Zheng Ying-Ying, Wu Ting-Ting, Liu Zhi-Qiang, Li Ang, Guo Qian-Qian, Ma Yan-Yan, Zhang Zeng-Lei, Xun Yi-Li, Zhang Jian-Chao, Wang Wan-Rong, Kadir Patigvl, Wang Ding-Yu, Ma Yi-Tong, Zhang Jin-Ying, Xie Xiang
Department of Cardiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052 P. R. China.
Key Laboratory of Cardiac Injury and Repair of Henan Province, Zhengzhou 450052, China.
J Agric Food Chem. 2020 Mar 18;68(11):3548-3557. doi: 10.1021/acs.jafc.0c00225. Epub 2020 Mar 6.
In the present study, we aimed to characterize gut microbiome and develop a gut microbiome-based diagnostic model in patients with coronary artery disease (CAD). Prospectively, we collected 309 fecal samples from Central China and Northwest China and carried out the sequencing of the V3-V4 regions of the 16S rRNA gene. The gut microbiome was characterized, and microbial biomarkers were identified in 152 CAD patients and 105 healthy controls (Xinjiang cohort, = 257). Using the biomarkers, we constructed a diagnostic model and validated it externally in 34 CAD patients and 18 healthy controls (Zhengzhou cohort, = 52). Fecal microbial diversity was increased in CAD patients compared to that in healthy controls ( = 0.021). Phylum was increased in CAD patients versus healthy controls ( = 0.001). Correspondingly, 48 microbial markers were identified through a 10-fold cross-validation on a random forest model, and an area under the curve (AUC) of 87.7% (95% CI: 0.832 to 0.916, < 0.001) was achieved in the Xinjiang cohort (development cohort, = 257). Notably, an AUC of 90.4% (95% CI: 0.848 to 0.928, < 0.001) was achieved using combined analysis of gut microbial markers and clinical variables. This model provided a robust tool for the prediction of CAD. It could be widely employed to complement the clinical assessment and prevention of CAD.
在本研究中，我们旨在对冠心病（CAD）患者的肠道微生物群进行特征分析，并建立基于肠道微生物群的诊断模型。前瞻性地，我们从中国中部和西北部收集了309份粪便样本，并对16S rRNA基因的V3-V4区域进行了测序。对152例CAD患者和105例健康对照（新疆队列，n = 257）的肠道微生物群进行了特征分析，并鉴定了微生物生物标志物。利用这些生物标志物，我们构建了一个诊断模型，并在34例CAD患者和18例健康对照（郑州队列，n = 52）中进行了外部验证。与健康对照相比，CAD患者的粪便微生物多样性增加（P = 0.021）。与健康对照相比，CAD患者的厚壁菌门增加（P = 0.001）。相应地，通过随机森林模型的10倍交叉验证鉴定了48个微生物标志物，在新疆队列（开发队列，n = 257）中获得了87.7%的曲线下面积（AUC）（95%CI：0.832至0.916，P < 0.001）。值得注意的是，使用肠道微生物标志物和临床变量的联合分析获得了90.4%的AUC（95%CI：0.848至0.928，P < 0.001）。该模型为CAD的预测提供了一个强大的工具。它可广泛用于补充CAD的临床评估和预防。
