research paper childhood obesity

• Introduction
• Conclusions
• Article Information

eTable 1. Searching strategy for prevalence of overweight and obesity in children and adolescents

eTable 2. Quality assessment for including studies

eTable 3. Characteristics of the studies for prevalence of obesity in children and adolescents

eTable 4. Characteristics of the studies for prevalence of overweight in children and adolescents

eTable 5. Characteristics of the studies for prevalence of excess weight in children and adolescents

eTable 6. Sensitivity analysis and leave-one-out results performed in Metafor package

eTable 7. Sensitivity analysis performed by using a built-in function

eTable 8. Univariate meta-regression

eTable 9. Multi-variable meta-regression

eTable 10. Subgroup analysis for obesity in children and adolescents

eTable 11. Analysis of risk factors for obesity in children and adolescents

eTable 12. Analysis of comorbidities for obesity in children and adolescents

eTable 13. Subgroup analysis for overweight in children and adolescents

eTable 14. Subgroup analysis for excess weight in children and adolescents.

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Zhang X , Liu J , Ni Y, et al. Global Prevalence of Overweight and Obesity in Children and Adolescents : A Systematic Review and Meta-Analysis . JAMA Pediatr. 2024;178(8):800–813. doi:10.1001/jamapediatrics.2024.1576

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Global Prevalence of Overweight and Obesity in Children and Adolescents : A Systematic Review and Meta-Analysis

• 1 Division of Thyroid Surgery, Department of General Surgery, Laboratory of Thyroid and Parathyroid Diseases, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
• 2 Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
• 3 Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
• 4 Frontiers Medical Center, Tianfu Jincheng Laboratory, Sichuan University, Chengdu, China
• 5 Department of Obstetrics and Gynecology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
• 6 Department of Gynecology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
• 7 Department of Pediatrics, West China Hospital, Sichuan University, Chengdu, China
• 8 Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China

Question   What is the global prevalence of overweight and obesity in children and adolescents?

Findings   In this systematic review and meta-analysis, we found high prevalence of overweight and obesity in children and adolescents. Various possible risk factors were identified, including inherent, dietary, and environmental factors.

Meaning   These findings suggest that excess weight commonly occurrs in children and adolescents, indicating a need for more control measures incorporating behavioral, environmental, and sociocultural factors.

Importance   Overweight and obesity in childhood and adolescence is a global health issue associated with adverse outcomes throughout the life course.

Objective   To estimate worldwide prevalence of overweight and obesity in children and adolescents from 2000 to 2023 and to assess potential risk factors for and comorbidities of obesity.

Data Sources   MEDLINE, Web of Science, Embase, and Cochrane.

Study Selection   The inclusion criteria were: (1) studies provided adequate information, (2) diagnosis based on body mass index cutoffs proposed by accepted references, (3) studies performed on general population between January 2000 and March 2023, (4) participants were younger than 18 years.

Data Extraction and Synthesis   The current study was performed in accordance with the Meta-analysis of Observational Studies in Epidemiology guidelines. DerSimonian-Laird random-effects model with Free-Tukey double arcsine transformation was used for data analysis. Sensitivity analysis, meta-regression, and subgroup analysis of obesity among children and adolescents were conducted.

Main Outcomes and Measures   Prevalence of overweight and obesity among children and adolescents assessed by World Health Organization, International Obesity Task Force, the US Centers for Disease Control and Prevention, or other national references.

Results   A total of 2033 studies from 154 different countries or regions involving 45 890 555 individuals were included. The overall prevalence of obesity in children and adolescents was 8.5% (95% CI 8.2-8.8). We found that the prevalence varied across countries, ranging from 0.4% (Vanuatu) to 28.4% (Puerto Rico). Higher prevalence of obesity among children and adolescents was reported in countries with Human Development Index scores of 0.8 or greater and high-income countries or regions. Compared to 2000 to 2011, a 1.5-fold increase in the prevalence of obesity was observed in 2012 to 2023. Substantial differences in rates of obesity were noted when stratified by 11 risk factors. Children and adolescents with obesity had a high risk of depression and hypertension. The pooled estimates of overweight and excess weight in children and adolescents were 14.8% (95% CI 14.5-15.1) and 22.2% (95% CI 21.6-22.8), respectively.

Conclusions and Relevance   This study’s findings indicated 1 of 5 children or adolescents experienced excess weight and that rates of excess weight varied by regional income and Human Development Index. Excess weight among children and adolescents was associated with a mix of inherent, behavioral, environmental, and sociocultural influences that need the attention and committed intervention of primary care professionals, clinicians, health authorities, and the general public.

Overweight and obesity in children and adolescents is an emerging worldwide health concern. Estimates of the prevalence have shown heterogeneity across countries and regions, typically demonstrating a growing trend. 1 - 4 The Global Burden of Disease Obesity Collaborators 5 reported an overall prevalence of 5.0% for childhood obesity, with 107.7 million children having obesity globally in 2015, and data from the World Obesity Federation 6 indicate that the rising trend has not yet been stopped, as it estimated that 158 million children and adolescents aged 5 to 19 years would experience obesity in 2020, 206 million in 2025, and 254 million in 2030. Awareness is growing that the epidemiological burden of childhood obesity has posed incremental expenses for both individuals and society. 7

Obesity could result from multidimensional biological, behavioral, and environmental causes, and unbalanced diet and sedentary habits appearing to be the main drivers. 8 - 10 Since obesity is a disease in and of itself, managing it becomes more difficult when it coexists with other pathological illnesses including diabetes, cardiovascular disease, and psychological disorders. 11 Furthermore, childhood overweight and obesity have been shown to persist into adulthood, 12 and their related adverse outcomes include not only certain health conditions in childhood, but also a greater risk and earlier onset of chronic disorders in later life. 13 - 15 Hence, there is a demand for routine surveillance of weight status in children and adolescents.

There has been a dearth of studies into the prevalence of obesity among children and adolescents from global perspective since the Non-Communicable Diseases Risk Factor Collaboration 16 reported an estimation of 5.6% of girls and 7.8% of boys with obesity in 2016. The present study pooled a larger and more recent set of national surveys than previously reported to estimate global prevalence as well as risk factors and comorbidities associated with overweight and obesity among children and adolescents under 18 years old from 2000 to 2023.

The study followed the Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) reporting guideline. A comprehensive literature search were performed in MEDLINE, Web of Science, Embase, and Cochrane databases between January 1, 2000, and March 31, 2023. The search strategy was structured to include terms pertaining to “overweight,” “obesity,” “excess weight,” “children,” “adolescent,” and “prevalence.” eTable 1 in Supplement 1 contains a full list of the search terms used. The study protocol was registered in PROSPERO ( CRD42023483885 ).

Predefined inclusion criteria were cohort studies, case-control trials, and randomized clinical trials that (1) reported the prevalence of obesity, overweight, and excess weight (overweight and obesity) assessed by body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) cutoffs in children and adolescents younger than 18 years; (2) were conducted in the general population (defined as apparently healthy children or adolescents from school, community, or national demographic census); (3) used standardized instruments, self-reported questionnaires, or clinically structured interviews for assessment of overweight and obesity; and (4) completed data collection between January 2000 and March 2023. We excluded studies of hospitalized patients or a mix of hospitalized and general populations. Title and abstract screening were done by X.Z, J.L, K.L, and C.Y based on the selection criteria. If articles seemed relevant, then the full text was assessed for inclusion.

