Causal factors for osteoarthritis risk revealed by mendelian randomization analysis

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.

Keywords: Causality; Genetics; Mendelian randomization; Osteoarthritis.

Fig. 1

Basic principles and core assumptions of Mendelian randomization (MR). The relevance assumption asserts that genetic variation is strongly correlated with exposure. The independence assumption indicates that genetic variation is independent of potential confounding factors. Lastly, the exclusion restriction assumption proposes that genetic variation influences the outcome solely through its effect on exposure

Fig. 2

Literature screening process diagram