Rare variants at KCNJ2 are associated with LDL-cholesterol levels in a cross-population study.

Leveraging whole genome sequencing data of 1751 individuals from the UK and 2587 Qatari subjects, we suggest here an association of rare variants mapping to the sour taste-associated gene KCNJ2 with reduced low-density lipoprotein cholesterol (LDL-C, P = 2.10 × 10-12) and with a 22% decreased dietary trans-fat intake. This study identifies a novel candidate rare locus for LDL-C, adding insights into the genetic architecture of a complex trait implicated in cardiovascular disease.

Unraveling the genetic background of individuals with a clinical familial hypercholesterolemia phenotype.

Familial hypercholesterolemia (FH) is a common genetic disorder of lipid metabolism caused by pathogenic/likely pathogenic variants in LDLR, APOB, and PCSK9 genes. Variants in FH-phenocopy genes (LDLRAP1, APOE, LIPA, ABCG5, and ABCG8), polygenic hypercholesterolemia, and hyperlipoprotein (a) [Lp(a)] can also mimic a clinical FH phenotype. We aim to present a new diagnostic tool to unravel the genetic background of clinical FH phenotype. Biochemical and genetic study was performed in 1,005 individuals with clinical diagnosis of FH, referred to the Portuguese FH Study. A next-generation sequencing panel, covering eight genes and eight SNPs to determine LDL-C polygenic risk score and LPA genetic score, was validated, and used in this study. FH was genetically confirmed in 417 index cases: 408 heterozygotes and 9 homozygotes. Cascade screening increased the identification to 1,000 FH individuals, including 11 homozygotes. FH-negative individuals (phenotype positive and genotype negative) have Lp(a) >50 mg/dl (30%), high polygenic risk score (16%), other monogenic lipid metabolism disorders (1%), and heterozygous pathogenic variants in FH-phenocopy genes (2%). Heterozygous variants of uncertain significance were identified in primary genes (12%) and phenocopy genes (7%). Overall, 42% of our cohort was genetically confirmed with FH. In the remaining individuals, other causes for high LDL-C were identified in 68%. Hyper-Lp(a) or polygenic hypercholesterolemia may be the cause of the clinical FH phenotype in almost half of FH-negative individuals. A small part has pathogenic variants in ABCG5/ABCG8 in heterozygosity that can cause hypercholesterolemia and should be further investigated. This extended next-generation sequencing panel identifies individuals with FH and FH-phenocopies, allowing to personalize each person's treatment according to the affected pathway.

Generation and validation of a classification model to diagnose familial hypercholesterolaemia in adults.

BACKGROUND AND AIMS: The early diagnosis of familial hypercholesterolaemia is associated with a significant reduction in cardiovascular disease (CVD) risk. While the recent use of statistical and machine learning algorithms has shown promising results in comparison with traditional clinical criteria, when applied to screening of potential FH cases in large cohorts, most studies in this field are developed using a single cohort of patients, which may hamper the application of such algorithms to other populations. In the current study, a logistic regression (LR) based algorithm was developed combining observations from three different national FH cohorts, from Portugal, Brazil and Sweden. Independent samples from these cohorts were then used to test the model, as well as an external dataset from Italy. METHODS: The area under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves was used to assess the discriminatory ability among the different samples. Comparisons between the LR model and Dutch Lipid Clinic Network (DLCN) clinical criteria were performed by means of McNemar tests, and by the calculation of several operating characteristics. RESULTS: AUROC and AUPRC values were generally higher for all testing sets when compared to the training set. Compared with DLCN criteria, a significantly higher number of correctly classified observations were identified for the Brazilian (p < 0.01), Swedish (p < 0.01), and Italian testing sets (p < 0.01). Higher accuracy (Acc), G mean and F1 score values were also observed for all testing sets. CONCLUSIONS: Compared to DLCN criteria, the LR model revealed improved ability to correctly classify observations, and was able to retain a similar number of FH cases, with less false positive retention. Generalization of the LR model was very good across all testing samples, suggesting it can be an effective screening tool if applied to different populations.

High-Throughput Microscopy Characterization of Rare LDLR Variants.

