LDLR variant classification for improved cardiovascular risk prediction in familial hypercholesterolemia.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a genetic disorder marked by high LDL cholesterol and an increased premature coronary artery disease (CAD) risk. Current dichotomous classification of LDL receptor gene (LDLR) variants may inadequately capture patient variability in LDL cholesterol levels and CAD risk. This study assessed a novel approach for determining LDLR variant severity using variant-specific LDL cholesterol percentiles. METHODS: Participants of the Dutch FH cascade screening program were screened for 456 LDLR variants. For each LDLR variant carrier, a sex- and age-specific LDL cholesterol percentile was derived from the LDL cholesterol level measured at study entry, i.e. generally from the blood drawn for DNA analysis. These percentiles were used to calculate the mean LDL cholesterol percentile for each variant. Based on the variant-specific LDL cholesterol percentiles, carriers were grouped into the following LDL cholesterol strata: <75th, 75th-88th, 88th-92nd, 92nd-96.5th, 96.5th-98th, and ≥98th percentile. Additionally, variants were categorized into class 1 (LDLR deficient) and non-class 1 (often LDLR defective) variants. CAD risk between carriers in the different LDL cholesterol strata and non-carriers was compared using a Cox proportional hazard model. RESULTS: Out of 35,067 participants, 12,485 (36 %) LDLR variant carriers (mean age 38.0 ± 20.0 years, 47.7 % male) were identified. Carriers had a 5-fold higher CAD risk compared with non-carriers. Hazard ratios for CAD increased gradually from 2.2 (95%CI 0.97-5.0) to 12.0 (95%CI 5.5-24.8) across the LDL cholesterol strata. A 7.3-fold and 3.9-fold increased CAD risk was observed in carriers of class 1 and non-class 1 LDLR variants, respectively. CONCLUSIONS: This study presents a refined approach for classifying LDLR variants based on their impact on LDL cholesterol levels, allowing for more precise, genotype-specific CAD risk estimation in FH patients compared with traditional methods.

PSMD11 loss-of-function variants correlate with a neurobehavioral phenotype, obesity, and increased interferon response.

Primary proteasomopathies have recently emerged as a new class of rare early-onset neurodevelopmental disorders (NDDs) caused by pathogenic variants in the PSMB1, PSMC1, PSMC3, or PSMD12 proteasome genes. Proteasomes are large multi-subunit protein complexes that maintain cellular protein homeostasis by clearing ubiquitin-tagged damaged, misfolded, or unnecessary proteins. In this study, we have identified PSMD11 as an additional proteasome gene in which pathogenic variation is associated with an NDD-causing proteasomopathy. PSMD11 loss-of-function variants caused early-onset syndromic intellectual disability and neurodevelopmental delay with recurrent obesity in 10 unrelated children. Our findings demonstrate that the cognitive impairment observed in these individuals could be recapitulated in Drosophila melanogaster with depletion of the PMSD11 ortholog Rpn6, which compromised reversal learning. Our investigations in subject samples further revealed that PSMD11 loss of function resulted in impaired 26S proteasome assembly and the acquisition of a persistent type I interferon (IFN) gene signature, mediated by the integrated stress response (ISR) protein kinase R (PKR). In summary, these data identify PSMD11 as an additional member of the growing family of genes associated with neurodevelopmental proteasomopathies and provide insights into proteasomal biology in human health.

