Genome-Wide Association Study of Beta-Blocker Survival Benefit in Black and White Patients with Heart Failure with Reduced Ejection Fraction.

In patients with heart failure with reduced ejection fraction (HFrEF), individual responses to beta-blockers vary. Candidate gene pharmacogenetic studies yielded significant but inconsistent results, and they may have missed important associations. Our objective was to use an unbiased genome-wide association study (GWAS) to identify loci influencing beta-blocker survival benefit in HFrEF patients. Genetic variant × beta-blocker exposure interactions were tested in Cox proportional hazards models for all-cause mortality stratified by self-identified race. The models were adjusted for clinical risk factors and propensity scores. A prospective HFrEF registry (469 black and 459 white patients) was used for discovery, and linkage disequilibrium (LD) clumped variants with a beta-blocker interaction of p < 5 × 10-5, were tested for Bonferroni-corrected validation in a multicenter HFrEF clinical trial (288 black and 579 white patients). A total of 229 and 18 variants in black and white HFrEF patients, respectively, had interactions with beta-blocker exposure at p < 5 × 10-5 upon discovery. After LD-clumping, 100 variants and 4 variants in the black and white patients, respectively, remained for validation but none reached statistical significance. In conclusion, genetic variants of potential interest were identified in a discovery-based GWAS of beta-blocker survival benefit in HFrEF patients, but none were validated in an independent dataset. Larger cohorts or alternative approaches, such as polygenic scores, are needed.

Common Variants on FGD5 Increase Hazard of Mortality or Rehospitalization in Patients With Heart Failure From the ASCEND-HF Trial.

BACKGROUND: Heart failure remains a global health burden, and patients hospitalized are particularly at risk, but genetic associates for subsequent death or rehospitalization are still lacking. METHODS: The genetic substudy of the ASCEND-HF trial (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) was used to perform genome-wide association study and transethnic meta-analysis. The overall trial included the patients of self-reported European ancestry (n=2173) and African ancestry (n=507). The end point was death or heart failure rehospitalization within 180 days. Cox models adjusted for 11 a priori predictors of rehospitalization and 5 genetic principal components were used to test the association between single-nucleotide polymorphisms and outcome. Summary statistics from the 2 populations were combined via meta-analysis with the significance threshold considered P<5×10-8. RESULTS: Common variants (rs2342882 and rs35850039 in complete linkage disequilibrium) located in FGD5 were significantly associated with the primary outcome in both ancestry groups (European Americans: hazard ratio [HR], 1.38; P=2.42×10-6; African ancestry: HR, 1.51; P=4.43×10-3; HR in meta-analysis, 1.41; P=4.25×10-8). FGD5 encodes a regulator of VEGF (vascular endothelial growth factor)-mediated angiogenesis, and in silico investigation revealed several previous genome-wide association study hits in this gene, among which rs748431 was associated with our outcome (HR, 1.20; meta P<0.01). Sensitivity analysis proved FGD5 common variants survival association did not appear to operate via coronary artery disease or nesiritide treatment (P>0.05); and the signal was still significant when changing the censoring time from 180 to 30 days (HR, 1.39; P=1.59×10-5). CONCLUSIONS: In this multiethnic genome-wide association study of ASCEND-HF, single-nucleotide polymorphisms in FGD5 were associated with increased risk of death or rehospitalization. Additional investigation is required to examine biological mechanisms and whether FGD5 could be a therapeutic target. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00475852.

Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program.

