Genetic inhibition of angiopoietin-like protein-3, lipids, and cardiometabolic risk.

BACKGROUND AND AIMS: RNA-based, antibody-based, and genome editing-based therapies are currently under investigation to determine if the inhibition of angiopoietin-like protein-3 (ANGPTL3) could reduce lipoprotein-lipid levels and atherosclerotic cardiovascular disease (ASCVD) risk. Mendelian randomisation (MR) was used to determine whether genetic variations influencing ANGPTL3 liver gene expression, blood levels, and protein structure could causally influence triglyceride and apolipoprotein B (apoB) levels as well as coronary artery disease (CAD), ischaemic stroke (IS), and other cardiometabolic diseases. METHODS: RNA sequencing of 246 explanted liver samples and genome-wide genotyping was performed to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of ANGPTL3. Genome-wide summary statistics of plasma protein levels of ANGPTL3 from the deCODE study (n = 35 359) were used. A total of 647 carriers of ANGPTL3 protein-truncating variants (PTVs) associated with lower plasma triglyceride levels were identified in the UK Biobank. Two-sample MR using SNPs that influence ANGPTL3 liver expression or ANGPTL3 plasma protein levels as exposure and cardiometabolic diseases as outcomes was performed (CAD, IS, heart failure, non-alcoholic fatty liver disease, acute pancreatitis, and type 2 diabetes). The impact of rare PTVs influencing plasma triglyceride levels on apoB levels and CAD was also investigated in the UK Biobank. RESULTS: In two-sample MR studies, common genetic variants influencing ANGPTL3 hepatic or blood expression levels of ANGPTL3 had a very strong effect on plasma triglyceride levels, a more modest effect on low-density lipoprotein cholesterol, a weaker effect on apoB levels, and no effect on CAD or other cardiometabolic diseases. In the UK Biobank, the carriers of rare ANGPTL3 PTVs providing lifelong reductions in median plasma triglyceride levels [-0.37 (interquartile range 0.41) mmol/L] had slightly lower apoB levels (-0.06 ± 0.32 g/L) and similar CAD event rates compared with non-carriers (10.2% vs. 10.9% in carriers vs. non-carriers, P = .60). CONCLUSIONS: PTVs influencing ANGPTL3 protein structure as well as common genetic variants influencing ANGPTL3 hepatic expression and/or blood protein levels exhibit a strong effect on circulating plasma triglyceride levels, a weak effect on circulating apoB levels, and no effect on ASCVD. Near-complete inhibition of ANGPTL3 function in patients with very elevated apoB levels may be required to reduce ASCVD risk.

Proteome-Wide Mendelian Randomization Identifies Causal Links Between Blood Proteins and Acute Pancreatitis.

BACKGROUND & AIMS: Acute pancreatitis (AP) is a complex disease and the leading cause of gastrointestinal disease-related hospital admissions. Few therapeutic options exist for AP prevention. Blood proteins with causal evidence may represent promising drug targets, but few have been causally linked with AP. Our objective was to identify blood proteins linked with AP by combining genome-wide association meta-analysis and proteome-wide Mendelian randomization (MR) studies. METHODS: We performed a genome-wide association meta-analysis totalling 10,630 patients with AP and 844,679 controls and a series of inverse-variance weighted MR analyses using cis-acting variants on 4719 blood proteins from the deCODE study (N = 35,559) and 4979 blood proteins from the Fenland study (N = 10,708). RESULTS: The meta-analysis identified genome-wide significant variants (P <5 × 10-8) at 5 loci (ABCG5/8, TWIST2, SPINK1, PRSS2 and MORC4). The proteome-wide MR analyses identified 68 unique blood proteins that may causally be associated with AP, including 29 proteins validated in both data sets. Functional annotation of these proteins confirmed expression of many proteins in metabolic tissues responsible for digestion and energy metabolism, such as the esophagus, adipose tissue, and liver as well as acinar cells of the pancreas. Genetic colocalization and investigations into the druggable genome also identified potential drug targets for AP. CONCLUSIONS: This large genome-wide association study meta-analysis for AP identified new variants linked with AP as well as several blood proteins that may be causally associated with AP. This study provides new information on the genetic architecture of this disease and identified pathways related to AP, which may be further explored as possible therapeutic targets for AP.

Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets.

Aims: Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. Methods and results: To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses. Conclusion: These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

Lipoprotein(a) and Calcific Aortic Valve Stenosis Progression: A Systematic Review and Meta-Analysis.

