Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances.

BACKGROUND: Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. METHODS: Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. RESULTS: Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. CONCLUSIONS: Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.

Influence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia.

BACKGROUND: Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH. METHODS: We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease. RESULTS: Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease is higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing variant. CONCLUSIONS: Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.

Prevalence of Dysbetalipoproteinemia in the UK Biobank According to Different Diagnostic Criteria.

CONTEXT: Dysbetalipoproteinemia (DBL) is a multifactorial disorder that disrupts the normal metabolism of remnant lipoproteins, causing increased risk of cardiovascular disease. However, establishing a proper diagnosis is difficult and the true prevalence of the disease in the general population remains unknown. OBJECTIVE: The objectives were to study the prevalence of the disease and to validate the performance of different clinical diagnostic criteria in a large population-based cohort. METHODS: This study included 453 437 participants from the UK Biobank. DBL was established in participants having an ε2ε2 genotype with mixed dyslipidemia or lipid-lowering therapy use (n=964). The different diagnostic criteria for DBL were applied in individuals without lipid-lowering medication (n=370 039, n=534 DBL), to compare their performance. RESULTS: Overall, 0.6% of participants had an ε2ε2 genotype, of which 36% were classified as DBL, for a disease prevalence of 0.2% (1:469). The prevalence of DBL was similar between the different genetic ancestries (≤0.2%). Several diagnostic criteria showed good sensitivity for the diagnosis of DBL (>90%), but they suffered from a very low positive predictive value (0.6%-15.4%). CONCLUSION: This study reported for the first time the prevalence of DBL in the UK Biobank according to genetic ancestry. Furthermore, we provided the first external validation of different diagnostic criteria for DBL in a large population-based cohort and highlighted the fact that these criteria should not be used to diagnose DBL alone but should rather be used as a first screening step to determine which individuals may benefit from genetic testing to confirm the diagnosis.

CETP inhibition enhances monocyte activation and bacterial clearance and reduces streptococcus pneumonia-associated mortality in mice.

Sepsis is a leading cause of mortality worldwide, and pneumonia is the most common cause of sepsis in humans. Low levels of high-density lipoprotein cholesterol (HDL-C) levels are associated with an increased risk of death from sepsis, and increasing levels of HDL-C by inhibition of cholesteryl ester transfer protein (CETP) decreases mortality from intraabdominal polymicrobial sepsis in APOE*3-Leiden.CETP mice. Here, we show that treatment with the CETP inhibitor (CETPi) anacetrapib reduced mortality from Streptococcus pneumoniae-induced sepsis in APOE*3-Leiden.CETP and APOA1.CETP mice. Mechanistically, CETP inhibition reduced the host proinflammatory response via attenuation of proinflammatory cytokine transcription and release. This effect was dependent on the presence of HDL, leading to attenuation of immune-mediated organ damage. In addition, CETP inhibition promoted monocyte activation in the blood prior to the onset of sepsis, resulting in accelerated macrophage recruitment to the lung and liver. In vitro experiments demonstrated that CETP inhibition significantly promoted the activation of proinflammatory signaling in peripheral blood mononuclear cells and THP1 cells in the absence of HDL; this may represent a mechanism responsible for improved bacterial clearance during sepsis. These findings provide evidence that CETP inhibition represents a potential approach to reduce mortality from pneumosepsis.

