The Brain-Derived Neurotrophic Factor Val66Met Polymorphism,Delivery Method,Birth Weight,and Night Sleep Duration as Determinants of Obesity in Vietnamese Children of Primary School Age

Abstract

Background:

Obesity is a complex disease that involves both environmental and genetic factors in its pathogenesis. Several studies have identified multiple obesity-associated loci in many populations. However, their contribution to obesity in the Vietnamese population is not fully described, especially in children. The study aimed to investigate the association of obesity with Val66Met polymorphism in brain-derived neurotrophic factor (BDNF) gene, delivery method, birth weight, and lifestyle factors in Vietnamese primary school children.

Methods:

A case–control study was conducted on 559 children aged 6–11 years (278 obese cases and 281 normal controls). The obesity of the children was classified using both criteria of International Obesity Task Force (IOTF, 2000) and World Health Organization (WHO, 2007). Lifestyle factors, birth delivery, and birth weight of the children were self-reported by parents. The BDNF genotype was analyzed using the polymerase chain reaction–restriction fragment length polymorphism method. Association was evaluated by multivariate logistic regression and cross-validated by the Bayesian model averaging method.

Results:

The most significantly independent factors for obesity were delivery method (cesarean section vs. vaginal delivery, β = 0.56, p = 0.007), birth weight (>3500 to <4000 g vs. 2500–3500 g, β = 0.52, p = 0.035; ≥4000 g vs. 2500–3500 g, β = 1.06, p = 0.015), night sleep duration (<8 h/day vs. ≥8 h/day, β = 0.99, p < 0.0001), and BDNF Val66Met polymorphism (AA and GG vs. AG, β = 0.38, p = 0.039).

Conclusions:

The study suggested the significant association of delivery method, birth weight, night sleep duration, and BDNF Val66Met polymorphism, with obesity in Vietnamese primary school children.

Introduction

Obesity in children is recognized as a global public health concern. The prevalence of childhood obesity has increased in most parts of the world.¹ In Vietnam, the prevalence of adolescent overweight and obesity in urban districts of Ho Chi Minh City doubled over a period of only 2 years, from 5.6% in 2002 to 13.7% in 2004.² In addition, obesity increases the risk of many diseases, including hypertension, type 2 diabetes, cardiovascular diseases, stroke, osteoarthritis, and several types of cancers.³ Moreover, ∼50% of individuals who are obese in adolescence remain obese in adulthood.⁴ Therefore, preventing obesity from early childhood is of great importance in terms of public health to reduce obesity prevalence and related diseases in adults.

Obesity is a complex disease that involves interactions between environmental and genetic factors in its pathogenesis.⁵ Sedentary lifestyle and excess food intake are the main environmental factors that lead to obesity.⁶ Another important factor is the duration of sleep, which could be a potential contributor to obesity in children.^7–9 Both low and high birth weight would likely to lead to higher possibility of obesity.^10,11 Studies on twins and nontwin siblings have shown that the genetic components contribute from 40% to 70% of the interindividual variation in common obesity.¹² The variation is reflected in differences of obese prevalence among racial populations and age groups because of differences in environmental factors, risk-factor profiles, and genetic background.^6,13 It is necessary to identify the characteristics of genetic and environmental factors, which would increase the risk of obesity among different populations.

Recent advances in genome-wide association studies (GWAS) have evidenced the association of common variants of several genes with the BMI and other measurements of obesity.^14,15 Among these genes, the brain-derived neurotrophic factor (BDNF) gene has been considered to confer obesity risk through its effects in the regulation of feeding. Studies in human and rodents have identified the role of BDNF in regulation of feeding and energy balance. Protein BDNF and its receptor (tropomyosin-related kinase B) are highly expressed in hypothalamus and hindbrain—two important energy homeostasis centers of adult animals.¹⁶ The secretion of precursor and mature BDNF proteins is affected by a single-nucleotide polymorphism (SNP) rs6265 (Val66Met, G196A) in the coding region of exon V of the BDNF gene. This SNP resulted in the substitution of valine toward methionine in the 66th position of the amino acid sequence of protein, and the Met variants were found to be significantly responsible for the unusual intracellular packaging of the precursory BDNF, as well as the decline in producing mature BDNF.¹⁷ In addition, several studies have noted a greater connection between BDNF Val66Met polymorphism and BMI not only in adults^14,18,19 but also in children.^6,13,20–22 However, some studies did not find the association of this SNP with obesity and BMI.^23,24 Furthermore, both the A allele^20,25 and G allele^26,27 were reported to associate with obesity.