Familial hypercholesterolemia (FH) is the most common inherited life-threatening disorder of lipid metabolism. Early diagnosis and treatment are the key to reduce the cumulative life-long cardiovascular burden of patients with FH. The high number of LDLR variants described as variants of unknown significance is the largest obstacle to achieve a definitive FH diagnosis. This study established a time- and cost-effective high-throughput cell-based assay to functionally profile LDLR variants, which allowed us to discriminate disruptive rare variants from silent ones. This work generated a valuable resource for systematic functional characterization of LDLR variants solving 1 of the major issues to achieve a definitive FH diagnosis.

Genetic Characteristics of Latvian Patients with Familial Hypercholesterolemia: The First Analysis from Genome-Wide Sequencing.

BACKGROUND: There is limited data on the genetic characteristics of patients with familial hypercholesterolemia (FH) in Latvia. We aim to describe monogenic variants in patients from the Latvian Registry of FH (LRFH). METHODS: Whole genome sequencing with 30× coverage was performed in unrelated index cases from the LRFH and the Genome Database of Latvian Population. LDLR, APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, LIPA, LPA, CYP27A1, and APOE genes were analyzed. Only variants annotated as pathogenic (P) or likely pathogenic (LP) using the FH Variant Curation Expert Panel guidelines for LDLR and adaptations for APOB and PCSK9 were reported. RESULTS: Among 163 patients, the mean highest documented LDL-cholesterol level was 7.47 ± 1.60 mmol/L, and 79.1% of patients had LDL-cholesterol ≥6.50 mmol/L. A total of 15 P/LP variants were found in 34 patients (diagnostic yield: 20.9%): 14 in the LDLR gene and 1 in the APOB gene. Additionally, 24, 54, and 13 VUS were detected in LDLR, APOB, and PCSK9, respectively. No P/LP variants were identified in the other tested genes. CONCLUSIONS: Despite the high clinical likelihood of FH, confirmed P/LP variants were detected in only 20.9% of patients in the Latvian cohort when assessed with genome-wide next generation sequencing.

The Hypocholesterolemic Potential of the Edible Algae Fucus vesiculosus: Proteomic and Quantitative PCR Analysis.

A brown seaweed consumed worldwide, Fucus vesiculosus, has been used to prevent atherosclerosis and hypercholesterolemia, among other uses. However, the mechanisms of action that lead to these effects are not yet fully understood. This work aims to study the in vitro effect of an aqueous extract of F. vesiculosus, previously characterized as rich in phlorotannins and peptides, on the expression of different proteins involved in the synthesis and transport of cholesterol. A proteomic analysis, Western blot, and qRT-PCR analysis were performed to identify protein changes in HepG2 cells exposed to 0.25 mg/mL of the F. vesiculosus extract for 24 h. The proteomic results demonstrated that, in liver cells, the extract decreases the expression of four proteins involved in the cholesterol biosynthesis process (CYP51A1, DHCR24, HMGCS1 and HSD17B7). Additionally, a 12.76% and 18.40% decrease in the expression of two important transporters proteins of cholesterol, NPC1L1 and ABCG5, respectively, was also observed, as well as a 30% decrease in NPC1L1 mRNA levels in the cells exposed to the extract compared to control cells. Our study reveals some of the mechanisms underlying the actions of bioactive compounds from F. vesiculosus that may explain its previously reported hypocholesterolemic effect, future prospecting its use as a functional food.

Contemporary Homozygous Familial Hypercholesterolemia in the United States: Insights From the CASCADE FH Registry.

