High Throughput Plasma Proteomics and Risk of Heart Failure and Frailty in Late Life.

Importance: Heart failure (HF) and frailty frequently coexist and may share a common pathobiology, although the underlying mechanisms remain unclear. Understanding these mechanisms may provide guidance for preventing and treating both conditions. Objective: To identify shared pathways between incident HF and frailty in late life using large-scale proteomics. Design, Setting, and Participants: In this cohort study, 4877 aptamers (Somascan v4) were measured among participants in the community-based longitudinal Atherosclerosis Risk In Communities (ARIC) cohort study at visit 3 (V3; 1993-1995; n = 10 638) and at visit 5 (V5; 2011-2013; n = 3908). Analyses were externally replicated among 3189 participants in the Cardiovascular Health Study (CHS). Data analysis was conducted from February 2022 to June 2023. Exposures: Protein aptamers, measured at study V3 and V5. Main Outcomes and Measures: Outcomes assessed included incident HF hospitalization after V3 and after V5, prevalent frailty at V5, and incident frailty between V5 and visit 6 (V6; 2016-2017; n = 4131). Frailty was assessed using the Fried criteria. Analyses were adjusted for age, gender, race, field center, hypertension, diabetes, smoking status, body mass index, estimated glomerular filtration rate, prevalent coronary heart disease, prevalent atrial fibrillation, and history of myocardial infarction. Mendelian randomization (MR) analysis was performed to assess potential causal effects of candidate proteins on HF and frailty. Results: A total of 4877 protein aptamers were measured among 10 638 participants at V3 (mean [SD] age, 60 [6] years; 4886 [46%] men). Overall, 286 proteins were associated with incident HF after V3 (822 events; P < 1.0 × 10-5), 83 of which were also associated with incident after V5 (336 events; P < 1.7 × 10-4). Among HF-free participants at V5 (n = 3908; mean [SD] age, 75 [5] years; 1861 [42%] men), 48 of 83 HF-associated proteins were associated with prevalent frailty (223 cases; P < 6.0 × 10-4), 18 of which were also associated with incident frailty at V6 (152 cases; P < 1.0 × 10-3). These proteins enriched fibrosis and inflammation pathways and demonstrated stronger associations with incident HF with preserved ejection fraction (HFpEF) than HF with reduced ejection fraction. All 18 proteins were associated with both prevalent frailty and incident HF in CHS. MR identified potential causal effects of several proteins on frailty and HF. Conclusions and Relevance: In this study, the proteins associated with risk of HF and frailty enrich for pathways related to inflammation and fibrosis as well as risk of HFpEF. Several of these proteins could potentially contribute to the shared pathophysiology of frailty and HF.

Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification.

BACKGROUND AND AIMS: Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed. METHODS: In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score. RESULTS: Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787). CONCLUSIONS: Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

Large scale plasma proteomics identifies novel proteins and protein networks associated with heart failure development.

Heart failure (HF) causes substantial morbidity and mortality but its pathobiology is incompletely understood. The proteome is a promising intermediate phenotype for discovery of novel mechanisms. We measured 4877 plasma proteins in 13,900 HF-free individuals across three analysis sets with diverse age, geography, and HF ascertainment to identify circulating proteins and protein networks associated with HF development. Parallel analyses in Atherosclerosis Risk in Communities study participants in mid-life and late-life and in Trøndelag Health Study participants identified 37 proteins consistently associated with incident HF independent of traditional risk factors. Mendelian randomization supported causal effects of 10 on HF, HF risk factors, or left ventricular size and function, including matricellular (e.g. SPON1, MFAP4), senescence-associated (FSTL3, IGFBP7), and inflammatory (SVEP1, CCL15, ITIH3) proteins. Protein co-regulation network analyses identified 5 modules associated with HF risk, two of which were influenced by genetic variants that implicated trans hotspots within the VTN and CFH genes.

Large-Scale Proteomics Identifies Novel Biomarkers and Circulating Risk Factors for Aortic Stenosis.

