(OR03-02-25) Genetic Risk, Diet, and Lifestyle: Differential Contributions to DXA Body Composition Phenotypes in a Nutritional Cohort

Objectives: To assess how combining genetic, diet, and lifestyle factors describe the variation in body composition and distribution phenotypes.

Methods: A cohort of men and women 18-66y (n =230) balanced by age, sex, and BMI from the USDA Nutritional Phenotyping Study (NCT02367287) provided demographic data, body composition, fitness, resting metabolic rate, diet, and biological samples. Imputed genetic data was used to calculate a BMI polygenic risk score (PRS; PGS002313) via the pgs_calc pipeline. Principle components (PC) analysis was used to capture genetic variation. Linear regression and ANCOVA assessed variables impact on outcomes measured by dual-energy X-ray absorption (DXA) including body fat percentage (BF%), lean mass index (LMI), trunk fat percentage (TF%), and the android-to-gynoid ratio (A:G). Explained variance was evaluated using sum of squares and partial R², followed by model comparison with Akaike’s (AIC) and Bayesian (BIC) information criteria. Statistical analysis and visualizations were done using R.

Results: The PRS explained 15.6% of the variance in BMI (R² =0.156) and predicted obesity (BMI >30) with good discriminatory ability (AUROC =0.71). ANCOVA confirmed the PRS as a BMI predictor (p =1.1x10⁻⁷), accounting for 10.9% of variation after adjusting for covariates (age, sex, PC 1-5). Healthy eating index (HEI), fitness, and resting metabolic rate (RMR) showed mixed independent associations with body composition phenotypes. In models constructed with all variables, variance analysis revealed that: a) RMR explained 20.1% of BMI variation and 16.5% of LMI, but less for other variables; b) Sex explained 27.3% of BF% variation and 16% of TF% variation; c) Diet and genetics had minimal contributions, with BMI PRS accounting for 1.4% of BMI variation; d) Fitness explained 10-15% of variation in all phenotypes except LMI ( < 1%). In final models of BF%, TF% and A:G, age, sex, HEI, RMR, fitness, and BMI PRS were retained by AIC, but BMI PRS was excluded by BIC. For LMI, HEI, PRS and RMR were retained, but HEI was excluded by BIC.

Conclusions: The BMI PRS significantly predicts BMI and obesity, explaining a portion of the variance in body composition outcomes. However, lifestyle factors such as fitness and RMR also play important roles, with their contributions varying across different phenotypes.

Funding Sources: USDA-ARS 2032-10700-003-000D