Background Homozygous familial hypercholesterolemia (HoFH) is a rare, treatment-resistant disorder characterized by early-onset atherosclerotic and aortic valvular cardiovascular disease if left untreated. Contemporary information on HoFH in the United States is lacking, and the extent of underdiagnosis and undertreatment is uncertain. Methods and Results Data were analyzed from 67 children and adults with clinically diagnosed HoFH from the CASCADE (Cascade Screening for Awareness and Detection) FH Registry. Genetic diagnosis was confirmed in 43 patients. We used the clinical characteristics of genetically confirmed patients with HoFH to query the Family Heart Database, a US anonymized payer health database, to estimate the number of patients with similar lipid profiles in a "real-world" setting. Untreated low-density lipoprotein cholesterol levels were lower in adults than children (533 versus 776 mg/dL; P=0.001). At enrollment, atherosclerotic cardiovascular disease and supravalvular and aortic valve stenosis were present in 78.4% and 43.8% and 25.5% and 18.8% of adults and children, respectively. At most recent follow-up, despite multiple lipid-lowering treatment, low-density lipoprotein cholesterol goals were achieved in only a minority of adults and children. Query of the Family Heart Database identified 277 individuals with profiles similar to patients with genetically confirmed HoFH. Advanced lipid-lowering treatments were prescribed for 18%; 40% were on no lipid-lowering treatment; atherosclerotic cardiovascular disease was reported in 20%; familial hypercholesterolemia diagnosis was uncommon. Conclusions Only patients with the most severe HoFH phenotypes are diagnosed early. HoFH remains challenging to treat. Results from the Family Heart Database indicate HoFH is systemically underdiagnosed and undertreated. Earlier screening, aggressive lipid-lowering treatments, and guideline implementation are required to reduce disease burden in HoFH.

The time is now: Achieving FH paediatric screening across Europe - The Prague Declaration.

Familial hypercholesterolaemia (FH) is the most common inherited metabolic disorder characterized by high cholesterol and if left untreated leads to premature cardiovascular disease, such as heart attacks. Treatment that begins early in life, particularly in childhood, is highly efficacious in preventing cardiovascular disease and cost-effective, thus early detection of FH is crucial. However, in Europe, less than 10% of people living with FH are diagnosed and even less receive life-saving treatment. The Prague Declaration is a call to action for national and European Union policymakers and decision-makers and a result of the Czech EU Presidency meeting on FH Paediatric Screening (early detection of inherited high cholesterol) at the Czech Senate in Prague on 6th September 2022. It builds on a considerable body of evidence which was discussed at the Technical Meeting under the auspices of the Slovenian EU Presidency in October 2021. The Prague meeting addressed the outstanding barriers to the systematic implementation of FH paediatric screening across Europe. In this article, we present the key points from the Prague meeting and concrete actions needed to move forward.

Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia.

Familial Hypercholesterolemia (FH) is an inherited disorder of cholesterol metabolism. Current criteria for FH diagnosis, like Simon Broome (SB) criteria, lead to high false positive rates. The aim of this work was to explore alternative classification procedures for FH diagnosis, based on different biological and biochemical indicators. For this purpose, logistic regression (LR), naive Bayes classifier (NB), random forest (RF) and extreme gradient boosting (XGB) algorithms were combined with Synthetic Minority Oversampling Technique (SMOTE), or threshold adjustment by maximizing Youden index (YI), and compared. Data was tested through a 10 × 10 repeated k-fold cross validation design. The LR model presented an overall better performance, as assessed by the areas under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves, and several operating characteristics (OC), regardless of the strategy to cope with class imbalance. When adopting either data processing technique, significantly higher accuracy (Acc), G-mean and F1 score values were found for all classification algorithms, compared to SB criteria (p < 0.01), revealing a more balanced predictive ability for both classes, and higher effectiveness in classifying FH patients. Adjustment of the cut-off values through pre or post-processing methods revealed a considerable gain in sensitivity (Sens) values (p < 0.01). Although the performance of pre and post-processing strategies was similar, SMOTE does not cause model's parameters to loose interpretability. These results suggest a LR model combined with SMOTE can be an optimal approach to be used as a widespread screening tool.

Functional profiling of LDLR variants: Important evidence for variant classification: Functional profiling of LDLR variants.

