Branched chain amino acids are associated with muscle mass in functionally limited older adults

Abstract

Background: Metabolic profiling may provide insight into biologic mechanisms related to the maintenance of muscle and fat-free mass in functionally limited older adults. The objectives of the study were to characterize the association between thigh muscle cross-sectional area (CSA) and the fat-free mass index (FFMI; total lean mass/height(2)) with the serum metabolite profile, to further identify significant metabolites as associated with markers of insulin resistance or inflammation, and to develop a metabolite predictor set representative of muscle CSA and the FFMI in functionally limited older adults.

Methods: Multivariable-adjusted linear regression was used on mass spectrometry-based metabolomic data to determine significant associations between serum metabolites with muscle CSA and the FFMI in 73 functionally limited (Short Physical Performance Battery ≤ 10) older adults (age range: 70-85 years). Significant metabolites were further examined for associations with markers of insulin resistance (homeostasis model assessment of insulin resistance) or inflammation (tumor necrosis factor-α and interleukin-6). Multivariable-adjusted stepwise regression was used to develop a metabolite predictor set representative of muscle CSA and the FFMI.

Results: Seven branched chain amino acid-related metabolites were found to be associated with both muscle CSA and the FFMI. Separately, two metabolites were identified as insulin resistance-associated markers of the FFMI, whereas four metabolites were identified as inflammation-associated markers of either muscle CSA or the FFMI. Stepwise models identified combinations of metabolites to explain approximately 68% of the variability inherent in muscle CSA or the FFMI.

Conclusions: Collectively, we report multiple branched chain amino acids and novel inflammation-associated tryptophan metabolites as markers of muscle CSA or the FFMI in functionally limited older adults.

Keywords: Biomarkers; Inflammation; Insulin resistance; Metabolomics.; Muscle.