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Background:We sought to determine subtypes of patients with heart failure (HF) with a distinct clinical profile and treatment response, using a wide range of biomarkers from various pathophysiological domains. Method and results:We performed unsupervised cluster analysis using 92 established cardiovascular biomarkers to identify mutually exclusive subgroups (endotypes) of 1802 patients with HF and reduced ejection fraction (HFrEF) from the BIOSTAT-CHF project. We validated our findings in an independent cohort of 813 patients.Based on their biomarker profile, six endotypes were identified. Patients with endotype 1 were youngest, less symptomatic, had the lowest NT-proBNP levels and lowest risk for all-cause mortality or hospitalization for HF. Patients with endotype 4 had more severe symptoms and signs of HF, higher NT-proBNP levels and were at highest risk for all-cause mortality or hospitalization for HF (HR 1.4; 95%CI 1.1-1.8). Patients with endotypes 2, 3 and 5 were better up-titrated to target doses of beta-blockers (p<0.02 for all). In contrast to other endotypes, patients with endotype 5 derived no potential survival benefit from uptitration of ACEi/ARB and beta-blockers (Pinteraction <0.001). Patients with endotype 2 (HR 1.29; 95%CI 1.10-1.42) experienced possible harm from uptitration of beta-blockers in contrast to patients with endotype 4 and 6 that experienced benefit (Pinteraction for all <0.001). Results were strikingly similar in the independent validation cohort. Conclusion:Using unsupervised cluster analysis, solely based on biomarker profiles, six distinct endotypes were identified with remarkable differences in characteristics, clinical outcome, and response to uptitration of guideline directed medical therapy.

Background. There is increasing recognition that heart failure (HF) and cancer are conditions with a number of shared characteristics. Objectives. To explore the association between tumour biomarkers and HF outcomes. Methods. In 2,079 patients of BIOSTAT-CHF cohort, we measured six established tumour biomarkers: CA125, CA15-3, CA19-9, CEA, CYFRA 21-1 and AFP. Results. During a median follow-up of 21 months, 555 (27%) patients reached the primary end-point of all-cause mortality. CA125, CYFRA 21-1, CEA and CA19-9 levels were positively correlated with NT-proBNP quartiles (all P < 0.001, P for trend < 0.001) and were, respectively, associated with a hazard ratio of 1.17 (95% CI 1.12-1.23; P < 0.0001), 1.45 (95% CI 1.30-1.61; P < 0.0001), 1.19 (95% CI 1.09-1.30; P = 0.006) and 1.10 (95% CI 1.05-1.16; P < 0.001) for all-cause mortality after correction for BIOSTAT risk model (age, BUN, NT-proBNP, haemoglobin and beta blocker). All tumour biomarkers (except AFP) had significant associations with secondary end-points (composite of all-cause mortality and HF hospitalization, HF hospitalization, cardiovascular (CV) mortality and non-CV mortality). ROC curves showed the AUC of CYFRA 21-1 (0.64) had a noninferior AUC compared with NT-proBNP (0.68) for all-cause mortality (P = 0.08). A combination of CYFRA 21-1 and NT-proBNP (AUC = 0.71) improved the predictive value of the model for all-cause mortality (P = 0.0002 compared with NT-proBNP). Conclusions. Several established tumour biomarkers showed independent associations with indices of severity of HF and independent prognostic value for HF outcomes. This demonstrates that pathophysiological pathways sensed by these tumour biomarkers are also dysregulated in HF.