Monogenic Familial Hypercholesterolemia Research Articles

The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia.

Genetic testing is key in modern healthcare, particularly for monogenic disorders such as familial hypercholesterolemia. This Tohoku Medical Megabank Project study explored the impact of first-degree relatives' dyslipidemia history on individual responses to familial hypercholesterolemia genomic results. Involving 214 participants and using Japan's 3.5KJPN genome reference panel, the study assessed preferences and intentions regarding familial hypercholesterolemia genetic testing results. The data revealed a significant inclination among participants with a family history of dyslipidemia to share their genetic test results, with more than 80% of participants intending to share positive results with their partners and children and 98.1% acknowledging the usefulness of positive results for personal health management. The study underscores the importance of family health history in genetic-testing perceptions, highlighting the need for family-centered approaches in genetic counseling and healthcare. Notable study limitations include the regional scope and reliance on questionnaire data. The study results emphasize the association between family health history and genetic-testing attitudes and decisions.

Polygenic Risk, Rare Variants, and Family History: Independent and Additive Effects on Coronary Heart Disease

BackgroundGenetic factors are not included in prediction models for coronary heart disease (CHD). ObjectivesThe authors assessed the predictive utility of a polygenic risk score (PRS) for CHD (defined as myocardial infarction, coronary revascularization, or cardiovascular death) and whether the risks due to monogenic familial hypercholesterolemia (FH) and family history (FamHx) are independent of and additive to the PRS. MethodsIn UK-biobank participants, PRSCHD was calculated using metaGRS, and 10-year risk for incident CHD was estimated using the pooled cohort equations (PCE). The area under the curve (AUC) of the receiver operator curve and net reclassification improvement (NRI) were assessed. FH was defined as the presence of a pathogenic or likely pathogenic variant in LDLR, APOB, or PCSK9. FamHx was defined as a diagnosis of CHD in first-degree relatives. Independent and additive effects of PRSCHD, FH, and FamHx were evaluated in stratified analyses. ResultsIn 323,373 participants with genotype data, the addition of PRSCHD to PCE increased the AUC from 0.759 (95% CI: 0.755-0.763) to 0.773 (95% CI: 0.769-0.777). The AUC and NRIEvent for PRSCHD were higher before the age of 55 years. Of 199,997 participants with exome sequence data, 10,000 had a PRSCHD ≥95th percentile (PRSP95), 673 had FH, and 46,163 had FamHx. The CHD risk associated with PRSP95 was independent of FH and FamHx. The risks associated with combinations of PRSCHD, FH, and FamHx were additive and comprehensive estimates could be obtained by multiplying the risk from each genetic factor. ConclusionsIncorporating PRSCHD into the PCE improves risk prediction for CHD, especially at younger ages. The associations of PRSCHD, FH, and FamHx with CHD were independent and additive.

Subtyping Severe Hypercholesterolemia by Genetic Determinant to Stratify Risk of Coronary Artery Disease.