Until now, there has been paucity of published data on the association of BDNF gene with obesity among Vietnamese children. Moreover, given the multifactorial pathogenesis of obesity, it is crucial to use an approach to analyze the association of the BDNF gene with obesity, taking into account the influence of lifestyle factors. Therefore, the study aimed to investigate the association between BDNF Val66Met polymorphism and obesity, considering the contribution of delivery method, birth weight, and lifestyle-related factors in primary school children in Vietnam. The most significant independent predictors for obesity were also reported.

Methods

Recruitment of Subjects

A case–control study was conducted with a total of 559 unrelated subjects (278 obese cases and 281 normal controls) recruited from a cross-sectional population-based study. First, 7750 children aged 6–11 years in 31 Hanoi primary school were recruited randomly between October and December 2011 in five urban and three suburban districts of Hanoi. These children were measured anthropometric indices and were then classified into four groups, including underweight-, normal weight-, overweight-, and obese group. For genetic analysis, 2 mL of venous blood was collected from 321 normal children and 366 obese children. Written consent to participate in the study was given by the parents of all subjects. The Ethics Committee of the National Institute of Nutrition approved this study. Next, a self-report questionnaire was completed by parents to collect data on birth delivery, birth weight, and lifestyle factors of the children. Out of the 687 blood-donated children, only 559 subjects with complete data of lifestyle were used for analysis.

Measurements

Anthropometric indices, including weight, height, waist circumference (WC), and hip circumference (HC), were measured twice for each individual, and the mean was used for the purpose of analysis. Body weight and height were measured with subjects in light clothing and without shoes. BMI was calculated as the weight per square of the height (kg/m²). WC was measured midway between the lower rib margin and the iliac crest, while HC was measured at the broadest circumference below the waist. Waist–hip ratio (WHR) was calculated as the WC (cm) divided by the HC (cm).

Obesity children and normal-weight children were classified using the criteria of age- and sex-specific BMI cutoff points proposed by the International Obesity Task Force (IOTF, 2000). Children who were classified underweight or stunting or wasting by the criteria of World Health Organization (WHO, 2007) were excluded from the study. None of the recruited obese children had any medical causes of obesity, including disorders (Hypothyroidism and Cushing syndrome) or drugs (steroids, antidepressants, antipsychotics, and seizure medications).

Genotyping Method

Genomic DNA was extracted from peripheral blood leukocytes by using the Wizard^® Genomic DNA Purification Kit (Promega Corporation, Madison, Wisconsin). Genotyping of BDNF Val66Met was carried out by using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) method. The following primers were used: forward primer 5′-ccaggtgagaagagtgatg-3′ and reverse primer 5′-agtctgcgtccttattgtt-3′. A 15 μL PCR reaction was performed according to the following conditions: 94°C for 3 minutes and 35 cycles of denaturation at 94°C for 30 seconds, primer annealing at 56°C for 40 seconds, primer extension at 72°C for 30 seconds, and final extension at 72°C for 8 minutes. PCR products (281 bp) were detected on Redsafe-stained 2.5% agarose gel. Five microliters PCR products were digested with 0.5 μL FastDigest Eco72I (PmlI) restriction enzyme. Alleles were visualized as fragments by electrophoresis through a Redsafe-stained 2.5% agarose gel. Distribution patterns of the BDNF-rs6265 polymorphisms were AA (281 bp band), AG (three bands: 281, 211, and 70 bp), and GG (two bands: 211 and 70 bp).

