PDF(Pubmed)
Abstract：
Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model\'s predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.
摘要：
急性心肌梗死(AMI),一种可以产生严重后果的常见疾病，当心肌血流由于冠状动脉阻塞而停止时发生。早期准确预测AMI对于快速预后和改善患者预后至关重要。代谢组学，研究生物系统中的小分子，是用于发现与许多疾病相关的生物标志物的有效工具。这项研究旨在利用代谢组学数据和一种称为可解释的机器学习方法（EBM）来构建AMI的预测模型。EBM模型是在从99个个体收集的102个预后代谢物的数据集上进行训练的，包括34名健康对照和65名AMI患者。经过全面的数据预处理,确定了21种代谢物作为预测AMI的候选预测因子。EBM模型在预测AMI方面表现出令人满意的性能，具有各种分类性能指标。该模型的预测是基于个体代谢物及其相互作用的综合效应。在这种情况下，在两个不同的EBM建模中获得的结果，仅包括个体代谢物特征和它们的相互作用效应，进行了讨论。最重要的预测指标包括肌酐,烟酰胺,和等柠檬酸盐。这些代谢物参与不同的生物活性，比如能量代谢，DNA修复,和细胞信号。结果表明，代谢组学和EBM模型的组合在构建可靠和可解释的AMI预测输出中的潜力。讨论的代谢物生物标志物可能有助于早期诊断，风险评估，和AMI患者的个性化治疗方法。这项研究成功地开发了一个包含广泛的数据预处理和EBM模型的管道，以识别潜在的代谢物生物标志物来预测AMI。EBM模型,具有整合交互术语的能力，表现出令人满意的分类性能，并揭示了显著的代谢物相互作用，这在评估AMI风险方面可能是有价值的。然而,从这项研究中获得的结果应通过在较大且定义明确的样本中进行的研究进行验证.