Severe hypercholesterolemia, defined as LDL (low-density lipoprotein) cholesterol (LDL-C) measurement ≥190 mg/dL, is associated with increased risk for coronary artery disease (CAD). Causes of severe hypercholesterolemia include monogenic familial hypercholesterolemia, polygenic hypercholesterolemia, elevated lipoprotein(a) [Lp(a)] hypercholesteremia, polygenic hypercholesterolemia with elevated Lp(a) (two-hit), or nongenetic hypercholesterolemia. The added value of using a genetics approach to stratifying risk of incident CAD among those with severe hypercholesterolemia versus using LDL-C levels alone for risk stratification is not known. To determine whether risk stratification by genetic cause provided better 10-year incident CAD risk stratification than LDL-C level, a retrospective cohort study comparing incident CAD risk among severe hypercholesterolemia subtypes (genetic and nongenetic causes) was performed among 130 091 UK Biobank participants. Analyses were limited to unrelated, White British or Irish participants with available exome sequencing data. Participants with cardiovascular disease at baseline were excluded from analyses of incident CAD. Of 130 091 individuals, 68 416 (52.6%) were women, and the mean (SD) age was 56.7 (8.0) years. Of the cohort, 9.0% met severe hypercholesterolemia criteria. Participants with LDL-C between 210 and 229 mg/dL and LDL-C ≥230 mg/dL showed modest increases in incident CAD risk relative to those with LDL-C between 190 and 209 mg/dL (210-229 mg/dL: hazard ratio [HR], 1.3 [95% CI, 1.1-1.7]; ≥230 mg/dL: HR, 1.3 [95% CI, 1.0-1.7]). In contrast, when risk was stratified by genetic subtype, monogenic familial hypercholesterolemia, elevated Lp(a), and two-hit hypercholesterolemia subtypes had increased rates of incident CAD relative to the nongenetic hypercholesterolemia subtype (monogenic familial hypercholesterolemia: HR, 2.3 [95% CI, 1.4-4.0]; elevated Lp(a): HR, 1.5 [95% CI, 1.2-2.0]; two-hit: HR, 1.9 [95% CI, 1.4-2.6]), while polygenic hypercholesterolemia did not. Genetics-based subtyping for monogenic familial hypercholesterolemia and Lp(a) in those with severe hypercholesterolemia provided better stratification of 10-year incident CAD risk than LDL-C-based stratification.

Possible explanations for the common clinical familial hypercholesterolemia phenotypes in the Faroe Islands

The prevalence of clinical familial hypercholesterolemia (FH) is very high in the Faroe Islands, but the possible causes are unknown. We aimed to describe potential genetic causes of FH in the Faroe Islands and to investigate whether levels of lipoprotein(a) and measures of dietary habits were associated with clinical FH in the Faroe Islands. In this case-control study, we identified potential clinical FH cases aged 18-75 years registered within a nationwide clinical laboratory database in the Faroe Islands and invited them for diagnostic evaluation according to clinical FH scoring systems. Controls were identified in the background population. Lipoprotein(a) was measured in plasma, while the fatty acid composition was determined in adipose tissue. The habitual diet of the participants was assessed using a food frequency questionnaire. Genetic testing for FH and polygenic variants was performed in a selection of clinical FH cases. A total of 121 clinical FH cases and 123 age- and sex-matched controls were recruited. We found a very low frequency of monogenic FH (2.5%), but a high level of polygenic FH (63%) in those genetically tested (67%). High levels of plasma lipoprotein(a) were associated with high odds of clinical FH. Clinical FH cases had a lower intake of saturated fatty acids (SFAs) measured by a high fat-score and a lower content of SFAs in adipose tissue compared with controls. The high prevalence of FH in the Faroe Islands may be due to polygenic causes of hypercholesterolemia and to a lesser extent other genetic factors and elevated plasma lipoprotein(a) levels.

Familial Hypercholesterolemia: How Far Should We Go?

Familial hypercholesterolemia (FH) is a common inherited disorder, yet the diagnosis methods and management are still challenging. The heterozygous FH (known as FH) is more common than the homozygous form, yet a mutation only found in only about 40% of patients with the phenotype of FH. In fact, most cases are most likely caused by polygenic aetiology with less severe clinical presentations than monogenic FH. Therefore, understanding FH is essential, so that the diagnosis methods and management, particularly the patients’ preventive measures of the patients can be improved. This review aims to describe and discuss the type of FH, including the clinical and lipid presentation, molecular pathogenesis of monogenic FH, and further discussion on how polygenic FH should be diagnosed and managed. Keywords: hypercholesterolemia, SNP, DNA testing

