Kizer Jorge R, Krauser Daniel G, Rodeheffer Richard J, Burnett John C, Okin Peter M, Roman Mary J, Umans Jason G, Best Lyle G, Lee Elisa T, Devereux Richard B
Department of Medicine, Weill Cornell Medical Center, New York, NY, USA.
Am J Cardiol. 2009 Jul 15;104(2):247-53. doi: 10.1016/j.amjcard.2009.03.026. Epub 2009 Jun 3.
Several biomarkers have been documented, singly or jointly, to improve risk prediction, but the extent to which they improve prediction-model performance in populations with high prevalences of obesity and diabetes has not been specifically examined. The aim of this study was to evaluate the ability of various biomarkers to improve prediction-model performance for death and major cardiovascular disease (CVD) events in a high-risk population. The relations of 6 biomarkers with outcomes were examined in 823 American Indians free of prevalent CVD or renal insufficiency, as were their contributions to risk prediction. In single-marker models adjusting for standard clinical and laboratory risk factors, 4 of 6 biomarkers significantly predicted mortality and major CVD events. In multimarker models, these 4 biomarkers-urinary albumin/creatinine ratio (UACR), glycosylated hemoglobin, B-type natriuretic peptide, and fibrinogen-significantly predicted mortality, while 2-UACR and fibrinogen-significantly predicted CVD. On the basis of its robust association in participants with diabetes, UACR was the strongest predictor of mortality and CVD, individually improving model discrimination or classification in the entire cohort. Singly, all remaining biomarkers also improved risk classification for mortality and enhanced average sensitivity for mortality and CVD. The addition of > or =1 biomarker to the single marker UACR further improved discrimination or average sensitivity for these outcomes. In conclusion, biomarkers derived from diabetic cohorts, and novel biomarkers evaluated primarily in lower risk populations, improve risk prediction in cohorts with prevalent obesity and diabetes. Risk stratification of these populations with multimarker models could enhance selection for aggressive medical or surgical approaches to prevention.
已有文献记载，多种生物标志物单独或联合使用可改善风险预测，但它们在肥胖和糖尿病高患病率人群中改善预测模型性能的程度尚未得到专门研究。本研究的目的是评估各种生物标志物在高危人群中改善死亡和主要心血管疾病（CVD）事件预测模型性能的能力。在823名无CVD或肾功能不全的美国印第安人中，研究了6种生物标志物与结局的关系，以及它们对风险预测的贡献。在调整了标准临床和实验室风险因素的单标志物模型中，6种生物标志物中有4种显著预测了死亡率和主要CVD事件。在多标志物模型中，这4种生物标志物——尿白蛋白/肌酐比值（UACR）、糖化血红蛋白、B型利钠肽和纤维蛋白原——显著预测了死亡率，而UACR和纤维蛋白原这2种生物标志物显著预测了CVD。基于其在糖尿病患者中的强关联，UACR是死亡率和CVD的最强预测因子，单独改善了整个队列中的模型辨别力或分类。单独来看，所有其余生物标志物也改善了死亡率的风险分类，并提高了死亡率和CVD的平均敏感性。在单标志物UACR基础上增加≥1种生物标志物进一步改善了这些结局的辨别力或平均敏感性。总之，来自糖尿病队列的生物标志物以及主要在低风险人群中评估的新型生物标志物，改善了肥胖和糖尿病高患病率队列中的风险预测。使用多标志物模型对这些人群进行风险分层可加强对积极的医学或手术预防方法的选择。
