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Review article, childhood and adolescent obesity: a review.

• 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 ).

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 ).

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 ).

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 ).

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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• Published: 20 October 2015

Biological, environmental, and social influences on childhood obesity

• M. Karen Campbell 1 , 2 , 3 , 4  

Pediatric Research volume  79 ,  pages 205–211 ( 2016 ) Cite this article

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The prevalence of childhood obesity has increased globally over the past three decades, with evidence of recent leveling off in developed countries. Reduction in the, currently high, prevalence of obesity will require a full understanding of the biological and social pathways to obesity in order to develop appropriately targeted prevention strategies in early life. Determinants of childhood obesity include individual level factors, including biological, social, and behavioral risks, acting within the influence of the child’s family environment, which is, in turn, imbedded in the context of the community environment. These influences act across childhood, with suggestions of early critical periods of biological and behavioral plasticity. There is evidence of sex and gender differences in the responses of boys and girls to their environments. The evidence that determinants of childhood obesity act at many levels and at different stages of childhood is of policy relevance to those planning early health promotion and primary prevention programs as it suggests the need to address the individual, the family, the physical environment, the social environment, and social policy. The purpose of this narrative review is to summarize current, and emerging, literature in a multilevel, life course framework.

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Introduction.

The prevalence of childhood obesity has increased globally over the past three decades, with more rapid increases recently occurring in low-income countries ( 1 ). In the United States, more than 30% of children are now overweight or obese ( 1 ), with evidence that the prevalence has leveled off ( 2 ). Children and adolescents are exhibiting obesity-related conditions such as type 2 diabetes, elevated blood pressure, low-density lipoprotein cholesterol, and higher fasting insulin levels ( 3 , 4 , 5 , 6 ). In addition, childhood obesity predicts adulthood obesity and its known health consequences ( 7 , 8 ). Treatment of obesity is notoriously difficult, with weight loss rarely sustained in adults ( 9 ). Therapeutic interventions in childhood are somewhat more successful, particularly if the intervention occurs prior to onset of puberty ( 10 ). However, real and sustained progress in combating the obesity epidemic will require a full understanding of the biological and social pathways to obesity in order to develop appropriately targeted prevention strategies in early life.

Pathways to childhood obesity are complex. It is therefore helpful to discuss determinants of obesity within a conceptual framework. A multilevel conceptual model, Bronfenbrenner’s Bioecological Systems Theory ( 11 ), has previously been applied to the conceptualization of childhood obesity by Davison and Birch ( 12 ). This framework depicts individual-level factors, including biological, social, and behavioral risks, as acting within the influence of the child’s family environment, which is, in turn imbedded in the context of the community environment. It is also helpful to consider critical periods for obesity risk and, as will be further illustrated in a later section, there are likely critical periods of biological and behavioral plasticity beginning as early as fetal life ( 13 ) with risk factors accumulating, and interacting with each other, across the life course. This is consistent with a life course model of chronic disease epidemiology ( 14 ). Specific determinants of obesity will be discussed below within this multilevel framework and life course perspective.

This narrative review will discuss both biological and social determinants of childhood obesity at three levels (individual, family, and community) and across early childhood. The relationship between childhood stress and obesity will be explored in greater detail as this is an important pathway of active interest in current literature. In addition, the review will address recent attention to sex- and gender-based differences in obesity risk. A key purpose in undertaking this review was to summarize evidence regarding pathways to obesity in boys and girls by integrating established plus emerging perspectives in the literature. These include an overview of important factors at each level. Given the breadth of the literature, it was not the intention to cover all literature on each determinant but rather to provide these as key examples of the many dimensions of obesity risk.

Individual-, Family-, and Community-Level Determinants of Obesity

At the individual level, the most direct determinant of children’s obesity is the energy balance between nutritional intake and activity, the latter being influenced by both physical activity and sedentary behaviors ( 15 , 16 , 17 ). These behavioral factors are therefore frequent targets for both preventive and therapeutic interventions. However, nutrition and activity are “downstream” factors that can be influenced by many “upstream” causes. The energy balance required to maintain an appropriate fat mass varies among individuals due to differences in metabolism and in lipostatic set point, which will influence appetite and activity preferences ( 18 ). Metabolism and lipostatic set point, while to some degree influenced by genetic predisposition ( 18 ), can be altered by gene–environment interactions ( 19 , 20 , 21 ).

The family, physical, and social environment influence children’s obesity risk in two ways: through a direct influence on children’s nutrition and activity behaviors and through indirect influences via stress as will be discussed later in this paper. Higher parental education, parental nurturing, and higher self-esteem reduce obesity risk in girls ( 22 ). There is an abundance of evidence that the home food environment ( 23 , 24 , 25 ), shared family meals ( 26 , 27 ), and electronic media use influence children’s obesity ( 28 ) largely through behavioral pathways. Mothers primarily establish the home food environment and are role models for eating behaviors ( 29 ) with evidence of strong correlation between the eating patterns of mothers and children ( 25 , 29 ). Appetite control and food preferences are established early in life ( 30 ), and there is a high correlation between parental obesity and their children’s obesity ( 20 , 22 ).

The community environment is increasingly obesogenic, with increased use of convenience foods, automobiles, and electronic and televised forms of entertainment ( 31 , 32 , 33 ) leading to higher consumption of calorie-dense foods and more sedentary lifestyles. Food choices have been shown to be influenced by proximity to fast food outlets, supermarkets, and farmers markets ( 34 , 35 , 36 , 37 , 38 ). Physical activity levels are influenced by public recreation opportunities, transit availability, and neighborhood walkability ( 35 , 37 , 39 , 40 , 41 , 42 ). In addition, lower obesity levels are observed in areas where the natural environment has high recreational value ( 43 ). While evidence suggests that the above environmental factors affect risk behaviors and obesity, there is still a gap in understanding how children interface with the obesogenic environment ( 44 ).

Prenatal and Postnatal Influences

There is emerging interest in prenatal factors, postnatal factors, and their interactions. These are critical time periods of metabolic and endocrine plasticity and may condition later physiologic responses to environmental influences ( 13 ). This field of research has been labeled as the developmental origins of health and disease and is the subject of much attention in the biomedical and epidemiologic sciences.

For the past two decades, there has been intense interest in the possible effect of fetal undernutrition on later obesity. The interest in this proposed association was precipitated by seminal work by Barker ( 45 ). In humans, fetal undernutrition may be a consequence of maternal undernutrition, maternal smoking, or placental dysfunction from preeclampsia. Markers of fetal undernutrition, which include fetal growth restriction and its proxy indicator small birth weight for gestational age, have been shown to be associated with a modestly elevated risk of obesity. It has been suggested that this effect is due to an in utero adaptation that becomes a mismatch to a postnatal environment in which nutrition is abundant ( 46 , 47 ). Animal studies, often based on maternal dietary restriction, confirm evidence for such fetal metabolic adaptations to undernutrition ( 48 ). In both animal and human studies, there is evidence of the permanence of these adaptations. The greatest elevation in obesity risk is for those who were born small, but experienced rapid “catch up growth” postnatally ( 48 , 49 , 50 , 51 , 52 ).

Emerging literature is challenging the relationship of fetal undernutrition as a determinant of obesity. First, if the association does exist, is a genetic component partially responsible? Specific adult obesity gene loci have been implicated as associated both with fetal growth ( 53 ) and with growth velocity in infancy ( 54 ). In this genomic era, this will be an aspect of the literature to watch, although to date the predictive value of individual gene loci for obesity risk has been modest. There is emerging speculation as to whether this association indeed exists at all, despite the abundance of literature on the topic. Part of this speculation is based on a statistical argument that, in the zealous effort to control for the myriad of potential confounders, most studies looking at the relationship between fetal growth restriction or small birth weight for gestational age and later chronic conditions have controlled for variables along the causal pathway and thus introduced bias ( 55 , 56 ). Moreover, recent carefully analyzed studies have suggested the inverse; that small birth weight for gestational age is associated with a lower risk of obesity ( 57 ). This question remains an active topic of interest in the literature, despite the recognition that the association, if real, is a small magnitude association with no clear implications for prevention ( 58 ).

