Surrogate markers of gut dysfunction are related to heart failure severity and outcome–from the BIOSTAT-CHF consortium

Background: The contribution of gut dysfunction to heart failure (HF) pathophysiology is not routinely assessed. We sought to investigate whether biomarkers of gut dysfunction would be useful in assessment of HF (eg, severity, adverse outcomes) and risk stratification. Methods: A panel of gut-related biomarkers including metabolites of the choline/carnitine- pathway (acetyl-L-carnitine, betaine, choline, γ-butyrobetaine, L-carnitine and trimethylamine-N-oxide [TMAO]) and the gut peptide, Trefoil factor-3 (TFF-3), were investigated in 1,783 patients with worsening HF enrolled in the systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) cohort and associations with HF severity and outcomes, and use in risk stratification were assessed. Results: Metabolites of the carnitine-TMAO pathway (acetyl-L-carnitine, γ-butyrobetaine, L-carnitine, and TMAO) and TFF-3 were associated with the composite outcome of HF hospitalization or all-cause mortality at 3 years (hazards ratio [HR] 2.04-2.93 [95% confidence interval {CI} 1.30-4.71] P≤.002). Combining the carnitine-TMAO metabolites with TFF-3, as a gut dysfunction panel, showed a graded association; a greater number of elevated markers was associated with higher New York Heart Association class (P<.001), higher plasma concentrations of B-type natriuretic peptide (P<.001), and worse outcome (HR 1.90-4.58 [95% CI 1.19-6.74] P≤ 0.008). Addition of gut dysfunction biomarkers to the contemporary BIOSTAT HF risk model also improved prediction for the aforementioned composite outcome (C-statistics P≤.011, NRI 13.5-21.1 [95% CI 2.7-31.9] P≤.014). Conclusions: A panel of biomarkers of gut dysfunction showed graded association with severity of HF and adverse outcomes. Biomarkers as surrogate markers are potentially useful for assessment of gut dysfunction to HF pathophysiology and in risk stratification

Bio-adrenomedullin as a marker of congestion in patients with new-onset and worsening heart failure.

BACKGROUND: Secretion of adrenomedullin (ADM) is stimulated by volume overload to maintain endothelial barrier function, and higher levels of biologically active (bio-) ADM in heart failure (HF) are a counteracting response to vascular leakage and tissue oedema. This study aimed to establish the value of plasma bio-ADM as a marker of congestion in patients with worsening HF. METHODS AND RESULTS: The association of plasma bio-ADM with clinical markers of congestion, as well as its prognostic value was studied in 2179 patients with new-onset or worsening HF enrolled in BIOSTAT-CHF. Data were validated in a separate cohort of 1703 patients. Patients with higher plasma bio-ADM levels were older, had more severe HF and more signs and symptoms of congestion (all P < 0.001). Amongst 20 biomarkers, bio-ADM was the strongest predictor of a clinical congestion score (r2  = 0.198). In multivariable regression analysis, higher bio-ADM was associated with higher body mass index, more oedema, and higher fibroblast growth factor 23. In hierarchical cluster analysis, bio-ADM clustered with oedema, orthopnoea, rales, hepatomegaly and jugular venous pressure. Higher bio-ADM was independently associated with impaired up-titration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers after 3 months, but not of beta-blockers. Higher bio-ADM levels were independently associated with an increased risk of all-cause mortality and HF hospitalization (hazard ratio 1.16, 95% confidence interval 1.06-1.27, P = 0.002, per log increase). Analyses in the validation cohort yielded comparable findings. CONCLUSIONS: Plasma bio-ADM in patients with new-onset and worsening HF is associated with more severe HF and more oedema, orthopnoea, hepatomegaly and jugular venous pressure. We therefore postulate bio-ADM as a congestion marker, which might become useful to guide decongestive therapy

Effects of the coronary artery disease associated LPA and 9p21 loci on risk of aortic valve stenosis

BACKGROUND: Aortic valve stenosis (AVS) and coronary artery disease (CAD) have a significant genetic contribution and commonly co-exist. To compare and contrast genetic determinants of the two diseases, we investigated associations of the LPA and 9p21 loci, i.e. the two strongest CAD risk loci, with risk of AVS. METHODS: We genotyped the CAD-associated variants at the LPA (rs10455872) and 9p21 loci (rs1333049) in the GeneCAST (Genetics of Calcific Aortic STenosis) Consortium and conducted a meta-analysis for their association with AVS. Cases and controls were stratified by CAD status. External validation of findings was undertaken in five cohorts including 7880 cases and 851,152 controls. RESULTS: In the meta-analysis including 4651 cases and 8231 controls the CAD-associated allele at the LPA locus was associated with increased risk of AVS (OR 1.37; 95%CI 1.24-1.52, p = 6.9 × 10-10) with a larger effect size in those without CAD (OR 1.53; 95%CI 1.31-1.79) compared to those with CAD (OR 1.27; 95%CI 1.12-1.45). The CAD-associated allele at 9p21 was associated with a trend towards lower risk of AVS (OR 0.93; 95%CI 0.88-0.99, p = 0.014). External validation confirmed the association of the LPA risk allele with risk of AVS (OR 1.37; 95%CI 1.27-1.47), again with a higher effect size in those without CAD. The small protective effect of the 9p21 CAD risk allele could not be replicated (OR 0.98; 95%CI 0.95-1.02). CONCLUSIONS: Our study confirms the association of the LPA locus with risk of AVS, with a higher effect in those without concomitant CAD. Overall, 9p21 was not associated with AVS

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure

Background: Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. Objectives: We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. Methods: We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. Results: The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro–B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. Conclusions: A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin