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Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction
Journal of the American College of Cardiology ( IF 24.0 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.jacc.2019.12.069
Julio A Chirinos 1 , Alena Orlenko 2 , Lei Zhao 3 , Michael D Basso 3 , Mary Ellen Cvijic 3 , Zhuyin Li 3 , Thomas E Spires 3 , Melissa Yarde 3 , Zhaoqing Wang 3 , Dietmar A Seiffert 3 , Stuart Prenner 1 , Payman Zamani 1 , Priyanka Bhattacharya 1 , Anupam Kumar 4 , Kenneth B Margulies 1 , Bruce D Car 3 , David A Gordon 3 , Jason H Moore 1 , Thomas P Cappola 1
Affiliation  

BACKGROUND Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF). OBJECTIVES The purpose of this study was to assess the value of a targeted plasma multi-marker approach to enhance our phenotypic characterization and risk prediction in HFpEF. METHODS In this study, the authors measured 49 plasma biomarkers from TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial participants (n = 379) using a Multiplex assay. The relationship between biomarkers and the risk of all-cause death or heart failure-related hospital admission (DHFA) was assessed. A tree-based pipeline optimizer platform was used to generate a multimarker predictive model for DHFA. We validated the model in an independent cohort of HFpEF patients enrolled in the PHFS (Penn Heart Failure Study) (n = 156). RESULTS Two large, tightly related dominant biomarker clusters were found, which included biomarkers of fibrosis/tissue remodeling, inflammation, renal injury/dysfunction, and liver fibrosis. Other clusters were composed of neurohormonal regulators of mineral metabolism, intermediary metabolism, and biomarkers of myocardial injury. Multiple biomarkers predicted incident DHFA, including 2 biomarkers related to mineral metabolism/calcification (fibroblast growth factor-23 and OPG [osteoprotegerin]), 3 inflammatory biomarkers (tumor necrosis factor-alpha, sTNFRI [soluble tumor necrosis factor-receptor I], and interleukin-6), YKL-40 (related to liver injury and inflammation), 2 biomarkers related to intermediary metabolism and adipocyte biology (fatty acid binding protein-4 and growth differentiation factor-15), angiopoietin-2 (related to angiogenesis), matrix metalloproteinase-7 (related to extracellular matrix turnover), ST-2, and N-terminal pro-B-type natriuretic peptide. A machine-learning-derived model using a combination of biomarkers was strongly predictive of the risk of DHFA (standardized hazard ratio: 2.85; 95% confidence interval: 2.03 to 4.02; p < 0.0001) and markedly improved the risk prediction when added to the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure Risk Score) risk score. In an independent cohort (PHFS), the model strongly predicted the risk of DHFA (standardized hazard ratio: 2.74; 95% confidence interval: 1.93 to 3.90; p < 0.0001), which was also independent of the MAGGIC risk score. CONCLUSIONS Various novel circulating biomarkers in key pathophysiological domains are predictive of outcomes in HFpEF, and a multimarker approach coupled with machine-learning represents a promising strategy for enhancing risk stratification in HFpEF.

中文翻译:

用于心力衰竭和保留射血分数患者风险分层的多种血浆生物标志物

背景 需要更好的风险分层策略来增强射血分数保留的心力衰竭 (HFpEF) 的临床护理和试验设计。目标 本研究的目的是评估靶向血浆多标志物方法的价值,以增强我们在 HFpEF 中的表型特征和风险预测。方法 在这项研究中,作者使用多重分析测量了来自 TOPCAT(使用醛固酮拮抗剂治疗保留的心脏功能心力衰竭)试验参与者(n = 379)的 49 种血浆生物标志物。评估了生物标志物与全因死亡或心力衰竭相关住院 (DHFA) 风险之间的关系。基于树的管道优化器平台用于生成 DHFA 的多标记预测模型。我们在参加 PHFS(宾夕法尼亚州心力衰竭研究)(n = 156)的独立 HFpEF 患者队列中验证了该模型。结果发现了两个大的、紧密相关的主要生物标志物簇,其中包括纤维化/组织重塑、炎症、肾损伤/功能障碍和肝纤维化的生物标志物。其他簇由矿物质代谢的神经激素调节剂、中间代谢和心肌损伤的生物标志物组成。多种生物标志物预测了 DHFA,包括 2 个与矿物质代谢/钙化相关的生物标志物(成纤维细胞生长因子 23 和 OPG [骨保护素])、3 个炎症生物标志物(肿瘤坏死因子-α、sTNFRI [可溶性肿瘤坏死因子受体 I],以及白细胞介素-6)、YKL-40(与肝损伤和炎症有关)、2 种与中间代谢和脂肪细胞生物学相关的生物标志物(脂肪酸结合蛋白 4 和生长分化因子 15)、血管生成素 2(与血管生成相关)、基质金属蛋白酶 7(与细胞外基质转换相关)、ST-2 和N 端前 B 型利钠肽。使用生物标志物组合的机器学习衍生模型可强烈预测 DHFA 的风险(标准化风险比:2.85;95% 置信区间:2.03 至 4.02;p < 0.0001),并在添加到MAGGIC(慢性心力衰竭风险评分中的 Meta-Analysis Global Group)风险评分。在独立队列 (PHFS) 中,该模型强烈预测了 DHFA 的风险(标准化风险比:2.74;95% 置信区间:1.93 至 3.90;p < 0.0001),这也独立于 MAGGIC 风险评分。结论 关键病理生理学领域的各种新型循环生物标志物可预测 HFpEF 的结果,多标志物方法与机器学习相结合代表了增强 HFpEF 风险分层的有前景的策略。
更新日期:2020-03-01
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