Increased FH-Risk-Score and Diabetes Are Cardiovascular Risk Equivalents in Heterozygous Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a genetic condition causing premature atherosclerotic cardiovascular disease (ASCVD). It is well established that patients with FH should be treated with statin therapy. However, there exists discordance concerning low-density lipoprotein cholesterol-lowering goals in the management of these patients between different guidelines worldwide. The objective was to compare the 10-year ASCVD risk of different subgroups of patients with and without FH including those with diabetes or a history of ASCVD and patients with FH within different FH-Risk-Score categories. METHODS: This multinational observational study used data from 3 different prospective cohorts. A total of 3383 FH and 6917 non-FH controls matched for age and sex were included (104 363 person-years of follow-up). The 10-year incident ASCVD risk was assessed using Kaplan-Meier estimates, whereas the relative risk was estimated using Cox proportional hazards regression models. RESULTS: FH patients with a high (score >20%) FH-Risk-Score (hazard ratio, 8.45 [95% CI, 6.69-10.67]; P<0.0001), FH patients with diabetes (hazard ratio, 7.67 [95% CI, 4.82-12.21]; P<0.0001), and non-FH patients with ASCVD (hazard ratio, 6.78 [95% CI, 5.45-8.42]; P<0.0001) had a significantly higher incident ASCVD risk over 10 years than the reference group (non-FH without ASCVD or diabetes). The observed 10-year risks in these groups were 32.1%, 30.8%, 30.0%, and 5.1%, respectively. The 10-year ASCVD risk associated with both FH and ASCVD was extremely high (observed risk of 50.7%; hazard ratio, 14.53 [95% CI, 12.14-17.38]; P<0.0001). CONCLUSIONS: This study strongly suggests that the observed risk of FH patients with diabetes, history of ASCVD, and FH-Risk-Score >20% is as high or higher than non-FH individuals with a history of ASCVD. More aggressive management should be recommended for these patients.

Concordance of a High Lipoprotein(a) Concentration Among Relatives.

Importance: Lipoprotein(a) (Lp[a]) concentrations are a highly heritable and potential causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Recent consensus statements by the European Atherosclerosis Society and American Heart Association recommend screening of relatives of individuals with high Lp(a) concentrations, but the expected yield of this approach has not been quantified in large populations. Objective: To measure the prevalence of high Lp(a) concentrations among first- and second-degree relatives of individuals with high Lp(a) concentrations compared with unrelated participants. Design, Setting, and Participants: In this cross-sectional analysis, pairs of first-degree (n = 19 899) and second-degree (n = 9715) relatives with measured Lp(a) levels from the UK Biobank study and random pairs of unrelated individuals (n = 184 764) were compared. Data for this study were collected from March 2006 to August 2010 and analyzed from December 2021 to August 2023. Exposure: Serum Lp(a) levels, with a high Lp(a) level defined as at least 125 nmol/L. Main Outcome and Measure: Concordance of clinically relevant high Lp(a) levels in first- and second-degree relatives of index participants with high Lp(a) levels. Results: A total of 52 418 participants were included in the analysis (mean [SD] age, 57.3 [8.0] years; 29 825 [56.9%] women). Levels of Lp(a) were correlated among pairs of first-degree (Spearman ρ = 0.45; P < .001) and second-degree (Spearman ρ = 0.22; P < .001) relatives. A total of 1607 of 3420 (47.0% [95% CI, 45.3%-48.7%]) first-degree and 514 of 1614 (31.8% [95% CI, 29.6%-34.2%]) second-degree relatives of index participants with high Lp(a) levels also had elevated concentrations compared with 4974 of 30 258 (16.4% [95% CI, 16.0%-16.9%]) pairs of unrelated individuals. The concordance in high Lp(a) levels was generally consistent among subgroups (eg, those with prior ASCVD, postmenopausal women, and statin users). The odds ratios for relatives to have high Lp(a) levels if their index relative had a high Lp(a) level compared with those whose index relatives did not have high Lp(a) levels were 7.4 (95% CI, 6.8-8.1) for first-degree relatives and 3.0 (95% CI, 2.7-3.4) for second-degree relatives. Conclusions and Relevance: The findings of this cross-sectional study suggest that the yield of cascade screening of first-degree relatives of individuals with high Lp(a) levels is over 40%. These findings support recent recommendations to use this approach to identify additional individuals at ASCVD risk based on Lp(a) concentrations.

Polygenic prediction of preeclampsia and gestational hypertension.

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

Metabolic syndrome predicts cardiovascular risk and mortality in familial hypercholesterolemia.

