Abstract
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.
Highlights
Cardiovascular disease (CVD) is the leading cause of death in persons with diabetes[1]
We evaluated the impact of adding retinal signs to traditional CVD risk factors, and a CVD risk prediction algorithm that included retinopathy and retinal vascular caliber assessed from retinal photographs
Reclassified to Lower risk Net Reclassification Improvement (NRI), % P-value. In this prospective population-based study, we characterized CVD risk models with the addition of biochemical markers of high sensitivity C-reactive protein (hsCRP) and estimated glomerular filtration rate (eGFR), as well as retinal markers of retinopathy and retinal vascular caliber measured from retinal photographs, assessed the performance of the models with measures of discrimination, calibration and reclassification
Summary
Cardiovascular disease (CVD) is the leading cause of death in persons with diabetes[1]. The presence of retinopathy in persons with diabetes is known to be associated with increased CVD risk[16,17] Besides these qualitative markers of microvascular pathology, new indicators of microvascular damage such as changes to retinal vascular caliber can be measured from the same retinal photographs using computer-assisted programs[18,19]. Studies show that these measures of microvascular damage are associated with CVD in the general population[20,21], and in cohorts with diabetes[19,22] This suggests that retinal measures captured from retinal photographs, reflecting generalized microvascular disease, may be potentially useful in further refining CVD risk in persons with diabetes. We examined the robustness of this new prediction algorithm in an independent cohort with diabetes
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