Enhanced identification of familial hypercholesterolemia using central laboratory algorithms.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a highly prevalent genetic disorder resulting in markedly elevated LDL cholesterol levels and premature coronary artery disease. FH underdiagnosis and undertreatment require novel detection methods. This study evaluated the effectiveness of using an LDL cholesterol cut-off ≥99.5th percentile (sex- and age-adjusted) to identify clinical and genetic FH, and investigated underutilization of genetic testing and undertreatment in FH patients. METHODS: Individuals with at least one prior LDL cholesterol level ≥99.5th percentile were selected from a laboratory database containing lipid profiles of 590,067 individuals. The study comprised three phases: biochemical validation of hypercholesterolemia, clinical identification of FH, and genetic determination of FH. RESULTS: Of 5614 selected subjects, 2088 underwent lipid profile reassessment, of whom 1103 completed the questionnaire (mean age 64.2 ± 12.7 years, 48% male). In these 1103 subjects, mean LDL cholesterol was 4.0 ± 1.4 mmol/l and 722 (65%) received lipid-lowering therapy. FH clinical diagnostic criteria were met by 282 (26%) individuals, of whom 85% had not received guideline-recommended genetic testing and 97% failed to attain LDL cholesterol targets. Of 459 individuals consenting to genetic validation, 13% carried an FH-causing variant, which increased to 19% in clinically diagnosed FH patients. CONCLUSIONS: The identification of a substantial number of previously undiagnosed and un(der)treated clinical and genetic FH patients within a central laboratory database highlights the feasibility and clinical potential of this targeted screening strategy; both in identifying new FH patients and in improving treatment in this high-risk population.

Low-Cost High-Throughput Genotyping for Diagnosing Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a common but underdiagnosed genetic disorder characterized by high low-density lipoprotein cholesterol levels and premature cardiovascular disease. Current sequencing methods to diagnose FH are expensive and time-consuming. In this study, we evaluated the accuracy of a low-cost, high-throughput genotyping array for diagnosing FH. METHODS: An Illumina Global Screening Array was customized to include probes for 636 variants, previously classified as FH-causing variants. First, its theoretical coverage was assessed in all FH variant carriers diagnosed through next-generation sequencing between 2016 and 2022 in the Netherlands (n=1772). Next, the performance of the array was validated in another sample of FH variant carriers previously identified in the Dutch FH cascade screening program (n=1268). RESULTS: The theoretical coverage of the array for FH-causing variants was 91.3%. Validation of the array was assessed in a sample of 1268 carriers of whom 1015 carried a variant in LDLR, 250 in APOB, and 3 in PCSK9. The overall sensitivity was 94.7% and increased to 98.2% after excluding participants with variants not included in the array design. Copy number variation analysis yielded a 89.4% sensitivity. In 18 carriers, the array identified a total of 19 additional FH-causing variants. Subsequent DNA analysis confirmed 5 of the additionally identified variants, yielding a false-positive result in 16 subjects (1.3%). CONCLUSIONS: The FH genotyping array is a promising tool for genetically diagnosing FH at low costs and has the potential to greatly increase accessibility to genetic testing for FH. Continuous customization of the array will further improve its performance.

Use of Lipoprotein(a) to improve diagnosis and management in clinical familial hypercholesterolemia.

BACKGROUND AND AIMS: Lipoprotein(a) (Lp(a)) is an LDL-like particle whose plasma levels are largely genetically determined. The impact of measuring Lp(a) in patients with clinical familial hypercholesterolemia (FH) referred for genetic testing is largely unknown. We set out to evaluate the contribution of (genetically estimated) Lp(a) in a large nation-wide referral population of clinical FH. METHODS: In 1504 patients referred for FH genotyping, we used an LPA genetic instrument (rs10455872 and rs3798220) as a proxy for plasma Lp(a) levels. The genetic Lp(a) proxy was used to correct LDL-cholesterol and reclassify patients with clinical FH based on Dutch Lipid Criteria Network (DLCN) scoring. Finally, we used estimated Lp(a) levels to reclassify ASCVD risk using the SCORE and SMART risk scores. RESULTS: LPA SNPs were more prevalent among mutation-negative compared with mutation-positive patients (296/1280 (23.1%) vs 35/224 (15.6%), p = 0.016). Among patients with genetically defined high Lp(a) levels, 9% were reclassified to the DLCN category 'unlikely FH' using Lp(a)-corrected LDL-cholesterol (LDL-Ccor) and all but one of these patients indeed carried no FH variant. Furthermore, elevated Lp(a) reclassified predicted ASCVD risk into a higher category in up to 18% of patients. CONCLUSIONS: In patients referred for FH molecular testing, we show that taking into account (genetically estimated) Lp(a) levels not only results in reclassification of probability of genetic FH, but also has an impact on individual cardiovascular risk evaluation. However, to avoid missing the diagnosis of an FH variant, clear thresholds for the use of Lp(a)-cholesterol adjusted LDL-cholesterol levels in patients referred for genetic testing of FH must be established.