A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia

BackgroundPeople with monogenic familial hypercholesterolemia (FH) are at an increased risk of premature coronary heart disease and death. With a prevalence of 1:250, FH is relatively common; but currently there is no population screening strategy in place and most carriers are identified late in life, delaying timely and cost-effective interventions. ObjectivesThe purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade screening. MethodsA least absolute shrinkage and selection operator logistic regression model was used to identify predictors that accurately identified people with FH in 139,779 unrelated participants of the UK Biobank. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS). Model derivation and evaluation were performed in independent training and testing data. ResultsA total of 488 FH variant carriers were identified using whole-exome sequencing of the low-density lipoprotein receptor, apolipoprotein B, apolipoprotein E, proprotein convertase subtilisin/kexin type 9 genes. A 14-variable algorithm for FH was derived, with an area under the curve of 0.77 (95% CI: 0.71-0.83), where the top 5 most important variables included triglyceride, LDL-C, apolipoprotein A1 concentrations, self-reported statin use, and LDL-C PGS. Excluding the PGS as a candidate feature resulted in a 9-variable model with a comparable area under the curve: 0.76 (95% CI: 0.71-0.82). Both multivariable models (w/wo the PGS) outperformed screening-prioritization based on LDL-C adjusted for statin use. ConclusionsDetecting individuals with FH can be improved by considering additional predictors. This would reduce the sequencing burden in a 2-stage population screening strategy for FH.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.

A new 165-SNP low-density lipoprotein cholesterol polygenic risk score based on next generation sequencing outperforms previously published scores in routine diagnostics of familial hypercholesterolemia

Genetic diagnosis of familial hypercholesterolemia (FH) remains unexplained in 30 to 70% of patients after exclusion of monogenic disease. There is now a growing evidence that a polygenic burden significantly modulates LDL-cholesterol (LDL-c) concentrations. Several LDL-c polygenic risk scores (PRS) have been set up. However, the balance between their diagnosis performance and their practical use in routine practice is not clearly established. Consequently, we set up new PRS based on our routine panel for sequencing and compared their diagnostic performance with previously-published PRS. After a meta-analysis, four new PRS including 165 to 1633 SNP were setup using different softwares. They were established using two French control cohorts (MONA LISA n=1082 and FranceGenRef n=856). Then the explained LDL-c variance and the ability of each PRS to discriminate monogenic negative FH patients (M-) versus healthy controls were compared with 4 previously-described PRS in 785 unrelated FH patients. Between all PRS, the 165-SNP PRS developed with PLINK showed the best LDL-c explained variance (adjusted R²=0.19) and the best diagnosis abilities (AUROC=0.77, 95%CI=0.74-0.79): it significantly outperformed all the previously-published PRS (p<1 × 10−4). By using a cut-off at the 75th percentile, 61% of M- patients exhibited a polygenic hypercholesterolemia with the 165-SNP PRS versus 48% with the previously published 12-SNP PRS (p =3.3 × 10−6). These results were replicated using the UK biobank. This new 165-SNP PRS, usable in routine diagnosis, exhibits better diagnosis abilities for a polygenic hypercholesterolemia diagnosis. It would be a valuable tool to optimize referral for whole genome sequencing.

Subclinical atherosclerosis determined by coronary artery calcium deposition in patients with clinical familial hypercholesterolemia

Background and aimsLimited knowledge exists regarding the association between coronary artery calcium (CAC) deposition in patients with clinical familial hypercholesterolemia (FH) and FH subtypes such as polygenic causes. We studied CAC score in patients with clinical FH and subtypes including polygenic causes of FH compared to healthy controls. MethodsIn a case-control study, we identified potential clinical FH cases registered with an LDL-C >6.7 mmol/l within a nationwide clinical laboratory database on the Faroe Islands and invited them for diagnostic evaluation according to clinical FH scoring systems. Controls were identified in the background population. All subjects were aged 18–75 years and without a history of cardiovascular disease. FH mutation testing and genotypes of twelve LDL-C associated single nucleotide polymorphisms were determined using conventional methods in selected individuals. CAC scores were assessed by cardiac CT. Odds ratios obtained using multivariate logistic regression were used as measures of association. ResultsA total of 120 clinical FH patients and 117 age- and sex-matched controls were recruited. We found a very low frequency of monogenic FH (3%), but a high level of polygenic FH (60%) in those genetically tested (54%). There was a statistically significant association between the CAC score and a diagnosis of clinical FH with the highest observed odds ratio of 5.59 (95% CI 1.65; 18.94, p = 0.006) in those with a CAC score ≥300 compared to those with a CAC of zero. In supplemental analyses, there was a strong association between CAC scores and clinical FH of a polygenic cause. ConclusionWe found a statistically significant association between CAC levels and clinical FH with the highest observed risk estimates among clinical FH cases of a presumed polygenic cause.

