare, M. et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 17 , 212 (2019).

Swinburn, B., Egger, G. & Raza, F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev. Med. 29 , 563–570 (1999).

Ford, N. D., Patel, S. A. & Narayan, K. V. Obesity in low-and middle-income countries: burden, drivers, and emerging challenges. Annu. Rev. Public Health 38 , 145–164 (2017).

Shah, B., Tombeau Cost, K., Fuller, A., Birken, C. S. & Anderson, L. N. Sex and gender differences in childhood obesity: contributing to the research agenda. BMJ Nutr. Prev. Health 3 , 387–390 (2020).

Public Health England. Research and analysis: differences in child obesity by ethnic group. GOV.UK https://www.gov.uk/government/publications/differences-in-child-obesity-by-ethnic-group/differences-in-child-obesity-by-ethnic-group#data (2019).

Cole, T. J. & Lobstein, T. Extended international (IOTF) body mass index cut‐offs for thinness, overweight and obesity. Pediatr. Obes. 7 , 284–294 (2012).

Kelly, A. S. et al. Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation 128 , 1689–1712 (2013).

Garnett, S. P., Baur, L. A., Jones, A. M. & Hardy, L. L. Trends in the prevalence of morbid and severe obesity in Australian children aged 7–15 years, 1985-2012. PLoS ONE 11 , e0154879 (2016).

Spinelli, A. et al. Prevalence of severe obesity among primary school children in 21 European countries. Obes. Facts 12 , 244–258 (2019).

Twig, G. et al. Body-Mass Index in 2.3 million adolescents and cardiovascular death in adulthood. N. Engl. J. Med. 374 , 2430–2440 (2016).

González-Muniesa, P. et al. Obesity. Nat. Rev. Dis. Primers   3 , 17034 (2017).

Geserick, M. et al. Acceleration of BMI in early childhood and risk of sustained obesity. N. Engl. J. Med. 379 , 1303–1312 (2018).

Larqué, E. et al. From conception to infancy – early risk factors for childhood obesity. Nat. Rev. Endocrinol. 15 , 456–478 (2019).

Barker, D. J. Fetal origins of coronary heart disease. Br. Med. J. 311 , 171–174 (1995).

Article   CAS   Google Scholar  

Gluckman, P. D., Hanson, M. A., Cooper, C. & Thornburg, K. L. Effect of in utero and early-life conditions on adult health and disease. N. Engl. J. Med. 359 , 61–73 (2008).

Philips, E. M. et al. Changes in parental smoking during pregnancy and risks of adverse birth outcomes and childhood overweight in Europe and North America: an individual participant data meta-analysis of 229,000 singleton births. PLoS Med. 17 , e1003182 (2020).

Voerman, E. et al. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: an individual participant data meta-analysis. PLoS Med. 16 , e1002744 (2019). Individual participant data meta-analysis of >160,000 mothers and their children on the associations of maternal BMI and gestational weight gain and childhood overweight or obesity.

McIntyre, H. D. et al. Gestational diabetes mellitus. Nat. Rev. Dis. Primers   5 , 47 (2019).

Oken, E. & Gillman, M. W. Fetal origins of obesity. Obes. Res. 11 , 496–506 (2003).

Hughes, A. R., Sherriff, A., Ness, A. R. & Reilly, J. J. Timing of adiposity rebound and adiposity in adolescence. Pediatrics 134 , e1354–e1361 (2014).

Rolland-Cachera, M. F. et al. Tracking the development of adiposity from one month of age to adulthood. Ann. Hum. Biol. 14 , 219–229 (1987).

Koletzko, B. et al. Prevention of childhood obesity: a position paper of the Global Federation of International Societies of Paediatric Gastroenterology, Hepatology and Nutrition (FISPGHAN). J. Pediatr. Gastroenterol. Nutr. 70 , 702–710 (2020).

Weber, M. et al. Lower protein content in infant formula reduces BMI and obesity risk at school age: follow-up of a randomized trial. Am. J. Clin. Nutr. 99 , 1041–1051 (2014).

Cope, M. B. & Allison, D. B. Critical review of the World Health Organization’s (WHO) 2007 report on ‘evidence of the long‐term effects of breastfeeding: systematic reviews and meta‐analysis’ with respect to obesity. Obes. Rev. 9 , 594–605 (2008).

Totzauer, M. et al. Different protein intake in the first year and its effects on adiposity rebound and obesity throughout childhood: 11 years follow‐up of a randomized controlled trial. Pediatr. Obes. 17 , e12961 (2022).

Deren, K. et al. Consumption of sugar-sweetened beverages in paediatric age: a position paper of the European academy of paediatrics and the European Childhood Obesity Group. Ann. Nutr. Metab. 74 , 296–302 (2019).

Felső, R., Lohner, S., Hollódy, K., Erhardt, É. & Molnár, D. Relationship between sleep duration and childhood obesity: systematic review including the potential underlying mechanisms. Nutr. Metab. Cardiovasc. Dis. 27 , 751–761 (2017).

Farooq, A. et al. Longitudinal changes in moderate‐to‐vigorous‐intensity physical activity in children and adolescents: a systematic review and meta‐analysis. Obes. Rev. 21 , e12953 (2020).

Mahumud, R. A. et al. Association of dietary intake, physical activity, and sedentary behaviours with overweight and obesity among 282,213 adolescents in 89 low and middle income to high-income countries. Int. J. Obes. 45 , 2404–2418 (2021).

Ballon, M. et al. Socioeconomic inequalities in weight, height and body mass index from birth to 5 years. Int. J. Obes. 42 , 1671–1679 (2018).

Buoncristiano, M. et al. Socioeconomic inequalities in overweight and obesity among 6- to 9-year-old children in 24 countries from the World Health Organization European region. Obes. Rev. 22 , e13213 (2021).

Jiwani, S. S. et al. The shift of obesity burden by socioeconomic status between 1998 and 2017 in Latin America and the Caribbean: a cross-sectional series study. Lancet Glob. Health 7 , e1644–e1654 (2019).

Monteiro, C. A., Conde, W. L., Lu, B. & Popkin, B. M. Obesity and inequities in health in the developing world. Int. J. Obes. 28 , 1181–1186 (2004).

Guo, S. S. & Chumlea, W. C. Tracking of body mass index in children in relation to overweight in adulthood. Am. J. Clin. Nutr. 70 , 145S–148S (1999).

Aarestrup, J. et al. Birthweight, childhood overweight, height and growth and adult cancer risks: a review of studies using the Copenhagen School Health Records Register. Int. J. Obes. 44 , 1546–1560 (2020).

Eslam, M. et al. Defining paediatric metabolic (dysfunction)-associated fatty liver disease: an international expert consensus statement. Lancet Gastroenterol. Hepatol. 6 , 864–873 (2021).

Daniels, S. R. et al. Overweight in children and adolescents: pathophysiology, consequences, prevention, and treatment. Circulation 111 , 1999–2012 (2005).

Cioana, M. et al. The prevalence of obesity among children with type 2 diabetes: a systematic review and meta-analysis. JAMA Netw. Open 5 , e2247186 (2022).

Gepstein, V. & Weiss, R. Obesity as the main risk factor for metabolic syndrome in children. Front. Endocrinol. 10 , 568 (2019).

Kuvat, N., Tanriverdi, H. & Armutcu, F. The relationship between obstructive sleep apnea syndrome and obesity: a new perspective on the pathogenesis in terms of organ crosstalk. Clin. Respir. J. 14 , 595–604 (2020).

Baker, J. L., Olsen, L. W. & Sorensen, T. I. Childhood body-mass index and the risk of coronary heart disease in adulthood. N. Engl. J. Med. 357 , 2329–2337 (2007).

Bjerregaard, L. G. et al. Change in overweight from childhood to early adulthood and risk of type 2 diabetes. N. Engl. J. Med. 378 , 1302–1312 (2018).

Kelsey, M. M., Zaepfel, A., Bjornstad, P. & Nadeau, K. J. Age-related consequences of childhood obesity. Gerontology 60 , 222–228 (2014).

Sharma, V. et al. A systematic review and meta-analysis estimating the population prevalence of comorbidities in children and adolescents aged 5 to 18 years. Obes. Rev. 20 , 1341–1349 (2019).

Lobstein, T. & Jackson-Leach, R. Planning for the worst: estimates of obesity and comorbidities in school-age children in 2025. Pediatr. Obes. 11 , 321–325 (2016).

Berthoud, H. R., Münzberg, H. & Morrison, C. D. Blaming the brain for obesity: integration of hedonic and homeostatic mechanisms. Gastroenterology 152 , 1728–1738 (2017).

Devoto, F. et al. Hungry brains: a meta-analytical review of brain activation imaging studies on food perception and appetite in obese individuals. Neurosci. Biobehav. Rev. 94 , 271–285 (2018).

Blum, W. F., Englaro, P., Attanasio, A. M., Kiess, W. & Rascher, W. Human and clinical perspectives on leptin. Proc. Nutr. Soc. 57 , 477–485 (1998).

Friedman, J. M. Leptin and the endocrine control of energy balance. Nat. Metab. 1 , 754–764 (2019).

Kühnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375 , 240–246 (2016).

Clément, K. et al. Efficacy and safety of setmelanotide, an MC4R agonist, in individuals with severe obesity due to LEPR or POMC deficiency: single-arm, open-label, multicentre, phase 3 trials. Lancet Diabetes Endocrinol. 8 , 960–970 (2020).

Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156 , 20–44 (2014).

Fischer-Posovszky, P., Roos, J., Zoller, V. & Wabitsch, M. in Pediatric Obesity: Etiology, Pathogenesis and Treatment (ed. Freemark, M. S.) 81–93 (Springer, 2018).

Hammarstedt, A., Gogg, S., Hedjazifar, S., Nerstedt, A. & Smith, U. Impaired adipogenesis and dysfunctional adipose tissue in human hypertrophic obesity. Physiol. Rev. 98 , 1911–1941 (2018).

Silventoinen, K. et al. Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the collaborative project of development of anthropometrical measures in twins (CODATwins) study. Am. J. Clin. Nutr. 104 , 371–379 (2016).

Silventoinen, K. et al. Differences in genetic and environmental variation in adult BMI by sex, age, time period, and region: an individual-based pooled analysis of 40 twin cohorts. Am. J. Clin. Nutr. 106 , 457–466 (2017).

Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum. Mol. Genet. 27 , 3641–3649 (2018).

Vogelezang, S. et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 16 , e1008718 (2020).

Bradfield, J. P. et al. A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity. Hum. Mol. Genet. 28 , 3327–3338 (2019). To our knowledge, currently the largest genome-wide association study meta-analysis on childhood obesity in >13,000 individuals with obesity and >15,500 controls.

Couto Alves, A. et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. Sci. Adv. 5 , eaaw3095 (2019).

Ding, X. et al. Genome-wide screen of DNA methylation identifies novel markers in childhood obesity. Gene 566 , 74–83 (2015).

Huang, R. C. et al. Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Epigenetics 10 , 995–1005 (2015).

Rzehak, P. et al. DNA-methylation and body composition in preschool children: epigenome-wide-analysis in the European Childhood Obesity Project (CHOP)-Study. Sci. Rep. 7 , 14349 (2017).

Alfano, R. et al. Perspectives and challenges of epigenetic determinants of childhood obesity: a systematic review. Obes. Rev. 23 , e13389 (2022).

Vehmeijer, F. O. L. et al. DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies. Genome Med. 12 , 105 (2020). Meta-analysis of epigenome-wide association studies of childhood BMI in >4,000 children.

Richmond, R. C. et al. DNA methylation and BMI: investigating identified methylation sites at HIF3A in a causal framework. Diabetes 65 , 1231–1244 (2016).

Wahl, S. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541 , 81–86 (2017).

Kivimäki, M. et al. Substantial intergenerational increases in body mass index are not explained by the fetal overnutrition hypothesis: the Cardiovascular Risk in Young Finns Study. Am. J. Clin. Nutr. 86 , 1509–1514 (2007).

Whitaker, R. C., Wright, J. A., Pepe, M. S., Seidel, K. D. & Dietz, W. H. Predicting obesity in young adulthood from childhood and parental obesity. N. Engl. J. Med. 337 , 869–873 (1997).

Davey Smith, G., Steer, C., Leary, S. & Ness, A. Is there an intrauterine influence on obesity? Evidence from parent child associations in the Avon Longitudinal Study of Parents and Children (ALSPAC). Arch. Dis. Child. 92 , 876–880 (2007).

Fleten, C. et al. Parent-offspring body mass index associations in the Norwegian Mother and Child Cohort Study: a family-based approach to studying the role of the intrauterine environment in childhood adiposity. Am. J. Epidemiol. 176 , 83–92 (2012).

Gaillard, R. et al. Childhood cardiometabolic outcomes of maternal obesity during pregnancy: the Generation R Study. Hypertension 63 , 683–691 (2014).

Lawlor, D. A. et al. Exploring the developmental overnutrition hypothesis using parental-offspring associations and FTO as an instrumental variable. PLoS Med. 5 , e33 (2008).

Patro, B. et al. Maternal and paternal body mass index and offspring obesity: a systematic review. Ann. Nutr. Metab. 63 , 32–41 (2013).

Sorensen, T. et al. Comparison of associations of maternal peri-pregnancy and paternal anthropometrics with child anthropometrics from birth through age 7 y assessed in the Danish National Birth Cohort. Am. J. Clin. Nutr. 104 , 389–396 (2016).

Styne, D. M. et al. Pediatric obesity – assessment, treatment, and prevention: an Endocrine Society Clinical Practice guideline. J. Clin. Endocrinol. Metab. 102 , 709–757 (2017).

National Institute for Health and Care Excellence. Obesity: identification, assessment and managenent: clinical guideline [CG189]. NICE https://www.nice.org.uk/guidance/cg189 (2022). A high quality clinical practice guideline for obesity management.

Barlow, S. E. Expert Committee Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 120 (Suppl. 4), S164–S192 (2007).

Canadian Task Force on Preventive Health Care. Recommendations for growth monitoring, and prevention and management of overweight and obesity in children and youth in primary care. Can. Med. Assoc. J. 187 , 411–421 (2015).

US Preventive Services Task Force. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. JAMA 317 , 2417–2426 (2017).

McConnell-Nzunga, J. et al. Classification of obesity varies between body mass index and direct measures of body fat in boys and girls of Asian and European ancestry. Meas. Phys. Educ. Exerc. Sci. 22 , 154–166 (2018).

Reinehr, T. et al. Definable somatic disorders in overweight children and adolescents. J. Pediatr. 150 , 618–622.e5 (2007).

Reinehr, T. Thyroid function in the nutritionally obese child and adolescent. Curr. Opin. Pediatr. 23 , 415–420 (2011).

Kohlsdorf, K. et al. Early childhood BMI trajectories in monogenic obesity due to leptin, leptin receptor, and melanocortin 4 receptor deficiency. Int. J. Obes. 42 , 1602–1609 (2018).

Armstrong, S. et al. Physical examination findings among children and adolescents with obesity: an evidence-based review. Pediatrics 137 , e20151766 (2016).

Reinehr, T. & Roth, C. L. Is there a causal relationship between obesity and puberty? Lancet Child Adolesc. Health 3 , 44–54 (2019).

Garnett, S. P., Baur, L. A. & Cowell, C. T. Waist-to-height ratio: a simple option for determining excess central adiposity in young people. Int. J. Obes. 32 , 1028–1030 (2008).

Maffeis, C., Banzato, C., Talamini, G. & Obesity Study Group of the Italian Society of Pediatric Endocrinology and Diabetology. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. J. Pediatr. 152 , 207–213.e2 (2008).

Hampl, S. E. et al. Clinical practice guideline for the evaluation and treatment of children and adolescents with obesity. Pediatrics 151 , e2022060640 (2023). A new, comprehensive clinical practice guideline outlining current recommendations on assessment and treatment of children and adolescents with obesity.

Reinehr, T. et al. Comparison of cardiovascular risk factors between children and adolescents with classes III and IV obesity: findings from the APV cohort. Int. J. Obes. 45 , 1061–1073 (2021).

Reinehr, T. Metabolic syndrome in children and adolescents: a critical approach considering the interaction between pubertal stage and insulin resistance. Curr. Diabetes Rep. 16 , 8 (2016).

Zeitler, P. et al. ISPAD Clinical Practice Consensus Guidelines 2018: type 2 diabetes mellitus in youth. Pediatr. Diabetes 19 , 28–46 (2018).

Ibáñez, L. et al. An International Consortium update: pathophysiology, diagnosis, and treatment of polycystic ovarian syndrome in adolescence. Horm. Res. Paediatr. 88 , 371–395 (2017).

Brockmann, P. E., Schaefer, C., Poets, A., Poets, C. F. & Urschitz, M. S. Diagnosis of obstructive sleep apnea in children: a systematic review. Sleep Med. Rev. 17 , 331–340 (2013).

Taylor, E. D. et al. Orthopedic complications of overweight in children and adolescents. Pediatrics 117 , 2167–2174 (2006).

Winck, A. D. et al. Effects of obesity on lung volume and capacity in children and adolescents: a systematic review. Rev. Paul. Pediatr. 34 , 510–517 (2016).

PubMed   PubMed Central   Google Scholar  

Jebeile, H., Lister, N., Baur, L., Garnett, S. & Paxton, S. J. Eating disorder risk in adolescents with obesity. Obes. Rev. 22 , e13173 (2021).

Quek, Y. H., Tam, W. W. S., Zhang, M. W. B. & Ho, R. C. M. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes. Rev. 18 , 742–754 (2017).

World Health Organization. Consideration of the Evidence on Childhood Obesity for the Commission on Ending Childhood Obesity . Report of the Ad Hoc Working Group on Science and Evidence for Ending Childhood Obesity (WHO, 2016).

Pickett, K. et al. The Child of the North: building a fairer future after COVID-19. The Northern Health Science Alliance and N8 Research Partnership https://www.thenhsa.co.uk/app/uploads/2022/01/Child-of-the-North-Report-FINAL-1.pdf (2021).

Bronfenbrenner, U. Toward an experimental ecology of human development. Am. Psychol. 32 , 513–531 (1977).

Nuffield Council on Bioethics. Public Health: Ethical Issues (Nuffield Council on Bioethics, 2007).

Lorenc, T., Petticrew, M., Welch, V. & Tugwell, P. What types of interventions generate inequalities? Evidence from systematic reviews. J. Epidemiol. Community Health 67 , 190–193 (2013).

Adams, J., Mytton, O., White, M. & Monsivais, P. Why are some population interventions for diet and obesity more equitable and effective than others? The role of individual agency. PLoS Med. 13 , e1001990 (2016).

Backholer, K. et al. A framework for evaluating the impact of obesity prevention strategies on socioeconomic inequalities in weight. Am. J. Public Health 104 , e43–e50 (2014).

McGill, R. et al. Are interventions to promote healthy eating equally effective for all? Systematic review of socioeconomic inequalities in impact. BMC Public Health 15 , 457 (2015).

Brown, T. et al. Interventions for preventing obesity in children. Cochrane Database Syst. Rev. 7 , Cd001871 (2019). A Cochrane review involving 153 RCTs of diet and/or physical activity interventions to prevent obesity in children and adolescents, highlighting varying effectiveness of interventions in different age groups.

PubMed   Google Scholar  

Le, L. K.-D. et al. Prevention of high body mass index and eating disorders: a systematic review and meta-analysis. Eat. Weight Disord. 27 , 2989–3003 (2022).

Nobles, J., Summerbell, C., Brown, T., Jago, R. & Moore, T. A secondary analysis of the childhood obesity prevention Cochrane Review through a wider determinants of health lens: implications for research funders, researchers, policymakers and practitioners. Int. J. Behav. Nutr. Phys. Act. 18 , 22 (2021).

Rai, K. K., Dogra, S. A., Barber, S., Adab, P. & Summerbell, C. A scoping review and systematic mapping of health promotion interventions associated with obesity in Islamic religious settings in the UK. Obes. Rev. 20 , 1231–1261 (2019).

World Health Organization. Nutrition Action in Schools: A Review of Evidence Related to the Nutrition-Friendly Schools Initiative (WHO, 2021).

Daly-Smith, A. et al. Using a multi-stakeholder experience-based design process to co-develop the Creating Active Schools Framework. Int. J. Behav. Nutr. Phys. Act. 17 , 13 (2020).

Tibbitts, B. et al. Considerations for individual-level versus whole-school physical activity interventions: stakeholder perspectives. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph18147628 (2021).

Askie, L. M. et al. Interventions commenced by early infancy to prevent childhood obesity-The EPOCH Collaboration: an individual participant data prospective meta-analysis of four randomized controlled trials. Pediatr. Obes. 15 , e12618 (2020). To our knowledge, the first prospective individual participant data meta-analysis showing that interventions commencing in late pregnancy or very early childhood are associated with healthier BMI z -score at age 18–24 months.

Seidler, A. L. et al. Examining the sustainability of effects of early childhood obesity prevention interventions: follow-up of the EPOCH individual participant data prospective meta-analysis. Pediatr. Obes. 17 , e12919 (2022).

Taylor, R. W. et al. Sleep, nutrition, and physical activity interventions to prevent obesity in infancy: follow-up of the prevention of overweight in infancy (POI) randomized controlled trial at ages 3.5 and 5 y. Am. J. Clin. Nutr. 108 , 228–236 (2018).

Mihrshahi, S. et al. A review of registered randomized controlled trials for the prevention of obesity in infancy. Int. J. Environ. Res. Public Health 18 , 2444 (2021).

Farooq, M. A. et al. Timing of the decline in physical activity in childhood and adolescence: Gateshead Millennium Cohort Study. Br. J. Sports Med. 52 , 1002–1006 (2018).

van Sluijs, E. M. F. et al. Physical activity behaviours in adolescence: current evidence and opportunities for intervention. Lancet 398 , 429–442 (2021).

Griffin, N. et al. A critique of the English national policy from a social determinants of health perspective using a realist and problem representation approach: the ‘Childhood Obesity: a plan for action’ (2016, 2018, 2019). BMC Public Health 21 , 2284 (2021).

Knai, C., Lobstein, T., Petticrew, M., Rutter, H. & Savona, N. England’s childhood obesity action plan II. Br. Med. J. 362 , k3098 (2018).

World Health Organization. WHO European Regional Obesity Report 2022 (WHO, 2022).

Zemrani, B., Gehri, M., Masserey, E., Knob, C. & Pellaton, R. A hidden side of the COVID-19 pandemic in children: the double burden of undernutrition and overnutrition. Int. J. Equity Health 20 , 44 (2021).

Alman, K. L. et al. Dietetic management of obesity and severe obesity in children and adolescents: a scoping review of guidelines. Obes. Rev. https://doi.org/10.1111/obr.13132 (2020).

Pfeiffle, S. et al. Current recommendations for nutritional management of overweight and obesity in children and adolescents: a structured framework. Nutrients https://doi.org/10.3390/nu11020362 (2019).

Scottish Intercollegiate Guidelines Network. Management of obesity. A National Clinical Guideline . SIGN 115 (SIGN, 2010).

Reinehr, T. et al. Two-year follow-up in 21,784 overweight children and adolescents with lifestyle intervention. Obesity 17 , 1196–1199 (2009).

Ells, L. J. et al. Interventions for treating children and adolescents with overweight and obesity: an overview of Cochrane reviews. Int. J. Obes. 42 , 1823–1833 (2018).

Al‐Khudairy, L. et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese adolescents aged 12 to 17 years. Cochrane Database Syst. Rev. 6 , CD012691 (2017). One of three Cochrane reviews looking at lifestyle treatment of paediatric obesity, in this case in adolescents, which identified 44 completed trials, finding low quality evidence of improvements in BMI and moderate quality evidence of improvements in weight.

Mead, E. et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese children from the age of 6 to 11 years. Cochrane Database Syst. Rev. 6 , CD012651 (2017). A Cochrane Review, involving 70 RCTs, showing that multicomponent behavioural interventions can lead to small, short-term reductions in BMI and related measures in children aged 6–11 years with obesity.

Ho, M. et al. Effectiveness of lifestyle interventions in child obesity: systematic review with meta-analysis. Pediatrics 130 , e1647–e1671 (2012). To our knowledge, the first systematic review of lifestyle interventions in children and adolescents with obesity to show improvements in cardiometabolic outcomes (LDL cholesterol, triglycerides, fasting insulin and blood pressure), as well as weight.

Clinical Practice Guideline Panel. Clinical practice guideline for multicomponent behavioral treatment of obesity and overweight in children and adolescents: current state of the evidence and research needs. American Psychological Association https://www.apa.org/obesity-guideline/clinical-practice-guideline.pdf (2018).

Nowicka, P. & Flodmark, C. E. Family therapy as a model for treating childhood obesity: useful tools for clinicians. Clin. Child. Psychol. Psychiatry 16 , 129–145 (2011).

Wilfley, D. E. et al. Improving access and systems of care for evidence-based childhood obesity treatment: conference key findings and next steps. Obesity 25 , 16–29 (2017).

Amiri, P. et al. Does motivational interviewing improve the weight management process in adolescents? A systematic review and meta-analysis. Int. J. Behav. Med. 29 , 78–103 (2022).

Kao, T. A., Ling, J., Hawn, R. & Vu, C. The effects of motivational interviewing on children’s body mass index and fat distributions: a systematic review and meta-analysis. Obes. Rev. 22 , e13308 (2021).

Hassapidou, M. et al. European Association for the Study of Obesity (EASO) position statement on medical nutrition therapy for the management of overweight and obesity in children and adolescents developed in collaboration with the European Federation of the Associations of Dietitians (EFAD). Obes. Facts https://doi.org/10.1159/000527540 (2022).

Hoelscher, D. M., Kirk, S., Ritchie, L. & Cunningham-Sabo, L. Position of the Academy of Nutrition and Dietetics: interventions for the prevention and treatment of pediatric overweight and obesity. J. Acad. Nutr. Diet. 113 , 1375–1394 (2013).

Hoare, J. K., Jebeile, H., Garnett, S. P. & Lister, N. B. Novel dietary interventions for adolescents with obesity: a narrative review. Pediatr. Obes. 16 , e12798 (2021).

Lister, N. et al. Nutritional adequacy of diets for adolescents with overweight and obesity: considerations for dietetic practice. Eur. J. Clin. Nutr. 71 , 646–651 (2017).

Andela, S. et al. Efficacy of very low-energy diet programs for weight loss: a systematic review with meta-analysis of intervention studies in children and adolescents with obesity. Obes. Rev. 20 , 871–882 (2019).

Srivastava, G. & Apovian, C. M. Current pharmacotherapy for obesity. Nat. Rev. Endocrinol. 14 , 12–24 (2018).

Apperley, L. J. et al. Childhood obesity: a review of current and future management options. Clin. Endocrinol. 96 , 288–301 (2022).

European Medicines Agency. Saxenda. European Medicines Agency https://www.ema.europa.eu/en/medicines/human/EPAR/saxenda (2022).

US Food and Drug Administration. FDA approves weight management drug for patients aged 12 and older. FDA https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-weight-management-drug-patients-aged-12-and-older (2021).

Kelly, A. S. et al. A randomized, controlled trial of liraglutide for adolescents with obesity. N. Engl. J. Med. 382 , 2117–2128 (2020). To our knowledge, the first RCT of liraglutide, administered daily via subcutaneous injection, in adolescents with obesity.

US Food and Drug Administration. FDA approves novel, dual-targeted treatment for type 2 iabetes. FDA https://www.fda.gov/news-events/press-announcements/fda-approves-novel-dual-targeted-treatment-type-2-diabetes (2022).

Jastreboff, A. M. et al. Tirzepatide once weekly for the treatment of obesity. N. Engl. J. Med. 387 , 205–216 (2022).

Holst, J. J. & Rosenkilde, M. M. GIP as a therapeutic target in diabetes and obesity: insight from incretin co-agonists. J. Clin. Endocrinol. Metab. https://doi.org/10.1210/clinem/dgaa327 (2020).

US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/study/NCT05260021 (2023).

Müller, T. D., Blüher, M., Tschöp, M. H. & DiMarchi, R. D. Anti-obesity drug discovery: advances and challenges. Nat. Rev. Drug Discov. 21 , 201–223 (2022).

Chalklin, C. G., Ryan Harper, E. G. & Beamish, A. J. Metabolic and bariatric surgery in adolescents. Curr. Obes. Rep. 10 , 61–69 (2021).

Albaugh, V. L. et al. Regulation of body weight: lessons learned from bariatric surgery. Mol. Metab. https://doi.org/10.1016/j.molmet.2022.101517 (2022).

Uhe, I. et al. Roux-en-Y gastric bypass, sleeve gastrectomy, or one-anastomosis gastric bypass? A systematic review and meta-analysis of randomized-controlled trials. Obesity 30 , 614–627 (2022).

Inge, T. H. et al. Long-term outcomes of bariatric surgery in adolescents with severe obesity (FABS-5+): a prospective follow-up analysis. Lancet Diabetes Endocrinol. 5 , 165–173 (2017).

Olbers, T. et al. Laparoscopic Roux-en-Y gastric bypass in adolescents with severe obesity (AMOS): a prospective, 5-year, Swedish nationwide study. Lancet Diabetes Endocrinol. https://doi.org/10.1016/S2213-8587(16)30424-7 (2017).

Pratt, J. S. et al. ASMBS pediatric metabolic and bariatric surgery guidelines, 2018. Surg. Obes. Relat. Dis. 14 , 882–901 (2018).

Jarvholm, K. et al. 5-year mental health and eating pattern outcomes following bariatric surgery in adolescents: a prospective cohort study. Lancet Child Adolesc. Health 4 , 210–219 (2020).

Van Der Heijden, L., Feskens, E. & Janse, A. Maintenance interventions for overweight or obesity in children: a systematic review and meta‐analysis. Obes. Rev. 19 , 798–809 (2018).

Park, J., Park, M.-J. & Seo, Y.-G. Effectiveness of information and communication technology on obesity in childhood and adolescence: systematic review and meta-analysis. J. Med. Internet Res. 23 , e29003 (2021).

Brissman, M., Beamish, A. J., Olbers, T. & Marcus, C. Prevalence of insufficient weight loss 5 years after Roux-en-Y gastric bypass: metabolic consequences and prediction estimates: a prospective registry study. BMJ Open 11 , e046407 (2021).

El Ansari, W. & Elhag, W. Weight regain and insufficient weight loss after bariatric surgery: definitions, prevalence, mechanisms, predictors, prevention and management strategies, and knowledge gaps – a scoping review. Obes. Surg. 31 , 1755–1766 (2021).

World Health Organization Regional Office for Europe. Weight Bias and Obesity Stigma: Considerations for the WHO European Region (WHO, 2017).

Puhl, R. M. & Latner, J. D. Stigma, obesity, and the health of the nation’s children. Psychol. Bull. 133 , 557–580 (2007).

Black, W. R. et al. Health-related quality of life across recent pediatric obesity classification recommendations. Children 8 , 303 (2021).

Finne, E., Reinehr, T., Schaefer, A., Winkel, K. & Kolip, P. Changes in self-reported and parent-reported health-related quality of life in overweight children and adolescents participating in an outpatient training: findings from a 12-month follow-up study. Health Qual. Life Outcomes 11 , 1 (2013).

Hill, A. J. Obesity in children and the ‘myth of psychological maladjustment’: self-esteem in the spotlight. Curr. Obes. Rep. 6 , 63–70 (2017).

McGregor, S., McKenna, J., Gately, P. & Hill, A. J. Self‐esteem outcomes over a summer camp for obese youth. Pediatr. Obes. 11 , 500–505 (2016).

Jansen, P., Mensah, F., Clifford, S., Nicholson, J. & Wake, M. Bidirectional associations between overweight and health-related quality of life from 4–11 years: longitudinal study of Australian children. Int. J. Obes. 37 , 1307–1313 (2013).

Mannan, M., Mamun, A., Doi, S. & Clavarino, A. Is there a bi-directional relationship between depression and obesity among adult men and women? Systematic review and bias-adjusted meta analysis. Asian J. Psychiatr. 21 , 51–66 (2016).

Lindberg, L., Hagman, E., Danielsson, P., Marcus, C. & Persson, M. Anxiety and depression in children and adolescents with obesity: a nationwide study in Sweden. BMC Med. 18 , 30 (2020).

Jebeile, H. et al. Association of pediatric obesity treatment, including a dietary component, with change in depression and anxiety: a systematic review and meta-analysis. JAMA Pediatr. 173 , e192841 (2019).

Gow, M. L. et al. Pediatric obesity treatment, self‐esteem, and body image: a systematic review with meta‐analysis. Pediatr. Obes. 15 , e12600 (2020).

Jebeile, H. et al. Treatment of obesity, with a dietary component, and eating disorder risk in children and adolescents: a systematic review with meta-analysis. Obes. Rev. 20 , 1287–1298 (2019). To our knowledge, the first systematic review to show that structured and professionally led weight management interventions in children and adolescents with obesity are associated with reductions in eating disorder risk and symptoms.

