Causal relationship between blood metabolites and sarcopenia-related traits:a Mendelian randomization study

Chen Tianxin; Dong Tingting; Li Yan; Zhang Sheng; Zhang Lei

doi:10.12307/2024.572

Abstract：BACKGROUND:Clinical evidences have suggested a correlation between metabolic factors and sarcopenia.Blood metabolites have been found as biological factors underlying the mechanisms of musculoskeletal disorders.However,the causal relationship between blood metabolites and sarcopenia is unclear. OBJECTIVE:To explore the causal relationship between blood metabolites and sarcopenia-related traits through a two-sample Mendelian randomization analysis and to analyze their metabolic pathways. METHODS:A dataset of 486 blood metabolites and sarcopenia-related traits was obtained from public databases.The inverse variance weighting,MR-Egger and weighted median methods were used to assess the causal relationship of blood metabolites with muscle mass and strength across genders.Sensitivity analyses,including heterogeneity and gene pleiotropy,were performed to explore the robustness of the results.Metabolic pathway analysis of potential causal relationships was performed using the Metaboanayst 5.0 tool. RESULTS AND CONCLUSION:A total of 124 metabolites and sarcopenia-related traits were observed to have potential causal relationships(P<0.05).Mannose and 1-arachidonoylglycerophosphocholine were significantly causally associated with an increased muscle mass in males(P<1.03×10-4).Pentadecanoate and glycine were significantly causally associated with decreased muscle mass and muscle strength in females,respectively(P<1.03×10-4).Metabolic pathway analysis identified eight metabolic pathways associated with altered levels of muscle mass and muscle strength in sarcopenia,including the"glyoxylate and dicarboxylate metabolism"and"Glycine,serine and threonine metabolism."The identified metabolites are considered as useful circulating metabolic biomarkers for screening and prevention of sarcopenia in clinical practice,serving as candidate molecules for future mechanistic exploration and drug target selection.