Abstract 10822: Familial Hypercholesterolemia in the EMERGE Network: 1-Year Outcomes After Return of Results

Introduction: Relatively little is known about outcomes following return of secondary findings detected in large scale sequencing projects. We assessed 1-year outcomes following return of pathogenic/likely pathogenic (P/LP) variants associated with familial hypercholesterolemia (FH) in phase III of the eMERGE network (2015-2019). Methods: Adult participants (n=18,544) at 7 sites were enrolled in a prospective cohort study to study the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR , APOB , and PCSK9 . Process, intermediate, and/or clinical outcomes within 1-year after return of results (RoR) were ascertained by electronic health record (EHR) review. A process outcome was defined as referral to a specialist or ordering new tests, including lipid profile and screening tests for coronary heart disease. An intermediate outcome was defined as new diagnosis of FH or atherosclerotic cardiovascular disease. A clinical outcome was defined as initiation or change in lipid-lowering medication. The primary outcomes were a new diagnosis of FH and modification of lipid lowering therapy. Results: Results were returned to 134 individuals, and data for 119 participants were available for the outcomes analysis. Process (86%), intermediate (49%), and/or clinical (26%) outcomes occurred in 92% of participants. Process outcomes included 72% with new tests ordered and 61% referred to a specialist. Before RoR, 86.5% of individuals with FH associated P/LP variants had a diagnosis of hypercholesterolemia but only 17.6% had a diagnosis of FH; 89% were on a statin but only 58% were on a high intensity statin. Following RoR, 44% (52/119) received a new diagnosis of FH. Lipid lowering treatment was modified in 26% (31/119), including initiation of a statin in 5%, increasing statin dose in 13%, and adding ezetimibe or a PCSK9 inhibitor in 12.6%. Conclusions: In a multisite cohort of EHR-linked biobanks, monogenic FH was underdiagnosed (only 17.6% had a specific diagnosis) and undertreated (only 58% were on a high intensity statin); 44% of participants received a new diagnosis of FH and 26% had lipid lowering treatment modified after RoR. These results highlight the clinical utility of returning FH variants in EHR-linked biobanks.

Abstract 11554: Biochemical and Molecular Newborn Screening for Familial Hypercholesterolemia

Introduction: Familial Hypercholesterolemia (FH) is a common genetic disorder resulting in elevated low-density lipoprotein cholesterol (LDL-C) causing premature atherosclerosis. Despite guidelines, universal screening rates remain low. Newborn screening could increase FH diagnostic rates. Hypothesis: Biochemical newborn screening for LDL-C, total cholesterol (TC), and apolipoprotein B (apoB) followed by selective molecular testing identifies pathogenic and likely pathogenic variants causing FH. Methods: De-identified, residual newborn screening bloodspots collected for routine screening at 24-48 hours of life were analyzed for LDL-C, TC, and apoB. Values are expressed as multiples of median (MoM). Mahalanobis distance, measuring how far MoM values differed positively from 1, was computed. Samples with the most extreme Mahalanobis distance were selected for molecular testing (panel includes LDLR , APOB , PCSK9 , LDLRAP1 , APOE , STAP1 , LIPA , ABCG5 , and ABCG8 ). Results: Distributions of apoB, TC, and LDL-C from 5,248 samples are shown below (figure). ApoB followed a Gamma distribution; TC and LDL-C followed non-normal but symmetric distributions. ApoB and LDL-C accounted for 93% of variance among biomarkers. Of the first 2500 samples, 192 samples were selected for molecular testing; a pathogenic variant for monogenic FH was detected in 1 sample and risk factors for polygenic FH in 4 samples (table below). Conclusions: Pathogenic or likely pathogenic variants for FH were identified with an incidence of 1 in 500 in the first 2500 newborn specimens tested. Further study will determine if this two-tiered screening strategy adequately detects FH in newborns.

