Haller Paul M, Goßling Alina, Magnussen Christina, Brenner Hermann, Schöttker Ben, Iacoviello Licia, Costanzo Simona, Kee Frank, Koenig Wolfgang, Linneberg Allan, Sujana Chaterina, Thorand Barbara, Salomaa Veikko, Niiranen Teemu J, Söderberg Stefan, Völzke Henry, Dörr Marcus, Sans Susana, Padró Teresa, Felix Stephan B, Nauck Matthias, Petersmann Astrid, Palmieri Luigi, Donfrancesco Chiara, De Ponti Roberto, Veronesi Giovanni, Ferrario Marco M, Kuulasmaa Kari, Zeller Tanja, Ojeda Francisco M, Blankenberg Stefan, Westermann Dirk
Department for Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany.
German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Martinistrasse 52, 20246 Hamburg, Germany.
Eur J Prev Cardiol. 2023 Sep 6;30(12):1218-1226. doi: 10.1093/eurjpc/zwad122.
The role of biomarkers in predicting cardiovascular outcomes in high-risk individuals is not well established. We aimed to investigate benefits of adding biomarkers to cardiovascular risk assessment in individuals with and without diabetes.
We used individual-level data of 95 292 individuals of the European population harmonized in the Biomarker for Cardiovascular Risk Assessment across Europe consortium and investigated the prognostic ability of high-sensitivity cardiac troponin I (hs-cTnI), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP). Cox-regression models were used to determine adjusted hazard ratios of diabetes and log-transformed biomarkers for fatal and non-fatal cardiovascular events. Models were compared using the likelihood ratio test. Stratification by specific biomarker cut-offs was performed for crude time-to-event analysis using Kaplan-Meier plots. Overall, 6090 (6.4%) individuals had diabetes at baseline, median follow-up was 9.9 years. Adjusting for classical risk factors and biomarkers, diabetes [HR 2.11 (95% CI 1.92, 2.32)], and all biomarkers (HR per interquartile range hs-cTnI 1.08 [95% CI 1.04, 1.12]; NT-proBNP 1.44 [95% CI 1.37, 1.53]; hs-CRP 1.27 [95% CI 1.21, 1.33]) were independently associated with cardiovascular events. Specific cut-offs for each biomarker identified a high-risk group of individuals with diabetes losing a median of 15.5 years of life compared to diabetics without elevated biomarkers. Addition of biomarkers to the Cox-model significantly improved the prediction of outcomes (likelihood ratio test for nested models P < 0.001), accompanied by an increase in the c-index (increase to 0.81).
Biomarkers improve cardiovascular risk prediction in individuals with and without diabetes and facilitate the identification of individuals with diabetes at highest risk for cardiovascular events.
生物标志物在预测高危个体心血管结局中的作用尚未明确。我们旨在研究在有糖尿病和无糖尿病个体中，将生物标志物添加到心血管风险评估中的益处。
我们使用了欧洲人群95292名个体的个体水平数据，这些数据在欧洲心血管风险评估生物标志物联盟中进行了统一整理，并研究了高敏心肌肌钙蛋白I（hs-cTnI）、脑钠肽N末端前体激素（NT-proBNP）和高敏C反应蛋白（hs-CRP）的预后能力。采用Cox回归模型确定糖尿病以及经对数转换的生物标志物对于致命和非致命心血管事件的校正风险比。使用似然比检验对模型进行比较。使用Kaplan-Meier图对特定生物标志物临界值进行分层，以进行粗事件发生时间分析。总体而言，6090名（6.4%）个体在基线时患有糖尿病，中位随访时间为9.9年。在调整经典危险因素和生物标志物后，糖尿病[风险比2.11（95%置信区间1.92, 2.32）]以及所有生物标志物（每四分位间距hs-cTnI的风险比为1.08 [95%置信区间1.04, 1.12]；NT-proBNP为1.44 [95%置信区间1.37, 1.53]；hs-CRP为1.27 [95%置信区间1.21, 1.33]）均与心血管事件独立相关。每种生物标志物的特定临界值确定了一组高危糖尿病个体，与生物标志物未升高的糖尿病患者相比，他们的中位寿命损失了15.5年。将生物标志物添加到Cox模型中显著改善了结局预测（嵌套模型的似然比检验P < 0.001），同时c指数增加（增加到0.81）。
生物标志物可改善有糖尿病和无糖尿病个体的心血管风险预测，并有助于识别心血管事件风险最高的糖尿病个体。