Fetal overnutrition, evidenced by large infant birth weight for gestational age, is a strong predictor of obesity in childhood and later life ( 59 , 60 , 61 ). A caveat is that, while large infant birth weight for gestational age is generally an indicator of excess fat mass, it may also reflect other growth parameters such that a subset of large infant birth weight for gestational age infants may have increased lean mass ( 62 , 63 ). Risk factors for large infant birth weight for gestational age include maternal obesity and maternal gestational diabetes ( 64 , 65 ) with African-American women exhibiting risk at lower maternal BMI thresholds ( 66 ). It is suggested that fetal hyperglycemia triggers fetal insulin production which in turn triggers fetal growth and adiposity ( 67 ). Animal studies demonstrate that fetal hyperinsulinemia may invoke permanent changes in the CNS mechanisms for regulating metabolism and body weight ( 67 ). Thus, fetal overnutrition may be a mechanism of intergenerational transmission of obesity and diabetes ( 67 , 68 ).

Early postnatal experiences are also important contributors to obesity risk. Breastfed infants are at lower risk for later obesity ( 69 , 70 , 71 , 72 , 73 ) for hypothesized reasons including that formula-fed infants develop greater reliance on external hunger cues ( 74 ) and have higher intake of protein ( 75 ), which may contribute to obesity risk through behavioral and physiologic mechanisms, respectively. The benefits of breastfeeding appear to be confined to exclusive breastfeeding; mixed infant feeding of breastmilk and formula do not reduce obesity risks associated with formula feeding ( 76 ). In addition, the timing and choice of complementary foods introduced into an infant’s diet may influence their food preferences in the long term ( 77 ). In general, obesity risk is elevated for those who experienced rapid early weight gain in infancy ( 78 , 79 , 80 ). Based on this knowledge, strategies for primary prevention in high-income countries may include support for long-term breastfeeding ( 81 ).

Psychosocial Vulnerabilities

There is evidence that psychosocial stress is associated with obesity in children. Measures of stress vary from study to study ( 82 ), but the findings are consistent. Whether this association is causal is not known, but there are theoretical frameworks that suggest causality. For example, the life course–stress process perspective introduced by Pearlin et al . ( 83 ) has been discussed by Wickrama et al . ( 84 ) in the context of body mass. A pathway from stress to obesity could include inflammatory mechanisms ( 85 ) including arousal of the hypothalamic–pituitary–adrenal axis leading to increased cortisol levels and subsequent metabolic disruption and increased hunger ( 84 , 86 , 87 , 88 ). If so, nutrition may mediate the relationship between stress and obesity, or lifestyle factors may be coexisting with environmental stressors ( 89 , 90 ). Some of the reported associations of environmental stressors with childhood overweight and obesity include negative life events ( 82 ), maltreatment ( 91 ), how well the family communicates ( 90 ), and parental stress ( 92 ).

Depression and obesity are often comorbid in both children and adults. This comorbidity may be due to common genetic and environmental etiologies ( 93 , 94 , 95 , 96 ) or common pathways via dysregulation of the hypothalamic–pituitary–adrenal system ( 93 , 95 , 96 ). Increased food intake and reduced physical activity are characteristic of both conditions ( 94 ). Bidirectional causation is also plausible, with suggestions that obesity may be a determinant of later depression in children ( 97 , 98 , 99 ) and conversely hypothesized mechanisms for depression causing obesity ( 93 , 95 , 98 , 99 , 100 ). Indeed, it has more recently been suggested that these two comorbid conditions may mutually reinforce a progressive downward spiral in each other ( 101 ) and that additional insight into their longitudinal interaction may be important for intervention strategies ( 102 ).

Mothers’ mental and emotional well-being has been shown to be associated with childhood obesity. Children of mothers with depressive symptoms are more likely to be obese or overweight in infancy ( 103 , 104 ), childhood ( 105 , 106 ), and adolescence ( 107 ). Prenatal exposure to maternal stress and distress has been shown to be associated with both children’s obesity and rapid postnatal growth ( 108 , 109 ). Proposed mechanisms for the association include infant feeding practices ( 110 ), mother–infant interaction ( 111 ), mother–infant feeding interactions ( 112 ), parenting style ( 113 ), and a direct effect of stressors leading to central adiposity via arousal of the child’s hypothalamic–pituitary–adrenal axis ( 86 ). It has also been suggested that, due to the comorbidity between maternal overweight and emotion regulation, these pathways may also play into the intergenerational transfer of overweight and obesity ( 112 ), as well as the roles of shared genes and environment ( 86 ). A recent systematic review noted the need for more prospective studies to confirm and explain these associations ( 114 ).

Consistently, in high-income countries, socioeconomic disadvantage has been shown to be associated with obesity risk in childhood and persistently throughout life ( 115 , 116 , 117 ). Socioeconomic disadvantage may exert its influence as early as the prenatal and postnatal period, through its association with maternal depression ( 106 , 118 ) and its consequences. Moreover, poverty may be associated with poorer individual diet ( 119 ), poorer retail food and recreational environment ( 34 , 120 , 121 ), suboptimal family food routines ( 118 , 122 ), and environmental stressors such as living in a higher crime neighborhood ( 121 ). The risks associated with socioeconomic disadvantage may accumulate and compound throughout childhood ( 123 ). Miller and Chen ( 124 ) present a theoretical model, with corresponding research evidence, linking poverty to the development of a proinflammatory phenotype and subsequent elevated risk for chronic conditions in childhood and beyond. Overall, it appears that poverty is associated with later obesity through its association with other obesity risk factors and through the stress process.

There is an increasing attention in the literature to the differences in vulnerabilities in boys and girls, suggesting different pathways to obesity. Much of the literature, to date, has looked at determinants of childhood obesity while statistically controlling for children’s sex. However, to truly understand the developmental processes leading to obesity, researchers may need to look at boys and girls separately in order to recognize both sex-specific (biological) and gender-specific (social and cultural) differences in the ways in which boys and girls interact with their physical and social environments. Some biological differences include body composition and growth patterns, with clear sex differences in the distribution of adiposity beginning as early as the neonatal period and continuing through adulthood ( 125 ). Energy requirements and the aptitude for specific physical activities exhibit sex differences, while specific gender differences include how boys and girls interact with their family and their food environment as well as their overall physical activity levels ( 126 ). There are also gender differences in metabolic responses to stress ( 87 ) and family disruption or conflict ( 127 ). Responses to the physical and social environment will influence, and be influenced by, pubertal development ( 47 , 48 , 125 ). Pubertal timing itself has significant influence on insulin resistance and metabolic syndrome, particularly in girls ( 128 , 129 ). The pubertal transition is also well established as a time when depression rates rise dramatically, particularly for females; indeed, this developmental stage is when the gender difference in depression emerges ( 130 , 131 ). Finally, pubertal timing and growth influence later adult cardiovascular risk in both males and females ( 128 ). Additional research focusing on the gendered dimensions of childhood obesity is needed.

Summary and Implications

In undertaking a review of this broad area of significant health promotion interest, I have used the narrative review method. It has been argued that narrative reviews have advantages when the scope and literature coverage is broad and covers a range of issues within a given topic ( 132 ). This broader coverage comes at the expense of the more explicit methods, reporting and reproducibility, that are associated with systematic reviews, which tend to focus on narrower topics using prescribed search methods ( 132 ). Given the methodological limitations of the narrative method, and the acknowledged potential for selection biases in study selection when a nonsystematic review is undertaken, the reader should turn to determinant-specific systematic reviews for exhaustive discussion of the specific determinants covered in this review. The main objective of this review was to summarize key early determinants of childhood obesity within the important framework of individual-, family-, and community-level biological and social influences acting across early life.

