Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Diabetes Obes Metab. 2020 Jul;22(7):1151-1156. doi: 10.1111/dom.14017. Epub 2020 Mar 20.
To perform post-hoc analyses of the EMPA-REG OUTCOME trial examining the degree to which empagliflozin-induced changes in conventional cardiovascular (CV) risk factors might explain the observed CV benefits.
We estimated 3-year EMPA-REG OUTCOME CV event rates using a type 2 diabetes-specific clinical outcomes simulation model applied to annual patient-level data. Variables included were atrial fibrillation, smoking, albuminuria, HDL cholesterol, LDL cholesterol, systolic blood pressure, glycated haemoglobin, heart rate, white cell count, haemoglobin, estimated glomerular filtration rate, and histories of ischaemic heart disease, heart failure, amputation, blindness, renal failure, stroke, myocardial infarction or diabetic ulcer. Multiple simulations were performed for each participant to minimize uncertainty and optimize confidence interval precision around CV risk point estimates. Observed and simulated cardiovascular relative risk reductions were compared.
Model-predicted relative risk reductions were smaller than those observed in the trial, with empagliflozin-associated changes in conventional CV risk factor values appearing to explain only 12% of the observed relative risk reduction for all-cause death (4% of 32%), 7% for CV death (3% of 39%) and 15% for heart failure (4% of 29%).
Empagliflozin-associated changes in conventional CV risk factors in EMPA-REG OUTCOME appear to explain only a small proportion of the CV and all-cause death reductions observed. Alternative risk-reduction mechanisms need to be explored to determine if the observed CV risk changes can be explained by other factors, or possibly by a direct drug-specific effect.
对 EMPA-REG OUTCOME 试验进行事后分析,考察恩格列净引起的传统心血管(CV)危险因素变化在多大程度上可以解释观察到的 CV 获益。
我们使用一种适用于年度患者水平数据的 2 型糖尿病特定临床结局模拟模型来估计 EMPA-REG OUTCOME 的 3 年 CV 事件发生率。纳入的变量包括心房颤动、吸烟、白蛋白尿、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、收缩压、糖化血红蛋白、心率、白细胞计数、血红蛋白、估算肾小球滤过率以及缺血性心脏病、心力衰竭、截肢、失明、肾衰竭、中风、心肌梗死或糖尿病溃疡的病史。对每个参与者进行多次模拟,以最小化不确定性并优化 CV 风险点估计的置信区间精度。比较了观察到的和模拟的心血管相对风险降低。
模型预测的相对风险降低小于试验中观察到的,恩格列净相关的传统 CV 危险因素变化似乎仅解释了全因死亡(4%的 32%)、CV 死亡(3%的 39%)和心力衰竭(4%的 29%)观察到的相对风险降低的 12%、7%和 15%。
在 EMPA-REG OUTCOME 中,恩格列净相关的传统 CV 危险因素变化似乎仅能解释 CV 和全因死亡降低的一小部分。需要探索其他的风险降低机制,以确定观察到的 CV 风险变化是否可以由其他因素或可能由直接的药物特异性作用来解释。