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The interaction between physical activity and obesity gene variants in association with BMI: Does the obesogenic environment matter?

ABSTRACT: Little is known about how obesity susceptibility single nucleotide polymorphisms (SNPs) interact with moderate to vigorous physical activity (MVPA) in relation to BMI during adolescence, once obesogenic neighborhood factors are accounted for. In race stratified models, including European (EA; N=4977), African (AA; N=1726), and Hispanic Americans (HA; N=1270) from the National Longitudinal Study of Adolescent to Adult Health (1996; ages 12-21), we assessed the evidence for a SNPxMVPA interaction with BMI-for-age Z score, once accounting for obesogenic neighborhood factors including physical activity amenities, transportation and recreation infrastructure, poverty and crime. Eight SNPxMVPA interactions with suggestive significance (p<0.10; three in each EA, and AA, two in HA) were observed showing attenuation on BMI-for-age Z score in adolescents with ?5 versus <5 bouts/week MVPA, except for rs10146997 (near NRXN3). Findings were robust to the inclusion of neighborhood-level variables as covariates. These findings suggest that any attenuation from MVPA on a genetic susceptibility to obesity during adolescence is likely not operating through obesogenic neighborhood factors.

SUBMITTER: Graff M

PROVIDER: S-EPMC5116401 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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The interaction between physical activity and obesity gene variants in association with BMI: Does the obesogenic environment matter?

Graff M M Richardson A S AS Young K L KL Mazul A L AL Highland Heather H North K E KE Mohlke K L KL Lange L A LA Lange E M EM Harris K M KM Gordon-Larsen P P

Health & place 20161020

Little is known about how obesity susceptibility single nucleotide polymorphisms (SNPs) interact with moderate to vigorous physical activity (MVPA) in relation to BMI during adolescence, once obesogenic neighborhood factors are accounted for. In race stratified models, including European (EA; N=4977), African (AA; N=1726), and Hispanic Americans (HA; N=1270) from the National Longitudinal Study of Adolescent to Adult Health (1996; ages 12-21), we assessed the evidence for a SNPxMVPA interaction ...[more]

PMID: 27771443

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Project description:ObjectivesThe home environment is thought to play a key role in early weight trajectories, although direct evidence is limited. There is general agreement that multiple factors exert small individual effects on weight-related outcomes, so use of composite measures could demonstrate stronger effects. This study therefore examined whether composite measures reflecting the 'obesogenic' home environment are associated with diet, physical activity, TV viewing, and BMI in preschool children.MethodsFamilies from the Gemini cohort (n = 1096) completed a telephone interview (Home Environment Interview; HEI) when their children were 4 years old. Diet, physical activity, and TV viewing were reported at interview. Child height and weight measurements were taken by the parents (using standard scales and height charts) and reported at interview. Responses to the HEI were standardized and summed to create four composite scores representing the food (sum of 21 variables), activity (sum of 6 variables), media (sum of 5 variables), and overall (food composite/21 + activity composite/6 + media composite/5) home environments. These were categorized into 'obesogenic risk' tertiles.ResultsChildren in 'higher-risk' food environments consumed less fruit (OR; 95% CI = 0.39; 0.27-0.57) and vegetables (0.47; 0.34-0.64), and more energy-dense snacks (3.48; 2.16-5.62) and sweetened drinks (3.49; 2.10-5.81) than children in 'lower-risk' food environments. Children in 'higher-risk' activity environments were less physically active (0.43; 0.32-0.59) than children in 'lower-risk' activity environments. Children in 'higher-risk' media environments watched more TV (3.51; 2.48-4.96) than children in 'lower-risk' media environments. Neither the individual nor the overall composite measures were associated with BMI.ConclusionsComposite measures of the obesogenic home environment were associated as expected with diet, physical activity, and TV viewing. Associations with BMI were not apparent at this age.