Kjeldbjerg, M. L. & Clausen, L. Prevalence of binge-eating disorder among children and adolescents: a systematic review and meta-analysis. Eur. Child Adolesc. Psychiatry https://doi.org/10.1007/s00787-021-01850-2 (2021).

Patton, G. C., Selzer, R., Coffey, C., Carlin, J. B. & Wolfe, R. Onset of adolescent eating disorders: population based cohort study over 3 years. BMJ 318 , 765–768 (1999).

Cortese, S. The association between ADHD and obesity: intriguing, progressively more investigated, but still puzzling. Brain Sci. 9 , 256 (2019).

Griffiths, L. J., Dezateux, C. & Hill, A. Is obesity associated with emotional and behavioural problems in children? Findings from the Millennium Cohort Study. Int. J. Pediatr. Obes. 6 , e423–e432 (2011).

Harrist, A. W. et al. The social and emotional lives of overweight, obese, and severely obese children. Child. Dev. 87 , 1564–1580 (2016).

Van Geel, M., Vedder, P. & Tanilon, J. Are overweight and obese youths more often bullied by their peers? A meta-analysis on the relation between weight status and bullying. Int. J. Obes. 38 , 1263–1267 (2014).

Albuquerque, D., Nóbrega, C., Manco, L. & Padez, C. The contribution of genetics and environment to obesity. Br. Med. Bull. 123 , 159–173 (2017).

Rancourt, D. & McCullough, M. B. Overlap in eating disorders and obesity in adolescence. Curr. Diabetes Rep. 15 , 78 (2015).

House, E. T. et al. Identifying eating disorders in adolescents and adults with overweight or obesity: a systematic review of screening questionnaires. Int. J. Eat. Disord. 55 , 1171–1193 (2022).

Lister, N. B., Baur, L. A., Paxton, S. J. & Jebeile, H. Contextualising eating disorder concerns for paediatric obesity treatment. Curr. Obes. Rep. 10 , 322–331 (2021).

Hagman, E. et al. Effect of an interactive mobile health support system and daily weight measurements for pediatric obesity treatment, a 1-year pragmatical clinical trial. Int. J. Obes. 46 , 1527–1533 (2022).

Swinburn, B. A. et al. The global syndemic of obesity, undernutrition, and climate change: The Lancet commission report. Lancet 393 , 791–846 (2019).

Whitehead, M., Taylor-Robinson, D. & Barr, B. Poverty, health, and covid-19. Br. Med. J. 372 , n376 (2021).

Morrison, K. M. et al. The CANadian Pediatric Weight Management Registry (CANPWR): lessons learned from developing and initiating a national, multi-centre study embedded in pediatric clinical practice. BMC Pediatr. 18 , 237 (2018).

Kirk, S. et al. Establishment of the pediatric obesity weight evaluation registry: a national research collaborative for identifying the optimal assessment and treatment of pediatric obesity. Child. Obes. 13 , 9–17 (2017).

Hagman, E., Danielsson, P., Lindberg, L. & Marcus, C., BORIS Steering Committee. Paediatric obesity treatment during 14 years in Sweden: lessons from the Swedish Childhood Obesity Treatment Register – BORIS. Pediatr. Obes. 15 , e12626 (2020).

Bohn, B. et al. Changing characteristics of obese children and adolescents entering pediatric lifestyle intervention programs in Germany over the last 11 years: an adiposity patients registry multicenter analysis of 65,453 children and adolescents. Obes. Facts 10 , 517–530 (2017).

Seidler, A. L. et al. A guide to prospective meta-analysis. BMJ 367 , l5342 (2019).

Hunter, K. E. et al. Transforming obesity prevention for children (TOPCHILD) collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity. BMJ Open 12 , e048166 (2022).

Lister, N. B. et al. Eating disorders in weight-related therapy (EDIT) collaboration: rationale and study design. Nutr. Res. Rev. https://doi.org/10.1017/S0954422423000045 (2023).

Hadjiyannakis, S. et al. Obesity class versus the Edmonton Obesity Staging System for Pediatrics to define health risk in childhood obesity: results from the CANPWR cross-sectional study. Lancet Child Adolesc. Health 3 , 398–407 (2019).

Brown, V. et al. Core outcome set for early intervention trials to prevent obesity in childhood (COS-EPOCH): agreement on “what” to measure. Int. J. Obes. 46 , 1867–1874 (2022). A stakeholder-informed study that identified the minimum outcomes recommended for collecting and reporting in obesity prevention trials in early childhood.

Han, J. C., Lawlor, D. A. & Kimm, S. Y. Childhood obesity. Lancet 375 , 1737–1748 (2010).

Shin, A. C., Zheng, H. & Berthoud, H. R. An expanded view of energy homeostasis: neural integration of metabolic, cognitive, and emotional drives to eat. Physiol. Behav. 97 , 572–580 (2009).

Lennerz, B., Wabitsch, M. & Eser, K. Ätiologie und genese [German]. Berufl. Rehabil. 1 , 14 (2014).

Google Scholar  

Pont, S. J., Puhl, R., Cook, S. R. & Slusser, W. Stigma experienced by children and adolescents with obesity. Pediatrics 140 , e20173034 (2017).

Talumaa, B., Brown, A., Batterham, R. L. & Kalea, A. Z. Effective strategies in ending weight stigma in healthcare. Obes. Rev. 23 , e13494 (2022).

Rubino, F. et al. Joint international consensus statement for ending stigma of obesity. Nat. Med. 26 , 485–497 (2020).

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Natalie B. Lister & Louise A. Baur

Institute of Endocrinology and Diabetes, The Children’s Hospital at Westmead, Sydney, New South Wales, Australia

Natalie B. Lister

Sydney School of Public Health, The University of Sydney, Sydney, New South Wales, Australia

Louise A. Baur

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Janine F. Felix

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Andrew J. Hill

Division of Paediatrics, Department of Clinical Science Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

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Introduction (L.A.B., J.F.F. and N.B.L.); Epidemiology (L.A.B. and J.F.F.); Mechanisms/pathophysiology (L.A.B., J.F.F., T.R. and M.W.); Diagnosis, screening and prevention (L.A.B., N.B.L., T.R., C.S. and M.W.); Management (L.A.B., N.B.L., A.J.H., C.M. and T.R.); Quality of life (L.A.B., N.B.L. and A.J.H.); Outlook (L.A.B., N.B.L., J.F.F., A.J.H., C.M., T.R., C.S. and M.W.); Overview of the Primer (L.A.B. and N.B.L.).

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A.J.H. reports receiving payment for consultancy advice for Slimming World (UK). L.A.B. reports receiving honoraria for speaking in forums organized by Novo Nordisk in relation to management of adolescent obesity and the ACTION-Teens study, which is sponsored by Novo Nordisk. L.A.B. is the Australian lead of the study. T.R. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). T.R. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk and Lilly, as well as honoraria for lectures in symposia organized by Novo Nordisk, Novartis and Merck. C.M. receives payments for consultancy advice and advisory board participation from Novo Nordisk, Oriflame Wellness, DeFaire AB and Itrim AB. C.M. also receives honoraria for speaking at meetings organized by Novo Nordisk and Astra Zeneca. C.M. is a shareholder and founder of Evira AB, a company that develops and sells systems for digital support for weight loss, and receives grants from Novo Nordisk for epidemiological studies of the effects of weight loss on future heath. M.W. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). M.W. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk, Regeneron, Boehringer Ingelheim and LG Chem, as well as honoraria for speaking in symposia organized by Novo Nordisk, Rhythm Pharmaceuticals and Infectopharm. M.W. is principal investigator in phase II and phase III studies of setmelanotide sponsored by Rhythm Pharmaceuticals. N.B.L., J.F.F. and C.S. declare no competing interests.

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Lister, N.B., Baur, L.A., Felix, J.F. et al. Child and adolescent obesity. Nat Rev Dis Primers 9 , 24 (2023). https://doi.org/10.1038/s41572-023-00435-4

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Changes in the incidence of childhood obesity.

Drs Cunningham and Hardy contributed equally as co-first authors.

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Solveig A. Cunningham , Shakia T. Hardy , Rebecca Jones , Carmen Ng , Michael R. Kramer , K.M. Venkat Narayan; Changes in the Incidence of Childhood Obesity. Pediatrics August 2022; 150 (2): e2021053708. 10.1542/peds.2021-053708

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Video Abstract

Examine childhood obesity incidence across recent cohorts.

We examined obesity incidence and prevalence across 2 cohorts of children in the United States 12 years apart using the Early Childhood Longitudinal Studies, parallel data sets following the kindergarten cohorts of 1998 and 2010 with direct anthropometric measurements at multiple time points through fifth grade in 2004 and 2016, respectively. We investigated annualized incidence rate and cumulative incidence proportion of obesity (BMI z-score ≥95 th percentile based on Centers for Disease Control and Prevention weight-for-age z -scores).

Among children who did not have obesity at kindergarten entry, there was a 4.5% relative increase in cumulative incidence of new obesity cases by end of fifth grade across cohorts (15.5% [14.1%–16.9%] vs 16.2% [15.0%–17.3%]), though annual incidence did not change substantially. The risk of incident obesity for children who had normal BMI at kindergarten entry stayed the same, but the risk of incident obesity among overweight kindergartners increased slightly. Social disparities in obesity incidence expanded: incidence of new cases during primary school among non-Hispanic Black children increased by 29% (95% confidence interval, 25%–34%), whereas risk for other race–ethnic groups plateaued or decreased. Children from the most socioeconomically disadvantaged households experienced 15% higher cumulative incidence across primary school in 2010 than 1998.

Incidence of childhood obesity was higher, occurred at younger ages, and was more severe than 12 years previous; thus, more youths may now be at risk for health consequences associated with early onset of obesity.

The prevalence of childhood obesity in the United States is high, and obesity in early life is linked with long-term poor physical and mental health.

The age-specific incidence of childhood obesity during primary school has grown higher during the 2000s compared with a decade earlier, is occurring at younger ages, and is reaching more severe levels, indicating the need for more comprehensive programs.

Obesity in early life remains a leading public health challenge because it is linked with long-term poor physical and mental health. 1 – 5   The prevalence of childhood obesity in the United States is among the highest in the world. 6 , 7   Although some data suggested that increases in the prevalence of obesity among primary school children had stalled in the early 2000s, 8   recent studies report that the prevalence of obesity among children and adolescents has continued to increase. 9  

To understand how the public health challenge of childhood obesity is evolving, and consequently the long-term prospects for population health, it is important to investigate temporal changes in incidence at the national level. Evidence from incidence can uncover patterns not observed from prevalence data. For example, earlier research indicated that, although the prevalence of childhood obesity in the United States was increasing with age, the incidence of new cases decreased with age, and that the children at highest risk of experiencing incident obesity by adolescence were those who had entered kindergarten overweight. 10   Prevalence estimates help us understand the proportion of the population affected by obesity and its health and psychosocial consequences; it also guides policy and programs for treatment and for long-term health care needs of the population. On the other hand, to inform prevention efforts, for example, the population groups at greatest risk if incidence and the ages when new cases are most likely to occur, we need age-specific incidence estimates. Furthermore, to monitor whether the force of disease is changing in the population and to evaluate whether prevention efforts are working, we must understand how the incidence of new cases is changing over time. With this information, we can understand the risk of developing obesity and how the epicenter of new obesity cases may be changing. We can also assess whether the differences in prevalence of obesity over time that have been reported in the literature are simply glitches, shifts in risk groups, or temporary stalls, or whether they portend coming improvements or worsening in health.

In this article, we examine changes in the incidence of obesity among cohorts of US children growing up 12 years apart, going through the same developmental stages at different points in time: 1 cohort in the late 1990s to early 2000s, the other in the 2010s. We used 2 parallel nationally representative kindergarten cohorts with direct anthropometric measurements at multiple points from average age 6 years to 11 years.

The Early Childhood Longitudinal Studies–Kindergarten are observational cohort studies conducted by the National Center for Education Statistics (NCES) of the U.S. Department of Education. They are nationally representative of the United States cohorts entering kindergarten in 1998 and 2010, thus approximately the birth cohorts of 1993 and 2005, respectively. Through a school-based, multistage sampling strategy, 21 069 kindergartners were enrolled in 1998 and 17 937 in the 2010 cohort. From each cohort, respectively 9796 and 8542 were retained and had follow-up at every data wave through the fifth grade, and we use this longitudinal sample for our analysis. Most attrition resulted from random selection for nonsampling because of survey costs of children who moved to different schools. The NCES constructed longitudinal weights and survey adjustments to maintain the representativeness of the analytic sample to the US population of children who entered kindergarten in 1998 and 2010. 11 , 12  

Data on the first cohort were collected 6 times between kindergarten entry (average age 5.7 years) and end of fifth grade (average age 11.2 years); data from the second cohort were collected 9 times using similar sampling and data collection methods. A visualization of the survey waves is shown in Supplemental Fig 3 .

All study procedures were approved by the NCES ethics review board; parents provided written informed consent and children assented to participate. The analysis in this report was submitted to the institutional review board of our institution, but deemed not applicable because it is a secondary data analysis of deidentified, public-use data.

Trained interviewers took anthropometric measurements at each data wave using a ShorrBoard to measure height and a digital scale to measure weight. We used these measurements to compute BMI z -scores by age and sex, with reference to growth charts developed by the Centers for Disease Control and Prevention. 13   Following standard recommendations, we defined normal weight as BMI z-score <85th percentile, overweight as 85th percentile ≤ BMI z -score <95 th percentile, and obesity as BMI z -score ≥95 th percentile. We additionally distinguished between moderate obesity, calculated as BMI z -score ≥95th percentile to <120% of the 95 th percentile and severe obesity as BMI z -score ≥120% of the 95th percentile. 14 – 16  

For each cohort, we calculated the distribution, prevalence, and incidence of obesity across data waves. We calculated obesity prevalence as the percentage of children with obesity at a given point in time. We then calculated annual obesity incidence rates as the annualized risk of developing obesity between data waves, calculated by dividing the number of new obesity cases in the wave by the number of person–years of follow-up contributed by children when they were nonobese. 10   For each individual, the follow-up period was defined as half of the time between each survey wave and the following wave for an incident case, or the entire time between these 2 waves if the child remained without obesity. This approach makes the implicit assumption that the obesity threshold was crossed at the midpoint between 2 data waves.

It could be that having more frequent data waves in the more recent cohort could allow us to observe more events of incident obesity that turn out to be fleeting, which would affect estimates of incident cases. Therefore, we estimated all incidence rates for the 2010–2011 cohort using the same waves of data as the 1998–1999, thus, using a balanced panel. The estimates, including all data waves available, are shown for reference in Supplemental Fig 4 .

We calculated cumulative obesity incidence as the proportion of children who developed obesity by the end of fifth grade, among children who did not have obesity at kindergarten entry. To examine the risk of obesity for demographic groups, we stratified cumulative incidence estimates by gender (boys or girls) and parent-reported race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other race, which includes Asian American, Pacific Islander, American Indian, and multiracial). To measure whether there were changes in socioeconomic patterning of obesity, we stratified cumulative incidence by household socioeconomic status (SES) quartiles at baseline (1 being the most disadvantaged households and 4 the most socioeconomically advantaged) and by school location at baseline (city, urban fringe and large town, small town and rural area). To consider importance of prenatal growth, we stratified by birth weight (low birth weight: <2500 g; normal birth weight: 2500–3999 g; high birth weight: ≥4000 g).

BMI was missing for <5% of observations in every full sample wave. However, some survey waves were designed as representative subsamples of the full cohorts to reduce survey costs, so some children were not measured at all data waves by design. When an observation was missing a BMI value at the first or last observation, we used the value from the closest available wave to avoid making estimates outside the available range. For observations with missing BMI values at other waves, the missing value was linearly interpolated. This is a reasonable assumption because the time between measurements was fairly short; the alternative method, multiple imputation, has been found to yield little difference in conclusions for within-person trajectories. 17   Missing data on demographic and socioeconomic variables was generally <1%, with the exception of SES in 1998 (5.9%) and urbanicity in 2010 (8.3%). We used listwise deletion for each subgroup analysis. Information on birth weight was missing for 17.2% in 1998 and 27.1% in 2010, so we show associations with weight status for those with missing birth weight.

Analyses were performed with R (version 3.5.1). 18  

Table 1 shows children by BMI categories in each of the 2 cohorts. When the birth cohorts of 1992/93 entered Kindergarten in 1998, 72.9% (95% confidence interval 71.2%–74.6%) had normal BMI, whereas 15.1% (13.6%–16.5%) had overweight and 12% already had obesity: 9.2% (8.1%–10.2% had moderate obesity and 2.9% had severe obesity (2.2%–3.5%). Twelve years later, when the birth cohorts of 2004 to 2005 entered kindergarten in 2010, the percentage of children starting school with a normal BMI had decreased to 69% (67.6%–70.5%). The percentage entering kindergarten with overweight in 2010 had not changed substantially, at 15.7% (14.6%–16.7%), but the percentage that already had obesity increased to 15.3%. Thus, unhealthy BMI levels shifted to younger ages between children growing up in the 1990s and those growing up in the 2000s as evidenced by increase in prevalence at kindergarten entry; in the more-recent cohort, obesity already affected 1 in 6 children before they entered school.

National Prevalence of Normal, Overweight, Obesity, and Severe Obesity BMI Between Kindergarten and Fifth Grade for the 1998 and 2010 Kindergarten Cohorts

Note: Prevalence of obesity is shown for the cohorts of United States children who entered kindergarten in 1998 and in 2010, followed yearly until the end of fifth grade. Other survey waves omitted for brevity. Percentages may not sum because of rounding. Data: Early Childhood Longitudinal Study–Kindergarten cohorts of 1998 and 2010. Normal weight, <85 th percentile; overweight, ≥85 th to <95 th percentile; all obesity, ≥95 th percentile; moderate obesity, ≥95th percentile to <120% of the 95th percentile; severe obesity, ≥120% of 95th percentile. CI, confidence interval.

At kindergarten entry, at average age 6 years, not only had a higher proportion of children in the more recent cohort already entered overweight and obesity, but a higher proportion had already reached severe obesity: 3.9% (3.3%–4.5%) in 2010, compared with 2.9% (2.2%–3.5%) in 1998. In both cohorts, the prevalence of severe obesity increased with increasing age. Additionally, the age-specific prevalence of severe obesity was greater at every age point in the 2010 as compared with 1998–1999 cohort. For example, in fifth grade (∼ age 11), the prevalence of severe obesity was 5.6% (4.8%–6.4%) in the 1998–1999 cohort versus 7.2% (6.4%–8.0%) in the 2010–2011 cohort. Thus, obesity was more severe in the recent cohort.

Fig 1 shows that the prevalence of obesity during primary school was higher in the 2010 cohort at all ages, with the largest differences being at kindergarten entry.

Prevalence of obesity between kindergarten and fifth grade for the 1998 and 2010 kindergarten cohorts. Notes: Prevalence of obesity is shown for the cohorts of US children who entered kindergarten in 1998 and in 2010, followed yearly until the end of fifth grade. Whiskers indicate 95% confidence intervals. Data: Early Childhood Longitudinal Study–Kindergarten cohorts of 1998 and 2010.

Prevalence of obesity between kindergarten and fifth grade for the 1998 and 2010 kindergarten cohorts. Notes: Prevalence of obesity is shown for the cohorts of US children who entered kindergarten in 1998 and in 2010, followed yearly until the end of fifth grade. Whiskers indicate 95% confidence intervals. Data: Early Childhood Longitudinal Study–Kindergarten cohorts of 1998 and 2010.

Fig 2 shows the annualized incidence rate of obesity during primary school. The annual incidence of obesity was highest during kindergarten for the 1998 cohort, at 5.12 (4.01–6.22) per 100 person–years, and lower thereafter, oscillating around 3 per 100 person–years. In the 2010 cohort, annual incidence was somewhat lower during kindergarten but peaked in first grade at 4.12 (3.15–5.09) per 100 person–years and remained higher than for the 1998 cohort thereafter, reaching the largest difference in fifth grade at 3.86 (3.44–4.28) per 100 person–years, compared with 2.83 (2.35–3.31) per 100 person–years in the 1998 cohort.

Annualized incidence of obesity between kindergarten and fifth grade for the 1998 and 2010 kindergarten cohorts. Notes: Annualized incidence of obesity is calculated using balanced measurement waves, with person–years at risk in the 2010 cohort calculated after excluding the spring second-grade, fall third-grade, and spring fourth-grade waves, for which no parallel measure was taken in the 1998 cohort. Children with obesity at any previous wave were excluded from calculations for subsequent waves because they were not eligible for incident obesity. Whiskers indicate 95% confidence intervals. Data: Early Childhood Longitudinal Study–Kindergarten Cohorts of 1998 and 2010.

Annualized incidence of obesity between kindergarten and fifth grade for the 1998 and 2010 kindergarten cohorts. Notes: Annualized incidence of obesity is calculated using balanced measurement waves, with person–years at risk in the 2010 cohort calculated after excluding the spring second-grade, fall third-grade, and spring fourth-grade waves, for which no parallel measure was taken in the 1998 cohort. Children with obesity at any previous wave were excluded from calculations for subsequent waves because they were not eligible for incident obesity. Whiskers indicate 95% confidence intervals. Data: Early Childhood Longitudinal Study–Kindergarten Cohorts of 1998 and 2010.

Table 2 shows the cumulative incidence proportion (eg, “risk”) of obesity by age 11 among children who entered kindergarten nonobese for the 1998 and 2010 kindergarten cohorts. Among all children who did not already have obesity when they entered kindergarten, cumulative incidence of obesity between kindergarten entry and the end of fifth grade (ages 6 and 11 years) was 16.2% (15.0–17.3) in 2010; this is a 4.5% relative increase over the cumulative incidence of 15.5% for children over the same age span 12 years earlier.

Cumulative Incidence Proportion of Obesity From Kindergarten Through Fifth Grade Across Sociodemographic Groups and Risk Ratios Comparing the US 1998 and 2010 Kindergarten Cohorts

Note: Number of incident cases of obesity per 100 person–years between the time the children entered kindergarten and when they completed fifth grade are shown for the cohorts of United States children who entered kindergarten in 1998 and in 2010. Children with obesity in kindergarten are excluded from this table because they were not eligible for incident obesity. Race or ethnic group was reported by parents of the children or collected from school records. The category designated as “other” includes Asian American, Pacific Islander, American Indian, and multiracial background. Data: Early Childhood Longitudinal Study–Kindergarten cohorts of 1998 and 2010. CI, confidence interval.

When further stratified by BMI category at kindergarten entry, differences emerge. There was no change between the 2 cohorts in the risk of developing obesity for children who entered with normal BMI (9.8% cumulative incidence for both cohorts). However, the risk of developing obesity for children who entered kindergarten overweight increased slightly from 42.9% (38.0–47.9) to 44.3% (41.3%–47.3%) in the later cohort. These patterns suggest that the cumulative risk of developing obesity during primary school was higher for the 2010–2011 kindergarten entrants than for new kindergartners 12 years earlier for children who already had overweight at average age 6 years.

Table 2 shows 5.5-year cumulative incidence proportion of obesity stratified by sociodemographic groups. Boys had higher risk of developing obesity than girls in each cohort, but only boys experienced an increase in risk in 2010 to 2011 (17.7% [16.1%–19.4%]) as compared with 1998 to 1999 (16.2% [14.2%–18.3%]), with a relative risk of 1.09 (1.08–1.11).

Non-Hispanic Black kindergartners were 29% (risk ratio [RR] 1.29, 95% confidence interval 1.25–1.34) more likely to develop new onset obesity by fifth grade in the 2010–2011 cohort compared with non-Hispanic Black children in the 1998–1999 cohort. Although in the late 1990s, Hispanic children were experiencing the highest incidence of obesity during primary school (19.9% [16.1–23.8]), they were surpassed by non-Hispanic Black children in the 2000s. Thus, there were widening disadvantages in obesity for non-Hispanic Black children compared with all other groups. The risk of incident obesity during primary school plateaued for Hispanic and Non-Hispanic White children, though at a higher level for Hispanic (19.9%) than for White children (13.6%). Children in other race groups, which includes Asian American, Pacific Islander, and American Indian, had a 16% reduction in risk of developing obesity during primary school between the 2 cohorts (RR 0.84 [0.80–0.87]); thus, they surpassed non-Hispanic White children as the group with lowest incidence in the 2010–2011 cohort (13.4% [10.1%–16.7%]).

The risks of developing obesity stayed similar for children in the middle of the socioeconomic spectrum across the 2 cohorts, but increased by 15% for children from the least advantaged households (RR 1.15 [1.12–1.17]), from 17.7% (14.6%–20.8%) to 20.3% (18.0–22.6)) and from the most advantaged households (RR 1.15 [1.14–1.17]). The socioeconomic gradient of obesity incidence was maintained and the disadvantages in terms of unhealthy weight increased for the most socioeconomically disadvantaged children relative to all other SES groups.

The risks of developing obesity were similarly distributed across urban, suburban, and rural schools in 1998, though the lowest risks were in suburban schools. Children in suburban schools and large towns had the largest increases in incidence, leading to even more similarity across environments for the 2010 cohort (RR 1.13 [1.11–1.15]).

To consider intergenerational transmission of risk, Table 2 presents obesity incidence in relation to birth weight. These preliminary data indicate that incidence of obesity in primary school increased linearly with birth weight in the early 2000s but followed a J-shape a decade later because of large relative increases in risk for children born with low birth weight and high birth weight.

Previous studies reporting on the prevalence of childhood obesity have shown continued increases in obesity during the past decade, with plateauing of obesity prevalence among some age groups. 9   This study contributes by identifying how the incidence of new cases has changed between recent cohorts.

Compared with children born in the early 1990s, those born in the mid-2000s experienced obesity with higher incidence, at younger ages, and at more severe levels. In 2010, a higher proportion of children arrived at kindergarten with moderate or severe obesity, compared with the cohort of children passing through this age group 12 years earlier. This pattern suggests earlier onset of elevated BMI in the more-recent cohort occurring during the preschool years. Increases in BMI across the 2 cohorts and higher proportions reaching severe obesity indicate temporal trends to more children having elevated BMI and severely high levels of BMI.

Across the 2 cohorts, social disparities in unhealthy weight increased. Most markedly, the risk of developing obesity during primary school increased significantly for non-Hispanic Black children, surpassing that of Hispanic children. During this period, other race and ethnic groups experienced plateauing or even decreasing incidence of obesity. Recognizing that the obesity risks faced by Black children are continuing to increase highlights the need to identify factors that may underlie their vulnerability to develop more successful prevention efforts. 19   Although extensive public health efforts have been directed toward childhood obesity since 2010, such as the Let’s Move! campaign and the Healthy, Hunger-Free Kids Act, these policies have had no impact on reducing population-level obesity. 20   The heterogenous risk of obesity by race–ethnicity, coupled with the lack of impact of interventions, highlights the need for public health policies to be tailored to counterbalance obesogenic factors. Notably, we identified increasing heterogeneity in incidence of obesity across schools in urban, suburban, and rural areas because all locations converged toward higher obesity incidence.

That children from both the highest and lowest socioeconomic households were more likely to develop obesity in the 2010s is a reminder that children of all walks of life are at risk for obesity. At the same time, the socioeconomic gradient of obesity has persisted because children from the most disadvantaged households experienced increases in incidence higher than other children; thus, their disadvantages in terms of long-term health continue to grow. Boys experienced increases in incidence of obesity during primary school but girls did not, suggesting the importance of psychosocial, behavioral, and epigenetic factors beyond those originating from race, ethnic, and socioeconomic circumstances.

Among these factors, the intergenerational transmission of obesity may offer insights into changes in obesity across cohorts. Specifically, obesity has been increasing among women of reproductive ages, 9 , 21 , 22   and maternal obesity predisposes children to develop obesity 23 – 25   ; consequently, more children in recent cohorts may be predisposed to developing obesity. Information on mothers’ health was not collected in the Early Childhood Longitudinal Study, so we cannot assess this proposition directly. To attempt to capture some prenatal factors, we examined associations between birth weight and obesity incidence. The proportion of children born with low and high birth weight was similar across the 2 cohorts (6.3% and 6.7% low birth weight and 8.9% and 6.6% high birth weight), but the incidence of obesity for children who were born small or large was substantially higher in the more-recent cohort. These patterns show mixed support for the possibility that childhood obesity incidence is increasing because of changing maternal and prenatal exposures; because of the high proportion of children missing data on birth weight, this hypothesis should be examined with other data sets.

In light of the observed higher incidence of obesity across primary school, more children in the recent cohort will be at risk for the health consequences that can develop with obesity, including diabetes, cardiovascular conditions, and mobility limitations. Data from the SEARCH for Diabetes in Youth (SEARCH) and Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) studies, among others, indicate that children with obesity have 5 times higher risk of diabetes in childhood, which in turn is associated with cardiovascular complications, lower quality of life, and mortality. 26 – 29  

Given the observed younger onset of obesity and severe obesity, more-recent cohorts will be exposed to obesity at more developmental periods and for longer durations. Obesity is difficult to reverse, even in childhood, and tends to endure into adolescence and even adulthood. 10 , 30 – 35   Earlier onset of obesity is associated with higher risk of severe obesity in adulthood and type 2 diabetes. 25 , 36 – 38   It will be important for studies to quantify the implications for health of age of onset and duration of childhood obesity.

The short-term and long-term consequences of obesity are most strongly associated with severity, including in children. 39 , 40   Children with severe obesity have more cardiovascular risk factors than children with moderate obesity; these include elevated blood pressure, atherosclerosis, and cardiac abnormalities. 4 , 5   They also have higher prevalence of metabolic syndrome, sleep apnea, nonalcoholic fatty liver disease, and musculoskeletal problems. 39 – 42   With more children experiencing severe obesity in the recent cohort, we can expect higher risks of these comorbidities in today’s high-schoolers and future adults. As these youths reach childbearing years, maternal obesity could increase intergenerational transmission of obesity for children born in the 2030s and 2040s.

This study has limitations. The cohorts examined here are representative of children who entered kindergarten in the United States in 1998 and in 2010, and may not reflect the experiences of even more-recent cohorts. However, these are the currently available nationally representative cohort studies of children and represent today’s teens and young adults. These cohorts experienced periods of time when obesity was high and also experienced health interventions that are still being used or considered today. We are comparing cohorts of children only 12 years apart. These data allow us to observe trends over contemporary cohorts, but, in a period of just >1 decade, population-level changes that can be observed will be small. We did not account for the fact that children who had obesity at 1 time point might have subsequently lost weight and transitioned to overweight or to normal BMI. We also do not have information on growth patterns before kindergarten, so we cannot track incidence of obesity across childhood, nor identify the age at which children who entered kindergarten with overweight or obesity had first developed excess BMI. Lacking data before and after the period of observation, called left and right censoring, is common in studies of disease incidence. Observational data do not allow us to assess possible causal pathways, but they are the only option for identifying trends over time at the national level.

Approximately 40% of today’s high school students and young adults experienced obesity or overweight before leaving primary school. Young people who were born in the 2000s experienced obesity incidence at even higher levels, at younger ages, and at higher severity during the developmentally important stages of childhood, compared even with the cohort 12 years earlier; this was despite the fact that they were exposed to more intensive efforts to prevent obesity than had earlier cohorts. These data have several implications for policies and prevention. First, they point to hitherto insufficient knowledge about susceptibility to childhood obesity to make substantial progress in obesity prevention. The data provide strong justification to focus efforts on research and policies aimed at preschool children. With advances in molecular and analytic technologies, integrative research connecting the biological factors and social determinants of early onset of obesity is warranted. Such approaches may shed light on childhood weight phenotypes and point to more precise interventions in terms of strategy and timing. We speculate that prevention programs need to look beyond simple solutions to obesity, including addressing the substantial changes in physical activity and in food environments that have progressed in recent decades, as well as the epigenetic and neuro–psycho–behavioral pathways to obesity. Ongoing surveillance is required to monitor changes in health at population levels. There are no national longitudinal studies of health for today’s children; such data will be necessary to map the changing incidence of obesity across age, gender, social and economic factors, and geographies.

Dr Cunningham conceptualized, designed and led completion of the study and drafted and revised the manuscript; Dr Hardy contributed to drafting the initial manuscript and to carrying out the initial analyses, and reviewed and revised the manuscript; Dr Jones carried out analyses for the revision and contributed to revising the manuscript; Dr Ng carried out the initial analyses and reviewed the manuscript; Dr Kramer contributed to conceptualizing and designing the study, carried out the analyses, and reviewed and revised the manuscript; Dr Narayan contributed to conceptualizing and designing the study and critically reviewed the manuscript for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award R01DK115937, and by the National Heart, Lung, and Blood Institute under award T32HL130025. The funder played no role in the design and conduct of the study.