Researchers reviewed and extracted data from included studies by using a data extraction form that included country or region, geographic region, publication year, study period, income of country or region, Human Development Index (HDI) of the country or region, study design, sample source, diagnostic reference, sample size, study quality, risk factors, and comorbidities. We also included race and ethnicity in subgroup analyses for comprehensive assessment, and the categories in this study were in accordance with our data sources, using a 4-level variable (Asian, Black, Hispanic, and White). Initial data extraction was done by X.Z, J.L, K.L, and C.Y. For quality assurance, data collected from all the included studies were validated by a second team member (Y.F, Q.N, B.S, or Y.N) for accuracy and completeness against the original source. All discrepancies were reviewed and resolved either by consensus or by a third team member if consensus was not reached. When duplicate data were identified, the duplicate with the smallest sample size or shortest duration of follow-up was excluded. We assessed the quality of included studies using an assessment scale based on the Joanna Briggs Institute Tool in accordance with previous published studies. 15 , 16 Studies scoring 1 to 3 were defined as low quality, 4 to 6 as average quality, and 7 to 9 as high quality. Studies were not excluded regardless of their quality score to increase transparency and to ensure all available evidence in this area was reported.

All data analysis was performed using R version 4.0.0 (R Foundation) with the meta and metafor statistical packages. A 95% CI was estimated using the Wilson score method, and the pooled prevalence was calculated using the DerSimonian-Laird random-effects model with Free-Tukey double arcsine transformation. Heterogeneity among the included studies was evaluated through the Cochran Q and I 2 statistics. Given the anticipated heterogeneity in global data, a random-effects model was used to estimate the prevalence of obesity, overweight, and excess weight. Sensitivity analyses were conducted by performing a set of leave-1-out diagnostic tests focusing on the significant heterogeneity associated with obesity where individual studies were systematically removed from the meta-analysis and the pooled-effect estimate recalculated. The results were then verified by using a build-in function in metafor. As sensitivity analysis was unable to decrease the heterogeneity, meta-regression was performed by using a mixed-effects model. Univariable and multivariable meta-regression (multimodel inference) were performed by using the dmetar package in synthesizing evidence from multiple studies and exploring heterogeneity. The random-effects weighting method was used for assigning weights in meta-regression. To assess the potential confounding effects of heterogeneity, subgroup analyses were conducted. Characteristics of participants were compared with the prevalence of obesity to determine the pooled estimates of risk factors and comorbidities. IQR was defined as the difference between the first and the third quartile. P  < .05 was considered as significant difference.

The search identified 65 448 records, 39 243 of which were retained after removing duplicates. Titles and abstracts were screened, resulting in the exclusion of 33 417 ineligible records. Full texts of the remaining 5826 records were assessed for eligibility, and 3793 were excluded. Overall, 2033 eligible studies involving 45 890 555 children and adolescents from 154 countries or regions were included in the final analysis ( Figure 1 ).

The characteristics and quality assessment score of all 2033 included studies are presented in eTables 2-5 in Supplement 1 . The sample size ranged from 30 to 3 190 300 participants. The cross-sectional design was used in most of the included research. The mean or median age and sex of participants was reported in 737 and 1090 studies. The median (IQR) age was 10.0 (7.1-12.5) years, and the median (IQR) proportion of participants who were female was 49.64% (48.1-51.5).

The prevalence of obesity in children and adolescents was reported by 1668 studies comprising 44 414 245 individuals from 152 countries or regions (eTable 3 in Supplement 1 ). A total of 4 519 587 participants were diagnosed as having obesity with a pooled prevalence of 8.5% (95% CI, 8.2-8.8; I 2 , 99.9%). To gain a deeper understanding of the heterogeneity, we conducted a sensitivity analysis by performing a set of leave-1-out diagnostic tests (eTables 6-7 in Supplement 1 ). After removing the outliers, the pooled estimate of obesity for children and adolescents was 8.3% (95% CI, 8.0-8.6; I 2 , 99.9%). To further explore the source of heterogeneity, meta-regression analysis was performed. Our univariate meta-regression model indicated that country or region ( R 2 , 66.6%; P  < .001), geographic region ( R 2 , 46.8%; P  < .001), diagnostic reference ( R 2 , 0; P  < .001), HDI level (R 2 , 41.9%; P  < .001), sample size ( R 2 , 0.01%; P  < .001), sample source ( R 2 , 2.4%; P  < .001), and publication year ( R 2 , 1.4%; P  = .02) were associated with heterogeneity, while study design was not ( R 2  = 4.4%; P  = .63) (eTable 8 in Supplement 1 ). By performing multivariable meta-regression, it was found that the geographic region, income level of the country or region, sample sources, diagnostic reference, and sample size showed the highest predictor importance of 99.99% (eTable 9 in Supplement 1 ).

In subgroup analyses, prevalence of obesity varied substantially across different countries and regions, from 0.4% (Vanuatu, 95% CI, 0.1-0.8) to 28.4% (Puerto Rico, 95% CI, 23.6-33.4). Stratified data by geographic regions, the highest obesity prevalence was found in Polynesia with an estimated rate of 19.5% (95% CI, 16.1-23.1), and the lowest prevalence appeared in Middle Africa (2.4%; 95% CI, 1.8-3.0). The prevalence of obesity in countries and regions with HDI scores of 0.8 or greater was 9.5% (95% CI, 9.2-9.8), whereas countries and regions with HDI scores lower than 0.8 showed a significantly lower prevalence of 7.6% (95% CI, 7.3-7.9; P  < .001). Likewise, there was a positive association between income of countries and regions and prevalence of children and adolescents’ obesity, with high-income countries showing the highest prevalence (9.3%; 95% CI, 9.0-9.6) and low-income countries exhibiting the lowest (3.6%; 95% CI, 2.5-4.8; P  < .001). We also discovered significant disparity among race and ethnicity, with the highest prevalence appearing in the Hispanic population (23.55; 95% CI, 20.66-26.56) and the lowest appearing in the Asian population (10.0%; 95% CI 8.73-11.29; P  < .001). Regarding sample sources, participants from medical institutions presented the highest prevalence of 13.6% (95% CI, 12.2-15.1), although sample sources drawn from databases contained most participants. Considering the diagnostic references for assessing obesity, 466 studies used the World Health Organization reference 17 (8.6%; 95% CI, 7.9-9.3), 807 used the International Obesity Task Force reference 18 (5.4%; 95% CI, 5.1-5.7), 453 used the US Centers for Disease Control and Prevention reference 19 (14.5%; 95% CI, 13.6-15.3), and 282 studies used various national references (9.7%; 95% CI, 9.0-10.3). A pattern of decreased prevalence was found in studies having more than 5000 participants (7.7%; 95% CI, 7.1-8.2) than those with fewer than 5000 participants (8.7%; 95% CI, 8.4-9.1; P  < .001). Moreover, studies performed from 2000 to 2011 showed significantly lower rates (7.1%; 95% CI, 6.8-7.3) than those performed from 2012 to 2023 (11.3%; 95% CI, 10.8-11.8; P  < .001) ( Table 1 ; eTable 10 in Supplement 1 .

