Multi-trait analysis reveals risk loci for heart failure and the shared genetic etiology with blood lipids, blood pressure, and blood glucose.

Phenotypic associations have been reported between heart failure (HF) and blood lipids (BLs), blood pressure (BP), and blood glucose (BG). However, the shared genetic etiology underlying these associations remains incompletely understood. Conducting a large-scale multi-trait association study for HF with these traits, we discovered 143 previously unreported genomic risk loci for HF. Results showed that 46, 35, and 14 colocalized loci were shared by HF with BLs, BP, and BG, respectively. Notably, the loci shared by HF with these traits rarely overlapped, indicating distinct mechanisms. The combination of gene-mapping, gene-based, and transcriptome-wide association analyses prioritized noteworthy candidate genes (such as lipoprotein lipase [LPL], G protein-coupled receptor kinase 5 [GRK5], and troponin C1, slow skeletal and cardiac type [TNNC1]) for HF. Enrichment analysis revealed that HF exhibited comparable characteristics to cardiovascular traits and metabolic traits correlated to BLs, BP, and BG. Finally, we reported drug repurposing candidates and plasma protein targets for HF. These results provide biological insights into the pathogenesis of these comorbidities of HF.

Identification of pleiotropic and specific therapeutic targets for cardio-cerebral diseases: A large-scale proteome-wide mendelian randomization and colocalization study.

BACKGROUND: The cardiac-brain connection has been identified as the basis for multiple cardio-cerebral diseases. However, effective therapeutic targets for these diseases are still limited. Therefore, this study aimed to identify pleiotropic and specific therapeutic targets for cardio-cerebral diseases using Mendelian randomization (MR) and colocalization analyses. METHODS: This study included two large protein quantitative trait loci studies with over 4,000 plasma proteins were included in the discovery and replication cohorts, respectively. We initially used MR to estimate the associations between protein and 20 cardio-cerebral diseases. Subsequently, Colocalization analysis was employed to enhance the credibility of the results. Protein target prioritization was based solely on including highly robust significant results from both the discovery and replication phases. Lastly, the Drug-Gene Interaction Database was utilized to investigate protein-gene-drug interactions further. RESULTS: A total of 46 target proteins for cardio-cerebral diseases were identified as robust in the discovery and replication phases by MR, comprising 7 pleiotropic therapeutic proteins and 39 specific target proteins. Followed by colocalization analysis and prioritization of evidence grades for target protein, 6 of these protein-disease pairs have achieved the highly recommended level. For instance, the PILRA protein presents a pleiotropic effect on sick sinus syndrome and Alzheimer's disease, whereas GRN exerts specific effects on the latter. APOL3, LRP4, and F11, on the other hand, have specific effects on cardiomyopathy and ischemic stroke, respectively. CONCLUSIONS: This study successfully identified important therapeutic targets for cardio-cerebral diseases, which benefits the development of preventive or therapeutic drugs.

Genetically predicted osteoprotegerin levels and the risk of cardiovascular diseases: A Mendelian randomization study.

PURPOSE: The relationship between circulating osteoprotegerin (OPG) levels and the risk of cardiovascular diseases (CVDs) has been the subject of conflicting results in previous observational and experimental studies. To assess the causal effect of genetically predicted OPG levels on the risk of a wide range of CVDs, we used the Mendelian randomization design. DESIGN: We initially extracted information of genetic variants on OPG levels and their corresponding effect values from the summary data based on the European ancestry genome-wide association study. Subsequently, we performed two-sample Mendelian randomization analyses to assess the causal effect of genetically predicted OPG levels on CVDs by using inverse variance weighting (IVW), MR-Egger, weighted median, and MR-PRESSO methods. We also conducted sensitivity analyzes as well as complementary analyses with a more relaxed threshold for the exposure genetic instrumental variable (P < 5 × 10-6) to test the robustness of our results. RESULTS: Our results indicated that genetically predicted OPG levels causally reduce the risk of atrial fibrillation (IVW OR = 0.84; 95% CI = 0.72-0.98; P = 0.0241), myocardial infarction(IVW OR = 0.89; 95% CI = 0.80-0.98; P = 0.0173) and coronary heart disease (IVW: OR = 0.90; 95% CI = 0.82-0.99; P = 0.0286). Further complementary analyses also confirmed the above results remain robust and we also identified a potential causal association of OPG levels with a reduced risk of hypertensive diseases(IVW OR = 0.94;95% CI = 0.88-1.00; P = 0.0394). CONCLUSION: This study provides compelling evidence for a causal relationship between genetically predicted OPG levels and risk reduction of coronary heart disease, myocardial infarction, and atrial fibrillation, indicating that OPG could potentially serve as a cardiovascular risk marker in clinical practice.