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<p>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.</p>

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Harnessing Statistical and Machine Learning Approaches to Analyze Oxidized LDL in Clinical Research

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Emir Veledar,
  • Omar Veledar,
  • Hannah Gardener,
  • Tatjana Rundek,
  • Mahdi Garelnabi

摘要

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.