To gain a more comprehensive view of obesity in children and adolescents, further analysis regarding potential risk factors were performed ( Table 2 ; eTable 11 in Supplement 1 ). Results indicated that a significant difference in the prevalence of obesity was found in the pooled estimate by age (0-5, 6-12, or 13-18 years; 8.5% vs 9.4% vs 6.9%, respectively; P  < .001), sex (male or female; 9.4% vs 7.5%, respectively; P  < .001), school type (public or private; 6.5% vs 11.6%, respectively; P  < .001), maternal weight status (obesity or nonobesity; 15.9% vs 8.1%, respectively; P  = .001), breakfast (having breakfast daily or usually skipping breakfast; 7.1% vs 10.0%, respectively; P  = .03), numbers of meals per day (>3 or ≤3; 3.3% vs 11.6%, respectively; P  = .008), hours of playing on the computer per day (≥2 or <2 hours; 11.9% vs 5.5%, respectively; P  = .01), maternal smoking in pregnancy (smoking or never; 7.7% vs 4.7%, respectively; P  = .006), birth weight (low, normal, or high; 6.2% vs 9.2% vs 12.8%, respectively; P  = .005), physical activity (regular or irregular; 7.7% vs 12.1%, respectively; P  = .006), and nightly sleep duration (<10 or ≥10 hours; 13.7% vs 7.2%, respectively; P  = .03). Minimal differences were observed among other factors.

Eight comorbidities associated with obesity among children and adolescents were investigated ( Table 3 ; eTable 12 in Supplement 1 ). There were 26 studies reporting on hypertension in children and adolescents with obesity, with a pooled rate of 28.0% (95% CI, 20.2-36.6). In addition, 13 studies documented dental caries (17.9%; 95% CI, 12.6-23.8), 8 included vitamin D deficiency (11.6% 95% CI, 5.4-19.9), 7 included asthma (18.8%; 95% CI, 12.5-26.2), 3 reported on diabetes (1.2%; 95% CI, 0.2-3.0), 3 included flatfoot (26.1%; 95% CI, 6.7-52.2), 2 reported on anxiety (25.1%; 95% CI, 0-94.2), and 2 included depression (35.2%; 95% CI, 0.4-87.0).

We further performed analyses on the prevalence of overweight and excess weight in children and adolescents. In total, 5 621 782 participants were diagnosed as having overweight with a pooled prevalence of 14.8% (95% CI, 14.5-15.1; I 2 , 99.8%), and 5 621 782 participants were diagnosed as having excess weight with a pooled prevalence of 22.2% (95% CI, 21.6-22.8; I 2 , 100.0%) ( Figure 2 ). Details on subgroup analyses for overweight and excess weight are listed in eTables 13-14 in Supplement 1 .

This systematic review and meta-analysis provided a comprehensive analysis of the global epidemiology of overweight and obesity from 2000 to 2023 in children and adolescents younger than 18 years. The overall prevalence of pediatric obesity, overweight, and excess weight was 8.5%, 14.8%, and 22.2%, respectively. According to our findings, there were notable regional variations, with Polynesia exhibiting the highest prevalence across all 3 categories and Middle and Western Africa displaying the lowest rates. Furthermore, a number of factors demonstrated a noteworthy association with the prevalence of pediatric obesity, including age, sex, school type, maternal obesity, having breakfast, number of meals per day, hours of playing on the computer per day, maternal smoking in pregnancy, birth weight, regular exercise, and sleep duration. Besides, children and adolescents with obesity are more likely to experience mental and physical comorbidities, such as depression and hypertension.

The Non-Communicable Diseases Risk Factor Collaboration 16 provided data on global prevalence of obesity in children and adolescents aged 5 to 19 years from 1975 to 2016 and found the prevalence had grown for both boys and girls, from 0.9% to 7.8% and 0.7% to 5.6%, respectively. Their key finding was that, although the prevalence of obesity in high-income nations had plateaued around the year 2000, in other parts of Asia it was still rising. Our findings reconfirmed that obesity was more common in boys than girls. More importantly, we found a sharply increased prevalence of obesity from 2012 to 2023 to 2000 to 2011. Even though obesity is growing more widespread globally, there are still notable regional differences to be aware of. According to previous studies, Polynesia, the Caribbean, Northern America, and Central America have the highest rates of obesity (above 15%). 16 , 20 Apart from the fact that many countries in these regions, such as the US, are well developed, which may contribute to the high prevalence of childhood obesity, it is noteworthy that most of these regions are adjacent to each other geographically, indicating that the genetic traits and unique diet habits of the habitants may also be potential drivers. Interestingly, the lowest prevalence (under 4%) appeared in Western European, Middle Africa, Melanesia, and Western Africa, covering highly developed countries as well as a large number of the least-developed countries. While the prevalence in Western Europe may be attributed to the quality of the health care system and health-conscious lifestyle choices, the similar prevalence in Middle Africa, Melanesia, and Western Africa were mainly due to their poverty. Furthermore, current findings revealed that pediatric obesity prevalence was closely linked to country development and national or regional income, which is in line with prior research. 20 Notably, even among nations in similar economic strata, there are differences in the estimates of prevalence. For example, the prevalence of pediatric obesity in the US is 18.6%, while that in Japan, another high-income country, is 3.9%. Differences in dietary habits may play a role in this disparity. European countries and the US often embrace a diet preference of processed food, which are typically abundant in unhealthy fats, added sugars, and refined carbohydrates. In contrast, diets rich in whole grains and vegetables, which are generally regarded as healthier options, have historically been prioritized in Southeast Asian countries.

Prevalence of obesity in children and adolescents shows disparities across different ages. Our results revealed a lower prevalence of obesity in adolescents than that of preschool and school-age children, which is largely in accordance with prior studies. 20 This decline in obesity prevalence could be mainly attributed to the hormone shifts as boys and girls approach puberty. 21 Besides, teenagers tend to be more conscious about their appearance, thus making more effort toward weight control. Furthermore, heavier pressure from middle and high school could partly contribute to weight loss in adolescents.

Early life is a pivotal period for childhood obesity development. 22 Prior analyses have linked preconception and prenatal environmental exposures to childhood obesity, including high maternal prepregnancy BMI, 23 gestational weight gain, 24 gestational diabetes, 25 and maternal smoking, 26 potentially through effects on the environment in uterus. The current study determined maternal obesity and smoking in pregnancy as risk factors for childhood and adolescent obesity, while maternal diabetes, gastrointestinal diabetes and gestational weight gain exhibited positive yet modest impact on it. Although prior studies considered paternal obesity to be a risk factor for childhood obesity, our findings revealed otherwise. 27 , 28 Furthermore, our results revealed low birthweight was associated with lowest prevalence of obesity. However, Yuan et al 29 claimed that children weighing less than 1500 g were most likely to be centrally obese. This mismatch may be due to the fact that we used BMI to quantify general obesity, whereas central obesity is measured by sex-specific waist to height ratio. Additionally, different infant feeding strategies, such as breastfeeding duration and formula addition, exhibit varying effects on childhood obesity in several meta-analyses. 30 - 32 Nevertheless, our findings showed no discernible impact from these parameters.

The rise in prevalence of obesity has been profoundly influenced by environmental and behavioral factors, 33 including dietary patterns, 34 , 35 physical activity level, 36 and use of technology. 37 The current study revealed that skipping breakfast was associated with an increased risk of pediatric obesity, which was consistent with previous research. 38 Surprisingly, having more than 3 meals per day was associated with a lower risk of being obese, which might be explained by the theory that having several small meals throughout a day is healthier than 3 large ones. 39 , 40 As previously noted, children with obesity tend to participate in less physical activity than their peers without obesity, 36 and decreasing levels of exercise as well as increasing sedentary behaviors contribute to obesity development. Our findings also showed that children with regular exercise had a much lower chance of obesity. Moreover, we observed that playing on the computer for more than 2 hours a day was associated with an increase in risk of excess weight, and time spent watching TV also showed a positive correlation, yet not significant. A connection between screen time and obesity in the pediatric population was initially demonstrated in studies of TV viewing, 41 , 42 while mobile and gaming devices are gaining more and more attention. 43 , 44 Screen exposure may raise the risk of obesity via increased exposure to food marketing, increased mindless eating while watching screens, displacement of time spent in physical activities, reinforcement of sedentary behaviors, and reduced sleep duration.