Statistical Analysis

Categorical variables were represented as percentages and were tested by the χ² test to compare their distribution between cases and controls. Quantitative variables were checked for normal distribution and compared to identify the difference between cases and controls, using either independent-sample t-test or Mann–Whitney U test, when appropriate. The Hardy–Weinberg equilibrium (HWE) was assessed using the χ² test. Binary logistic regression analysis was used to test several models for the associations of obesity to the BDNF Val66Met risk allele and other variables, taking into account the covariates (age, sex, birth weight, living region, and lifestyle-related factors). The variables included in the analyses were checked for multicollinearity to ensure the stability of the parameter estimates. In this study, data are presented as odds ratios with 95% confidence intervals (CI). The Akaike information criterion (AIC) was used to assess model fit for a given set of data; lower values indicated improved model fit. To assess the model performance, a receiver operating characteristic (ROC) curve was built to plot probabilities resulted from the multivariate logistic regression analysis, and the area under curve (AUC) was used to measure the power to predict individuals with obesity. The above statistical procedures were performed using SPSS version 16.0 (SPSS, Chicago, Illinois). The Bayesian model averaging method was used to cross-validate the final model using Bayesian Model Averaging Software with the R Statistical Environment version 3.1.3.²⁸

Results

The characteristics of subjects in cases and controls are shown in Table 1. There were significant differences between obesity and control groups with regard to living region, birth weight, height, weight, BMI, WC, HC, and WHR. Supplementary Table S1 (Supplementary Data are available online at www.liebertpub.com/chi) shows that the frequencies of the AA, AG, and GG genotypes differed significantly between case and control groups (p = 0.03). The G allele frequency was 0.42 in the controls and 0.46 in the cases. The observed genotype frequencies in the control group were in HWE (p = 0.893), but not in the obese group (p = 0.001). The analysis of the best-fit model for the BDNF Val66Met polymorphism with obesity among genetic modes of inheritance (additive, codominant, dominant, overdominant, and recessive) showed that the lowest AIC values was found in the overdominant mode, indicating that it is the best-fit model in the data (Supplementary Table S2). This mode was used to further multivariate analyses.

Table 1.

Characteristics of Participants in Control and Obese Groups

Characteristics	Control group (N = 281)	Obese group (N = 278)	p
Boy (%)	62.2	71.9	0.084
Age (year)	8.1 ± 1.4	8.0 ± 1.3	0.337
Urban region (%)	52.3	61.9	0.014
Birth weight (g)	3178 (2900–3500)	3320 (3000–3600)	0.001^a
Height (cm)	125.5 ± 9.0	130.0 ± 8.7	<0.0001
Weight (kg)	23.8 (20.8–27.4)	38.8 (34.2–45.7)	<0.0001^a
BMI (kg/m²)	15.21 (14.5–16.3)	23.45 (21.9–25.1)	<0.0001^a
WC (cm)	52.7 (49.5–55.5)	72.1 (68.3–77.9)	<0.0001^a
HC (cm)	62.2 (61.4–63.0)	77.9 (77.0–78.8)	<0.0001^b
WHR	0.86 ± 0.06	0.94 ± 0.05	<0.0001

Data are the mean ± SD unless otherwise indicated.

p-value by Student's t-test or Mann–Whitney U test or chi-square test.

Data are median (interquartile range).

Data are geometric mean (95CI).

HC, hip circumference; WC, waist circumference; WHR, waist–hip ratio.

Univariate and multivariate logistic regression analyses of potential associated factors for obesity are presented in Table 2. The results showed that delivery method, birth weight, living region, night sleep duration, and BDNF Val66Met polymorphism were significantly associated with children obesity in univariate analysis. The association remained significant in the model after adjustments for sex, age, residence, time spent for television and computer games, school travel mode, and sport participation. The variables including delivery method, birth weight, living region, night sleep duration, and BDNF Val66Met polymorphism were also confirmed using the Bayesian model averaging, in which they were selected in almost models (Fig. 1). Table 3 shows the most significant independent predictors for obesity in final prediction model. The area under ROC curve was 0.657 (95%CI: 0.610–0.704, p < 0.0001) (Fig. 2) for the model of obesity on the predictors, including delivery method, birth weight, living region, and night sleep duration. Adding the genetic marker to these covariates improved the area under ROC curve slightly from 0.657 to 0.668 (p = 0.001, Wilcoxon Signed-Ranks Test).

Figure 1.

Analysis BMA analysis to cross-validate the final model. The above figure shows the finding of the BMA analysis to cross-validate the final model found by multivariate logistic regression analysis. There were 22 models selected by BMA, in which delivery method, birth weight, living region, night sleep duration, and BDNF Val66Met polymorphism were presented in most of the selected models. BDNF, brain-derived neurotrophic factor; BMA, Bayesian Model Averaging.