CONFLICT OF INTEREST DISCLAIMER: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2022-056547 .

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Review article, childhood and adolescent obesity: a review.

child obesity research paper

  • 1 Division of Endocrinology, Diabetes and Metabolism, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States
  • 2 Division of Adolescent Medicine, Department of Pediatrics, Medical College of Wisconsin Affiliated Hospitals, Milwaukee, WI, United States
  • 3 Division of Adolescent Medicine, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, United States

Obesity is a complex condition that interweaves biological, developmental, environmental, behavioral, and genetic factors; it is a significant public health problem. The most common cause of obesity throughout childhood and adolescence is an inequity in energy balance; that is, excess caloric intake without appropriate caloric expenditure. Adiposity rebound (AR) in early childhood is a risk factor for obesity in adolescence and adulthood. The increasing prevalence of childhood and adolescent obesity is associated with a rise in comorbidities previously identified in the adult population, such as Type 2 Diabetes Mellitus, Hypertension, Non-alcoholic Fatty Liver disease (NAFLD), Obstructive Sleep Apnea (OSA), and Dyslipidemia. Due to the lack of a single treatment option to address obesity, clinicians have generally relied on counseling dietary changes and exercise. Due to psychosocial issues that may accompany adolescence regarding body habitus, this approach can have negative results. Teens can develop unhealthy eating habits that result in Bulimia Nervosa (BN), Binge- Eating Disorder (BED), or Night eating syndrome (NES). Others can develop Anorexia Nervosa (AN) as they attempt to restrict their diet and overshoot their goal of “being healthy.” To date, lifestyle interventions have shown only modest effects on weight loss. Emerging findings from basic science as well as interventional drug trials utilizing GLP-1 agonists have demonstrated success in effective weight loss in obese adults, adolescents, and pediatric patients. However, there is limited data on the efficacy and safety of other weight-loss medications in children and adolescents. Nearly 6% of adolescents in the United States are severely obese and bariatric surgery as a treatment consideration will be discussed. In summary, this paper will overview the pathophysiology, clinical, and psychological implications, and treatment options available for obese pediatric and adolescent patients.

Introduction

Obesity is a complex issue that affects children across all age groups ( 1 – 3 ). One-third of children and adolescents in the United States are classified as either overweight or obese. There is no single element causing this epidemic, but obesity is due to complex interactions between biological, developmental, behavioral, genetic, and environmental factors ( 4 ). The role of epigenetics and the gut microbiome, as well as intrauterine and intergenerational effects, have recently emerged as contributing factors to the obesity epidemic ( 5 , 6 ). Other factors including small for gestational age (SGA) status at birth, formula rather than breast feeding in infancy, and early introduction of protein in infant's dietary intake have been reportedly associated with weight gain that can persist later in life ( 6 – 8 ). The rising prevalence of childhood obesity poses a significant public health challenge by increasing the burden of chronic non-communicable diseases ( 1 , 9 ).

Obesity increases the risk of developing early puberty in children ( 10 ), menstrual irregularities in adolescent girls ( 1 , 11 ), sleep disorders such as obstructive sleep apnea (OSA) ( 1 , 12 ), cardiovascular risk factors that include Prediabetes, Type 2 Diabetes, High Cholesterol levels, Hypertension, NAFLD, and Metabolic syndrome ( 1 , 2 ). Additionally, obese children and adolescents can suffer from psychological issues such as depression, anxiety, poor self-esteem, body image and peer relationships, and eating disorders ( 13 , 14 ).

So far, interventions for overweight/obesity prevention have mainly focused on behavioral changes in an individual such as increasing daily physical exercise or improving quality of diet with restricting excess calorie intake ( 1 , 15 , 16 ). However, these efforts have had limited results. In addition to behavioral and dietary recommendations, changes in the community-based environment such as promotion of healthy food choices by taxing unhealthy foods ( 17 ), improving lunch food quality and increasing daily physical activity at school and childcare centers, are extra measures that are needed ( 16 ). These interventions may include a ban on unhealthy food advertisements aimed at children as well as access to playgrounds and green spaces where families can feel their children can safely recreate. Also, this will limit screen time for adolescents as well as younger children.

However, even with the above changes, pharmacotherapy and/or bariatric surgery will likely remain a necessary option for those youth with morbid obesity ( 1 ). This review summarizes our current understanding of the factors associated with obesity, the physiological and psychological effects of obesity on children and adolescents, and intervention strategies that may prevent future concomitant issues.

Definition of Childhood Obesity

Body mass index (BMI) is an inexpensive method to assess body fat and is derived from a formula derived from height and weight in children over 2 years of age ( 1 , 18 , 19 ). Although more sophisticated methods exist that can determine body fat directly, they are costly and not readily available. These methods include measuring skinfold thickness with a caliper, Bioelectrical impedance, Hydro densitometry, Dual-energy X-ray Absorptiometry (DEXA), and Air Displacement Plethysmography ( 2 ).

BMI provides a reasonable estimate of body fat indirectly in the healthy pediatric population and studies have shown that BMI correlates with body fat and future health risks ( 18 ). Unlike in adults, Z-scores or percentiles are used to represent BMI in children and vary with the age and sex of the child. BMI Z-score cut off points of >1.0, >2.0, and >3.0 are recommended by the World Health Organization (WHO) to define at risk of overweight, overweight and obesity, respectively ( 19 ). However, in terms of percentiles, overweight is applied when BMI is >85th percentile <95th percentile, whereas obesity is BMI > 95th percentile ( 20 – 22 ). Although BMI Z-scores can be converted to BMI percentiles, the percentiles need to be rounded and can misclassify some normal-weight children in the under or overweight category ( 19 ). Therefore, to prevent these inaccuracies and for easier understanding, it is recommended that the BMI Z-scores in children should be used in research whereas BMI percentiles are best used in the clinical settings ( 20 ).

As BMI does not directly measure body fat, it is an excellent screening method, but should not be used solely for diagnostic purposes ( 23 ). Using 85th percentile as a cut off point for healthy weight may miss an opportunity to obtain crucial information on diet, physical activity, and family history. Once this information is obtained, it may allow the provider an opportunity to offer appropriate anticipatory guidance to the families.

Pathophysiology of Obesity

The pathophysiology of obesity is complex that results from a combination of individual and societal factors. At the individual level, biological, and physiological factors in the presence of ones' own genetic risk influence eating behaviors and tendency to gain weight ( 1 ). Societal factors include influence of the family, community and socio-economic resources that further shape these behaviors ( Figure 1 ) ( 3 , 24 ).

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Figure 1 . Multidimensional factors contributing to child and adolescent obesity.

Biological Factors

There is a complex architecture of neural and hormonal regulatory control, the Gut-Brain axis, which plays a significant role in hunger and satiety ( Figure 2 ). Sensory stimulation (smell, sight, and taste), gastrointestinal signals (peptides, neural signals), and circulating hormones further contribute to food intake ( 25 – 27 ).

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Figure 2 . Pictorial representation of the Hunger-Satiety pathway a and the various hormones b involved in the pathway. a, Y1/Y5R and MC3/4 are second order neuro receptors which are responsible in either the hunger or satiety pathway. Neurons in the ARC include: NPY, Neuropeptide Y; AgRP, Agouti-Related Peptide; POMC, Pro-Opiomelanocortin; CART, Cocaine-and Amphetamine-regulated Transcript; α-MSH, α-Melanocyte Stimulating Hormone. b, PYY, Peptide YY; PP, Pancreatic Polypeptide; GLP-1, Glucagon-Like Peptide- I; OMX, Oxyntomodulin.

The hypothalamus is the crucial region in the brain that regulates appetite and is controlled by key hormones. Ghrelin, a hunger-stimulating (orexigenic) hormone, is mainly released from the stomach. On the other hand, leptin is primarily secreted from adipose tissue and serves as a signal for the brain regarding the body's energy stores and functions as an appetite -suppressing (anorexigenic) hormone. Several other appetite-suppressing (anorexigenic) hormones are released from the pancreas and gut in response to food intake and reach the hypothalamus through the brain-blood barrier (BBB) ( 28 – 32 ). These anorexigenic and orexigenic hormones regulate energy balance by stimulating hunger and satiety by expression of various signaling pathways in the arcuate nucleus (ARC) of the hypothalamus ( Figure 2 ) ( 28 , 33 ). Dysregulation of appetite due to blunted suppression or loss of caloric sensing signals can result in obesity and its morbidities ( 34 ).

Emotional dysfunction due to psychiatric disorders can cause stress and an abnormal sleep-wake cycles. These modifications in biological rhythms can result in increased appetite, mainly due to ghrelin, and can contribute to emotional eating ( 35 ).

Recently, the role of changes in the gut microbiome with increased weight gain through several pathways has been described in literature ( 36 , 37 ). The human gut serves as a host to trillions of microorganisms, referred to as gut microbiota. The dominant gut microbial phyla are Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia, with Firmicutes and Bacteroidetes representing 90% of human gut microbiota ( 5 , 38 ). The microbes in the gut have a symbiotic relationship within their human host and provide a nutrient-rich environment. Gut microbiota can be affected by various factors that include gestational age at birth, mode of infant delivery, type of neonatal and infant feeding, introduction of solid food, feeding practices and external factors like antibiotic use ( 5 , 38 ). Also, the maturation of the bacterial phyla that occurs from birth to adulthood ( 39 ), is influenced by genetics, environment, diet, lifestyle, and gut physiology and stabilizes in adulthood ( 5 , 39 , 40 ). Gut microbiota is unique to each individual and plays a specific role in maintaining structural integrity, and the mucosal barrier of the gut, nutrient metabolism, immune response, and protection against pathogens ( 5 , 37 , 38 ). In addition, the microbiota ferments the indigestible food and synthesizes other essential micronutrients as well as short chain fatty acids (SCFAs') ( 40 , 41 ). Dysbiosis or imbalance of the gut microbiota, in particularly the role of SCFA has been linked with the patho-physiology of obesity ( 36 , 38 , 41 , 42 ). SCFAs' are produced by anaerobic fermentation of dietary fiber and indigestible starch and play a role in mammalian energy metabolism by influencing gut-brain communication axis. Emerging evidence has shown that increased ratio of Firmicutes to Bacteroidetes causes increased energy extraction of calories from diets and is evidenced by increased production of short chain fatty acids (SCFAs') ( 43 – 45 ). However, this relationship is not affirmed yet, as a negative relationship between SCFA levels and obesity has also been reported ( 46 ). Due to the conflicting data, additional randomized control trials are needed to clarify the role of SCFA's in obese and non-obese individuals.

The gut microbiota also has a bidirectional interaction with the liver, and various additional factors such as diet, genetics, and the environment play a key role in this relationship. The Gut- Liver Axis is interconnected at various levels that include the mucus barrier, epithelial barrier, and gut microbiome and are essential to maintain normal homeostasis ( 47 ). Increased intestinal mucosal permeability can disrupt the gut-liver axis, which releases various inflammatory markers, activates an innate immune response in the liver, and results in a spectrum of liver diseases that include hepatic steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC) ( 48 , 49 ).

Other medical conditions, including type 2 Diabetes Mellitus, Metabolic Syndrome, eating disorders as well as psychological conditions such as anxiety and depression are associated with the gut microbiome ( 50 – 53 ).

Genetic Factors

Genetic causes of obesity can either be monogenic or polygenic types. Monogenic obesity is rare, mainly due to mutations in genes within the leptin/melanocortin pathway in the hypothalamus that is essential for the regulation of food intake/satiety, body weight, and energy metabolism ( 54 ). Leptin regulates eating behaviors, the onset of puberty, and T-cell immunity ( 55 ). About 3% of obese children have mutations in the leptin ( LEP ) gene and the leptin receptor (LEPR) and can also present with delayed puberty and immune dysfunction ( 55 , 56 ). Obesity caused by other genetic mutations in the leptin-melanocortin pathway include proopiomelanocortin (POMC) and melanocortin receptor 4 (MC4R), brain-derived neurotrophic factor (BDNF), and the tyrosine kinase receptor B (NTRK2) genes ( 57 , 58 ). Patients with monogenic forms generally present during early childhood (by 2 years old) with severe obesity and abnormal feeding behaviors ( 59 ). Other genetic causes of severe obesity are Prader Willi Syndrome (PWS), Alström syndrome, Bardet Biedl syndrome. Patients with these syndromes present with additional characteristics, including cognitive impairment, dysmorphic features, and organ-specific developmental abnormalities ( 60 ). Individuals who present with obesity, developmental delay, dysmorphic features, and organ dysfunction should receive a genetics referral for further evaluation.

Polygenic obesity is the more common form of obesity, caused by the combined effect of multiple genetic variants. It is the result of the interplay between genetic susceptibility and the environment, also known as the Gene-Environment Interaction (GEI) ( 61 – 64 ). Genome-wide association studies (GWAS) have identified gene variants [single nucleotide polymorphism (SNPs)] for body mass index (BMI) that likely act synergistically to affect body weight ( 65 ). Studies have identified genetic variants in several genes that may contribute to excessive weight gain by increasing hunger and food intake ( 66 – 68 ). When the genotype of an individual confers risk for obesity, exposure to an obesogenic environment may promote a state of energy imbalance due to behaviors that contribute to conserving rather than expending energy ( 69 , 70 ). Research studies have shown that obese individuals have a genetic variation that can influence their actions, such as increased food intake, lack of physical activity, a decreased metabolism, as well as an increased tendency to store body fat ( 63 , 66 , 67 , 69 , 70 ).

Recently the role of epigenetic factors in the development of obesity has emerged ( 71 ). The epigenetic phenomenon may alter gene expression without changing the underlying DNA sequence. In effect, epigenetic changes may result in the addition of chemical tags known as methyl groups, to the individual's chromosomes. This alteration can result in a phenomenon where critical genes are primed to on and off regulate. Complex physiological and psychological adjustment occur during infancy and can thereafter set the stage for health vs. disease. Developmental origins of health and disease (DOHaD) shows that early life environment can impact the risk of chronic diseases later in life due to fetal programming secondary to epigenetic changes ( 72 ). Maternal nutrition during the prenatal or early postnatal period may trigger these epigenetic changes and increase the risk for chronic conditions such as obesity, metabolic and cardiovascular disease due to epigenetic modifications that may persist and cause intergenerational effect on the health children and adults ( 58 , 73 , 74 ). Similarly, adverse childhood experiences (ACE) have been linked to a broad range of negative outcomes through epigenetic mechanisms ( 75 ) and promote unhealthy eating behaviors ( 76 , 77 ). Other factors such as diet, physical activity, environmental and psychosocial stressors can cause epigenetic changes and place an individual at risk for weight gain ( 78 ).

Developmental Factors

Eating behaviors evolve over the first few years of life. Young children learn to eat through their direct experience with food and observing others eating around them ( 79 ). During infancy, feeding defines the relationship of security and trust between a child and the parent. Early childhood eating behaviors shift to more self-directed control due to rapid physical, cognitive, communicative, and social development ( 80 ). Parents or caregivers determine the type of food that is made available to the infant and young child. However, due to economic limitations and parents having decreased time to prepare nutritious meals, consumption of processed and cheaper energy-dense foods have occurred in Western countries. Additionally, feeding practices often include providing large or super-sized portions of palatable foods and encouraging children to finish the complete meal (clean their plate even if they do not choose to), as seen across many cultures ( 81 , 82 ). Also, a segment of parents are overly concerned with dietary intake and may pressurize their child to eat what they perceive as a healthy diet, which can lead to unintended consequences ( 83 ). Parents' excessive restriction of food choices may result in poor self-regulation of energy intake by their child or adolescent. This action may inadvertently promote overconsumption of highly palatable restricted foods when available to the child or adolescent outside of parental control with resultant excessive weight gain ( 84 , 85 ).

During middle childhood, children start achieving greater independence, experience broader social networks, and expand their ability to develop more control over their food choices. Changes that occur in the setting of a new environment such as daycare or school allow exposure to different food options, limited physical activity, and often increased sedentary behaviors associated with school schedules ( 24 ). As the transition to adolescence occurs, physical and psychosocial development significantly affect food choices and eating patterns ( 25 ). During the teenage years, more independence and interaction with peers can impact the selection of fast foods that are calorically dense. Moreover, during the adolescent years, more sedentary behaviors such as video and computer use can limit physical exercise. Adolescence is also a period in development with an enhanced focus on appearance, body weight, and other psychological concerns ( 86 , 87 ).

Environmental Factors

Environmental changes within the past few decades, particularly easy access to high-calorie fast foods, increased consumption of sugary beverages, and sedentary lifestyles, are linked with rising obesity ( 88 ). The easy availability of high caloric fast foods, and super-sized portions, are increasingly common choices as individuals prefer these highly palatable and often less expensive foods over fruits and vegetables ( 89 ). The quality of lunches and snacks served in schools and childcare centers has been an area of debate and concern. Children and adolescents consume one-third to one-half of meals in the above settings. Despite policies in place at schools, encouraging foods, beverages, and snacks that are deemed healthier options, the effectiveness of these policies in improving children's dietary habits or change in obesity rate has not yet been seen ( 90 ). This is likely due to the fact that such policies primarily focus on improving dietary quality but not quantity which can impact the overweight or obese youth ( 91 ). Policies to implement taxes on sugary beverages are in effect in a few states in the US ( 92 ) as sugar and sugary beverages are associated with increased weight gain ( 2 , 3 ). This has resulted in reduction in sales of sugary drinks in these states, but the sales of these types of drinks has risen in neighboring states that did not implement the tax ( 93 ). Due to advancements in technology, children are spending increased time on electronic devices, limiting exercise options. Technology advancement is also disrupting the sleep-wake cycle, causing poor sleeping habits, and altered eating patterns ( 94 ). A study published on Canadian children showed that the access to and night-time use of electronic devices causes decreased sleep duration, resulting in excess body weight, inferior diet quality, and lower physical activity levels ( 95 ).

Infant nutrition has gained significant popularity in relation to causing overweight/obesity and other diseases later in life. Breast feeding is frequently discussed as providing protection against developing overweight/obesity in children ( 8 ). Considerable heterogeneity has been observed in studies and conducting randomized clinical trials between breast feeding vs. formula feeding is not feasible ( 8 ). Children fed with a low protein formula like breast milk are shown to have normal weight gain in early childhood as compared to those that are fed formulas with a high protein load ( 96 ). A recent Canadian childbirth cohort study showed that breast feeding within first year of life was inversely associated with weight gain and increased BMI ( 97 ). The effect was stronger if the child was exclusively breast fed directly vs. expressed breast milk or addition of formula or solid food ( 97 ). Also, due to the concern of poor growth in preterm or SGA infants, additional calories are often given for nutritional support in the form of macronutrient supplements. Most of these infants demonstrate “catch up growth.” In fact, there have been reports that in some children the extra nutritional support can increase the risk for overweight/obesity later in life. The association, however, is inconsistent. Recently a systemic review done on randomized controlled trials comparing the studies done in preterm and SGA infants with feeds with and without macronutrient supplements showed that macronutrient supplements may increase weight and length in toddlers but did not show a significant increase in the BMI during childhood ( 98 ). Increased growth velocity due to early introduction of formula milk and protein in infants' diet, may influence the obesity pathways, and can impact fetal programming for metabolic disease later in life ( 99 ).

General pediatricians caring for children with overweight/obesity, generally recommend endocrine testing as parents often believe that there may be an underlying cause for this condition and urge their primary providers to check for conditions such as thyroid abnormalities. Endocrine etiologies for obesity are rarely identified and patients with underlying endocrine disorders causing excessive weight gain usually are accompanied by attenuated growth patterns, such that a patient continues to gain weight with a decline in linear height ( 100 ). Various endocrine etiologies that one could consider in a patient with excessive weight gain in the setting of slow linear growth: severe hypothyroidism, growth hormone deficiency, and Cushing's disease/syndrome ( 58 , 100 ).

Clinical-Physiology of Pediatric Obesity

It is a well-known fact that early AR(increased BMI) before the age of 5 years is a risk factor for adult obesity, obesity-related comorbidities, and metabolic syndrome ( 101 – 103 ). Typically, body mass index (BMI) declines to a minimum in children before it starts increasing again into adulthood, also known as AR. Usually, AR happens between 5 and 7 years of age, but if it occurs before the age of 5 years is considered early AR. Early AR is a marker for higher risk for obesity-related comorbidities. These obesity-related health comorbidities include cardiovascular risk factors (hypertension, dyslipidemia, prediabetes, and type 2 diabetes), hormonal issues, orthopedic problems, sleep apnea, asthma, and fatty liver disease ( Figure 3 ) ( 9 ).

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Figure 3 . Obesity related co-morbidities a in children and adolescents. a, NAFLD, Non-Alcoholic Fatty Liver Disease; SCFE, Slipped Capital Femoral Epiphysis; PCOS, Polycystic Ovary Syndrome; OSA, Obstructive Sleep Apnea.

Clinical Comorbidities of Obesity in Children

Growth and puberty.

Excess weight gain in children can influence growth and pubertal development ( 10 ). Childhood obesity can cause prepubertal acceleration of linear growth velocity and advanced bone age in boys and girls ( 104 ). Hyperinsulinemia is a normal physiological state during puberty, but children with obesity can have abnormally high insulin levels ( 105 ). Leptin resistance also occurs in obese individuals who have higher leptin levels produced by their adipose tissue ( 55 , 106 ). The insulin and leptin levels can act on receptors that impact the growth plates with a resultant bone age advancement ( 55 ).

Adequate nutrition is essential for the typical timing and tempo of pubertal onset. Excessive weight gain can initiate early puberty, due to altered hormonal parameters ( 10 ). Obese children may present with premature adrenarche, thelarche, or precocious puberty (PP) ( 107 ). The association of early pubertal changes with obesity is consistent in girls, and is well-reported; however, data is sparse in boys ( 108 ). One US study conducted in racially diverse boys showed obese boys had delayed puberty, whereas overweight boys had early puberty as compared to normal-weight boys ( 109 ). Obese girls with PP have high leptin levels ( 110 , 111 ). Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) is a cross-sectional study and suggested an indirect relationship between elevated leptin levels, early puberty, and cardiometabolic and inflammatory markers in obese girls ( 112 ). Additionally, obese girls with premature adrenarche carry a higher risk for developing polycystic ovary syndrome (PCOS) in the future ( 113 , 114 ).

Sleep Disorders

Obesity is an independent risk factor for obstructive sleep apnea (OSA) in children and adolescents ( 12 , 115 ). Children with OSA have less deleterious consequences in terms of cardiovascular stress of metabolic syndrome when compared to adolescents and adults ( 116 , 117 ). In children, abnormal behaviors and neurocognitive dysfunction are the most critical and frequent end-organ morbidities associated with OSA ( 12 ). However, in adolescents, obesity and OSA can independently cause oxidative systemic stress and inflammation ( 118 , 119 ), and when this occurs concurrently, it can result in more severe metabolic dysfunction and cardiovascular outcomes later in life ( 120 ).

Other Comorbidities

Obesity is related to a clinical spectrum of liver abnormalities such as NAFLD ( 121 ); the most important cause of liver disease in children ( 122 – 124 ). NAFLD includes steatosis (increased liver fat without inflammation) and NASH (increased liver fat with inflammation and hepatic injury). While in some adults NAFLD can progress to an end-stage liver disease requiring liver transplant ( 125 , 126 ), the risk of progression during childhood is less well-defined ( 127 ). NAFLD is closely associated with metabolic syndrome including central obesity, insulin resistance, type 2 diabetes, dyslipidemia, and hypertension ( 128 ).

Obese children are also at risk for slipped capital femoral epiphysis (SCFE) ( 129 ), and sedentary lifestyle behaviors may have a negative influence on the brain structure and executive functioning, although the direction of causality is not clear ( 130 , 131 ).

Clinical Comorbidities of Obesity in Adolescents

Menstrual irregularities and pcos.

At the onset of puberty, physiologically, sex steroids can cause appropriate weight gain and body composition changes that should not affect normal menstruation ( 132 , 133 ). However, excessive weight gain in adolescent girls can result in irregular menstrual cycles and puts them at risk for PCOS due to increased androgen levels. Additionally, they can have excessive body hair (hirsutism), polycystic ovaries, and can suffer from distorted body images ( 134 , 135 ). Adolescent girls with PCOS also have an inherent risk for insulin resistance irrespective of their weight. However, weight gain further exacerbates their existing state of insulin resistance and increases the risk for obesity-related comorbidities such as metabolic syndrome, and type 2 diabetes. Although the diagnosis of PCOS can be challenging at this age due to an overlap with predictable pubertal changes, early intervention (appropriate weight loss and use of hormonal methods) can help restore menstrual cyclicity and future concerns related to childbearing ( 11 ).

Metabolic Syndrome and Sleep Disorders

Metabolic syndrome (MS) is a group of cardiovascular risk factors characterized by acanthosis nigricans, prediabetes, hypertension, dyslipidemia, and non-alcoholic steatohepatitis (NASH), that occurs from insulin resistance caused by obesity ( 136 ). Diagnosis of MS in adults requires at least three out of the five risk factors: increased central adiposity, hypertension, hyperglycemia, hypertriglyceridemia, or low HDL level. Definitions to diagnose MS are controversial in younger age groups, and many definitions have been proposed ( 136 ). This is due to the complex physiology of growth and development during puberty, which causes significant overlap between MS and features of normal growth. However, childhood obesity is associated with an inflammatory state even before puberty ( 137 ). In obese children and adolescents, hyperinsulinemia during puberty ( 138 , 139 ) and unhealthy sleep behaviors increase MS's risk and severity ( 140 ). Even though there is no consensus on diagnosis regarding MS in this age group, when dealing with obese children and adolescents, clinicians should screen them for MS risk factors and sleep behaviors and provide recommendations for weight management.

Social Psychology of Pediatric Obesity in Children and Adolescents

Obese children and adolescents may experience psychosocial sequelae, including depression, bullying, social isolation, diminished self-esteem, behavioral problems, dissatisfaction with body image, and reduced quality of life ( 13 , 141 ). Compared with normal-weight counterparts, overweight/obesity is one of the most common reasons children and adolescents are bullied at school ( 142 ). The consequence of stigma, bullying, and teasing related to childhood obesity are pervasive and can have severe implications for emotional and physical health and performance that can persist later in life ( 13 ).

In adolescents, psychological outcomes associated with obesity are multifactorial and have a bidirectional relationship ( Figure 4 ). Obese adolescents due to their physique may have a higher likelihood of psychosocial health issues, including depression, body image/dissatisfaction, lower self-esteem, peer victimization/bullying, and interpersonal relationship difficulties. They may also demonstrate reduced resilience to challenging situations compared to their non-obese/overweight counterparts ( 9 , 143 – 146 ). Body image dissatisfaction has been associated with further weight gain but can also be related to the development of a mental health disorder or an eating disorder (ED) or disorder eating habits (DEH). Mental health disorders such as depression are associated with poor eating habits, a sedentary lifestyle, and altered sleep patterns. ED or DEH that include anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED) or night eating syndrome (NES) may be related to an individual's overvaluation of their body shape and weight or can result during the treatment for obesity ( 147 – 150 ). The management of obesity can place a patient at risk of AN if there is a rigid focus on caloric intake or if a patient overcorrects and initiates obsessive self-directed dieting. Healthcare providers who primarily care for obese patients, usually give the advice to diet to lose weight and then maintain it. However, strict dieting (hypocaloric diet), which some patients may later engage in can lead to an eating disorder such as anorexia nervosa ( 151 ). This behavior leads to a poor relationship with food, and therefore, adolescents perseverate on their weight and numbers ( 152 ).

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Figure 4 . Bidirectional relationship of different psychological outcomes of obesity.

Providers may not recognize DEHs when a morbidly obese patient loses the same weight as a healthy weight individual ( 149 ). It may appear as a positive result with families and others praising the individual without realizing that this youth may be engaging in destructive behaviors related to weight control. Therefore, it is essential to screen regarding the process of how weight loss was achieved ( 144 , 150 ).

Support and attention to underlying psychological concerns can positively affect treatment, overall well-being, and reduce the risk of adult obesity ( 150 ). The diagram above represents the complexity of the different psychological issues which can impact the clinical care of the obese adolescent.

Eating family meals together can improve overall dietary intake due to enhanced food choices mirrored by parents. It has also may serve as a support to individuals with DEHs if there is less attention to weight and a greater focus on appropriate, sustainable eating habits ( 148 ).

Prevention and Anticipatory Guidance

It is essential to recognize and provide preventive measures for obesity during early childhood and adolescence ( 100 , 153 , 154 ). It is well-established that early AR is a risk factor for adult obesity ( 66 – 68 ). Therefore, health care providers caring for the pediatric population need to focus on measures such as BMI but provide anticipatory guidance regarding nutritional counseling without stigmatizing or judging parents for their children's overweight/obesity ( 155 ). Although health care providers continue to pursue effective strategies to address the obesity epidemic; ironically, they frequently exhibit weight bias and stigmatizing behaviors. Research has demonstrated that the language that health care providers use when discussing a patient's body weight can reinforce stigma, reduce motivation for weight loss, and potentially cause avoidance of routine preventive care ( 155 ). In adolescents, rather than motivating positive changes, stigmatizing language regarding weight may negatively impact a teen and result in binge eating, decreased physical activity, social isolation, avoidance of health care services, and increased weight gain ( 156 , 157 ). Effective provider-patient communication using motivational interviewing techniques are useful to encourage positive behavior changes ( 155 , 158 ).

Anticipatory guidance includes educating the families on healthy eating habits and identifying unhealthy eating practices, encouraging increased activity, limiting sedentary activities such as screen time. Lifestyle behaviors in children and adolescents are influenced by many sectors of our society, including the family ( Figure 1 ) ( 3 , 24 ). Therefore, rather than treating obesity in isolation as an individual problem, it is crucial to approach this problem by focusing on the family unit. Family-based multi-component weight loss behavioral treatment is the gold standard for treating childhood obesity, and it is having been found useful in those between 2 and 6 years old ( 150 , 159 ). Additionally, empowering the parents to play an equal role in developing and implementing an intervention for weight management has shown promising results in improving the rate of obesity by decreasing screen time, promoting healthy eating, and increasing support for children's physical activity ( 160 , 161 ).

When dietary/lifestyle modifications have failed, the next option is a structured weight -management program with a multidisciplinary approach ( 15 ). The best outcomes are associated with an interdisciplinary team comprising a physician, dietician, and psychologist generally 1–2 times a week ( 15 , 162 ). However, this treatment approach is not effective in patients with severe obesity ( 122 ). Although healthier lifestyle recommendations for weight loss are the current cornerstone for obesity management, they often fail. As clinicians can attest, these behavioral and dietary changes are hard to achieve, and all too often is not effective in patients with severe obesity. Failure to maintain substantial weight loss over the long term is due to poor adherence to the prescribed lifestyle changes as well as physiological responses that resist weight loss ( 163 ). American TV hosts a reality show called “The Biggest Loser” that centers on overweight and obese contestants attempting to lose weight for a cash prize. Contestants from “The Biggest Loser” competition, had metabolic adaptation (MA) after drastic weight loss, regained more than they lost weight after 6 years due to a significant slow resting metabolic rate ( 164 ). MA is a physiological response which is a reduced basal metabolic rate seen in individuals who are losing or have lost weight. In MA, the body alters how efficient it is at turning the food eaten into energy; it is a natural defense mechanism against starvation and is a response to caloric restriction. Plasma leptin levels decrease substantially during caloric restriction, suggesting a role of this hormone in the drop of energy expenditure ( 165 ).

Pharmacological Management

The role of pharmacological therapy in the treatment of obesity in children and adolescents is limited.

Orlistat is the only FDA approved medication for weight loss in 12-18-year-olds but has unpleasant side effects ( 166 ). Another medicine, Metformin, has been used in children with signs of insulin resistance, may have some impact on weight, but is not FDA approved ( 167 ). The combination of phentermine/topiramate (Qsymia) has been FDA approved for weight loss in obese individuals 18 years and older. In studies, there has been about 9–10% weight loss over 2 years. However, caution must be taken in females as it can lead to congenital disabilities, especially with use in the first trimester of pregnancy ( 167 ).

GLP-1 agonists have demonstrated great success in effective weight loss and are approved by the FDA for adult obesity ( 168 – 170 ). A randomized control clinical trial recently published showed a significant weight loss in those using liraglutide (3.0 mg)/day plus lifestyle therapy group compared to placebo plus lifestyle therapy in children between the ages of 12–18 years ( 171 ).

