Metabolome-wide Mendelian Randomization Assessing the Causal Relationship between Blood Metabolites and Sarcopenia-related Traits

孟德尔随机化 代谢组 肌萎缩 混淆 生物 代谢物 遗传学 内科学 生物信息学 医学 代谢组学 内分泌学 基因 遗传变异 基因型
作者
Simin Chen,Yiran Dong,Nuerbiyamu Aiheti,Sheng Wang,Shikang Yan,Kaidiriyan Kuribanjiang,Huilong Li,Xing Peng,Abudunaibi Wupuer,Yihan Li,Lei Yang,Jianping Zhao
出处
期刊:The Journals of Gerontology [Oxford University Press]
卷期号:79 (4) 被引量:1
标识
DOI:10.1093/gerona/glae051
摘要

Abstract Sarcopenia is among the most common musculoskeletal illnesses, yet its underlying biochemical mechanisms remain incompletely understood. In this study, we used Mendelian randomization (MR) to investigate the causal relationship between the genetically determined blood metabolites and sarcopenia, with the overall objective of identifying likely molecular pathways for sarcopenia. We used 2-sample MR to investigate the effects of blood metabolites on sarcopenia-related traits. 452 metabolites were exposure, and 3 sarcopenia-related traits as the outcomes: handgrip strength, appendicular lean mass, and walking pace. The inverse-variance weighted (IVW) causal estimates were determined. For sensitivity analysis, methods such as MR-Egger regression, the weighted median, the weighted mode, and the heterogeneity test were used. Additionally, for complementation, we performed replication, meta-analysis, and metabolic pathway analyses. Candidate biomarkers were defined by meeting one of the following criteria: (1) significant metabolites are defined as pIVW < pBonferroni [1.11 × 10−4 (.05/452)]; (2) strong metabolites are defined as 4 MR methods p < .05; and (3) suggestive metabolites are defined as passing sensitivity analysis. Three metabolites (creatine, 1-arachidonoylglycerophosphocholine, and pentadecanoate [15:0]) with significant causality, 3 metabolites (glycine, 1-arachidonoylglycerophosphocholine, and epiandrosterone sulfate) with strong causality, and 25 metabolites (including leucylleucin, pyruvic acid, etc.) with suggestive causality were associated with sarcopenia-related traits. After further replication analyses and meta-analysis, these metabolites maintained substantial effects on sarcopenia-related traits. We additionally identified 14 important sarcopenia-related trait metabolic pathways. By combining metabolomics with genomics, these candidate metabolites and metabolic pathways identified in our study may provide new clues regarding the mechanisms underlying sarcopenia.
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