Importance: There are currently no pharmacological treatments available to slow hemodynamic progression of aortic stenosis. Plasma lipoprotein(a) concentrations predict incident aortic stenosis but its association with hemodynamic progression is controversial. Objective: To determine the association between plasma lipoprotein(a) concentrations and hemodynamic progression in patients with aortic stenosis. Design, Settings and Participants: The study included patients with aortic stenosis from 5 longitudinal clinical studies conducted from March 2001 to March 2023 in Canada and the UK. Of 757 total patients, data on plasma lipoprotein(a) concentrations and rates of hemodynamic progression assessed by echocardiography were available for 710, who were included in this analysis. Data were analyzed from March 2023 to April 2024. Exposure: Cohort-specific plasma lipoprotein(a) concentration tertiles. Main Outcomes and Measures: Hemodynamic aortic stenosis progression on echocardiography as assessed by annualized change in peak aortic jet velocity, mean transvalvular gradient, and aortic valve area. Results: Among the included patients, 497 (70%) were male and 213 (30%) were female. The mean (SD) age was 65.2 (13.1) years. Patients in the top lipoprotein(a) tertile demonstrated 41% (estimate, 1.41; 95% CI, 1.13-1.75) faster progression of peak aortic jet velocity and 57% (estimate, 1.57; 95% CI, 1.18-2.10) faster progression of mean transvalvular gradient than patients in the bottom tertile. There was no evidence of heterogeneity across the individual cohorts. Progression of aortic valve area was comparable between groups (estimate, 1.23; 95% CI, 0.71-2.12). Similar results were observed when plasma lipoprotein(a) concentrations were treated as a continuous variable. Conclusions and Relevance: In this study, higher plasma lipoprotein(a) concentrations were associated with faster rates of hemodynamic progression in patients with aortic stenosis. Lowering plasma lipoprotein(a) concentrations warrants further investigation in the prevention and treatment of aortic stenosis.

Contribution of Lipoprotein(a) to Polygenic Risk Prediction of Coronary Artery Disease: A Prospective UK Biobank Analysis.

BACKGROUND: Lp(a) (lipoprotein[a]) is a highly atherogenic lipoprotein subfraction that may contribute to polygenic risk of coronary artery disease (CAD), but the extent of this contribution is unknown. Our objective was to estimate the contribution of Lp(a) to polygenic risk of CAD and to evaluate the respective contributions of Lp(a) and a CAD polygenic risk score (PRS) to CAD. METHODS: A total of 372 385 UK Biobank participants of European ancestry free of CAD at baseline were included. Plasma Lp(a) levels were measured and a CAD-PRS was calculated using the LDpred2 algorithm. Over the median follow-up of 12.6 years, 13 538 participants had incident CAD (myocardial infarction, coronary artery bypass grafting, or coronary angioplasty). RESULTS: The LPA region contribution to the CAD-PRS-mediated CAD risk was modest (7.2% [95% CI, 6.1-8.3]). Lp(a) levels significantly increased the predictive performance of a CAD-PRS including age and sex in Cox regression (C statistic 0.751 versus 0.746, difference, 0.005 [95% CI, 0.004-0.006]). Compared with participants in the bottom CAD-PRS quintile with Lp(a) levels <25 nmol/L (CAD event rate, 1.4%), the hazard ratio for incident CAD in participants in the top CAD-PRS quintile with Lp(a) levels ≥125 nmol/L was 5.45 (95% CI, 4.93-6.03; P=9.35×10-242, CAD event rate 6.6%). CONCLUSIONS: Compared with individuals with a low genetic risk of CAD (low CAD-PRS and low Lp[a] levels), those with a high genetic risk (high CAD-PRS and high Lp[a] levels) had a 5-fold higher CAD risk. These results highlight a substantial contribution of genetic risk factors to CAD and that accurate estimation of genetic risk of CAD may need to consider blood levels of Lp(a).

Large-scale metabolomic profiling and incident non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent disease with no specific drug therapy. High-throughput metabolomics present an unprecedented opportunity to identify biomarkers and potentially causal risk factors for NAFLD. Here, we determined the impact of 21 circulating metabolites, 17 lipids, and 132 lipoprotein particle characteristics on NAFLD combining prospective observational and two-sample Mendelian randomization (MR) analyses in 121,032 UK Biobank participants. We identified several metabolic factors associated with NAFLD risk in observational and MR analyses including triglyceride-rich and high-density lipoprotein particles composition, as well as the ratio of polyunsaturated fatty acids to total fatty acids. This study, is one of the largest to investigate incident NAFLD, provides concordant observational and genetic evidence that therapies aimed at reducing circulating triglycerides and increasing large HDL particles, as well as interventions aimed at increasing polyunsaturated fatty acid content may warrant further investigation into NAFLD prevention and treatment.