Recently during the EASL conference, academic researchers and industry partners presented novel interventions targeting different gut- liver axis levels that include intestinal content, intestinal microbiome, intestinal mucosa, and peritoneal cavity ( 47 ). The focus for these therapeutic interventions within the gut-liver axis was broad and ranged anywhere from newer drugs protecting the intestinal mucus lining, restoring the intestinal barriers and improvement in the gut microbiome. One of the treatment options was Hydrogel technology which was shown to be effective toward weight loss in patients with metabolic syndrome. Hydrogel technology include fibers and high viscosity polysaccharides that absorb water in the stomach and increasing the volume, thereby improving satiety ( 47 ). Also, a clinical trial done in obese pregnant mothers using Docosahexaenoic acid (DHA) showed that the mothers' who got DHA had children with lower adiposity at 2 and 4 years of age ( 172 ). Recently the role of probiotics in combating obesity has emerged. Probiotics are shown to alter the gut microbiome that improves intestinal digestive and absorptive functions of the nutrients. Intervention including probiotics may be a possible solution to manage pediatric obesity ( 173 , 174 ). Additionally, the role of Vitamin E for treating the comorbidities of obesity such as diabetes, hyperlipidemia, NASH, and cardiovascular risk, has been recently described ( 175 , 176 ). Vitamin E is a lipid- soluble compound and contains both tocopherols and tocotrienols. Tocopherols have lipid-soluble antioxidants properties that interact with cellular lipids and protects them from oxidation damage ( 177 ). In metabolic disease, certain crucial pathways are influenced by Vitamin E and some studies have summarized the role of Vitamin E regarding the treatment of obesity, metabolic, and cardiovascular disease ( 178 ). Hence, adequate supplementation of Vitamin E as an appropriate strategy to help in the treatment of the prevention of obesity and its associated comorbidities has been suggested. Nonetheless, some clinical trials have shown contradictory results with Vitamin E supplementation ( 177 ). Although Vitamin E has been recognized as an antioxidant that protects from oxidative damage, however, a full understanding of its mechanism of action is still lacking.

Bariatric Surgery

Bariatric surgery has gained popularity since the early 2000s in the management of severe obesity. If performed earlier, there are better outcomes for reducing weight and resolving obesity-related comorbidities in adults ( 179 – 182 ). Currently, the indication for bariatric in adolescents; those who have a BMI >35 with at least one severe comorbidity (Type 2 Diabetes, severe OSA, pseudotumor cerebri or severe steatohepatitis); or BMI of 40 or more with other comorbidities (hypertension, hyperlipidemia, mild OSA, insulin resistance or glucose intolerance or impaired quality of life due to weight). Before considering bariatric surgery, these patients must have completed most of their linear growth and participated in a structured weight-loss program for 6 months ( 159 , 181 , 183 ). The American Society for Metabolic and Bariatric Surgery (AMBS) outlines the multidisciplinary approach that must be taken before a patient undergoing bariatric surgery. In addition to a qualified bariatric surgeon, the patient must have a pediatrician or provider specialized in adolescent medicine, endocrinology, gastroenterology and nutrition, registered dietician, mental health provider, and exercise specialist ( 181 ). A mental health provider is essential as those with depression due to obesity or vice versa may have persistent mental health needs even after weight loss surgery ( 184 ).

Roux-en-Y Gastric Bypass (RYGB), laparoscopic Sleeve Gastrectomy (LSG), and Gastric Banding are the options available. RYGB and LSG currently approved for children under 18 years of age ( 166 , 181 , 185 ). At present, gastric banding is not an FDA recommended procedure in the US for those under 18y/o. One study showed some improvements in BMI and severity of comorbidities but had multiple repeat surgeries and did not believe a suitable option for obese adolescents ( 186 ).

Compared to LSG, RYGB has better outcomes for excess weight loss and resolution of obesity-related comorbidities as shown in studies and clinical trials ( 183 , 184 , 187 ). Overall, LSG is a safer choice and may be advocated for more often ( 179 – 181 ). The effect on the Gut-Brain axis after Bariatric surgery is still inconclusive, especially in adolescents, as the number of procedures performed is lower than in adults. Those who underwent RYGB had increased fasting and post-prandial PYY and GLP-1, which could have contributed to the rapid weight loss ( 185 ); this effect was seen less often in patients with gastric banding ( 185 ). Another study in adult patients showed higher bile acid (BA) subtype levels and suggested a possible BA's role in the surgical weight loss response after LSG ( 188 ). Adolescents have lower surgical complication rates than their adult counterparts, hence considering bariatric surgery earlier rather than waiting until adulthood has been entertained ( 180 ). Complications after surgery include nutritional imbalance in iron, calcium, Vitamin D, and B12 and should be monitored closely ( 180 , 181 , 185 ). Although 5-year data for gastric bypass in very obese teens is promising, lifetime outcome is still unknown, and the psychosocial factors associated with adolescent adherence post-surgery are also challenging and uncertain.

Obesity in childhood and adolescence is not amenable to a single easily modified factor. Biological, cultural, and environmental factors such as readily available high-density food choices impact youth eating behaviors. Media devices and associated screen time make physical activity a less optimal choice for children and adolescents. This review serves as a reminder that the time for action is now. The need for interventions to change the obesogenic environment by instituting policies around the food industry and in the schools needs to be clarified. In clinical trials GLP-1 agonists are shown to be effective in weight loss in children but are not yet FDA approved. Discovery of therapies to modify the gut microbiota as treatment for overweigh/obesity through use of probiotics or fecal transplantation would be revolutionary. For the present, ongoing clinical research efforts in concert with pharmacotherapeutic and multidisciplinary lifestyle programs hold promise.

Author Contributions

AK, SL, and MJ contributed to the conception and design of the study. All authors contributed to the manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1. Gurnani M, Birken C, Hamilton. J. Childhood obesity: causes, consequences, and management. Pediatr Clin North Am. (2015) 62:821–40. doi: 10.1016/j.pcl.2015.04.001

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria. AS. Childhood obesity: causes and consequences. J Family Med Prim Care. (2015) 4:187–92. doi: 10.4103/2249-4863.154628

3. Brown CL, Halvorson EE, Cohen GM, Lazorick S, Skelton JA. Addressing childhood obesity: opportunities for prevention. Pediatr Clin North Am. (2015) 62:1241–61. doi: 10.1016/j.pcl.2015.05.013

4. Qasim A, Turcotte M, de Souza RJ, Samaan MC, Champredon D, Dushoff J, et al. On the origin of obesity: identifying the biological, environmental, and cultural drivers of genetic risk among human populations. Obes Rev. (2018) 19:121–49. doi: 10.1111/obr.12625

5. Rinninella E, Raoul P, Cintoni M, Fransceschi F, Miggiano GAD, Gasbarrini A, et al. What is the healthy gut microbiota composition? a changing ecosystem across age, environment, diet, and diseases. Microorganisms. (2019) 7:14. doi: 10.3390/microorganisms7010014

6. Indrio F, Martini S, Francavilla R, Corvaglia L, Cristofori F, Mastrolia SA, et al. Epigenetic matters: the link between early nutrition, microbiome, and long-term health development. Front Pediatr. (2017) 5:178. doi: 10.3389/fped.2017.00178

7. Marcovecchio ML, Gorman S, Watson LPE, Dunger DB, Beardsall K. Catch-up growth in children born small for gestational age related to body composition and metabolic risk at six years of age in the UK. Horm Res Paediatr. (2020) 93:119–27. doi: 10.1159/000508974

8. Koletzko B, Fishbein M, Lee WS, Moreno L, Mouane N, Mouzaki M, et al. Prevention of childhood obesity: a position paper of the global federation of international societies of paediatric gastroenterology, hepatology nutrition (FISPGHAN). J Pediatr Gastroenterol Nutr. (2020) 70:702–10. doi: 10.1097/MPG.0000000000002708

9. Pulgarón ER. Childhood obesity: a review of increased risk for physical and psychological comorbidities. Clin Ther. (2013) 35:A18–32. doi: 10.1016/j.clinthera.2012.12.014

10. De Leonibus C, Marcovecchio ML, Chiarelli F. Update on statural growth and pubertal development in obese children. Pediatr Rep. (2012) 4:e35. doi: 10.4081/pr.2012.e35

11. Witchel SF, Burghard AC, Tao RH, Oberfield SE. The diagnosis and treatment of PCOS in adolescents. Curr Opin Pediatr . (2019) 31:562–9. doi: 10.1097/MOP.0000000000000778

12. Marcus CL, Brooks LJ, Draper KA, Gozal D, Halbower AC, Jones J, et al. Diagnosis and management of childhood obstructive sleep apnea syndrome. Pediatrics . (2012) 130:e714–55. doi: 10.1542/peds.2012-1672

CrossRef Full Text | Google Scholar

13. Rankin J, Matthews L, Cobley S, Han A, Sanders R, Wiltshire HD, et al. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Ther . (2016) 7:125–46. doi: 10.2147/AHMT.S101631

14. Topçu S, Orhon FS, Tayfun M, Uçaktürk SA, Demirel F. Anxiety, depression, and self-esteem levels in obese children: a case-control study. J Pediatr Endocrinol Metabol. (2016) 29:357–61. doi: 10.1515/jpem-2015-0254

15. Katzmarzyk PT, Barlow S, Bouchard C, Catalano PM, Hsia DS, Inge TH, et al. An evolving scientific basis for the prevention and treatment of pediatric obesity. Int J Obes. (2014) 38:887–905. doi: 10.1038/ijo.2014.49

16. Brown T, Moore TH, Hooper L, Gao Y, Zayegh A, Ijaz S, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev . (2019) 7:CD001871. doi: 10.1002/14651858.CD001871.pub4

17. Smith E, Scarborough P, Rayner M, Briggs ADM. Should we tax unhealthy food and drink? Proc Nutr Soc. (2019) 77:314–20. doi: 10.1017/S0029665117004165

18. Adab P, Pallan M, Whincup PH. Is BMI the best measure of obesity? BMJ. (2018) 360:k 1274. doi: 10.1136/bmj.k1274

19. Anderson LN, Carsley S, Lebovic G, Borkhoff CM, Maguire JL, Parkin PC, et al. Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores. BMC Res Notes. (2017) 10:639. doi: 10.1186/s13104-017-2983-0

20. Must A, Anderson SE. Body mass index in children and adolescents: consideration for population-based applications. Int J Obes. (2006) 30:590–4. doi: 10.1038/sj.ijo.0803300

21. Flegal KM, Wei R, Ogden C. Weight-for-stature compared with body mass index-for-age growth charts for the United States from the centers for disease control and prevention. Am J Clin Nutr. (2002) 75:761–6.22. doi: 10.1093/ajcn/75.4.761

22. Himes JH, Dietz WH. Guidelines for overweight in adolescent preventive services: recommendations from an expert committee. The expert committee on clinical guidelines for overweight in adolescent preventive services. Am J Clin Nutr. (1994) 59:307–16. doi: 10.1093/ajcn/59.2.307

23. Lazarus R, Baur L, Webb K, Blyth F. Body mass index in screening for adiposity in children and adolescents: systematic evaluation using receiver operating characteristic curves. Am J Clin Nutr. (1996) 63:500–6. doi: 10.1093/ajcn/63.4.500

24. McGinnis JM, Gootman JA. Food Marketing to Children and Youth: Threat or Opportunity? Institute of Medicine of the National Academies. Washington, DC: The National Academies Press. (2006).

Google Scholar

25. Chaudhri OB, Salem V, Murphy KG, Bloom SR. Gastrointestinal satiety signals. Annu Rev Physiol. (2008) 70:239–55. doi: 10.1146/annurev.physiol.70.113006.100506

26. Scaglioni S, De Cosmi V, Ciappolino V, Parazzini F, Brambilla P, Agostoni C. Factors influencing children's eating behaviours. Nutrients. (2018) 10:706. doi: 10.3390/nu10060706

27. Ahima RS, Antwi DA. Brain regulation of appetite and satiety. Endocrinol Metab Clin North Am. (2008) 37:811–23. doi: 10.1016/j.ecl.2008.08.005

28. Niswender KD, Baskin DG, Schwartz MW. Review insulin and its evolving partnership with leptin in the hypothalamic control of energy homeostasis. Trends Endocrinol Metab. (2004) 15:362–9. doi: 10.1016/j.tem.2004.07.009

29. Niswender KD, Schwartz MW. Review insulin and leptin revisited: adiposity signals with overlapping physiological and intracellular signaling capabilities. Front Neuroendocrinol. (2003) 24:1–10. doi: 10.1016/S0091-3022(02)00105-X

30. Amitani M, Asakawa A, Amitani H, Inui. A. The role of leptin in the control of insulin-glucose axis. Front Neurosci. (2013) 7:51. doi: 10.3389/fnins.2013.00051

31. Cowley MA, Smith RG, Diano S, Tschöp M, Pronchuk N, Grove KL, et al. The distribution and mechanism of action of ghrelin in the CNS demonstrates a novel hypothalamic circuit regulating energy homeostasis. Neuron. (2003) 37:649–61. doi: 10.1016/S0896-6273(03)00063-1

32. Buhmann H, le Roux CW, Bueter M. The gut–brain axis in obesity. Best Prac Res Clin Gastroenterol. (2014) 28:559–71. doi: 10.1016/j.bpg.2014.07.003

33. Cone RD. Review anatomy and regulation of the central melanocortin system. Nat Neurosci. (2005) 8:571–8. doi: 10.1038/nn1455

34. Timper K, Brüning JC. Hypothalamic circuits regulating appetite and energy homeostasis: pathways to obesity. Dis Model Mech. (2017) 10:679–89. doi: 10.1242/dmm.026609

35. Labarthe A, Fiquet O, Hassouna R, Zizzari P, Lanfumey L, Ramoz N, et al. Ghrelin-derived peptides: a link between appetite/reward, gh axis, and psychiatric disorders? Front Endocrinol. (2014) 5:163. doi: 10.3389/fendo.2014.00163

36. Hills R. D Jr, Pontefract BA, Mishcon HR, Black CA, Sutton SC, Theberge CR. Gut microbiome: profound implications for diet and disease. Nutrients. (2019) 11:1613. doi: 10.3390/nu11071613

37. Torres-Fuentes C, Schellekens H, Dinan TG, Cryan JF. The microbiota-gut-brain axis in obesity. Lancet Gastroenterol Hepatol. (2017) 2:747–56. doi: 10.1016/S2468-1253(17)30147-4

38. Gérard P. Gut microbiota and obesity. Cell Mol Life Sci. (2016) 73:147–62. doi: 10.1007/s00018-015-2061-5

39. Derrien M, Alvarez AS, de Vos WM. The gut microbiota in the first decade of life. Trends Microbiol. (2019) 27:997–1010.40. doi: 10.1016/j.tim.2019.08.001

40. Dao MC, Clément K. Gut microbiota and obesity: concepts relevant to clinical care. Eur J Intern Med . (2018) 48:18–24.41. doi: 10.1016/j.ejim.2017.10.005

41. Kim KN, Yao Y., Ju SY. Short chain fatty acids and fecal microbiota abundance in humans with obesity: a systematic review and meta-analysis. Nutrients. (2019) 11:2512. doi: 10.3390/nu11102512

42. Castaner O, Goday A, Park YM, Lee SH, Magkos F, Shiow STE, et al. The gut microbiome profile in obesity: a systematic review. Int J Endocrinol. (2018) 2018:4095789. doi: 10.1155/2018/4095789

43. Riva A, Borgo F, Lassandro C, Verduci E, Morace G, Borghi E, et al. Pediatric obesity is associated with an altered gut microbiota and discordant shifts in firmicutes populations. Enviroin Microbiol. (2017) 19:95–105. doi: 10.1111/1462-2920.13463

44. Fernandes J, Su W, Rahat-Rozenbloom S, Wolever TMS, Comelli EM. Adiposity, gut microbiota and faecal short chain fatty acids are linked in adult humans. Nutr Diabetes . (2014) 4:e121. doi: 10.1038/nutd.2014.23

45. Rahat-Rozenbloom S, Fernandes J, Gloor GB, Wolever TMS. Evidence for greater production of colonic short-chain fatty acids in overweight than lean humans. Int J Obes . (2014) 38:1525–31. doi: 10.1038/ijo.2014.46

46. Barczyńska R, Litwin M, Slizewska K, Szalecki M, Berdowska A, Bandurska K, et al. Bacterial microbiota fatty acids in the faeces of overweight obese children. Pol. J. Microbiol. (2018) 67:339–45. doi: 10.21307/pjm-2018-041

47. Albillos A, de Gottardi A, Rescigno M. The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol. (2020) 72:558–77. doi: 10.1016/j.jhep.2019.10.003

48. Yu EL, Golshan S, Harlow KE, Angeles JE, Durelle J, Goyal NP, et al. Prevalence of nonalcoholic fatty liver disease in children with obesity. J Pediatr. (2019) 207:64–70. doi: 10.1016/j.jpeds.2018.11.021

49. Ranucci G, Spagnuolo MI, Iorio R. Obese children with fatty liver: Between reality and disease mongering. World J Gastroenterol. (2017) 23:8277–82. doi: 10.3748/wjg.v23.i47.8277

50. Cox AJ, West NP, Cripps A. W. Obesity, inflammation, and the gut microbiota. Lancet Diabet Endocrinol. (2015) 3:207–15. doi: 10.1016/S2213-8587(14)70134-2

51. Seitz J, Trinh S, Herpertz-Dahlmann B. The microbiome and eating disorders. Psychiatr Clin North Am. . (2019) 42:93–103. doi: 10.1016/j.psc.2018.10.004

52. Deans E. Microbiome and mental health in the modern environment. J Physiol Anthropol. (2016) 36:1. doi: 10.1186/s40101-016-0101-y

53. Peirce JM, Alviña K. The role of inflammation and the gut microbiome in depression and anxiety. J Neurosci Res . (2019) 97:1223–41. doi: 10.1002/jnr.24476

54. Ranadive SA, Vaisse C. Lessons from extreme human obesity: monogenic disorders. Endocrinol Metab Clin North Am. (2008) 37:733–51. doi: 10.1016/j.ecl.2008.07.003

55. Soliman AT, Yasin M, Kassem A. Leptin in pediatrics: a hormone from adipocyte that wheels several functions in children. Indian J Endocrinol Metab . (2012) 16(Suppl. 3):S577–87. doi: 10.4103/2230-8210.105575

56. Farooqi IS, Wangensteen T, Collins S, Kimber W, Matarese G, Keogh JM, et al. Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. N Engl J Med. (2007) 356:237–47. doi: 10.1056/NEJMoa063988

57. Mutch DM, Clément K. Unraveling the genetics of human obesity. PLoS Genet. (2006) 2:e188. doi: 10.1371/journal.pgen.0020188

58. Crocker MK, Yanovski JA. Pediatric obesity: etiology and treatment. Endocrinol Metab Clin North Am. (2009) 38:525–48. doi: 10.1016/j.ecl.2009.06.007

59. Huvenne H, Dubern B, Clément K, Poitou C. Rare genetic forms of obesity: clinical approach and current treatments in 2016. Obes Facts. (2016) 9:158–73. doi: 10.1159/000445061

60. Stefan M, Nicholls RD. What have rare genetic syndromes taught us about the pathophysiology of the common forms of obesity? Curr Diab Rep. (2004) 4:143–50. doi: 10.1007/s11892-004-0070-0

61. Hetherington MM, Cecil JE. Gene-Environment interactions in obesity. Forum Nutr. (2009) 63:195–203. doi: 10.1159/000264407

62. Reddon H, Guéant JL, Meyre D. The importance of gene-environment interactions in human obesity. Clin Sci. (2016) 130:1571–97. doi: 10.1042/CS20160221

63. Castillo JJ, Orlando RA, Garver WS. Gene-nutrient interactions and susceptibility to human obesity. Genes Nutr. (2017) 12:29. doi: 10.1186/s12263-017-0581-3

64. Heianza Y, Qi L. Gene-Diet interaction and precision nutrition in obesity. Int J Mol Sci. (2017) 18:787. doi: 10.3390/ijms18040787

65. Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. (2018) 6:223–36. . doi: 10.1016/S2213-8587(17)30200-0

66. Bouchard L, Drapeau V, Provencher V, Lemieux S, Chagnon Y, Rice T, et al. Neuromedin beta: a strong candidate gene linking eating behaviors and susceptibility to obesity. Am J Clin Nutr. (2004) 80:1478–86. . doi: 10.1093/ajcn/80.6.1478

67. Grimm ER, Steinle NI. Genetics of eating behavior: established and emerging concepts. Nutr Rev. (2011) 69:52–60. . doi: 10.1111/j.1753-4887.2010.00361.x

68. van der Klaauw AA, Farooqi IS. The hunger genes: pathways to obesity. Cell. (2015) 161:119–32. . doi: 10.1016/j.cell.2015.03.008

69. Martinez JA. Bodyweight regulation causes of obesity. Proc Nutr Soc. (2000) 59:337–45. Review. doi: 10.1017/S0029665100000380

70. Rask-Andersen M, Karlsson T, Ek WE, Johansson Å. Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status. PLoS Genet. (2017) 5:1. doi: 10.1371/journal.pgen.1006977

71. Xulong S, Pengzhou L, Xiangwu Y, Weizheng L, Xianjie Q, Shaihong Z, et al. From genetics and epigenetics to the future of precision treatment for obesity. Gastroenterol Rep. (2017) 5:266–70. doi: 10.1093/gastro/gox033

72. Bianco-Miotto T, Craig JM, Gasser YP, van dijk SJ, Ozanne SE. Epigenetics and DOHaD: from basics to birth and beyond. J Dev Orig Health Dis. (2017) 8:513–9. doi: 10.1017/S2040174417000733

73. van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS, Members of EpiSCOPE. Epigenetics and human obesity. Int J Obes . (2015) 39:85–97. doi: 10.1038/ijo.2014.34

74. Li Y. Epigenetic mechanisms link maternal diets and gut microbiome to obesity in the offspring. Front Genet . (2018) 9:342. doi: 10.3389/fgene.2018.00342

75. Kaufman J, Montalvo-Ortiz JL, Holbrook H, O'Loughlin K, Orr C, Kearney C, et al. Adverse childhood experiences, epigenetic measures, and obesity in youth. J Pediatr. (2018) 202:150–6.76. doi: 10.1016/j.jpeds.2018.06.051

76. May Gardner R, Feely A, Layte R, Williams J, McGavock J. Adverse childhood experiences are associated with an increased risk of obesity in early adolescence: a population-based prospective cohort study. Pediatr Res. (2019) 86:522–28. doi: 10.1038/s41390-019-0414-8

77. Cheon BK„ Hong YY. Mere experience of low subjective socioeconomic status stimulates appetite food intake. Proc Natl Acad Sci USA . (2017) 114:72–7. doi: 10.1073/pnas.1607330114

78. Alegría-Torres JA, Baccarelli A, Bollati V. Epigenetics lifestyle. Epigenomics . (2011) 3:267-77. doi: 10.2217/epi.11.22

79. Birch LL, Fisher JO. Development of eating behaviors among children and adolescents. Pediatrics . (2011) 101:539–49.

PubMed Abstract | Google Scholar

80. Birch L, Savage JS, Ventura A. Influences on the development of children's eating behaviours: from infancy to adolescence. Can J Diet Pract Res. (2007) 68:s1–s56.

81. Nielsen SJ, Popkin BM. Patterns and trends in food portion sizes, 1977- 1998. JAMA. (2003) 289:450–53. . doi: 10.1001/jama.289.4.450

82. Munoz KA, Krebs-Smith SM, Ballard-Barbash R, Cleveland LE. Food intakes of US children and adolescents compared with recommendations. Pediatrics. (1997) 100:323–29. doi: 10.1542/peds.100.3.323

83. Fisher JO, Birch LL. Restricting access to palatable foods affects children's behavioral response, food selection, and intake. Am J Clin Nutr. (1999) 69:1264–72. doi: 10.1093/ajcn/69.6.1264

84. Faith MS, Scanlon KS, Birch LL, Francis LA, Sherry B. Parent-child feeding strategies and their relationships to child eating and weight status. Obes Res. (2004) 12:1711–22. . doi: 10.1038/oby.2004.212

85. Smith AD, Sanchez N, Reynolds C, Casamassima M, Verros M, Annameier SK, et al. Associations of parental feeding practices and food reward responsiveness with adolescent stress-eating. Appetite. (2020) 152:104715. doi: 10.1016/j.appet.2020.104715

86. Lowe CJ, Morton JB, Reichelt AC. Adolescent obesity and dietary decision making-a brain-health perspective. Lancet Child Adolesc Health. (2020) 4:388–96. doi: 10.1016/S2352-4642(19)30404-3

87. Goran MI, Treuth MS. Energy expenditure, physical activity, and obesity in children. Pediatr Clin North Am. (2001) 48:931–53. doi: 10.1016/S0031-3955(05)70349-7

88. Romieu I, Dossus L, Barquera S, Blottière HM, Franks PW, Gunter M, et al. Energy balance and obesity: what are the main drivers? Cancer Causes Control. (2017) 28:247–58. doi: 10.1007/s10552-017-0869-z

89. Mattes R, Foster GD. Food environment and obesity. Obesity. (2014) 22:2459–61. doi: 10.1002/oby.20922

90. Ickovics JR, O'Connor Duffany K, Shebl FM, Peters SM, Read MS, Gilstad-Hayden KR, et al. Implementing school-based policies to prevent obesity: cluster randomized trial. Am J Prev Med. (2019) 56:e1–11. doi: 10.1016/j.amepre.2018.08.026

91. Micha R, Karageorgou D, Bakogianni I, Trichia E, Whitsel LP, Story M, et al. Effectiveness of school food environment policies on children's dietary behaviors: A systematic review and meta-analysis. PLoS ONE. ( 2018 ) 13:e0194555. doi: 10.1371/journal.pone.0194555

92. Cawley J, Frisvold D, Hill A, Jones DJ. The impact of the philadelphia beverage tax on purchases and consumption by adults and children. Health Econ. (2019) 67:102225. doi: 10.1016/j.jhealeco.2019.102225

93. John Cawley J, Thow AM, Wen K, Frisvold D. The economics of taxes on sugar-sweetened beverages: a review of the effects on prices, sales, cross-border shopping, and consumption. Annu Rev Nutr. (2019) 39:317–38. doi: 10.1146/annurev-nutr-082018-124603

94. Fuller C, Lehman E, Hicks S, Novick MB. Bedtime use of technology and associated sleep problems in children. Glob Pediatr Health. (2017) 4:2333794X17736972. doi: 10.1177/2333794X17736972

95. Chahal H, Fung C, Kuhle S, Veugelers PJ. Availability and night-time use of electronic entertainment and communication devices are associated with short sleep duration and obesity among Canadian children. Pediatr Obes. (2012) 8:42–51. doi: 10.1111/j.2047-6310.2012.00085.x

96. Minghua T. Protein intake during the first two years of life and its association with growth and risk of overweight. Int J Environ Res Public Health. ( 2018 ) 15:1742. doi: 10.3390/ijerph15081742

97. Azad MB, Vehling L, Chan D, Klopp A, Nickel NC, McGavock JM, et al. Infant feeding and weight gain: separating breast milk from breastfeeding and formula from food. Pediatrics. (2018) 142:e20181092. doi: 10.1542/peds.2018-1092

98. Lin L, Amissah E, Gamble GD, Crowther CA, Harding JE. Impact of macronutrient supplements on later growth of children born preterm or small for gestational age: a systematic review and meta-analysis of randomised and quasirandomised controlled trials. PLoS Med. (2020) 17:e1003122. . doi: 10.1371/journal.pmed.1003122

99. Rzehak P, Oddy WH, Mearin ML, Grote V, Mori TA, Szajewska H, et al. Infant feeding and growth trajectory patterns in childhood and body composition in young adulthood. Am J Clin Nutr. (2017) 106:568–80. doi: 10.3945/ajcn.116.140962

100. Styne DM, Arslanian SA, Connor EL, Farooqi IS, Murad MH, Silverstein JH. Pediatric obesity-assessment, treatment, and prevention: an endocrine society clinical practice guideline. J Clin Endocrinol Metab. (2017) 102:709–57. doi: 10.1210/jc.2016-2573

101. Whitaker RC, Pepe MS, Wright JA, Seidel KD, Dietz WH. Early adiposity rebound and the risk of adult obesity. Pediatrics . (1998) 101:E5. doi: 10.1542/peds.101.3.e5

102. Geserick M, Vogel M, Gausche R, Lipek T, Spielau U, Keller E, et al. Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med. (2018) 379:1303–12. doi: 10.1056/NEJMoa1803527

103. Jabakhanji SB, Boland F, Ward M, Biesma RJ. Body mass index changes in early childhood. Pediatrics. (2018) 202:106–14. doi: 10.1016/j.jpeds.2018.06.049

104. Chung S. Growth and puberty in obese children and implications of body composition. J Obes Metab Syndr. (2017) 26:243–50. doi: 10.7570/jomes.2017.26.4.243

105. Tagi VM, Giannini C, Chiarelli F. Insulin resistance in children. Front Endocrinol. (2019) 10:342. doi: 10.3389/fendo.2019.00342

106. Kelesidis I, Mantzoros CS. Leptin and its emerging role in children and adolescents. Clin Pediatr Endocrinol . (2006) 15:1–14. doi: 10.1297/cpe.15.1

107. Burt Solorzano CM, McCartney CR, Obesity and the pubertal transition in girls and boys. Reproduction . (2010) 140:399–410. doi: 10.1530/REP-10-0119

108. Li W, Liu Q, Deng X, Chen Y, Liu S, Story M. Association between obesity and puberty timing: a systematic review and meta-analysis. Int J Environ Res Public Health. (2017) 14:1266. doi: 10.3390/ijerph14101266

109. Lee JM, Wasserman R, Kaciroti N, Gebremariam A, Steffes J, Dowshen S, et al. Timing of puberty in overweight vs. obese boys. Pediatrics. (2016) 137:e20150164. doi: 10.1542/peds.2015-0164

110. He J, Kang Y, Zheng L. Serum levels of LH, IGF-1 and leptin in girls with idiopathic central precocious puberty (ICPP) and the correlations with the development of ICPP. Minerva Pediatr . (2018). doi: 10.23736/S0026-4946.18.05069-7

111. Kang MJ, Oh YJ, Shim YS, Baek JW, Yang S, Hwang IT. The usefulness of circulating levels of leptin, kisspeptin, and neurokinin B in obese girls with precocious puberty. Gynecol Endocrinol. (2018) 34:627–30. doi: 10.1080/09513590.2017.1423467

112. Rendo-Urteaga T, Ferreira de Moraes AC, Torres-Leal FL, Manios Y, Gottand F, Sjöström M, et al. Leptin and adiposity as mediators on the association between early puberty and several biomarkers in European adolescents: the helena study. J Pediatr Endocrinol Metab. (2018) 31:1221–29. doi: 10.1515/jpem-2018-0120

113. Franks S. Adult polycystic ovary syndrome begins in childhood. Best Pract Res Clin Endocrinol Metab. (2002) 16:263–72. doi: 10.1053/beem.2002.0203

114. Franks S. Polycystic ovary syndrome in adolescents. Int J Obes. (2008) 32:1035–41. doi: 10.1038/ijo.2008.61

115. Jehan S, Zizi F, Pandi-Perumal SR, Wall S, Auguste E, Myers K, et al. Obstructive sleep apnea and obesity: implications for public health. Sleep Med Disord. (2017) 1:00019.

