A new phenotypic classification system for dyslipidemias based on the standard lipid panel

Background Dyslipoproteinemias can be classified by their distinct lipoprotein patterns, which helps determine atherosclerotic cardiovascular disease (ASCVD) risk and directs lipid management but this has required advanced laboratory testing. Objective To develop a new algorithm for classifying lipoprotein disorders that only relies on the standard lipid panel. Methods Lipid thresholds for defining the different lipoprotein phenotypes were derived for Non-High-Density Lipoprotein-Cholesterol (NonHDL-C) and Triglycerides (TG) to be concordant when possible with the current US Multi-Society guidelines for blood cholesterol management. Results The new classification method categorizes patients into all the classical Fredrickson-like phenotypes except for Type III dysbetalipoproteinemia. In addition, a new hypolipidemic phenotype (Type VI) due to genetic mutations in apoB-metabolism is described. The validity of the new algorithm was confirmed by lipid analysis by NMR (N = 11,365) and by concordance with classification by agarose gel electrophoresis/beta-quantification (N = 5504). Furthermore, based on the Atherosclerosis Risk in Communities (ARIC) cohort (N = 14,742), the lipoprotein phenotypes differ in their association with ASCVD (TypeV>IIb > IVb > IIa > IVa > normolipidemic) and can be used prognostically as risk enhancer conditions in the management of patients. Conclusions We describe a clinically useful lipoprotein phenotyping system that is only dependent upon the standard lipid panel. It, therefore, can be easily implemented for increasing compliance with current guidelines and for improving the care of patients at risk for ASCVD.

Value of the Clinical Pharmacist Interventions in the Application of the American College of Cardiology (ACC/AHA) 2018 Guideline for Cholesterol Management

The study aims to examine the extent of implementing the ACC/AHA cholesterol guideline and its updates in practice as well as the role of clinical pharmacists in making such guidelines effective. A cross-sectional observational study was conducted on 272 adult patients who visited the hospital internal medicine clinic in the UAE and were candidates for statin therapy based on the 2018 ACC/AHA guidelines for cholesterol management. Clinical pharmacist interventions were determined. The chi-square test was used to compare compliance with the guidelines before and after clinical pharmacist interventions. Compliance with the recommendations for cholesterol management was significantly improved from 60.3% to 92.6% (X2=79.1, p=0.0001) after clinical pharmacist interventions. Among patients who were on statin therapy, the percentage of those who were on proper statin intensity increased significantly from 47.6% to 94.4% (X2=72.5, p=0.0001). The combination of statins with nonstatin therapies such as ezetimibe and PCSK9 inhibitors increased from 8.5% to 30.6% (X2=95, p<0.0001) and from 0.0% to 1.6% (X2=6, p=0.014), respectively. The use of other lipid-lowering agents was diminished from 14.6% to 3.2% (X2=19.2, p<0.0001). Collaboration between physicians and clinical pharmacists is a crucial strategy to achieve better health outcomes among patients suffering from dyslipidemia.

A New Phenotypic Classification System for Dyslipidemias Based on the Standard Lipid Panel

BACKGROUND Dyslipoproteinemias can be classified by their distinct lipoprotein patterns, which helps determine atherosclerotic cardiovascular disease (ASCVD) risk and directs lipid management but this has required advanced laboratory testing. OBJECTIVE To develop a new algorithm for classifying lipoprotein disorders that only relies on the standard lipid panel. METHODS Lipid thresholds for defining the different lipoprotein phenotypes were derived for Non-High-Density Lipoprotein-Cholesterol (NonHDL-C) and Triglycerides (TG) to be concordant when possible with the current US Multi-Society guidelines for blood cholesterol management. RESULTS The new classification method categorizes patients into all the classical Fredrickson-like phenotypes except for Type III dysbetalipoproteinemia. In addition, a new hypolipidemic phenotype (Type VI) due to genetic mutations in apoB-metabolism is described. The validity of the new algorithm was confirmed by lipid analysis by NMR (N = 11,365) and by concordance with classification by agarose gel electrophoresis/beta-quantification (N = 5504). Furthermore, based on the Atherosclerosis Risk in Communities (ARIC) cohort (N = 14742), the lipoprotein phenotypes differ in their association with ASCVD (TypeV > IIb > IVb > IIa > IVa > normolipidemic) and can be used prognostically as risk enhancer conditions in the management of patients. CONCLUSIONS We describe a clinically useful lipoprotein phenotyping system that is only dependent upon the standard lipid panel. It, therefore, can be easily implemented for increasing compliance with current guidelines and for improving the care of patients at risk for ASCVD.