全代谢组孟德尔随机化评估血液代谢物与肌肉疏松症相关特征之间的因果关系。

Simin Chen, Yiran Dong, Nuerbiyamu Aiheti, Jie Wang, Shikang Yan, Kaidiriyan Kuribanjiang, Huilong Li, Xing Peng, Abudunaibi Wupuer, Yihan Li, Lei Yang, Jianping Zhao
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引用次数: 0

摘要

肌肉疏松症是最常见的肌肉骨骼疾病之一,但人们对其潜在的生化机制仍知之甚少。在这项研究中,我们采用孟德尔随机化方法(MR)来研究由基因决定的血液代谢物与肌肉疏松症之间的因果关系,总体目标是找出可能导致肌肉疏松症的分子途径。我们采用双样本 MR 方法研究血液代谢物对肌肉疏松症相关性状的影响。我们暴露了 452 种代谢物,并以三种与肌肉疏松症相关的性状为结果:手握强度、关节瘦体重和步行速度。确定了反方差加权(IVW)因果估计值。在敏感性分析中,使用了 MR-Egger 回归、加权中位数、加权模式和异质性检验等方法。此外,为了进行补充,我们还进行了复制、荟萃分析和代谢通路分析。候选生物标志物的定义需满足以下标准之一:(1) 重要的代谢物定义为 Pivw
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Sarcopenia-Related Traits.

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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