An oral microbiome model for predicting atherosclerotic cardiovascular disease

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.

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).

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 Fusobacterium nucleatum, Prevotella, Porphyromonas, Leptotrichia, Streptococcus and Actinomyces were significantly associated with ASCVD event risk (all P < 0.05). Three machine learning models (RF, GB, and KNN) were constructed, with the RF model achieving the highest predictive performance. The AUC values of the RF, GB, and KNN models in the training set were 0.888 (95% CI: 0.818-0.958), 0.823 (95% CI: 0.745-0.901), and 0.812 (95% CI: 0.727-0.898) respectively, and in the validation set were 0.845 (95% CI: 0.740-0.951), 0.746 (95% CI: 0.621-0.871), and 0.767 (95% CI: 0.647-0.887) respectively. Additionally, the integrated model showed significant improvements in net reclassification improvement (NRI = 0.315, P < 0.05) and integrated discrimination improvement (IDI = 0.227, P < 0.05) compared to traditional clinical models.

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.