Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes

Previous studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.

We recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).

Compared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925-1) and an AUC value of 0.948 (95% CI: 0.549-0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.

Our study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS.