TY - JOUR
T1 - Retinopathy Signs Improved Prediction and Reclassification of Cardiovascular Disease Risk in Diabetes
T2 - A prospective cohort study
AU - Ho, Henrietta
AU - Cheung, Carol Y.
AU - Sabanayagam, Charumathi
AU - Yip, Wanfen
AU - Ikram, Mohammad Kamran
AU - Ong, Peng Guan
AU - Mitchell, Paul
AU - Chow, Khuan Yew
AU - Cheng, Ching Yu
AU - Shyong Ta, E.
AU - Wong, Tien Yin
N1 - Funding Information:
National Medical Research Council (NMRC)
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/2/2
Y1 - 2017/2/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85011333327&partnerID=8YFLogxK
U2 - 10.1038/srep41492
DO - 10.1038/srep41492
M3 - Article
C2 - 28148953
AN - SCOPUS:85011333327
SN - 2045-2322
VL - 7
JO - Scientific Reports
JF - Scientific Reports
M1 - 41492
ER -