Figure 2.

ROC curves for obesity prediction on the BDNF Val66Met polymorphism, delivery method, birth weight, living region, and night sleep duration in Model 2 and Model 1 without genetic marker. Predictors were adjusted for age and sex. AUC, area under the curve; ROC, receiver operating characteristic.

Table 2.

Associated Factors of Obesity in Primary School Children

	Univariate analysis		Multivariate analysis
Variable	OR (95% CI)	p	OR (95% CI)	p
Age (year)	0.94 (0.83–1.07)	0.337	0.87 (0.74–1.01)	0.075
Sex
Female	1		1
Male	1.31 (0.91–1.88)	0.142	1.37 (0.91–2.05)	0.129
Delivery methods
Vaginal delivery	1		1
Cesarean section	2.09 (1.45–3.01)	<0.0001	1.60 (1.05–2.46)	0.030
Birth weight
2500–3500 g	1		1
<2500 g	0.47 (0.18–1.24)	0.127	0.47 (0.17–1.30)	0.146
>3500 and <4000 g	1.85 (1.16–2.96)	0.010	1.79 (1.09–2.96)	0.022
≥4000 g	3.14 (1.42–6.93)	0.005	2.86 (1.20–6.83)	0.018
Living region
Suburban	1		1
Urban	1.48 (1.06–2.07)	0.023	1.12 (0.76–1.66)	0.568
Time watching television + playing computer game				0.712
<2 h/day	1		1
2–3 h/day	1.07 (0.74–1.55)	0.704	1.12 (0.75–1.70)	0.577
>3 h/day	1.16 (0.69–1.95)	0.581	1.26 (0.70–2.27)	0.433
Playing sports
No	1		1
Yes	1.08 (0.73–1.61)	0.691	1.08 (0.70–1.69)	0.723
Night sleep duration			1
≥8 h/day	1
<8 h/day	2.60 (1.62–4.19)	<0.0001	2.56 (1.53–4.29)	<0.0001
School travel mode
Active transport (walk, bicycle)	1		1
Inactive transport (car, motorbike)	1.40 (0.97–2.02)	0.070	1.32 (0.86–2.02)	0.200
BDNF rs5256 (overdominant)
AA + GG	1		1
AG	1.51 (1.08–2.11)	0.015	1.49 (1.02–2.17)	0.039

BDNF, brain-derived neurotrophic factor: CI, confidence intervals; OR, odds ratio.

Table 3.

The Most Significant Independent Predictors for Obesity in Vietnamese Children in Primary Schools

Variable	β ± SE	p
Delivery method
Vaginal delivery	0
Cesarean section	0.56 ± 0.21	0.007
Birth weight
2500–3500 g	0
<2500 g	−0.80 ± 0.51	0.120
>3500 to <4000 g	0.52 ± 0.25	0.035
≥4000 g	1.06 ± 0.44	0.015
Night sleep duration
≥8 hours	0
<8 hours	0.99 ± 0.25	<0.0001
BDNF-rs6265
AA + GG	0
AG	0.38 ± 0.19	0.039
Constant	0.19 ± 0.59	<0.0001

β and p-values were calculated by multivariate logistic regression adjusted for sex and age.

Discussion

To the best of our knowledge, this is the initial report on the significant association of BDNF Val66Met polymorphism, delivery method, birth weight, and night sleep duration with obesity among primary school children in Vietnam. Other factors, such as living region, time spent for watching TV and playing computer games, participation in sports, and school travel modes, were not significantly related to obesity in the cohort.

