Can small dense LDL cholesterol be estimated from the lipid profile?

Abstract

Purpose of review: Small dense low-density lipoprotein cholesterol (sdLDL-C) is recognized for its strong atherosclerogenic potential. However, its direct measurement remains impractical in clinical settings due to its high cost, time constraints, and labor-intensive nature. This review discusses the benefits and limitations of estimating sdLDL-C using conventional lipid fractions, highlighting recent advancements in estimation methods.

Recent findings: Sampson et al. proposed a novel equation for estimating sdLDL-C based on conventional lipid parameters, offering a more accessible alternative to direct measurement. Recent studies, including ours, demonstrated that this estimation method achieves sufficiently high accuracy for overall application. However, its accuracy can be improved by incorporating machine learning. Furthermore, sdLDL-C estimated by Sampson's equation has been shown to be a superior risk marker for hypertension, an intermediate phenotype of atherosclerosis, and ischemic heart disease, a major cardiovascular event, compared to conventional lipid profiles alone, although further research is needed to determine whether estimated sdLDL-C is equivalent to directly measured sdLDL-C in risk assessment.

Summary: Estimated sdLDL-C presents a promising alternative to direct measurement. While estimated sdLDL-C levels can serve a risk marker for cardiovascular diseases, further research is needed to refine estimation models and explore their integration into clinical practice.

Keywords: atherosclerosis; machine learning; sampson's equation; small dense low-density lipoprotein cholesterol.