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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.

Guidelines for prevention of cardiovascular diseases use risk scores to guide the intensity of treatment. A comparison of these scores in a German population has not been performed. We have evaluated the correlation, discrimination and calibration of ten commonly used risk equations in primary care in 4044 participants of the DETECT (Diabetes and Cardiovascular Risk Evaluation: Targets and Essential Data for Commitment of Treatment) study. The risk equations correlate well with each other. All risk equations have a similar discriminatory power. Absolute risks differ widely, in part due to the components of clinical endpoints predicted: The risk equations produced median risks between 8.4% and 2.0%. With three out of 10 risk scores calculated and observed risks well coincided. At a risk threshold of 10 percent in 10 years, the ACC/AHA atherosclerotic cardiovascular disease (ASCVD) equation has a sensitivity to identify future CVD events of approximately 80%, with the highest specificity (69%) and positive predictive value (17%) among all the equations. Due to the most precise calibration over a wide range of risks, the large age range covered and the combined endpoint including non-fatal and fatal events, the ASCVD equation provides valid risk prediction for primary prevention in Germany.