Imputation of Baseline LDL Cholesterol Concentration in Patients with Familial Hypercholesterolemia on Statins or Ezetimibe.

BACKGROUND: Familial hypercholesterolemia (FH) is the most frequent genetic disorder seen clinically and is characterized by increased LDL cholesterol (LDL-C) (>95th percentile), family history of increased LDL-C, premature atherosclerotic cardiovascular disease (ASCVD) in the patient or in first-degree relatives, presence of tendinous xanthomas or premature corneal arcus, or presence of a pathogenic mutation in the LDLR, PCSK9, or APOB genes. A diagnosis of FH has important clinical implications with respect to lifelong risk of ASCVD and requirement for intensive pharmacological therapy. The concentration of baseline LDL-C (untreated) is essential for the diagnosis of FH but is often not available because the individual is already on statin therapy. METHODS: To validate a new algorithm to impute baseline LDL-C, we examined 1297 patients. The baseline LDL-C was compared with the imputed baseline obtained within 18 months of the initiation of therapy. We compared the percent reduction in LDL-C on treatment from baseline with the published percent reductions. RESULTS: After eliminating individuals with missing data, nonstandard doses of statins, or medications other than statins or ezetimibe, we provide data on 951 patients. The mean ± SE baseline LDL-C was 243.0 (2.2) mg/dL [6.28 (0.06) mmol/L], and the mean ± SE imputed baseline LDL-C was 244.2 (2.6) mg/dL [6.31 (0.07) mmol/L] (P = 0.48). There was no difference in response according to the patient's sex or in percent reduction between observed and expected for individual doses or types of statin or ezetimibe. CONCLUSIONS: We provide a validated estimation of baseline LDL-C for patients with FH that may help clinicians in making a diagnosis.

Predicting proprotein convertase subtilisin kexin type-9 loss of function mutations using plasma PCSK9 concentration.

BACKGROUND: Low plasma proprotein convertase subtilisin kexin type-9 (PCSK9) concentration has been associated with loss of function (LOF) PCSK9 mutations in several studies. However, the current standard for detection of these LOF mutations is through gene sequencing. Gene sequencing is labor intensive and expensive. Identifying a simple test to help predict PCSK9 LOF mutations would help to better target subjects requiring gene sequencing. OBJECTIVES: Determine the diagnostic accuracy of plasma PCSK9 concentration in detecting PCSK9 LOF mutations using genotyping as the gold standard. METHODS: We carried out a retrospective analysis of 1412 French-Canadian participants of the Quebec Child and Adolescent Health and Social Survey who had been screened for the PCSK9 R46L and Leucine insertion (InsLEU) LOF mutations by genotyping. Their plasma PCSK9 concentrations were measured using a well-described enzyme-linked immunosorbent assay. We used the Youden index to determine the optimal cutoff for PCSK9 concentration to predict mutation status. We further investigated the use of low-density lipoprotein cholesterol (LDL-C) concentration in combination with plasma PCSK9 concentration to refine the prediction of mutation status. RESULTS: Plasma PCSK9 had a moderate accuracy (area under the curve, 0.71) in detecting the PCSK9 R46L mutation with a sensitivity of 71% and specificity of 70% at cutoff of 70 ng/mL. Combining PCSK9 and LDL-C increased the diagnostic accuracy for the detection of the R46L mutation (area under the curve, 0.75). However, plasma PCSK9 concentration was no better than chance at detecting PCSK9 InsLEU mutation. CONCLUSION: This analysis suggests that plasma PCSK9 combined with LDL-C concentrations might be useful to predict certain PCSK9 LOF mutations such as the R46L mutation but may fail to predict others such as the InsLEU mutation.