Establishing the relationship between familial dysbetalipoproteinemia and genetic variants in the APOE gene.

Familial Dysbetalipoproteinemia (FD) is the second most common monogenic dyslipidemia and is associated with a very high cardiovascular risk due to cholesterol-enriched remnant lipoproteins. FD is usually caused by a recessively inherited variant in the APOE gene (ε2ε2), but variants with dominant inheritance have also been described. The typical dysbetalipoproteinemia phenotype has a delayed onset and requires a metabolic hit. Therefore, the diagnosis of FD should be made by demonstrating both the genotype and dysbetalipoproteinemia phenotype. Next Generation Sequencing is becoming more widely available and can reveal variants in the APOE gene for which the relation with FD is unknown or uncertain. In this article, two approaches are presented to ascertain the relationship of a new variant in the APOE gene with FD. The comprehensive approach consists of determining the pathogenicity of the variant and its causal relationship with FD by confirming a dysbetalipoproteinemia phenotype, and performing in vitro functional tests and, optionally, in vivo postprandial clearance studies. When this is not feasible, a second, pragmatic approach within reach of clinical practice can be followed for individual patients to make decisions on treatment, follow-up, and family counseling.

Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia.

BACKGROUND AND AIMS: Low-density lipoprotein cholesterol (LDL-C) levels vary in patients with familial hypercholesterolemia (FH) and can be explained by a single deleterious genetic variant or by the aggregate effect of multiple, common small-effect variants that can be captured in a polygenic score (PS). We set out to investigate the contribution of a previously published PS to the inter-individual LDL-C variation and coronary artery disease (CAD) risk in patients with a clinical FH phenotype. METHODS: First, in a cohort of 628 patients referred for genetic FH testing, we evaluated the distribution of a PS for LDL-C comprising 12 genetic variants. Next, we determined its association with coronary artery disease (CAD) risk using UK Biobank data. RESULTS: The mean PS was higher in 533 FH-variant-negative patients (FH/M-) compared with 95 FH-variant carriers (1.02 vs 0.94, p < 0.001). 39% of all patients had a PS equal to the top 20% from a population-based reference cohort and these patients were less likely to carry an FH variant (OR 0.22, 95% CI 0.10-0.48) compared with patients in the lowest 20%. In UK Biobank data, the PS explained 7.4% of variance in LDL-C levels and was associated with incident CAD. Addition of PS to a prediction model using age and sex and LDL-C did not increase the c-statistic for predicting CAD risk. CONCLUSIONS: This 12-variant PS was higher in FH/M- patients and associated with incident CAD in UK Biobank data. However, the PS did not improve predictive accuracy when added to the readily available characteristics age, sex and LDL-C, suggesting limited discriminative value for CAD.

Successful Genetic Screening and Creating Awareness of Familial Hypercholesterolemia and Other Heritable Dyslipidemias in the Netherlands.

The genetic screening program for familial hypercholesterolemia (FH) in the Netherlands, which was embraced by the Dutch Ministry of Health from 1994 to 2014, has led to twenty years of identification of at least 1500 FH cases per year. Although funding by the government was terminated in 2014, the approach had proven its effectiveness and had built the foundation for the development of more sophisticated diagnostic tools, clinical collaborations, and new molecular-based treatments for FH patients. As such, the community was driven to continue the program, insurance companies were convinced to collaborate, and multiple approaches were launched to find new index cases with FH. Additionally, the screening was extended, now also including other heritable dyslipidemias. For this purpose, a diagnostic next-generation sequencing (NGS) panel was developed, which not only comprised the culprit LDLR, APOB, and PCSK9 genes, but also 24 other genes that are causally associated with genetic dyslipidemias. Moreover, the NGS technique enabled further optimization by including pharmacogenomic genes in the panel. Using such a panel, more patients that are prone to cardiovascular diseases are being identified nowadays and receive more personalized treatment. Moreover, the NGS output teaches us more and more about the dyslipidemic landscape that is less straightforward than we originally thought. Still, continuous progress is being made that underlines the strength of genetics in dyslipidemia, such as discovery of alternative genomic pathogenic mechanisms of disease development and polygenic contribution.