BACKGROUND: Familial Hypercholesterolemia (FH) is a semidominant disorder of the lipid metabolism associated with premature atherosclerosis and coronary heart disease. So far, about 3,000 unique LDLR variants have been described, most of which lack functional evidence proving their effect on LDLR function, despite the important role that functional studies play in variant classification. OBJECTIVE: In this work, we aimed to functionally characterize 13 rare missense variants, identified worldwide and in Portugal, in clinical FH patients. METHODS: LDLR-deficient CHO-ldlA7 cells were transfected with plasmids carrying different LDLR variants generated by site-directed mutagenesis. LDLR activity and expression were assessed by FACS. RESULTS: 11/13 variants affect LDLR function (p.Cys109Phe; p.Cys143Arg; p.Glu267Lys; p.Cys352Ser; p.Ile451Thr; p.His485Gln; p.Asp492Asn; p.Val500Ala; p.Gly529Arg; p.Phe614Ile; p.Glu626Lys) and 2/13 are inconclusive (p.Arg81Cys; p.Gly98Arg;). CONCLUSION: Of the 13 variants studied, 8 were classified as VUS by ACMG criteria, but for 7 of these 8, our functional studies were able to reassign them as Likely pathogenic or Pathogenic. For an accurate diagnosis, an effort must be made to improve functional characterization of putative disease-causing variants.

Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects.

Familial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The main purpose of the current work was to explore alternative classification methods to traditional clinical criteria for FH diagnosis, based on several biochemical and biological indicators. Logistic regression (LR), decision tree (DT), random forest (RF) and naive Bayes (NB) algorithms were developed for this purpose, and thresholds were optimized by maximization of Youden index (YI). All models presented similar accuracy (Acc), specificity (Spec) and positive predictive values (PPV). Sensitivity (Sens) and G-mean values were significantly higher in LR and RF models, compared to the DT. When compared to Simon Broome (SB) biochemical criteria for FH diagnosis, all models presented significantly higher Acc, Spec and G-mean values (p < 0.01), and lower negative predictive value (NPV, p < 0.05). Moreover, LR and RF models presented comparable Sens values. Adjustment of the cut-off point by maximizing YI significantly increased Sens values, with no significant loss in Acc. The obtained results suggest such classification algorithms can be a viable alternative to be used as a widespread screening method. An online application has been developed to assess the performance of the LR model in a wider population.

Rare primary dyslipidaemias associated with low LDL and HDL cholesterol values in Portugal.

Background: Dyslipidaemia represents a group of disorders of lipid metabolism, characterized by either an increase or decrease in lipid particles, usually associated with triglycerides, LDL cholesterol (LDL-C) and/or HDL cholesterol (HDL-C). Most hyperlipidaemias and HDL deficiencies confer an increased cardiovascular risk, while hypolipidaemia, such as abeta or hypobetalipoproteinemia, may present different manifestations ranging from poor weight progression to neurological manifestations. The aim of this study is to present 7 cases with rare dyslipidaemias associated with low LDL or low HDL cholesterol values, referred to our laboratory for the genetic identification of the cause of the dyslipidaemia. Methods: Lipid profile was determined for each individual in an automated equipment Integra Cobas (Roche). Molecular analysis was performed by NGS with a target panel of 57 genes involved in lipid metabolism (Sure select QXT, Agilent) and samples were run in a NextSEQ Sequencer (Illumina). Only genes associated to rare forms of low HDL-c or LDL-c were analysed for this work, namely: ABCA1, APOA1, LCAT, SCARB1, APOB, PCSK9, MTTP, SAR1B, and ANGPTL3. All rare variants (MAF<5%) found in these genes were confirmed by Sanger sequencing. Results and discussion: This study includes 7 index cases (IC), with the following clinical diagnoses: Fish Eye Disease (1), Hypoalphalipoproteinemia (1) and Abetalipoproteinemia (ABL) / Familial Hypobetalipoproteinemia (FHBL) (5). We have identified one IC with a compound heterozygosity in LCAT causing Fish Eye Disease and one IC with a variant in ABCA1 in homozygosity causing Tangier disease. We found variants causing homozygous FHBL in 2 IC, one of whom has an undescribed pathogenic variant in homozygosity in APOB (c.12087+1G>A) and the other is a possible compound heterozygous for APOB variants c.2604+1G>A and c.4651C>T/p.(Gln1551*). In two patients only a variant in heterozygosity (c.3365delG/p.(Gly1122Vfs*62) and c.11095A>T/p.(Arg3699*)). In the remaining patient, no variants were identified. NGS proved to be a fundamental key for genetic testing of rare lipid disorders, allowing us to find the genetic cause of disease in 6/7 patients with low HDL-c and LDL-c. Patients with these rare conditions should be identified as early as possible in order to minimize or prevent clinical manifestations. The unsolved case is still under investigation.