Consideration of determinants of obesity within this broader multilevel framework may imply that strategies for health promotion and primary prevention should include attention to determinants at all levels. The upstream influences on childhood obesity occur at many levels, including the family and the community, and begin very early in the life course. Health promotion activities typically target individual lifestyle factors, despite emerging evidence of the importance of broader environmental prevention targets ( 133 ). Family-based interventions to improve the home food environment ( 90 ) and parenting style ( 134 ) and policies to reduce the costs of healthy food choices ( 135 ) are needed. Prevention efforts should also include programs to reduce financial stress in families and programs aimed at teaching children on how to cope with stressors in their environment ( 86 ). It has been suggested that overweight and obesity reductions may accrue if the prevention focus is shifted, more broadly, to promoting healthy lifestyles and healthy environments and beyond the focus on individual children’s body weight as the outcome ( 136 ). The opportunities for early health promotion require attention simultaneously to many levels ( 30 ), suggesting the need to address the individual, family, and physical environment, the social environment, and social policy.

Statement of Financial Support

none; there are no conflicts of interest.

Lobstein T, Jackson-Leach R, Moodie ML, et al. Child and adolescent obesity: part of a bigger picture. Lancet 2015; 385 :2510–20.

Article   PubMed   PubMed Central   Google Scholar  

Rokholm B, Baker JL, Sørensen TI. The levelling off of the obesity epidemic since the year 1999–a review of evidence and perspectives. Obes Rev 2010; 11 :835–46.

Article   CAS   PubMed   Google Scholar  

Daniels SR. The consequences of childhood overweight and obesity. Future Child 2006; 16 :47–67.

Article   PubMed   Google Scholar  

Amed S, Dean HJ, Panagiotopoulos C, et al. Type 2 diabetes, medication-induced diabetes, and monogenic diabetes in Canadian children: a prospective national surveillance study. Diabetes Care 2010; 33 :786–91.

Clarson CL, Mahmud FH, Baker JE, et al. Metformin in combination with structured lifestyle intervention improved body mass index in obese adolescents, but did not improve insulin resistance. Endocrine 2009; 36 :141–6.

Thompson DR, Obarzanek E, Franko DL, et al. Childhood overweight and cardiovascular disease risk factors: the National Heart, Lung, and Blood Institute Growth and Health Study. J Pediatr 2007; 150 :18–25.

Guo SS, Wu W, Chumlea WC, Roche AF. Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr 2002; 76 :653–8.

Steinberger J, Moran A, Hong CP, Jacobs DR Jr, Sinaiko AR. Adiposity in childhood predicts obesity and insulin resistance in young adulthood. J Pediatr 2001; 138 :469–73.

Stelmach-Mardas M, Mardas M, Walkowiak J, Boeing H. Long-term weight status in regainers after weight loss by lifestyle intervention: status and challenges. Proc Nutr Soc 2014; 73 :509–18.

Wiegand S, Keller KM, Lob-Corzilius T, et al. Predicting weight loss and maintenance in overweight/obese pediatric patients. Horm Res Paediatr 2014; 82 :380–7.

Bronfenbrenner U, Ceci SJ. Nature-nurture reconceptualized in developmental perspective: a bioecological model. Psychol Rev 1994; 101 :568–86.

Davison KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obes Rev 2001; 2 :159–71.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hanson MA, Gluckman PD. Early developmental conditioning of later health and disease: physiology or pathophysiology? Physiol Rev 2014; 94 :1027–76.

Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol 2002; 31 :285–93.

Boone-Heinonen J, Gordon-Larsen P, Adair LS. Obesogenic clusters: multidimensional adolescent obesity-related behaviors in the U.S. Ann Behav Med 2008; 36 :217–30.

Nelson MC, Gordon-Larsen P, Adair LS, Popkin BM. Adolescent physical activity and sedentary behavior: patterning and long-term maintenance. Am J Prev Med 2005; 28 :259–66.

Owen N, Leslie E, Salmon J, Fotheringham MJ. Environmental determinants of physical activity and sedentary behavior. Exerc Sport Sci Rev 2000; 28 :153–8.

CAS   PubMed   Google Scholar  

Speakman JR. Obesity: the integrated roles of environment and genetics. J Nutr 2004; 134 :Suppl 8:2090S–105S.

Bouchard C. Gene-environment interactions in the etiology of obesity: defining the fundamentals. Obesity (Silver Spring) 2008; 16 :Suppl 3:S5–S10.

Article   CAS   Google Scholar  

Kosti RI, Panagiotakos DB, Tountas Y, et al. Parental body mass index in association with the prevalence of overweight/obesity among adolescents in Greece; dietary and lifestyle habits in the context of the family environment: the Vyronas study. Appetite 2008; 51 :218–22.

Qi L, Cho YA. Gene-environment interaction and obesity. Nutr Rev 2008; 66 :684–94.

Crossman A, Anne Sullivan D, Benin M. The family environment and American adolescents’ risk of obesity as young adults. Soc Sci Med 2006; 63 :2255–67.

Rosenkranz RR, Dzewaltowski DA. Model of the home food environment pertaining to childhood obesity. Nutr Rev 2008; 66 :123–40.

Haines J, Neumark-Sztainer D, Wall M, Story M. Personal, behavioral, and environmental risk and protective factors for adolescent overweight. Obesity (Silver Spring) 2007; 15 :2748–60.

Article   Google Scholar  

Arcan C, Neumark-Sztainer D, Hannan P, van den Berg P, Story M, Larson N. Parental eating behaviours, home food environment and adolescent intakes of fruits, vegetables and dairy foods: longitudinal findings from Project EAT. Public Health Nutr 2007; 10 :1257–65.

Pinard CA, Yaroch AL, Hart MH, Serrano EL, McFerren MM, Estabrooks PA. Measures of the home environment related to childhood obesity: a systematic review. Public Health Nutr 2012; 15 :97–109.

Haines J, Kleinman KP, Rifas-Shiman SL, Field AE, Austin SB. Examination of shared risk and protective factors for overweight and disordered eating among adolescents. Arch Pediatr Adolesc Med 2010; 164 :336–43.

Haines J, Neumark-Sztainer D. Prevention of obesity and eating disorders: a consideration of shared risk factors. Health Educ Res 2006; 21 :770–82.

Campbell KJ, Crawford DA, Salmon J, Carver A, Garnett SP, Baur LA. Associations between the home food environment and obesity-promoting eating behaviors in adolescence. Obesity (Silver Spring) 2007; 15 :719–30.

Gluckman P, Nishtar S, Armstrong T. Ending childhood obesity: a multidimensional challenge. Lancet 2015; 385 :1048–50.

Jeffery RW, Utter J. The changing environment and population obesity in the United States. Obes Res 2003; 11 :Suppl:12S–22S.

Gilliland J . The built environment and obesity: trimming waistlines through neighbourhood design. In: Bunting E, Filion P, Walker R, eds. Canadian Cities in Transition . Don Mills, ON: Oxford University Press, 2010:391–410.

Google Scholar  

Gilliland JA, Rangel CY, Healy MA, et al. Linking childhood obesity to the built environment: a multi-level analysis of home and school neighbourhood factors associated with body mass index. Can J Public Health 2012; 103 :Suppl 3:eS15–21.

Ford PB, Dzewaltowski DA. Disparities in obesity prevalence due to variation in the retail food environment: three testable hypotheses. Nutr Rev 2008; 66 :216–28.

Rahman T, Cushing RA, Jackson RJ. Contributions of built environment to childhood obesity. Mt Sinai J Med 2011; 78 :49–57.

Zhang X, van der Lans I, Dagevos H. Impacts of fast food and the food retail environment on overweight and obesity in China: a multilevel latent class cluster approach. Public Health Nutr 2012; 15 :88–96.

Epstein LH, Raja S, Daniel TO, et al. The built environment moderates effects of family-based childhood obesity treatment over 2 years. Ann Behav Med 2012; 44 :248–58.

He M, Tucker P, Gilliland J, Irwin JD, Larsen K, Hess P. The influence of local food environments on adolescents’ food purchasing behaviors. Int J Environ Res Public Health 2012; 9 :1458–71.