BACKGROUND: Limited data exist regarding risk factors for aortic stenosis (AS). The plasma proteome is a promising phenotype for discovery of novel biomarkers and potentially causative mechanisms. OBJECTIVES: The aim of this study was to discover novel biomarkers with potentially causal associations with AS. METHODS: We measured 4,877 plasma proteins (SomaScan aptamer-affinity assay) among ARIC (Atherosclerosis Risk In Communities) study participants in mid-life (visit 3 [V3]; n = 11,430; age 60 ± 6 years) and in late-life (V5; n = 4,899; age 76 ± 5 years). We identified proteins cross-sectionally associated with aortic valve (AV) peak velocity (AVmax) and dimensionless index by echocardiography at V5 and with incident AV-related hospitalization after V3 with the use of multivariable linear and Cox proportional hazard regression. We assessed associations of candidate proteins with changes in AVmax over 6 years and with AV calcification with the use of cardiac computed tomography, replicated analysis in an independent sample, performed Mendelian randomization, and evaluated gene expression in explanted human AV tissue. RESULTS: Fifty-two proteins cross-sectionally were associated with AVmax and dimensionless index at V5 and with risk of incident AV-related hospitalization after V3. Among 3,413 participants in the Cardiovascular Health Study, 6 of those proteins were significantly associated with adjudicated moderate or severe AS, including matrix metalloproteinase 12 (MMP12), complement C1q tumor necrosis factor-related protein 1 (C1QTNF1), and growth differentiation factor-15. MMP12 was also associated with greater increase in AVmax over 6 years, greater degree of AV calcification, and greater expression in calcific compared with normal or fibrotic AV tissue. C1QTNF1 had consistent potential causal effects on both AS and AVmax according to Mendelian randomization analysis. CONCLUSIONS: These findings identify MMP12 as a potential novel circulating biomarker of AS risk and C1QTNF1 as a new putative target to prevent AS progression.

Plasma Ferritin Levels, Incident Heart Failure, and Cardiac Structure and Function: The ARIC Study.

BACKGROUND: Whether iron deficiency contributes to incident heart failure (HF) and cardiac dysfunction has important implications given the prevalence of iron deficiency and the availability of several therapeutics for iron repletion. OBJECTIVES: The aim of this study was to estimate the associations of plasma ferritin level with incident HF overall, HF phenotypes, and cardiac structure and function measures in older adults. METHODS: Participants in the ongoing, longitudinal ARIC (Atherosclerosis Risk In Communities) study who were free of prevalent HF and anemia were studied. The associations of plasma ferritin levels with incident HF overall and heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) were estimated using Cox proportional hazards regression models. Linear regression models estimated the cross-sectional associations of plasma ferritin with echocardiographic measures of cardiac structure and function. RESULTS: The cohort included 3,472 individuals with a mean age of 75 ± 5 years (56% women, 14% Black individuals). In fully adjusted models, lower ferritin was associated with higher risk for incident HF overall (HR: 1.20 [95% CI: 1.08-1.34] per 50% lower ferritin level) and higher risk for incident HFpEF (HR: 1.28 [95% CI: 1.09-1.50]). Associations with incident HFrEF were not statistically significant. Lower ferritin levels were associated with higher E/e' ratio and higher pulmonary artery systolic pressure after adjustment for demographics and HF risk factors but not with measures of left ventricular structure or systolic function. CONCLUSIONS: Among older adults without prevalent HF or anemia, lower plasma ferritin level is associated with a higher risk for incident HF, HFpEF, and higher measures of left ventricular filling pressure.

