An oral microbiome model for predicting atherosclerotic cardiovascular disease.

Objective: This study aimed to construct a predictive model for the early onset of atherosclerotic cardiovascular disease (ASCVD) by integrating oral microbiome data with traditional clinical risk factors. Methods: A retrospective study was conducted involving participants aged 50-70 years without pre-existing ASCVD. The patients were divided into a training set and a validation set at a ratio of 7:3 by the complete randomization method. The characteristics of the oral microbiome were characterized by 16S rRNA/metagenomic sequencing. In the training set, univariate analysis and multivariate Logistic regression analysis were applied to screen predictive variables, and Random Forest (RF), Gradient Boosting (GB), and K-nearest Neighbor (KNN) were constructed. The receiver operating characteristic (ROC) curve was validated. The model performance was evaluated by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: A total of 331 patients were enrolled and randomly divided into a training set (n=231) and a validation set (n=100). 40 out of 331 participants experienced major adverse cardiovascular events (MACE). Multivariate Logistic regression analysis confirmed that age, relative abundance of Conclusion: The integration of the oral microbiome and clinical data can improve the accuracy of the ASCVD risk prediction model, providing a novel biomarker strategy for primary cardiovascular prevention.