The relationship between delivery method and obesity has been reported in many studies. In the United Kingdom, children born by cesarean section involved harvesting fat tissue increases from 6 weeks after birth, at the age of 11 they had a 1.83-times higher risk of overweight and obesity than those born by vaginal delivery.²⁹ The association between cesarean section and childhood obesity was reported in Europe³⁰ and Brazil³¹; while no association was found in Canada³² and Hong Kong.³³ An association between cesarean delivery with obesity was observed in children at 2 years, but not in those aged from 6 to 10 years.³⁴ In a recent systematic review and meta-analysis, including 28 studies,³⁵ children born by cesarean section were at higher risk of developing obesity in childhood. In the present study, the children born by cesarean delivery were 1.6 times more likely to have obesity than those born by vaginal delivery. The difference of the associations can be explained by the fact that the cesarean section is often done for children with high birth weight,³⁶ early gestational age, or higher age mothers.³³ Moreover, recent studies on microbiome may also explain why cesarean section born children are more likely to be obese compared with vaginal born children.³⁷ The vaginal born infants carried gut microbiome similar to that of mother, whereas the cesarean born babies lacked this similarity of gut microbiome.³⁸ Individuals born by cesarean delivery were 15% more likely to become obese during follow-up than those born by vaginal delivery; those born via cesarean delivery had 64% higher odds of obesity compared with their siblings born via vaginal delivery.³⁹

The present data showed that primary school children with birth weight of 3500–4000 g and over 4000 g had higher risk of obesity odds ratio (OR = 1.8 and 2.86, respectively) than those with birth weight of 2500–3500 g. Epidemiological studies have demonstrated that both low and high birth weight have increased obesity in later life because the utero environment may influence the fetus's epigenetic process.^40,41 However, how the four epigenetic modalities—DNA methylation, noncoding RNA, transcription factors, and histone modifications—contribute to epigenetic memory and how epigenomic changes may mediate the altered control of fetal gene expression as a consequence of maternal obesity are not well characterized.⁴²

Experimental studies in humans have found that a short period of sleep duration could result in important changes in the secretion of cortisol and the growth hormone,⁴³ as well as levels of leptin and ghrelin, which are related to the regulation of hunger and satiety.⁴⁴ Several cross-sectional studies discovered positive association between short sleep duration and obesity,^9,45 while some did not find the similarity results.⁴⁶ In this study, children who slept fewer hours (<8 h/night) had much higher risk of obesity (OR = 2.56). It could be suggested that parents should be well aware of their child's night sleep duration to prevent children from becoming obese from an early age.

Studies on the relationship between BDNF Val66Met variants and obesity reported that both the A allele^20,25 and G allele^26,27 were associated with obesity. Several reports found the relationship between this SNP and obesity in childhood Asians^22,47 and Caucasians.^20,48 Meanwhile, other research efforts indicated a lack of association between the SNP and obesity traits in Spanish school children⁴⁹ and in young German participants.⁵⁰ The varied results may be explained, in part, by differences in environmental factors, risk-factor profiles, and genetic backgrounds of those populations.⁶ Reports of GWAS¹⁸ and meta-analysis¹⁹ indicated that G allele of BDNF rs6265 polymorphism was significantly associated with obesity. In this study, according to the model of overdominant genetic, the heterozygous genotype AG increased the risk of obesity by 1.49 times compared with the other two genotypes (95% CI = 1.02–2.17). Compared with AA homozygotes, subjects with G allele (AG or GG) had an increased odds of obesity [OR (95%CI) = 1.58 (1.09–2.29) in the codominant model]. However, GG homozygotes in the dominant and copy number of G allele in the additive model were not significantly associated with obesity. This is likely due to the small sample size of the study.

In association studies, the p-value correction of multiple hypotheses testing is necessary, especially for studies with a large number of SNP such as GWAs. Actually, we have only checked one hypothesis on the relationship between BDNF Val66Met polymorphism and obesity in the present study, considering the contribution of delivery method, birth weight, and lifestyle-related factors. Therefore, the correction for multiple testing was not been applied in the study. The multivariate logistic regression analysis indicated that the association reported was not changed by covariates. In addition, the independent variables in the final model were cross-validated by the Bayesian model averaging method.

Major factors which affect the genetic equilibrium are as follows: mutations, recombinations during sexual reproduction, genetic drift, gene migration, and natural selection. In the present study, given that obese and normal subjects were recruited randomly in a large population of 7750 children aged 6–11 years in 31 Hanoi primary schools in five urban and three suburban districts of Hanoi City, the disturbance of HWE in the obese group could be partly explained by natural selection, but not the others. Obese children were a specific group who may have higher frequency of risk alleles susceptible to obesity. For instance, the observed frequency (24.1%) of AA genotype of the BDNF Val66Met polymorphism in our cohort was significantly lower than expected frequency (28.9%) estimated by Hardy–Weinberg principle.

