Abstract 13514: Associations Between Plasma Metabolites With Body Composition Traits and Their Responses to Exercise Training

INTRODUCTION: Although exercise training can improve body composition, the molecular biomarkers and mechanisms related to these changes have not been fully elucidated. HYPOTHESIS: We hypothesized that distinct metabolite signatures exist for both baseline levels and exercise responsiveness of body composition traits, with substantial overlap across traits. METHODS: Measurements were taken before and after 20 weeks of endurance training in self-identified Black and White adults of the HERITAGE Family Study (n=652). 300 targeted plasma metabolites were measured using LC-MS. Underwater weighing, CT scans, and anthropometric measurements were used to derive the 11 body composition traits included in this study: BMI, body surface area, fat mass, fat free mass, %fat, waist circumference, waist-to-hip ratio, body weight, and abdominal visceral, subcutaneous, and total fat. Linear mixed models were used to test the association between plasma metabolites and each body composition trait at baseline and in response to training with full covariate adjustment. Significance was set to FDR<0.05. Results: On average, subjects were [mean and (SD)] 35 (14) years old, 33% Black, 54% female, and had BMI of 26.2 (5.2) kg/m 2 . The number of metabolites significantly associated with body composition traits at baseline ranged from 57-141 ( Table 1 ). DMGV was among the top 3 associated metabolites at baseline for all 11 traits, while SM(d18:1/16:1) was associated with 8 of 11 baseline traits. Few if any significant associations were found between change in metabolites and change in body composition measures in response to exercise training (range: 0-10) ( Table 1 ). CONCLUSIONS: Although numerous metabolites were associated with body composition traits at baseline, few associations were observed with trait responses to training. These results suggest that intrinsic body composition and its response to exercise training may have differing underlying metabolic signatures.