Preiss David, Zetterstrand Sofia, McMurray John J V, Ostergren Jan, Michelson Eric L, Granger Christopher B, Yusuf Salim, Swedberg Karl, Pfeffer Marc A, Gerstein Hertzel C, Sattar Naveed
BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK.
Diabetes Care. 2009 May;32(5):915-20. doi: 10.2337/dc08-1709. Epub 2009 Feb 5.
The purpose of this study was to identify predictors of incident diabetes during follow-up of nondiabetic patients with chronic heart failure (CHF) in the Candesartan in Heart Failure Assessment of Reduction in Mortality and Morbidity (CHARM) program.
A total of 1,620 nondiabetic patients had full baseline datasets. We compared baseline demographic, medication, and laboratory data for patients who did or did not develop diabetes and conducted logistic regression and receiver operator characteristic curve analyses.
Over a median period of 2.8 years, 126 of the 1,620 patients (7.8%) developed diabetes. In multiple logistic regression analysis, the following baseline characteristics were independently associated with incident diabetes in decreasing order of significance by stepwise selection: higher A1C (odds ratio [OR] 1.78 per 1 SD increase; P < 0.0001), higher BMI (OR 1.64 per 1 SD increase; P < 0.0001), lipid-lowering therapy (OR 2.05; P = 0.0005), lower serum creatinine concentration (OR 0.68 per 1 SD increase; P = 0.0018), diuretic therapy (OR 4.81; P = 0.003), digoxin therapy (OR 1.65; P = 0.022), higher serum alanine aminotransferase concentration (OR 1.15 per 1 SD increase; P = 0.027), and lower age (OR 0.81 per 1 SD increase; P = 0.048). Using receiver operating characteristic curve analysis, A1C and BMI yielded areas under the curve of 0.723 and 0.712, respectively, increasing to 0.788 when combined. Addition of other variables independently associated with diabetes risk minimally improved prediction of diabetes.
In nondiabetic patients with CHF in CHARM, A1C and BMI were the strongest predictors of the development of diabetes. Other minor predictors in part reflected CHF severity or drug-associated diabetes risk. Identifying patients with CHF at risk of diabetes through simple criteria appears possible and could enable targeted preventative measures.
本研究旨在确定在心力衰竭降低死亡率和发病率坎地沙坦评估（CHARM）项目中，慢性心力衰竭（CHF）非糖尿病患者随访期间新发糖尿病的预测因素。
共有1620例非糖尿病患者拥有完整的基线数据集。我们比较了发生或未发生糖尿病患者的基线人口统计学、用药及实验室数据，并进行了逻辑回归和受试者工作特征曲线分析。
在中位时间2.8年期间，1620例患者中有126例（7.8%）发生糖尿病。在多元逻辑回归分析中，按逐步选择的显著程度降序排列，以下基线特征与新发糖尿病独立相关：较高的糖化血红蛋白（A1C）（每增加1个标准差，优势比[OR]为1.78；P<0.0001）、较高的体重指数（BMI）（每增加1个标准差，OR为1.64；P<0.0001）、降脂治疗（OR为2.05；P = 0.0005）、较低的血清肌酐浓度（每增加1个标准差，OR为0.68；P = 0.0018）、利尿剂治疗（OR为4.81；P = 0.003）、地高辛治疗（OR为1.65；P = 0.022）、较高的血清丙氨酸氨基转移酶浓度（每增加1个标准差，OR为1.15；P = 0.027）以及较低的年龄（每增加1个标准差，OR为0.81；P = 0.048）。使用受试者工作特征曲线分析，A1C和BMI的曲线下面积分别为0.723和0.712，联合使用时增至0.788。添加其他与糖尿病风险独立相关的变量对糖尿病预测的改善极小。
在CHARM研究中的CHF非糖尿病患者中，A1C和BMI是糖尿病发生的最强预测因素。其他次要预测因素部分反映了CHF的严重程度或药物相关的糖尿病风险。通过简单标准识别有糖尿病风险的CHF患者似乎是可行的，并且可以采取有针对性的预防措施。