All body systems can be affected by obesity in the short or long term, depending upon age and obesity severity. Plenty of previous studies have discussed potential comorbidities of multiple system related to childhood obesity. 45 - 51 According to a systematic analysis, children and adolescents with obesity have a 1.4 times higher likelihood of developing prediabetes, 1.7 times higher likelihood of developing asthma, 4.4 times higher likelihood of developing high blood pressure, and 26.1 times higher likelihood of developing fatty liver disease compared to those who are of a healthy weight. 52 Likewise, our research disclosed high prevalence of comorbidities in children and adolescents with obesity. The highest pooled prevalence was found in depression, which approximately 1 in 3 children with obesity might experience, followed by hypertension, with a pooled prevalence of 28.0%. Compared to previously reported incidence in general population, which is approximately 25% for depression 53 and 4% for hypertension, 54 children and adolescents with obesity seemed to be more vulnerable to those health condition. The association between obesity and mentioned comorbidities had been shown to be bidirectional. 55 , 56 In the management of childhood and adolescent obesity, it is pivotal that comorbidities are assessed and treated alongside to prevent progression of both.

There are some limitations in the present research. To our knowledge, this is the most comprehensive study to date, covering all geographic regions, but some countries and regions had limited data, making it challenging to accurately estimate. Besides, different criteria for recognizing overweight and obesity in children may influence the accuracy of the estimation. Moreover, limited studies concerning comorbidities were included in our analysis, since we focused on the epidemiology in the literature search process. Additionally, we simply divided the study period into 2 categories, namely 2000 to 2011 and 2012 to 2023, which resulted in less detailed information of the time trajectory of prevalence for childhood and adolescent obesity.

In conclusion, the current study provided new epidemiological insights of overweight and obesity among children and adolescents worldwide. Our findings indicated high prevalence of overweight and obesity in children and adolescent with a pooled estimation of 8.5% and 14.8%, meaning approximately 1 of every 5 children or adolescents experience excess weight. Various risk factors, including inherent, dietary, and environmental factors, were significantly associated with the prevalence of pediatric obesity. It is noteworthy that children and adolescents with obesity were at high risk of mental and physical comorbidities. Global coordinated action and national control program are paramount to comprehend, prevent, and manage childr and adolescent obesity.

Accepted for Publication: April 17, 2024.

Published Online: June 10, 2024. doi:10.1001/jamapediatrics.2024.1576

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Zhang X et al. JAMA Pediatrics .

Corresponding Authors: Kewei Li, PhD, Department of Pediatrics, West China Hospital, Sichuan University, No 37. Guoxue Alley, 610000, Chengdu, China ( [email protected] ) and Zhihui Li, PhD, Division of Thyroid Surgery, Department of General Surgery, West China Hospital, Sichuan University, No 37. Guoxue Alley, 610000, Chengdu, China ( [email protected] ).

Author Contributions: Ms X. Zhang and Dr J. Liu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Ms X. Zhang, Dr J. Liu, Ms Ni, Mr Yi, and Ms Fang are co–first authors.

Concept and design : X. Zhang, J. Liu, Ni, Yi, Fang, K. Li, Yong Liu, Huang, Z. Li.

Acquisition, analysis, or interpretation of data : All authors.

Drafting of the manuscript : X. Zhang, J. Liu, Ni, Yi, Fang, K. Li, Yong Liu, Huang, Z. Li.

Critical review of the manuscript for important intellectual content : All authors.

Statistical analysis : X. Zhang, J. Liu, Ni, Yi, Fang, K. Li, Yong Liu.

Obtained funding : J. Liu, K. Li, Yong Liu, Z. Li.

Administrative, technical, or material support : X. Zhang, J. Liu, Ni, Yi, Fang, Huang, Z. Li.

Supervision : K. Li, Yong Liu, Huang, Z. Li.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research is supported by the fellowship of China Postdoctoral Science Foundation (2021M702340), the Science and Technology Department of Sichuan Province (2021ZYCD016 and 2022NSFSC1441), a postdoctoral research grant of Sichuan University (2023SCU12047), the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (2016105 and ZYGD20006), the National Nature Science Foundation of China (NSFC82303674), “From 0 to 1” Innovative Research Project (2023SCUH0038), and the Sichuan Science and Technology Program (2023YFS0123).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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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.

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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

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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

Affiliations.

• 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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• Distinct association patterns of chemokine profile and cardiometabolic status in children and adolescents with type 1 diabetes and obesity. Špehar Uroić A, Filipović M, Šućur A, Kelava T, Kovačić N, Grčević D. Špehar Uroić A, et al. Front Endocrinol (Lausanne). 2024 Jul 23;15:1335371. doi: 10.3389/fendo.2024.1335371. eCollection 2024. Front Endocrinol (Lausanne). 2024. PMID: 39109081 Free PMC article.
• Effect of School-Based Educational Intervention on Childhood Obesity in Croatian Urban and Rural Settings. Cobal S, Bender DV, Kljusurić JG, Rumora Samarin I, Krznarić Ž. Cobal S, et al. Children (Basel). 2024 Jul 17;11(7):867. doi: 10.3390/children11070867. Children (Basel). 2024. PMID: 39062316 Free PMC article.
• Differentiating monogenic and syndromic obesities from polygenic obesity: Assessment, diagnosis, and management. Fitch AK, Malhotra S, Conroy R. Fitch AK, et al. Obes Pillars. 2024 Apr 22;11:100110. doi: 10.1016/j.obpill.2024.100110. eCollection 2024 Sep. Obes Pillars. 2024. PMID: 38766314 Free PMC article. Review.

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Childhood obesity

Childhood obesity is defined by a Body mass index (BMI) > 3 standard deviations above the WHO growth standard median. The prevalence of obesity in children and adolescents worldwide increased 10-fold between 1975 and 2016 and continues to increase year on year. Childhood obesity is associated with earlier onset of conditions including cardiovascular disease, diabetes and cancer and excess weight is now a greater cause of global death than undernourishment.

The cause of this rise in childhood obesity is multifactorial, with genetic, psychological and socioeconomic risks implicated. Treatment will involve teams of specialists working together to implement dietary and lifestyle changes.

This Collection gathers original research with a focus on treatment and management of childhood obesity in order to prevent the onset of future disease complications.

Alison Fildes, PhD

School of Psychology, University of Leeds, UK

Miaobing Jazzmin Zheng, PhD

Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Australia

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Article Contents

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

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

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

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

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

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

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

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

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

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

Basic behavioral and social sciences research in childhood obesity

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

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

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

Early childhood

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

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

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

Policies, programs, and environmental strategies

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

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

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

Health disparities

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

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

Transagency and public–private partnership

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

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

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

Severe obesity

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

Measurement

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

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

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

Sleep and obesity

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

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

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

Funding: This commentary was not funded.

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

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

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Overweight/Obesity Prevalence among Under-Five Children and Risk Factors in India: A Cross-Sectional Study Using the National Family Health Survey (2015–2016)

1 Department of Geography, University of Gour Banga (UGB), Malda 732103, West Bengal, India

Pradip Chouhan

Farooq ahmed.