Project description:BackgroundAccounting for nearly one-third of all deaths, cardiovascular disease is the leading cause of mortality and morbidity in the United States. Adverse health behaviors are major determinants of this high incidence of disease. Examining local food and physical activity environments and population characteristics in a poor, rural state may highlight underlying drivers of these behaviors. We aimed to identify demographic and environmental factors associated with both obesity and overall poor cardiovascular health (CVH) behaviors in Maine counties.MethodsOur cross-sectional study analyzed 40,398 Behavioral Risk Factor Surveillance System (BRFSS) 2011-2014 respondents alongside county-level United States Department of Agriculture (USDA) Food Environment Atlas 2010-2012 measures of the built environment (i.e., density of restaurants, convenience stores, grocery stores, and fitness facilities; food store access; and county income). Poor CVH score was defined as exhibiting greater than 5 out of the 7 risk factors defined by the American Heart Association (current smoking, physical inactivity, obesity, poor diet, hypertension, diabetes, and high cholesterol). Multivariable logistic regression models described the contributions of built environment variables to obesity and overall poor CVH score after adjustment for demographic controls.ResultsBoth demographic and environmental factors were associated with obesity and overall poor CVH. After adjustment for demographics (age, sex, personal income, and education), environmental characteristics most strongly associated with obesity included low full-service restaurant density (OR 1.34; 95% CI 1.24-1.45), low county median household income (OR 1.31; 95% CI 1.21-1.42) and high convenience store density (OR 1.21; 95% CI 1.12-1.32). The strongest predictors of overall poor CVH behaviors were low county median household income (OR 1.30; 95% CI 1.13-1.51), low full-service restaurant density (OR 1.38; 95% CI 1.19-1.59), and low fitness facility density (OR 1.27; 95% CI 1.11-1.46).ConclusionsIn a rural state, both demographic and environmental factors predict overall poor CVH. These findings may help inform communities and policymakers of the impact of both social determinants of health and local environments on health outcomes.

Project description:BACKGROUND:Meta-analysis of genome-wide association studies identified obesity-related genetic variants. Due to the pleiotropic effects of related phenotypes, we tested six of these obesity-related genetic variants for their association with physical activity: fat mass and obesity-associated (FTO)(rs9939609)T>A, potassium channel tetramerization domain containing (KCTD15) (rs11084753)G>A, melanocortin receptor4 (MC4R)(rs17782313)T>C, neuronal growth regulator 1 (NEGR1)(rs2815752)A>G, SH2B adapter protein 1 (SH2B1)(rs7498665)A>G, and transmembrane protein18 (TMEM18)(rs6548238)C>T. METHOD:European-American women (n = 263) and men (n = 229) (23.5 ± 0.3 years, 24.6 ± 0.2 kg/m2) were genotyped and completed the Paffenbarger physical activity Questionnaire. Physical activity volume in metabolic energy equivalents [MET]-hour/week was derived from the summed time spent (hour/week) times the given MET value for vigorous, moderate, and light intensity physical activity, and sitting and sleeping, respectively. Multivariable adjusted [(age, sex, and body mass index (BMI)] linear regression tested associations among genotype (dominant/recessive model) and the log of physical activity volume. RESULT:MC4R (rs17782313)T>C explained 1.1 % (p = 0.02), TMEM18(rs6548238)C>T 1.2 % (p = 0.01), and SH2B1 (rs7498665)A>G 0.6 % (p = 0.08) of the variability in physical activity volume. Subjects with the MC4R C allele spent 3.5 % less MET-hour/week than those with the TT genotype (p = 0.02). Subjects with the TMEM18 T allele spent 4.1 % less MET-hour/week than those with the CC genotype (p = 0.01). Finally, subjects with the SH2B1 GG genotype spent 3.6 % less MET-hour/week than A allele carriers (p = 0.08). CONCLUSION:Our findings suggest a shared genetic influence among some obesity-related gene loci and physical activity phenotypes that should be explored further. Physical activity volume differences by genotype have public health importance equating to 11-13 lb weight difference annually.

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The interaction between physical activity and obesity gene variants in association with BMI: Does the obesogenic environment matter?

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The interaction between physical activity and obesity gene variants in association with BMI: Does the obesogenic environment matter?

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