Relationship between obstructive sleep apnea and metabolic syndrome based on the NHANES and mendelian randomization study.

Background: Obstructive Sleep Apnea (OSA) is associated with metabolic disturbances, including hypertension, insulin resistance, and dyslipidemia, collectively known as Metabolic Syndrome (MetS). The relationship between OSA and MetS remains controversial. This study aims to investigate the relationship between OSA and MetS by conducting an observational study using data from NHANES and applying Mendelian Randomization (MR) analysis to examine the bidirectional causal relationship.

Methods: We utilized data from the National Health and Nutrition Examination Survey (NHANES) collected between 2005 and 2008. Diagnosis of OSA was evaluated using the multivariate apnea predictive index (MAP index), while MetS was identified based on established clinical criteria. Genetic data were sourced from recent genome-wide association studies (GWAS) to perform a two-sample MR analysis. Instrumental variables were selected based on strict significance thresholds and linkage disequilibrium criteria. The primary MR analysis employed the inverse variance weighted (IVW) method, supplemented by weighted median and MR-Egger regression methods.

Results: A strong association was identified between OSA and Mets through multivariate logistic regression analysis by using data from the NHANES. The MR analysis revealed a significant bidirectional causal relationship between OSA and MetS. Specifically, OSA was found to increase the risk of MetS (IVW OR = 1.52, 95% CI = 1.08-2.14, P = 0.016), and MetS was similarly found to elevate the risk of OSA (IVW OR = 1.97, 95% CI = 1.76-2.21, P = 7.58E-32). Sensitivity analyses confirmed the robustness of these findings, with no significant evidence of pleiotropy.

Conclusion: This study provides genetic evidence supporting a bidirectional causal relationship between OSA and MetS. These findings underscore the interlinked nature of sleep-disordered breathing and metabolic health, suggesting that addressing one condition could benefit the other. Integrated treatment strategies targeting shared risk factors, such as obesity, may enhance overall health outcomes.