penetrance. Autosomal dominant FH is attributed to mutations in three different genes: LDL receptor (LDLR), APOB, and proprotein convertase subtilisin/kexin type 9 (PCSK9) (1,(3)(4)(5). Other FH genes have been searched for using exome sequencing without success (2). FH caused by mutations in LDLR adaptor protein (LDLRAP) is known as autosomal recessive FH (2). FH is the most common monogenic disorder leading to premature CHD; despite this fact, it is notoriously underdiagnosed and undertreated worldwide (6).Homozygous FH (HoFH) is characterized by extremely high levels of LDL-C (460-1,160 mg/dl) and early onset coronary artery disease (typically by the second decade of life) (7). Mean LDL-C concentration in untreated patients is close to 615 mg/dl (7,8). Patients are classified into two groups based on the level of LDLR activity, either <2% (receptor negative) or 2-25% (receptor defective). Receptor defective patients have a better prognosis than receptor negative cases (9-11).Heterozygous FH (HeFH) is caused by a single inherited copy of a mutation. The frequency of a heterozygous mutation is >90, 5, and <1% in the LDLR, APOB, and PCSK9 genes, respectively (5). A causal mutation in one of these genes is identified in 60-80% of cases. Affected individuals are characterized by LDL-C levels two to three times greater than normal (190-400 mg/dl). The mean untreated LDL-C concentration is 199.9 mg/dl (12). HeFH is suspected

BackgroundPostprandial lipemia is an important cardiovascular risk factor. The assessment of postprandial lipid metabolism is a newly trend that several consortiums and countries have adopted. The aim of the study is to determine a postprandial triglyceride concentration cut-off point that accurately discriminate individuals with fasting normal triglyceride concentrations from those with fasting hypertriglyceridemia.MethodsCross sectional population-based study. A total of 212 subjects underwent an eight hours’ oral fat tolerance test. Samples were taken fasting, three, four, five, six and eight hours after the meal. The area under the receiver operating characteristic curve (c-statistic) was computed using postprandial triglycerides concentrations as independent predictor, and fasting hypertriglyceridemia as dependent variable.ResultsThe best threshold of postprandial lipemia to discriminate fasting hypertriglyceridemia was 280 mg/dL at any hour area under the curve 0.816 (95% confidence interval 0.753–0.866), bootstrap-corrected c-statistic = 0.733 (95% confidence interval 0.68–0.86). The same value was compared with apolipoprotein B concentrations (>90th percentile) having a good performance: area under the curve 0.687 95% confidence interval 0.624–0.751). Likewise, subjects with high postprandial lipemia have higher Globo risk scores.ConclusionThe 280 mg/dL cut-off point value of postprandial triglycerides concentration any time after a test meal discriminate subjects with fasting hypertriglyceridemia. This threshold has a good performance in a heterogeneous population and has a good concordance with cardiovascular risk surrogates.

Context Statins are the lipid-lowering therapy of choice for the prevention of atherosclerotic cardiovascular disease (ASCVD) but their effectiveness in lowering Low-Density Lipoprotein-Cholesterol (LDL-C) can substantially differ between individuals. In this mini-review, we describe the different causes for a suboptimal statin response and an algorithm for the diagnosis and clinical management of these patients. Evidence Acquisition A PubMed search, using terms “statin resistance”, “statin sensitivity”, “statin pharmacokinetics”, “cardiovascular disease”, and “lipid-lowering therapies” was performed. Published papers in the past 10 years that were relevant to the topic were examined to provide content for this mini-review. Evidence Synthesis Suboptimal lowering of LDL-C by statins is a major problem in the clinical management of patients and limits the value of this therapeutic approach. There are multiple causes of statin hypo-responsiveness with compliance being the most common explanation. Other causes, such as analytical issues with LDL-C measurement and the presence of common lipid disorders (Familial Hypercholesterolemia, elevated lipoprotein(a)(Lp(a)) and secondary dyslipidemias) should be excluded before considering primary statin resistance from rare genetic variants in lipoprotein-related or drug-metabolism genes. A wide variety of non-statin lipid-lowering drugs are now available and can be added to statins to achieve more effective LDL-C lowering. Conclusions The evaluation of statin hypo-responsiveness is a multi-step process that can lead to the optimization of lipid-lowering therapy for the prevention of ASCVD. It may also lead to the identification of distinct types of dyslipidemias that require specific therapies and/or the genetic screening of family members.

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.

No abstract

No abstract

No abstract

No abstract