基于孟德尔随机化分析的骨关节炎风险的因果因素。

Causal factors for osteoarthritis risk revealed by mendelian randomization analysis.

机构信息

State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, 400016, China.

Chongqing Key Laboratory of Ultrasound Molecular Imaging, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.

出版信息

Aging Clin Exp Res. 2024 Aug 22;36(1):176. doi: 10.1007/s40520-024-02812-9.

Abstract

Osteoarthritis (OA), a prevalent chronic disease among the elderly, presents a complex pathogenesis and currently lacks effective treatment. Traditional observational studies are time-consuming, labor-intensive, susceptible to confounding factors, and cannot establish causal relationships. Mendelian randomization (MR) analysis, leveraging genetic variation to assess causal associations between exposures and outcomes, offers a cost-effective and efficient alternative. Over the past decade, large-scale genome-wide association studies have identified numerous genetic variants linked to OA risk factors, facilitating MR study design. In this review, we systematically identified 52 MR studies meeting specific criteria and evaluated their quality, exploring the impact of lifestyle, nutrition, comorbidities, circulating metabolites, plasma proteins, and other health factors on OA risk. We discuss the results and potential mechanisms of MR findings, addressing conflicting evidence based on existing literature and our prior research. With the ongoing expansion of genome-wide association data, we anticipate MR's role in future OA studies to broaden, particularly in drug development research using targeted MR approaches. We thus aim for this paper to offer valuable insights for researchers and clinicians in related fields.

摘要

骨关节炎(OA)是一种常见的老年慢性疾病,其发病机制复杂,目前缺乏有效的治疗方法。传统的观察性研究耗时、费力,容易受到混杂因素的影响,并且无法建立因果关系。孟德尔随机化(MR)分析利用遗传变异来评估暴露与结局之间的因果关联,提供了一种具有成本效益和效率的替代方法。在过去的十年中,大规模的全基因组关联研究已经确定了许多与 OA 风险因素相关的遗传变异,这为 MR 研究设计提供了便利。在本综述中,我们系统地确定了 52 项符合特定标准的 MR 研究,并评估了它们的质量,探讨了生活方式、营养、合并症、循环代谢物、血浆蛋白和其他健康因素对 OA 风险的影响。我们根据现有文献和我们之前的研究讨论了 MR 研究结果及其潜在机制,解决了基于现有文献的相互矛盾的证据。随着全基因组关联数据的不断扩展,我们预计 MR 在未来 OA 研究中的作用将会扩大,特别是在使用靶向 MR 方法的药物开发研究中。因此,我们希望本文能为相关领域的研究人员和临床医生提供有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7f0/11341639/5c920fccc918/40520_2024_2812_Fig1_HTML.jpg

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