Why the study?
Current algorithms for assessing ASCVD risk and reliance on LDL cholesterol fail to identify a sizeable population at high risk for adverse cardiovascular events, particularly those with metabolic syndrome, prediabetes, and diabetes.
Key points are not available for this paper at this time.
Design
Review
LDL reliance in ASCVD algorithms may miss high-risk metabolic patients; leaves open whether apoB integration refines risk stratification.
Current algorithms for assessing risk of atherosclerotic cardiovascular disease (ASCVD) and, in particular, the reliance on low-density lipoprotein (LDL) cholesterol in conditions where this measurement is discordant with apoB and LDL-particle concentrations fail to identify a sizeable part of the population at high risk for adverse cardiovascular events. This results in missed opportunities for ASCVD prevention, most notably in those with metabolic syndrome, prediabetes, and diabetes. There is substantial evidence that accumulation of ectopic fat and associated metabolic traits are markers for and pathogenic components of high-risk atherosclerosis. Conceptually, the subset of advanced lesions in high-risk atherosclerosis that triggers vascular complications is closely related to a set of coordinated high-risk traits clustering around a distinct metabolic phenotype. A key feature of this phenotype is accumulation of ectopic fat, which, coupled with age-related muscle loss, creates a milieu conducive for the development of ASCVD: atherogenic dyslipidemia, nonresolving inflammation, endothelial dysfunction, hyperinsulinemia, and impaired fibrinolysis. Sustained vascular inflammation, a hallmark of high-risk atherosclerosis, impairs plaque stabilization in this phenotype. This review describes how metabolic and inflammatory processes that are promoted in large measure by ectopic adiposity, as opposed to subcutaneous adipose tissue, relate to the pathogenesis of high-risk atherosclerosis. Clinical biomarkers indicative of these processes provide incremental information to standard risk factor algorithms and advanced lipid testing identifies atherogenic lipoprotein patterns that are below the discrimination level of standard lipid testing. This has the potential to enable improved identification of high-risk patients who are candidates for therapeutic interventions aimed at prevention of ASCVD.
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Lechner et al. (2020) studied this question.
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