2 Vanke School of Public Health, Tsinghua University, Beijing 100029, China

3 Department of Anthropology, Quaid-i-Azam University Islamabad 44000, Pakistan

Tanmoy Ghosh

Sabbir mondal, muhammad shahid.

4 School of Insurance and Economics, University of International Business and Economics (UIBE), Beijing 100029, China

Saireen Fatima

5 Fazaia Medical College, Air University, Islamabad 44000, Pakistan

Associated Data

The general datasets are accessible through the Demographic Health Surveys (DHS) repository. The data used in this work are accessible upon reasonable request from the first author.

The occurrence of overweight and obesity has increased in recent years in India. In this study, we investigate the prevalence and associated risk factors of overweight/obesity among children aged 0–59 months in India. Using data from the 2015–2016 National Family Health Survey-4 (NFHS-4), the research sample included 176,255 children aged 0 to 59 months. Bivariate and multivariate techniques were used to analyze children’s risk factors for overweight/obesity. We identified that the prevalence of overweight/obesity among children aged 0–59 was 2.6% in India. The study findings reveal that factors such as child sex, age, birth weight, birth rank, maternal education, number of children, age at marriage, mother’s BMI, media exposure, social group, and dietary diversity score were most significantly correlated with childhood overweight and obesity in India. Furthermore, we found that male children (ARR: 1.08) aged between 0 and 11 months (ARR: 3.77) with low birth rank (ARR: 1.24), obese (ARR: 1.81) children whose mothers married after the age of 18 (ARR: 1.15), children who belong to a scheduled tribe family (ARR: 1.46), and children who consumed 7–9 food items (ARR: 1.22) were at highest risk of being overweight and obese. However, breastfeeding (ARR: 0.85) and Muslim families (ARR: 0.87) appeared to be protective factors with respect to childhood overweight and obesity in India. Pertinent public health programs, clinical follow-up, and awareness about sedentary lifestyles can help to reduce overweight/obesity risks in children.

1. Introduction

Childhood obesity and overweight were initially considered a disease in developed countries with higher per capita income [ 1 ]. Overweight is defined as excess body weight relative to height, whereas obesity refers to surplus body fat [ 2 ]. According to the World Health Organization (WHO), when body mass index (BMI) is more than 25, the situation is denoted as overweight, and a BMI of more than 30 is considered an obesity condition [ 3 ]. The burden of overweight and obesity among children has increased, becoming a global public health concern [ 4 , 5 ]. In developing countries with emerging economies, the increasing trend of overweight and obesity among children poses a significant challenge to the healthcare system [ 6 ]. The occurrence of overweight and obesity is higher in developed countries than in developing countries [ 7 ]. The prevalence of childhood obesity has increased in developed countries. However, obesity prevalence is also increasing in developing countries [ 8 ]. The conditions of overweight and obesity primarily occur due to energy imbalances between calories consumed, calories exhausted, and excessive calorie intake or insufficient physical activity. Childhood overweight/obesity is a precursor to metabolic syndrome, poor physical health, mental disorders, respiratory problems, and glucose intolerance, which can continue into adulthood [ 9 ]. Childhood overweight and obesity are determined mainly by insufficient nutrition, physical inactivity, high socioeconomic status, urban residency, traditional beliefs, and marketing of transitional food companies [ 7 , 10 ].

Childhood overweight/obesity is a significant public health concern in the 21st century. At the global level, many middle- and low-income countries are affected by overweight/obesity, particularly in urban areas [ 8 ]. According to the WHO, approximately 39 million under-five-year-old children are overweight or obese [ 3 ]. Globally, childhood overweight and obesity are associated with more deaths than childhood underweight conditions. Worldwide, overweight/obesity is considered the fifth leading mortality risk factor, now representing a global epidemic. According to Global Burden of Disease 2017, more than four million people die annually as a result of being overweight or obese [ 4 ].

On average, 60% of children suffering from overweight/obesity have at least one additional risk factor for cardiovascular diseases, such as hypertension, hyperlipidemia, or hyperinsulinemia [ 11 ]. The risk factor for developing abnormal lipid profiles is high among children with overweight/obesity [ 12 , 13 ]. In obese children, high blood pressure is three times greater than in non-obese or normal children [ 14 , 15 , 16 ]. A cluster of non-communicable diseases and unhealthy lifestyles described as “lifestyle syndrome” or “new world syndrome” has been observed due to the rapid advancement of urbanization and expanding demographic trends [ 10 ].

In India, a dual burden has been observed whereby children and adolescents suffer from obesity or overweight on the one hand and malnourishment or underweight on the other hand [ 17 ]. The Global Burden of Disease (GBD) report shows that in India, the predominance of overweight children aged 2 to 4 years was 11.5% in 2017 [ 4 ]. The tendency of children to be overweight in India increased significantly between 2010 and 2017 and is projected to increase to 17.5% by 2030 [ 4 ]. The occurrence of overweight among children in India has increased from 1.6% (2006) to 3.8% (2020) [ 18 ]. According to the NFHS report, the prevalence of overweight children under five years of age has increased from 2.1% (2015–2016) to 3.4% (2019–2021).

More than 14.4 million children are obese in India, the second-highest rate globally, behind China [ 4 ]. Various studies have suggested possible reasons for the increasing trends of overweight/obesity among children in India. Possible explanations include insufficient physical activity, increased television screen time, urban residency, and family social status [ 19 , 20 , 21 , 22 , 23 , 24 ]. Further research is required to examine global health issues, such as child obesity/overweight, among young children to protect them from future consequences. Significant differences in overweight/obesity prevalence can be observed according to various factors among under-five children in India. Significant differences have been reported in countrywide representative studies on nutritional status in India according to sociodemographic and household characteristics, as well as dietary characteristics. Therefore, the present research aims to investigate and identify the prevalence of overweight or obesity among Indian children under five years of age, as well as sociodemographic and household risk factors.

2. Materials and Methods

2.1. study design and sampling weights.

Data were taken from the fourth round of the NFHS conducted in 2015–2016, a cross-sectional national representative survey to estimate overweight/obesity and its associated factors among children under five years of age. The NFHS 2015–2016 was conducted under the stewardship of India’s Ministry of Health and Family Welfare (MoHFW), with coordination and technical guidance provided by the International Institute of Population Science (IIPS), Mumbai. The countrywide representative sample survey provides comprehensive data on women’s health, child health, and family planning. The NFHS-4 includes data from a population-representative sample of 699,686 women aged 15–49 years and 112,122 men aged 15–54 from 601,509 households. The response rates for women and men were 97% and 92%, respectively. Municipal corporation offices provided a list of 28,586 clusters for this stratified sample (20,509 clusters in rural areas, 8397 clusters in urban areas, and 130 clusters in slums). Sampling weights are necessary for any analysis using the NFHS-4 data to ensure the representativeness of the survey results at the national and domain levels due to the non-proportional allocation of the sample to the different survey domains and urban and rural areas [ 25 ]. Because the NFHS-4 sample is a two-stage stratified cluster sample, sampling weights were determined using independent sampling probabilities for each stage and each cluster. The NFHS-4 report of India includes further information on the sampling technique.

