Division of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, Texas.
Department of Medicine, Section of Cardiovascular Research and Center for Cardiometabolic Disease Prevention, Baylor College of Medicine, Houston TX.
Am J Cardiol. 2023 Oct 1;204:295-301. doi: 10.1016/j.amjcard.2023.07.118. Epub 2023 Aug 9.
We sought to determine how biomarkers known to be associated with hypertension-induced end-organ injury complement the use of systolic blood pressure (SBP) for cardiovascular disease (CVD) risk prediction at different ages. Using data from visits 2 (1990 to 1992) and 5 (2011 to 2013) of the Atherosclerosis Risk in Communities (ARIC) study, 3 models were used to predict CVD (composite of coronary heart disease, stroke, and heart failure). Model A included traditional risk factors (TRFs) except SBP, model B-TRF plus SBP, and model C-TRF plus biomarkers (high-sensitivity troponin T [hsTnT] and N-terminal pro-B-type natriuretic peptide [NT-proBNP]). Harrel's C-statistics were used to assess risk discrimination for CVD comparing models B and A and C and B. At visit 2, the addition of SBP to TRF (model B vs model A) significantly improved the C-statistic (∆C-statistic, 95% confidence interval 0.010, 0.007 to 0.013) whereas the addition of hsTnT to TRF (model C vs model B) decreased the C-statistic (∆C-statistic -0.0038, -0.0075 to -0.0001) compared with SBP. At visit 5, the addition of SBP to TRF did not significantly improve the C-statistic (∆C-statistic 0.001, -0.002 to 0.005) whereas the addition of both hsTnT and NT-proBNP to TRF significantly improved the C-statistic compared with SBP (∆C-statistic 0.028, 0.015 to 0.041 and 0.055, 0.036 to 0.074, respectively). In summary, the incremental value of SBP for CVD risk prediction diminishes with age whereas the incremental value of hsTnT and NT-proBNP increases with age.
我们旨在确定与高血压引起的靶器官损伤相关的生物标志物如何补充收缩压(SBP)在不同年龄的心血管疾病(CVD)风险预测中的作用。使用来自动脉粥样硬化风险社区(ARIC)研究的第 2 次(1990 年至 1992 年)和第 5 次(2011 年至 2013 年)就诊的数据,使用 3 种模型来预测 CVD(冠心病、中风和心力衰竭的复合)。模型 A 包括传统危险因素(TRFs),除 SBP 外,模型 B-TRF 加 SBP,模型 C-TRF 加生物标志物(高敏肌钙蛋白 T [hsTnT]和 N 端 pro-B 型利钠肽 [NT-proBNP])。哈雷尔 C 统计量用于评估模型 B 和 A 以及 C 和 B 之间 CVD 风险的区分。在第 2 次就诊时,将 SBP 添加到 TRF(模型 B 与模型 A)中显著提高了 C 统计量(ΔC 统计量,95%置信区间为 0.010,0.007 至 0.013),而将 hsTnT 添加到 TRF(模型 C 与模型 B)中则降低了 C 统计量(ΔC 统计量-0.0038,-0.0075 至-0.0001)与 SBP 相比。在第 5 次就诊时,将 SBP 添加到 TRF 中并未显著提高 C 统计量(ΔC 统计量 0.001,-0.002 至 0.005),而将 hsTnT 和 NT-proBNP 添加到 TRF 中则显著提高了 C 统计量与 SBP 相比(ΔC 统计量 0.028,0.015 至 0.041 和 0.055,0.036 至 0.074,分别)。综上所述,SBP 对 CVD 风险预测的增量价值随着年龄的增长而减小,而 hsTnT 和 NT-proBNP 的增量价值随着年龄的增长而增加。