Genetic Architecture and Cardiovascular Risk of Familial Combined Hyperlipidemia

<h3>Lead Author's Financial Disclosures</h3> Nothing to disclose. <h3>Study Funding</h3> None. <h3>Background/Synopsis</h3> Familial combined hyperlipidemia (FCHL) is one of most common inherited lipid phenotypes and is characterized by elevated plasma concentrations of apolipoprotein B-100 and triglycerides. However, many aspects of FCHL remain incompletely understood, such as its genetic basis and the magnitude of cardiovascular risk with which it is associated. <h3>Objective/Purpose</h3> The goals of this study were to investigate the genetic architecture and cardiovascular risk associated with FCHL. <h3>Methods</h3> We identified individuals with a FCHL phenotype among 349,222 unrelated participants of European ancestry in the UK Biobank using modified versions of 5 different diagnostic criteria. The prevalence of the FCHL phenotype was 11.44% (n=39,961), 5.01% (n=17,485), 1.48% (n=5,153), 1.10% (n=3,838), and 0.48% (n=1,688) according to modified versions of the Consensus Conference, Dutch, Mexico, Brunzell, and Goldstein clinical criteria, respectively. We performed discovery, case-control genome-wide association studies for these different FCHL criteria. <h3>Results</h3> We identified 175 independent loci associated with FCHL at genome-wide significance. All loci have known associations with plasma lipid levels. We investigated the combined impact of genetic and environmental risk on FCHL and found that the combination of polygenic susceptibility to hypercholesterolemia or hypertriglyceridemia and features of metabolic syndrome was associated wa ith greater prevalence of FCHL. Lastly, we observed that individuals with an FCHL phenotype had a comparable risk of incident coronary artery disease compared to individuals with monogenic Familial Hypercholesterolemia (FH) (adjusted hazard ratio vs controls [95% confidence interval]: 2.72 [2.31-3.21] and 1.90 [1.30-2.78]) <h3>Conclusions</h3> Our results suggest that, rather than being a single genetic entity, the FCHL phenotype represents a polygenic susceptibility to dyslipidemia in combination with metabolic abnormalities. Importantly, the cardiovascular of the FCHL phenotype is similar to that of monogenic FH, despite being at least 5 times more common.

Monogenic Versus Polygenic Forms of Hypercholesterolemia and Cardiovascular Risk: Are There Any Differences?

Common DNA variants with small effects work together to create susceptibility to polygenic hypercholesterolemia. Some clinicians wonder whether patients with polygenic hypercholesterolemia have less severe clinical features compared to patients with monogenic familial hypercholesterolemia (FH) caused by rare deleterious variants. Studies performed in cohorts of patients with both monogenic and polygenic hypercholesterolemia have assessed lipid levels, non-invasive markers of atherosclerosis, and clinical end points, including major adverse cardiovascular events. The totality of data suggests a gradient across genotypes. Specifically, individuals with polygenic hypercholesterolemia have deleterious phenotypes that are intermediate in severity between those in patients with monogenic hypercholesterolemia and in control subjects. Although clinical variables in patients with polygenic hypercholesterolemia are less severe than in those with monogenic hypercholesterolemia, cardiovascular risk is still very high in these patients compared to controls. Patients with polygenic hypercholesterolemia must be treated assertively.

LDL-C Concentrations and the 12-SNP LDL-C Score for Polygenic Hypercholesterolaemia in Self-Reported South Asian, Black and Caribbean Participants of the UK Biobank.

