Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, 1309 Beacon Street, Brookline, MA 02446, USA.
Diabetologia. 2013 Feb;56(2):275-83. doi: 10.1007/s00125-012-2772-1. Epub 2012 Nov 10.
AIMS/HYPOTHESIS: We sought to derive and validate a cardiovascular disease (CVD) prediction algorithm for older adults with diabetes, and evaluate the incremental benefit of adding novel circulating biomarkers and measures of subclinical atherosclerosis.
As part of the Cardiovascular Health Study (CHS), a population-based cohort of adults aged ≥65 years, we examined the 10 year risk of myocardial infarction, stroke and cardiovascular death in 782 older adults with diabetes, in whom 265 events occurred. We validated predictive models in 843 adults with diabetes, who were followed for 7 years in a second cohort, the Multi-Ethnic Study of Atherosclerosis (MESA); here 71 events occurred.
The best fitting standard model included age, smoking, systolic blood pressure, total and HDL-cholesterol, creatinine and the use of glucose-lowering agents; however, this model had a C statistic of 0.64 and poorly classified risk in men. Novel biomarkers did not improve discrimination or classification. The addition of ankle-brachial index, electrocardiographic left ventricular hypertrophy and internal carotid intima-media thickness modestly improved discrimination (C statistic 0.68; p = 0.002) and classification (net reclassification improvement [NRI] 0.12; p = 0.01), mainly in those remaining free of CVD. Results were qualitatively similar in the MESA, with a change in C statistic from 0.65 to 0.68 and an NRI of 0.09 upon inclusion of subclinical disease measures.
CONCLUSIONS/INTERPRETATION: Standard clinical risk factors and novel biomarkers poorly discriminate and classify CVD risk in older adults with diabetes. The inclusion of subclinical atherosclerotic measures modestly improves these features, but to develop more robust risk prediction, a better understanding of the pathophysiology and determinants of CVD in this patient group is needed.
目的/假设：我们旨在为老年糖尿病患者开发并验证一种心血管疾病（CVD）预测算法，并评估添加新型循环生物标志物和亚临床动脉粥样硬化测量值的增量收益。
作为基于人群的老年人队列心血管健康研究（CHS）的一部分，我们检查了 782 名老年糖尿病患者中 10 年内心肌梗死、中风和心血管死亡的风险，其中发生了 265 例事件。我们在第二个队列——动脉粥样硬化多民族研究（MESA）中的 843 名糖尿病患者中验证了预测模型，其中 71 例事件发生，这些患者随访 7 年。
最佳拟合的标准模型包括年龄、吸烟、收缩压、总胆固醇和高密度脂蛋白胆固醇、肌酐和降糖药物的使用；然而，该模型的 C 统计量为 0.64，并且在男性中风险分类较差。新型生物标志物并未改善区分度或分类。踝臂指数、心电图左心室肥厚和颈内动脉内膜中层厚度的增加适度改善了区分度（C 统计量 0.68；p=0.002）和分类（净重新分类改善[NRI]0.12；p=0.01），主要在那些仍然没有 CVD 的患者中。在 MESA 中，结果定性相似，纳入亚临床疾病测量值后，C 统计量从 0.65 变为 0.68，NRI 为 0.09。
结论/解释：标准临床危险因素和新型生物标志物在老年糖尿病患者中对 CVD 风险的区分度和分类能力较差。亚临床动脉粥样硬化测量值的纳入适度改善了这些特征，但为了开发更强大的风险预测模型，需要更好地了解该患者群体中 CVD 的病理生理学和决定因素。