The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for LDLR variant classification.

PURPOSE: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for sequence-level variant classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource Familial Hypercholesterolemia (FH) Variant Curation Expert Panel was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. In this study, we provide consensus recommendations for the most common FH-associated gene, LDLR, where >2300 unique FH-associated variants have been identified. METHODS: The multidisciplinary FH Variant Curation Expert Panel met in person and through frequent emails and conference calls to develop LDLR-specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate, and commentary, consensus among experts was reached. RESULTS: The consensus LDLR variant modifications to existing ACMG/AMP guidelines include (1) alteration of population frequency thresholds, (2) delineation of loss-of-function variant types, (3) functional study criteria specifications, (4) cosegregation criteria specifications, and (5) specific use and thresholds for in silico prediction tools, among others. CONCLUSION: Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of patients with FH.

Applicability of Martin-Hopkins formula and comparison with Friedewald formula for estimated low-density lipoprotein cholesterol in e_COR study population.

INTRODUCTION: Low-density lipoprotein cholesterol (LDL) is essential in managing cardiovascular disease risk. Since 1972, the Friedewald formula has been used to estimate LDL concentration, although with some limitations. In 2013, Martin et al. proposed a similar but more accurate formula for calculating LDL. AIM: To assess the applicability of the new formula, which we have named the Martin-Hopkins formula, in the Portuguese population and compare it with the Friedewald formula using direct LDL. METHODS: Cross-sectional study, including 1689 participants from the e_COR study. We applied the Martin-Hopkins and Friedewald formulas for estimated LDL (LDL-M and LDL-F). The Friedewald formula was not applied in 12 cases due to triglycerides ≥400mg/dL. Direct LDL was measured and the accepted significance level was p<0.05. RESULTS: Of the total subjects, 50.2% were male and had a median age of 51 (34) years. LDL-D was 117.0 (44.0) mg/dL, LDL-M was 114.6 (43.7) mg/dL and LDL-F was 113.8 (43.2) mg/dL. The Spearman coefficient (ρ) between LDL-M/LDL-D was 0.987 and between LDL-F/LDL-D was 0.983, p=0.001. This strong correlation was maintained in the group with diabetes (LDL-M/LDL-D ρ=0.987; LDL-F/LDL-D ρ=0.978, p=0.001) and hypertriglyceridemia (LDL-M/LDL-D ρ=0.983; LDL-F/LDL-D ρ=0.982, p=0.001). In terms of agreement, the highest value of κ=0.90 was obtained for LDL-M when LDL-D <100mg/dL. CONCLUSION: The Martin-Hopkins formula performed well and had good applicability, showing superiority in relation to the Friedewald formula, especially for LDL-D values <100mg/dL, diabetes, and hypertriglyceridemia.

Polygenic contribution for familial hypercholesterolemia (FH).

