Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis
Authors: Vidula Mahesh K. , Orlenko Alena , Zhao Lei , Salvador Lisa , Small Aeron M. , Horton Edward , Cohen Jordana B. , Adusumalli Srinath , Denduluri Srinivas , Kobayashi Taisei , Hyman Matthew , Fiorilli Paul , Magro Caroline , Singh Bibi , Pourmussa Bianca , Greczylo Candy , Basso Michael , Ebert Christina , Yarde Melissa , Li Zhuyin , Cvijic Mary Ellen , Wang Zhaoqing , Walsh Alice , Maranville Joseph , Kick Ellen , Luettgen Joseph , Adam Leonard , Schafer Peter , Ramirez‐Valle Francisco , Seiffert Dietmar , Moore Jason H. , Gordon David , Chirinos Julio A. 
Published: October 2021

AbstractAimsEnhanced risk stratification of patients with aortic stenosis (AS) is necessary to identify patients at high risk for adverse outcomes, and may allow for better management of patient subgroups at high risk of myocardial damage. The objective of this study was to identify plasma biomarkers and multimarker profiles associated with adverse outcomes in AS.Methods and resultsWe studied 708 patients with calcific AS and measured 49 biomarkers using a Luminex platform. We studied the correlation between biomarkers and the risk of (i) death and (ii) death or heart failure‐related hospital admission (DHFA). We also utilized machine‐learning methods (a tree‐based pipeline optimizer platform) to develop multimarker models associated with the risk of death and DHFA. In this cohort with a median follow‐up of 2.8 years, multiple biomarkers were significantly predictive of death in analyses adjusted for clinical confounders, including tumour necrosis factor (TNF)‐α [hazard ratio (HR) 1.28, P < 0.0001], TNF receptor 1 (TNFRSF1A; HR 1.38, P < 0.0001), fibroblast growth factor (FGF)‐23 (HR 1.22, P < 0.0001), N‐terminal pro B‐type natriuretic peptide (NT‐proBNP) (HR 1.58, P < 0.0001), matrix metalloproteinase‐7 (HR 1.24, P = 0.0002), syndecan‐1 (HR 1.27, P = 0.0002), suppression of tumorigenicity‐2 (ST2) (IL1RL1; HR 1.22, P = 0.0002), interleukin (IL)‐8 (CXCL8; HR 1.22, P = 0.0005), pentraxin (PTX)‐3 (HR 1.17, P = 0.001), neutrophil gelatinase‐associated lipocalin (LCN2; HR 1.18, P < 0.0001), osteoprotegerin (OPG) (TNFRSF11B; HR 1.26, P = 0.0002), and endostatin (COL18A1; HR 1.28, P = 0.0012). Several biomarkers were also significantly predictive of DHFA in adjusted analyses including FGF‐23 (HR 1.36, P < 0.0001), TNF‐α (HR 1.26, P < 0.0001), TNFR1 (HR 1.34, P < 0.0001), angiopoietin‐2 (HR 1.26, P < 0.0001), syndecan‐1 (HR 1.23, P = 0.0006), ST2 (HR 1.27, P < 0.0001), IL‐8 (HR 1.18, P = 0.0009), PTX‐3 (HR 1.18, P = 0.0002), OPG (HR 1.20, P = 0.0013), and NT‐proBNP (HR 1.63, P < 0.0001). Machine‐learning multimarker models were strongly associated with adverse outcomes (mean 1‐year probability of death of 0%, 2%, and 60%; mean 1‐year probability of DHFA of 0%, 4%, 97%; P < 0.0001). In these models, IL‐6 (a biomarker of inflammation) and FGF‐23 (a biomarker of calcification) emerged as the biomarkers of highest importance.ConclusionsPlasma biomarkers are strongly associated with the risk of adverse outcomes in patients with AS. Biomarkers of inflammation and calcification were most strongly related to prognosis.