ArticleNutrients2025
High-Density Lipoprotein Particles, Inflammation, and Coronary Heart Disease Risk.
Article in Nutrients, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Effect of HDL disk and LDL dimer presence on lipoprotein particle number determination and subclassification.Analytical and bioanalytical chemistry · 2026Article
- Quantitative metabolomics and machine learning identify differential metabolomic biomarkers across normoglycaemia, pre-diabetes and diabetes in a Qinghai Plateau population.BMJ public health · 2026Article
- Serum-Amyloid-A to High-Density-Lipoprotein-Cholesterol Ratio: superior biomarker for early diagnosis of coronary artery disease with clinically relevant stenoses and development of machine learning diagnostic model.Frontiers in cardiovascular medicine · 2026Article
- Article
- The interaction between blood lipids and ASCVD increases the risk of DKD: a nonlinear relationship transforms into a linear relationship, a cross-sectional study.Frontiers in endocrinology · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundCoronary heart disease (CHD) remains a leading cause of death and has been associated with alterations in plasma lipoprotein particles and inflammation markers. This study aimed to evaluate and compare standard and advanced lipid parameters and inflammatory biomarkers in CHD cases and matched control subjects. We hypothesized that incorporating advanced lipid and inflammatory biomarkers into risk models would improve CHD risk prediction beyond the standard lipid measures.
methodsCHD cases (
resultsThe most significant percent differences between male and female cases versus controls were for hsCRP (+78%, +200%), MPO (+109%, +106%), SAA (+84%, +33%), sdLDL-C (+48%; +43%), Lp(a) (+43%,+70%), apoA-I in very large α-1 HDL (-34%, -26%), HDL-C (-24%, -27%), and apoA-I in very small preβ-1 HDL (+17%; +16%). Total C, non-HDL-C, and direct and calculated LDL-C levels were only modestly higher in the cases. Multivariate models incorporating advanced parameters were statistically superior to a standard model (C statistic: men: 0.913 vs. 0.856; women: 0.903 versus 0.838). Machine learning identified apoA-I in preβ-1-HDL, α-2-HDL, α-1-HDL, α-3-HDL, MPO, and sdLDL-C as the top predictors of CHD.
conclusionsThis study introduces a novel approach to CHD risk assessment by integrating advanced HDL particle analysis and machine learning. By assessing HDL subpopulations (α-1, α-2, preβ-1 HDL), inflammatory biomarkers (MPO, SAA), and small dense LDL, we provide a more refined stratification model. Notably, preβ-1 HDL, an independent risk factor reflecting impaired cholesterol efflux from the artery wall, is highlighted as a critical marker of CHD risk. Our approach allows for earlier identification of high-risk individuals, particularly those with subtle lipid or inflammatory abnormalities, supporting more personalized interventions. These findings demonstrate the potential of advanced lipid profiling and machine learning to enhance CHD risk prediction.
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