Circulating multimarker approach to identify patients with preclinical left ventricular remodelling and/or diastolic dysfunction

Abstract

Aims: Biomarkers reflecting myocardial fibrosis and inflammation have been individually associated with left ventricular hypertrophy (LVH) and diastolic dysfunction (DD). However, the added value of a fibrosis-inflammation multimarker approach in a populational setting is yet to be studied. We evaluated the value of a multimarker approach to detect LVH and DD in a large population-based cohort. Methods and results: In a prespecified analysis (BioSe-PreIC study) of the 4th visit of the STANISLAS cohort (1705 subjects, 47 ± 14 years, 47.4% men), we evaluated the ability of brain natriuretic peptide (BNP), Galectin-3 (GAL3), N-terminal propeptide of procollagen type III (P3NP), and soluble ST2 to predict LVH (LV mass > 116/100 g/m2 for men/women) and DD using discrimination (C-index) and reclassification analysis (NRI). Participants with LVH and/or DD had significantly higher levels of BNP, GAL3, and ST2. Overall, the predictive value of clinical variables for LVH and/or DD was good (C-index ranging from 0.76 to 0.82) and the addition of BNP, Gal3, P3NP, and ST2 moderately but significantly improved predictive value (delta C-index = 0.03, P = 0.03 for LVH and 0.01, P = 0.01 for DD) and reclassification (NRI = 25.3, P = 0.02 for LVH and NRI = 32.7 for DD, P < 0.0001). Gal3, P3NP, and ST2 significantly improved predictive value (delta C-index = 0.01, P = 0.01) and reclassification (NRI = 31.3, P < 0.0001) for DD of top of clinical variables and BNP. Conclusions: As the measurement of Gal3, P3NP, and ST2 results in marginal (even if significant) increase in the prediction of DD/LVH on top of routine evaluation, their systematic use should not be promoted in unselected healthy individuals to screen for preclinical DD. Further research is needed to determine whether a more personalized medicine approach combing proteomic and clinical scoring can amplify the added value of biomarkers to identify preclinical DD.