Saharan Seema Singh, Creasy Kate Townsend, Birnbaum Lauren, Stock Eveline O, Mustra Rakic Jelena, Tian Xiaoli, Prakash Arun, Malloy Mary, Kane John
Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, United States.
Department of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania. Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, United States.
Proc (Int Conf Comput Sci Comput Intell). 2025;2507:139-153. doi: 10.1007/978-3-031-94950-0_13. Epub 2025 Aug 29.
Coronary heart disease (CHD) remains the leading cause of global mortality, per the Center for Disease Control. Thus, it is important to develop novel and improved methods for CHD prediction, detection, and early intervention. Our study aims to assess the predictive efficacy of plasma Preβ High-Density Lipoprotein (HDL) and cytokines as biomarkers of CHD, utilizing machine learning (ML) algorithms to enhance risk predictions. In a case-control study, we explored the potential of 35 plasma cytokines in conjunction with Preβ HDL levels to discriminate "at risk" CHD patients from non-affected, control subjects. The dataset contains data on 108 individuals and is divided into two cohorts: 41 individuals with CHD and 67 individuals in the Control group. Leveraging random forest, coupled with feature engineering and importance techniques, the dataset underwent synthetic augmentation, yielding a total of 20,000 samples. In comparison to the Control group, individuals in the CHD group exhibited significantly higher levels of Plasma Preβ HDL, with mean values of 13.5 mg/dL apoA1 and 10.2 mg/dL apoA1 respectively (p < 0.05). The second random forest classifier incorporating: Preβ HDL, FGF-Basic, MCP-1, Eotaxin, IL-10, IL-9, IL-1β achieved a F1 score, prediction accuracy, and AUROC score of 100%. The remarkable results derived from the random forest classifiers underscore the need for further exploration into the predictive potential of Preβ HDL and plasma cytokines in the development of CHD, using ML methodologies. Further investigation may lead to the identification of novel drug targets for more effective therapeutic interventions.
根据疾病控制中心的数据，冠心病（CHD）仍然是全球死亡的主要原因。因此，开发用于冠心病预测、检测和早期干预的新颖且改进的方法非常重要。我们的研究旨在评估血浆前β高密度脂蛋白（HDL）和细胞因子作为冠心病生物标志物的预测效力，利用机器学习（ML）算法来增强风险预测。在一项病例对照研究中，我们探索了35种血浆细胞因子与前β HDL水平相结合，以区分“有风险”的冠心病患者与未受影响的对照受试者的潜力。该数据集包含108个人的数据，并分为两个队列：41名冠心病患者和67名对照组个体。利用随机森林，结合特征工程和重要性技术，对数据集进行了合成扩充，共产生了20,000个样本。与对照组相比，冠心病组个体的血浆前β HDL水平显著更高，载脂蛋白A1的平均值分别为13.5 mg/dL和10.2 mg/dL（p < 0.05）。包含前β HDL、碱性成纤维细胞生长因子（FGF - Basic）、单核细胞趋化蛋白 - 1（MCP - 1）、嗜酸性粒细胞趋化因子（Eotaxin）、白细胞介素 - 10（IL - 10）、白细胞介素 - 9（IL - 9）、白细胞介素 - 1β（IL - 1β）的第二个随机森林分类器的F1分数、预测准确率和曲线下面积（AUROC）分数达到了100%。随机森林分类器得出的显著结果强调了使用ML方法进一步探索前β HDL和血浆细胞因子在冠心病发展中的预测潜力的必要性。进一步的研究可能会导致识别出用于更有效治疗干预的新型药物靶点。
