Unraveling the relationship between obstructive sleep apnea and osteoarthritis: A multivariate mendelian randomization highlighting the role of BMI as a confounding factor

Background

Osteoarthritis (OA) and obstructive sleep apnea (OSA) are prevalent chronic conditions with emerging evidence suggesting a potential link. However, the causality of this association remains unclear, possibly influenced by confounders like high body mass index (BMI). This study aimed to explore causal relationships between OA and OSA using Mendelian randomization (MR).

Methods

MR analysis was performed to assess causality between OA and OSA. Inverse variance weighting (IVW) was the primary MR method, complemented by sensitivity analyses, including MR steiger, MR-Egger, MR-PRESSO, weighted median, heterogeneity tests, and leave-one-out approaches to evaluate pleiotropy and confirm the robustness of the causal estimates. To exclude confounding effects of BMI, we also used a multivariate MR (MVMR).

Results

After adjusting for BMI through MVMR, no significant causal relationship was identified between genetically predicted OSA and OA phenotypes, including knee (KOA) and hip osteoarthritis (HOA), suggesting that obesity largely drives the observed relationship between these conditions. Similarly, MR steiger doesn't support a causal effect from OA on OSA. Sensitivity analyses confirmed the robustness of these results, with no significant evidence of horizontal pleiotropy or heterogeneity affecting outcomes. The findings indicate that BMI acts as a critical confounder in the relationship between OSA and OA, rather than OSA directly contributing to OA development.

Conclusions

Our findings indicate that there is no significant causal relationship between genetically predicted OSA and OA after adjusting for BMI. These findings underscore obesity as the primary shared risk factor, highlighting the importance of weight management as a key strategy for mitigating the risks of both conditions. Future research should aim to validate these findings in diverse populations and explore other metabolic pathways that may contribute to these complex associations.