116. Patinkin ZW, Feinn R, Santos M. Metabolic consequences of obstructive sleep apnea in adolescents with obesity: a systematic literature review and meta-analysis. Childhood Obes. (2017) 13:102–10. doi: 10.1089/chi.2016.0248

117. Kaditis A. From obstructive sleep apnea in childhood to cardiovascular disease in adulthood: what is the evidence? Sleep. (2010) 33:1279–80. doi: 10.1093/sleep/33.10.1279

118. Marseglia L, Manti S, D'Angelo G, Nicotera A, Parisi E, Di Rose G, et al. Oxidative stress in obesity: a critical component in human diseases. Int J Mol Sci . (2014) 16:378–400. doi: 10.3390/ijms16010378

119. Eisele HJ, Markart P, Schulz R. Obstructive sleep apnea, oxidative stress, and cardiovascular disease: evidence from human studies. Oxid Med Cell Longev . (2015) 2015:608438. doi: 10.1155/2015/608438

120. Hui W, Slorach C, Guerra V, Parekh RS, Hamilton J, Messiha S, et al. Effect of obstructive sleep apnea on cardiovascular function in obese youth. Am J Cardiol. (2019) 123:341–7. doi: 10.1016/j.amjcard.2018.09.038

121. Matteoni CA, Younossi Z .m., Gramlich T, Boparai N, Liu YC, et al. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology. (1999) 1999:116:1413. doi: 10.1016/S0016-5085(99)70506-8

122. Lavine JE, Schwimmer JB. Nonalcoholic fatty liver disease in the pediatric population. Clin Liver Dis. ( 2004 ) 8:549. doi: 10.1016/j.cld.2004.04.010

123. Huang JS, Barlow SE, Quiros-Tejeira RE, Scheimann A, Skelton J, Suskind D, et al. Childhood obesity for pediatric gastroenterologists. J Pediatr Gastroenterol Nutr. (2013) 2013:56:99. doi: 10.1097/MPG.0b013e31826d3c62

124. Anderson EL, Howe LD, Jones HE, Higgins JPT, Lawlor DA, Fraser A. The prevalence of non-alcoholic fatty liver disease in children and adolescents: a systematic review and meta-analysis. PLoS ONE. ( 2015 ) 10:e0140908. doi: 10.1371/journal.pone.0140908

125. Nobili V, Alisi A, Newton KP, Schwimmer JB. Comparison of the phenotype and approach to pediatric vs adult patients with nonalcoholic fatty liver disease. Gastroenterology. (2016) 150:1798–810. doi: 10.1053/j.gastro.2016.03.009

126. Rafiq N, Bai C, Fang Y, Srishord M, McCullough A, Gramlich T, et al. Long-term follow-up of patients with nonalcoholic fatty liver. Clin Gastroenterol Hepatol. (2009) 7:234–38. doi: 10.1016/j.cgh.2008.11.005

127. Feldstein AE, Charatcharoenwitthaya P, Treeprasertsuk S, Benson JT, Enders FB, Angula P. The natural history of non-alcoholic fatty liver disease in children: a follow-up study for up to 20 years. Gut. (2009) 58:1538. doi: 10.1136/gut.2008.171280

128. Schwimmer JB, Pardee PE, Lavine JE, Blumkin AK, Cook S. Cardiovascular risk factors and the metabolic syndrome in pediatric nonalcoholic fatty liver disease. Circulation . (2008) 118:277. doi: 10.1161/CIRCULATIONAHA.107.739920

129. Perry DC, Metcalfe D, Lane S, Turner S. Childhood obesity and slipped capital femoral epiphysis. Pediatrics. (2018) 142:e20181067. doi: 10.1542/peds.2018-1067

130. Zavala-Crichton JP, Esteban-Cornejo I, Solis-Urra P, Mora-Gonzalez J, Cadenas-Sanchez C, Rodriguez-Ayllon M, et al. Association of sedentary behavior with brain structure and intelligence in children with overweight or obesity: Active Brains Project . (2020) 9:1101. doi: 10.3390/jcm9041101

131. Ronan L, Alexander-Bloch A, Fletcher PC. Childhood obesity, cortical structure, and executive function in healthy children. Cereb Cortex. (2019) 30:2519–28. doi: 10.1093/cercor/bhz257

132. Baker ER. Body weight and the initiation of puberty. Clin Obstetr Gynecol. (1985) 28:573–9. doi: 10.1097/00003081-198528030-00013

133. Siervogel RM, Demerath EW, Schubert C, Remsberg KE, Chumlea WM, Sun S, et al. Puberty and body composition. Horm Res. (2003) 60:36–45. doi: 10.1159/000071224

134. Sadeeqa S, Mustafa T, Latif S. Polycystic ovarian syndrome- related depression in adolescent girls. J Pharm Bioallied Sci. (2018) 10:55–9. doi: 10.4103/JPBS.JPBS_1_18

135. Himelein MJ, Thatcher SS. Depression and body image among women with polycystic ovary syndrome. J Health Psychol . (2006) 11:613–25. doi: 10.1177/1359105306065021

136. Magge SN, Goodman E, Armstrong SC. The metabolic syndrome in children and adolescents: shifting the focus to cardiometabolic risk factor clustering. Pediatrics. (2017) 140:e20171603. doi: 10.1542/peds.2017-1603

137. Mauras N, Delgiorno C, Kollman C, Bird K, Morgan M, Sweeten S, et al. Obesity without established comorbidities of the metabolic syndrome is associated with a proinflammatory and prothrombotic state, even before the onset of puberty in children. J Clin Endocrinol Metab. (2010) 95:1060–8. doi: 10.1210/jc.2009-1887

138. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. (2004) 350:2362–74. doi: 10.1056/NEJMoa031049

139. Erdmann J, Kallabis B, Oppel U, Sypchenko O, Wagenpfeil S, Schusdziarra V. Development of hyperinsulinemia and insulin resistance during the early stage of weight gain. Am J Physiol Endocrinol Metabol. (2008) 294:e568–75. . doi: 10.1152/ajpendo.00560.2007

140. Pulido-Arjona L, Correa-Bautista JE, Agostinis-Sobrinho C, Mota J, Santos R, Correa-Rodrigues M, et al. Role of sleep duration and sleep- related problems in the metabolic syndrome among children and adolescents. Ital J Pediatr. (2018) 44:9. doi: 10.1186/s13052-018-0451-7

141. Harriger JA, Thompson JK. Psychological consequences of obesity: weight bias and body image in overweight and obese youth. Int Rev Psychiatry. (2012) 24:247–53. . doi: 10.3109/09540261.2012.678817

142. Bacchini D, Licenziati MR, Garrasi A, Corciulo N, Driul D, Tanas R, et al. Bullying and victimization in overweight and obese outpatient children and adolescents: an italian multicentric study. PLoS ONE. (2015) 10:e0142715. doi: 10.1371/journal.pone.0142715

143. Loth KA, Watts AW, Berg PVD, Neumark-Sztainer D. Does body satisfaction help or harm overweight teens? A 10-year longitudinal study of the relationship between body satisfaction and body mass index. J Adolesc Health. (2015) 57:559–61. doi: 10.1016/j.jadohealth.2015.07.008

144. Gowey MA, Lim CS, Clifford LM, Janicke DM. Disordered eating and health-related quality of life in overweight and obese children. J Pediatr Psychol. (2014) 39:552–61. doi: 10.1093/jpepsy/jsu012

145. Mannan M, Mamun A, Doi S, Clavarino A. Prospective associations between depression and obesity for adolescent males and females- a systematic review and meta-analysis of longitudinal studies. PLoS ONE. (2016) 11:e0157240. doi: 10.1371/journal.pone.0157240

146. Ruiz LD, Zuelch ML, Dimitratos SM, Scherr RE. Adolescent obesity: diet quality, psychosocial health, and cardiometabolic risk factors. Nutrients. (2019) 12:43. doi: 10.3390/nu12010043

147. Goldschmidt AB, Aspen VP, Sinton MM, Tanofsky-Kraff M, Wilfley DE. Disordered eating attitudes and behaviors in overweight youth. Obesity. (2008) 16:257–64. doi: 10.1038/oby.2007.48

148. Golden NH, Schneider M, Wood C. Preventing obesity and eating disorders in adolescents. Pediatrics. (2016) 138:e1–e12. doi: 10.1542/peds.2016-1649

149. Rastogi R, Rome ES. Restrictive eating disorders in previously overweight adolescents and young adults. Cleve Clin J Med. (2020) 87:165–71. doi: 10.3949/ccjm.87a.19034

150. Hayes JF, Fitzsimmons-Craft EE, Karam AM, Jakubiak JL, Brown ME, Wilfley D. Disordered eating attitudes and behaviors in youth with overweight and obesity: implications for treatment. Curr Obes Rep. (2018) 7:235. doi: 10.1007/s13679-018-0316-9

151. Goldschmidt AB, Wall MM, Loth KA, Neumark-Sztainer D. Risk factors for disordered eating in overweight adolescents and young adults: Table I. J Pediatr Psychol. (2015) 40:1048–55. doi: 10.1093/jpepsy/jsv053

152. Follansbee-Junger K, Janicke DM, Sallinen BJ. The influence of a behavioral weight management program on disordered eating attitudes and behaviors in children with overweight. J Am Diet Assoc. (2010) 110:653–9. doi: 10.1016/j.jada.2010.08.005

153. Blake-Lamb TL, Locks LM, Perkins ME, Woo Baidal JA, Cheng ER, Taveras EM. Interventions for childhood obesity in the first 1,000 days a systematic review. Am J Prev Med. (2016) 50:780–9. doi: 10.1016/j.amepre.2015.11.010

154. McGuire S. Institute of Medicine (IOM). Early childhood obesity prevention policies. Washington, DC: The National Academies Press. Adv Nutr . (2011) 3:56–7. doi: 10.3945/an.111.001347

155. Pont SJ, Puhl R, Cook SR, Slusser W. Stigma experienced by children and adolescents with obesity. Pediatrics. (2017) 140:e20173034. doi: 10.1542/peds.2017-3034

156. Puhl R, Suh Y. Health consequences of weight stigma: implications for obesity prevention and treatment. Curr Obes Rep. (2015) 4:182–90. doi: 10.1007/s13679-015-0153-z

157. Schwimmer JB, Burwinkle TM, Varni JW. Health-related quality of life of severely obese children and adolescents. JAMA. (2003) 289:1813–9. doi: 10.1001/jama.289.14.1813

158. Carcone AI, Jacques-Tiura AJ, Brogan Hartlieb KE, Albrecht T, Martin T. Effective patient-provider communication in pediatric obesity. Pediatr Clin North Am. (2016) 63:525–38. doi: 10.1016/j.pcl.2016.02.002

159. Coppock JH, Ridolfi DR, Hayes JF, Paul MS, Wilfley DE. Current approaches to the management of pediatric overweight and obesity. Curr Treat Options Cardiovasc Med. (2014) 16:343. doi: 10.1007/s11936-014-0343-0

160. Davison KK, Jurkowski JM, Li K, Kranz S, Lawson HA. A childhood obesity intervention developed by families for families: results from a pilot study. Int J Behav Nutr Phys Act. (2013) 10:3. doi: 10.1186/1479-5868-10-3

161. Krystia O, Ambrose T, Darlington G, Ma DWL, Buchholz AC, Haines J. A randomized home- based childhood obesity prevention pilot intervention has favourable effects on parental body composition: preliminary evidence from the guelph family health study. BMC Obes. (2019) 6:10. doi: 10.1186/s40608-019-0231-y

162. Skjåkødegård HF, Danielsen YS, Morken M, Linde SRF, Kolko RP, Balantekin KN, et al. Study protocol: a randomized controlled trial evaluating the effect of family-based behavioral treatment of childhood and adolescent obesity–The FABO-study. BMC Public Health. (2016) 16:1106. doi: 10.1186/s12889-016-3755-9

163. Hall KD, Kahan S. Maintenance of lost weight and long-term management of obesity. Med Clin North Am. (2018) 102:183–97. doi: 10.1016/j.mcna.2017.08.012

164. Hall KD. Diet vs. exercise in “the biggest loser” weight loss competition. Obesity. (2013) 21:957–9. doi: 10.1002/oby.20065

165. Lecoultre V, Ravussin E, Redman LM. The fall in leptin concentration is a major determinant of the metabolic adaptation induced by caloric restriction independently of the changes in leptin circadian rhythms. J Clin Endocrinol Metabol. (2011) 96:E1512–E516. doi: 10.1210/jc.2011-1286

166. Kaur KK, Allahbadia G, Singh M. Childhood obesity: a comprehensive review of epidemiology, aetiopathogenesis and management of this global threat of the 21st century. Acta Sci Paediatr. (2019) 2:56–66. doi: 10.31080/ASPE.2019.02.0132

167. Crimmins NA, Xanthakos SA. Obesity. in Neinstein's Adolescent and Young Adult Health , Guide. Philadelphia, PA: Wolters Kluwer (2016). p. 295–300.

168. Astrup A, Rossner S, Van Gaal L, Rissanen A, Niskanen L, Al Hakim M, et al. Effects of liraglutide in the treatment of obesity: a randomized, double-blind, placebo-controlled study. Lancet. (2009) 374:1606–16. doi: 10.1016/S0140-6736(09)61375-1

169. Monami M, Dicembrini I, Marchionni N, Rotella CM, Mannucci E. Effects of glucagon-like peptide-1 receptor agonists on body weight: a meta-analysis. Exp Diabetes Res. (2012) 2012:672658. doi: 10.1155/2012/672658

170. Pi-Sunyer X, Astrup A, Fujioka K, Greenway F, Halpern A, Krempf, et al. A randomized, controlled trial of 3.0 mg of liraglutide in weight management. N Engl J Med. (2015) 373:11–22 . doi: 10.1056/NEJMoa1411892

171. Kelly AS, Auerbach P, Barrientos-Perez M, Gies I, Hale PM, Marcus C, et al. A randomized, controlled trial of liraglutide for adolescents with obesity. N Engl J Med. (2020) 382:2117–28. doi: 10.1056/NEJMoa1916038

172. Foster BA, Escaname E, Powell T, Larsen B, Siddiqui SK, Menchaca J, et al. Randomized controlled trial of DHA supplementation during pregnancy: child adiposity outcomes. Nutrients . (2017) 9:566. doi: 10.3390/nu9060566

173. Abenavoli L, Scarpellini E, Colica C, Boccuto L, Salehi B, Sharifi-Rad J, et al. Gut microbiota and obesity: a role for probiotics. Nutrients. (2019) 11:2690. doi: 10.3390/nu11112690

174. Vajro P, Mandato C, Veropalumbo C, De Micco I. Probiotics: a possible role in treatment of adult and pediatric nonalcoholic fatty liver disease. Ann Hepatol. (2013) 12:161–63. doi: 10.1016/S1665-2681(19)31401-2

175. Zhao L, Fang X, Marshall M, Chung S. Regulation of obesity and metabolic complications by gamma and delta tocotrienols. Molecules. (2016) 21:344. doi: 10.3390/molecules21030344

176. Wong SK, Chin K-Y, Suhaimi FH, Ahmad F, Ima-Nirwana S. Vitamin E as a potential interventional treatment for metabolic syndrome: evidence from animal and human studies. Front Pharmacol. (2017) 8:444. doi: 10.3389/fphar.2017.00444

177. Galli F, Azzi A, Birringer A, Cook-Mills JM, Eggersdorfer M, Frank J, et al. Vitamin E: Emerging aspects and new directions. Free Radic Biol Med. (2017) 102:16–36. doi: 10.1016/j.freeradbiomed.2016.09.017

178. Galmés S, Serra F, Palou A. Vitamin E metabolic effects and genetic variants: a challenge for precision nutrition in obesity and associated disturbances. Nutrients . (2018) 10:1919. doi: 10.3390/nu10121919

179. Ahn SM. Current issues in bariatric surgery for adolescents with severe obesity: durability, complications, and timing of intervention. J. Obes Metabol Syndrome. (2020) 29:4–11. doi: 10.7570/jomes19073

180. Lamoshi A, Chernoguz A, Harmon CM, Helmrath M. Complications of bariatric surgery in adolescents. Semin Pediatr Surg. (2020) 29:150888. doi: 10.1016/j.sempedsurg.2020.150888

181. Weiss AL, Mooney A, Gonzalvo JP. Bariatric surgery. Adv Pediatr. (2017) 6:269–83. doi: 10.1016/j.yapd.2017.03.005

182. Stanford FC, Mushannen T, Cortez P, Reyes KJC, Lee H, Gee DW, et al. Comparison of short and long-term outcomes of metabolic and bariatric surgery in adolescents and adults. Front Endocrinol. (2020) 11:157. doi: 10.3389/fendo.2020.00157

183. Inge TH, Zeller MH, Jenkins TM, Helmrath M, Brandt ML, Michalsky MP, et al. Perioperative outcomes of adolescents undergoing bariatric surgery: the teen-longitudinal assessment of bariatric surgery (Teen-LABS) study . JAMA Pediatr . (2014) 168:47–53. doi: 10.1001/jamapediatrics.2013.4296

184. Järvholm K, Bruze G, Peltonen M, Marcus C, Flodmark CE, Henfridsson P, et al. 5-year mental health and eating pattern outcomes following bariatric surgery in adolescents: a prospective cohort study. Lancet Child AdolescHealth . (2020) 4:210–9. doi: 10.1016/S2352-4642(20)30024-9

185. Xanthakos SA. Bariatric surgery for extreme adolescent obesity: indications, outcomes, and physiologic effects on the gut–brain axis. Pathophysiology. (2008) 15:135–46. doi: 10.1016/j.pathophys.2008.04.005

186. Zitsman JL, Digiorgi MF, Kopchinski JS, Sysko R, Lynch L, Devlin M, et al. Adolescent Gastric Banding: a five-year longitudinal study in 137 individuals. Surg Obes Relat Dis. (2018) 14. doi: 10.1016/j.soard.2018.09.030

187. Inge TH, Jenkins TM, Xanthakos SA, Dixon JB, Daniels SR, Zeller MH, et al. Long-term outcomes of bariatric surgery in adolescents with severe obesity (FABS-5+). A prospective follow-up analysis. Lancet Diabet Endocrinol . (2017) 5:165–73. doi: 10.1016/S2213-8587(16)30315-1

188. Kindel TL, Krause C, Helm MC, Mcbride CL, Oleynikov D, Thakare R, et al. Increased glycine-amidated hyocholic acid correlates to improved early weight loss after sleeve gastrectomy. Surg Endosc. (2017) 32:805–12. doi: 10.1007/s00464-017-5747-y

Keywords: obesity, childhood, review (article), behavior, adolescent

Citation: Kansra AR, Lakkunarajah S and Jay MS (2021) Childhood and Adolescent Obesity: A Review. Front. Pediatr. 8:581461. doi: 10.3389/fped.2020.581461

Received: 08 July 2020; Accepted: 23 November 2020; Published: 12 January 2021.

Reviewed by:

Copyright © 2021 Kansra, Lakkunarajah and Jay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alvina R. Kansra, akansra@mcw.edu

This article is part of the Research Topic

Pediatric Obesity: From the Spectrum of Clinical-Physiology, Social-Psychology, and Translational Research

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Introduction, nih efforts, gaps and opportunities, acknowledgments, compliance with ethical standards.

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Childhood obesity research at the NIH: Efforts, gaps, and opportunities

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S Sonia Arteaga, Layla Esposito, Stavroula K Osganian, Charlotte A Pratt, Jill Reedy, Deborah Young-Hyman, Childhood obesity research at the NIH: Efforts, gaps, and opportunities, Translational Behavioral Medicine , Volume 8, Issue 6, December 2018, Pages 962–967, https://doi.org/10.1093/tbm/iby090

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Childhood obesity is a major public health challenge. This article describes an overview of the National Institutes of Health (NIH) behavioral and social sciences childhood obesity research efforts. The overview will highlight five areas of childhood obesity research supported by the NIH: (a) basic behavioral and social sciences; (b) early childhood; (c) policies, programs, and environmental strategies; (d) health disparities; and (e) transagency and public–private collaboration. The article also describes potential gaps and opportunities in the areas of childhood obesity and severe obesity, measurement, and sleep.

Practice: The National Institutes of Health (NIH) supports a number of funding announcements, workshops, and dietary assessment tools related to childhood obesity.

Policy: Childhood obesity continues to be a major public health challenge, and research related to programs, policies, and/or environmental strategies could be further explored to assess factors related to the promotion of healthy weight among children.

Research: To address the childhood obesity epidemic, the NIH supports a broad spectrum of biomedical and behavioral research that seeks to identify the causes and consequences of childhood obesity to develop new and more effective approaches to its prevention and treatment, and synergize and disseminate evidence within the NIH and with other stakeholder organizations.

Childhood obesity continues to be a major public health challenge with 18.5% of children aged 2–19 years having obesity [ 1 ]. Despite earlier reports that there may be stabilization of obesity among children [ 2 ], recent findings suggest that obesity is not decreasing and severe obesity is increasing among Hispanic children [ 3 , 4 ]. Children who have obesity are more likely to have cardiovascular risk factors [ 5 , 6 ], type 2 diabetes [ 7 ], and are at increased risk for morbidity and mortality as adults [ 8 ] including increased risk of developing several types of cancer [ 9 ].

To address the childhood obesity epidemic, the National Institutes of Health (NIH) supports a broad spectrum of biomedical and behavioral research that seeks to identify the causes and consequences of childhood obesity and to develop new and more effective approaches to its prevention and treatment [ 10 ]. The childhood obesity research that NIH supports includes studies in pregnancy, infancy, childhood, adolescence, and prevention and treatment approaches in families, schools, and other community settings, as well as in health care settings. The NIH also supports basic behavioral and social science research that is providing insights into factors related to the development, prevention, and treatment of childhood obesity, as well as environmental and policy-related research.

In the following section, we provide an overview of the NIH behavioral and social sciences childhood obesity research efforts. This overview is not meant to be a comprehensive summary of NIH’s childhood obesity activities, but instead is based on active and recently completed NIH-funded research activities including workshops and funding announcements as they relate to the behavioral and social sciences. This overview highlights five areas of childhood obesity research supported by the NIH: (a) basic behavioral and social sciences; (b) early childhood; (c) policies, programs, and environmental strategies; (d) health disparities; and (e) transagency and public–private collaboration. Based on research findings and workshop recommendations, discussions on potential gaps and future opportunities in childhood obesity research are provided.

Basic behavioral and social sciences research in childhood obesity

The NIH has long recognized the importance of basic behavioral and social science research related to pediatric obesity and has supported numerous efforts through various Institute and Center initiatives as well as through investigator-initiated research [ 11 ]. In particular, one major initiative, the Obesity-Related Behavioral Intervention Trials (ORBIT) consortium ( www.nihorbit.org ), was a trans-NIH program led by the National Heart, Lung, and Blood Institute (NHLBI) that facilitated the translation of basic behavioral and social science findings into pediatric and adult obesity-related interventions [ 12 ]. The findings from ORBIT and other investigator-initiated research have advanced our understanding of several drivers of food intake and eating behaviors such as taste preferences, self-regulation, impulsivity, sensitization to the relative reinforcing value of food, food reward and inhibition, emotional eating, habituation to food, and ability to delay gratification [ 13 ]. Another important trans-NIH initiative is the Science of Behavior Change (SOBC) that focuses on understanding mechanisms for novel targets of behavior change. Self-regulation, stress resilience and reactivity, and interpersonal and social processes have all been identified by SOBC as promising targets of behavior change and intervention development [ 14 ], and all of these targets can be considered relevant for obesity prevention and control.

Despite significant advances in our understanding of eating behaviors, the individual characteristics and processes that predict and explain physical activity behaviors are not well understood. In response, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) held a Workshop on Behavioral Phenotyping of Physical Activity and Sedentary Behavior in December 2015 to identify gaps and promising research opportunities in behavioral and psychological phenotyping related to variation in physical activity and sedentary behaviors as they relate to obesity [ 15 ]. This workshop resulted in the release of an NIDDK-led, trans-NIH program announcement (PAR-18–105) Ancillary Studies to Identify Behavioral and/or Psychological Phenotypes Contributing to Obesity.

Finally, research has demonstrated that characterizing and influencing individuals’ behaviors in relation to obesity prevention and treatment is increasingly complex and will require more personalized intervention approaches. Individuals’ behaviors do not operate in a vacuum nor are individuals necessarily characterized by one behavioral phenotype[ 16 ]. Future research in this area could work toward deciphering underlying behavioral mechanisms and developing theoretical frameworks that incorporate a more comprehensive and interdisciplinary approach, identifying patterns of behavioral and psychosocial phenotypes in the context of their various environmental influences.

Early childhood

Early childhood is a critical time period in the development of obesity, and the NIH supports several efforts focusing on the prenatal period through age 5. Recognizing the importance of the role of early childhood in the development of obesity, the NIH sponsored a 2013 workshop on the “Prevention of Obesity in Infancy and Early Childhood” [ 17 ], which resulted in a funding announcement, PA-18–032: Understanding Factors in Infancy and Early Childhood (Birth to 24 Months) that Influence Obesity Development (R01 Clinical Trial Optional).

In addition to studying obesity during infancy, the NIH also recognizes the importance of trans-generational impacts and has two large research initiatives that offer opportunities to better explore the trans-generational effects of obesity and its mechanisms: (a) Lifestyle Interventions for Expectant Moms (LIFE-Moms) and (b) Environmental influences on Child Health Outcomes (ECHO) program. Pregnancy is an opportunity to intervene and influence outcomes for the mother and offspring. In 2011, the NIH launched the LIFE-Moms consortium to determine whether behavioral and lifestyle interventions in overweight and obese pregnancy would have an effect on excessive gestational weight gain and impact maternal and child outcomes [ 18 ]. The findings from the LIFE-Moms consortium show that women randomized to the intervention group gained less weight compared with the standard care group [ 19 ]. The de-identified LIFE-Moms data will be available for investigators to access and analyze for future manuscripts. For more information, see https://repository.niddk.nih.gov/home/ .

In 2016, the NIH launched ECHO to fund multiple, synergistic, longitudinal studies using 83 pediatric cohorts to investigate environmental exposures—including physical, chemical, biological, social, behavioral, natural, and built environments—on child health and development [ 20 ]. Obesity is a key pediatric outcome with data to be contributed by all cohorts, enabling investigators to explore how obesity emerges from a complex web of exposures in early childhood. Future research could continue to explore the mechanisms of how early-life exposures contribute to the development of obesity and what factors (e.g., home and pediatric settings) may be leveraged to encourage healthy weight development.

Policies, programs, and environmental strategies

Policies, programs, and environmental strategies have an important influence on childhood obesity, but how and to what extent they affect childhood obesity warrants further study. Many of the factors addressable by policy and environmental change, such as large infrastructure changes or implementation of taxes or subsidies, are not under the control of researchers and may not be studied using traditional randomized study designs, relying instead on a study design referred to as a natural experiment [ 21 ]. A 2010 Institute of Medicine report and 2011 NIH Strategic Plan for Obesity recommended increased emphasis on evaluation of policy and environmental changes to determine their impact on improved diet, physical activity, and weight outcomes [ 22 , 23 ].

The NIH supports the evaluation of natural experiments through funding announcements PAR-17–178: Evaluating Natural Experiments in Healthcare to Improve Diabetes Prevention and Treatment (R18), PA-16–165: Obesity Policy Evaluation Research (R01), and PAR-18-854: Time-Sensitive Obesity Policy and Program Evaluation (R01). The grants funded through the aforementioned funding announcements cover a wide range of policy and environmental strategies including changes to the built environment through light rails, parks, and transportation improvements and the influence on physical activity and health; policies targeting sugar-sweetened beverages and the impact on diet and added sugars; and how later school start times are associated with weight and health outcomes among adolescents.

In addition to investigator-initiated research, the NIH has also launched large initiatives to assess how multi-level environmental factors affect childhood obesity. The NIH Healthy Communities Study was an observational study of 130 diverse communities that sought to determine the associations between characteristics of community programs and policies and body mass index (BMI), diet, and physical activity in children [ 24 ]. Data were collected on children (retrospectively up to 10 years using medical abstraction), their parents, the home environment, school lunch and physical activity environments, and community programs and policies (retrospectively up to 10 years). The results show that over time, more intense programs and policies are related to lower childhood BMI and that there are disparities in this association by sociodemographic family and community characteristics [ 25 ]. A de-identified public use dataset of the Healthy Communities Study is available for researchers to access at https://biolincc.nhlbi.nih.gov/home/ . Future research could investigate how contextual factors within communities (e.g., race/ethnicity of the community, crime, housing) interact with community programs and policies to promote healthy or obesogenic environments.

Health disparities

Obesity prevalence has risen to epidemic levels, particularly among various racial and ethnic minority groups, including Hispanics, African Americans, American Indians/Alaskan Natives, and low-income populations both in urban and rural communities and in all age groups across the lifespan [ 2 ]. To promote the health of future generations of adults, many NIH institutes have funded research addressing health disparities to gain a better understanding of the etiology of obesity as well as interventions that would lower the prevalence of obesity. The Childhood Obesity Prevention and Treatment Research (COPTR) consortium is an example of a large NIH initiative addressing health disparities and childhood obesity. COPTR tested multi-level multicomponent intervention approaches [ 26 ] to prevent excess weight gain in nonoverweight and overweight youth and to reduce weight in obese and severely obese youth [ 27 ]. Research funded under this consortium targeted preschoolers (2–5 years old) and preadolescents and adolescents (7–15 years old) with a total sample size of ~1,750 ( N ~50% females and ~70% minorities) for 3 years of intervention [ 27 ]. Two obesity prevention trials tested approaches that target home, community, and primary care settings for preschool children living in low-income and ethnically diverse neighborhoods. Two obesity treatment trials examined therapies for overweight and obese children, 7–15 years old, in school and home settings in collaboration with local youth organizations. The findings from COPTR could contribute to future understanding of the multiple factors, including social determinants of health indicators, to prevent or treat obesity among a diverse population of low-income children [ 28 , 29 ].

Recently, NIH staff led a systematic review of interventions addressing obesity disparities with the goal of providing guidance for future research, particularly in populations with a high prevalence of obesity and obesity-related cardiometabolic risk. The review noted a dearth of high-quality research that targets minority populations and a limited number of clinical trials in youth [ 30 ]. NIH staff also convened workshops such as the Multi-Level Intervention Research Methods: Recommendations for Targeting Hard-to-Reach, High-Risk or Vulnerable Populations and Communities. Recommendations from the workshop have been published elsewhere [ 31 ] and include recommendations under the following topics: study design and analytical approaches, intervention implementation, cultural adaptation of intervention, use of community health workers, and training of interventionists. Funding opportunity announcements that are relevant to health disparities research include PA-18–412: Addressing Health Disparities in NIDDK Diseases (R01 Clinical Trial Not Allowed); PA-18–152: Reducing Health Disparities Among Minority and Underserved Children (R01 Clinical Trial Optional); and PA-18–169: Reducing Health Disparities Among Minority and Underserved Children (R21 Clinical Trial Optional). Future research needs to better understand the biological and behavioral mechanisms of childhood obesity as well as the contextual and environmental factors that may alleviate or exacerbate obesity disparities [ 32 ].

Transagency and public–private partnership

Launched in 2009, the National Collaborative on Childhood Obesity Research (NCCOR; www.nccor.org ) brings together the nation’s four largest childhood obesity research funders—Centers for Disease Control and Prevention, NIH, United States Department of Agriculture, and Robert Wood Johnson Foundation—in a public–private collaboration to accelerate progress in reducing childhood obesity. Major NCCOR foci are identifying and evaluating practical and sustainable interventions; improving research resources (see Measurement section in this article for examples) to facilitate childhood obesity research and program evaluation; providing national leadership to accelerate implementation of evidence-informed practice and policy; and developing synergistic childhood obesity initiatives across multiple stakeholders [ 33 ].

NCCOR uses this collaborative approach to combine resources and expertise from stakeholder organizations to identify emerging areas of research need, formulate projects within the scope of the NCCOR mission, and identify external collaborators and funding sources by which to implement projects. Examples of NCCOR NIH led or co-led activities include (a) the Healthy Communities Study ( https://www.nhlbi.nih.gov/science/healthy-communities-study-hcs/ ), (b) the Johns Hopkins Global Obesity Center ( www.globalobesity.org ), (c) the Envision Research Network ( https://www.nccor.org/envision/publications.html ), and (d) the Childhood Obesity Declines ( https://www.nccor.org/projects/obesity-declines/ ) among others. Of note is that NCCOR recognizes that many and varied research design and evaluation approaches are needed to better understand the difficulties in reducing rates of childhood obesity, especially in the context of community-based initiatives. Thus, the initiatives cited here span research efforts, targeting individual behavior change to policy implementation, environmental to systemic social determinants of childhood obesity, and recognize the importance of community and academic partnerships. In addition to facilitating research resources and improving intervention and research methods, NCCOR is dedicated to the dissemination of promising evidence regarding intervention strategies and evidence-informed programs to policy makers and program implementers, particularly those embedded in the community and those addressing health inequities and underserved communities. Future research could continue to explore how partnerships with various entities such as housing, transportation, education, and social services can work together to more effectively deliver childhood obesity interventions.

In addition to the abovementioned NIH efforts, severe obesity, measurement issues in childhood obesity research, and the mechanisms associated with sleep and obesity have emerged as gaps and opportunities for further childhood obesity research.

Severe obesity

Severe obesity in youth, defined as BMI ≥ 1.2 times the 95th percentile or an absolute BMI ≥ 35 kg/m 2 , is a prevalent and serious disease with a limited number of effective and safe treatment options [ 34 ]. The prevalence of severe obesity among all children is 5.6% and is highest (7.7%) among adolescents aged 12–19 years [ 3 ]. To address the issue of severe obesity among adolescents, a workshop led by NIDDK, in cooperation with several NIH Institutes and Centers, entitled “Developing Precision Medicine Approaches to the Treatment of Severe Obesity in Adolescents” ( https://www.niddk.nih.gov/news/meetings-workshops/2017/workshop-developing-precision-medicine-approaches-treatment-severe-obesity-adolescents ) was convened in September 2017 to explore the current state of the science and identify (a) what is known regarding the epidemiology and biopsychosocial determinants of severe obesity in adolescents, (b) what is known regarding effectiveness of treatments for severe obesity in adolescents and predictors of response, and (c) gaps and opportunities for future research to develop more effective and targeted treatments for adolescents with severe obesity. Several gaps were identified and recommendations were made for opportunities to accelerate research to advance precision medicine approaches to treat severe obesity in adolescents and to enhance methodological rigor in pediatric obesity research. More research is needed to better understand the underlying etiology and pathophysiology of severe obesity in children and developing effective intervention approaches.