Background: Monogenic familial hypercholesterolaemia (FH) is an autosomal dominant disorder characterised by elevated low-density lipoprotein cholesterol (LDL-C) concentrations due to monogenic mutations in LDLR, APOB, PCSK9, and APOE. Some mutation-negative patients have a polygenic cause for elevated LDL-C due to a burden of common LDL-C-raising alleles, as demonstrated in people of White British (WB) ancestry using a 12-single nucleotide polymorphism (SNP) score. This score has yet to be evaluated in people of South Asian (SA), and Black and Caribbean (BC) ethnicities. Objectives: 1) Compare the LDL-C and 12-SNP score distributions across the three major ethnic groups in the United Kingdom: WB, SA, and BC individuals; 2) compare the association of the 12-SNP score with LDL-C in these groups; 3) evaluate ethnicity-specific and WB 12-SNP score decile cut-off values, applied to SA and BC ethnicities, in predicting LDL-C concentrations and hypercholesterolaemia (LDL-C>4.9 mmol/L). Methods: The United Kingdom Biobank cohort was used to analyse the LDL-C (adjusted for statin use) and 12-SNP score distributions in self-reported WB (n = 353,166), SA (n = 7,016), and BC (n = 7,082) participants. To evaluate WB and ethnicity-specific 12-SNP score deciles, the total dataset was split 50:50 into a training and testing dataset. Regression analyses (logistic and linear) were used to analyse hypercholesterolaemia (LDL-C>4.9 mmol/L) and LDL-C. Findings: The mean (±SD) measured LDL-C differed significantly between the ethnic groups and was highest in WB [3.73 (±0.85) mmol/L], followed by SA [3.57 (±0.86) mmol/L, p < 2.2 × 10−16], and BC [3.42 (±0.90) mmol/L] participants (p < 2.2 × 10−16). There were significant differences in the mean (±SD) 12-SNP score between WB [0.90 (±0.23)] and BC [0.72 (±0.25), p < 2.2 × 10−16], and WB and SA participants [0.86 (±0.19), p < 2.2 × 10−16]. In all three ethnic groups the 12-SNP score was associated with measured LDL-C [R 2 (95% CI): WB = 0.067 (0.065–0.069), BC = 0.080 (0.063–0.097), SA = 0.027 (0.016–0.038)]. The odds ratio and the area under the curve for hypercholesterolaemia were not statistically different when applying ethnicity-specific or WB deciles in all ethnic groups. Interpretation: We provide information on the differences in LDL-C and the 12-SNP score distributions in self-reported WB, SA, and BC individuals of the United Kingdom Biobank. We report the association between the 12-SNP score and LDL-C in these ethnic groups. We evaluate the performance of ethnicity-specific and WB 12-SNP score deciles in predicting LDL-C and hypercholesterolaemia.

Abstract 068: Investigation Of Familial Hypercholesterolemia Subtypes In The UK Biobank

Familial hypercholesterolemia (FH) is an inherited disorder characterized by lifelong elevated low density lipoprotein cholesterol (LDL-C) and dramatically increased risk for premature atherosclerotic cardiovascular disease (ASCVD). FH diagnosis currently requires either the presence of a pathogenic variant in an FH gene, or phenotypic characteristics. However, genetic studies now suggest that FH encompasses four discrete subtypes: 1) presence of a monogenic FH variant, 2) a high LDL-C polygenic score, “polygenic FH,” 3) elevated lipoprotein(a), and 4) LDL-C &gt; 190 mg/dl and a positive family history without an identifiable genetic cause, or true “phenotypic FH.” Here, we screened 134,444 unrelated white British individuals with exome sequences available in the UK Biobank for individuals with FH subtypes. We classified 358 individuals (1 in 376; 0.27%) with a pathogenic variant in their exome sequence as monogenic FH, 2,800 had polygenic FH (1 in 48; 2.1%), 2,246 had elevated lipoprotein (a) (1 in 60; 1.7%), and 3,146 had phenotypic FH (1 in 43; 2.3%). The remaining 125,894 samples without an FH subtype were classified as controls. We stratified the cohort into statin treated and untreated individuals. Those with an FH subtype were 2.8 times more likely to be prescribed a lipid lowering medication at the baseline interview compared to controls. In the statin treated group (n=21,875), those with monogenic FH were at the highest risk for 10-year incident ASCVD. We observed a 2.7 (95% confidence interval (95% CI): 1.6-4.3) fold higher ASCVD risk in monogenic FH compared to controls as measured by the hazard ratio (HR). Those with elevated lipoprotein (a) and phenotypic FH were also at increased risk for ASCVD (HR = 1.5 (95% CI: 1.1-2.1) and HR = 1.6 (95% CI: 1.3-2.1), respectively). In contrast, we do not observe elevated rates of incident ASCVD in those with polygenic FH relative to controls among treated individuals. In the untreated group (n=112,569), the risk of ASCVD in FH subtypes was more pronounced relative to controls. Individuals with elevated lipoprotein (a) were at the highest risk of incident ASCVD (HR = 2.9, 95% CI: 2.4-3.7) among the four subgroups. In conclusion, we observed differences in ASCVD risk among subtypes of FH. These findings demonstrate the importance of considering subtypes in ASCVD risk assessment for patients who present with the FH phenotype.

