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.

Novel echocardiographic pixel intensity quantification method for differentiating transthyretin cardiac amyloidosis from light chain cardiac amyloidosis and other phenocopies.

AIMS: Differentiating cardiac amyloidosis (CA) subtypes is important considering the significantly different therapies for transthyretin (ATTR)-CA and light chain (AL)-CA. Therefore, an echocardiographic method to distinguish ATTR-CA from AL-CA would provide significant value. We assessed a novel echocardiographic pixel intensity method to quantify myocardial calcification to differentiate ATTR-CA from phenocopies of CA and from AL-CA, specifically. METHODS AND RESULTS: 167 patients with ATTR-CA (n=53), AL-CA (n=32), hypertrophic cardiomyopathy (n=37), and advanced chronic kidney disease (n=45) were retrospectively evaluated. The septal reflectivity ratio (SRR) was measured as the average pixel intensity of the visible anterior septal wall divided by the average pixel intensity of the visible posterior lateral wall. SRR and other myocardial strain-based echocardiographic measures were evaluated with receiver operator characteristic analysis to evaluate accuracy in distinguishing ATTR-CA from AL-CA and other forms of left ventricular hypertrophy. Mean septal reflectivity ratio (SRR) was significantly higher in the ATTR-CA cohort compared to the other cohorts (p <0.001). SRR demonstrated the largest AUC (0.91, p<0.0001) for distinguishing ATTR from all other cohorts and specifically for distinguishing ATTR-CA from AL-CA (AUC=0.90, p<0.0001, specificity 96%, sensitivity 63%). There was excellent inter- and intra-operator reproducibility with an ICC of 0.91 (p <0.001) and 0.89 (p <0.001), respectively. CONCLUSION: The SRR is a reproducible and robust parameter for differentiating ATTR-CA from other phenocopies of CA and specifically ATTR-CA from AL-CA.

Gut microbiota, blood metabolites, and left ventricular diastolic dysfunction in US Hispanics/Latinos.

BACKGROUND: Left ventricular diastolic dysfunction (LVDD) is an important precursor of heart failure (HF), but little is known about its relationship with gut dysbiosis and microbial-related metabolites. By leveraging the multi-omics data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a study with population at high burden of LVDD, we aimed to characterize gut microbiota associated with LVDD and identify metabolite signatures of gut dysbiosis and incident LVDD. RESULTS: We included up to 1996 Hispanic/Latino adults (mean age: 59.4 years; 67.1% female) with comprehensive echocardiography assessments, gut microbiome, and blood metabolome data. LVDD was defined through a composite criterion involving tissue Doppler assessment and left atrial volume index measurements. Among 1996 participants, 916 (45.9%) had prevalent LVDD, and 212 out of 594 participants without LVDD at baseline developed incident LVDD over a median 4.3 years of follow-up. Using multivariable-adjusted analysis of compositions of microbiomes (ANCOM-II) method, we identified 7 out of 512 dominant gut bacterial species (prevalence > 20%) associated with prevalent LVDD (FDR-q < 0.1), with inverse associations being found for Intestinimonas_massiliensis, Clostridium_phoceensis, and Bacteroide_coprocola and positive associations for Gardnerella_vaginali, Acidaminococcus_fermentans, Pseudomonas_aeruginosa, and Necropsobacter_massiliensis. Using multivariable adjusted linear regression, 220 out of 669 circulating metabolites with detection rate > 75% were associated with the identified LVDD-related bacterial species (FDR-q < 0.1), with the majority being linked to Intestinimonas_massiliensis, Clostridium_phoceensis, and Acidaminococcus_fermentans. Furthermore, 46 of these bacteria-associated metabolites, mostly glycerophospholipids, secondary bile acids, and amino acids, were associated with prevalent LVDD (FDR-q < 0.1), 21 of which were associated with incident LVDD (relative risk ranging from 0.81 [p = 0.001, for guanidinoacetate] to 1.25 [p = 9 × 10-5, for 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4)]). The inclusion of these 21 bacterial-related metabolites significantly improved the prediction of incident LVDD compared with a traditional risk factor model (the area under the receiver operating characteristic curve [AUC] = 0.73 vs 0.70, p = 0.001). Metabolite-based proxy association analyses revealed the inverse associations of Intestinimonas_massilliensis and Clostridium_phoceensis and the positive association of Acidaminococcus_fermentans with incident LVDD. CONCLUSION: In this study of US Hispanics/Latinos, we identified multiple gut bacteria and related metabolites linked to LVDD, suggesting their potential roles in this preclinical HF entity. Video Abstract.