Background/aimsDemonstrate that subretinal drusenoid deposits (SDDs) in age-related macular degeneration (AMD) are linked to coexistent high-risk vascular diseases (HRVDs).MethodsCross-sectional study. Two hundred AMD subjects (aged 51–100 years; 121 women, 79 men) were recruited. Spectral domain optical coherence tomography, autofluorescence and near-infrared reflectance imaging, and lipid profiles were obtained. Subjects were assigned by health history questionnaires into those with or without HRVDs, defined as: cardiac valve defect (eg, aortic stenosis), myocardial defect (eg, myocardial infarction) and stroke/transient ischaemic attack. Masked readers assigned subjects into two groups: SDD (with or without drusen) and drusen (only). Univariate testing was performed by χ2test. We built multivariate regression models to test relationships of coexistent HRVD to SDD status, lipid levels and other covariates.ResultsThe prevalence of HRVD was 41.2% (40/97) and 6.8% (7/103) in the SDD and non-SDD groups, respectively (correlation of SDD with HRVD, p=9×10−9, OR 9.62, 95% CI 4.04 to 22.91). Multivariate regressions: only SDDs and high-density lipoprotein (HDL) in the first two HDL quartiles remained significant for HRVD (p=9.8×10−5, 0.021, respectively). Multivariate regression model: SDDs and an HDL in Q1 or Q2 identified the presence of HRVD with the accuracy of 78.5%, 95% CI 72.2% to 84.0%.ConclusionsHigh-risk cardiovascular and neurovascular diseases were accurately identified in an AMD cohort from SDDs and HDL levels. The SDDs may be related to inadequate ocular perfusion resulting from the systemic vasculopathies. Further research with this paradigm is warranted and might reduce mortality and morbidity from vascular disease.

Bangladeshis are prone to develop type 2 diabetes mellitus (T2DM), hypertension (sHTN and dHTN) and atherosclerotic heart diseases, observed more predominantly in the urban population. Though metabolic syndrome (MetS) is a related disorder, there are few studies in this regard. The prevalence of obesity, T2DM and MetS in three urban communities of Bangladesh were addressed in this study. Nine hundred non-slum urban households in three Dhaka City Wards were randomly selected. One member (age ≥ 25y) from each household was invited for investigation with an overnight fast. Socio-demographic information as well as height, weight, waist-girth, hip-girth and blood pressure were measured. Fasting plasma glucose (FPG), total cholesterol (chol), triglycerides (TG) and high-density lipoproteins-c (HDL) were estimated. A total of 705 (m / f = 239 / 466) subjects volunteered for the study. The mean value with 95% confidence interval (CI) of age was 42.4 (40.9 -43.1) years for men and 37.8 (36.8 -38.7) for women. The mean (CI) body mass index (BMI) was 21.0 (20.6 -21.5) and 22.6 (22.2 -22.9) and waist hip ratio (WHR) was 0.84 (0.83 -0.84) and 0.82 (0.81 -0.83), respectively for men and women. The mean (CI) FPG (fasting plasma glucose) was 5.5 (5.2 -5.7) for men and 5.2 (5.0 -5.4) for women. The prevalence of obesity (BMI ≥ 25.0) was 21%, T2DM (FPG ≥ 6.1 mmol/l) was 22.2%, triglyceridemia (TG ≥ 150mg/dl) was 45.1% and low HDL-c (HDL<40mg/ dl) was 43.8%. The crude prevalence of MetS varied based on different cluster combinations, being the lowest (0.3%) recommended by WHO cluster (FPG + BMI + SBP/DBP) and the highest (8.7%) by International Diabetes Federation (IDF) cluster (waist + FPG + HDL). The MetS was found higher in male than female by NCEP criteria and higher in female than male by IDF criteria. The study revealed an increased prevalence of obesity, T2DM and MetS in the urban communities. It also revealed that T2DM and MetS are moderately common and of growing healthcare burden in the rapidly growing urban population. Additionally, the study observed the wide ranging prevalence rates of MetS in the same study population indicating the need to establish a consistent and useful MetS-cluster depending on population characteristics.Ibrahim Med. Coll. J. 2008; 2(2): 44-48

Purpose: Soft drusen and subretinal drusenoid deposits (SDDs) characterize two pathways to advanced age-related macular degeneration (AMD), with distinct genetic risks, serum risks, and associated systemic diseases.Methods: One hundred and twenty-six subjects with AMD were classified as SDD (with or without soft drusen) or non-SDD (drusen only) by retinal imaging, with serum risks, genetic testing, and histories of cardiovascular disease (CVD) and stroke.Results: There were 62 subjects with SDD and 64 non-SDD subjects, of whom 51 had CVD or stroke. SDD correlated significantly with lower mean serum high-density lipoprotein (61 ± 18 vs. 69 ± 22 mg/dL, P = 0.038, t-test), CVD and stroke (34 of 51 SDD, P = 0.001, chi square), ARMS2 risk allele (P = 0.019, chi square), but not with CFH risk allele (P = 0.66). Non-SDD (drusen only) correlated/trended with APOE2 (P = 0.032) and CETP (P = 0.072) risk alleles (chi square). Multivariate independent risks for SDD were CVD and stroke (P = 0.008) and ARMS2 homozygous risk (P = 0.038).Conclusion: Subjects with subretinal drusenoid deposits and non-SDD subjects have distinct systemic associations and serum and genetic risks. Subretinal drusenoid deposits are associated with CVD and stroke, ARMS2 risk, and lower high-density lipoprotein; non-SDDs are associated with higher high-density lipoprotein, CFH risk, and two lipid risk genes. These and other distinct associations suggest that these lesions are markers for distinct diseases.

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Background: Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases. Methods: iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen’s telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders. Results: In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76. Conclusions: The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.