In addition to genetic factors, a variety of studies have provided strong evidence demonstrating that sedentary behavior was positively related to obesity among children.^51–53 Hence, reducing television viewing time and increasing outdoor activities are effective strategies for managing the obesity epidemic in children.^7,54 In addition, walking and cycling to school are regarded as valuable opportunities for children to accumulate physical activity⁴² and maintain a healthy weight.^56,57 In this study, children's total time consumption for watching television and playing computer games per day was classified into three different categories (<2, ≥2–3, and >3 hours) as recommended by the American Academy of Pediatrics, children should only spend a maximum of 2 h/day for watching television and playing video/computer games.⁵⁸ The present results showed that the total time spent on television and computer games, sport participation, and active transport were not significantly associated to children obesity in Vietnam.

The present findings must be interpreted in the context of several limitations. First, the case–control design did not allow a causal conclusion. Second, given the relatively small sample size, the association of obesity with sedentary behaviors, including total time spent on television and computer games, sport participation, and school travel modes, was found to be not statistically significant in the cohort; and it should be further evaluated in prospective study with larger sample. Third, the lifestyle factors of the children who were self-reported by parents could be recorded incompletely. Next, we were unable to collect information regarding the food intake and physical activity levels of the participants, so the relationship between those two factors toward obesity could not be evaluated in the study. Finally, genetic factor was only the BDNF Val66Met polymorphism. Although it was significantly associated with childhood obesity in the cohort, adding the genetic marker in clinical covariates improved the area under ROC curve slightly.

Conclusions

This study showed the significant association of BDNF Val66Met polymorphism, delivery method, birth weight, and sleep duration with the risk of obesity in primary school children in Vietnam. It is necessary to extend the scale of the research and collect more information on both genetic and environmental factors to identify the gene–environment interactions to childhood obesity in Vietnam.

Footnotes

Acknowledgments

We thank colleagues in Hanoi National University of Education and National Institute of Nutrition for their kindly help and support. This study was supported by the Grant No. DHSP2015-422 from Hanoi National University of Education, the Grant No. B2014-17-47 from the Ministry of Education and Training, and the Grant No. 01C-08/05-2011-2 from Hanoi Department of Science and Technology, Vietnam.

Author Disclosure Statement

No competing financial interests exist.

References

WHO 2014 Obesity and overweight Fact sheet No. 311. Updated August 2014. Available at www.who.int/mediacentre/factsheets/fs311/en/ (last accessed January 4, 2017).

Hong

, Dibley

, Sibbritt

, et al. Overweight and obesity are rapidly emerging among adolescents in Ho Chi Minh City, Vietnam, 2002–2004. Int J Pediatr Obes, 2007; 2:194–201.

Must

, Spadano

, Coakley

, et al. The disease burden associated with overweight and obesity. JAMA, 1999; 282:1523–1529.

Must

. Does overweight in childhood have an impact on adult health?. Nutr Rev, 2003; 61:139–142.

Andreasen

, Andersen

. Gene–environment interactions and obesity–Further aspects of genome wide association studies. Nutrition, 2009; 25:998–1003.

Manco

, Dallapiccola

. Genetics of pediatric obesity. Pediatrics, 2012; 130:123–133.

Danielzik

, Czerwinski–Mast

, Langnäse

, et al. Parental overweight, socioeconomic status and high birth weight are the major determinants of overweight and obesity in 5–7 y–old children: Baseline data of the Kiel Obesity Prevention Study (KOPS). Int J Obes Relat Metab Disord, 2004; 28:1494–1502.

McLaren

. Socioeconomic status and obesity. Epidemiol Rev, 2007; 29:29–48.

Cappuccio

, Taggart

, Kandala

, et al. Meta–analysis of short sleep duration and obesity in children and adults. Sleep, 2008; 31:619–626.

10.

Schaefer–Graf

, Pawliczak

, Passow

, et al. Birth weight and parental BMI predict overweight in children from mothers with gestational diabetes. Diabetes Care, 2005; 28:1745–1750.

11.

Fernandez–Twinn

, Ozanne

. Mechanisms by which poor early growth programs type–2 diabetes, obesity and the metabolic syndrome. Physiol Behav, 2006; 88:234–243.

12.

Maes

, Neale

, Eaves

. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet, 1997; 27:325–351.

13.

Zhao

, Grant

. Genetics of childhood obesity. J Obes, 2011; 2011:845148.

14.

Fall

, Ingelsson

. Genome–wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol, 2012; 382:740–757.

15.

, Loos

. Obesity genomics: Assessing the transferability of susceptibility loci across diverse populations. Genome Med, 2013; 5:55.

16.

Rios

. BDNF and the central control of feeding: Accidental bystander or essential player?. Trends Neurosci, 2013; 36:83–90.

17.

Zegers

, Hendrickx

, Verrijken

, et al. Screening for genetic variants in BDNF that contribute to childhood obesity. Pediatr Obes, 2014; 9:36–42.

18.

Thorleifsson

, Walters

, Gudbjartsson

, et al. Genome–wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet, 2009; 41:18–24.

19.

Wen

, Cho

, Zheng

, et al. Meta–analysis identifies common variants associated with body mass index in east Asians. Nat Genet, 2012; 44:307–311.

20.

Skledar

, Nikolac

, Dodig–Curkovic

, et al. Association between brain–derived neurotrophic factor Val66Met and obesity in children and adolescents. Prog Neuropsychopharmacol Biol Psychiatry, 2012; 36:136–140.

21.

Mitchell

, Hakonarson

, Rebbeck

, Grant

. Obesity–susceptibility loci and the tails of the pediatric BMI distribution. Obesity (Silver Spring), 2013; 21:1256–1260.

22.

Zhao

, Xi

, Shen

, et al. An obesity genetic risk score is associated with metabolic syndrome in Chinese children. Gene, 2014; 535:299–300.

23.

Marti

, Santos

, Gratacos

, et al. Association between leptin receptor (LEPR) and brain–derived neurotrophic factor (BDNF) gene variants and obesity: A case–control study. Nutr Neurosci, 2009; 12:183–188.

24.

Nikolac Perkovic

, Mustapic

, Pavlovic

, et al. Lack of association between brain–derived neurotrophic factor Val66Met polymorphism and body mass index change over time in healthy adults. Neurosci Lett, 2013; 545:127–131.

25.

Beckers

, Peeters

, Zegers

, et al. Association of the BDNF-rs6265 variation with obesity in women. Mol Genet Metab, 2008; 95:110–112.

26.

Gunstad

, Schofield

, Paul

, et al. BDNF-rs6265 polymorphism is associated with body mass index in healthy adults. Neuropsychobiology, 2006; 53:153–156.

27.

Hong

, Lim

, Go

, et al. Recapitulation of the association of the Val66Met polymorphism of BDNF gene with BMI in Koreans. Obesity (Silver Spring), 2012; 20:1871–1875.

28.

Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. Available at www.R-project.org/ (last accessed November 15, 2016).

29.

Blustein

, Attina

, Liu

, et al. Association of caesarean delivery with child adiposity from age 6 weeks to 15 years. Int J Obes (Lond), 2013; 37:900–906.

30.

Wang

, Alamian

, Southerland

, et al. Cesarean section and the risk of overweight in grade 6 children. Eur J Pediatr, 2013; 172:1341–1347.

31.

Portela

, Vieira

, Matos

, et al. Maternal obesity, environmental factors, cesarean delivery and breastfeeding as determinants of overweight and obesity in children: Results from a cohort. BMC Pregnancy Childbirth, 2015; 15:94.

32.

Flemming

, Woolcott

, Allen

, et al. The association between caesarean section and childhood obesity revisited: A cohort study. Arch Dis Child, 2013; 98:526–532.

33.

Lin

, Leung

, Schooling

. Mode of delivery and adiposity: Hong Kong's Children of 1997 birth cohort. Ann Epidemiol, 2013; 23:693–699.

34.

Pei

, Heinrich

, Fuertes

, et al. Cesarean delivery and risk of childhood obesity. J Pediatr, 2014; 164:1068–1073.

35.