BACKGROUND: The association between familial hypercholesterolemia (FH) and premature atherosclerotic cardiovascular disease (ASCVD) is well established. Several risk factors other than the cumulative low-density lipoprotein cholesterol (LDL-C) have been shown to modulate the severity of the phenotype in these patients. However, the effect of the metabolic syndrome (MetS) on ASCVD risk in FH remains to be determined. OBJECTIVES: The objective was to study the association between the presence of MetS and the incidence of different ASCVD endpoints and all-cause mortality. METHODS: This prospective follow up study used data from 5 independent FH cohorts from Europe and North America. We analysed data of 2401 adult heterozygous FH without history of a prior ASCVD event (21,139 person-years of follow-up). Multivariate Cox proportional hazards regression was used to estimate the association between MetS and the incidence of the different endpoints. RESULTS: The prevalence of MetS was 14% in the study population. The presence of MetS was a significant predictor of incident 10-year ASCVD after adjustment for traditional cardiovascular risk factors (HR 2.07, 95% CI 1.34-3.19), as well as of 10-year major adverse cardiovascular event (MACE) (HR 4.59, 95% CI 2.27-9.30), 10-year myocardial infarction (MI) (HR 4.29, 95% CI 1.91-9.63), and 30-year all-cause mortality (HR 4.87, 95% CI 1.99-11.89). CONCLUSION: Our findings suggests that FH patients with MetS, have an increased cardiovascular risk that is independent from LDL-C and other traditional risk factors. Future studies are required to determine the most appropriate strategy to reduce the cardiovascular burden associated with MetS in this population.

Genetic and clinical factors underlying a self-reported family history of heart disease.

AIMS: To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors. METHODS AND RESULTS: Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27-1.33) and HeFH (OR 1.31, 1.11-1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15-1.21), lipoprotein(a) (OR 1.17, CI 1.14-1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10-1.16), and triglycerides (OR 1.07, CI 1.04-1.10). For the PAR analyses: 21.9% (CI 18.19-25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44-23.88) is attributed to genetic factors, and 36.0% (CI 33.31-38.68) is attributed to genetic and clinical factors combined. CONCLUSIONS: A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.

With advances in genetics, it is tempting to assume that the 'family history' of a patient is an imperfect proxy for information we can already glean from genetics and laboratory tests. However, this study shows that much of the information contained in the self-reported family history of heart disease is not captured by currently available genetic and clinical biomarkers and highlights an important knowledge gap. Clinically used biomarkers explained only 21.9% of the likelihood of a patient reporting a family history of heart disease, while genetics explained 22.2%, and a combined model explained 36% of this likelihoodThe majority of the risk of reporting a family history went unexplained, implying that family history still has major relevance in clinical practice.

Genetic Identification of Homozygous Familial Hypercholesterolemia by Long-Read Sequencing Among Patients With Clinically Diagnosed Heterozygous Familial Hypercholesterolemia.

BACKGROUND: Homozygous familial hypercholesterolemia (HoFH) is a rare genetic disorder characterized by extremely elevated plasma low-density lipoprotein cholesterol and accelerated atherosclerosis. Accurate identification of patients with HoFH is essential as they may be eligible for specialized treatments. We hypothesized that a subset of patients with clinically diagnosed heterozygous FH (HeFH) may in fact have HoFH, and this could be identified by genetic diagnosis. METHODS: We recruited patients with a clinical diagnosis of HeFH based on a Dutch Lipid Clinic Network score ≥6 and no secondary cause of hypercholesterolemia. We performed targeted next-generation sequencing of the low-density lipoprotein receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), and low-density lipoprotein receptor adapter protein 1 (LDLRAP1) genes, followed by long-read sequencing of the LDLR gene in patients with >1 pathogenic LDLR variant. We examined lipid levels and cardiovascular events. RESULTS: Among 705 patients with clinically diagnosed HeFH, we identified a single pathogenic variant in 300 (42.6%) and >1 pathogenic variant in the LDLR gene in 11 patients (1.6%). We established a genetic diagnosis of HoFH in 6 (0.9%) patients (3 true homozygotes and 3 compound heterozygotes). The mean baseline low-density lipoprotein cholesterol and prevalence of premature cardiovascular disease of patients with genetically identified HoFH was significantly higher than patients with HeFH. CONCLUSIONS: In a cohort of patients with clinically diagnosed HeFH, genetic testing including long-read sequencing revealed that 0.9% had HoFH. These patients tended to have a more severe clinical phenotype. Genetic testing of patients with clinical FH may identify patients with HoFH that had eluded clinical diagnosis.

TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease.

Somatic mutations in blood indicative of clonal hematopoiesis of indeterminate potential (CHIP) are associated with an increased risk of hematologic malignancy, coronary artery disease, and all-cause mortality. Here we analyze the relation between CHIP status and incident peripheral artery disease (PAD) and atherosclerosis, using whole-exome sequencing and clinical data from the UK Biobank and Mass General Brigham Biobank. CHIP associated with incident PAD and atherosclerotic disease across multiple beds, with increased risk among individuals with CHIP driven by mutation in DNA Damage Repair (DDR) genes such as TP53 and PPM1D. To model the effects of DDR-induced CHIP on atherosclerosis, we used a competitive bone marrow transplantation strategy, and generated atherosclerosis-prone Ldlr-/- chimeric mice carrying 20% p53-deficient hematopoietic cells. The chimeric mice were analyzed 13-weeks post-grafting and showed increased aortic plaque size and accumulation of macrophages within the plaque, driven by increased proliferation of p53-deficient plaque macrophages. In summary, our findings highlight the role of CHIP as a broad driver of atherosclerosis across the entire arterial system beyond the coronary arteries, and provide genetic and experimental support for a direct causal contribution of TP53-mutant CHIP to atherosclerosis.

Effect of the LDL receptor mutation type on incident major adverse cardiovascular events in familial hypercholesterolaemia.

AIMS: Patients with familial hypercholesterolaemia (FH) are at increased risk of cardiovascular disease (CVD) due to extremely high circulating LDL cholesterol (LDL-C) concentrations. Our objective was to study the effect of the type of LDL receptor (LDLR) mutation on the incidence of major adverse cardiovascular events (MACEs). METHODS AND RESULTS: This was a multinational prospective cohort study, which included patients with heterozygous FH aged 18-65 years, without a prior history of CVD, and carrying a pathogenic or likely pathogenic variant in the LDLR gene. A total of 2131 patients (20 535person-years of follow-up) were included in the study, including 1234 subjects carrying a defective mutation in the LDLR and 897 subjects carrying a null mutation. During the follow-up, a first MACE occurred in 79 cases (6%) in the defective group and in 111 cases (12%) in the null group. The mean baseline LDL-C concentration was 17% higher in the null group than in the defective group (7.90 vs. 6.73 mmoL/L, P < 0.0001). In a Cox regression model corrected for traditional cardiovascular risk factors, the presence of a null mutation was associated with a hazard ratio of 2.09 (1.44-3.05), P = 0.0001. CONCLUSION: Carriers of a null mutation have an independent ∼2-fold increased risk of incident MACE compared with patients carrying a defective mutation. This study highlights the importance of genetic screening in FH in order to improve patient care.

Genome-wide and phenome-wide analysis of ideal cardiovascular health in the VA Million Veteran Program.

BACKGROUND: Genetic studies may help identify causal pathways; therefore, we sought to identify genetic determinants of ideal CVH and their association with CVD outcomes in the multi-population Veteran Administration Million Veteran Program. METHODS: An ideal health score (IHS) was calculated from 3 clinical factors (blood pressure, total cholesterol, and blood glucose levels) and 3 behavioral factors (smoking status, physical activity, and BMI), ascertained at baseline. Multi-population genome-wide association study (GWAS) was performed on IHS and binary ideal health using linear and logistic regression, respectively. Using the genome-wide significant SNPs from the IHS GWAS, we created a weighted IHS polygenic risk score (PRSIHS) which was used (i) to conduct a phenome-wide association study (PheWAS) of associations between PRSIHS and ICD-9 phenotypes and (ii) to further test for associations with mortality and selected CVD outcomes using logistic and Cox regression and, as an instrumental variable, in Mendelian Randomization. RESULTS: The discovery and replication cohorts consisted of 142,404 (119,129 European American (EUR); 16,495 African American (AFR)), and 45,766 (37,646 EUR; 5,366 AFR) participants, respectively. The mean age was 65.8 years (SD = 11.2) and 92.7% were male. Overall, 4.2% exhibited ideal CVH based on the clinical and behavioral factors. In the multi-population meta-analysis, variants at 17 loci were associated with IHS and each had known GWAS associations with multiple components of the IHS. PheWAS analysis in 456,026 participants showed that increased PRSIHS was associated with a lower odds ratio for many CVD outcomes and risk factors. Both IHS and PRSIHS measures of ideal CVH were associated with significantly less CVD outcomes and CVD mortality. CONCLUSION: A set of high interest genetic variants contribute to the presence of ideal CVH in a multi-ethnic cohort of US Veterans. Genetically influenced ideal CVH is associated with lower odds of CVD outcomes and mortality.