Measurement

Measurement is a fundamental component of all forms of research, including research on childhood obesity. The development and consistent use of high-quality, comparable measures and research methods is a priority. To address this need and encourage innovative research with novel assessment approaches, better statistical methods and modeling, and tools for culturally diverse populations and/or children at various ages, the NIH supports the Diet and Physical Activity Assessment Methodology (PA-16–167). However, the advancement and application of appropriate diet and physical activity measures remains challenging, as highlighted at two workshops at NIH, “Extending Dietary Patterns Research Methods” [ 35 ] and “Research Strategies for Nutritional and Physical Activity Epidemiology and Cancer Prevention” [ 36 ].

NIH resources are available to provide guidance on selecting measures and to provide tools for research. For example, NCI developed the Dietary Assessment Primer ( https://dietassessmentprimer.cancer.gov/ ) to help determine the best way to assess diet, and specific dietary assessment tools, such as the Automated Self-Administered 24-Hour (ASA24; https://epi.grants.cancer.gov/asa24/ ) and Dietary Assessment Tool and the Diet History Questionnaire ( https://epi.grants.cancer.gov/dhq2/ ). In addition, NCCOR’s Measures Registry and User Guides ( https://www.nccor.org/nccor-tools/measures/ ) were developed for four relevant domains, including diet, physical activity, food environment, and physical activity environment, and were designed to provide an overview of measurement, describe general principles of measurement selection, present case studies, and direct researchers to additional resources across the lifespan.

Although these tools are useful, opportunities exist to further develop objective measurements of diet and physical activity through new technologies that integrate and exploit advances in wearable sensors and other novel image-based tools. More sophisticated exposure characterization for childhood obesity researchers could allow for measurement of individual diet and physical activity behaviors as well as a linkage in real time to other details that include geospatial location, time, and context, providing opportunities to examine new research questions and identify potential targets for intervention.

Sleep and obesity

Recent meta-analyses have found an association between shortened sleep duration and increased risk of obesity in children [ 37–39 ]. The relationship between sleep and obesity is stronger in younger children than in adolescents [ 37 ], and more research is needed to better understand why the relationship varies with age. Future research could also investigate the mechanisms of sleep/circadian rhythms and the development of obesity including how in utero factors may affect those mechanisms. Sleep is a modifiable behavior, and research is needed to better understand how improving sleep may affect weight gain, weight loss, and weight maintenance. For instance, a recent study found that it was possible to increase sleep in children, and the increased sleep condition versus decreased sleep condition was associated with lower self-reported caloric intake and weight, but the study was short in duration and had a small sample size [ 40 ]. More research is needed to better understand how intervention approaches including sleep can lead to the prevention and treatment of obesity. Furthermore, future research could address how health disparities may interact with sleep to affect obesity. NIH is currently supporting funding announcement PAR-17–234: Mechanisms and Consequences of Sleep Disparities in the U.S. (R01).

This article highlights NIH childhood obesity research efforts in the behavioral and social sciences. There are several activities that the NIH has undertaken to further the knowledge, prevention, and treatment of childhood obesity. In addition to the aforementioned NIH efforts, there are emerging gaps and opportunities related to severe obesity, measurement issues, and sleep and obesity. The childhood obesity epidemic continues to grow, and the NIH is committed to supporting research that will help alleviate the obesity epidemic. NIH will continue to support behavioral and social science approaches to better understand the drivers of childhood obesity and to develop effective interventions.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Diabetes and Digestive and Kidney Diseases, National Cancer Institute, Office of Behavioral and Social Science Research, the National Institutes of Health, or the U.S. Department of Health and Human Services.

Funding: This commentary was not funded.

Conflicts of Interest: All authors declare they have no conflicts of interest.

Ethical Approval: Human rights, informed consent, and animal welfare ethical statements are not applicable.

Hales CM , Carroll MD , Fryar CD , Ogden CL . Prevalence of obesity among adults and youth: United States, 2015–2016 . NCHS Data Brief . 2017 October;( 288 ): 1 – 8 .

Google Scholar

Ogden CL , Carroll MD , Lawman HG , et al.  Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014 . JAMA . 2016 ; 315 ( 21 ): 2292 – 2299 .

Hales CM , Fryar CD , Carroll MD , Freedman DS , Ogden CL . Trends in obesity and severe obesity prevalence in us youth and adults by sex and age, 2007–2008 to 2015–2016 . JAMA . 2018;319(16):1723–1725 . doi: 10.1001/jama.2018.3060

Skinner AC , Ravanbakht SN , Skelton JA , Perrin EM , Armstrong SC . Prevalence of obesity and severe obesity in US children, 1999–2016 . Pediatrics . 2018;141(3):e20173459 . doi: 10.1542/peds.2017–3459

Freedman DS , Mei Z , Srinivasan SR , Berenson GS , Dietz WH . Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study . J Pediatr . 2007 ; 150 ( 1 ): 12 – 17.e2 .

Koskinen J , Juonala M , Dwyer T , et al.  Impact of lipid measurements in youth in addition to conventional clinic-based risk factors on predicting preclinical atherosclerosis in adulthood: International Childhood Cardiovascular Cohort Consortium . Circulation . 2018 ; 137 ( 12 ): 1246 – 1255 .

Goran MI , Ball GD , Cruz ML . Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents . J Clin Endocrinol Metab . 2003 ; 88 ( 4 ): 1417 – 1427 .

Reilly JJ , Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review . Int J Obes (Lond) . 2011 ; 35 : 891 – 898 .

World Cancer Research Fund/American Institute for Cancer Research . Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective . Washington, DC: American Institute for Cancer Research ; 2007 .

Google Preview

Rodgers GP , Collins FS . The next generation of obesity research: no time to waste . JAMA . 2012 ; 308 ( 11 ): 1095 – 1096 .

Czajkowski SM . National Institutes of Health update: translating basic behavioral science into new pediatric obesity interventions . Pediatr Clin North Am . 2016 ; 63 ( 3 ): 389 – 399 .

Naar S . From bench to bedside: T1 translation of basic behavioral science into novel pediatric obesity interventions . Pediatr Clin North Am . 2016 ; 63 ( 3 ): xv – xvi .

Young-Hyman D . Introduction to special issue: self-regulation of appetite – it’s complicated . Obesity (Silver Spring) . 2017 ; 25 ( suppl 1 ): S5 – S7 .

Nielsen L , Riddle M , King JW , et al.  ; NIH Science of Behavior Change Implementation Team . The NIH science of behavior change program: transforming the science through a focus on mechanisms of change . Behav Res Ther . 2018 ; 101 : 3 – 11 .

Bryan AD , Jakicic JM , Hunter CM , Evans ME , Yanovski SZ , Epstein LH . Behavioral and psychological phenotyping of physical activity and sedentary behavior: implications for weight management . Obesity (Silver Spring) . 2017 ; 25 ( 10 ): 1653 – 1659 .

Kral TVE, Moore RH, Chittams J, Jones E, O’Malley L, Fisher JO. Identifying behavioral phenotypes for childhood obesity. Appetite. 2018;127:87–96. doi:10.1016/j.appet.2018.04.021

Lumeng JC , Taveras EM , Birch L , Yanovski SZ . Prevention of obesity in infancy and early childhood: a National Institutes of Health workshop . JAMA Pediatr . 2015 ; 169 ( 5 ): 484 – 490 .

Clifton RG , Evans M , Cahill AG , et al.  ; LIFE-Moms Research Group . Design of lifestyle intervention trials to prevent excessive gestational weight gain in women with overweight or obesity . Obesity . 2016 ; 24 ( 2 ): 305 – 313 .

Peaceman AM, Clifton RG, Phelan S, et al.; LIFE‐Moms Research Group. Lifestyle interventions limit gestational weight gain in women with overweight or obesity: LIFE-Moms prospective meta-analysis. Obesity . 2018;26(9):1396–1404. doi:10.1002/oby.22250

Gillman MW , Blaisdell CJ . Environmental influences on child health outcomes, a research program of the National Institutes of Health . Curr Opin Pediatr . 2018 ; 30 ( 2 ): 260 – 262 .

Hunter CM , McKinnon RA , Esposito L . News from the NIH: research to evaluate “natural experiments” related to obesity and diabetes . Transl Behav Med . 2014 ; 4 ( 2 ): 127 – 129 .

Institute of Medicine . Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making . Washington, DC : The National Academies Press ; 2010 .

The NIH Obesity Research Task Force . Strategic Plan for NIH Obesity Research . NIH Publication No. 11–5493. Bethesda, MD: The National Institutes of Health ; 2011 .

Arteaga SS , Loria CM , Crawford PB , et al.  The Healthy Communities Study: its rationale, aims, and approach . Am J Prev Med . 2015 ; 49 ( 4 ): 615 – 623 .

Strauss WJ , Nagaraja J , Landgraf AJ , et al.  The longitudinal relationship between community programmes and policies to prevent childhood obesity and BMI in children: the Healthy Communities Study . Pediatr Obes . 2018 . Epub ahead of print. doi: 10.1111/ijpo.12266

Pratt CA , Arteaga S , Loria C . Forging a future of better cardiovascular health: addressing childhood obesity . J Am Coll Cardiol . 2014 ; 63 ( 4 ): 369 – 371 .

Pratt CA , Boyington J , Esposito L , et al.  Childhood Obesity Prevention and Treatment Research (COPTR): interventions addressing multiple influences in childhood and adolescent obesity . Contemp Clin Trials . 2013 ; 36 ( 2 ): 406 – 413 .

Barkin SV , Heerman WJ , Sommer EC , et al.  Effect of a behavioral intervention for underserved preschool-age children on change in body mass index: a randomized clinical trial . JAMA . 2018;320(5):450–460 .

French SA , Sherwood NE , et al.  NET-works randomized clinical trial: Results of a three-year childhood obesity prevention intervention for preschool-aged children . Am J Public Health. 2018 July. in press

Pratt CA , Loria CM , Arteaga SS , et al.  A systematic review of obesity disparities research . Am J Prev Med . 2017 ; 53 ( 1 ): 113 – 122 .

Stevens J , Pratt C , Boyington J , et al.  Multilevel interventions targeting obesity: research recommendations for vulnerable populations . Am J Prev Med . 2017 ; 52 ( 1 ): 115 – 124 .

Krueger PM , Reither EN . Mind the gap: race/ethnic and socioeconomic disparities in obesity . Curr Diab Rep . 2015 ; 15 ( 11 ): 95 .

Writing Group for the National Collaborative on Childhood Obesity Research . A national collaborative for building the field of childhood obesity research . Am J Prev Med . 2018 ; 54 ( 3 ): 453 – 464 .

Kelly AS , Barlow SE , Rao G , et al.  ; American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young, Council on Nutrition, Physical Activity and Metabolism, and Council on Clinical Cardiology . Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association . Circulation . 2013 ; 128 ( 15 ): 1689 – 1712 .

Reedy J , Subar AF , George SM , Krebs-Smith SM . Extending methods in dietary patterns research . Nutrients . 2018 ; 10 ( 5 ) :571.

Mahabir S , Willett WC , Friedenreich CM , et al.  Research strategies for nutritional and physical activity epidemiology and cancer prevention . Cancer Epidemiol Biomarkers Prev . 2018 ; 27 ( 3 ): 233 – 244 .

Fatima Y , Doi SA , Mamun AA . Longitudinal impact of sleep on overweight and obesity in children and adolescents: a systematic review and bias-adjusted meta-analysis . Obes Rev . 2015 ; 16 ( 2 ): 137 – 149 .

Miller MA , Kruisbrink M , Wallace J , Ji C , Cappuccio FP . Sleep duration and incidence of obesity in infants, children, and adolescents: a systematic review and meta-analysis of prospective studies . Sleep . 2018 ; 41 ( 4 ). doi:10.1093/sleep/zsy018

Wu Y , Gong Q , Zou Z , Li H , Zhang X . Short sleep duration and obesity among children: a systematic review and meta-analysis of prospective studies . Obes Res Clin Pract . 2017 ; 11 ( 2 ): 140 – 150 .

Hart CN , Hawley NL , Wing RR . Development of a behavioral sleep intervention as a novel approach for pediatric obesity in school-aged children . Sleep Med Clin . 2016 ; 11 ( 4 ): 515 – 523 .

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ASPE Childhood Obesity White Paper

Aspe research brief, childhood obesity.

By: Jennifer Bishop, Rebecca Middendorf, Tori Babin, Wilma Tilson

The document provides an overview of the research literature on causes of childhood obesity.

This research brief is available on the Internet at:

http://aspe.hhs.gov/health/reports/child_obesity/index.cfm

Overweight and obesity in children are significant public health problems in the United States. The number of adolescents who are overweight has tripled since 1980 and the prevalence among younger children has more than doubled. According to the 1999-2002 NHANES survey, 16 percent of children age 6-19 years are overweight (see Figure 1). 1,2,3  Not only have the rates of overweight increased, but the heaviest children in a recent NHANES survey were markedly heavier than those in previous surveys.

Figure 1. Prevalence of overweight among children and adolescents ages 6-19 years

Figure 1. Prevalence of overweight among children and adolescents ages 6-19 years

NOTE: Excludes pregnant women starting with 1971-74. Pregnancy status not available for 1963-65 and 1966-70. Data for 1963-65 are for children 6-11 years of age; data for 1966-70 are for adolescents 12-17 years of age, not 12-19 years.

SOURCE: CDC/NCHS, NHES and NHANES.

Obesity disproportionately affects certain minority youth populations. NHANES found that African American and Mexican American adolescents ages 12-19 were more likely to be overweight, at 21 percent and 23 percent respectively, than non-Hispanic White adolescents (14 percent). 4  In children 6-11 years old, 22 percent of Mexican American children were overweight, whereas 20 percent of African American children and 14 percent of non-Hispanic White children were overweight. 5  In addition to the children and teens who were overweight in 1999-2002, another 15 percent were at risk of becoming overweight. 6,7  In a national survey of American Indian children 5-18 years old, 39 percent were found to be overweight or at risk for overweight. 8

Being overweight during childhood and adolescence increases the risk of developing high cholesterol, hypertension, respiratory ailments, orthopedic problems, depression and type 2 diabetes as a youth. One disease of particular concern is Type 2 diabetes, which is linked to overweight and obesity and has increased dramatically in children and adolescents, particularly in American Indian, African American and Hispanic/Latino populations. 9  The hospital costs alone associated with childhood obesity were estimated at $127 million during 1997–1999 (in 2001 constant U.S. dollars), up from $35 million during 1979–1981. 10

Looking at the long-term consequences, overweight adolescents have a 70 percent chance of becoming overweight or obese adults, which increases to 80 percent if one or more parent is overweight or obese. 11  Obesity in adulthood increases the risk of diabetes, high blood pressure, high cholesterol, asthma, arthritis, and a general poor health status. 12  In 2000, the total cost of obesity for children and adults in the United States was estimated to be $117 billion ($61 billion in direct medical costs). 13

Understanding the causes of childhood obesity can provide the opportunity to focus resources, interventions and research in directions that would be most beneficial in addressing the problem. The remainder of this document provides an overview of the existing research into the causes of childhood obesity, and a discussion of data limitations.

The causes of childhood obesity are multi-factorial. Overweight in children and adolescents is generally caused by a lack of physical activity, unhealthy eating patterns resulting in excess energy intake, or a combination of the two. Genetics and social factors - socio-economic status, race/ethnicity, media and marketing, and the physical environment – also influence energy consumption and expenditure. Most factors of overweight and obesity do not work in isolation and solely targeting one factor may not going to make a significant impact on the growing problem.

To date, research has been unable to isolate the effects of a single factor due to the co-linearity of the variables as well as research constraints. 14  Specific causes for the increase in prevalence of childhood obesity are not clear and establishing causality is difficult since longitudinal research in this area is limited. Such research must employ long study times to discern if there is an interaction of factors leading to an increase in the prevalence or the prevention of obesity during childhood and adolescence. Underreporting total food intake, misreporting of what was eaten, and over reporting physical activity are all likely potential biases that may affect the outcomes of studies in this area. 15

Nutrition and Eating Habits

It is difficult to correlate nutritional choices and childhood obesity using observational research. However, trend data suggest some changes in eating patterns and consumption that may be correlated with increases in obesity. In general, children and adolescents are eating more food away from home, drinking more sugar-sweetened drinks, and snacking more frequently. Convenience has become one of the main criteria for American’s food choices today, leading more and more people to consume ‘away-from-home’ quick service or restaurant meals or to buy ready-to-eat, low cost, quickly accessible meals to prepare at home. The nutritional composition of children’s diets as well as the number of calories consumed are of interest to determine the effect of food consumption on childhood obesity.

Below are notable trends gleaned from studies that used the USDA’s Nationwide Food Consumption Survey and the Continuing Survey of Food Intakes by Individuals. These studies demonstrate changes in eating patterns among American youth that illustrate the complexity that exists relating food intake to the increased prevalence of obesity. 16

  • Children are getting more of their food away from home. Energy intake from away-from-home food sources increased from 20 to 32 percent from 1977-1978 to 1994-1996. 17
  • Daily total energy intake did not significantly increase for children 6-11, but did increase for adolescent girls and boys (ages 12-19 years) by 113 and 243 kilocalories, respectively. 18,19
  • Daily total energy intake that children derived from energy dense (high calorie) snacks increased by approximately 121 kilocalories between 1977 and 1996. 20
  • There has been a decline in breakfast consumption - especially for children of working mothers.
  • Portion sizes increased between 1977 and 1996. Average portion sizes increased for salty snacks from 1.0 oz to 1.6 oz and for soft drinks from 12.2 oz to 19.9 oz. 21

Figure 2: Proportion of Vegetable Servings, 1999-2000

Figure 2: Proportion of Vegetable Servings, 1999-2000

Note: Children 2-19 years.

Source: National Health and Nutrition Examination Survey, NCHS, CDC.

Figure 3: Proportion of Grain Servings, 1999-2000

Figure 3: Proportion of Grain Servings, 1999-2000

Source: National Health and Nutrition Examination Survey, NCHS, CDC

Other studies indicate that children are not eating the recommended servings of foods featured in the USDA food pyramid and that there have been significant changes in the types of beverages that children are consuming:

  • Only 21 percent of young people eat the recommended five or more servings of fruits and vegetables each day. 22  As shown in figure 2, nearly half of all vegetable servings are fried potatoes.
  • Percent total energy from fat actually decreased between 1965 and 1996 for children, from 39 to 32 percent for total fat, and 15 to 12 percent for saturated fat. 23
  • In 1994-1996, adolescent girls and boys only consumed 12 and 30 percent, respectively, of the Food Guide Pyramid’s serving recommendations for dairy; and 18 and 14 percent, respectively, of the serving recommendations for fruit. 24
  • Soda consumption increased dramatically in the early to mid 1990s. Thirty-two percent of adolescent girls and 52 percent of adolescent boys consume three or more eight ounce servings of soda per day. 25  Soft drink consumption for adolescent boys has nearly tripled, from seven to 22 oz. per day (1977-1978 to 1994). 26,27  Children as young as seven months old are consuming soda. 28
  • Milk consumption has declined during the same period. In 1977-78, children age 6-11 drank four times as much milk as any other beverage. In 1994-1996 that decreased to 1.5 times as much milk as sugar sweetened beverages. 29  In 1977-1978, adolescents drank 1.5 times as much milk as any other beverage and in 1996 they consumed twice as much sugar sweetened beverages as milk. 30  Milk consumption decreased for adolescent boys and girls 37 and 30 percent respectively, between 1965 and 1996. 31

Studies on Diet

Several studies have been published that attempt to link children’s diets with the onset of obesity. However, none have been able to show a causal link between diet and obesity. 32,33  Two such studies include the Bogalusa Heart Study and a USDA Economic Research Service study.

  • The Bogalusa Heart Study analyzed children’s eating patterns over two decades (1973-1994) using a series of seven cross-sectional surveys given to 1,584 ten year old children. The study discovered changes in children’s eating patterns over this 20 year period including: increased incidence of missed breakfasts, increased numbers of children eating dinners outside the home, and increased snacking. No causal associations were found between changes in meal patterns and overweight status. 34
  • The USDA Economic Research Service study on fruit consumption indicated that higher fruit consumption is linked with a lower BMI in both adults and children. A large cohort of 3,064 children between the ages of 5 and 18 years were surveyed between 1994 and 1996 using the USDA’s Continuing Survey of Food Intakes by Individuals (CSFII). The study hypothesized that people who incorporate nutrient-dense, low-fat foods into their diets like those found in fruits and vegetables will have a healthier BMI. However, the study only found a weak correlation between body weight and vegetable consumption. 35

Figure 4: Vigorous Physical Activity for Adolescents by Grade Level: 2001

Figure 4: Vigorous Physical Activity for Adolescents by Grade Level: 2001

Note: Vigorous physical activity is activity that made students in grades 9-12 sweat or breathe hard for 20+ minutes minutes on 3+ of the past 7 days. I = 95% confidence interval.

Source: Youth Risk Behavior Surveillance System, NCCDPHP, CDC

Figure 5. Vigorous Physical Activity for Adolescents by Sex and Race/Ethnicity

Figure 5. Vigorous Physical Activity for Adolescents by Sex and Race/Ethnicity

Note: Black and white exclude persons of Hispanic origin. Hispanic can be any race. Vigorous physical activity is activity that made students in grades 9-12 sweat or breathe hard for 20+ minutes minutes on 3+ of the past 7 days. I = 95% confidence interval.

Source: Youth Risk Behavior Surveillance System, NCCDPHP, CDC.

Physical Inactivity and Sedentary Behaviors

Research indicates that a decrease in daily energy expenditure without a concomitant decrease in total energy consumption may be the underlying factor for the increase in childhood obesity. Physical activity trend data for children are limited, but cross sectional data indicates that one third of adolescents are not getting recommended levels of moderate or vigorous activity, 10 percent are completely inactive, and physical activity levels fall as adolescents age (see figures 4 and 5). 36  This situation may actually be worse than these data describe. Activity measured by physical activity monitors tends to be significantly lower than what is reported on surveys. 37

Watching television, using the computer, and playing video games occupy a large percentage of children’s leisure time, influencing their physical activity levels. It is estimated that children in the United States are spending 25 percent of their waking hours watching television and statistically, children who watch the most hours of television have the highest incidence of obesity. 38,39  This trend is apparent not only because little energy is expended while viewing television but also because of the concurrent consumption of high-calorie snacks.

A recent examination of the Department of Education’s Early Childhood Longitudinal Survey (ECLS-K) found that a one-hour increase in physical education per week resulted in a 0.31 point drop (approximately 1.8%) in body mass index among overweight and at-risk first grade girls. There was a smaller decrease for boys. The study concluded that expanding physical education in kindergarten to at least five hours per week could reduce the percentage of girls classified as overweight from 9.8 to 5.6 percent. 40

Figure 6: Percentage of children aged 9-13 years who reported participation in organized and free-time physical activity during the preceding 7 days, by selected characteristics

Figure 6: Percentage of children aged 9-13 years who reported participation in organized and free-time physical activity during the preceding 7 days, by selected characteristics

Source: Youth Media Campaign Longitudinal Survey, US 2002

Currently, schools are decreasing the amount of free play or physical activity that children receive during school hours. Only about one-third of elementary children have daily physical education, and less than one-fifth have extracurricular physical activity programs at their schools. Daily enrollment in physical education classes among high school students decreased from 42 percent in 1991 to 25 percent in 1995, subsequently increasing slightly to 28 percent in 2003. 41  Outside of school hours, only 39 percent of children ages 9-13 participate in an organized physical activity, although 77 percent engage in free-time physical activity (Figure 6). 42

Physical Environment

Experts have increasingly looked to the physical environment as a driver in the rapid increase of obesity in the United States. 43  In urban and suburban areas, the developed environment can create obstacles to being physically active. In urban areas, space for outdoor recreation can be scarce, preventing kids from having a protected place to play; neighborhood crime, unattended dogs, or lack of street lighting may also inhibit children from being able to walk safely outdoors; and busy traffic can impede commuters from walking or biking to work as a means of daily exercise. Though few studies are available on the direct effects of the physical environment on physical activity, there are signs of the potential for improvement, evidenced by Toronto’s 23 percent increase in bicycle use after the addition of bike lanes, and London’s footpath use increase within the range of 34-101 percent (depending on location) as a result of improved lighting. 44,45  There has been less research on the relationship between the physical environment and physical activity for children than for adults, however the findings for children appear to be consistent with those of the adult population. 46  The percentage of trips to school that children walked declined from 20 percent in 1977 to 12 percent in 2001. 47  Because children spend a substantial amount of time traveling to and from school, this may be an area in which to incorporate and increase physical activity into children’s daily habits. 48  Additionally, in-school environments have an impact on children’s health. In a study of available school environments such as courts, fields and nets for physical activity in middle schools, environmental characteristics including the area type and size, supervision, temperature and organized activities explained 42 percent of the variance in the proportion of girls who were physically active and 59 percent of the variance in boys. 49

In suburban areas, the evolution of ‘sprawl’ can prevent residents from walking or biking and contributes to the great dependence on rising vehicle use. 50  Suburban residents frequently lack adequate resources for physical recreation or sidewalks. 51  In the first national study to establish a direct association between the form of the community and the health of the people who live there, analysts from Smart Growth America and the Centers for Disease Control and Prevention (CDC) found that “sprawl appears to have direct relationships to BMI and obesity. ”52  In the study, 488 counties were assigned a ‘sprawl index’ value, which ranged from 63 for the most sprawling county to 352 for the least sprawling county; the results showed that for a 50-point decrease in sprawl index value, the average BMI rose 0.17 points. 53  Results also indicate that at the extremes, residents of the highest sprawling areas are likely to weigh six pounds more, on average, than residents of the most compact areas. 54  Researchers reported that people in high sprawl counties were likely to weigh more, walk less, and have a higher prevalence of hypertension. 55  Analysts agree that further research is required to determine direct causality between sprawl and health problems such as obesity, overweight, and hypertension.

Socio-Economic Status and Race/Ethnicity

Among adults, a negative relationship between socioeconomic status (SES) (e.g., parental income, parental education, occupation status) and being overweight or obese has been well established, however, the relationship appears weaker and less consistent in children. 56,57,58,59  A number of studies find that SES is negatively associated with children being overweight or obese. 60  It appears likely that the relationship between SES and obesity varies by race/ethnicity, such that the negative relationship is only apparent among White adolescents and is not apparent among Black or Mexican-American (and presumably other Latino) adolescents. 61  In other words, Black and Latino children from families with higher socioeconomic status are no less likely to be overweight or obese than those in families with lower socioeconomic status. Despite the more pronounced impact of SES among White children, they are substantially less likely to be overweight or obese than Black, Latino, or Native American children, who are disproportionately affected by obesity. 62,63  In 1998, 21.5 percent of Black children and 21.8 percent of Latino children were overweight, while 12.3 percent of White children were. 64  In a 2003 regional survey in the Aberdeen area, American Indian boys ages 5-17 years old had a prevalence of overweight at 22 percent and 18 percent for girls for the same age group. 65  Furthermore, the prevalence at which obesity has been increasing in children in the recent years has been even more pronounced and rapid among minority children: between 1986 and 1998, obesity prevalence among African Americans and Hispanics increased 120 percent, as compared to a 50 percent increase among non-Hispanic Whites. 66,67

Findings from studies suggest that the effects of race/ethnicity and SES on the prevalence of childhood obesity cannot be individually determined because they are collinear. Therefore evidence is often inconsistent as a result of the difficulty of separating the overlapping factors. 68  Furthermore, the relationship among race/ethnicity, SES, and childhood obesity may result from a number of underlying causes, including less healthy eating patterns (e.g., eating fewer fruits and vegetables, more saturated fats), engaging in less physical activity, more sedentary behavior, and cultural attitudes about body weight. 69  Clearly these factors tend to co-occur and are likely to contribute jointly to differentials in increased risk of obesity in children.

Parental Influences

Numerous parental influences shape the eating habits of youth including; the choice of an infant feeding method, the foods they make available and accessible, the amount of time children are left unsupervised and their eating interactions with others in the social context. Several studies suggest that breastfeeding offers a small but consistent protective effect against obesity in children. 70  This effect is most pronounced in early childhood. It has been hypothesized that exposure to complex sugars and fats contained in bottle formula influence “obesogenic factors” in infants, which predispose them to weight gain later on in life. 71  A recent study postulated that breastfeeding may promote healthier eating habits because breastfed infants may eat until satiated, whereas bottle fed babies may be encouraged to eat until they have consumed all of the formula. Breast feeding also may expose babies to more variability in terms of nutrition and tastes since formula fed infants have experience with only a single flavor, whereas breastfed infants are exposed to a variety of flavors from the maternal diet that are transmitted through the milk. 72  Research indicates that the perception of flavors in mother’s milk is one of the human infant’s earliest sensory experiences, and there is support for the idea that early experience with flavors has an effect on milk intake and the subsequent acceptance of a variety of foods 73 .

Studies suggest that parental food preferences directly influence and shape those of their children. In a study by Oliveria and colleagues, they reported that parents who ate diets high in saturated fats also had children that ate diets high in saturated fats. 74  It is suspected that this observation is not merely due to the foods parents feed their children, but rather due to the preferences children develop through exposure to foods that their parents prefer early in their lives. Birch and Fisher posit that exposure to fruits and vegetables and foods high in energy, sugar and fat may play an important role in establishing a hierarchy of food preferences and selection in kids. 75  Other studies have confirmed that availability and accessibility of fruits and vegetables was positively related to fruit and vegetable preferences and consumption by school children. 76

Additionally, child-feeding practices that control what and how much children eat can also affect their food preferences. Studies have determined that parents who attempt to encourage the consumption of food(s) may inadvertently cause children to dislike the food(s). Whereas parents that attempt to limit food(s) may actually promote increased preference and consumption of the limited food(s) in children. 77,78

Researchers also indicate that the social context in which a child is introduced to or has experiences with food is instrumental in shaping food preferences because the eating environment serves as a model for the developing child. 79  For many children, eating is a social event that often times occurs in the presence of parents, other adults, older siblings and peers. In these contexts, children observe the behaviors and preferences of others around them. These role models have been found to have an influential effect on future food selection, especially when the model is similar to the child, or perceived as being powerful as in the case of older peers. 80,81,82

Over the last three decades there has been an increase in the number of dual income families as more women have entered the workforce and there has been an increase in the number of women serving as the sole supporter for their families. 83  It has been hypothesized that increased rates and hours of parental employment may be correlated with the weight increases in American children (particularly for women because they still bear the bulk of the responsibility of caring for children). Studies have demonstrated that children in single-parent families are more likely to be overweight or obese than children in two-parent families and that the rise in women working outside the home coincides with the rise in childhood weight problems. 84,85  Several potential mechanisms have been proposed to explain this phenomenon including the following:

  • Constraints on parent’s time potentially contribute to children’s weight problems, as working parents probably rely more heavily than non-working parents on prepared, processed, and fast foods, which generally have high calorie, high fat, and low nutritional content.
  • Children left unsupervised after school may make poor nutritional choices and engage in more sedentary activities.
  • Child care providers may not offer as many opportunities for physical activity and may offer less nutritious food alternatives.
  • Unsupervised children may spend a great deal of time indoors, perhaps due to safety concerns, watching TV or playing video games rather than engaging in more active outdoor pursuits. 86

In short, the recent social and economic changes in American society have encouraged the consumption of excess energy and have had a detrimental effect on energy expenditure among youth. These changes have impacted the foods available in the homes, the degree of influence parents have when children make food selections and has led to increases in sedentary behaviors among youth.

There is an abundance of evidence that supports genetic susceptibility as an important risk factor for obesity. Evidence from twin, adoption and family studies strongly suggests that biological relatives exhibit similarities in maintenance of body weight, and that heredity contributes between five and 40 percent of the risk for obesity. 87,88,89  Other studies indicate that 50-70 percent of a person’s BMI and degree of adiposity (fatness) is determined by genetic influences and that there is a 75 percent chance that a child will be overweight if both parents are obese, and a 25-50 percent chance if just one parent is obese. 90,91,92

Though this relationship is well established, the role of genetics in obesity is complex. While over 250 obesity-associated genes have been identified, there is no one ‘smoking gun’. 93  Cases of monogenic obesity and related syndromes do exist, but they are extremely rare and only account for a small number of those who are overweight and obese. To date only six single gene specific defects that result in obesity have been found, and appear to affect fewer than 150 people. 94  Genetic susceptibility to obesity in most cases is due to multiple genes that interact with environmental and behavioral factors. Simply having a genetic predisposition to obesity does not guarantee that an individual will develop the disease.