Lopez RP, Hynes HP. Obesity, physical activity, and the urban environment: public health research needs. Environ Health 2006; 5 :25.

Oreskovic NM, Winickoff JP, Kuhlthau KA, Romm D, Perrin JM. Obesity and the built environment among Massachusetts children. Clin Pediatr (Phila) 2009; 48 :904–12.

Tucker P, Irwin JD, Gilliland J, He M, Larsen K, Hess P. Environmental influences on physical activity levels in youth. Health Place 2009; 15 :357–63.

Larsen K, Gilliland J, Hess PM. Route-based analysis to capture the environmental influences on a child’s mode of travel between home and school. Ann Assoc Am Geogr 2012; 102 :1348–65.

Björk J, Albin M, Grahn P, et al. Recreational values of the natural environment in relation to neighbourhood satisfaction, physical activity, obesity and wellbeing. J Epidemiol Community Health 2008; 62 :e2.

Penney TL, Almiron-Roig E, Shearer C, McIsaac JL, Kirk SF. Modifying the food environment for childhood obesity prevention: challenges and opportunities. Proc Nutr Soc 2014; 73 :226–36.

Barker DJ. The fetal and infant origins of adult disease. BMJ 1990; 301 :1111.

Zafon C. Oscillations in total body fat content through life: an evolutionary perspective. Obes Rev 2007; 8 :525–30.

Gluckman PD, Hanson MA, Beedle AS, Raubenheimer D. Fetal and neonatal pathways to obesity. Front Horm Res 2008; 36 :61–72.

Gluckman PD, Hanson MA. Developmental and epigenetic pathways to obesity: an evolutionary-developmental perspective. Int J Obes (Lond) 2008; 32 :Suppl 7:S62–71.

Eriksson JG, Forsén T, Tuomilehto J, Winter PD, Osmond C, Barker DJ. Catch-up growth in childhood and death from coronary heart disease: longitudinal study. BMJ 1999; 318 :427–31.

Eriksson JG, Forsén T, Tuomilehto J, Osmond C, Barker DJ. Early growth and coronary heart disease in later life: longitudinal study. BMJ 2001; 322 :949–53.

Soto N, Bazaes RA, Peña V, et al. Insulin sensitivity and secretion are related to catch-up growth in small-for-gestational-age infants at age 1 year: results from a prospective cohort. J Clin Endocrinol Metab 2003; 88 :3645–50.

Nobili V, Alisi A, Panera N, Agostoni C. Low birth weight and catch-up-growth associated with metabolic syndrome: a ten year systematic review. Pediatr Endocrinol Rev 2008; 6 :241–7.

PubMed   Google Scholar  

Morgan AR, Thompson JM, Murphy R, et al. Obesity and diabetes genes are associated with being born small for gestational age: results from the Auckland Birthweight Collaborative study. BMC Med Genet 2010; 11 :125.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Elks CE, Loos RJ, Sharp SJ, et al. Genetic markers of adult obesity risk are associated with greater early infancy weight gain and growth. PLoS Med 2010; 7 :e1000284.

Kramer MS. Invited commentary: association between restricted fetal growth and adult chronic disease: is it causal? Is it important? Am J Epidemiol 2000; 152 :605–8.

Tu YK, West R, Ellison GT, Gilthorpe MS. Why evidence for the fetal origins of adult disease might be a statistical artifact: the “reversal paradox” for the relation between birth weight and blood pressure in later life. Am J Epidemiol 2005; 161 :27–32.

Kramer MS, Martin RM, Bogdanovich N, Vilchuk K, Dahhou M, Oken E. Is restricted fetal growth associated with later adiposity? Observational analysis of a randomized trial. Am J Clin Nutr 2014; 100 :176–81.

Joseph KS. Should we intervene to improve fetal and infant growth? In: Kuh D, Ben-Shlomo Y, eds. A Life Course Approach to Chronic Disease Epidemiology . New York: Oxford University Press, 2004:399–414.

Adair LS. Child and adolescent obesity: epidemiology and developmental perspectives. Physiol Behav 2008; 94 :8–16.

Huang JS, Lee TA, Lu MC. Prenatal programming of childhood overweight and obesity. Matern Child Health J 2007; 11 :461–73.

Boney CM, Verma A, Tucker R, Vohr BR. Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics 2005; 115 :e290–6.

Murphy MJ, Metcalf BS, Jeffery AN, Voss LD, Wilkin TJ. Does lean rather than fat mass provide the link between birth weight, BMI, and metabolic risk? EarlyBird 23. Pediatr Diabetes 2006; 7 :211–4.

Singhal A, Wells J, Cole TJ, Fewtrell M, Lucas A. Programming of lean body mass: a link between birth weight, obesity, and cardiovascular disease? Am J Clin Nutr 2003; 77 :726–30.

Gillman MW, Rifas-Shiman S, Berkey CS, Field AE, Colditz GA. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics 2003; 111 :e221–6.

Kim SY, Sharma AJ, Sappenfield W, Wilson HG, Salihu HM. Association of maternal body mass index, excessive weight gain, and gestational diabetes mellitus with large-for-gestational-age births. Obstet Gynecol 2014; 123 :737–44.

Sridhar SB, Ferrara A, Ehrlich SF, Brown SD, Hedderson MM. Risk of large-for-gestational-age newborns in women with gestational diabetes by race and ethnicity and body mass index categories. Obstet Gynecol 2013; 121 :1255–62.

Dabelea D, Crume T. Maternal environment and the transgenerational cycle of obesity and diabetes. Diabetes 2011; 60 :1849–55.

Ma RC, Tutino GE, Lillycrop KA, Hanson MA, Tam WH. Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring. Prog Biophys Mol Biol 2015; 118 :55–68.

Gillman MW, Rifas-Shiman SL, Camargo CA Jr, et al. Risk of overweight among adolescents who were breastfed as infants. JAMA 2001; 285 :2461–7.

Hediger ML, Overpeck MD, Kuczmarski RJ, Ruan WJ. Association between infant breastfeeding and overweight in young children. JAMA 2001; 285 :2453–60.

Hawkins SS, Cole TJ, Law C ; Millennium Cohort Study Child Health Group. An ecological systems approach to examining risk factors for early childhood overweight: findings from the UK Millennium Cohort Study. J Epidemiol Community Health 2009; 63 :147–55.

Arenz S, Rückerl R, Koletzko B, von Kries R. Breast-feeding and childhood obesity–a systematic review. Int J Obes Relat Metab Disord 2004; 28 :1247–56.

Owen CG, Martin RM, Whincup PH, Smith GD, Cook DG. Effect of infant feeding on the risk of obesity across the life course: a quantitative review of published evidence. Pediatrics 2005; 115 :1367–77.

Fisher JO, Birch LL, Smiciklas-Wright H, Picciano MF. Breast-feeding through the first year predicts maternal control in feeding and subsequent toddler energy intakes. J Am Diet Assoc 2000; 100 :641–6.

Kirchberg FF, Harder U, Weber M, et al.; European Childhood Obesity Trial Study Group. Dietary protein intake affects amino acid and acylcarnitine metabolism in infants aged 6 months. J Clin Endocrinol Metab 2015; 100 :149–58.

Rossiter MD, Colapinto CK, Khan MK, et al. Breast, formula and combination feeding in relation to childhood obesity in Nova Scotia, Canada. Matern Child Health J 2015; 19 :2048–56.

Mennella JA, Trabulsi JC. Complementary foods and flavor experiences: setting the foundation. Ann Nutr Metab 2012; 60 :Suppl 2:40–50.

Stettler N, Zemel BS, Kumanyika S, Stallings VA. Infant weight gain and childhood overweight status in a multicenter, cohort study. Pediatrics 2002; 109 :194–9.

Baird J, Fisher D, Lucas P, Kleijnen J, Roberts H, Law C. Being big or growing fast: systematic review of size and growth in infancy and later obesity. BMJ 2005; 331 :929.

Dubois L, Girard M. Early determinants of overweight at 4.5 years in a population-based longitudinal study. Int J Obes (Lond) 2006; 30 :610–7.

Çamurdan MO, Çamurdan AD, Polat S, Beyazova U. Growth patterns of large, small, and appropriate for gestational age infants: impacts of long-term breastfeeding: a retrospective cohort study. J Pediatr Endocrinol Metab 2011; 24 :463–8.

Lumeng JC, Wendorf K, Pesch MH, et al. Overweight adolescents and life events in childhood. Pediatrics 2013; 132 :e1506–12.

Pearlin LI, Schieman S, Fazio EM, Meersman SC. Stress, health, and the life course: some conceptual perspectives. J Health Soc Behav 2005; 46 :205–19.

Wickrama KK, O’Neal CW, Oshri A. Are stressful developmental processes of youths leading to health problems amplified by genetic polymorphisms? The case of body mass index. J Youth Adolesc 2014; 43 :1096–109.

Magrone T, Jirillo E. Childhood obesity: immune response and nutritional approaches. Front Immunol 2015; 6 :76.

Gundersen C, Mahatmya D, Garasky S, Lohman B. Linking psychosocial stressors and childhood obesity. Obes Rev 2011; 12 :e54–63.

Huybrechts I, De Vriendt T, Breidenassel C, et al.; HELENA Study Group. Mechanisms of stress, energy homeostasis and insulin resistance in European adolescents–the HELENA study. Nutr Metab Cardiovasc Dis 2014; 24 :1082–9.

Pasquali R. The hypothalamic-pituitary-adrenal axis and sex hormones in chronic stress and obesity: pathophysiological and clinical aspects. Ann NY Acad Sci 2012; 1264 :20–35.

Vanaelst B, Michels N, Clays E, et al. The association between childhood stress and body composition, and the role of stress-related lifestyle factors–cross-sectional findings from the baseline ChiBSD survey. Int J Behav Med 2014; 21 :292–301.

Renzaho AM, Dau A, Cyril S, Ayala GX. The influence of family functioning on the consumption of unhealthy foods and beverages among 1- to 12-y-old children in Victoria, Australia. Nutrition 2014; 30 :1028–33.

Danese A, Tan M. Childhood maltreatment and obesity: systematic review and meta-analysis. Mol Psychiatry 2014; 19 :544–54.

Shankardass K, McConnell R, Jerrett M, et al. Parental stress increases body mass index trajectory in pre-adolescents. Pediatr Obes 2014; 9 :435–42.

Bornstein SR, Schuppenies A, Wong ML, Licinio J. Approaching the shared biology of obesity and depression: the stress axis as the locus of gene-environment interactions. Mol Psychiatry 2006; 11 :892–902.

de Wit L, Luppino F, van Straten A, Penninx B, Zitman F, Cuijpers P. Depression and obesity: a meta-analysis of community-based studies. Psychiatry Res 2010; 178 :230–5.

McElroy SL, Kotwal R, Malhotra S, Nelson EB, Keck PE, Nemeroff CB. Are mood disorders and obesity related? A review for the mental health professional. J Clin Psychiatry 2004; 65 :634–51, quiz 730.

Stunkard AJ, Faith MS, Allison KC. Depression and obesity. Biol Psychiatry 2003; 54 :330–7.

Erickson SJ, Robinson TN, Haydel KF, Killen JD. Are overweight children unhappy?: Body mass index, depressive symptoms, and overweight concerns in elementary school children. Arch Pediatr Adolesc Med 2000; 154 :931–5.

Needham BL, Crosnoe R. Overweight status and depressive symptoms during adolescence. J Adolesc Health 2005; 36 :48–55.

Ross CE. Overweight and depression. J Health Soc Behav 1994; 35 :63–79.

Duclos M, Gatta B, Corcuff JB, Rashedi M, Pehourcq F, Roger P. Fat distribution in obese women is associated with subtle alterations of the hypothalamic-pituitary-adrenal axis activity and sensitivity to glucocorticoids. Clin Endocrinol (Oxf) 2001; 55 :447–54.

Penninx BW, Milaneschi Y, Lamers F, Vogelzangs N. Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile. BMC Med 2013; 11 :129.

Luppino FS, de Wit LM, Bouvy PF, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67 :220–9.

Surkan PJ, Kawachi I, Peterson KE. Childhood overweight and maternal depressive symptoms. J Epidemiol Community Health 2008; 62 :e11.

Wang L, Anderson JL, Dalton Iii WT, et al. Maternal depressive symptoms and the risk of overweight in their children. Matern Child Health J 2013; 17 :940–8.

Ramasubramanian L, Lane S, Rahman A. The association between maternal serious psychological distress and child obesity at 3 years: a cross-sectional analysis of the UK Millennium Cohort Data. Child Care Health Dev 2013; 39 :134–40.

Gross RS, Velazco NK, Briggs RD, Racine AD. Maternal depressive symptoms and child obesity in low-income urban families. Acad Pediatr 2013; 13 :356–63.

Zeller MH, Reiter-Purtill J, Modi AC, Gutzwiller J, Vannatta K, Davies WH. Controlled study of critical parent and family factors in the obesigenic environment. Obesity (Silver Spring) 2007; 15 :126–36.

Stout SA, Espel EV, Sandman CA, Glynn LM, Davis EP. Fetal programming of children’s obesity risk. Psychoneuroendocrinology 2015; 53 :29–39.

Hohwü L, Henriksen TB, Grønborg TK, Hedegaard M, Sørensen TI, Obel C. Maternal salivary cortisol levels during pregnancy are positively associated with overweight children. Psychoneuroendocrinology 2015; 52 :143–52.

Article   PubMed   CAS   Google Scholar  

Farrow CV, Blissett JM. Is maternal psychopathology related to obesigenic feeding practices at 1 year? Obes Res 2005; 13 :1999–2005.

Wachs TD. Multiple influences on children’s nutritional deficiencies: a systems perspective. Physiol Behav 2008; 94 :48–60.

de Campora G, Giromini L, Larciprete G, Li Volsi V, Zavattini GC. The impact of maternal overweight and emotion regulation on early eating behaviors. Eat Behav 2014; 15 :403–9.

McConley RL, Mrug S, Gilliland MJ, et al. Mediators of maternal depression and family structure on child BMI: parenting quality and risk factors for child overweight. Obesity (Silver Spring) 2011; 19 :345–52.

Milgrom J, Skouteris H, Worotniuk T, Henwood A, Bruce L. The association between ante- and postnatal depressive symptoms and obesity in both mother and child: a systematic review of the literature. Womens Health Issues 2012; 22 :e319–28.

O’Dea JA, Chiang H, Peralta LR. Socioeconomic patterns of overweight, obesity but not thinness persist from childhood to adolescence in a 6-year longitudinal cohort of Australian schoolchildren from 2007 to 2012. BMC Public Health 2014; 14 :222.

Kakinami L, Séguin L, Lambert M, Gauvin L, Nikiema B, Paradis G. Poverty’s latent effect on adiposity during childhood: evidence from a Québec birth cohort. J Epidemiol Community Health 2014; 68 :239–45.

Lee H, Andrew M, Gebremariam A, Lumeng JC, Lee JM. Longitudinal associations between poverty and obesity from birth through adolescence. Am J Public Health 2014; 104 :e70–6.

McCurdy K, Gorman KS, Kisler T, Metallinos-Katsaras E. Associations between family food behaviors, maternal depression, and child weight among low-income children. Appetite 2014; 79 :97–105.

de Jong E, Visscher TL, HiraSing RA, Seidell JC, Renders CM. Home environmental determinants of children’s fruit and vegetable consumption across different SES backgrounds. Pediatr Obes 2015; 10 :134–40.

Carroll-Scott A, Gilstad-Hayden K, Rosenthal L, et al. Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: the role of built, socioeconomic, and social environments. Soc Sci Med 2013; 95 :106–14.

Lovasi GS, Schwartz-Soicher O, Quinn JW, et al. Neighborhood safety and green space as predictors of obesity among preschool children from low-income families in New York City. Prev Med 2013; 57 :189–93.

