High-Density Lipoprotein Particles, Inflammation, and Coronary Heart Disease Risk
This 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.
Authors
- Kate Townsend Creasy (ORCID: https://orcid.org/0000-0003-1870-4586)
- Margaret R. Diffenderfer (ORCID: https://orcid.org/0000-0002-3803-2812)
- Eveline O. Stock (ORCID: https://orcid.org/0000-0001-7085-5790)
- Bela F. Asztalos
- Rachel Schwemberger (ORCID: https://orcid.org/0000-0003-3481-2982)
- John P. Kane (ORCID: https://orcid.org/0000-0001-8049-3850)
- Mary J. Malloy (ORCID: https://orcid.org/0000-0001-6189-5993)
- Clive R. Pullinger (ORCID: https://orcid.org/0000-0003-3213-1374)
- Alexander T. Quinn
- Lihong He
- John M. Miller (ORCID: https://orcid.org/0000-0002-1655-9621)
Institutions
- Tufts University (US)
- University of California, San Francisco (US)
- Hospital of the University of Pennsylvania (US)
- Alameda Health System (US)
- Highland Hospital (US)
Publication Details
- Journal
- Nutrients
- Published
- 2025-03-28
- DOI
- https://doi.org/10.3390/nu17071182
- Citations
- 6
- Primary Topic
- Lipoproteins and Cardiovascular Health
- Type
- article
- Field-Weighted Citation Impact
- 12.04