BACKGROUND: The All of Us Research Program enrolls diverse US participants which provide a unique opportunity to better understand the problem of opioid use. This study aims to estimate the prevalence of opioid use and its association with sociodemographic characteristics from survey data and electronic health record (EHR). METHODS: A total of 214,206 participants were included in this study who competed survey modules and shared EHR data. Adjusted logistic regressions were used to explore the associations between sociodemographic characteristics and opioid use. RESULTS: The lifetime prevalence of street opioids was 4%, and the nonmedical use of prescription opioids was 9%. Men had higher odds of lifetime opioid use (aOR: 1.4 to 3.1) but reduced odds of current nonmedical use of prescription opioids (aOR: 0.6). Participants from other racial and ethnic groups were at reduced odds of lifetime use (aOR: 0.2 to 0.9) but increased odds of current use (aOR: 1.9 to 9.9) compared with non-Hispanic White participants. Foreign-born participants were at reduced risks of opioid use and diagnosed with opioid use disorders (OUD) compared with US-born participants (aOR: 0.36 to 0.67). Men, Younger, White, and US-born participants are more likely to have OUD. CONCLUSIONS: All of Us research data can be used as an indicator of national trends for monitoring the prevalence of receiving prescription opioids, diagnosis of OUD, and non-medical use of opioids in the US. The program employs a longitudinal design for routinely collecting health-related data including EHR data, that will contribute to the literature by providing important clinical information related to opioids over time. Additionally, this data will enhance the estimates of the prevalence of OUD among diverse populations, including groups that are underrepresented in the national survey data.

The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation.

BACKGROUND: The correlation between human gut microbiota and psychiatric diseases has long been recognized. Based on the heritability of the microbiome, genome-wide association studies on human genome and gut microbiome (mbGWAS) have revealed important host-microbiome interactions. However, establishing causal relationships between specific gut microbiome features and psychological conditions remains challenging due to insufficient sample sizes of previous studies of mbGWAS. METHODS: Cross-cohort meta-analysis (via METAL) and multi-trait analysis (via MTAG) were used to enhance the statistical power of mbGWAS for identifying genetic variants and genes. Using two large mbGWAS studies (7,738 and 5,959 participants respectively) and12 disease-specific studies from the Psychiatric Genomics Consortium (PGC), we performed bidirectional two-sample mendelian randomization (MR) analyses between microbial features and psychiatric diseases (up to 500,199 individuals). Additionally, we conducted downstream gene- and gene-set-based analyses to investigate the shared biology linking gut microbiota and psychiatric diseases. RESULTS: METAL and MTAG conducted in mbGWAS could boost power for gene prioritization and MR analysis. Increases in the number of lead SNPs and mapped genes were witnessed in 13/15 species and 5/10 genera after using METAL, and MTAG analysis gained an increase in sample size equivalent to expanding the original samples from 7% to 63%. Following METAL use, we identified a positive association between Bacteroides faecis and ADHD (OR, 1.09; 95 %CI, 1.02-1.16; P = 0.008). Bacteroides eggerthii and Bacteroides thetaiotaomicron were observed to be positively associated with PTSD (OR, 1.11; 95 %CI, 1.03-1.20; P = 0.007; OR, 1.11; 95 %CI, 1.01-1.23; P = 0.03). These findings remained stable across statistical models and sensitivity analyses. No genetic liabilities to psychiatric diseases may alter the abundance of gut microorganisms.Using biological annotation, we identified that those genes contributing to microbiomes (e.g., GRIN2A and RBFOX1) are expressed and enriched in human brain tissues. CONCLUSIONS: Our statistical genetics strategy helps to enhance the power of mbGWAS, and our genetic findings offer new insights into biological pleiotropy and causal relationship between microbiota and psychiatric diseases.

MAP3K19 regulatory variation in populations with African ancestry may increase COVID-19 severity.

To identify ancestry-linked genetic risk variants associated with COVID-19 hospitalization, we performed an integrative analysis of two genome-wide association studies and resolved four single nucleotide polymorphisms more frequent in COVID-19-hospitalized patients with non-European ancestry. Among them, the COVID-19 risk SNP rs16831827 shows the largest difference in minor allele frequency (MAF) between populations with African and European ancestry and also shows higher MAF in hospitalized COVID-19 patients among cohorts of mixed ancestry (odds ratio [OR] = 1.20, 95% CI: 1.10-1.30) and entirely African ancestry (OR = 1.30, 95% CI: 1.02-1.67). rs16831827 is an expression quantitative trait locus of MAP3K19. MAP3K19 expression is induced during ciliogenesis and most abundant in ciliated tissues including lungs. Single-cell RNA sequencing analyses revealed that MAP3K19 is highly expressed in multiple ciliated cell types. As rs16831827∗T is associated with reduced MAP3K19 expression, it may increase the risk of severe COVID-19 by reducing MAP3K19 expression.

