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
Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Eur J Heart Fail. 2021 Dec;23(12):2021-2032. doi: 10.1002/ejhf.2361. Epub 2021 Oct 21.
AIMS: Enhanced 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 RESULTS: We 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. CONCLUSIONS: Plasma 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.
目的：加强主动脉瓣狭窄（AS）患者的风险分层对于识别不良结局高风险患者很有必要，且可能有助于更好地管理心肌损伤高风险的患者亚组。本研究的目的是识别与AS不良结局相关的血浆生物标志物和多标志物谱。 方法与结果：我们研究了708例钙化性AS患者，并使用Luminex平台测量了49种生物标志物。我们研究了生物标志物与（i）死亡风险和（ii）死亡或心力衰竭相关住院（DHFA）风险之间的相关性。我们还利用机器学习方法（基于树的管道优化器平台）来开发与死亡风险和DHFA风险相关的多标志物模型。在这个中位随访时间为2.8年的队列中，在针对临床混杂因素进行调整的分析中，多种生物标志物可显著预测死亡，包括肿瘤坏死因子（TNF）-α[危险比（HR）1.28，P<0.0001]、TNF受体1（TNFRSF1A；HR 1.38，P<0.0001）、成纤维细胞生长因子（FGF）-23（HR 1.22，P<0.0001）、N末端B型利钠肽原（NT-proBNP）（HR 1.58，P<0.0001）、基质金属蛋白酶-7（HR 1.24，P = 0.0002）、多配体蛋白聚糖-1（HR 1.27，P = 0.0002）、抑瘤素-2（ST2）（IL1RL1；HR 1.22，P = 0.0002）、白细胞介素（IL）-8（CXCL8；HR 1.22，P = 0.0005）、五聚素（PTX）-3（HR 1.17，P = 0.001）、中性粒细胞明胶酶相关脂质运载蛋白（LCN2；HR 1.18，P<0.0001）、骨保护素（OPG）（TNFRSF11B；HR 1.26，P = 0.0002）和内皮抑素（COL18A1；HR 1.28，P = 0.0012）。在调整分析中，几种生物标志物也可显著预测DHFA，包括FGF-23（HR 1.36，P<0.0001）、TNF-α（HR 1.26，P<0.0001）、TNFR1（HR 1.34，P<0.0001）、血管生成素-2（HR 1.26，P<0.0001）、多配体蛋白聚糖-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）和NT-proBNP（HR 1.63，P<0.0001）。机器学习多标志物模型与不良结局密切相关（1年死亡平均概率为0%、2%和60%；1年DHFA平均概率为0%、4%、97%；P<0.0001）。在这些模型中，IL-6（一种炎症生物标志物）和FGF-23（一种钙化生物标志物）成为最重要的生物标志物。 结论：血浆生物标志物与AS患者的不良结局风险密切相关。炎症和钙化生物标志物与预后的关系最为密切。
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