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Objective To determine the association between systemic medication use and intraocular pressure (IOP) in a population of older British men and women. Design Population-based, cross-sectional study. Participants We included 7093 participants from the European Prospective Investigation into Cancer–Norfolk Eye Study. Exclusion criteria were a history of glaucoma therapy (medical, laser, or surgical), IOP asymmetry between eyes of >5 mmHg, and missing data for any covariables. The mean age of participants was 68 years (range, 48–92) and 56% were women. Methods We measured IOP using the Ocular Response Analyzer. Three readings were taken per eye and the best signal value of the Goldmann-correlated IOP value considered. Participants were asked to bring all their medications and related documentation to the health examination, and these were recorded by the research nurse using an electronic case record form. The medication classes examined were angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, α-blockers, β-blockers, calcium channel blockers, diuretics, nitrates, statins, insulin, biguanides, sulfonylureas, aspirin, and other nonsteroidal anti-inflammatory drugs. We examined associations between medication use and IOP using multivariable linear regression models adjusted for age, sex, and body mass index. Models containing diabetic medication were further adjusted for glycosylated hemoglobin levels. Main Outcome Measures Mean IOP of the right and left eyes. Results Use of systemic β-blockers (−0.92 mmHg; 95% CI, −1.19, −0.65; P

ObjectiveTo determine the association between systemic medication use and intraocular pressure (IOP) in a population of older British men and women.DesignPopulation-based, cross-sectional study.ParticipantsWe included 7093 participants from the European Prospective Investigation into Cancer–Norfolk Eye Study. Exclusion criteria were a history of glaucoma therapy (medical, laser, or surgical), IOP asymmetry between eyes of >5 mmHg, and missing data for any covariables. The mean age of participants was 68 years (range, 48–92) and 56% were women.MethodsWe measured IOP using the Ocular Response Analyzer. Three readings were taken per eye and the best signal value of the Goldmann-correlated IOP value considered. Participants were asked to bring all their medications and related documentation to the health examination, and these were recorded by the research nurse using an electronic case record form. The medication classes examined were angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, α-blockers, β-blockers, calcium channel blockers, diuretics, nitrates, statins, insulin, biguanides, sulfonylureas, aspirin, and other nonsteroidal anti-inflammatory drugs. We examined associations between medication use and IOP using multivariable linear regression models adjusted for age, sex, and body mass index. Models containing diabetic medication were further adjusted for glycosylated hemoglobin levels.Main Outcome MeasuresMean IOP of the right and left eyes.ResultsUse of systemic β-blockers (−0.92 mmHg; 95% CI, −1.19, −0.65; P

Purpose To examine associations between retinal vessel morphometry and cardiometabolic risk factors in older British men and women. Design Retinal imaging examination as part of the European Prospective Investigation into Cancer—Norfolk Eye Study. Participants Retinal imaging and clinical assessments were carried out in 7411 participants. Retinal images were analyzed using a fully automated validated computerized system that provides novel measures of vessel morphometry. Methods Associations between cardiometabolic risk factors, chronic disease, and retinal markers were analyzed using multilevel linear regression, adjusted for age, gender, and within-person clustering, to provide percentage differences in tortuosity and absolute differences in width. Main Outcomes Measures Retinal arteriolar and venular tortuosity and width. Results In all, 279 802 arterioles and 285 791 venules from 5947 participants (mean age, 67.6 years; standard deviation [SD], 7.6 years; 57% female) were analyzed. Increased venular tortuosity was associated with higher body mass index (BMI; 2.5%; 95% confidence interval [CI], 1.7%–3.3% per 5 kg/m 2 ), hemoglobin A1c (HbA1c) level (2.2%; 95% CI, 1.0%–3.5% per 1%), and prevalent type 2 diabetes (6.5%; 95% CI, 2.8%–10.4%); wider venules were associated with older age (2.6 μm; 95% CI, 2.2–2.9 μm per decade), higher triglyceride levels (0.6 μm; 95% CI, 0.3–0.9 μm per 1 mmol/l), BMI (0.7 μm; 95% CI, 0.4–1.0 per 5 kg/m 2 ), HbA1c level (0.4 μm; 95% CI, –0.1 to 0.9 per 1%), and being a current smoker (3.0 μm; 95% CI, 1.7–4.3 μm); smoking also was associated with wider arterioles (2.1 μm; 95% CI, 1.3–2.9 μm). Thinner venules were associated with high-density lipoprotein (HDL) (1.4 μm; 95% CI, 0.7–2.2 per 1 mmol/l). Arteriolar tortuosity increased with age (5.4%; 95% CI, 3.8%–7.1% per decade), higher systolic blood pressure (1.2%; 95% CI, 0.5%–1.9% per 10 mmHg), in females (3.8%; 95% CI, 1.4%–6.4%), and in those with prevalent stroke (8.3%; 95% CI, –0.6% to 18%); no association was observed with prevalent myocardial infarction. Narrower arterioles were associated with age (0.8 μm; 95% CI, 0.6–1.0 μm per decade), higher systolic blood pressure (0.5 μm; 95% CI, 0.4–0.6 μm per 10 mmHg), total cholesterol level (0.2 μm; 95% CI, 0.0–0.3 μm per 1 mmol/l), and HDL (1.2 μm; 95% CI, 0.7–1.6 μm per 1 mmol/l). Conclusions Metabolic risk factors showed a graded association with both tortuosity and width of retinal venules, even among people without clinical diabetes, whereas atherosclerotic risk factors correlated more closely with arteriolar width, even excluding those with hypertension and cardiovascular disease. These noninvasive microvasculature measures should be evaluated further as predictors of future cardiometabolic disease.