Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is closely associated with cardiometabolic abnormalities. However, the role of a comprehensive index integrating multiple cardiometabolic parameters in OSA risk assessment remains unclear. This study aimed to explore the potential association between the Cardiometabolic Index (CMI) and OSA risk in a large-scale population cohort. METHODS: Data were derived from the 2005-2008 and 2015-2018 cycles of the National Health and Nutrition Examination Survey (NHANES), with a total of 39,722 participants included. OSA was defined using self-reported indicators (e.g., daytime sleepiness, nocturnal choking/snoring) rather than objective criteria such as polysomnography (PSG). Logistic regression analysis was performed to examine the association between CMI and OSA. Segmented regression and non-linear models were constructed to verify the non-linear relationship between the two variables. Propensity score matching (PSM) was used to validate the stability of the results, and finally, subgroup analysis was conducted to investigate heterogeneity across different populations. RESULTS: The study found a potential association between elevated CMI and OSA. Multivariate adjusted regression analysis showed that in Model 3, the continuous CMI variable was significantly associated with OSA risk (odds ratio [OR] = 1.014, P = 0.024), and the OSA risk in the Q4 group (highest CMI quartile) was significantly higher than that in the Q1 group (lowest CMI quartile) (OR = 1.272, P < 0.001), showing a linear upward trend. The threshold for the association between CMI and OSA was 2.03: when CMI < 2.03, the OR was 1.50 (P < 0.001), while no statistically significant association was observed when CMI exceeded this threshold, indicating a significant "inverted L-shaped" non-linear relationship between the two. After PSM, the OSA risk in the Q4 group was 1.174 times that in the Q1 group (P = 0.018). Subgroup analysis revealed that the association between CMI and OSA might be stronger in populations without hypertension or stroke, especially among females. CONCLUSIONS: This study found a non-linear positive association between CMI and OSA risk. Specifically, the OSA risk in the Q4 group was significantly higher than that in the Q1 group (OR = 1.272, P < 0.001). Additionally, the association was more prominent in young people, females, and populations without hypertension or stroke. These findings suggest that CMI may serve as a convenient indicator for early screening, particularly applicable to high-risk OSA populations with metabolic disorders. The results of this study provide a new perspective for public health interventions and clinical risk assessment of OSA.