Abstract
BACKGROUND: The triglyceride-glucose (TyG) index is an indirect marker of insulin resistance used to assess diabetes mellitus and cardiovascular disease risk. However, its clinical evidence regarding atrial fibrillation (AF) remains limited. Similarly, the atherogenic index of plasma (AIP), a recently discovered cardiovascular risk biomarker, has not been evaluated in relation to AF. Therefore, this study aimed to analyze the relationship between the TyG index, AIP, and AF, and compare their ability to predict AF risk. METHODS: This retrospective study included 1,122 patients hospitalized at the First Hospital of Jilin University between January 1, 2023, and February 29, 2024. The associations between the TyG index, AIP, and AF risk were analyzed using multivariate logistic regression, stratified subgroup analyses, and restricted cubic spline regression. Correlation and mediation analyses were performed to evaluate the relationship between the two biomarkers and potential indirect interactions linking the TyG index, AIP, and AF occurrence. Receiver operating characteristic (ROC) curves were generated to compare the predictive accuracies of the TyG index and AIP for AF risk. RESULTS: The TyG index and AIP were identified as independent predictors of AF development. Significant positive and nonlinear relationships were identified between both indices and AF (overall, P < 0.001; nonlinearity, P < 0.001). Subgroup analyses confirmed an elevated AF risk associated with increased TyG and AIP values across various patient subcategories, without significant interaction effects. A strong positive correlation was observed between the TyG index and AIP. Mediation analysis indicated no significant indirect effects of the TyG index and AIP on AF risk (P = 0.132). ROC curve analysis showed that AIP and TyG had comparable predictive abilities. CONCLUSIONS: The TyG index and AIP were independently associated with increased AF risk. Additionally, AIP demonstrated predictive accuracy comparable to that of the TyG index in predicting AF risk.