It must also be noted that the recent increases in weight observed in the American population are not correlated with genetics. Despite the strong influence that genetics has on obesity, the genetic composition of a population does not change rapidly, and moreover, the characteristics of the American population have not dramatically changed. Therefore, increases in the incidence and prevalence rates of obesity in the US are likely due to behavioral or environmental factors, which have interacted with genes, and not the effects of genetics alone.

Advertising and Marketing

There has been considerable debate over whether exposure to food advertising affects incidence rates of childhood obesity. While the positive correlation between the hours of television viewed, body mass index, and obesity incidence has been documented, the exact mechanisms through which this occurs are still being investigated. It has been estimated that the average child currently views more than 40,000 commercials on television each year, a sharp increase from 20,000 in the 1970s. 95  Moreover, an accumulated body of research reveals that more than 50 percent of television advertisements directed at children promote foods and beverages such as candy, convenience foods, snack foods, sugar sweetened beverages and sweetened breakfast cereals that are high in calories and fat and low in fiber and nutrient density. 96  The statistics on food advertising to children indicate that:

  • Annual sales of foods and beverages to young consumers exceeded $27 billion in 2002. 97
  • Food and beverage advertisers collectively spend $10 to $12 billion annually to reach children and youth: more than $1 billion is spent on media advertising to children (primarily on television); more than $4.5 billion is spent on youth-targeted public relations; and $3 billion is spent on packaging designed for children. 98,99
  • Fast food outlets spend $3 billion in television ads targeted to children. 100

A growing body of research suggests that there may be a link between exposure to food advertising and the increasing rates of obesity among youth. In the 1970s and 1980s a number of experimental studies were conducted that demonstrated young children (under age eight) were much more likely than older children to believe that television advertisements were telling the truth; and that exposure to television advertisements influenced the food choices among children (enticing them to choose more sugary foods instead of natural options) which increased requests to parents for high sugar foods they saw advertised. 101,102,103,104  Though many of these studies did find significant correlations between advertising and behavioral change, the reliability of these findings are equivocal because many of the studies use small sample sizes, and some of them are more than 25 years old.

A recent literature review by Kaiser Family Foundation highlighted a number of studies that suggested that advertising influenced dietary and other food choices in children, which likely contributed to energy imbalance and weight gain 105 . One study found that among children as young as three, the amount of weekly television viewing was significantly related to their caloric intake as well as requests and parental purchases of specific foods they saw advertised on television. Several other studies found that the amount of time children spent watching TV was correlated with how often they requested products at the grocery store and their product and brand preferences.

In 2003, Gerald Hastings of the University of Strathclyde in the United Kingdom (UK), conducted a review of the available literature on advertising and obesity to test the relationship between advertising to children and obesity. 106  After reviewing more than 30,000 articles, only 120 were determined to be most relevant. Based on these articles, Hastings reported qualified findings that advertising to children does in fact have an adverse effect on food preferences, purchasing behavior and consumption. However, these findings must be weighed against the fact that the strongest and most cited study in the review does not fully support this notion. The study investigated the impact of commercials on 262 children in Ohio, and yielded a statistically significant relationship between a child’s exposure to advertising and the number of snacks eaten. 107  However, though the commercial exposure did reduce children’s nutrition efficiency (quality of nutrition), it only explained two percent of the change in nutrient intake and had no direct effect on caloric intake.

Since Hastings, more research has been published that supports his conclusion. A notable example from the UK by Halford et al. studied 42 elementary-school aged children and found that lean, overweight and obese children who watched television programs with snack food advertising were more likely to choose high fat savory food options than lower fat sweet options. They also ate a greater volume of food than their similar weight peers in a non-advertisement control group. 108  The study also found that weight status modified the ability to recall advertised products among a list of similar products (where more obese children displayed greater recall). The authors suggest that these results support the notion that exposure to food advertising on television can affect eating behavior, stimulating energy intake from a range of advertised foods and exaggerating unhealthy choices in foods. They also proffer that the observed association between remembering food ads and eating more indicates that a susceptibility to food cues could potentially contribute to overeating and promotes weight gain in children.

Those who discount the idea that advertising is a factor in childhood obesity cite the limited research findings, question the methodological validity of much of the available literature and look to observational outcomes of policy changes in Canada and Sweden. In 1980, Quebec banned all food advertising to children, however the rates of obesity for children in Quebec are currently no different from those in other Canadian Provinces. A similar ban on advertising has existed in Sweden for over a decade, and also has not resulted in reductions of obesity rates. 109  Though these observations undermine the conclusions of the Hastings review and others, no definitive answers are apparent. In order to close the loop on the causal pathway between food advertising and childhood obesity, many questions need to be answered using longitudinal studies designed with a sufficient statistical power.

The excess intake of calories above the daily expenditure of energy leads to weight gain and can eventually lead to obesity. The main components of this equation are energy intake (diet) and energy expenditure (physical activity, metabolic rate, etc.). The nutrition and physical activity habits of U.S. children have been changing over the past 40 years. Research shows some correlation of these changes to the increases in obesity levels in children. The physical environment, socio-economic status and race/ethnicity, family structure, genetics, and advertising may also influence diet and levels of physical activity among American youth.

Available research shows that there are a number of root causes of obesity in children. Selecting one or two main causes or essential factors is next to impossible given the current data, because the potential influences of obesity are multiple and intertwined. There are large gaps in knowledge, limiting the ability to pinpoint a particular cause and determine the most effective ways to combat childhood obesity. Another research gap stems from lack of a prospective longitudinal study that links dietary and other behavior patterns to development of obesity. Another complication of current data is that there is a need for more precise and reliable measures of dietary intake and activity levels, as individual recall of events and diet are not the most dependable sources for information.

When thinking about early prevention of obesity, it is essential that more is understood about how genetics is involved and how the genes are triggered or react to environmental changes and stimuli. Additionally, research is only beginning to explain how taste preferences develop, their biochemical underpinnings and how this information may be useful in curbing childhood weight gain.

Primary prevention is not an option for many children who are already overweight. Research on successful interventions for children who are overweight or at risk of becoming overweight is extremely important to effectively reduce childhood obesity in this country. Overall, research has just begun to scratch the surface in elucidating the causes of obesity in children. Filling in the knowledge gaps will take time, as implementing some of the study designs that will best illuminate the complex interactions are time consuming and costly. However the fundamentals are clear, to stay healthy, eat a balanced diet and devote adequate time to physical activity. 110

1 Childhood is defined for the purposes of this paper as 6-19 years of age

2 Overweight and obesity are used interchangeably and are defined as a BMI on or above the 95 th percentile for gender and age (BMI-for-age). Downloaded from: http://www.cdc.gov/nccdphp/dnpa/bmi/bmi-for-age.htm Accessed: Feb. 2005. These terms have different connotations for adults.

3 National Center for Health Statistics. “Prevalence of Overweight Among Children and Adolescents: United States, 1999-2002” Downloaded from: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overwght99.htm Accessed: Feb. 2005.

6   At risk for overweight is considered a BMI-for-age between the 85th and 95th percentiles.

7 National Center for Health Statistics. Obesity Still a Major Problem, New Data Show. Downloaded from: http://www.cdc.gov/nchs/pressroom/04facts/obesity.htm Accessed: Feb. 2005.

8 Jackson, Yvonne. (1993) Height, weight, and body mass index of American Indian schoolchildren, 1990-1991. Journal of the American Dietetic Association. 93(10) 1136-1140.

9 Centers for Disease Control and Prevention. National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2003. Rev ed. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2004.

10 Centers for Disease Control and Prevention. “Preventing Obesity and Chronic Diseases Through Good Nutrition and Physical Activity” Downloaded from: http://www.cdc.gov/nccdphp/pe_factsheets/pe_pa.htm Accessed: Feb. 2005

11 Torgan, C. (2002). Childhood obesity on the rise. The NIH Word on Health. Downloaded from: http://www.nih.gov/news/WordonHealth/jun2002/childhoodobesity.htm Accessed: Feb. 2005.

12 Centers for Disease Control and Prevention. Overweight and Obesity Health Consequences. Downloaded from: http://www.cdc.gov/nccdphp/dnpa/obesity/consequences.htm Accessed: Feb. 2005

13 CDC, Preventing Obesity and Chronic Diseases, op. cit.

14 Co-linearity occurs when some or all of the independent variables (the variables believed to influence an outcome measure) in a regression model are so highly correlated that it is impossible to come up with reliable estimates of their individual impact on the outcome.

15 Livingstone MBE and Black AE “Markers of the validity and reported energy intake,” Journal of Nutrition (supplement) 2003; 895S – 920S.

16 In the comparison of the 1977-78 and 1994-96 studies, a number of methodological changes should be noted. The earlier studies sampled only the 48 contiguous states (later studies included all 50 states), included 3 days of dietary records (the later study only included 2 days), and asked the parents about dietary intake (later studies asked the children, with assistance from adults).

17 Lin BH, Guthrie J, Frazao E. 1999b. Quality of children’s diets at and away from home: 1994-96. Food Review 2-10.

18 Enns CW, Mickle SJ, Goldman JD. 2002. Trends in food and nutrient intakes by children in the United States. Family Economics and Nutrition Review 14(2):56-58.

19 Enns CW, Mickle SJ, Goldman JD. 2003. Trends in food and nutrient intakes by adolescents in the United States. Family Economics and Nutrition Review 15(2):15-27.

20 Jahns L, Siega-Riz AM, Popkin BM. 2001. The increasing prevalence of snacking among US children from 1977 to 1996. Journal of Pediatrics 138(4):493-498.

21 Nielsen SJ, Popkin BM. 2003. Patterns and trends in food portion sizes, 1977-1998. Journal of the American Medical Association 289(4):450-453.

22 Centers for Disease Control and Prevention. (2004). Physical activity and good nutrition essential elements to preventing chronic disease and obesity. Downloaded from: http://www.cdc.gov/nccdphp/aag/pdf/aag_dnpa2004.pdf Accessed: Feb. 2005.

23 Cavadini C, Siega-Riz AM, Popkin BM. 2000. US adolescent food intake trends from 1965 to 1996. Archives of Diseases in Children 83(1):18-24.

24 USDA (U.S. Department of Agriculture). 2000. Pyramid Servings Intakes by U.S. Children and Adults: 1994-96, 1998. Agricultural Research Service, Community Nutrition Research Group. Table Set No. 1.

25 Gleason P, Suitor C. 2001. Children’s Diets in the Mid-1990’s: Dietary Intake and Its Relationship with School Meal Participation. Alexandria, VA: U.S. Department of Agriculture. Report No. CN-01-CD1.

26 Guthrie JF, Morton JF. 2000. Food sources of added sweeteners in the diets of Americans. Journal of the American Dietetic Association 100(1):43-51.

27 French SA, Lin BH, Guthrie JF. 2003. National trends in soft drink consumption among children and adolescents age 6 to 17 years: Prevalence, amounts, and sources, 1977/78 to 1994/1998. Journal of the American Dietetic Association 103(10):1326-1331.

28 Fox MK, Pac S, Devaney B, Jankowski L. 2004. Feeding Infants and Toddlers Study: What foods are infants and toddlers eating? Journal of the American Dietetic Association 104(1, Supplement 1):S22-S30.

29 French SA, Story M, Jeffery RW. 2001. Environmental influences on eating and physical activity. Annual Review of Public Health 22:309-335.

31 Cavadini C, Siega-Riz AM, Popkin BM. 2000. US adolescent food intake trends from 1965 to 1996. Archives of Diseases in Children 83(1):18-24.

32 Alexy U, et al. “Pattern of long-term fat intake and BMI during childhood and adolescence—results from the DONALD Study,” International Journal of Obesity 2004; 28: 1203-9..

33 Sugimori H, et al. “Analysis of factors that influence body mass index from ages 3 to 6 years—a study based on the Toyama Cohort Study,” Pediatrics International 2004; 46: 302-10

34 Nicklas TA et al. “Children’s meal patterns have changed over a 21-year period: the Bogalusa Heart Study Journal of the American Dietetic Association 2004 May;104(5):753-61.

35 Lin, B-H and Morrison, RM. “Higher fruit consumption linked with lower Body Mass Index,” USDA Economic Research Service Food Review Winter 2002; 25(3): 28-32.

36 IOM, Preventing Childhood Obesity: Life in the Balance, 2004

37 Pate RR, Reedson PS, Sallis JF, Tayor WC, Sirard J, Trost SG, Dowda M. (2002) Compliance with physical activity guidelines: prevalence in a population of children and youth. Annals of Epidemiology. 12 (5), 303-308.

38 Robinson, T. N. (2001). Television viewing and childhood obesity. Pediatric clinics of North America , 48 (4), 1017-1025.

39 Torgan, op.cit

40   Ashlesha Datar, Roland Sturm. Physical Education in Elementary School and Body Mass Index: Evidence from the Early Childhood Longitudinal Study. American Journal of Public Health. 2004; 94 (9): 1501-1506.

41 YRBSS Fact Sheet: Physical Activity. Found at: http://www.cdc.gov/HealthyYouth/yrbs/pdfs/trends-pa.pdf

42 CDC. (2003). Physical Activity Levels Among Children Aged 9-13 Years --United States. MMWR 52 (33), 785-788.

43 J.O. Hill, J. C. Peters, Science. 280. 1371 (1998).; S. A. French, M. Story, R. W. Jeffery. Ann. Rev. Pub. Health 22, 63 (2001).

44 Macbeth AG. 1999. Bicycle lanes in Toronto. ITE Journal 69:38-40, 42, 44, 46.

45 Painter K. 1996. The influence of street lighting improvements on crime, fear, and pedestrian street use, after dark. Landsc Urban Plan. 35:193-201.

46 IOM (Institute of Medicine). 2005. Preventing Childhood Obesity: Health in the Balance. Washington, DC: National Academy Press.

47 Sturm R. 2005b (in press). Childhood obesity – What can we learn from existing data on social trends? Part 2. Preventing Chronic Disease .

48 IOM (Institute of Medicine). 2005. Preventing Childhood Obesity: Health in the Balance. Washington, DC: National Academy Press.

49 Sallis J, Conway T, Prochaska J, McKenzie T, et al. The Association of School Environments with Youth Physical Activity. American Journal of Public Health. 2001; 91, 4: 618-620

50 Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship Between Urban Sprawl and Physical Activity, Obesity, and Morbidity. The Science of Health Promotion. 2003 Sept/Oct, Vol 18, No 1. 47-57.

51 Environmental Health Perspectives, Vol 112, No 11 Aug 2004, Downloaded from: http://ehp.niehs.nih.gov/docs/allpubs.html Accessed: Feb. 2005

52 Ewing (2003), Relationship Between Urban, op. cit

53 McCann B, Ewing R. 2003. Measuring the Health Effects of Sprawl. Smart Growth America Surface Transportation Policy Project. Downloaded from: www.smartgrowthamerica.org . Accessed: Feb. 2005.

56 Sobal, J. & Stunkard, A.J. (1989). Socioeconomic status and obesity: A review of the literature. Psychological Bulletin, 105 , 260-275.

58 Strauss, R.S. & Knight, J. (1999). Influence of the home environment on the development of obesity in children. Pediatrics, 101 (6).

59 National Center for Health Statistics (1998). Health, United States with socioeconomic status and health chartbook. Hyattsville, MD.; Berkowitz, R.I. & Stunkard, A.J. (2002). Development of childhood obesity. In Wadden, & Stunkard (ed). Handbook of obesity treatment (pp. 515-531).

60 Sobal, J. & Stunkard, A.J. (1989).; Strauss, R.S. & Knight, J. (1999). Influence of the home environment on the development of obesity in children. Pediatrics, 101 (6); National Center for Health Statistics (1998). Health, United States with socioeconomic status and health chartbook. Hyattsville, MD.; Berkowitz, R.I. & Stunkard, A.J. (2002). Development of childhood obesity. In Wadden, & Stunkard (ed). Handbook of obesity treatment (pp. 515-531).

61 Troiano, R.P. & Flegal, K.M. (1998). Overweight children and adolescents: Description, epidemiology, and demographics. Pediatrics, 101 (3), 497-504.

62 Crawford, Story, Wang, Ritchie & Sabry (2001). Ethnic issues in the epidemiology of childhood obesity. Pediatric Clinics of North America, 48 (4), 855-878.

63 Strauss & Pollack (2001). Epidemic increase in childhood overweight, 1986-1998. Journal of the American Medical Association, 286 (22), 2845-2848.

65 Zephier E, Himes JH, Story M. Prevalence of overweight and obesity in American Indian school children and adolescents in the Aberdeen area: A population study. (1999) International Journal of Obesity. 23, S28-S30.

66 Strauss (2001) op. cit.

68 Troiano, R.P. & Flegal, K.M. (1998). Overweight children and adolescents: Description, epidemiology, and demographics. Pediatrics, 101 (3), 497-504.

69 Strauss, R.S. & Knight, J. (1999). Influence of the home environment on the development of obesity in children. Pediatrics, 101 (6).

70 Arenz S, Rucker R, and von Kries R. “Breast feeding and childhood obesity—a systematic review.” International Journal of Obesity 2004; 28: 1247-1256.

71 Yajnik, CS. “The lifecycle effects of nutrition and body size on adult adiposity, diabetes and cardiovascular disease.” Obesity Reviews 2002; 3: 217-224.

72 Bonuck, K et.al. “Is late bottle-weaning associated with overweight in young children? Analysis of NHANES III data. Clinical Pediatrics (Philadelphia) Jul.-Aug. 2004; 43(6): 535-40.

73 Sullivan, S., Birch, L. Infant dietary experience and acceptance of solid foods. Pediatrics. 93:271-277; 1993.

74 Oliveria, S. et al. Parent-child relationships in nutrient intake: the Framingham children’s study. American Journal of Clinical Nutrition. 56:593-598;1992.

75 Birch, L., Fisher, J. Development of eating behaviors among children and adolescents. Pediatrics. 101:539-549;1998.

76 Hearn, M., Baranowski, T., and Baranowski, J. et al. Environmental influences on dietary behavior among children: Availability and accessibility of fruits and vegetables enable consumption. Journal of Health Education . 1998.

77 Birch, L., Fisher, J. Op cit.

78 Fisher, J., Birch, L. 3-5 Year-old children’s fat preferences in consumption are related to parental adiposity. Journal of the American Dietetic Assn . 95:759-764; 1995.

79 Birch, L., Fisher, J. Op Cit.

80 Birch, LL. Effects of peer models’ food choices and eating behaviors on preschooler’s food preferences. Child Development. 51: 489-496; 1980.

81 Birch, LL. The relationship between children’s food preferences and those of their parents. Journal of Nutrition Education. 12:14-18, 1980.

82 Duncker, K. Experimental modification of children’s food preferences through social suggestion. Journal of Abnormal Social Psychology.33:490-507, 1983.

83 Sado, S., Bayer, A. The Changing American Family. Downloaded from the Population Resource Center’s website: http://www.prcdc.org/summaries/family/family.html . Accessed April, 2004.

85 United States Census Bureau. 2000. Statistical Abstract of the United States 2000. Washington, DC: Government Printing Office.

86 Anderson, P., Butcher, K., and Levine, P. (2003). Maternal Employment and Overweight Children. Journal of Health Economics, 22 , 477-504.

87 Center for Disease Control. Factors Contributing to Obesity. Downloaded from: www.cdc.gov/nccdphp/dnpa/obesity/contributing_factors.htm . Accessed: Jan. 2005.

88 Bouchard, C., Perusse, L. Genetic Aspects of Obesity. Annals of the New York Academy of Sciences. 699:26-35;1993

89 Bouchard, C., Perusse, L., Rice, T., Rao, D. 2003. Genetics of Human Obesity. In: Bray, G.A, Bouchard, C. Eds . Handbook of Obesity Etiology and Pathophysiology. 2 nd Edition. New York: Marcel Dekker.

90 Skelton, J. Childhood Obesity: Overview. Downloaded from: www.meadjohnson.com/professional/newsletters/0300app/0300a3.html . Accessed: Jan. 2005.

92 In adults, Overweight is defined as a BMI (Body Mass Index) score of 25-29.9 and Obese is defined as a BMI score of 30 or greater. To calculate your BMI, go to: http://www.cdc.gov/nccdphp/dnpa/bmi/calc-bmi.htm

93 Skelton op cit.n

94 Jeffrey P. Koplan, Catharyn T. Liverman, and Vivica A. Kraak, Editors, Committee on Prevention of Obesity in Children and Youth. 2004. Preventing Childhood Obesity: Health in the Balance. Washington, DC: National Academies Press.

95 Kunkel, D. 2002 “Children and Television Advertising,” Handbook of Children and the Media. Eds. Singer, D., and Singer, J. Thousand Oaks, CA: Sage Publications.

96 Kaiser Family Foundation. (2004) The Role of Media in Childhood Obesity. Downloaded from: http://www.kff.org/entmedia/entmedia022404pkg.cfm . Accessed: January 2005

97 US Market for Kids Foods and Beverages, 2003. Kids’ Lifestyles—US [Online] Downloaded from: http://www.marketresearch.com/researchindex/849192.html#pagetop . Accessed: Feb. 2005.

98 Brownell, K. 2004. Food Fight: The Inside Story of the Food Industry, America’s Obesity Crisis, and What We Can Do About it. New York, NY: MacGraw-Hill.

99 McNeal, J. 1999. The Kids Market: Myths and Realities. Ithaca, NY: Paramount Marketing Publishing.

100 Schosser, E. 2002. Fast Food Nation. New York, NY: Perennial Publishing.

101 Brody, G., Stoneman, Z., Lane, T.S., and Sanders, A. Television Food Commercials Aimed at Children, Family Grocery Shopping and Mother-Child Interactions. Family Relations. 30:435-439;1981.

102  Clancy-Hepburn, K., Hickey, K. and Nevill, G. Children’s Behaviour Responses to TV Food Advertisements. Journal of Nutrition Education. 63:93-95.

103 Woodward, D., Cumming, F., Ball, P., Williams, H., Hornsby, H., and Boon, J. Does Television Affect Teenagers’ Food Choices? Journal of Human Nutrition and Dietetics. 10:229-235:1997.

104 Ward, S., Wackman, D. Children’s Purchase Influence Attempts and Parental Yielding. Journal of Market Research. 9:316-321; 1972.

105 Kaiser op.cit.

106 Hastings, G., Stead, M., and McDermott, L. Review of Research on the Effects of Food Promotion to Children. Glasgow: University of Strathclyde Centre for Social Medicine., 2003. Downloaded from www.foodstandards.gov.uk/healtheireating/promotion/readreview

107 Bolton, R. Modeling the Impact of Television Food Advertising on Children’s Diets. In: Leigh, JH; Martin, CR jr. eds , Current Issues and Research on Advertising. Ann Arbor, MI; Division of Research Graduate School of Business Administration. University of Michigan, 1983.

108 Halford, J., Gillespie, J., Brown, V., Pontin, E., and Dovey, T. Effect of Television Advertisements for Foods on Food Consumption in Children. Appetite. 42:221-225;2004.

109 Ashton, D. Food Advertising and Childhood Obesity. Journal of the Royal Society of Medicine. 97(2): 51-52;2004.

110 See US Dietary Guidelines at: http://www.health.gov/dietaryguidelines/dga2005/document/pdf/dga2005.pdf

Childhood Obesity Research Demonstration (CORD) 3.0

A mother with her children

Childhood obesity remains a pressing public health concern, affecting nearly 1 in 5 US children . In addition, some groups experience higher rates, such as children from lower-income families.

Building on previous CORD projects, CORD 3.0 research teams focus on adapting, testing, and packaging effective programs to reduce obesity among children from lower-income families. In addition, CORD 3.0 projects work towards programs that are sustainable and cost-effective in multiple settings. Research teams will translate these programs into user-friendly, packaged materials and messages for healthcare, community, or public health organizations.

CORD 3.0 has the potential to reduce childhood obesity by increasing the availability of effective family healthy weight programs for millions of children from lower-income families.

CORD 3.0 funds five recipients for 5 years (2019-2024).

  • CORD 3.0 Publications

CDC launched CORD 1.0  in 2010 and funded four recipients to use a whole-community approach to address childhood obesity. Several sites saw reductions in children’s body mass index (BMI), a body measurement that can be used to screen for weight categories.

Findings from CORD 1.0 suggest that interventions that address two or more levels of influence, such as the patient, healthcare provider, family, or community, can improve childhood obesity prevention and management. In 2016, CDC launched CORD 2.0 , funding two recipients to continue to conduct research on the effectiveness of using obesity screening and behavioral counseling to combat childhood obesity. Recipients put into place and evaluated a family-centered, weight management intervention for children. Referrals to these interventions were based on electronic health records. Recipients also collaborated with state Child Health Insurance Programs and Medicaid offices to recommend sustainable and scalable program components.

An important step in translating evidence-based programs into routine practice is the creation of user-friendly intervention materials and messages. CORD 3.0 builds on prior CORD projects by increasing the availability and number of packaged, effective programs to address childhood obesity. Projects ensure that these programs can be used by healthcare, community, or public health organizations to serve families with lower incomes and that the programs are sustainable in multiple settings.

Title: A dissemination strategy to identify communities ready to implement a pediatric weight management intervention in geographically underserved areas. Authors: Golden C, Hill JL, Heelan KA, Bartee R, Abbey B, Malmakar A, Estabrooks PA. Journal: Preventing Chronic Disease Released: 2021 Link to Abstract:  https://www.cdc.gov//pcd/issues/2021/20_0248.htm

Title: Qualitative Comparative Analysis of Program and Participant Factors That Explain Success in a Micropolitan Pediatric Weight Management Intervention. Childhood Obesity. Authors: Golden CA, Heelan KA, Hill JL, Bartee RT, Abbey B, Estabrooks PA. Journal: Childhood Obesity Released: 2021 Link to Abstract: https://pubmed.ncbi.nlm.nih.gov/34780274/

Special supplement to the journal, Childhood Obesity: “Childhood Obesity Research Demonstration 3.0: Study Designs for Scaling Effective Pediatric Weight Management Interventions for High-Risk Children through Packaging and Implementation.”

Special supplement to the journal, Childhood Obesity: “ Childhood Obesity Research Demonstration 3.0: Study Designs for Scaling Effective Pediatric Weight Management Interventions for High-Risk Children through Packaging and Implementation.”

This supplement describes the study protocols and implementation and dissemination plans of five recipients of CDC’s  Childhood Obesity Research Demonstration (CORD) 3.0 Project , including their use of public health strategies to facilitate the spread and scale of family-centered, evidence-based pediatric healthy lifestyle interventions in venues engaged with low-income families.

Massachusetts General Hospital

Massachusetts General Hospital CORD 3.0 packages together two childhood obesity interventions –  Connect for Health  and the  Mass in Motion   Kids Healthy Weight Clinic – in collaboration with the American Academy of Pediatrics’ Institute for Healthy Childhood Weight. Massachusetts General Hospital works with three community-based health centers in Mississippi to ensure the packaged programs can be effective and sustainable in primary care settings where the majority of patients use Medicaid, and with substantially high prevalence of obesity.

The Miriam Hospital (Providence, Rhode Island)

The Miriam Hospital CORD 3.0 evaluates the  JOIN for ME  program in two settings: the housing authority and patient-centered medical home sites. The  JOIN for ME  program is a child weight management intervention that can be used in many community settings.  JOIN for ME  includes strong parental involvement and has found meaningful reductions in body mass index in children 6-12 years of age.

Stanford University

Stanford University CORD 3.0 uses technology, design, behavioral theory, and biomedical business innovation strategies to prepare the Stanford Pediatric Weight Control Program (SPWCP) to reach children throughout the United States. SPWCP is a family-based group program to address childhood obesity. Stanford University will evaluate the effectiveness of this program in four local organizations serving low-income families.

University of Nebraska

University of Nebraska CORD 3.0 builds on their previous work that adapted the Building Healthy Families (BHF) program to a micropolitan area of 10,000 to 50,000 residents. In the past BHF researchers saw significant decreases in child BMI. University of Nebraska CORD 3.0 will package BHF for successful adaptation to rural communities and other micropolitan areas to decrease the number of adults and children with obesity.

Washington University in St. Louis

Washington University CORD 3.0 evaluates the evidence-based Family-based Behavioral Treatment (FBT) for use in diverse primary care settings, such as in urban and rural communities, and with families with lower-incomes. FBT is a proven behavioral program that works with both children and parents. Research indicates that FBT can reduce child BMI, and the average parent loses about 20 pounds during treatment. This CORD 3.0 project tests whether FBT can be cost-effective and sustainable when used in diverse primary care settings.

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  • v.64(5); 2021 May

The impacts of exercise on pediatric obesity

Ronald j. headid iii.

School of Health and Kinesiology, University of Nebraska at Omaha, Omaha, NE, USA

Song-Young Park

Over the last few decades, the rates of pediatric obesity have more than doubled regardless of sociodemographic categorization, and despite these rates plateauing in recent years there continues to be an increase in the severity of obesity in children and adolescents. This review will discuss the pediatric obesity mediated cardiovascular disease (CVD) risk factors such as attenuated levels of satiety and energy metabolism hormones, insulin resistance, vascular endothelial dysfunction, and arterial stiffness. Additionally, early intervention to combat pediatric obesity is critical as obesity has been suggested to track into adulthood, and these obese children and adolescents are at an increased risk of early mortality. Current suggested strategies to combat pediatric obesity are modifying diet, limiting sedentary behavior, and increasing physical activity. The effects of exercise intervention on metabolic hormones such as leptin and adiponectin, insulin sensitivity/resistance, and body fat in obese children and adolescents will be discussed along with the exercise modality, intensity, and duration. Specifically, this review will focus on the differential effects of aerobic exercise, resistance training, and combined exercise on the cardiovascular risks in pediatric obesity. This review outlines the evidence that exercise intervention is a beneficial therapeutic strategy to reduce the risk factors for CVD and the ideal exercise prescription to combat pediatric obesity should contain both muscle strengthening and aerobic components with an emphasis on fat mass reduction and long-term adherence.

Introduction

In 2013 approximately 2.1 billion individuals were considered overweight or obese which was defined as a body mass index (BMI) greater than 25 kg/m 2 [ 1 ]. Over the last few decades the obesity rates in more than 70 countries have doubled [ 2 , 3 ], while the rate of this increase in obesity is higher in children than in adults regardless of sociodemographic categorization [ 2 ]. Additionally, epidemiological data indicate that the proportion of children and adolescents with obesity appears to be plateauing in recent years but the rate of more severe obesity cases continues to rise [ 4 , 5 ]. Although the development of pediatric obesity is a multifaceted process that involves genetic, behavioral, and environmental influences [ 6 , 7 ], the lifestyle behaviors during childhood and adolescence might have the greatest influence on the development of obesity [ 8 , 9 ].

Obesity is characterized by an energy imbalance that is affected by lifestyle behaviors such as poor dietary habits [ 10 - 13 ] and inadequate physical activity time [ 14 ] which are both strongly associated with the development of obesity. More specifically, sedentary behaviors such as increased video gaming, television watching, and computer screen time [ 15 , 16 ] are highly associated with the development of obesity [ 17 ]. The development of obesity often influences the onset of several cardiovascular disease (CVD) risk factors [ 18 ]. Additionally, the relative risk for CVDs such as hypertension, stroke, and heart disease is 1.5- to 5.1-times higher in obese children when compared to children with normal body composition [ 19 ]. Combating pediatric obesity is critical as there is a strong connection between pediatric obesity and adult obesity [ 4 , 20 ]. A previous meta-analysis identified that when compared to normal-weight children, obese children are 5 times more likely to be obese in adulthood [ 21 ], and about 80% of obese adolescents remain obese in adulthood [ 22 ].

Physical inactivity and sedentary behaviors are significant contributors to the development of pediatric obesity [ 14 , 23 , 24 ]. Physical activity and exercise have been suggested as powerful treatments to help prevent obesity as well as improve obesityrelated risk factors in children and adolescents [ 25 - 27 ]. For every one hour of moderate-to-vigorous activity there is a 10% decrease in the risk of developing obesity [ 14 ]. Additionally, regular exercise is considered an effective treatment for reducing inflammation [ 28 ], obesity-related risk factors, and the development of comorbidities [ 29 ]. Therefore, this review will focus on the health risks associated with pediatric obesity and further discuss optimal exercise strategies, specifically, aerobic exercise (AE) such as running, cycling, or jump rope, resistance training (RT) such as free weights, cable machines, or resistance bands, and combined aerobic and resistance exercise (CRAE) such as the combination of running and free weights, to combat pediatric obesity and the associated risk factors and comorbidities ( Table 1 ).

