Association of mid-life cerebral small vessel disease with diabetic retinopathy in type 2 diabetes in an Indian population.

PURPOSE: Prevalence of cerebral small vessel disease (SVD) in elderly patients with diabetic retinopathy (DR) is higher than in those without DR. We determined the prevalence and severity of SVD in middle-aged patients with DR and compared it with those without DR (NODR) in a subset of the Indian population. We feel this information is critical with evolving trends of an increasing incidence of stroke at younger ages. METHOD: Institution-based analytical cross-sectional study with 88 middle-aged type 2 diabetes patients; 44 in each group with <10 years diabetes duration, <8% HbA1C value, and with no history of cardiovascular disease. The presence and severity of SVD were determined by magnetic resonance imaging (MRI). RESULT: Prevalence of SVD was 59.1% among study participants; 70.5% in DR and 47.7% in NODR (p = .03). Significantly increased SVD score (p = .008), high SVD score (p = .030), and white matter hyperintensity (WMH) load (p = .017) were observed in DR compared to NODR. There was no difference in the load of lacune and microbleed. SVD score did not differ according to the severity of DR (p = .395). The location-wise study of MRI revealed a significantly higher SVD load at the centrum semiovale in DR than in NODR (p = .014). We observed a 2.6 times greater chance of SVD (Odds ratio: 2.6, 95% CI 1.1-6.3) and a 9.6 times greater chance of high SVD score (Odds ratio: 9.6, 95% CI 1.1-80.0) in DR compared to NODR. CONCLUSION: Significantly higher burden of SVD in DR was observed, particularly affecting the centrum semiovale suggesting an association of mid-life SVD with DR in this population.

Spectropathologic endorsement of ocular carotenoids for early detection of diabetic retinopathy.

Diabetic retinopathy (DR) is a common health concern. Unfortunately, the metabolic pathway causing DR is yet to be understood. The carotenoid level in the human body is known to protect the health of the eyes. In this work, resonance Raman spectroscopy and multivariate analysis of the spectral data of human serum are reported as next-generation spectropathologic tools to detect retinal degeneration efficiently. The proposed technique shows promise by endorsing ocular carotenoids as a critical biomarker for such pathosis. Furthermore, the multivariate analysis of the spectral data distinguishes between two different stages of the disease. The machine learning algorithm is used to estimate a significant accuracy of 94% of the proposed model for the classification. As the carotenoid level can be controlled by dietary intake, we believe that the reported results also indicate a therapeutic role of the same in DR.