Multimarker approach for the prediction of heart failure incidence in the community.

Background: Several biological pathways are activated in ventricular remodeling and in overt heart failure (HF). There are no data, however, on the incremental utility of a parsimonious set of biomarkers (reflecting pathways implicated in HF) for predicting HF risk in the community.
Methods and Results: We related a multibiomarker panel to the incidence of a first HF event in 2754 Framingham Heart Study participants (mean age, 58 years; 54 women) who were free of HF and underwent routine assays for 6 biomarkers (C-reactive protein, plasminogen activator inhibitor-1, homocysteine, aldosterone-to-renin ratio, B-type natriuretic peptide, and urinary albumin-to-creatinine ratio). We estimated model c statistic, calibration, and net reclassification improvement to assess the incremental predictive usefulness of biomarkers. We also related biomarkers to the incidence of nonischemic HF in participants without prevalent coronary heart disease. On follow-up (mean, 9.4 years), 95 first HF events occurred (54 in men). In multivariable-adjusted models, the biomarker panel was significantly related to HF risk (P=0.00005). On backward elimination, B-type natriuretic peptide and urinary albumin-to-creatinine ratio emerged as key biomarkers predicting HF risk; hazards ratios per 1-SD increment in log marker were 1.52 (95 confidence interval, 1.24 to 1.87) and 1.35 (95 confidence interval, 1.11 to 1.66), respectively. B-type natriuretic peptide and urinary albumin-to-creatinine ratio significantly improved the model c statistic from 0.84 (95 confidence interval, 0.80 to 0.88) in standard models to 0.86 (95 confidence interval, 0.83 to 0.90), enhanced risk reclassification (net reclassification improvement=0.13; P=0.002), and were independently associated with nonischemic HF risk.
Conclusion: Using a multimarker strategy, we identified B-type natriuretic peptide and urinary albumin-to-creatinine ratio as key risk factors for new-onset HF with incremental predictive utility over standard risk factors.