{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T05:48:06Z","timestamp":1792129686610,"version":"4.4.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"14th Five-Year Plan Key Discipline of Public Health and Preventive Medicine in Xinjiang Uygur Autonomous Region"},{"name":"2022 Doctoral Research Initiation Fund of Xinjiang Medical University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2005&amp;lt;\u2005pBonferroni [1.11\u2005\u00d7\u200510\u22124 (.05\/452)]; (2) strong metabolites are defined as 4 MR methods p\u2005&amp;lt;\u2005.05; 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.<\/jats:p>","DOI":"10.1093\/gerona\/glae051","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T02:24:43Z","timestamp":1707963883000},"source":"Crossref","is-referenced-by-count":9,"title":["Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Sarcopenia-Related Traits"],"prefix":"10.1093","volume":"79","author":[{"given":"Simin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Sciences, Xinjiang University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuerbiyamu","family":"Aiheti","sequence":"additional","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Sciences, Xinjiang University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shikang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaidiriyan","family":"Kuribanjiang","sequence":"additional","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Sciences, Xinjiang University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abudunaibi","family":"Wupuer","sequence":"additional","affiliation":[{"name":"School of Public Health, Xinjiang Medical University , Urumqi, Xinjiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Sciences, Xinjiang University , Urumqi, Xinjiang , 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