Repeat Measures of Lipoprotein(a) Molar Concentration and Cardiovascular Risk.

BACKGROUND: When indicated, guidelines recommend measurement of lipoprotein(a) for cardiovascular risk assessment. However, temporal variability in lipoprotein(a) is not well understood, and it is unclear if repeat testing may help refine risk prediction of coronary artery disease (CAD). OBJECTIVES: The authors examined the stability of repeat lipoprotein(a) measurements and the association between instability in lipoprotein(a) molar concentration with incident CAD. METHODS: The authors assessed the correlation between baseline and first follow-up measurements of lipoprotein(a) in the UK Biobank (n = 16,017 unrelated individuals). The association between change in lipoprotein(a) molar concentration and incident CAD was assessed among 15,432 participants using Cox proportional hazards models. RESULTS: Baseline and follow-up lipoprotein(a) molar concentration were significantly correlated over a median of 4.42 years (IQR: 3.69-4.93 years; Spearman rho = 0.96; P < 0.0001). The correlation between baseline and follow-up lipoprotein(a) molar concentration were stable across time between measurements of <3 (rho = 0.96), 3-4 (rho = 0.97), 4-5 (rho = 0.96), and >5 years (rho = 0.96). Although there were negligible-to-modest associations between statin use and changes in lipoprotein(a) molar concentration, statin usage was associated with a significant increase in lipoprotein(a) among individuals with baseline levels ≥70 nmol/L. Follow-up lipoprotein(a) molar concentration was significantly associated with risk of incident CAD (HR per 120 nmol/L: 1.32 [95% CI: 1.16-1.50]; P = 0.0002). However, the delta between follow-up and baseline lipoprotein(a) molar concentration was not significantly associated with incident CAD independent of follow-up lipoprotein(a) (P = 0.98). CONCLUSIONS: These findings suggest that, in the absence of therapies substantially altering lipoprotein(a), a single accurate measurement of lipoprotein(a) molar concentration is an efficient method to inform CAD risk.

Polygenic risk scores for the diagnosis and management of dyslipidemia.

PURPOSE OF REVIEW: To review current progress in the use of polygenic risk scores for lipid traits and their use in the diagnosis and treatment of lipid disorders. RECENT FINDINGS: Inherited lipid disorders, including those causing extremes of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglycerides were initially identified as monogenic traits, in which a single rare variant with large effect size is responsible for the phenotype. More recently, a polygenic basis for many lipid traits has also been identified. Patients with polygenic dyslipidemia can be identified through the use of polygenic risk scores (PRSs), which collapse information from a handful to several million genetic variants into a single metric. SUMMARY: PRSs for lipid traits may aid in the identification of the genetic basis for the lipid phenotype in individual patients, may provide additional information regarding the risk of cardiovascular disease, and could help in guiding therapeutic decision making.

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

BACKGROUND: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. METHODS: We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases-based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. RESULTS: Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling). CONCLUSIONS: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.

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.

Polygenic architecture and cardiovascular risk of familial combined hyperlipidemia.

BACKGROUND AND AIMS: Familial 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 RESULTS: We 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]). CONCLUSIONS: These 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.