Gut-microbiome-based predictive model for ST-elevation myocardial infarction in young male patients.
Gut-microbiome-based predictive model for ST-elevation myocardial infarction in young male patients.
复制标题
DOI：
10.3389/fmicb.2022.1031878
复制
发表时间：
2022
影响因子：
5.2
通讯作者：
Li, Yan
中科院分区：
文献类型：
--
作者：
Liu, Mingchuan;Wang, Min;Peng, Tingwei;Ma, Wenshuai;Wang, Qiuhe;Niu, Xiaona;Hu, Lang;Qi, Bingchao;Guo, Dong;Ren, Gaotong;Geng, Jing;Wang, Di;Song, Liqiang;Hu, Jianqiang;Li, Yan
年轻男性患者的ST段抬高型心肌梗死（STEMI）占总心脏病发作事件的很大比例。因此，临床意识和筛查急性心肌梗死（AMI）在年轻的无症状患者是必要的。肠道微生物组可能参与STEMI的发病机制。目前研究的目的是基于该人群的肠道微生物组和临床参数开发早期风险预测模型。共有81名年轻男性（年龄< 44岁）入组本研究。41例STEMI青年男性患者被纳入病例组，40例非冠状动脉疾病（CAD）青年男性患者被纳入对照组。为了确定这两组之间肠道微生物组标志物的差异，使用Illumina MiSeq平台进行基于16S rRNA的肠道微生物组测序。此外，一个诺模图和相应的网页构建。采用K折交叉验证、标准曲线和决策曲线分析（DCA）对模型的诊断有效性和实用性进行了分析。与对照组相比，在病例组患者中观察到α和β多样性的趋势显著降低，并被鉴定为以链球菌和普雷沃菌为代表的肠道微生物组显著改变。在临床参数方面，与对照组相比，病例组患者的体重指数（BMI）、收缩压（SBP）、甘油三酯（TG）、丙氨酸氨基转移酶（ALT）和天冬氨酸氨基转移酶（AST）较高，血尿素氮（BUN）较低。此外，BMI和SBP与链球菌和[瘤胃球菌]呈显著正相关（p<0.05）。此外，BMI和SBP与普雷沃菌属和巨球菌属呈显著负相关（p<0.05）。仅普氏菌与AST呈显著负相关（p < 0.05）。最后，基于肠道微生物组和临床参数构建了早期预测列线图和相应的网页，其中受试者工作特征（ROC）曲线下面积（AUC）为0.877，C指数为0.911。对于内部验证，分层K折交叉验证（K = 3）如下：AUC值为0.934。该模型的校准曲线显示出良好的一致性之间的实际和预测的概率。DCA结果表明，该模型在临床环境中使用时具有较高的净临床获益。在这项研究中，我们结合肠道微生物组和常见的临床参数来构建预测模型。我们的分析表明，所构建的模型是一种非侵入性的工具，在预测年轻男性STEMI的临床应用潜力。
ST-segment elevation myocardial infarction (STEMI) in young male patients accounts for a significant proportion of total heart attack events. Therefore, clinical awareness and screening for acute myocardial infarction (AMI) in asymptomatic patients at a young age is required. The gut microbiome is potentially involved in the pathogenesis of STEMI. The aim of the current study is to develop an early risk prediction model based on the gut microbiome and clinical parameters for this population. A total of 81 young males (age < 44 years) were enrolled in this study. Forty-one young males with STEMI were included in the case group, and the control group included 40 young non-coronary artery disease (CAD) males. To identify the differences in gut microbiome markers between these two groups, 16S rRNA-based gut microbiome sequencing was performed using the Illumina MiSeq platform. Further, a nomogram and corresponding web page were constructed. The diagnostic efficacy and practicability of the model were analyzed using K-fold cross-validation, calibration curves, and decision curve analysis (DCA). Compared to the control group, a significant decrease in tendency regarding α and β diversity was observed in patients in the case group and identified as a significantly altered gut microbiome represented by Streptococcus and Prevotella. Regarding clinical parameters, compared to the control group, the patients in the case group had a higher body mass index (BMI), systolic blood pressure (SBP), triglyceride (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and low blood urea nitrogen (BUN). Additionally, BMI and SBP were significantly (p<0.05) positively correlated with Streptococcus and [Ruminococcus]. Further, BMI and SBP were significantly (p<0.05) negatively correlated with Prevotella and Megasphaera. A significant negative correlation was only observed between Prevotella and AST (p < 0.05). Finally, an early predictive nomogram and corresponding web page were constructed based on the gut microbiome and clinical parameters with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.877 and a C-index of 0.911. For the internal validation, the stratified K-fold cross-validation (K = 3) was as follows: AUC value of 0.934. The calibration curves of the model showed good consistency between the actual and predicted probabilities. The DCA results showed that the model had a high net clinical benefit for use in the clinical setting. In this study, we combined the gut microbiome and common clinical parameters to construct a prediction model. Our analysis shows that the constructed model is a non-invasive tool with potential clinical application in predicting STEMI in the young males.
登录
查看更多内容
影响因子：
12.8
作者：
Kwun JS;Kang SH;Lee HJ;Park HK;Lee WJ;Yoon CH;Suh JW;Cho YS;Youn TJ;Chae IH
通讯作者：
Chae IH
影响因子：
9.8
作者：
McEvoy, Matthew D.;Gupta, Ruchir;Edwards, Mark R.
通讯作者：
Edwards, Mark R.
肠道微生物组和代谢随冠状动脉疾病严重程度的变化
影响因子：
15.5
作者：
Liu, Honghong;Chen, Xi;Zhang, Chenhong
通讯作者：
Zhang, Chenhong
影响因子：
5.9
作者：
Bangalore, Sripal;Fonarow, Gregg C.;Bhatt, Deepak L.
通讯作者：
Bhatt, Deepak L.
影响因子：
8.9
作者：
van den Munckhof, I. C. L.;Kurilshikov, A.;Rutten, J. H. W.
通讯作者：
Rutten, J. H. W.