Malhotra K, Herman AN, Wright G, Bruton Y, Fisher JO, Whitaker RC. Perceived benefits and challenges for low-income mothers of having family meals with preschool-aged children: childhood memories matter. J Acad Nutr Diet 2013; 113 :1484–93.

Hernandez DC, Pressler E. Accumulation of childhood poverty on young adult overweight or obese status: race/ethnicity and gender disparities. J Epidemiol Community Health 2014; 68 :478–84.

Miller GE, Chen E. The biological residue of childhood poverty. Child Dev Perspect 2013; 7 :67–73.

Wisniewski AB, Chernausek SD. Gender in childhood obesity: family environment, hormones, and genes. Gend Med 2009; 6 :Suppl 1:76–85.

Sweeting HN. Gendered dimensions of obesity in childhood and adolescence. Nutr J 2008; 7 :1.

Hernandez DC, Pressler E. Gender disparities among the association between cumulative family-level stress & adolescent weight status. Prev Med 2015; 73 :60–6.

Widén E, Silventoinen K, Sovio U, et al. Pubertal timing and growth influences cardiometabolic risk factors in adult males and females. Diabetes Care 2012; 35 :850–6.

Goran MI, Gower BA. Longitudinal study on pubertal insulin resistance. Diabetes 2001; 50 :2444–50.

Hankin BL, Abramson LY. Development of gender differences in depression: an elaborated cognitive vulnerability-transactional stress theory. Psychol Bull 2001; 127 :773–96.

Hankin BL, Abramson LY, Moffitt TE, Silva PA, McGee R, Angell KE. Development of depression from preadolescence to young adulthood: emerging gender differences in a 10-year longitudinal study. J Abnorm Psychol 1998; 107 :128–40.

Collins JA, Fauser BC. Balancing the strengths of systematic and narrative reviews. Hum Reprod Update 2005; 11 :103–4.

Alvaro C, Jackson LA, Kirk S, et al. Moving Canadian governmental policies beyond a focus on individual lifestyle: some insights from complexity and critical theories. Health Promot Int 2011; 26 :91–9.

Kakinami L, Barnett TA, Séguin L, Paradis G. Parenting style and obesity risk in children. Prev Med 2015; 75 :18–22.

Morrissey TW, Jacknowitz A, Vinopal K. Local food prices and their associations with children’s weight and food security. Pediatrics 2014; 133 :422–30.

Penney TL, Kirk SF. The health at every size paradigm and obesity: missing empirical evidence may help push the reframing obesity debate forward. Am J Public Health 2015; 105 :e38–42.

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Campbell, M. Biological, environmental, and social influences on childhood obesity. Pediatr Res 79 , 205–211 (2016). https://doi.org/10.1038/pr.2015.208

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Review of Childhood Obesity: From Epidemiology, Etiology, and Comorbidities to Clinical Assessment and Treatment

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• 1 Division of Pediatric Endocrinology and Metabolism, Mayo Clinic, Rochester, MN. Electronic address: [email protected].
• 2 Department of Pediatrics and Department of Medicine, University of Minnesota, Minneapolis.
• PMID: 28065514
• DOI: 10.1016/j.mayocp.2016.09.017

Childhood obesity has emerged as an important public health problem in the United States and other countries in the world. Currently 1 in 3 children in the United States is afflicted with overweight or obesity. The increasing prevalence of childhood obesity is associated with emergence of comorbidities previously considered to be "adult" diseases including type 2 diabetes mellitus, hypertension, nonalcoholic fatty liver disease, obstructive sleep apnea, and dyslipidemia. The most common cause of obesity in children is a positive energy balance due to caloric intake in excess of caloric expenditure combined with a genetic predisposition for weight gain. Most obese children do not have an underlying endocrine or single genetic cause for their weight gain. Evaluation of children with obesity is aimed at determining the cause of weight gain and assessing for comorbidities resulting from excess weight. Family-based lifestyle interventions, including dietary modifications and increased physical activity, are the cornerstone of weight management in children. A staged approach to pediatric weight management is recommended with consideration of the age of the child, severity of obesity, and presence of obesity-related comorbidities in determining the initial stage of treatment. Lifestyle interventions have shown only modest effect on weight loss, particularly in children with severe obesity. There is limited information on the efficacy and safety of medications for weight loss in children. Bariatric surgery has been found to be effective in decreasing excess weight and improving comorbidities in adolescents with severe obesity. However, there are limited data on the long-term efficacy and safety of bariatric surgery in adolescents. For this comprehensive review, the literature was scanned from 1994 to 2016 using PubMed using the following search terms: childhood obesity, pediatric obesity, childhood overweight, bariatric surgery, and adolescents.

Copyright © 2016 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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• Energy Intake / physiology*
• Exercise / physiology*
• Genetic Predisposition to Disease
• Pediatric Obesity* / complications
• Pediatric Obesity* / epidemiology
• Pediatric Obesity* / etiology
• Pediatric Obesity* / therapy
• Sedentary Behavior
• Sleep Wake Disorders / complications
• United States / epidemiology
• Weight Reduction Programs / methods
• Weight Reduction Programs / standards*

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Aspe research brief, childhood obesity.

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

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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

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

Note: Children 2-19 years.

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

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

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

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

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

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• v.4(2); Apr-Jun 2015

Childhood obesity: causes and consequences

Krushnapriya sahoo.

1 Phd Scholar, Department of Human Development and Family Studies, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan, India

Bishnupriya Sahoo

2 Senior Resident, Department of Pediatrics, Vardhmann Medical College and Safdarjung Hospital, New Delhi, India

Ashok Kumar Choudhury

3 Assistant Professor, Department of Hepatology, Institute of Liver and Biliary Sciences, New Delhi, India

Nighat Yasin Sofi

4 Research Scientist, Human Nutrition Unit, All India Institute of Medical Sciences, New Delhi, India

Raman Kumar

5 CMO In Charge Emergency, Department of Clinical Research, Institute of Liver and Biliary Sciences, New Delhi, India

Ajeet Singh Bhadoria

6 Epidemiologist and Public Health Specialist, Department of Clinical Research, Institute of Liver and Biliary Sciences, New Delhi, India

Childhood obesity has reached epidemic levels in developed as well as in developing countries. Overweight and obesity in childhood are known to have significant impact on both physical and psychological health. Overweight and obese children are likely to stay obese into adulthood and more likely to develop non-communicable diseases like diabetes and cardiovascular diseases at a younger age. The mechanism of obesity development is not fully understood and it is believed to be a disorder with multiple causes. Environmental factors, lifestyle preferences, and cultural environment play pivotal roles in the rising prevalence of obesity worldwide. In general, overweight and obesity are assumed to be the results of an increase in caloric and fat intake. On the other hand, there are supporting evidence that excessive sugar intake by soft drink, increased portion size, and steady decline in physical activity have been playing major roles in the rising rates of obesity all around the world. Childhood obesity can profoundly affect children's physical health, social, and emotional well-being, and self esteem. It is also associated with poor academic performance and a lower quality of life experienced by the child. Many co-morbid conditions like metabolic, cardiovascular, orthopedic, neurological, hepatic, pulmonary, and renal disorders are also seen in association with childhood obesity.