Shifting as an executive function separate from updating and inhibition in old age: Behavioral and genetic evidence.

This study aimed to examine the organization of executive functions (EFs), specifically working memory updating, prepotent response inhibition, and mental-set shifting in old age, with a particular focus on determining whether the shifting function was behaviorally and genetically separated from the other functions. A total of 248 healthy older Chinese individuals participated, and multiple measures of executive functions were collected. Additionally, measures of fluid intelligence were included to explore the varying relationships between the three executive functions and this higher-order cognitive ability. Furthermore, genetic data were gathered and analyzed to investigate the associations between EFs and six candidate single-nucleotide polymorphisms (SNPs) mapped to dopaminergic, serotonergic, or glutamatergic genes. The results indicated that both the three-factor model and the two-factor model, which combined updating and inhibition, demonstrated a good fit. Furthermore, shifting was found to be behaviorally separated from the other two functions, and the correlation between shifting and fluid intelligence was smaller compared to the correlations between updating and inhibition with fluid intelligence. Moreover, the DRD2 SNPs showed significant associations with shifting, rather than with updating and inhibition. These findings provide evidence that shifting is distinct and separate from updating and inhibition, highlighting the diversity of EFs among older adults.

Cross-phenotype relationship between opioid use disorder and suicide attempts: new evidence from polygenic association and Mendelian randomization analyses.

Clinical epidemiological studies have found high co-occurrence between suicide attempts (SA) and opioid use disorder (OUD). However, the patterns of correlation and causation between them are still not clear due to psychiatric confounding. To investigate their cross-phenotype relationship, we utilized raw phenotypes and genotypes from >150,000 UK Biobank samples, and genome-wide association summary statistics from >600,000 individuals with European ancestry. Pairwise association and a potential bidirectional relationship between OUD and SA were evaluated with and without controlling for major psychiatric disease status (e.g., schizophrenia, major depressive disorder, and alcohol use disorder). Multiple statistical and genetics tools were used to perform epidemiological association, genetic correlation, polygenic risk score prediction, and Mendelian randomizations (MR) analyses. Strong associations between OUD and SA were observed at both the phenotypic level (overall samples [OR = 2.94, P = 1.59 ×10-14]; non-psychiatric subgroup [OR = 2.15, P = 1.07 ×10-3]) and the genetic level (genetic correlation rg = 0.38 and 0.5 with or without conditioning on psychiatric traits, respectively). Consistently, increasing polygenic susceptibility to SA is associated with increasing risk of OUD (OR = 1.08, false discovery rate [FDR] =1.71 ×10-3), and similarly, increasing polygenic susceptibility to OUD is associated with increasing risk of SA (OR = 1.09, FDR = 1.73 ×10-6). However, these polygenic associations were much attenuated after controlling for comorbid psychiatric diseases. A combination of MR analyses suggested a possible causal association from genetic liability for SA to OUD risk (2-sample univariable MR: OR = 1.14, P = 0.001; multivariable MR: OR = 1.08, P = 0.001). This study provided new genetic evidence to explain the observed OUD-SA comorbidity. Future prevention strategies for each phenotype needs to take into consideration of screening for the other one.

Evaluation of a New Aptamer-Based Array for Soluble Suppressor of Tumorgenicity (ST2) and N-terminal Pro-B-Type Natriuretic Peptide (NTproBNP) in Heart Failure Patients.