2.2. Study Participants

In the study sample, a total of 259,627 under-five children were born in the last five years ( n = 259,627), of which 83,372 children were excluded: 11,884 due to death; children of multiparous mothers ( n = 3393); children whose weight/height data were not recorded ( n = 11,138); those whose height/age were outside of reasonable limits ( n = 1185); flagged cases ( n = 10,071); children whose/height data were <2SD ( n = 45,598) from the median of the reference population, considered acutely malnourished; and children who lived elsewhere without their mother ( n = 103) ( Figure 1 ). The height and weight of children between the ages of 0 and 59 months were assessed. The weight of children was determined using a Seca 874 digital scale. A Seca 213 stadiometer was used to measure children’s height between the ages of 24 and 59 months [ 25 ]. The recumbent length of children younger than two years old or with a height of less than 85 cm was measured with a Seca 417 infantometer. Children with height-for-age Z scores <−6 SD or >+6 SD, weight-for-age Z scores <−6 SD or >+5 SD, or weight-for-height Z scores <−5 SD or >+5 SD were flagged as having invalid data [ 25 ]. Ultimately, 176,255 children aged younger than 59 months were selected for this study ( Figure 1 ). A total of 5130 children were found to be overweight or obese, and these children were included in the study analysis. The remainder of the children ( n = 171,125) were considered normal-weight children.

Flow diagram showing children aged 0 to 59 months included in the study for analyses from the 2015–2016 NFHS-4, India.

2.3. Outcome Characteristics

The study’s outcome variable, child overweight or obesity between 0 and 59 months, assessed sociodemographic and household characteristics based on children’s body mass index Z scores. According to the WHO, a child with a BMI Z score >2SD is considered overweight and obese with a BMI Z score > 3SD [ 26 , 27 ]. In our study, children with a BMI Z score of more than 2 (>2SD) were considered overweight/obese, and those with a BMI Z score in the range of −2 to +2 were classified as normal-weight children. In this study, we dichotomized the binary variable into two categories: normal children, coded as “0”; and overweight/obese children, coded as “1”.

2.4. Explanatory Characteristics

We considered independent variables of sociodemographic and household characteristics (child, maternal, and household-level factors). Child-level factors included child sex (male and female), child’s age in months (0–11, 12–23, 24–35, 36–47, and 48–59 months), birth weight (<2.5 kg = low and ≥2.5 kg = normal), currently breastfeeding (no and yes), and birth rank (1, 2, 3, and 4+). Maternal factors included maternal education, comprising four categories (illiterate, primary, secondary, and higher), age at marriage (<18 years and ≥18 years), and the number of children (≥4 and <4). Maternal BMI was categorized into three classes (thin (<18.5 kg/m 2 ), normal (18.5–24.9 kg/m 2 ), and overweight/obese (>25 kg/m 2 )). Maternal BMI is determined by dividing a woman’s weight in kilograms by their height in square meters (kg/m 2 ) [ 25 ]. Finally, the household- or community-level factors were divided into the following categories: place of residence (rural and urban), region (categorized into six subdivisions: North: Punjab, Himachal Pradesh, Uttarakhand, Haryana, Chandigarh, Rajasthan, Jammu and Kashmir, and Delhi; Central: Madhya Pradesh, Chhattisgarh, and Uttar Pradesh; East: West Bengal, Bihar, Jharkhand, and Odisha; Northeast: Nagaland, Assam, Manipur, Mizoram, Meghalaya, Tripura, and Sikkim; West: Goa, Dadra and Nagar Haveli, Maharashtra, Daman and Diu, and Gujrat; and South: Andhra Pradesh, Karnataka, Kerala, Telangana, Tamil Nadu, Lakshadweep, and Puducherry) [ 25 ], social groups (Scheduled Caste, Scheduled Tribe, Other Backward Classes, and other), religious belief (Hindu, Muslim, and other), and wealth quintile (poorest, poorer, middle, richer, and richest).

Explanation of Explanatory Variables

Dietary Diversity Score (DDS): In the NFHS-4, dietary diversity was assessed immediately based on the number of food groups consumed within the last 24 h [ 25 ]. Expenditure information was collected on 21 different types of food eaten by children the day before data collection. These foods were initially divided into nine categories: milk or curd, pulses or beans, fruits, eggs, fish, chicken or meat, fried food, dark-green leafy vegetables, and aerated drinks [ 25 ]. Based on the data on food consumption (never/rarely, daily, and weekly), a dietary diversity score was calculated and divided into three categories: 3 food items (children who consumed 3 of 9 foods), 4–6 food items (children who consumed 4–6 of 9 foods), and 7–9 food items.

Exposure to media: The frequency of reading newspapers and magazines, watching television, and listening to the radio each week was used as a proxy for media exposure. Based on these three media categories, mothers were classified into three groups: low exposure (uses at least one of these media at least once a week), medium exposure (uses any two of these media at least once a week), and high exposure (uses at least three of these media at least once a week).

Wealth quintile: The household wealth quintile is a score of economic well-being based on housing properties and sustainable product ownership [ 28 ]. Based on these scores measured by principal component analysis, household wealth is categorized into five levels: poorest, poorer, middle, richer, and richest. Each level corresponds to 20% of the respondents, ranging from 1 (poorest) to 5 (richest).

2.5. Statistical Analyses

Bivariate and multivariate techniques were used to analyze the association between childhood overweight/obesity and sociodemographic, household, and dietary characteristics. The data were also examined using descriptive statistics. We first calculated the proportion of overweight/obese children and that of normal children. The frequency and percentage of the study variable were determined using descriptive statistics as the next step. Pearson’s chi-square tests were used in bivariate analysis to determine the sociodemographic and household characteristics associated with the prevalence of overweight/obesity and the significant level across the independent variables. Binary logistic regression models were used to assess the unadjusted risk ratio (URR) and adjusted risk ratio (ARR) with 95% confidence intervals (C.I.s) between childhood overweight/obesity and sociodemographic and household characteristics. The ARR was controlled for the sex of the child, the child’s age, currently breastfeeding, birth rank, mother’s educational level, age at marriage, mother’s BMI, place of residence, region, social group, religious beliefs, wealth quintile, and dietary diversity score. Data analyses were executed with STATA 12.1 version (StataCorp L.P., Lakeway Drive, College Station, TX, USA).

3.1. Children in Pairs from Different Sociodemographic and Household Characteristics in India

Sociodemographic and household characteristics of children aged 0–59 months and their mothers are depicted in Table 1 . Approximately 2.6% of the total sample population was found to have childhood overweight/obesity. The majority of children in the sample were aged between 36 and 47 months. Approximately 85% of children had a normal birth weight (≥2.5 kg), and more than two-thirds of children were currently breastfeeding. The most common birth rank was first or second. Nearly one-third of women had no educational attainment. More than 40% of women were married before the age of 18, and one-third of mothers had more than four children. The body mass index (BMI) of more than 60% of mothers was normal. Only 7% of women were fully exposed to mass media. Most of the children were members of OBCs (46.6%), belonging to the Hindu (78.3%), living in rural areas (71.9%), and from central (27.7%) and eastern (25.8%) regions of India. A large portion of the children were members of the poorest (24.1%) and poorer (21.9%) wealth quintiles.

Sociodemographic and household characteristics ( n = 176,255).

3.2. Dietary Characteristics of Children in India

Table 2 represents the dietary characteristics of children aged 0–59 months. A significant portion of children consumed milk or curd (42.3%), pulses or beans (44.6%), and dark-green leafy vegetables (46.7%) every day, and nearly all of the children rarely/never ate fruits (58.2%), eggs (59.2%), fish (66.8%), chicken or meat (67.8%), fried food (55.3%), and aerated drinks (78.6%).

Dietary characteristics of the sample population ( n = 176,255).

3.3. Prevalence of Overweight/Obesity by Sociodemographic and Household Characteristics of Under-Five Children in India

The prevalence of overweight/obesity relative to socioeconomic and household characteristics of under-five children in India is depicted in Table 3 . The prevalence of childhood overweight/obesity was found to be significantly higher in the following groups: age of 0–11 months (5.8%), normal birth weight (2.9%), currently breastfeeding (2.7%), and lower birth rank (3%). There was statistically significant variation in childhood overweight or obesity with respect to sex ( p = 0.006). The rate of obesity/overweight was significantly higher in children with mothers with higher educational qualifications (4.7%), who were married after the age of 18 (3.1%), had fewer children (2.8%), were obese (3.4%), and fully exposed to mass media (4%). The prevalence of overweight/obesity was significantly higher among children residing in urban areas (3.4%), southern regions (3.6%), scheduled tribes (2.9%), belonging to other communities (3.1%), and living in households belonging to the richest wealth quintile (4.1%).

Prevalence of overweight/obesity by sociodemographic and household characteristics of under-five children in India ( n = 176,255).

Note: Data from NFHS-4, India, 2015–2016. Percentages were computed by applying sample weights.

3.4. Prevalence of Overweight/Obesity According to Dietary Characteristics of Under-Five Children in India

Table 4 illustrates the analyses of the causes of overweight/obesity by dietary characteristics of under-five children in India. Children who consumed milk or curd (3.1%), pulses or beans (2.8%), dark-green leafy vegetables (3%), fruits (3.6%), eggs (3.5%), fish (3.3%), chicken or meat (3.6%), fried food (3.3%), and aerated drinks (3.6%) daily were more susceptible to overweight/obesity.

Prevalence of overweight/obesity according to dietary characteristics of under-five children in India ( n = 176,255).

3.5. Factors Associated with Childhood Overweight/Obesity in India

Table 5 shows the associations between study variables and childhood overweight/obesity among children aged 0–59 months. Male children had an increased risk of being overweight or obese relative to female children (ARR: 1.08 and 95% CI: 1.02–1.14). Children aged 0–11 months had a 3.7 times higher chance of being overweight/obese than children aged 48–59 months (ARR: 3.77 and 95% CI: 3.41–4.16). Normal birth weight was associated with 1.3 times increased probability of being overweight/obese relative to lower birth weight (LBW) (URR: 1.30 and 95% CI: 1.18–1.43). Children who were currently breastfeeding were at a lower risk of being overweight or obese than non-breastfeeding children (ARR: 0.85 and 95% CI: 0.79–0.92). The risk of overweight or obesity was 1.2 times higher among first-born children (ARR: 1.24 and 95% CI: 1.12–1.38). The unadjusted regression model identified a significant relationship between the educational status of mothers and childhood overweight or obesity. Our analysis also revealed that the likelihood of having an overweight or obese child was increased in mothers with a higher educational level relative to that of illiterate mothers. However, this association was not statistically significant ( p = 0.1).

Factors associated with childhood overweight/obesity in India, NFHS, 2015–2016.

*** if p < 0.01, ** if p < 0.05, * if p < 0.1. CI= confidence interval, † = reference category, VIF = variance inflation factor.

Children from families with fewer than four siblings had 1.44 times increased chances of being overweight or obese relative to children from families with four or more siblings. The odds of overweight or obesity were more than one time higher among children whose mothers were married after the age of 18 (ARR: 1.15 and 95% CI: 1.08–1.24) and obese (ARR: 1.81 and 95% CI: 1.62–2.02). The prevalence of overweight or obesity was 1.46 times increased among children whose mothers were fully engaged in mass media. Children living in urban areas in the north-eastern and southern regions among those who belonged to other communities and the richest household quintile had a higher probability of being overweight or obese than other children. However, the adjusted table did not show that this association was statistically significant. In terms of social category, children belonging to a scheduled tribe had 1.4 times increased possibility of being overweight compared to children belonging to a scheduled caste (ARR: 1.46 and 95% CI: 1.31–1.62), and children from families with Muslim religious beliefs had a lower prevalence of overweight/obesity than children from Hindu families (ARR: 0.87 and 95% CI: 0.79–0.96). Children who consumed 7–9 food items had an increased chance of overweight/obesity as compared to children who consumed <3 food items (ARR: 1.22 and 95% CI: 1.12–1.34) ( Table 5 ).

4. Discussion

In the present study, we examined the incidence of overweight and obesity among children in India, as well as the contributing factors. According to the survey, 2.6% of Indian children under five years of age were obese or overweight. Compared with other South Asian countries, childhood overweight/obesity was found to be higher in India (2.8%) than in Bangladesh (1.6%) and Nepal (1.4%) and lower than in Maldives (5.4%) and Pakistan (4.9%) [ 26 ]. Overweight/obesity among under-five children in India was significantly associated with sex, age, birth weight, birth rank, number of children, age at marriage, mother’s BMI, maternal education, media exposure, social groups, and dietary diversity score.

The study results reveal that male children were more likely to be overweight or obese than female children. This results of the present study are also compatible with evidence from Ethiopia [ 27 ], Ghana [ 29 ], Nepal [ 30 ], Pakistan [ 31 ], Cameroon [ 32 ], China [ 33 ], and Brazil [ 34 ]. However, our findings contradicts those of other research showing that female children were more likely to be overweight/obese than male children [ 35 , 36 ] or that sex had no considerable influence on overweight or obesity in children [ 37 ]. These contradictory results may be a result of genetic and environmental factors [ 27 ], calorie intake, physical activity behaviors [ 38 , 39 ], and social and individual psychology [ 40 ].

We found that younger children had an increased probability of being overweight or obese relative to their older counterparts. Previous studies carried out in Indonesia [ 41 ], Cameroon [ 32 ], and Malaysia [ 42 ] showed similar results. This phenomenon could be explained by the fact that young children who are fed formula instead of breast milk might become more overweight or obese than older children [ 43 ].

A significant association was also revealed between breastfeeding and childhood overweight in the present study. Children who were currently breastfeeding had a lower probability of being overweight or obese than non-breastfeeding children. These findings are consistent with those of previous research from the United States [ 44 ], China [ 45 ], and Denmark [ 46 ]. It is possible that breast milk supplies a moderate amount of calories and nutrients for children, such as sugar, water, protein, and fat [ 45 , 47 ], which can protect against childhood overweight or obesity.

First-born children were more likely to be overweight or obese compared to children with a higher birth rank (4+) in India. Few researchers have studied the link between birth rank and childhood obesity at an early age. This result is consistent with the results of an investigation in Ethiopia [ 27 , 32 ]. Children with a birth rank of 1–3 were more likely to be overweight/obese than children with a birth rank of >3, according to a cross-sectional examination of 4518 Cameroonian children aged 6–59 months.

A significant determinant of childhood overweight or obesity is maternal education. In India, mothers with higher levels of education had a higher risk of having overweight or obese children. Similar findings were reported in studies in Saudi Arabia [ 48 ], China [ 49 ], Kazakhstan [ 50 ], Nepal [ 30 ], and Bangladesh [ 31 ]. The following factors may explain this result: children from well-educated households may consume more protein, have higher dietary diversity and increased energy and fat intake, and be more likely to have high levels of lipoprotein in their blood, which might cause them to become overweight or obese [ 35 ]. Moreover, educated mothers are more likely to be employed, which could mean that they pay less attention to or observe their children’s physical activity or sitting behavior, such as watching television, less than unemployed mothers, which significantly increases their BMI and obesity [ 51 ]. Furthermore, we found that mothers with higher levels of education tended to feed their children different food and consume unnecessary nutrients, which may increase the risk of their children being overweight or obese [ 52 ].

