Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, 168751, Singapore.
Chinese University of Hong Kong Eye Centre, Department of Ophthalmology and Visual Sciences, Hong Kong.
Sci Rep. 2017 Feb 2;7:41492. doi: 10.1038/srep41492.
CVD risk prediction in diabetics is imperfect, as risk models are derived mainly from the general population. We investigate whether the addition of retinopathy and retinal vascular caliber improve CVD prediction beyond established risk factors in persons with diabetes. We recruited participants from the Singapore Malay Eye Study (SiMES, 2004-2006) and Singapore Prospective Study Program (SP2, 2004-2007), diagnosed with diabetes but no known history of CVD at baseline. Retinopathy and retinal vascular (arteriolar and venular) caliber measurements were added to risk prediction models derived from Cox regression model that included established CVD risk factors and serum biomarkers in SiMES, and validated this internally and externally in SP2. We found that the addition of retinal parameters improved discrimination compared to the addition of biochemical markers of estimated glomerular filtration rate (eGFR) and high-sensitivity C-reactive protein (hsCRP). This was even better when the retinal parameters and biomarkers were used in combination (C statistic 0.721 to 0.774, p = 0.013), showing improved discrimination, and overall reclassification (NRI = 17.0%, p = 0.004). External validation was consistent (C-statistics from 0.763 to 0.813, p = 0.045; NRI = 19.11%, p = 0.036). Our findings show that in persons with diabetes, retinopathy and retinal microvascular parameters add significant incremental value in reclassifying CVD risk, beyond established risk factors.
糖尿病患者的 CVD 风险预测并不完善，因为风险模型主要来自一般人群。我们研究了在糖尿病患者中，是否添加视网膜病变和视网膜血管口径可以改善超越既定危险因素的 CVD 预测。我们从新加坡马来人眼病研究（SiMES，2004-2006 年）和新加坡前瞻性研究计划（SP2，2004-2007 年）中招募了参与者，这些参与者在基线时被诊断患有糖尿病，但没有已知的 CVD 病史。在 SiMES 中，我们将 Cox 回归模型中包含的既定 CVD 风险因素和血清生物标志物的风险预测模型中添加了视网膜病变和视网膜血管（动脉和静脉）口径测量值，并在 SP2 中进行了内部和外部验证。我们发现，与添加估计肾小球滤过率（eGFR）和高敏 C 反应蛋白（hsCRP）的生化标志物相比，添加视网膜参数可提高判别能力。当视网膜参数和生物标志物结合使用时，效果更好（C 统计量为 0.721 至 0.774，p=0.013），显示出更好的判别能力和整体重新分类（NRI=17.0%，p=0.004）。外部验证结果一致（C 统计量从 0.763 到 0.813，p=0.045；NRI=19.11%，p=0.036）。我们的研究结果表明，在糖尿病患者中，视网膜病变和视网膜微血管参数在重新分类 CVD 风险方面具有显著的附加价值，超越了既定的危险因素。
