The Relative Contributions of Socioeconomic and Genetic Factors to Variations in Body Mass Index Among Young Adults. Issue 11 (14th April 2020)

• Record Type: Journal Article
• Authors: Kim, Rockli; Lippert, Adam M; Wedow, Robbee; Jimenez, Marcia P; Subramanian, S V
• Is Part Of: American journal of epidemiology. Volume 189:Issue 11(2020)
• Page Start: 1333
• Page End: 1341
• Publication Date: 2020-04-14
• Subjects: body mass index -- genetic -- obesity -- polygenic score -- socioeconomic status -- variation; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwaa058
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: In light of recent findings on the small proportion of variance in body mass index (BMI) explained by shared environment, and growing interests in the role of genetic susceptibility, we assessed the relative contribution of socioeconomic status (SES) and genome-wide polygenic score for BMI to explaining variation in BMI. Our final analytic sample included 4, 918 white and 1, 546 black individuals from the US National Longitudinal Study of Adolescent to Adult Health Wave IV (2007–2008) who had complete measures on BMI, demographics, SES, genetic data, and health behaviors. We used ordinary least-squares regression to assess variation in log(BMI) as a function of the aforementioned predictors, independently and mutually adjusted. All analyses were stratified by race/ethnicity in the main analysis, and further by sex. The age-adjusted variation in log(BMI) was 0.055 among whites and 0.066 among blacks. The contribution of SES and polygenic score ranged from less than1% to 6% and from 2% to 8%, respectively, and majority of the variation (87%–96%) in log(BMI) remained unexplained. Differential distribution of socioeconomic resources, stressors, and buffers may interact to produce systematically larger variation in vulnerable populations. More understanding of the contribution of biological, genetic, and environmental factors, as well as stochastic elements, in diverse phenotypic variance is needed in population health sciences.