Cost-Effectiveness of Screening Algorithms for Familial Hypercholesterolaemia in Primary Care.

Although familial hypercholesterolemia (FH) screening within primary care is considered cost-effective, which screening approach is cost-effective has not been established. This study determines the cost-effectiveness of six case-finding strategies for screening of electronic health records to identify index patients who have genetically confirmed monogenic FH in English primary care. A decision tree was constructed to represent pathways of care for each approach (FH Case Identification Tool (FAMCAT) versions 1 and 2, cholesterol screening, Dutch Lipid Clinic Network (DLCN), Simon Broome criteria, no active screening). Clinical effectiveness was measured as the number of monogenic FH cases identified. Healthcare costs for each algorithm were evaluated from an NHS England perspective over a 12 week time horizon. The primary outcome was the incremental cost per additional monogenic FH case identified (ICER). FAMCAT2 was found to dominate (cheaper and more effective) cholesterol and FAMCAT1 algorithms, and extendedly dominate DLCN. The ICER for FAMCAT2 vs. no active screening was 8111 GBP (95% CI: 4088 to 14,865), and for Simon Broome vs. FAMCAT2 was 74,059 GBP (95% CI: −1,113,172 to 1,697,142). Simon Broome found the largest number of FH cases yet required 102 genetic tests to identify one FH patient. FAMCAT2 identified fewer, but only required 23 genetic tests.

Evaluation of a novel rapid genomic test including polygenic risk scores for the diagnosis and management of familial hypercholesterolaemia.

Introduction: Familial hypercholesterolaemia (FH) is a common autosomal dominant genetic condition, characterised by elevated LDL cholesterol (LDL-C), leading to premature cardiovascular disease (CVD). Early and accurate diagnosis, with implementation of preventative therapies, has a major impact on reducing premature CVD, morbidity and mortality. Genetic testing is recommended to confirm clinical diagnosis in the proband and enable cascade testing in relatives. There is growing evidence that the risk of CVD conferred by hypercholesterolaemia depends not only on monogenic causes but also on polygenic factors. GENinCode has developed a novel genomic testing system (Lipid inCode®) which we have assessed against an accredited National Health Service (NHS UK) genetic screening service in order to validate its diagnostic and clinical utility.Methods: DNA samples from 40 index cases who had been referred for FH testing in an ISO15189-accredited NHS genetic screening service, were retrospectively tested using the Lipid inCode® assay. The results were compared with those from NHS testing.Results: There was absolute concordance in variant detection between both diagnostic tests for monogenic and polygenic FH, the only difference being in the interpretation and classification of DNA variants based on ACMG guidelines, which did not differ by more than one classification class. The Lipid inCode® test was equivalent to the NHS test in providing comprehensive genetic analysis that included the assessment of both monogenic (FH) and polygenic determinants of blood cholesterol and including a pharmacogenomic assessment of predisposition to statin-related myopathy.Conclusion: The Lipid inCode® diagnostic test can be undertaken with rapid turnaround and gave the same results as those reported by standard NHS genetic laboratory testing. In addition to assessment of monogenic FH, the Lipid inCode® assay provides additional genetic data, such as polygenic factors contributing to hypercholesterolaemia, a polygenic risk score (PRS) for coronary artery disease (CAD), pharmacogenomic testing for statin myopathy, and genetic predisposition to raised Lp(a).