Introduction

The world is undergoing a rapid epidemiological and nutritional transition characterized by persistent nutritional deficiencies, as evidenced by the prevalence of stunting, anemia, and iron and zinc deficiencies. Concomitantly, there is a progressive rise in the prevalence of obesity, diabetes and other nutrition related chronic diseases (NRCDs) like obesity, diabetes, cardiovascular disease, and some forms of cancer. Obesity has reached epidemic levels in developed countries. The highest prevalence rates of childhood obesity have been observed in developed countries; however, its prevalence is increasing in developing countries as well.[ 1 ] Females are more likely to be obese as compared to males, owing to inherent hormonal differences.[ 2 ]

It is emerging convincingly that the genesis of Type 2 Diabetes and Coronary Heart Disease begins in childhood, with childhood obesity serving as an important factor.[ 3 ] There has been a phenomenal rise in proportions of children having obesity in the last 4 decades, especially in the developed world. Studies emerging from different parts of India within last decade are also indicative of similar trend.[ 4 , 5 , 6 , 7 , 8 , 9 ] This view has been challenged over recent years and we presently consider these as different forms of the global malnutrition problem. This new conceptualization leads us to simultaneously address the root causes of nutritional deficiencies which in turn will contribute to the control of under nutrition and the prevention of obesity, diabetes, and other NRCDs. This summary provides a public health overview of selected key issues related to the prevention of obesity and chronic diseases with a life-course perspective of nutrition and child growth.

Childhood obesity is one of the most serious public health challenges of the 21 st century. The problem is global and is steadily affecting many low and middle income countries, particularly in urban settings. The prevalence has increased at an alarming rate. Globally in 2010, the number of overweight children under the age of five is estimated to be over 42 million. Close to 35 million of these are living in developing countries.

Definition of Childhood Obesity

Although definition of obesity and overweight has changed over time, it can be defined as an excess of body fat (BF). There is no consensus on a cut-off point for excess fatness of overweight or obesity in children and adolescents. A study by conducted by Williams et al . (1992), on 3,320 children in the age-group of 5–18 years classified children as fat if their percentage of body fat was at least 25% for males and 30% for females, respectively.[ 10 ] The Center for Disease Control and Prevention defined overweight as at or above the 95 th percentile of body mass index (BMI) for age and “at risk for overweight” as between 85 th to 95 th percentile of BMI for age.[ 11 , 12 ] European researchers classified overweight as at or above 85 th percentile and obesity as at or above 95 th percentile of BMI.[ 13 ]

An Indian research study has defined overweight and obesity as overweight (between ≥85 th and <95 th percentile) and obesity (≥95 th percentile).[ 14 ] Another study has followed World Health Organization 2007 growth reference for defining overweight and obesity.[ 15 ]

There are also several methods to measure the percentage of body fat. In research, techniques include underwater weighing (densitometry), multi-frequency bioelectrical impedance analysis (BIA), and magnetic resonance imaging (MRI). In the clinical environment, techniques such as BMI, waist circumference, and skin-fold thickness have been used extensively. Although, these methods are less accurate than research methods, they are satisfactory to identify risk. While BMI seems appropriate for differentiating adults, it may not be as useful in children because of their changing body shape as they progress through normal growth. In addition, BMI fails to distinguish between fat and fat-free mass (muscle and bone) and may exaggerate obesity in large muscular children. Furthermore, maturation pattern differs between genders and different ethnic groups. Studies that used BMI to identify overweight and obese children based on percentage of body fat have found high specificity (95–100%), but low sensitivity (36–66%) for this system of classification.[ 16 ] While health consequences of obesity are related to excess fatness, the ideal method of classification should be based on direct measurement of fatness. Although methods such as densitometry can be used in research practice, they are not feasible for clinical settings. For large population-based studies and clinical situations, bioelectrical impedance analysis (BIA) is widely used. Waist circumference seems to be more accurate for children because it targets central obesity, which is a risk factor for type II diabetes and coronary heart disease.

Causes of Childhood Obesity

It is widely accepted that increase in obesity results from an imbalance between energy intake and expenditure, with an increase in positive energy balance being closely associated with the lifestyle adopted and the dietary intake preferences. However, there is increasing evidence indicating that an individual's genetic background is important in determining obesity risk. Research has made important contributions to our understanding of the factors associated with obesity. The ecological model, as described by Davison et al ., suggests that child risk factors for obesity include dietary intake, physical activity, and sedentary behavior.[ 17 ] The impact of such risk factors is moderated by factors such as age, gender. Family characteristics parenting style, parents’ lifestyles also play a role. Environmental factors such as school policies, demographics, and parents’ work-related demands further influence eating and activity behaviors.

Genetics are one of the biggest factors examined as a cause of obesity. Some studies have found that BMI is 25–40% heritable.[ 18 ] However, genetic susceptibility often needs to be coupled with contributing environmental and behavioral factors in order to affect weight.[ 19 ] The genetic factor accounts for less than 5% of cases of childhood obesity.[ 18 ] Therefore, while genetics can play a role in the development of obesity, it is not the cause of the dramatic increase in childhood obesity.

Basal metabolic rate has also been studied as a possible cause of obesity. Basal metabolic rate, or metabolism, is the body's expenditure of energy for normal resting functions. Basal metabolic rate is accountable for 60% of total energy expenditure in sedentary adults. It has been hypothesized that obese individuals have lower basal metabolic rates. However, differences in basal metabolic rates are not likely to be responsible for the rising rates of obesity.[ 18 ]

Review of the literature investigates factors behind poor diet and offers numerous insights into how parental factors may impact on obesity in children.[ 20 ] They note that children learn by modeling parents’ and peers’ preferences, intake and willingness to try new foods. Availability of, and repeated exposure to, healthy foods is key to developing preferences and can overcome dislike of foods. Mealtime structure is important with evidence suggesting that families who eat together consume more healthy foods. Furthermore, eating out or watching TV while eating is associated with a higher intake of fat. Parental feeding style is also significant. The author's found that authoritative feeding (determining which foods are offered, allowing the child to choose, and providing rationale for healthy options) is associated with positive cognitions about healthy foods and healthier intake. Interestingly authoritarian restriction of “junk-food” is associated with increased desire for unhealthy food and higher weight.[ 21 ]

Government and social policies could also potentially promote healthy behavior. Research indicates taste, followed by hunger and price, is the most important factor in adolescents snack choices.[ 22 ] Other studies demonstrate that adolescents associate junk food with pleasure, independence, and convenience, whereas liking healthy food is considered odd.[ 23 ] This suggests investment is required in changing meanings of food, and social perceptions of eating behavior. As proposed by the National Taskforce on Obesity (2005), fiscal policies such as taxing unhealthy options, providing incentives for the distribution of inexpensive healthy food, and investing in convenient recreational facilities or the esthetic quality of neighborhoods can enhance healthy eating and physical activity.[ 24 ]

Dietary factors have been studied extensively for its possible contributions to the rising rates of obesity. The dietary factors that have been examined include fast food consumption, sugary beverages, snack foods, and portion sizes.

Fast food Consumption: Increased fast food consumption has been linked with obesity in the recent years. Many families, especially those with two parents working outside the home, opt for these places as they are often favored by their children and are both convenient and inexpensive.[ 25 ] Foods served at fast food restaurants tend to contain a high number of calories with low nutritional values. A study conducted examined the eating habits of lean and overweight adolescents at fast food restaurants.[ 26 ] Researchers found that both groups consumed more calories eating fast food than they would typically in a home setting but the lean group compensated for the higher caloric intake by adjusting their caloric intake before or after the fast food meal in anticipation or compensation for the excess calories consumed during the fast food meal. Though many studies have shown weight gain with regular consumption of fast food, it is difficult to establish a causal relationship between fast food and obesity.

