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The association between obesity and cardiovascular disease (CVD) remains unclear, particularly for those with established CVD risk factors. We analyzed follow-up data from the Aichi Workers' Cohort Study. We studied the association between the degree of obesity and risk of CVD and its subtypes specifically among individuals with hypertension, hyper-low-density lipoprotein (LDL)-cholesterolemia, or diabetes. Pooled data of 8972 adults (7076 men and 1896 women) who were recruited between 2002 and 2008 were used in the current analysis. We used multivariable Cox proportional hazard model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between the degree of obesity assessed with body mass index (BMI) and the risk of CVD and its subtypes, i.e., coronary heart disease (CHD) and stroke. During a median of 12 years, there were 197 CVDs (80 CHDs and 117 strokes). BMI ≥ 27.5 compared to 21.0–22.9 kg/m2 was positively and significantly associated with the risks of CVD, CHD, and total stroke. Hypertension, hyper-LDL-cholesterolemia, and diabetes mediated 15.9%, 5.8%, and 8.7% of obesity-CVD associations, respectively, and 28.3% by their combination. In the stratified analyses by the presence of risk factors, BMI ≥ 25.0 (overweight/obesity) compared to BMI < 25 kg/m2 was associated with a higher risk of CVD in those with and without hypertension, but only with hyper-LDL-cholesterolemia, and without diabetes. Overweight/obesity was associated with the risk of CVD and its subtypes. About 30% of the risk was explained by hypertension, hyper-LDL-cholesterolemia, and diabetes, of which hypertension accounted for approximately the half of the explained risk. However, overweight/obesity increased the risk of CVD even in those without hypertension. These findings highlight the importance of controlling and preventing overweight/obesity regardless of chronic disease status. [ABSTRACT FROM AUTHOR]

Objective: To characterize the central corneal thickness (CCT) in Koreans and assess CCT variation in different types of glaucoma. Design: Observational cross-sectional study. Participants: Patients previously diagnosed as primary open-angle glaucoma (POAG, n = 343), normal-tension glaucoma (NTG, n = 192), and ocular hypertension (OHT, n = 44), as well as nonglaucomatous patients (n = 224) examined in a glaucoma clinic in Korea. Methods: Participants’ CCT was measured by ultrasound pachymetry. Main Outcome Measures: Mean CCT and its correlations with glaucoma diagnosis, age, spherical equivalent, gender, diabetes, and hypertension. Results: Mean CCT was highest in eyes with OHT (582.1 μm, P<0.0001), lowest in NTG (537.5 μm, P<0.0001), and intermediate and similar in eyes with POAG and healthy eyes (550.7 and 553.6 μm, respectively, P = 0.289). Central corneal thickness was inversely correlated with age (r = −0.12; P<0.0001). There was no statistically significant difference in mean CCT between eyes with different gender or presence of diabetes and hypertension. Mean CCT in normal and OHT eyes was weakly correlated with refractive error (P<0.05). Conclusions: In Korean subjects, CCT is greatest in eyes with OHT and least in eyes with NTG. [Copyright &y& Elsevier]

Purpose: To compare the effectiveness of Humphrey Matrix perimetry, GDx VCC, Stratus OCT, and retinal nerve fiber layer (RNFL) photography using the Heidelberg Retina Angiograph 1 (HRA1) for early glaucoma detection. Design: Cross-sectional comparative study. Participants: Seventy-two primary open-angle glaucoma patients with early-stage visual field defects and 48 healthy controls were included. Methods: Measurements using Humphrey Matrix perimetry, GDx VCC, Stratus OCT, and RNFL photography using HRA1, as well as standard automated perimetry, were obtained. We constructed receiver operating characteristic (ROC) curves for all available parameters and calculated the area under the ROC curves (AUC) to seek the best discriminating parameter of each test. Subsequently, the ROC curves were calculated for the combinations of the best discriminating parameters of each test to seek the most effective combination for early glaucoma detection. Main Outcome Measure: The AUC for various parameters of Humphrey Matrix perimetry, GDx VCC, Stratus OCT, and RNFL photography using HRA1. Results: The AUCs of Humphrey Matrix perimetry, GDx VCC, Stratus OCT, and RNFL photography using HRA1 with the best discriminating parameter were 0.990, 0.906, 0.794, and 0.751, respectively. The AUC of the following best combination was 0.972, more than 5 points depressed below the level of 5% on the pattern deviation plot from Humphrey Matrix perimetry, and the nerve fiber indicator was larger than 20 from GDx VCC. Conclusions: The AUC of the Humphrey Matrix perimetry was greater than that of the GDx VCC, Stratus OCT, and RNFL photography using HRA1. [Copyright &y& Elsevier]