Evaluation of a novel rapid genomic test including polygenic risk scores for the diagnosis and management of familial hypercholesterolaemia

Introduction: Familial hypercholesterolaemia (FH) is a common autosomal dominant genetic condition, characterised by elevated LDL cholesterol (LDL-C), leading to premature cardiovascular disease (CVD). Early and accurate diagnosis, with implementation of preventative therapies, has a major impact on reducing premature CVD, morbidity and mortality.Genetic testing is recommended to confirm clinical diagnosis in the proband and enable cascade testing in relatives. There is growing evidence that the risk of CVD conferred by hypercholesterolaemia depends not only on monogenic causes but also on polygenic factors. GENinCode has developed a novel genomic testing system (Lipid inCode®) which we have assessed against an accredited National Health Service (NHS UK) genetic screening service in order to validate its diagnostic and clinical utility.Methods: DNA samples from 40 index cases who had been referred for FH testing in an ISO15189-accredited NHS genetic screening service, were retrospectively tested using the Lipid inCode® assay. The results were compared with those from NHS testing. Results: There was absolute concordance in variant detection between both diagnostic tests for monogenic and polygenic FH, the only difference being in the interpretation and classification of DNA variants based on ACMG guidelines, which did not differ by more than one classification class. The Lipid inCode® test was equivalent to the NHS test in providing comprehensive genetic analysis that included the assessment of both monogenic (FH) and polygenic determinants of blood cholesterol and including a pharmacogenomic assessment of predisposition to statin-related myopathy.Conclusion: The Lipid inCode® diagnostic test can be undertaken with rapid turnaround and gave the same results as those reported by standard NHS genetic laboratory testing. In addition to assessment of monogenic FH, the Lipid inCode® assay provides additional genetic data, such as polygenic factors contributing to hypercholesterolaemia, a polygenic risk score (PRS) for coronary artery disease (CAD), pharmacogenomic testing for statin myopathy, and genetic predisposition to raised Lp(a).

Polygenic architecture and cardiovascular risk of familial combined hyperlipidemia

Background and aimsFamilial combined hyperlipidemia (FCHL) is one of the most common inherited lipid phenotypes, characterized by elevated plasma concentrations of apolipoprotein B-100 and triglycerides. The genetic inheritance of FCHL remains poorly understood. The goals of this study were to investigate the polygenetic architecture and cardiovascular risk associated with FCHL. Methods and resultsWe identified individuals with an FCHL phenotype among 349,222 unrelated participants of European ancestry in the UK Biobank using modified versions of 5 different diagnostic criteria. The prevalence of the FCHL phenotype was 11.44% (n = 39,961), 5.01% (n = 17,485), 1.48% (n = 5,153), 1.10% (n = 3,838), and 0.48% (n = 1,688) according to modified versions of the Consensus Conference, Dutch, Mexico, Brunzell, and Goldstein criteria, respectively. We performed discovery, case-control genome-wide association studies for these different FCHL criteria and identified 175 independent loci associated with FCHL at genome-wide significance. We investigated the association of genetic and clinical risk with FCHL and found that polygenic susceptibility to hypercholesterolemia or hypertriglyceridemia and features of metabolic syndrome were associated with greater prevalence of FCHL. Participants with an FCHL phenotype had a similar risk of incident coronary artery disease compared to participants with monogenic familial hypercholesterolemia (adjusted hazard ratio vs controls [95% confidence interval]: 2.72 [2.31–3.21] and 1.90 [1.30–2.78]). ConclusionsThese results suggest that, rather than being a single genetic entity, the FCHL phenotype represents a polygenic susceptibility to dyslipidemia in combination with metabolic abnormalities. The cardiovascular risk associated with an FCHL phenotype is similar to that of monogenic familial hypercholesterolemia, despite being ∼5x more common.

Polygenic contribution for familial hypercholesterolemia (FH).

The present review summarizes different polygenic risk scores associated with hypercholesterolemia applied to cohorts with a genetic diagnosis of familial hypercholesterolemia (FH). 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. 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.