Sugary beverages

A study examining children aged 9–14 from 1996–1998, found that consumption of sugary beverages increased BMI by small amounts over the years.[ 18 ] Sugary drinks are another factor that has been examined as a potential contributing factor to obesity. Sugary drinks are often thought of as being limited to soda, but juice and other sweetened beverages fall into this category. Many studies have examined the link between sugary drink consumption and weight and it has been continually found to be a contributing factor to being overweight.[ 18 ] Sugary drinks are less filling than food and can be consumed quicker, which results in a higher caloric intake.[ 19 ]

Snack foods

Another factor that has been studied as a possible contributing factor of childhood obesity is the consumption of snack foods. Snack foods include foods such as chips, baked goods, and candy. Many studies have been conducted to examine whether these foods have contributed to the increase in childhood obesity. While snacking has been shown to increase overall caloric intake, no studies have been able to find a link between snacking and overweight.[ 18 ]

Portion size

Portion sizes have increased drastically in the past decade. Consuming large portions, in addition to frequent snacking on highly caloric foods, contribute to an excessive caloric intake. This energy imbalance can cause weight gain, and consequently obesity.[ 18 ]

Activity level

One of the factors that is most significantly linked to obesity is a sedentary lifestyle. Each additional hour of television per day increased the prevalence of obesity by 2%.[ 18 ] Television viewing among young children and adolescents has increased dramatically in recent years.[ 18 , 27 ] The increased amount of time spent in sedentary behaviors has decreased the amount of time spent in physical activity. Research which indicates the number of hours children spend watching TV correlates with their consumption of the most advertised goods, including sweetened cereals, sweets, sweetened beverages, and salty snacks.[ 22 ] Despite difficulties in empirically assessing the media impact, other research discussed emphasizes that advertising effects should not be underestimated. Media effects have been found for adolescent aggression and smoking and formation of unrealistic body ideals. Regulation of marketing for unhealthy foods is recommended, as is media advocacy to promote healthy eating.

Environmental factors

While extensive television viewing and the use of other electronic media has contributed to the sedentary lifestyles, other environmental factors have reduced the opportunities for physical activity. Opportunities to be physically active and safe environments to be active in have decreased in the recent years. The majority of children in the past walked or rode their bike to school. A study conducted in 2002 found that 53% of parents drove their children to school.[ 18 ] Of these parents, 66% said they drove their children to school since their homes were too far away from the school. Other reasons parents gave for driving their children to school included no safe walking route, fear of child predators, and out of convenience for the child.[ 18 ] Children who live in unsafe areas or who do not have access to safe, well-lit walking routes have fewer opportunities to be physically active.[ 18 ]

Socio-cultural factors

Socio-cultural factors have also been found to influence the development of obesity. Our society tends to use food as a reward, as a means to control others, and as part of socializing.[ 28 ] These uses of food can encourage the development of unhealthy relationships with food, thereby increasing the risk of developing obesity.

Family factors

Family factors have also been associated with the increase of cases of obesity. The types of food available in the house and the food preferences of family members can influence the foods that children eat. In addition, family mealtimes can influence the type of food consumed and the amount thereof. Lastly, family habits, whether they are sedentary or physically active, influence the child.[ 28 ] Studies have shown that having an overweight mother and living in a single parent household are associated with overweight and childhood obesity.[ 29 ]

Psychological factors

Depression and anxiety.

A recent review concluded that the majority of studies find a prospective relationship between eating disturbances and depression.[ 30 ] However, this relationship is not unidirectional; depression may be both a cause and a consequence of obesity.[ 31 ] Additionally, in a clinical sample of obese adolescents, a higher life-time prevalence of anxiety disorders was reported compared to non-obese controls.[ 32 ] Although some studies demonstrate no significant relationship between increased BMI and increased anxiety symptoms.[ 33 ] Thus, the relationship between obesity and anxiety may not be unidirectional and is certainly not conclusive.

Self-esteem

Research findings comparing overweight/obese children with normal-weight children in regards to self-esteem have been mixed.[ 34 ] Some studies have found that obese children have lower self-esteem while others do not.[ 35 , 36 , 37 ] There is some consensus in the literature that the global approach to self-esteem measurement with children who are overweight/obese is misleading as the physical and social domains of self-esteem seem to be where these children are most vulnerable.[ 38 ]

Body dissatisfaction

Research has consistently found that body satisfaction is higher in males than females at all ages.[ 39 ] Gender differences may reflect the westernized cultural ideals of beauty in that thinness is the only culturally defined ideal for females, while males are encouraged to be both lean and muscular. Thus, there is a linear relationship between body dissatisfaction and increasing BMI for girls; while for boys a U-shaped relationship suggests that boys with BMIs at the low and high extremes experience high levels of body dissatisfaction.[ 40 , 41 ]

Eating disorder symptoms

Traits associated with eating disorders appear to be common in adolescent obese populations, particularly for girls.[ 42 ] A number of studies have shown higher prevalence of eating-related pathology (i.e. Anorexia, Bulimia Nervosa, and impulse regulation) in obese children/youth.[ 43 , 44 ]

Emotional problems

In one of the few studies to investigate the psychological impact of being overweight/obese in children, a review of 10 published studies over a 10-year period (1995-2005) with sample sizes greater than 50 revealed that all participants reported some level of psychosocial impact as a result of their weight status.[ 45 ] Being younger, female, and with an increased perceived lack of control over eating seemed to heighten the psychosocial consequences.

Consequences of childhood obesity

Childhood obesity can profoundly affect children's physical health, social, and emotional well-being, and self esteem. It is also associated with poor academic performance and a lower quality of life experienced by the child. These potential consequences are further examined in the following sections.

Medical consequences

Childhood obesity has been linked to numerous medical conditions. These conditions include, but are not limited to, fatty liver disease, sleep apnea, Type 2 diabetes, asthma, hepatic steatosis (fatty liver disease), cardiovascular disease, high cholesterol, cholelithiasis (gallstones), glucose intolerance and insulin resistance, skin conditions, menstrual abnormalities, impaired balance, and orthopedic problems.[ 25 , 46 ] Until recently, many of the above health conditions had only been found in adults; now they are extremely prevalent in obese children. Although most of the physical health conditions associated with childhood obesity are preventable and can disappear when a child or adolescent reaches a healthy weight, some continue to have negative consequences throughout adulthood.[ 46 ] In the worst cases, some of these health conditions can even result in death. Below, three of the more common health problems associated with childhood obesity are discussed, diabetes, sleep apnea, and cardiovascular disease.

Socio-emotional consequences

In addition to being implicated in numerous medical concerns, childhood obesity affects children's and adolescent's social and emotional health. Obesity has been described as being “one of the most stigmatizing and least socially acceptable conditions in childhood.”[ 38 ] Overweight and obese children are often teased and/or bullied for their weight. They also face numerous other hardships including negative stereotypes, discrimination, and social marginalization.[ 46 ] Discrimination against obese individuals has been found in children as young as 2 years old.[ 28 ] Obese children are often excluded from activities, particularly competitive activities that require physical activity. It is often difficult for overweight children to participate in physical activities as they tend to be slower than their peers and contend with shortness of breath.[ 25 ] These negative social problems contribute to low self esteem, low self confidence, and a negative body image in children and can also affect academic performance.[ 46 ] All of the above-mentioned negative effects of overweight and obesity can be devastating to children and adolescents.

The social consequences of obesity may contribute to continuing difficulty in weight management. Overweight children tend to protect themselves from negative comments and attitudes by retreating to safe places, such as their homes, where they may seek food as a comfort. In addition, children who are overweight tend to have fewer friends than normal weight children, which results in less social interaction and play, and more time spent in sedentary activities.[ 25 ] As aforementioned, physical activity is often more difficult for overweight and obese children as they tend to get shortness of breath and often have a hard time keeping up with their peers. This in turn inevitably results in weight gain, as the amount of calories consumed exceeds the amount of energy burned.[ 25 ]

Academic consequences

Childhood obesity has also been found to negatively affect school performance. A research study concluded that overweight and obese children were four times more likely to report having problems at school than their normal weight peers.[ 38 ] They are also more likely to miss school more frequently, especially those with chronic health conditions such as diabetes and asthma, which can also affect academic performance.

The growing issue of childhood obesity can be slowed, if society focuses on the causes. There are many components that play into childhood obesity, some being more crucial than others. A combined diet and physical activity intervention conducted in the community with a school component is more effective at preventing obesity or overweight. Moreover, if parents enforce a healthier lifestyle at home, many obesity problems could be avoided. What children learn at home about eating healthy, exercising and making the right nutritional choices will eventually spill over into other aspects of their life. This will have the biggest influence on the choices kids make when selecting foods to consume at school and fast-food restaurants and choosing to be active. Focusing on these causes may, over time, decrease childhood obesity and lead to a healthier society as a whole.

Source of Support: Nil.

Conflict of Interest: None declared.