Changes in Pulmonary Artery Pressure Late in Life: The Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: Although the prognostic implications of higher pulmonary artery systolic pressure (PASP) are well established, few data exist regarding longitudinal change in pulmonary pressure in late life. OBJECTIVES: The aim of this study was to quantify changes in PASP over 6 years and determine the relative contributions of cardiac and pulmonary dysfunction. METHODS: Among 1,420 participants in the ARIC (Atherosclerosis Risk in Communities) study with echocardiographic measures of PASP at both the fifth (2011-2013) and seventh (2018-2019) visits, longitudinal changes in PASP over about 6.5 years were quantified. Multivariable regression was used to determine the extent to which cardiac and pulmonary dysfunction were associated with changes in PASP and to define the relationship of changes in PASP with dyspnea development. RESULTS: The mean age was 75 ± 5 years at visit 5 and 81 ± 5 years at visit 7, 24% of subjects were Black adults, and 68% were women. Over the 6.5 years, PASP increased by 5 ± 8 mm Hg, from 28 ± 5 to 33 ± 8 mm Hg. PASP increased more in older participants. Predictors of greater increases in PASP included worse left ventricular (LV) systolic and diastolic function, pulmonary function, and renal function. Increases in PASP were associated with concomitant increases in measures of LV filling pressure, including E/e' ratio and left atrial volume index. Each 5 mm Hg increase was associated with 16% higher odds of developing dyspnea (OR: 1.16; 95% CI: 1.07-1.27; P < 0.001). CONCLUSIONS: Pulmonary pressure increased over 6.5 years in late life, was associated with concomitant increases in LV filling pressure, and predicted the development of dyspnea. Interventions targeting LV diastolic function may be effective at mitigating age-related increases in PASP.

A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation.

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

Joint testing of donor and recipient genetic matching scores and recipient genotype has robust power for finding genes associated with transplant outcomes.

Genetic matching between transplant donor and recipient pairs has traditionally focused on the human leukocyte antigen (HLA) regions of the genome, but recent studies suggest that matching for non-HLA regions may be important as well. We assess four genetic matching scores for use in association analyses of transplant outcomes. These scores describe genetic ancestry distance using identity-by-state, or genetic incompatibility or mismatch of the two genomes and therefore may reflect different underlying biological mechanisms for donor and recipient genes to influence transplant outcomes. Our simulation studies show that jointly testing these scores with the recipient genotype is a powerful method for preliminary screening and discovery of transplant outcome related single nucleotide polymorphisms (SNPs) and gene regions. Following these joint tests with marginal testing of the recipient genotype and matching score separately can lead to further understanding of the biological mechanisms behind transplant outcomes. In addition, we present results of a liver transplant data analysis that shows joint testing can detect SNPs significantly associated with acute rejection in liver transplant.

IL1RN Variation Influences Both Disease Susceptibility and Response to Recombinant Human Interleukin-1 Receptor Antagonist Therapy in Systemic Juvenile Idiopathic Arthritis.

OBJECTIVE: To determine whether systemic juvenile idiopathic arthritis (JIA) susceptibility loci that were identified by candidate gene studies demonstrate association with systemic JIA in the largest study population assembled to date. METHODS: Single-nucleotide polymorphisms (SNPs) from 11 previously reported systemic JIA risk loci were examined for association in 9 populations, including 770 patients with systemic JIA and 6,947 controls. The effect of systemic JIA-associated SNPs on gene expression was evaluated in silico in paired whole genome and RNA sequencing data from the lymphoblastoid cell lines (LCLs) of 373 European subjects from the 1000 Genomes Project. Responses of systemic JIA-associated SNPs to anakinra treatment were evaluated in 38 US patients for whom treatment response data were available. RESULTS: We found no association between the previously reported 26 SNPs and systemic JIA. Expanded analysis of the regions containing the 26 SNPs revealed only 1 significant association: the promoter region of IL1RN (P < 1 × 10-4 ). Systemic JIA-associated SNPs correlated with IL1RN expression in LCLs, with an inverse correlation between systemic JIA risk and IL1RN expression. The presence of homozygous IL1RN high expression alleles correlated strongly with a lack of response to anakinra therapy (odds ratio 28.7 [95% confidence interval 3.2-255.8]). CONCLUSION: In our study, IL1RN was the only candidate locus associated with systemic JIA. The implicated SNPs are among the strongest known determinants of IL1RN and interleukin-1 receptor antagonist levels, linking low expression with increased systemic JIA risk. Homozygous high expression alleles predicted nonresponsiveness to anakinra therapy, making them ideal candidate biomarkers to guide systemic JIA treatment. This study is an important first step toward the personalized treatment of systemic JIA.

Genetic architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA. METHODS: We performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes. RESULTS: The major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes. CONCLUSIONS: The lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.