BACKGROUND: Recent advances in multi-marker platforms offer faster data generation, but the fidelity of these methods compared to the ELISA is not established. We tested the correlation and predictive performance of SOMAscan vs. ELISA methods for NTproBNP and ST2. METHODS: Patients ≥ 18 years with heart failure and ejection fraction < 50% were enrolled. We tested the correlation between SOMA and ELISA for each biomarker and their association with outcomes. RESULTS: There was good correlation of SOMA vs. ELISA for ST2 (ρ = 0.71) and excellent correlation for NTproBNP (ρ = 0.94). The two versions of both markers were not significantly different regarding survival association. The two ST2 assays and NTproBNP assays were similarly associated with all-cause mortality and cardiovascular mortality. These associations remained statistically significant when adjusted for MAGGIC risk score (all p < 0.05). CONCLUSION: SOMAscan quantifications of ST2 and NTproBNP correlate to ELISA versions and carry similar prognosis.

GBC: a parallel toolkit based on highly addressable byte-encoding blocks for extremely large-scale genotypes of species.

Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC's data structure and algorithms are valuable for accelerating large-scale genomic research.

Self-rated health as a predictor of hospitalizations in patients with bipolar disorder or major depressive disorder: A prospective cohort study of the UK Biobank.

BACKGROUND: To determine the association between self-rated health (SRH) and subsequent all-cause hospitalizations in patients with bipolar disorder (BD) or major depression (MDD). METHODS: We conducted a prospective cohort study on people with BD or MDD in the UK from 2006 to 2010 using UK Biobank touchscreen questionnaire data and linked administrative health databases. The association between SRH and 2-year all-cause hospitalizations was assessed using proportional hazard regression after adjustment for sociodemographics, lifestyle behaviors, previous hospitalization use, the Elixhauser comorbidity index, and environmental factors. RESULTS: A total of 29,966 participants were identified, experiencing 10,279 hospitalization events. Among the cohort, the average age was 55.88 (SD 8.01) years, 64.02 % were female, and 3029 (10.11 %), 15,972 (53.30 %), 8313 (27.74 %), and 2652 (8.85 %) reported excellent, good, fair, and poor SRH, respectively. Among patients reporting poor SRH, 54.19 % had a hospitalization event within 2 years compared with 22.65 % for those having excellent SRH. In the adjusted analysis, patients with good, fair, and poor SRH had 1.31 (95 % CI 1.21-1.42), 1.82 (95 % CI 1.68-1.98), and 2.45 (95 % CI 2.22, 2.70) higher hazards of hospitalization, respectively, than those with excellent SRH. LIMITATIONS: Selection bias can exist as our cohort cannot fully represent all the BD and MDD cases in the UK. Moreover, the causality is questionable. CONCLUSION: SRH was independently associated with subsequent all-cause hospitalizations in patients with BD or MDD. This large study underscores the need for proactive SRH screening in this population, which might inform resource allocation in clinical care and enhance high-risk population detection.

Genome-wide screening of sex-biased genetic variants potentially associated with COVID-19 hospitalization.

Sex-biased difference in coronavirus disease 2019 (COVID-19) hospitalization has been observed as that male patients tend to be more likely to be hospitalized than female patients. However, due to the insufficient sample size and existed studies that more prioritized to sex-stratified COVID-19 genome-wide association study (GWAS), the searching for sex-biased genetic variants showing differential association signals between sexes with COVID-19 hospitalization was severely hindered. We hypothesized genetic variants would show potentially sex-biased genetic effects on COVID-19 hospitalization if they display significant differential association effect sizes between male and female COVID-19 patients. By integrating two COVID-19 GWASs, including hospitalized COVID-19 patients vs. general population separated into males (case = 1,917 and control = 221,174) and females (case = 1,343 and control = 262,886), we differentiated the association effect sizes of each common single nucleotide polymorphism (SNP) within the two GWASs. Twelve SNPs were suggested to show differential COVID-19 associations between sexes. Further investigation of genes (n = 58) close to these 12 SNPs resulted in the identification of 34 genes demonstrating sex-biased differential expression in at least one GTEx tissue. Finally, 5 SNPs are mapped to 8 genes, including rs1134004 (GADD45G), rs140657166 (TRIM29 and PVRL1), rs148143613 (KNDC1 and STK32C), rs2443615 (PGAP2 and TRIM21), and rs2924725 (CSMD1). The 8 genes display significantly differential gene expression in blood samples derived from COVID-19 patients compared to healthy controls. These genes are potential genetic factors contributing to sex differences in COVID-19 hospitalization and warranted for further functional studies.

