JOURNAL ARTICLE

Causal Roles of Lifestyle, Psychosocial Characteristics, and Sleep Status in Sarcopenia: A Mendelian Randomization Study.

  • Published In: Journals of Gerontology Series A: Biological Sciences & Medical Sciences, 2024, v. 79, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Mingchong; Yu, Daqian; Pan, Yutao; Ji, Shengchao; Han, Ning; Yang, Chensong; Sun, Guixin 3 of 3

Abstract

This article focuses on identifying causal relationships between lifestyle, psychosocial characteristics, sleep status, and sarcopenia using two-sample Mendelian randomization (MR) analysis based on genome-wide association study (GWAS) data from UK Biobank, FinnGen, and meta-analyses. The study found significant causal roles for coffee intake, education years, smoking behaviors, leisure screen time, and moderate-to-vigorous intensity physical activity (MVPA) during leisure time in the development of sarcopenia, while no significant causal evidence was found for carbohydrate intake, protein intake, alcohol consumption, or sleep status. These findings suggest that interventions targeting coffee consumption, smoking cessation, physical activity enhancement, and educational factors may help prevent sarcopenia. The study was limited to individuals of European descent and employed rigorous genetic instrument filtration to reduce bias.

Additional Information

  • Source:Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2024/01, Vol. 79, Issue 1, p1
  • Document Type:Article
  • Subject Area:Complementary and Alternative Medicine
  • Publication Date:2024
  • ISSN:1079-5006
  • DOI:10.1093/gerona/glad191
  • Accession Number:174909886
  • Copyright Statement:Copyright of Journals of Gerontology Series A: Biological Sciences & Medical Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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