{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:30:55Z","timestamp":1766269855164,"version":"3.41.2"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The cardiometabolic Index (CMI) serves as a metric for evaluating the functional and metabolic health of the heart. It aids healthcare professionals in assessing cardiac health, predicting the risk of cardiovascular diseases, and determining the effectiveness of various treatments. Despite its significance, there is a scarcity of studies examining the relationship between CMI and obstructive sleep apnea (OSA). Consequently, our objective was to clarify the relationship between CMI and OSA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a cross-sectional study using data from the 2015\u20132018 National Health and Nutrition Examination Survey (NHANES), focusing on a cohort of adults aged 20 years and older. To assess the prevalence of OSA, we employed the Sleep Questionnaire (SLQ) included in the NHANES dataset, which identifies OSA based on symptom-based survey items. Various analytical methods were utilized to examine the relationship between CMI and OSA, including multivariate logistic regression, restricted cubic splines (RCS), threshold effect analysis, subgroup analyses, and mediation effect analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we included 3,912 participants, among whom 1,997 were diagnosed with OSA, resulting in a prevalence of 51%. After thoroughly accounting for relevant covariates, a positive correlation between the CMI and OSA was observed [OR (95% CI): 1.31 (1.21, 1.42), p &amp;lt; 0.001]. This association was further corroborated through restricted cubic spline (RCS) analyses. Additionally, threshold effect analyses indicated a significant inflection point, with the prevalence of OSA increasing significantly with CMI and then leveling off. Further subgroup analyses demonstrated a significant interaction based on smoking status (p &amp;lt; 0.05). Finally, mediation analyses confirmed that smoking served as a mediator in the relationship between CMI and OSA, exhibiting a mediation effect size of 0.002115.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In the adult population of the United States, a positive nonlinear relationship exists between the CMI and the prevalence of OSA. Smoking status partially mediates this association. Additionally, the findings from the threshold effects analysis indicate that maintaining CMI within an appropriate range can significantly decrease the likelihood of developing OSA.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1609585","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T05:47:48Z","timestamp":1752126468000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Association between cardiometabolic Index and obstructive sleep apnea and the mediating role of smoking: a cross-sectional 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