Tien Wong

The eye in hypertension

Hypertension has a range of effects on the eye. Hypertensive retinopathy refers to retinal microvascular signs that develop in response to raised blood pressure. Signs of hypertensive retinopathy are frequently seen in adults 40 years and older, and are predictive of incident stroke, congestive heart failure, and cardiovascular mortality--independently of traditional risk factors. Hypertension is also a major risk factor for the development of other retinal vascular diseases, such as retinal vein and artery occlusion, and ischaemic optic neuropathy. High blood pressure increases the risk of both development of diabetic retinopathy and its progression. Adequate control of blood pressure has been proven in randomised clinical trials to reduce vision loss associated with diabetic retinopathy. Finally, hypertension has been implicated in the pathogenesis of glaucoma and age-related macular degeneration. Recognition of the ocular effects of blood pressure could allow physicians to better manage patients with hypertension, and to monitor its end-organ effects.

Retinopathy predicts coronary heart disease mortality

Background: Retinopathy lesions are fairly common findings in clinic settings and may predict risk of coronary heart disease (CHD). Objective: To examine whether retinopathy independently predicts a risk of CHD-related mortality in people with and without diabetes. Methods: In an Australian population-based cohort of people with (n = 199) and without (n = 2768) diabetes (Blue Mountains Eye Study, total n = 2967), the presence and severity of retinopathy was assessed from retinal photographs. 12-Year cumulative CHD deaths were ascertained from Australian National Death Index records. Results: Over 12 years, 353 participants (11.9%) had incident CHD-related deaths. Retinopathy was present in 57/199 (28.6%) participants with, and in 268/2768 (9.7%) without, diabetes. The presence of retinopathy increased the CHD mortality rate per person-year by an amount (0.005) equivalent to the presence of diabetes itself (12-year CHD mortality rate per person-year of 0.010 in people with neither diabetes nor retinopathy, 0.015 in those with diabetes alone, 0.016 in those with retinopathy alone). After adjusting for cardiovascular risk factors, retinopathy remained an independent predictor of CHD death both in people with diabetes (hazard ratio (HR) = 2.21, 95% CI 1.20 to 4.05) and in those without diabetes (HR = 1.33, 95% CI 1.02 to 1.83). Moderate retinopathy was associated with adjusted HR = 6.68 (95% CI 2.24 to 20.0) in people with diabetes and adjusted HR = 2.29 (95% CI 1.10 to 4.76) in people without diabetes. Conclusions: A finding of retinopathy in people with or without diabetes may signal increased CHD risk. The increased CHD mortality associated with retinopathy in people without diabetes was equivalent to the presence of diabetes itself.

Prevalence and Determinants of Suboptimal Vitamin D Levels in a Multiethnic Asian Population

This population-based cross-sectional study examined the prevalence and risk factors of suboptimal vitamin D levels (assessed using circulating 25-hydroxycholecalciferol (25(OH)D)) in a multi-ethnic sample of Asian adults. Plasma 25(OH)D concentration of 1139 Chinese, Malay and Indians (40–80 years) were stratified into normal (≥30 ng/mL), and suboptimal (including insufficiency and deficiency, <30 ng/mL) based on the 2011 Endocrine Society Clinical Practice Guidelines. Logistic regression models were used to assess the associations of demographic, lifestyle and clinical risk factors with the outcome. Of the 1139 participants, 25(OH)D concentration was suboptimal in 76.1%. In multivariable models, age ≤65 years (compared to age >65 years), Malay and Indian ethnicities (compared to Chinese ethnicity), and higher body mass index, HbA1c, education and income levels were associated with suboptimal 25(OH)D concentration (p < 0.05). In a population-based sample of Asian adults, approximately 75% had suboptimal 25(OH)D concentration. Targeted interventions and stricter reinforcements of existing guidelines for vitamin D supplementation are needed for groups at risk of vitamin D insufficiency/deficiency.

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

OBJECTIVE The goal of this study was to describe the development and validation of an artificial intelligence–based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS A DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images. A panel of ophthalmologists graded DR severity in retinal photographs included in the development and internal validation data sets (n = 71,043); a reference standard grading was assigned once three graders achieved consistent grading outcomes. For external validation, we tested our DLA using 35,201 images of 14,520 eyes (904 eyes with any DR; 401 eyes with vision-threatening referable DR) from population-based cohorts of Malays, Caucasian Australians, and Indigenous Australians. RESULTS Among the 71,043 retinal images in the training and validation data sets, 12,329 showed vision-threatening referable DR. In the internal validation data set, the area under the curve (AUC), sensitivity, and specificity of the DLA for vision-threatening referable DR were 0.989, 97.0%, and 91.4%, respectively. Testing against the independent, multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%, respectively. Among false-positive cases, 85.6% were due to a misclassification of mild or moderate DR. Undetected intraretinal microvascular abnormalities accounted for 77.3% of all false-negative cases. CONCLUSIONS This artificial intelligence–based DLA can be used with high accuracy in the detection of vision-threatening referable DR in retinal images. This technology offers potential to increase the efficiency and accessibility of DR screening programs.