Genetic control of body weight by the human brain proteome.

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with body weight but the biological relevance of most remains unexplored. Given the critical role of the brain in body weight regulation, we set out to determine whether genetic variants linked with body mass index (BMI) could be mapped to brain proteins. Using genetic colocalization, we mapped 25 loci from the largest BMI GWAS (n = 806,834) to brain protein concentrations obtained from publicly available datasets. We also performed a proteome-wide Mendelian randomization on 696 brain proteins followed by genetic colocalization and identified 35 additional brain proteins. Only a minority of these proteins (<30%) had a colocalization signal with cortex gene expression levels, highlighting the value of moving beyond gene expression levels and examining brain protein levels. In conclusion, we identified 60 unique proteins expressed in the brain that may be critical regulators of body weight in humans.

Impact of C-reactive protein levels on lipoprotein(a)-associated aortic stenosis incidence and progression.

Aims: Elevated lipoprotein(a) [Lp(a)] levels are associated with the risk of coronary artery disease (CAD) and calcific aortic valve stenosis (CAVS). Observational studies revealed that Lp(a) and C-reactive protein (CRP) levels, a biomarker of systemic inflammation, may jointly predict CAD risk. Whether Lp(a) and CRP levels also jointly predict CAVS incidence and progression is unknown. Methods and results: We investigated the association of Lp(a) with CAVS according to CRP levels in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk study (n = 18 226, 406 incident cases) and the UK Biobank (n = 438 260, 4582 incident cases), as well as in the ASTRONOMER study (n = 220), which assessed the haemodynamic progression rate of pre-existing mild-to-moderate aortic stenosis. In EPIC-Norfolk, in comparison to individuals with low Lp(a) levels (<50 mg/dL) and low CRP levels (<2.0 mg/L), those with elevated Lp(a) (>50 mg/dL) and low CRP levels (<2.0 mg/L) and those with elevated Lp(a) (>50 mg/dL) and elevated CRP levels (>2.0 mg/L) had a higher CAVS risk [hazard ratio (HR) = 1.86 (95% confidence intervals, 1.30-2.67) and 2.08 (1.44-2.99), respectively]. A comparable predictive value of Lp(a) in patients with vs. without elevated CRP levels was also noted in the UK Biobank. In ASTRONOMER, CAVS progression was comparable in patients with elevated Lp(a) levels with or without elevated CRP levels. Conclusion: Lp(a) predicts the incidence and possibly progression of CAVS regardless of plasma CRP levels. Lowering Lp(a) levels may warrant further investigation in the prevention and treatment of CAVS, regardless of systemic inflammation.

Blood Levels of the SMOC1 Hepatokine Are Not Causally Linked with Type 2 Diabetes: A Bidirectional Mendelian Randomization Study.

Hepatokines are liver-derived proteins that may influence metabolic pathways such as insulin sensitivity. Recently, Sparc-related modular calcium-binding protein 1 (SMOC1) was identified as glucose-responsive hepatokine that is dysregulated in the setting of non-alcoholic fatty liver disease (NAFLD). While SMOC1 may influence glucose-insulin homeostasis in rodents, it is unknown if SMOC1 is influenced by NAFLD in humans. It is also unknown if SMOC1 is causally associated with metabolic and disease traits in humans. Therefore, we aimed to determine the effect of NAFLD on SMOC1 gene expression in the liver and aimed to explore the potential causal associations of SMOC1 levels with NAFLD, T2D, and glycemic traits in humans. Using an RNA sequencing dataset from a cohort of 216 patients with NAFLD, we assessed SMOC1 expression levels across the NAFLD spectrum. We performed a series of bidirectional inverse-variance weighted Mendelian randomization (MR) analyses on blood SMOC1 levels using two sources of genome-wide association studies (GWAS) (Fenland study, n = 10,708 and INTERVAL study, n = 3301). We utilized GWAS summary statistics for NAFLD in 8434 cases and 770,180 controls, as well as publicly available GWAS for type 2 diabetes (T2D), body mass index (BMI), waist-to-hip ratio (WHR), fasting blood insulin (FBI), fasting blood glucose (FBG), homeostatic Model Assessment of Insulin Resistance (HOMA-B and HOMA-IR), and hemoglobin A1c (HbA1C). We found that SMOC1 expression showed no significant differences across NAFLD stages. We also identified that the top single-nucleotide polymorphism associated with blood SMOC1 levels, was associated with SMOC1 gene expression in the liver, but not in other tissues. Using MR, we did not find any evidence that genetically predicted NAFLD, T2D, and glycemic traits influenced SMOC1 levels. We also did not find evidence that blood SMOC1 levels were causally associated with T2D, NAFLD, and glycemic traits. In conclusion, the hepatokine SMOC1 does not appear to be modulated by the presence of NAFLD and may not regulate glucose-insulin homeostasis in humans. Results of this study suggest that blood factors regulating metabolism in rodents may not always translate to human biology.