PURPOSE OF REVIEW: The present review summarizes different polygenic risk scores associated with hypercholesterolemia applied to cohorts with a genetic diagnosis of familial hypercholesterolemia (FH). RECENT FINDINGS: Several single-nucleotide polymorphisms associated with increased levels of LDL-C or Lp(a) have been genotyped in population cohorts with FH phenotype, to identify the cause of hypercholesterolemia in mutation negative individuals. In different studies, a large proportion of individuals without a monogenic causative variant (in low density lipoprotein receptor gene (LDLR), apolipoprotein B gene (APOB) or proprotein convertase subtilisin/kexin type 9 gene (PCSK9 genes) was considered to have a hypercholesterolemia with a polygenic basis. The heterogeneity in the phenotype of monogenic FH may also be explained by polygenic contributions to LDL-C. The elevated LDL-C genetic risk score (GRS) has been associated with increased risk of atherosclerotic cardiovascular disease in individuals with monogenic FH. Moreover, a poorer response to lipid lowering therapy has been associated with monogenic FH when compared to a polygenic basis. The reason why Lp(a) concentrations are raised in individuals with clinical FH is unclear, but it could be caused by a genetic variation in Lipoprotein(A) gene as a polygenic contribution. SUMMARY: Polygenic risk scores have revealed to be important tools to define the cause of hypercholesterolemia in FH mutation-negative individuals and should be included in FH diagnosis strategies, although there is still space for more specific LDL-C GRS to be developed. The use of GRS may be used to refine cardiovascular risk prediction in FH patients and could lead to a personalized approach to therapy. The identification of the genetic status of an individual with FH phenotype (monogenic or polygenic) may have implications on their risk stratification, cascade screening of relatives, disease management and therapeutic measures.

Characterization of Two Variants at Met 1 of the Human LDLR Gene Encoding the Same Amino Acid but Causing Different Functional Phenotypes.

Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism, characterized by increased levels of total and LDL plasma cholesterol, which leads to premature atherosclerosis and coronary heart disease. FH phenotype has considerable genetic heterogeneity and phenotypic variability, depending on LDL receptor activity and lifestyle. To improve diagnosis and patient management, here, we characterized two single nucleotide missense substitutions at Methionine 1 of the human LDLR gene (c.1A>T/p.(Met1Leu) and c.1A>C/p.(Met1Leu)). We used a combination of Western blot, flow cytometry, and luciferase assays to determine the effects of both variants on the expression, activity, and synthesis of LDLR. Our data show that both variants can mediate translation initiation, although the expression of variant c.1A>T is very low. Both variants are in the translation initiation codon and codify for the same amino acid p.(Met1Leu), yet they lead to different levels of impairment on LDLR expression and activity, corroborating different efficiencies of the translation initiation at these non-canonical initiation codons. The functional data of these variants allowed for an improved American College of Medical Genetics (ACMG) classification for both variants, which can allow a more personalized choice of the lipid-lowering treatment and dyslipidemia management, ultimately improving patients' prognosis.

Applicability of Martin-Hopkins formula and comparison with Friedewald formula for estimated low-density lipoprotein cholesterol in e_COR study population. / Aplicabilidade da fórmula Martin­Hopkins e comparação com a fórmula Friedewald na estimativa do colesterol LDL na população do estudo e_COR.

INTRODUCTION: Low-density lipoprotein cholesterol (LDL) is essential in managing cardiovascular disease risk. Since 1972, the Friedewald formula has been used to estimate LDL concentration, although with some limitations. In 2013, Martin et al. proposed a similar but more accurate formula for calculating LDL. AIM: To assess the applicability of the new formula, which we have named the Martin-Hopkins formula, in the Portuguese population and compare it with the Friedewald formula using direct LDL. MATERIAL AND METHODS: Cross-sectional study, including 1689 participants from the e_COR study. We applied the Martin-Hopkins and Friedewald formulas for estimated LDL (LDL-M and LDL-F). The Friedewald formula was not applied in 12 cases due to triglycerides ≥400mg/dL. Direct LDL was measured and the accepted significance level was p<0.05. RESULTS: Of the total subjects, 50.2% were male and had a median age of 51 (34) years. LDL-D was 117.0 (44.0) mg/dL, LDL-M was 114.6 (43.7) mg/dL and LDL-F was 113.8 (43.2) mg/dL. The Spearman coefficient (ρ) between LDL-M/LDL-D was 0.987 and between LDL-F/LDL-D was 0.983, p=0.001. This strong correlation was maintained in the group with diabetes (LDL-M/LDL-D ρ=0.987; LDL-F/LDL-D ρ=0.978, p=0.001) and hypertriglyceridemia (LDL-M/LDL-D ρ=0.983; LDL-F/LDL-D ρ=0.982, p=0.001). In terms of agreement, the highest value of κ=0.90 was obtained for LDL-M when LDL-D <100 mg/dL. CONCLUSION: The Martin-Hopkins formula performed well and had good applicability, showing superiority in relation to the Friedewald formula, especially for LDL-D values <100 mg/dL, diabetes, and hypertriglyceridemia.

LDLR variants functional characterization: Contribution to variant classification.

BACKGROUND AND AIMS: Familial hypercholesterolaemia (FH) is an autosomal disorder of lipid metabolism presenting with increased cardiovascular risk. LDLR mutations are the cause of disease in 90% of the cases but functional studies have only been performed for about 15% of all LDLR variants. In the Portuguese Familial Hypercholesterolemia Study (PFHS), 142 unique LDLR alterations were identified and 44 (30%) lack functional characterization. The aim of the present work is to increase evidence for variant classification by performing functional characterization of 13 LDLR missense alterations found in Portugal and in 20 other countries. METHODS: Different LDLR mutants were generated by site-directed mutagenesis and expressed in CHO-ldlA7 cells lacking endogenous expression of LDLR. To determine the effects of alterations on LDLR function, cell surface expression, binding and uptake of FITC-LDL were assessed by flow cytometry and Western blot. RESULTS: Of the 13 variants studied 7 were shown to affect LDLR function - expression, binding or uptake, with rates lower than 60%: p.(Cys184Tyr), p.(Gly207_Ser213del); p.(His211Asp); p.(Asp221Tyr); p.(Glu288Lys); p.(Gly592Glu) and p.(Asp601Val)). The remaining 6 variants do not alter the LDLR function. CONCLUSIONS: These studies contributed to an update of these variants classification: from the 9 variants classified as variants of unknown significance, 7 have reached now a final classification and 3 variants have improved classification from likely pathogenic to pathogenic. In Portugal, an additional 55 patients received an FH definite diagnosis thanks to these studies. Since only likely pathogenic and pathogenic variants are clinically actionable, this work shows the importance of functional studies for variant classification.

Pharmacogenomics of statins and familial hypercholesterolemia.

PURPOSE OF REVIEW: To collect evidence on statin pharmacogenomics, and review what is known in this field for familial hypercholesterolemia (FH) patients. RECENT FINDINGS: There are well-known associations between specific single nucleotide polymorphisms involved in statin transport and metabolism and either adverse effects or altered lipid-lowering efficacy. However, the applicability of this knowledge is uncertain, especially in high-risk populations. There are alternative approaches to study plasma concentrations of statins and new insights on why some association studies fail to be replicated. SUMMARY: Statin therapy recommendations are not always followed in primary and secondary prevention and, even when followed, patients often fail to reach therapeutic target values. Considering the stringent 2019 European Atherosclerosis Society and European Society of Cardiology recommended target lipid levels, as well as the persistently high cost for alternative lipid-lowering therapies such as PCSK9 inhibitors, the variability in low-density lipoprotein cholesterol reductions on statin therapy is still an important factor that needs to be addressed to ensure better cardiovascular disease risk management, especially for FH patients, who have not been well studied historically in this context.

Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia.

Familial hypercholesterolaemia increases circulating LDL-C levels and leads to premature cardiovascular disease when undiagnosed or untreated. Current guidelines support genetic testing in patients complying with clinical diagnostic criteria and cascade screening of their family members. However, most of hyperlipidaemic subjects do not present pathogenic variants in the known disease genes, and most likely suffer from polygenic hypercholesterolaemia, which translates into a relatively low yield of genetic screening programs. This study aims to identify new biomarkers and develop new approaches to improve the identification of individuals carrying monogenic causative variants. Using a machine-learning approach in a paediatric dataset of individuals, tested for disease causative genes and with an extended lipid profile, we developed new models able to classify familial hypercholesterolaemia patients with a much higher specificity than currently used methods. The best performing models incorporated parameters absent from the most common FH clinical criteria, namely apoB/apoA-I, TG/apoB and LDL1. These parameters were found to contribute to an improved identification of monogenic individuals. Furthermore, models using only TC and LDL-C levels presented a higher specificity of classification when compared to simple cut-offs. Our results can be applied towards the improvement of the yield of genetic screening programs and corresponding costs.