Metabolomics of Sarcopenia in Hong Kong Chinese

Abstract

Objective: Sarcopenia has recently been recognized as an independent condition by the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) Code. However, the pathophysiology of sarcopenia remains largely unknown. The aim of this study was to evaluate the role of metabolome in sarcopenia using untargeted metabolomics approach. Materials and methods: Untargeted metabolomic profiling in serum sample was performed in 287 participants from the Hong Kong Osteoporosis Study. In total, 725 metabolites with known identity and missingness<50% were included in the final analysis. The sarcopenia phenotypes, gait speed, handgrip strength, and appendicular lean mass (ALM), were studied. Multivariable linear regression was used to evaluate the association between metabolites and sarcopenia phenotypes. Multivariate analysis of covariance (MANCOVA) was used to evaluate the association of metabolite with multiple correlated quantitative sarcopenia phenotypes, with adjustment for age, sex, height, and weight. Pathway analysis was performed using MetaboAnalyst 3.0. Results: There were five, three, and one metabolites significantly associated with appendicular lean mass (ALM) measured using dual-energy X-ray absorptiometry, handgrip strength, and gait speed respectively with a false-discovery rate Q-value <0.05. The metabolite with the strongest association was creatine (Beta: +2.41kg per standard deviation [SD]), 3-methyl-2-oxobutyrate (Beta: +4.47kg per SD), and 5-hydroxylysine (Beta: -1.59ms-1 per SD) for ALM, handgrip strength, and gait speed, respectively. In MANCOVA analysis, 28 of the metabolites were significantly associated with the sarcopenia phenotype, two of which were further associated with fat mass. The variance explained by these 28 metabolites in handgrip strength, gait speed, ALM was 6.6%, 8.1%, and 10%, respectively. Conclusion: Our study identified muscle-related metabolites and demonstrated the power of a multivariate metabolomics approach.