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis.

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development.

A Comparative Analysis of the Lipoprotein(a) and Low-Density Lipoprotein Proteomic Profiles Combining Mass Spectrometry and Mendelian Randomization.

BACKGROUND: Lipoprotein(a) (Lp[a]), which consists of a low-density lipoprotein (LDL) bound to apolipoprotein(a), is one of the strongest genetic risk factors for atherosclerotic cardiovascular diseases. Few studies have performed hypothesis-free direct comparisons of the Lp(a) and the LDL proteomes. Our objectives were to compare the Lp(a) and the LDL proteomic profiles and to evaluate the effect of lifelong exposure to elevated Lp(a) or LDL cholesterol levels on the plasma proteomic profile. METHODS: We performed a label-free analysis of the Lp(a) and LDL proteomic profiles of healthy volunteers in a discovery (n = 6) and a replication (n = 9) phase. We performed inverse variance weighted Mendelian randomization to document the effect of lifelong exposure to elevated Lp(a) or LDL cholesterol levels on the plasma proteomic profile of participants of the INTERVAL study. RESULTS: We identified 15 proteins that were more abundant on Lp(a) compared with LDL (serping1, pi16, itih1, itih2, itih3, pon1, podxl, cd44, cp, ptprg, vtn, pcsk9, igfals, vcam1, and ttr). We found no proteins that were more abundant on LDL compared with Lp(a). After correction for multiple testing, lifelong exposure to elevated LDL cholesterol levels was associated with the variation of 18 plasma proteins whereas Lp(a) did not appear to influence the plasma proteome. CONCLUSIONS: Results of this study highlight marked differences in the proteome of Lp(a) and LDL as well as in the effect of lifelong exposure to elevated LDL cholesterol or Lp(a) on the plasma proteomic profile.

CONTEXTE: La lipoprotéine(a) (Lp[a]), qui est constituée d'une lipoprotéine de basse densité (LDL) liée à une apolipoprotéine(a), est l'un des plus importants facteurs de risque génétiques de survenue d'une maladie cardiovasculaire athéroscléreuse. Peu d'études comparatives directes sans hypothèse ont porté sur le protéome de la Lp(a) et celui des LDL. Nos objectifs étaient de comparer les profils protéomiques de la Lp(a) et des LDL et d'évaluer l'effet d'une exposition à vie à un taux élevé de Lp(a) ou de cholestérol LDL sur le profil protéomique plasmatique. MÉTHODOLOGIE: Nous avons réalisé une analyse sans marquage des profils protéomiques de la Lp(a) et des LDL chez des volontaires en bonne santé dans le cadre d'une phase de découverte (n = 6) et d'une phase de réplication (n = 9). Pour rendre compte de l'effet d'une exposition à vie à un taux élevé de Lp(a) ou de cholestérol des LDL sur le profil protéomique plasmatique des participants de l'étude INTERVAL, nous avons utilisé une analyse de randomisation Mendélienne avec pondération par l'inverse de la variance. RÉSULTATS: Nous avons relevé 15 protéines associées en plus grande abondance à la Lp(a) qu'aux LDL (serping1, pi16, itih1, itih2, itih3, pon1, podxl, cd44, cp, ptprg, vtn, pcsk9, igfals, vcam1 et ttr). Nous n'avons noté aucune protéine associée en plus grande abondance aux LDL qu'à la Lp(a). Après correction pour tenir compte de la multiplicité des tests, l'exposition à vie à un taux élevé de cholestérol LDL a été associée à la variation de 18 protéines plasmatiques, tandis que le taux de Lp(a) ne semblait pas influencer le protéome plasmatique. CONCLUSIONS: Les résultats de notre étude font ressortir les différences marquées entre le protéome de la Lp(a) et celui des LDL, ainsi qu'entre l'effet sur le profil protéomique plasmatique de l'exposition à vie à un taux élevé de cholestérol LDL et celui de l'exposition à vie à un taux élevé de Lp(a).