Temporal patterns of 14 blood biomarker candidates of cardiac remodeling in relation to prognosis of patients with chronic heart failure—the Bio-SHiFT study

Background—Remodeling biomarkers carry high potential for predicting adverse events in chronic heart failure (CHF) patients. However, temporal patterns during the course of CHF, and especially the trajectory before an adverse event, are unknown. We studied the prognostic value of temporal patterns of 14 cardiac remodeling biomarker candidates in stable patients with CHF from the Bio-SHiFT (Serial Biomarker Measurements and New Echocardiographic Techniques in Chronic Heart Failure Patients Result in Tailored Prediction of Prognosis) study. Methods and Results—In 263 CHF patients, we performed trimonthly blood sampling during a median follow-up of 2.2 years. For the analysis, we selected all baseline samples, the 2 samples closest to the primary end point (PE), or the last sample available for end point–free patients. Thus, in 567 samples, we measured suppression of tumorigenicity-2, galectin-3, galectin-4, growth differentiation factor-15, matrix metalloproteinase-2, 3, and 9, tissue inhibitor metalloproteinase-4, perlecan, aminopeptidase-N, caspase-3, cathepsin-D, cathepsin-Z, and cystatin-B. The PE was a composite of cardiovascular mortality, heart transplantation, left ventricular assist device implantation, and HF hospitalization. Associations between repeatedly measured biomarker candidates and the PE were investigated by joint modeling. Median age was 68 (interquartile range: 59–76) years with 72% men; 70 patients reached the PE. Repeatedly measured suppression of tumorigenicity-2, galectin-3, galectin-4, growth differentiation factor-15, matrix metalloproteinase-2 and 9, tissue inhibitor metalloproteinase-4, perlecan, cathepsin-D, and cystatin-B levels were significantly associated with the PE, and increased as the PE approached. The slopes of biomarker trajectories were also predictors of clinical outcome, independent of their absolute level. Associations persisted after adjustment for clinical characteristics and pharmacological treatment. Suppression of tumorigenicity-2 was the strongest predictor (hazard ratio: 7.55 per SD difference, 95% CI: 5.53–10.30), followed by growth differentiation factor-15 (4.06, 2.98–5.54) and matrix metalloproteinase-2 (3.59, 2.55–5.05). Conclusions—Temporal patterns of remodeling biomarker candidates predict adverse clinical outcomes in CHF.