Next-generation sequencing to confirm clinical familial hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia is characterised by high low-density lipoprotein-cholesterol levels and is caused by a pathogenic variant in LDLR, APOB or PCSK9. We investigated which proportion of suspected familial hypercholesterolemia patients was genetically confirmed, and whether this has changed over the past 20 years in The Netherlands. METHODS: Targeted next-generation sequencing of 27 genes involved in lipid metabolism was performed in patients with low-density lipoprotein-cholesterol levels greater than 5 mmol/L who were referred to our centre between May 2016 and July 2018. The proportion of patients carrying likely pathogenic or pathogenic variants in LDLR, APOB or PCSK9, or the minor familial hypercholesterolemia genes LDLRAP1, ABCG5, ABCG8, LIPA and APOE were investigated. This was compared with the yield of Sanger sequencing between 1999 and 2016. RESULTS: A total of 227 out of the 1528 referred patients (14.9%) were heterozygous carriers of a pathogenic variant in LDLR (80.2%), APOB (14.5%) or PCSK9 (5.3%). More than 50% of patients with a Dutch Lipid Clinic Network score of 'probable' or 'definite' familial hypercholesterolemia were familial hypercholesterolemia mutation-positive; 4.8% of the familial hypercholesterolemia mutation-negative patients carried a variant in one of the minor familial hypercholesterolemia genes. The mutation detection rate has decreased over the past two decades, especially in younger patients in which it dropped from 45% in 1999 to 30% in 2018. CONCLUSIONS: A rare pathogenic variant in LDLR, APOB or PCSK9 was identified in 14.9% of suspected familial hypercholesterolemia patients and this rate has decreased in the past two decades. Stringent use of clinical criteria algorithms is warranted to increase this yield. Variants in the minor familial hypercholesterolemia genes provide a possible explanation for the familial hypercholesterolemia phenotype in a minority of patients.

Intronic variant screening with targeted next-generation sequencing reveals first pseudoexon in LDLR in familial hypercholesterolemia.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is caused by pathogenic variants in LDLR, APOB, or PCSK9 genes (designated FH+). However, a significant number of clinical FH patients do not carry these variants (designated FH-). Here, we investigated whether variants in intronic regions of LDLR attribute to FH by affecting pre-mRNA splicing. METHODS: LDLR introns are partly covered in routine sequencing of clinical FH patients using next-generation sequencing. Deep intronic variants, >20 bp from intron-exon boundary, were considered of interest once (a) present in FH- patients (n = 909) with LDL-C >7 mmol/L (severe FH-) or after in silico analysis in patients with LDL-C >5 mmol/L (moderate FH-) and b) absent in FH + patients (control group). cDNA analysis and co-segregation analysis were performed to assess pathogenicity of the identified variants. RESULTS: Three unique variants were present in the severe FH- group. One of these was the previously described likely pathogenic variant c.2140+103G>T. Three additional variants were selected based on in silico analyses in the moderate FH- group. One of these variants, c.2141-218G>A, was found to result in a pseudo-exon inclusion, producing a premature stop codon. This variant co-segregated with the hypercholesterolemic phenotype. CONCLUSIONS: Through a screening approach, we identified a deep intronic variant causal for FH. This finding indicates that filtering intronic variants in FH- patients for the absence in FH + patients might enrich for true FH-causing variants and suggests that intronic regions of LDLR need to be considered for sequencing in FH- patients.