Department of Cardiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Department of Geriatric Cardiology, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
Front Cell Infect Microbiol. 2024 Jan 11;13:1305375. doi: 10.3389/fcimb.2023.1305375. eCollection 2023.
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 and were significantly increased in the ACS group, whereas certain SCFA-producing genera such as and 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.
先前的研究表明，肠道微生物群的改变与急性冠状动脉综合征（ACS）的发展密切相关。然而，肠道微生物群在 ACS 的早期诊断中的价值仍有待研究。
我们招募了 66 名志愿者，包括 29 名首次诊断为 ACS 的患者和同期的 37 名健康志愿者，收集他们的粪便样本，并对 16S rRNA 基因的 V4 区进行测序。使用 PICRUSt2 对微生物群进行功能预测。随后，我们基于微生物标志物构建了一个列线图和相应的网页，以辅助 ACS 的诊断。使用自举内部验证、校准曲线和决策曲线分析（DCA）来分析模型的诊断性能和实用性。
与健康对照组相比，ACS 患者的微生物群落多样性和组成明显异常。在 ACS 组中，潜在的致病属如 和 显著增加，而某些产生 SCFA 的属如 和 则减少。此外，在与临床指标的相关性分析中，观察到微生物群与炎症水平和冠状动脉粥样硬化严重程度相关。最后，我们基于肠道微生物群和临床变量开发了一种 ACS 诊断模型，其接受者操作特征（ROC）曲线下面积（AUC）为 0.963（95%CI：0.925-1），自举内部验证的 AUC 值为 0.948（95%CI：0.549-0.641）。该模型的校准曲线显示实际概率与预测概率之间具有良好的一致性。DCA 表明，该模型在临床应用中具有较高的净临床获益。
本研究首次描述了中国西南地区 ACS 患者和健康人群肠道微生物群的组成和功能，并表明了微生物群作为 ACS 早期诊断的非侵入性标志物的潜在作用。
