High-Density Lipoprotein Particles, Inflammation, and Coronary Heart Disease Risk

Abstract

Background: Coronary 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.

Methods: CHD cases (n = 227, mean age 61 years, 47% female) and matched controls (n = 526) underwent fasting blood collection while off lipid-lowering medications. Automated chemistry analyses were performed to measure total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-C (LDL-C), small dense LDL-C (sdLDL-C), apolipoproteins (apos) A-I and B, lipoprotein(a) (Lp(a)), high-sensitivity C-reactive protein (hsCRP), serum amyloid-A (SAA), myeloperoxidase (MPO), and apoA-I in HDL particles (via 2-dimensional electrophoresis and immunoblotting). Univariate, multivariate, and machine learning analyses compared the CHD cases with the controls.

Results: The 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.

Conclusions: 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.

Keywords: CHD; HDL; inflammation.

Figure 1

HDL Particle metabolism. After the production of very-small-discoidal lipid-poor preβ-1 HDL in the liver or intestine (step 1), these particles bind to cellular ABCA1, resulting in the removal of FC and PL from cells, including those in artery wall plaques (step 2), and the formation of small-discoidal α-4 HDL. These particles, in turn, are converted to medium-spherical α-3 HDL when their free cholesterol is esterified to CE by the action of LCAT. Moreover, efficient lipolysis via the action of lipoprotein lipase on TRL is essential to the formation of larger HDL particles and for normal HDL metabolism and the transfer of TRL lipid and apolipoprotein constituents to HDL (step 3). Medium α-3 HDL is, in turn, converted to large α-2 HDL with further cholesterol esterification via LCAT (step 4). These particles, in turn, pick up TG from TRL in exchange for CE via CETP to form very large α-1 HDL. There is also further cholesterol esterification via LCAT (step 5). These particles also pick up TG in exchange for CE via CETP with some conversion of α-1 to α-2 HDL via HL (step 6). Large and very large α-2 and α-1 HDL deliver cholesterol (mainly CE) to the liver via SR-BI with some catabolism of HDL particles (step 7). Moreover, there is a lot of recycling and exchange of HDL constituents between all HDL particles and TRL and recycling of HDL constituents to re-form preβ-1 and α-4 HDL, facilitated by the action of EL and sPLA2 (steps 8 and 9). The final step in HDL particle metabolism is the catabolism of preβ-1 HDL via the kidney (step 10). This latter process is enhanced in patients with hypertriglyceridemia [32,33]. Abbreviations: ABCA1, ATP-binding cassette transporter A1; apo, apolipoprotein; CE, cholesteryl ester; CETP, cholesteryl ester transfer protein; EL, endothelial lipase; FC, free cholesterol; HDL, high-density lipoprotein; HL, hepatic lipase; LCAT, lecithin–cholesterol acyltransferase; PL, phospholipid; sPLA2, secretory phospholipase A2; SR-B1, scavenger receptor class B type 1; TG, triglyceride; TRL, triglyceride-rich lipoprotein.