Head-to-head comparison of the ability of the cardiometabolic index and triglyceride-glucose index to predict 3-year major adverse cardiovascular events in patients with atrial fibrillation: insights from a community cohort.

backgroundAtrial fibrillation (AF) represents the most common sustained cardiac arrhythmia and confers an elevated risk of major adverse cardiovascular events (MACEs). Emerging evidence indicates that metabolic dysregulation substantially influences the AF prognosis. The cardiometabolic index (CMI) and triglyceride-glucose (TyG) index are non-insulin-dependent surrogate markers of metabolic dysfunction that are readily obtainable in clinical practice. However, their comparative prognostic value for predicting MACEs in patients with AF has not been previously evaluated within the same cohort.

methodsThis retrospective single-center cohort study enrolled 380 AF patients who received treatment at the Shanghai Jinyang Community Health Center between January 2022 and June 2025, with a maximum follow-up duration of 3 years. CMI and TyG were calculated from routinely collected baseline clinical and laboratory data. MACEs served as the primary endpoint. Predictive performance was examined using adjusted Cox regression with restricted cubic spline (RCS) to assess potential nonlinearity, along with Kaplan-Meier survival curves, receiver operating characteristic (ROC) curve-based discrimination analysis, machine learning approaches, and subgroup interaction testing. Incremental predictive benefit over the CHA2DS2-VASc score was further evaluated.

resultsA total of 53 patients (13.9%) experienced MACEs during follow-up. Baseline CMI and TyG values were statistically higher among patients with events (both P < 0.01). In multivariable Cox regression analyses, elevated CMI (hazard ratio [HR], 3.25; 95% confidence interval [CI], 1.89-5.58) and elevated TyG index (HR, 4.52; 95% CI, 1.83-11.12) emerged as independent predictors of MACEs. RCS analyses revealed nonlinear associations, with threshold effects at a CMI ≈ 0.85 and a TyG index ≈ 9.02. Their predictive ability was further supported by Kaplan-Meier and ROC curve analyses. Machine learning models, particularly extreme gradient boosting (XGBoost), demonstrated increased discrimination (area under the curve [AUC] reaching 0.93). Subgroup analyses revealed enhanced predictive performance in patients without heart failure, coronary artery disease, or diabetes, as well as in individuals aged ≥ 65 years. Incorporation of either the CMI or the TyG index into the CHA2DS2-VASc score yielded significant improvements in predictive accuracy, whereas adding both indices did not provide an additional benefit.

conclusionsCMI and the TyG index function as robust, independent predictors of 3-year MACEs in patients with atrial fibrillation, and may help identify metabolically impaired individuals who are not adequately captured by conventional risk scores. The TyG index, in particular, offers strong predictive accuracy combined with ease of measurement from routine laboratory tests, making it widely accessible across diverse healthcare settings. These simple, cost-effective indices enable the prompt recognition of high-risk patients and facilitate timely initiation of preventive interventions to reduce cardiovascular morbidity and mortality, serving as practical adjuncts to the CHA2DS2-VASc score for more precise risk stratification and personalized management of AF.