What your genes can (and can't) tell you about BMI and diabetes

• Type of Material: Article
• Authors: Carmen D Ng, Emory University; Jordan Weiss, University of Pennsylvania
• Language: English
• Date: 2020-01-02
• Publisher: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Rights managed by Taylor & Francis
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1080/19485565.2020.1806032
• Title of Journal or Parent Work: BIODEMOGRAPHY AND SOCIAL BIOLOGY
• Volume: 66
• Issue: 1
• Start Page: 40
• End Page: 49
• Grant/Funding Information: This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development [T32 HD007242] and National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK115937-01].
• Keywords: WEIGHT; Sociology; BODY-MASS INDEX; CONSEQUENCES; Social Sciences, Biomedical; Biomedical Social Sciences; HEIGHT; Demography; HEALTH; Social Sciences; OBESITY; OVERWEIGHT

Abstract

Body mass index (BMI) is commonly used as a proxy for adiposity in epidemiological and public health studies. However, BMI may suffer from issues of misreporting and, because it fluctuates over the life course, its association with morbidities such as diabetes is difficult to measure. We examined the associations between actual BMI, genetic propensity for high BMI, and diabetes to better understand whether a BMI polygenic score (PGS) explained more variation in diabetes than self-reported BMI. We used a sample of non-Hispanic white adults from the longitudinal Health and Retirement Study (1992–2016). Structural equation models were used to determine how much variation in BMI could be explained by a BMI PGS. Then, we used logistic regression models (n = 12,086) to study prevalent diabetes at baseline and Cox regression models (n = 11,129) to examine incident diabetes with up to 24 years of follow-up. We observed that while both actual BMI and the BMI PGS were significantly associated with diabetes, actual BMI had a stronger association than its genetic counterpart and resulted in better model performance. Moreover, actual BMI explained more variation in baseline and incident diabetes than its genetic counterpart which may suggest that actual BMI captures more than just adiposity as intended.