Supplemental Digital Content is available in the text. Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome–based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome–based ML approach for diagnostic screening of CVD.

中文翻译：

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基于肠道微生物组的心血管疾病诊断筛查的机器学习策略

文本中提供了补充数字内容。心血管疾病（CVD）是人类死亡的第一大原因。除遗传和环境因素外，近年来，肠道菌群已成为影响CVD的新因素。尽管因果关系尚未明确确定，但据报道，肠道微生物群的变化与心血管疾病之间的关联是显着的。因此，我们假设机器学习 (ML) 可用于基于肠道微生物组的 CVD 诊断筛查。为了验证我们的假设，使用 5 种监督机器学习算法（包括随机森林、支持向量机、决策树、弹性网络和神经网络）对通过美国肠道项目收集的 478 名 CVD 和 473 名非 CVD 人类受试者的粪便 16S 核糖体 RNA 测序数据进行了分析。网络。在 CVD 组和非 CVD 组之间鉴定出 39 个差异细菌类群。使用这些分类特征的 ML 建模在接收者操作特征曲线（0.0，完美反歧视；0.5，随机猜测；1.0，完美歧视）下实现了 ≈0.58（随机森林和神经网络）的测试面积。接下来，使用操作分类单元的前 500 个高方差特征（而不是细菌分类群）来训练 ML 模型，并在接收器操作特征曲线下实现了约 0.65（随机森林）的改进测试面积。此外，通过将选择限制为仅前 25 个高度贡献的操作分类单元特征，接收器操作特征曲线下的面积进一步显着增强至约 0.70。总体而言，我们的研究首次确定了 CVD 患者肠道菌群失调，并应用这些知识开发基于肠道微生物组的 ML 方法，用于 CVD 诊断筛查。