Patterns of Convergence and Divergence Between Bipolar Disorder Type I and Type II: Evidence From Integrative Genomic Analyses.

Aim: Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about the genetic structure of BD subtypes. We aimed to investigate the genetic overlap and distinction of bipolar type I (BD I) & type II (BD II) by conducting integrative post-GWAS analyses. Methods: We utilized single nucleotide polymorphism (SNP)-level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis, including exploring the potential causal associations between two BD subtypes and lithium responses and comparing the difference in genetic structures among four different psychiatric traits. Results: SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184, and RPL10AP3, shared by BD I and II, and one locus (MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes affecting BD I and II through expression in specific brain regions (nucleus accumbens for BD I). Cross-phenotype analyses indicated that BD I and II share continuous genetic structures with schizophrenia and major depressive disorder, which help fill the gaps left by the dichotomy of mental disorders. Conclusion: These combined evidences illustrate genetic convergence and divergence between BD I and II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.

Detecting and distinguishing indicators of risk for suicide using clinical records.

Health systems are essential for suicide risk detection. Most efforts target people with mental health (MH) diagnoses, but this only represents half of the people who die by suicide. This study seeks to discover and validate health indicators of suicide death among those with, and without, MH diagnoses. This case-control study used statistical modeling with health record data on diagnoses, procedures, and encounters. The study included 3,195 individuals who died by suicide from 2000 to 2015 and 249,092 randomly selected matched controls, who were age 18+ and affiliated with nine Mental Health Research Network affiliated health systems. Of the 202 indicators studied, 170 (84%) were associated with suicide in the discovery cohort, with 148 (86%) of those in the validation cohort. Malignant cancer diagnoses were risk factors for suicide in those without MH diagnoses, and multiple individual psychiatric-related indicators were unique to the MH subgroup. Protective effects across MH-stratified models included diagnoses of benign neoplasms, respiratory infections, and utilization of reproductive services. MH-stratified latent class models validated five subgroups with distinct patterns of indicators in both those with and without MH. The highest risk groups were characterized via high utilization with multiple healthcare concerns in both groups. The lowest risk groups were characterized as predominantly young, female, and high utilizers of preventive services. Healthcare data include many indicators of suicide risk for those with and without MH diagnoses, which may be used to support the identification and understanding of risk as well as targeting of prevention in health systems.

DLRAPom: a hybrid pipeline of Optimized XGBoost-guided integrative multiomics analysis for identifying targetable disease-related lncRNA-miRNA-mRNA regulatory axes.

The lack of a reliable and easy-to-operate screening pipeline for disease-related noncoding RNA regulatory axis is a problem that needs to be solved urgently. To address this, we designed a hybrid pipeline, disease-related lncRNA-miRNA-mRNA regulatory axis prediction from multiomics (DLRAPom), to identify risk biomarkers and disease-related lncRNA-miRNA-mRNA regulatory axes by adding a novel machine learning model on the basis of conventional analysis and combining experimental validation. The pipeline consists of four parts, including selecting hub biomarkers by conventional bioinformatics analysis, discovering the most essential protein-coding biomarkers by a novel machine learning model, extracting the key lncRNA-miRNA-mRNA axis and validating experimentally. Our study is the first one to propose a new pipeline predicting the interactions between lncRNA and miRNA and mRNA by combining WGCNA and XGBoost. Compared with the methods reported previously, we developed an Optimized XGBoost model to reduce the degree of overfitting in multiomics data, thereby improving the generalization ability of the overall model for the integrated analysis of multiomics data. With applications to gestational diabetes mellitus (GDM), we predicted nine risk protein-coding biomarkers and some potential lncRNA-miRNA-mRNA regulatory axes, which all correlated with GDM. In those regulatory axes, the MALAT1/hsa-miR-144-3p/IRS1 axis was predicted to be the key axis and was identified as being associated with GDM for the first time. In short, as a flexible pipeline, DLRAPom can contribute to molecular pathogenesis research of diseases, effectively predicting potential disease-related noncoding RNA regulatory networks and providing promising candidates for functional research on disease pathogenesis.