Characteristics of exercise studies investigating the effects of exercise in pediatric obesity

BMI, body mass index; LDL, low-density lipoprotein; WC, waist circumference; SBP, systolic blood pressure; HOMA-IR, homeostatic model assessment of insulin resistance; BF%, body fat percentage; CRP, C-reactive protein; baPWV, brachial-to-ankle pulse wave velocity; ET-1, endothelin-1; 1RM, 1 repetition maximum; VO 2 peak, peak oxygen uptake; TNF-alpha, tumor necrosis factor alpha; IL-6, interleukin 6; TC, total cholesterol; FMD, flowmediated dilation; MIIT/HIIT. moderate-intensity interval training/high-intensity interval training; HDL, high density lipoprotein; BW, body weight; GPR, glucose production rate; CRAE, combined resistance and aerobic exercise; RT, resistance training.

Pediatric obesity

The prevalence of pediatric obesity continues to increase around the world [ 1 ] and obesity is expected to affect 91 million children by the year 2025 [ 2 ]. Obesity is a multifactorial condition that can be affected by genetic, psychological, lifestyle, environmental, behavioral, and hormonal factors [ 3 ]. It is well-accepted that there is no single cause of pediatric obesity, however, obesity is characterized by the accumulation of excess fat mass which develops when caloric intake exceeds total energy expenditure [ 4 ]. The neuroendocrine regulation of fat stores is a complex system based on circulating hormones, which send signals to specialized neurons in the hypothalamus to indicate the status of body fat (BF) stores in the body, which in turn induces the appropriate response necessary to maintain these fat stores [ 5 ]. Amidst other important functions, the hypothalamus is the control center for feelings of hunger and satiety. An individual’s susceptibility to the development of obesity can in part be explained by mechanisms that may negatively affect hypothalamic neurons, leading to an improper hunger/satiety balance, and genetic and environmental modulators such as leptin and insulin resistance (IR) [ 5 ]. Both leptin resistance and IR are associated with feeding behavior and weight gain. Pediatric obesity also negatively effects cardiovascular health and is also accompanied by a host of other comorbidities and associated risk factors [ 8 ].

1. Health risks associated with obesity

Pediatric obesity is associated with a myriad of CVD risk factors including increased IR [ 9 ], impaired glucose tolerance, dyslipidemia [ 10 , 11 ], impaired microvascular function [ 12 ], systemic low-grade inflammation [ 13 ], increased artery wall thickness [ 14 ], and elevated blood pressure (BP) [ 9 ]. In addition to an increased risk of CVD, pediatric obesity is also associated with the development of nonalcoholic fatty liver disease [ 9 ], cancer, pulmonary disease, asthma, sleep apnea, orthopedic problems, and depression [ 15 - 17 ] and has also been identified as an independent risk factor for the development of insulin-resistant type 2 diabetes (T2D) [ 18 - 21 ]. Furthermore, the severity of these obesity-related risk factors and comorbidities significantly increases with the severity of obesity [ 17 , 18 , 22 , 23 ]. Combating obesity early in childhood is critical as even mild reductions in body mass before the onset of puberty has been shown to decrease the risk of CVD and other obesity-related risk factors such as hypertension, dyslipidemia, T2D, and coronary heart disease later in life if normal bodyweight is maintained [ 22 - 25 ]. If pediatric obesity cannot be treated appropriately, obese children and adolescents will be at an increased risk of premature death [ 26 ] and a significantly increased risk for CVD and CVD-related mortality in adulthood [ 27 ].

2. Pediatric obesity and health risks track into adulthood

In 2015, there were approximately 4 million international obesity-related deaths with 70% of these deaths being attributed to CVD [ 28 , 29 ]. Pediatric obesity has been known as a significant contributor to the current obesity and CVD epidemics in adults [ 27 ]. Individuals who are obese during childhood are more likely to become obese as adults [ 30 - 33 ] and there is compelling evidence suggesting that obesity-associated CVD risk factors, such as dyslipidemia, IR, and elevated BP, track from childhood into adulthood [ 18 , 27 , 34 , 35 ]. Obese children who were tracked from childhood to adulthood were more likely to suffer from CVD, digestive disease, metabolic diseases, and cancer as an adult when compared to children of normal weight [ 8 , 16 ]. Additionally, obesity in childhood is strongly associated with a 3.5-times higher risk of CVD mortality in adulthood and is projected to account for as much as 25% of all adult CVD-related deaths [ 27 ]. The manifestation of obesity-related CVD risk factors can appear in as early as the third year of life [ 15 , 20 ], and the duration of obesity during childhood and adolescence is associated with increased risk of developing obesity-related comorbidities and CVD-related mortality in adulthood [ 18 , 19 , 23 ]. In fact, pediatric obesity is a well-established predictor of CVD and premature mortality in adulthood [ 26 , 36 , 37 ] thereby highlighting the importance of early intervention to prevent the development of obesity. These types of interventions should serve to target improvements in several metabolic, hormonal, and cardiovascular parameters to better protect young populations from future CVD complications, as well as instilling a healthy lifestyle that can be maintained throughout adulthood.

Metabolic syndrome and adipokines

Metabolic syndrome (MetS) is defined as a cluster of conditions including high central adiposity, dyslipidemia, and high fasting blood glucose, all of which contribute to an increased risk of CVD [ 30 ]. Obesity has been identified as a major contributor to the development of MetS [ 30 ] and obesity-associated MetS risk factors have been shown to track from childhood into adulthood [ 31 , 32 ]. Obesity is characterized by an increase in adipose tissue which is considered an active metabolic endocrine organ and a source of inflammation through the production of inflammatory cytokines [ 33 , 34 ]. Adipose tissue-derived cytokines are referred to as, adipokines, and 2 of the most prominent adipokines are leptin and adiponectin which are negatively affected by obesity [ 35 , 36 ]. Additionally, T2D is the most common comorbidity associated with pediatric obesity [ 37 ]. T2D development, whether in childhood or adulthood, may be partially explained by the adverse effect of obesity on the dysregulation of leptin and adiponectin levels which play a crucial role in homeostatic status of IR/sensitivity [ 38 , 39 ]. Previous research suggests levels of these adipokines and insulin sensitivity may be positively affected by exercise in obese children and adolescents [ 27 , 40 - 42 ]. Therefore, exercise therapy use in children may support intact homeostatic regulation of leptin and adiponectin levels, which may reduce the likelihood of IR and T2D development in this population.

Leptin’s primary function is to promote body mass reduction through sympathetic-driven appetite reduction [ 43 , 44 ], improved lipid metabolism, and increased energy expenditure [ 39 ] in a healthy, nonobese individual. However, leptin is considered one of the primary hormone markers for obesity [ 45 ]. Leptin levels are paradoxically increased in obesity [ 46 , 47 ], indicating that obesity is associated with a state of leptin resistance and disturbed leptin bioactivity. Furthermore, increased levels of leptin are strongly correlated with IR and increased inflammation in adolescents [ 48 , 49 ] and also contributes to obesity-related hypertension through increased sympathetic tone [ 43 , 44 ].

Available literature indicates that exercise interventions have been shown to positively impact leptin levels when there is also a significant decrease in BF [ 36 , 50 ]. Many individual studies indicate that AE interventions improve body composition and leptin levels in obese children and adolescents [ 51 - 53 ]. However, previously completed pooled-analyses indicate that there was no signficant affect of AE on leptin levels in obese children and adolescents [ 36 ]. It is important to note by using nonrandomized control trials, Garcia-Hermoso et al. [ 36 ] did conclude that AE interventions resulted in significantly reduced leptin levels in obese children and adoelscents. To our knowledge, only a single previous study investigates the effects of RT on leptin levels in pediatric obesity. Shultz et al. [ 54 ] found that following a 16-week RT intervention leptin levels were not significanlty changed in obese adolescents. Interestingly, they did find that participants that had singificantly increased aerobic capacity also had significantly decrease leptin levels following RT. Additionally, Racil et al. [ 55 ] showed that following 12 weeks of high-intensity interval training (HIIT) there were significant improvements in body composition and reductions in leptin levels in obese adolescents. In the same study, they showed that obese adolescents could gain even greater benefits if they performed CRAE training. Utilizing the same protocol with an additional plyometric component, obese adolescents had significantly greater improvements in body compositions and leptin levels [ 55 ]. Dâmaso et al. [ 56 ] also showed that when compared to AE, CRAE training resulted in greater improvements in body composition and leptin levels in obese adolesccents. Overall, previous data show that exercise interventions reduce leptin levels in obese adolescents [ 25 ] and also indicate that the improvements in leptin levels are likely mediated by concomitant reductions in BF due to increased energy expenditure [ 27 , 41 , 50 , 53 , 57 , 58 ] which may be accomplished through exercise interventions of greater frequency, intensity, or duration.

2. Adiponectin

Adiponectin is an adipokine with antiatherogenic and anti-inflammatory properties [ 59 ] and is an important regulator of glucose homeostasis and insulin sensitivity [ 38 ] that helps protect against obesity-related MetS [ 46 ]. Adiponectin levels are often decreased in obesity [ 60 ]. However, in pediatric obesity, adiponectin levels may be increased with exercise training, and like leptin, significant improvements in adiponectin levels are strongly associated with decreases in BF and this relationship has been highlighted by many previous exercise training studies with obese adolescents [ 27 , 36 , 61 ]. Dâmaso et al. [ 56 ] found that after 1 year of an exercise program consisting of RT and AE obese adolescents experienced significant improvements in adiponectin/leptin ratio and body composition. Additionally, our group has shown that CRAE 3 days per week, for 12 weeks signficantly improved body composition and adiponectin/leptin ratio in obese adolescent girls [ 62 ]. These results are supported by another previous study utilizing CRAE training that observed significant improvements in body composition and adiponection levels in obese adolescents [ 41 ]. Additionally, the effects of HIIT highlight the relationship between decreased BF and improved adiponectin levels as Racil et al. [ 63 ] showed that 12 weeks of moderate-intensity interval training (MIIT) and HIIT both improved BF percentage (BF%), blood lipids, and adiponectin, but HIIT results in significantly greater improvements in these measures in addition to significantly reducing waist circumference (WC). It is important to note that a previous study has shown that combined resistance as CRAE training may positively impact body composition and insulin sensitivity without altering adiponectin levels in obese adolescents [ 40 ]. In this study, there were no changes in adiponectin levels following a 12-week exercise program in obese adolescents, however, there were significant improvements in BF, lean body mass, and insulin sensitivity. Similar results have been observed in other studies examining the impacts of AE [ 64 , 65 ] and it was suggested that the improvements in insulin sensitivity were due to improved glucose uptake and utilization by skeletal muscle in response to exercise [ 66 ]. The exact exercise-induced mechanisms and training volume and duration implicit in improved adiponectin levels in obese children and adolescents requires further investigation.

3. Insulin resistance

T2D is characterized by reduced insulin sensitivity leading to excess blood glucose levels which can contribute to a host of other complications including, CVD, cancer, and diabetic neuropathy, nephropathy, and retinopathy [ 67 ]. In recent years T2D has increased dramatically in children and adolescents throughout the world [ 68 ] and there is a strong relationship between the increase in pediatric obesity and rising incidence of T2D [ 69 ]. It is well established that increased physical activity and exercise are the most comprehensive treatment for the IR associated with pediatric obesity [ 26 ]. IR in pediatric obesity has previously been shown to be improved by a variety of types of exercise including AE [ 40 , 70 ], RT [ 71 , 72 ], MIIT [ 63 ], and HIIT [ 55 , 63 ]. It is important to note that previous studies have also shown that exercise interventions do not always result in improvements in IR in obese children and adolescents [ 73 , 74 ]. A meta-analysis completed by Marson et al. [ 74 ] compared the effects of AE, RT, and CRAE training on IR, fasting glucose, and insulin levels in overweight and obese children and adolescents. The analysis concluded that exercise training in general was not associated with a reduction in fasting glucose, however, AE does result in improvements in fasting insulin levels and IR. Additionally, Marson et al. [ 74 ] concluded that the efficacy of RT and CRAE training as interventions to improve IR in pediatric obesity could not be determined due to limited available literature. CRAE training is of particular interest as it has been shown to provide greater benefits than AE or RT alone [ 75 ]. Our group has shown in multiple studies that CRAE training improves fasting glucose [ 62 , 70 , 76 ] and insulin levels [ 62 , 70 , 76 ] as well as IR [ 42 , 70 ] in obese adolescents. Nonetheless, increasing physical activity in the obese pediatric population is of paramount importance as sedentary behaviors and physical inactivity have been identified as significant contributors to the development of obesity and MetS [ 14 , 23 , 24 ].

Vascular function

Obesity during adolescence is a well-established marker for increased arterial stiffness, coronary artery calcification, hypertension, and atherosclerosis in adulthood [ 77 ], with some atherosclerotic lesions appearing as early as the teenage years [ 78 ]. Pediatric obesity is associated with an increase in a plethora of proatherogenic and proinflammatory factors [ 59 , 79 - 86 ] which contribute to impaired vascular function [ 87 - 90 ] and the development of atherosclerosis [ 91 ]. Obesity-associated IR and leptin resistance as well as increased adipokine secretion promote inflammation and endothelial dysfunction [ 92 - 95 ]. Intact endothelial function is widely considered a critical component of a healthy vascular system [ 96 ] and endothelial dysfunction is highly predictive of cardiovascular mortality and morbidity [ 96 - 99 ]. Impaired endothelial function has also been identified as an important prerequisite to the development of atherosclerosis [ 96 , 100 ] and hypertension [ 101 ]. Early intervention to prevent atherosclerosis may be essential due to the progressive nature of atherosclerotic development [ 91 ]. Interventions that enhance vascular function and endothelial function, such as exercise [ 102 - 104 ], may reduce CVD risks for obese children and adolescents as well as protect against cardiovascular mortality and morbidity later in life.

1. Endothelial function and exercise

Early intervention to improve vascular function in obese adolescents is of paramount importance, as it may reduce the risk of CVD later in life in through a reduction in global CVD risk [ 105 ]. Flow-mediated dilation (FMD) is a well-established assessment of vascular endothelial function [ 96 ] and CVD risk [ 106 - 108 ] in children and adolescents and a decrease in FMD of 1% results in a 13% increase in future cardiovascular event risk [ 97 ]. Previous research has established that pediatric obesity is associated with attenuated vasodilator function through endothelium-dependent mechanisms [ 88 , 109 , 110 ]. Watts et al. [ 88 , 89 ] has shown that when compared to lean age-matched control participants, obese adolescent FMD is significantly attenuated, 12.32%±3.14% versus 6.00%±0.69% [ 88 ], and 8.9%±1.5% versus 5.3%±0.9% [ 89 ]. Additionally, this group has shown that following exercise training FMD was significantly improved (from 6.00%±0.69% to 7.35%±0.99% [ 88 ], from 5.3%±0.9% to 8.8%±0.8% [ 89 ]). Dias et al. [ 111 ] confirmed these results in a 2015 meta-analysis that concluded that obese adolescents had significantly impaired FMD compared to age-matched control participants and that following exercise training the obese adolescent FMD values were restored to the level of the nonobese age-matched counterparts. Furthermore, the effects of exercise training are not specific to the vasculature of the working muscles used during exercise but exercise results in global/systemic improvements in endothelial function [ 102 , 104 ]. It is important to note that endothelial function is often improved following exercise regardless of changes in body composition, BP, or glycemic control [ 89 , 112 ] which indicates that the improvements in endothelial function following exercise may be mediated through alterations in hemodynamic factors such as shear stress acting on the vessel wall [ 113 , 114 ]. Finally, following periods of detraining the improvements in FMD observed in obese adolescents do not persist [ 89 ]. These results are in agreement with the loss of endothelial function improvement observed in adults following the cessation of exercise [ 102 , 104 ]. This indicates the vascular benefits of exercise are reversible if a physically active lifestyle is not maintained, which therefore supports the incorporation of exercise as a lifestyle change to maintain intact endothelial function. All together the available literature suggests that exercise training is an efficacious therapy to reverse the attenuated endothelial and vascular function associated with pediatric obesity.

2. Arterial stiffness

Arterial stiffness indicates vascular compliance and distensibility and is a key player in vascular reactivity and vascular health [ 115 ]. Pulse wave velocity (PWV) is used as the gold standard measurement for small and larger arterial stiffness [ 116 , 117 ], with increased values corresponding to increased stiffness and decreased compliance and distensibility [ 118 ]. Considering the relationship between obesity with hypertension and atherosclerosis, it is reasonable to assume that obesity would be associated with increased arterial stiffness which has been shown to be true in obese adults [ 119 ]. Contradictory to this intuitive conclusion, Charakida et al. [ 120 ] found that arterial stiffness assessed by PWV was significantly lower in obese adolescents when compared to lean age-matched controls, 6.99±1.01 m/sec and 7.65± 1.23 m/sec ( P <0.05) respectively, with various other studies confirming these results [ 121 - 123 ]. The PWV values measured by Charakida et al. in obese adolescents are considered normal and healthy and the difference between groups does not meet the clinically significant threshold of 1.0 m/sec [ 124 ]. It is important to note that another group, Cote et al. [ 93 ], found that obese children and adolescents had significantly greater carotid and aortic PWV values when compared to age-matched nonobese controls. Nonetheless, these results indicate that pediatric obesity may affect vascular structure (stiffness) and function (FMD) differently. The relationship between exercise and arterial stiffness in pediatric obesity is unknown. In adults, measures of arterial stiffness are improved with exercise [ 125 ] and are significantly lower in adults with greater cardiorespiratory fitness [ 126 ]. Our group has shown that arterial stiffness was either significantly reduced [ 42 , 62 ] or unchanged [ 76 ] in obese adolescents following 12 weeks of CRAE training. However, the reduced arterial stiffness observed was likely due to an improved nitric oxide (NO) and endothelin-1 (ET-1) ratio and increased vasodilatory [ 62 ] capacity as arterial stiffness is a measurement of a vessel’s structural elasticity which tends to change with time [ 127 , 128 ]. It is also important to note that in response to exercise previous studies suggest that shear stress, or the force of blood flowing on the endothelial surface, mediates complimentary adaptations in artery function and structure with changes in function preceding changes in structure [ 114 , 129 ]. Future research should adopt long-term assessments utilizing multiple time points to determine the vascular adaptations associated with pediatric obesity and the effects of exercise over time on arterial stiffness.

Exercise modality and prescription

It is widely accepted that obesity is caused by an imbalance between energy intake and energy expenditure, specifically when energy intake exceeds energy expenditure resulting in increased adipose tissue accumulation. Dietary habits, levels of physical activity, and sedentary behaviors all affect an individual’s energy balance [ 130 ]. Interestingly, modern trends for physical activity reveal that in general, there is a significant increase in the frequency of sedentary behaviors, such as screen time and watching television, during childhood [ 131 ]. Increased frequency of sedentary behaviors and decreased physical activity time are significant contributors to the development of pediatric obesity as previous research has shown that decreased levels of physical activity are associated with increased BMI [ 132 ] and fat mass [ 133 ] and obesity [ 14 , 134 ]. Various obesity-related comorbidities and CVD risk factors can be attributed to the accumulation of excess fat mass [ 30 - 32 ]. Previous studies utilizing exercise interventions have shown that exercise improves body composition and has a positive impact on blood lipid profiles and BP as well as blood levels of metabolic hormones in obese children and adolescents ( Fig. 1 ) [ 135 , 136 ]. However, the effects of exercise may be dependent on the modality of exercise (AE, RT, and CRAE) as each modality may lead to distinct results.

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Obesity is associated with a number of comorbidities as well as increased risk factors for cardiovascular disease. In pediatric obesity, these conditions manifest early in life and have been shown to track into adulthood. Exercise has been shown to be an efficacious therapeutic strategy to combat pediatric obesity.

1. Aerobic exercise

Obesity is associated with an increased risk of all-cause morbidity and mortality [ 34 , 137 ] which can be reduced through improved cardiorespiratory fitness [ 138 ]. It is well established that in children, adolescents, and adults, AE is an effective means of improving cardiorespiratory fitness. In fact, AE training may be the most researched modality of exercise intervention in the pediatric population. AE is generally performed as a moderate-intensity physical exercise such as running, cycling, or jump rope for a sustained period of time, approximately 30–60 minutes per exercise bout, with the purpose of improving the body’s ability to transport and utilize oxygen in the skeletal muscle and the heart. Previous meta-analyses suggest that AE interventions are effective for reducing fasting insulin levels, IR [ 74 , 135 ], and BF% [ 139 ] as well as improving blood lipid levels [ 140 ] in obese adolescents. Additionally, previous studies have shown that AE training can lower overall body weight, BMI, low-density lipoprotein [ 52 ], as well improve blood levels of leptin, cortisol [ 52 ] and visfatin [ 141 ]. Previous studies generally prescribe AE for 30 to 90 minutes at a moderate or moderate-to-vigorous intensity for 3 to 5 days a week. These interventions prescribe progressively more intense AE over the duration of the interventions which range from 8 weeks to 48 weeks and utilize a variety of modalities including water activities, walking, jogging, and recreational sport [ 74 ]. Our group has previously utilized the same jump rope exercise protocol in 2 separate studies to investigate the effects of exercise on cardiovascular and metabolic parameters in obese adolescents [ 70 , 142 ]. The 12-week jump rope exercise program consisted of a 5-minute warm-up, 40 minutes of jump rope which increased in intensity every 4 weeks (weeks 1–4 at 40%–50% heart rate reserve [HRR], weeks 5–8 at 50%–60% HRR, and weeks 9–12 at 60%–70% HRR), and a 5-minute cool-down, which was completed 5 days a week for 12 weeks. The 12-week jump rope program resulted in improved body composition, BP, resting levels of NO, ET-1, insulin, and glucose, and reduced markers of inflammation and IR [ 70 , 142 ]. AE is more commonly prescribed to adolescents as AE training modalities such as jump rope, play, dancing, and sport may often be considered more fun and enjoyable which is an important factor for motivation, participation, and long-term adherence [ 143 , 144 ]. AE induces a multitude of positive effects; however, AE alone may not be the most efficacious exercise modality to combat pediatric obesity.

2. Resistance training

RT exercises utilize external loads in the form of free weights, resistance bands, cable machines, or body weight to apply resistance against the contraction of a skeletal muscle with the purpose of increasing muscular strength, power, hypertrophy, and/or endurance. RT is generally performed 1 to 3 times per week while the number of repetitions and sets as well as the duration and intensity of a bout of RT is dependent on the focus of the RT program, muscular strength, power, hypertrophy, or endurance. RT has traditionally been reserved for adult athletes as the primary purpose of RT is to improve muscular performance and it was believed children and adolescents did not experience the same benefits of RT as adults. However, more recent studies suggest that adolescents can improve physical performance, muscle size, and strength through RT [ 145 ]. Additionally, it is generally accepted that AE is optimal for reducing BF while RT is optimal for increasing lean body mass [ 146 ]. This may explain why AE is more commonly prescribed for weight management as reducing fat mass is commonly the primary focus of an exercise prescription for obese individuals due to the negative metabolic and inflammatory effects of excess adipose tissue [ 33 - 36 ]. However, AE alone may only minimally affect muscular strength and lean body mass in adults, children, and adolescents. In obese adults, RT has been shown to reduce fat mass and improve blood lipid levels and IR [ 147 , 148 ] but there is limited research focused on investigating the effects of RT on body composition and cardiovascular and metabolic parameters in obese adolescents. Lee et al. [ 149 ] found that abdominal adiposity was significantly reduced following 3 months of RT or AE in obese adolescent boys but only the RT group’s adiposity loss was associated with significant improvements in IR. However, in a follow-up study, the same group found that following 13 weeks of AE or RT, BF% was significantly reduced in obese adolescent girls but only AE resulted in improved insulin sensitivity which was believed to be attributed to greater reductions in BF in response to the AE modality [ 150 ]. These results indicate that there may be differential responses to RT between sexes in obese adolescents. Previous meta-analyses have identified that RT alone is not associated with significant decreases in fat mass or improvements in metabolic parameters and CVD risk factors in obese children and adolescents [ 74 , 151 ]. It was suggested that this may be due to the insufficient literature available and the variations of the study designs and methodologies. In the studies analyzed most prescribed RT interventions were similar to American College of Sports Medicine guidelines (1 to 2 sets of 10 to 15 repetitions of upper body and lower body multi-joint exercises, 2 to 3 times per week) but variance between age, sex, and adherence was present. Even so, current physical activity guidelines for the pediatric population suggest performing AE and muscle-strengthening activities 3 times per week [ 152 ] as muscular strength has been shown to be an important factor for protecting against chronic diseases and all-cause mortality [ 153 , 154 ].

3. CRAE training

As previously discussed, AE and RT interventions may have different effects on body composition and cardiovascular and metabolic parameters in pediatric obesity. CRAE training is a unique training modality that utilizes both AE and RT components in a single exercise protocol to provide the benefits of each modality, which may potentially be more beneficial for metabolic parameters, vascular function, and CVD risk factors than AE or RT alone. CRAE training generally involves completing a bout of RT, one set of 8–20 repetitions of multiple upper body and lower body resistance exercises, followed by a bout of AE, 20–30 minutes at a moderate intensity, during a single exercise session. CRAE training has been shown to improve both cardiorespiratory fitness and muscular strength [ 76 , 155 ] and previous reviews suggest that in adults, CRAE training is more effective for reducing BF% [ 156 ], WC, BP [ 76 ], levels of blood lipids, and improving glycemic control [ 157 ] when compared to AE or RT alone. One CRAE training protocol our group has previously developed consisted of a 5-minute warm-up, 20 minutes of RT (one set of 15–20 repetitions of 5 upper body and 3 lower body exercises), 30 minutes of walking or jogging at 60%–70% HRR, and a 5-minute cool-down performed 5 times a week for 12 weeks. Our group has shown in obese adolescents that CRAE training improves anthropometric measurements (BF%, WC) [ 42 , 76 ], reduces BP, arterial stiffness, IR, markers of inflammation, levels of ET-1, and increases NO bioavailability [ 44 ]. Our findings are supported by previously completed reviews that indicate CRAE training improves body composition, blood lipid profiles, blood levels of adipokines [ 151 , 158 ], and insulin sensitivity [ 151 , 159 ]. These reviews also conclude AE alone and CRAE training are more effective than RT for improving fat mass, lipid profiles, fasting insulin, fasting glucose, and IR but do not provide significantly different benefits [ 151 , 159 ]. However, Dâmaso et al. [ 56 ] found that following 1 year of CRAE training obese adolescents experienced significantly greater improvements in BF mass, blood lipid levels, lean body mass, blood levels of leptin and adiponectin, and leptin/adiponectin ratio when compared to obese adolescents that completed 1 year of AE. Further research is required to confirm the efficacy of CRAE training over AE and RT. Nonetheless, it is important that obese children and adolescents perform both AE and RT, or CRAE training, which is may be more beneficial than AE or RT alone to prevent the development of obesity-related metabolic diseases and CVD. Additionally, adherence rate of the exercise training is crucial to have positive effects of exercise training for obese children and adolescents. Although there is no study that has directly compared the exercise training adherent rates in AE, RT, and CRAE, our recent studies suggest that CRAE training may have greater exercise training adherence rates compared to AE and RT. Since CRAE training is able to combine multiple different exercise modalities, it may be more enjoyable and less demanding in these young individuals compared to AE or RT alone. This notion can be supported by previous studies that reported adolescents of various backgrounds have stated that they would be more inclined to engage in exercise if it is perceived as fun and enjoyable [ 143 , 144 , 160 ]. Studies about specific exercise modality, and adherence rate are warranted to develop optimal exercise modalities for pediatric populations.

Obesity is one of the most prominent public health concerns of modern times with the potential to place a substantial burden on healthcare systems. Pediatric obesity is a well-established risk factor for the development of MetS, T2D, CVD, cancer, and early mortality in adulthood. Although the exact cause of pediatric obesity is multifaceted, it is a condition that can be improved with effective and maintainable lifestyle changes. Exercise has proven to be an efficacious intervention to combat pediatric obesity and its related risk factors and comorbidities. While RT provides benefits, AE and CRAE training appear to be the most effective exercise modalities to reduce BF and combat pediatric obesity. Based off of previous research we suggest that the most appropriate exercise prescription to improve pediatric obesity would be a CRAE training protocol, which contains both muscle strengthening (RT) and aerobic components (AE), that places emphasis on reducing fat mass and long-term adherence.

Key message

Pediatric obesity contributes to the development of vascular dysfunction and metabolic and cardiovascular diseases which have all been shown to track into adulthood, increasing the risk of early mortality. Early exercise intervention is critical for combating obesity-related comorbidities and the optimal exercise prescription has yet to be well documented. Exercise prescriptions to combat pediatric obesity should incorporate both aerobic and muscle-strengthening exercises with an emphasis on long-term adherence.

No potential conflict of interest relevant to this article was reported.

COMMENTS

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    Alix Hall Sze Lin Yoong Carolyn Summerbell Open Access Published: October 19, 2022 DOI: https://doi.org/10.1016/j.eclinm.2022.101635 Interventions to prevent obesity in school-aged children 6-18 years: An update of a Cochrane systematic review and meta-analysis including studies from 2015-2021 Summary Background

  11. Childhood obesity research at the NIH: Efforts, gaps, and opportunities

    This overview highlights five areas of childhood obesity research supported by the NIH: (a) basic behavioral and social sciences; (b) early childhood; (c) policies, programs, and environmental strategies; (d) health disparities; and (e) transagency and public-private collaboration.

  12. PDF Running head: Childhood Obesity 1

    Childhood Obesity: Turning a Risk Factor into a Solution Obesity is a critical health problem that is increasing worldwide, and in the United States in particular. In 2012, The Center for Disease Control and Prevention (CDC) identified obesity as a leading cause of death of adults in the US, second only to heart disease, and

  13. Childhood obesity: a growing pandemic

    Children with overweight and obesity are more likely to become adults with obesity and to develop non-communicable diseases such as diabetes and cardiovascular disease at a younger age than children considered to have a healthy weight. There is also an increased risk of cancer, premature death, and disability later in life.

  14. Frontiers

    Obesity increases the risk of developing early puberty in children ( 10 ), menstrual irregularities in adolescent girls ( 1, 11 ), sleep disorders such as obstructive sleep apnea (OSA) ( 1, 12 ), cardiovascular risk factors that include Prediabetes, Type 2 Diabetes, High Cholesterol levels, Hypertension, NAFLD, and Metabolic syndrome ( 1, 2 ).

  15. Childhood obesity research at the NIH: Efforts, gaps, and opportunities

    The childhood obesity research that NIH supports includes studies in pregnancy, infancy, childhood, adolescence, and prevention and treatment approaches in families, schools, and other community settings, as well as in health care settings. The NIH also supports basic behavioral and social science research that is providing insights into ...

  16. American Heart Association Childhood Obesity Research Summit

    The Childhood Obesity Research Summit provided an overview of childhood obesity, and participants identified key questions that need to be answered, as well as recommendations for future work in the areas of education, public policy, and research. ... (2017) White Paper AGA: POWER — Practice Guide on Obesity and Weight Management, Education ...

  17. ASPE Childhood Obesity White Paper

    Abstract. The document provides an overview of the research literature on causes of childhood obesity. Overweight and obesity in children are significant public health problems in the United States. The number of adolescents who are overweight has tripled since 1980 and the prevalence among younger children has more than doubled.

  18. PDF CHILDHOOD OBESITY: CONFRONTING THE GROWING PROBLEM A Thesis Presented

    One third of American children are currently overweight or obese, putting them at an increased risk for a multitude of obesity-related health problems including heart disease, high blood pressure, various cancers, type 2 diabetes, osteoarthritis, and respiratory problems (Koh, 2010; Centers for Disease Control and Prevention).

  19. Prevention and Management of Childhood Obesity and its Psychological

    This chapter reviews the state-of-the-science for understanding the etiology of childhood obesity, the preventive interventions and treatment options for overweight and obesity, and the medical complications and co-occurring psychological conditions that result from excess adiposity, such as hypertension, non-alcoholic fatty liver disease, and d...

  20. A systematic literature review on obesity ...

    Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity.

  21. Obesity Effects on Child Health

    Introduction Obesity in childhood is the most challenging public health issue in the twenty-first century. It has emerged as a pandemic health problem worldwide. The children who are obese tend to stay obese in adulthood and prone to increased risk for diabetes and cardiac problems at a younger age.

  22. Childhood Obesity Research Demonstration (CORD) 3.0

    CORD 3.0 has the potential to reduce childhood obesity by increasing the availability of effective family healthy weight programs for millions of children from lower-income families. CORD 3.0 funds five recipients for 5 years (2019-2024). CDC launched CORD 1.0 in 2010 and funded four recipients to use a whole-community approach to address ...

  23. The impacts of exercise on pediatric obesity

    The effects of exercise intervention on metabolic hormones such as leptin and adiponectin, insulin sensitivity/resistance, and body fat in obese children and adolescents will be discussed along with the exercise modality, intensity, and duration.