Veledar Emir, Veledar Omar, Gardener Hannah, Rundek Tatjana, Garelnabi Mahdi
Department of Neurology, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA.
Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
Cell Biochem Biophys. 2025 Aug 30. doi: 10.1007/s12013-025-01837-9.
Oxidized low-density lipoprotein (OxLDL) is increasingly recognized as a critical mediator in the pathogenesis of atherosclerosis and several chronic diseases, including type 2 diabetes, metabolic syndrome, Alzheimer's disease, and chronic kidney disease. Given the biochemical heterogeneity of OxLDL, its accurate quantification remains a significant analytical challenge for precise statistical and Machine Learning (ML) methods. The paper examines statistical and computational methodologies used to assess OxLDL levels in clinical studies, highlighting strengths, limitations, and clinical relevance. This contribution provides current insights on standardizing analytic pipelines using statistical and machine learning tools for reproducibility, interpretability, and translational impact in clinical research. Traditional statistical methods have provided a foundational understanding of OxLDL's clinical implications. Meta-analyses, regression models, and survival analyses have consistently demonstrated associations between elevated OxLDL levels and increased disease risk, severity, and mortality. Comparative analyses (t-tests, ANOVA) and correlation studies further reveal its links with inflammation, lipid profiles, and cardiac function. Emerging ML and Artificial Intelligence (AI) approaches offer powerful tools to advance OxLDL research. Predictive models using ML algorithms enhance disease risk stratification, while deep learning facilitates automated image analysis to assess OxLDL-induced vascular changes. AI-integrated diagnostic platforms now combine clinical, biochemical, and imaging data to improve outcome prediction in CVD.
氧化型低密度脂蛋白（OxLDL）越来越被认为是动脉粥样硬化和几种慢性疾病发病机制中的关键介质，这些慢性疾病包括2型糖尿病、代谢综合征、阿尔茨海默病和慢性肾脏病。鉴于OxLDL的生化异质性，其准确定量对于精确的统计和机器学习（ML）方法而言仍然是一项重大的分析挑战。本文研究了临床研究中用于评估OxLDL水平的统计和计算方法，突出了其优势、局限性和临床相关性。本论文提供了关于使用统计和机器学习工具标准化分析流程以实现临床研究中的可重复性、可解释性和转化影响的当前见解。传统统计方法为OxLDL的临床意义提供了基础性的理解。荟萃分析、回归模型和生存分析一致表明，OxLDL水平升高与疾病风险增加、病情严重程度和死亡率之间存在关联。比较分析（t检验、方差分析）和相关性研究进一步揭示了其与炎症、血脂谱和心脏功能的联系。新兴的ML和人工智能（AI）方法为推进OxLDL研究提供了强大工具。使用ML算法的预测模型可增强疾病风险分层，而深度学习有助于自动化图像分析以评估OxLDL诱导的血管变化。现在，集成AI的诊断平台结合了临床、生化和影像数据，以改善心血管疾病（CVD）的预后预测。