Kuhle

, Tong

, Woolcott

. Association between caesarean section and childhood obesity: A systematic review and meta-analysis. Obes Rev, 2015; 16:295–303.

36.

Mueller

, Whyatt

, Hoepner

, et al. Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity. Int J Obes (Lond), 2015; 39:665–670.

37.

John

, Mullin

. The gut microbiome and obesity. Curr Oncol Rep, 2016; 18:45.

38.

Dominguez-Bello

, Costello

, Contreras

, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA, 2010; 107:11971–11975.

39.

Yuan

, Gaskins

, Blaine

, et al. Association between cesarean birth and risk of obesity in offspring in childhood, adolescence, and early adulthood. JAMA Pediatr, 2016; 170:e162385.

40.

Dabelea

, Harrod

. Role of developmental overnutrition in pediatric obesity and type 2 diabetes. Nutr Rev, 2013; 71(Suppl 1):S62–S67.

41.

Teh

, Pan

, Chen

, et al. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res, 2014; 24:1064–1074.

42.

, Sloboda

, Vickers

. Maternal obesity and developmental programming of metabolic disorders in offspring: Evidence from animal models. Exp Diabetes Res, 2011; 2011:592408.

43.

Spiegel

, Leproult

, Colecchia

, et al. Adaptation of the 24–h growth hormone profile to a state of sleep debt. Am J Physiol Regul Integr Comp Physiol, 2000; 279:R874–R883.

44.

Spiegel

, Tasali

, Penev

, Van Cauter

. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med, 2004; 141:846–850.

45.

Liu

, Croft

, Wheaton

, et al. Association between perceived insufficient sleep, frequent mental distress, obesity and chronic diseases among US adults, 2009 behavioral risk factor surveillance system. BMC Public Health, 2013; 13:84.

46.

Magee

, Hale

. Longitudinal associations between sleep duration and subsequent weight gain: A systematic review. Sleep Med Rev, 2012; 16:231–241.

47.

, Xi

, Zhang

, et al. Associations of six single nucleotide polymorphisms in obesity–related genes with BMI and risk of obesity in Chinese children. Diabetes, 2010; 59:3085–3089.

48.

Mei

, Chen

, Jiang

, et al. Longitudinal replication studies of GWAS risk SNPs influencing body mass index over the course of childhood and adulthood. PLoS One, 2012; 7:e31470.

49.

Arija

, Ferrer–Barcala

, Aranda

, Canals

. BDNF-rs6265 polymorphism, energy intake and BMI: A follow–up study in school children at risk of eating disorders. BMC Public Health, 2010; 10:363.

50.

Friedel

, Horro

, Wermter

, et al. Mutation screen of the brain derived neurotrophic factor gene (BDNF): Identification of several genetic variants and association studies in patients with obesity, eating disorders, and attention–deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet, 2005; 132B:96–99.

51.

Lioret

, Maire

, Volatier

, Charles

. Child overweight in France and its relationship with physical activity, sedentary behaviour and socioeconomic status. Eur J Clin Nutr, 2007; 61:509–516.

52.

Mitchell

, Mattocks

, Ness

, et al. Sedentary behavior and obesity in a large cohort of children. Obesity (Silver Spring), 2009; 17:1596–1602.

53.

Shan

, Xi

, Cheng

, et al. Prevalence and behavioral risk factors of overweight and obesity among children aged 2–18 in Beijing, China. Int J Pediatr Obes, 2010; 5:383–389.

54.

Dietz

. The obesity epidemic in young children. Reduce television viewing and promote playing. BMJ, 2001; 322:313–314.

55.

Cooper

, Andersen

, Wedderkopp

, et al. Physical activity levels of children who walk, cycle, or are driven to school. Am J Prev Med, 2005; 29:179–184.

56.

Gordon–Larsen

, Nelson

, Beam

. Associations among active transportation, physical activity, and weight status in young adults. Obes Res, 2005; 13:868–875.

57.

Rosenberg

, Sallis

, Conway

, et al. Active transportation to school over 2 years in relation to weight status and physical activity. Obesity (Silver Spring), 2006; 14:1771–1776.

58.

American Academy of Pediatrics. Committee on Public Education. American Academy of Pediatrics: Children, adolescents, and television. Pediatrics, 2001; 107:423–426.

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