Retinopathy Signs Improved Prediction and Reclassification of Cardiovascular Disease Risk in Diabetes: A prospective cohort study.

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Ho, H; Cheung, CY; Sabanayagam, C; Yip, W; Ikram, MK; Ong, PG; Mitchell, P; Chow, KY; Cheng, CY; Tai, ES; Wong, TY

Published Date

  • February 2, 2017

Published In

Volume / Issue

  • 7 /

Start / End Page

  • 41492 -

PubMed ID

  • 28148953

Pubmed Central ID

  • 28148953

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/srep41492

Language

  • eng

Conference Location

  • England