{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:17:12Z","timestamp":1787318232515,"version":"build-2736575974"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"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>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The cardiometabolic index, a composite indicator integrating central obesity and lipid abnormalities, has demonstrated predictive value in several cardiovascular diseases. However, its role in predicting major adverse cardiovascular events among patients with atrial fibrillation remains underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this single-center retrospective cohort study, 192 atrial fibrillation (AF) patients under management at the Jinyang Community Health Service Center in Pudong, Shanghai, from January 2022 to January 2024 were enrolled. Patients were stratified into tertiles based on baseline cardiometabolic index (CMI). The primary endpoint was major adverse cardiovascular events (MACE), comprising cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for worsening heart failure, and coronary revascularization due to unstable angina or ischemic events. Multivariable Cox proportional hazards models were used to assess the independent association between CMI and MACE. Kaplan\u2013Meier curves and Log-rank tests were applied to compare event incidence across groups. Restricted cubic spline analysis examined potential nonlinearity. An extreme gradient boosting model was developed to evaluate predictive performance, with SHapley Additive exPlanations used to assess variable importance. Subgroup analyses were conducted to evaluate the consistency of CMI\u2019s predictive value across different clinical populations. The median follow-up duration was 664 days (interquartile range: 384\u2013900 days), estimated using the reverse Kaplan\u2013Meier method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MACE incidence increased significantly with rising CMI levels. Compared to the low CMI group, the high CMI group had a significantly higher risk of MACE (HR = 5.56, 95% CI: 1.48 \u2013 20.90, P = 0.011). Kaplan\u2013Meier analysis showed significant differences in cumulative incidence among the three groups (Log-rank P &amp;lt; 0.001). restricted cubic spline (RCS) modeling revealed a nonlinear positive association, with a sharp increase in MACE risk above a CMI threshold of approximately 0.85 (P for nonlinearity &amp;lt; 0.001). The Extreme Gradient Boosting (XGBoost) model achieved a C-index of 0.737 in the test set, with SHapley Additive exPlanations (SHAP) analysis ranking CMI as the fourth most influential predictor, following age, left atrial diameter, and left ventricular ejection fraction. Subgroup analyses suggested that the predictive value of CMI was particularly evident in patients without chronic kidney disease and those without prior catheter ablation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Elevated CMI is independently associated with increased MACE risk in patients with atrial fibrillation and demonstrates a nonlinear dose\u2013response relationship. As a simple, accessible metabolic indicator, CMI shows promise for improving cardiovascular risk identification and guiding personalized management\u2014especially in high-risk AF patients without overt metabolic dysfunction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1682622","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T04:11:21Z","timestamp":1759464681000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Cardiometabolic index as a predictor of major adverse cardiovascular events in atrial fibrillation: insights from a community-based cohort"],"prefix":"10.3389","volume":"16","author":[{"given":"Jingjing","family":"Sha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayue","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunhan","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mangmang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caihong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichun","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siliang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001160","article-title":"2023 ACC\/AHA\/ACCP\/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology\/American Heart Association Clinical Practice Guidelines Committee","volume":"148","author":"January","year":"2023","journal-title":"Circulation"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S47385","article-title":"Epidemiology of atrial fibrillation: European perspective","volume":"6","author":"Zoni-Berisso","year":"2014","journal-title":"Clin Epidemiol."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3389\/fnut.2025.1560334","article-title":"Global, regional, and national burden of atrial fibrillation\/flutter attribute able to metabolic, behavioral, and environmental risk factors, 1990\u20132021: a longitudinal observational study","volume":"12","author":"Wei","year":"2025","journal-title":"Front Nutr"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.pcad.2015.07.004","article-title":"Atrial fibrillation and heart failure: update 2015","author":"Thihalolipavan","year":"2015","journal-title":"Prog Cardiovasc Dis"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1111\/jth.13690","article-title":"Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis","volume":"15","author":"van Doorn","year":"2017","journal-title":"J Thromb Haemost."},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.1032736","article-title":"Finding low CHA2DS2-VASc scores unreliable? 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