In the present study, we examined the significant impact that maternal age at marriage had on childhood obesity/overweight in Indian children aged 0–59 months. The odds ratios show that children whose mothers were married after the age of 18 were more likely to be overweight or obese. Children of older mothers or those who married after the age of 18 were more likely to be obese or overweight. [ 53 ]. We were not able to clearly interpret this finding; however, a possible explanation is that mothers married at an older age began investing more in their careers, which reduced mother-child interactions and gave them less time to monitor their children’s physical activity, which may lead to their children being overweight or obese.

Another prominent covariate is the mother’s BMI, which has been strongly associated with childhood overweight or obesity. In the current study, children whose mothers were overweight/obese had a higher risk of becoming overweight or obese than those whose mothers were underweight or thin. Numerous studies have reported maternal BMI as a risk factor for childhood obesity [ 54 , 55 ]. This might be explained by the fact that the evidence of epigenetic processes in the uterus, including DNA methylation and changes in the intestinal microbiome, contributes to obesity in children [ 56 ]. Excessive lifestyle exposure (socioeconomic status, food production, marketing, food scarcity, and an obese environment) promote unhealthy behaviors, to which some individuals are susceptible [ 57 , 58 ]. For example, it is possible that mothers were exposed to such complex factors, which contributed to the development of their obesity. In such a case, their children would be more likely to be exposed to the same complex factors, increasing the growth of the uterus and the tendency toward obesity [ 54 ].

Our findings are consistent with trends that have been identified in developing countries, but the relations did not remain significant upon multidisciplinary analysis. Children with urban residences were more overweight than rural children in India. However, the adjusted risk ratio was not significant. This result is consistent with those reported in a previous study in Cameroon [ 32 ]. Several studies have reported a significant association between childhood overweight and place of residence. Furthermore, overweight children have been reported to more often live in urban areas than rural areas. This finding is in line with those of studies conducted in Peru [ 59 ], Poland [ 60 ], China [ 33 ], and Hawaii [ 61 ].

The present study also highlights a strong association between region and childhood overweight/obesity. The odds of being overweight were almost 1.07 times higher in north-eastern and southern India than in northern India. However, this association was not statistically significant. Overweight rates were two times higher in northern and eastern India than in other regions [ 17 ]. Similarly, a higher prevalence of overweight was observed in north-eastern and southern India than in other regions [ 62 ], which could be explained by the higher socioeconomic status of these regions, which may be affected by rapid urbanization and a reduction in the number of urban playgrounds, which may lead to a sedentary lifestyle for children.

The present study also highlights the increased risk of childhood overweight or obesity among scheduled tribe families compared to scheduled caste families. No previous research has examined such an association, possibly ignoring the direct influence of social groups on the development of childhood overweight and obesity. A high accumulation of body fat percentage was observed among Indian tribes [ 63 ]. Because most tribes are still untouchable, these outcomes can be partially explained by the lack of healthcare awareness and vaccination confidence [ 64 ].

Multivariate analysis has shown a weaker protective effect on children overweight/obese of Muslim religion than Hindu religion in India. A previous study in Cameroon [ 32 ] reported that Muslim families might protect their children against being overweight, possibly due to parental choices with respect to a child’s diet that may be influenced by religion. In other words, religion can affect eating habits as a result of adherence to rules that separate religious groups [ 65 ].

With respect to the relationship between the dietary diversity score and childhood obesity or overweight, we observed a significant gradual increase in the risk of being overweight or obese among children who consumed 7–9 food items daily in India. This result is similar to the results of studies on children in Iran [ 66 ], Saudi Arabia [ 67 ], and the Dongcheng District of Beijing [ 68 ]. The dietary diversity score increased in tandem with the percentage consumption of most food groups, leading to excessive energy intake and obesity [ 69 ]. Higher dietary diversity scores were related to increased energy intake, increased consumption of all three components of micronutrients (vitamin A, iodine, and iron), and increased risk of obesity/overweight [ 70 ]. Higher dietary diversity scores were also associated with daily consumption of several foods, such as curd or milk, pulses or beans, fish, eggs, fruits, chicken or meat, vegetable, fried food, and aerated drinks, which may lead to increased energy accretion and an increased probability of being overweight or obese among children aged 0–59 months in India.

Strengths and Weaknesses of the Research

This study has several strengths. The nationally representative data used for respondent selection and the multilevel sampling method reinforce the study results [ 25 ], to a large extent, increasing the generalizability of our results for all children aged 0–59 months in India. This study highlights the dietary intake of children and related problems. Despite having its strengths, this study is also subject to some significant limitations. We were unable to determine the causal relationship between the predictive variable and explanatory variables due to the cross-sectional nature of the data, which may have distorted our estimates or resulted in the absence of an association. Another limitation is that this analysis did not include all possible sociodemographic and household variables. The present study explains some sociodemographic and household characteristics of overweight or obesity among Indian children under the age of under-five, but it cannot account for factors related to physical activities or children’s lifestyles. We have superscribed this limitation to address the corresponding bias through a verified data imputation method.

5. Conclusions

In the present study, we examined the sociodemographic and household factors associated with overweight or obesity among under-five children in India. Risk factors of overweight include being a male child, having a high birth weight, being aged between 0–23 months, and having a low birth rank, whereas breastfeeding protects against overweight or obesity among children between 0 and 59 months of age. The likelihood of being overweight or obese, having children with more than four siblings, getting married after turning 18, and increased media exposure were also higher in children whose mothers had higher levels of education. This study also indicates a high prevalence of early childhood overweight, with significant disparities between dietary diversity scores and scheduled tribe families in India. However, Muslim families appeared to be a protective factor against childhood overweight/obesity. In terms of preventative strategies, parents should focus on advocacy campaigns to reduce excess weight and obesity and strengthen clinical measures, such as antenatal weight gain monitoring, which could help to counteract overweight or obese children in later life. Further studies, i.e., nutrition education studies on feeding practice and physical activity, should be conducted in higher socioeconomic environments. The government should clinically follow up with children with high birth weight in an effort to prevent later childhood overweight. More studies are needed to investigate other possible risk factors linked to the increase in childhood overweight or obesity in India.

Acknowledgments

The authors are grateful to the International Institute for Population Sciences (IIPS), Mumbai, for providing access to the data used in this work.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, J.S.; methodology, J.S. and P.C.; formal analysis, J.S. and F.A.; investigation, J.S., P.C., T.G. and S.M.; resources, F.A.; data curation, J.S.; supervision, P.C. and F.A.; project administration, P.C.; writing—original draft preparation, J.S. and F.A.; writing—review and editing, F.A., P.C., T.G., S.M., M.S., S.F. and K.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The International Institute for Population Sciences (IIPS), Mumbai, provided ethical clearance for the National Family Health Survey (NFHS-4). The Inner City Fund (ICF) International Review Board (IRB) examined and approved this work. Respondents were provided written permission to take part in the survey.

Informed Consent Statement

The current investigation relied on secondary data that was freely accessible in the public domain. There is no identifying information about the survey participants in the dataset. As a result, no ethical clearance was necessary to perform this research.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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