Survival Association of Angiotensin Inhibitors in Heart Failure With Reduced Ejection Fraction: Comparisons Using Self-Identified Race and Genomic Ancestry.

BACKGROUND: It remains unclear whether there is a racial disparity in the response to angiotensin inhibitors in patients with heart failure with reduced ejection fraction (HFrEF) and whether the role of genomic ancestry plays a part. Therefore, we compared survival rates associated with angiotensin inhibitors in patients with HFrEF by self-identified race and proportion of West African genomic ancestry. METHODS: Three datasets totaling 1153 and 1480 self-identified Black and White patients, respectively, with HFrEF were meta-analyzed (random effects model) for race-based analyses. One dataset had genomic data for ancestry analyses (416 and 369 self-identified Black and White patients, respectively). Cox proportional hazards regression, adjusted for propensity scores, assessed the association of angiotensin inhibitor exposure with all-cause mortality by self-identified race or proportion of West African genomic ancestry. RESULTS: In meta-analysis of self-identified race, adjusted hazard ratios (95% CI) for exposure to angiotensin inhibitors were similar in self-identified Black and White patients with HFrEF: 0.52 (0.31-0.85) P = 0.006 and 0.54 (0.42-0.71) P = 0.001, respectively. Results were similar when the proportion of West African genomic ancestry was > 80% or < 5%: 0.66 (0.34-1.25) P = 0.200 and 0.56 (0.26-1.23) P = 0.147, respectively. CONCLUSIONS: Among self-identified Black and White patients with HFrEF, reduction in all-cause mortality associated with exposure to angiotensin inhibitors was similar regardless of self-identified race or proportion of West African genomic ancestry.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Plasma Proteomic Profile Predicts Survival in Heart Failure With Reduced Ejection Fraction.

BACKGROUND: It remains unclear whether the plasma proteome adds value to established predictors in heart failure (HF) with reduced ejection fraction (HFrEF). We sought to derive and validate a plasma proteomic risk score (PRS) for survival in patients with HFrEF (HFrEF-PRS). METHODS: Patients meeting Framingham criteria for HF with EF<50% were enrolled (N=1017) and plasma underwent SOMAscan profiling (4453 targets). Patients were randomly divided 2:1 into derivation and validation cohorts. The HFrEF-PRS was derived using Cox regression of all-cause mortality adjusted for clinical score and NT-proBNP (N-terminal pro-B-type natriuretic peptide), then was tested in the validation cohort. Risk stratification improvement was evaluated by C statistic, integrated discrimination index, continuous net reclassification index, and median improvement in risk score for 1-year and 3-year mortality. RESULTS: Participants' mean age was 68 years, 48% identified as Black, 35% were female, and 296 deaths occurred. In derivation (n=681), 128 proteins associated with mortality, 8 comprising the optimized HFrEF-PRS. In validation (n=336) the HFrEF-PRS associated with mortality (hazard ratio, 2.27 [95% CI, 1.84-2.82], P=6.3×10-14), Kaplan-Meier curves differed significantly between HFrEF-PRS quartiles (P=2.2×10-6), and it remained significant after adjustment for clinical score and NT-proBNP (hazard ratio, 1.37 [95% CI, 1.05-1.79], P=0.021). The HFrEF-PRS improved metrics of risk stratification (C statistic change, 0.009, P=0.612; integrated discrimination index, 0.041, P=0.010; net reclassification index=0.391, P=0.078; median improvement in risk score=0.039, P=0.016) and associated with cardiovascular death and HF phenotypes (eg, 6-minute walk distance, EF change). Most HFrEF-PRS proteins had little known connection to HFrEF. CONCLUSIONS: A plasma multiprotein score improved risk stratification in patients with HFrEF and identified novel candidates.