A mathematical model for thrombotic risk assessment in type 2 diabetes.

Hyperglycaemia is known to alter the circulating lipids in diabetics. Combinatorial effect of in vivo synthesis of lipids and dietary lipids leads to atherosclerosis. Uncontrolled diabetes is linked with the cardiovascular outcome. This data has correlated the Castelli's Risk Index (CRI-I and CRI-II), Atherogenic Index of Plasma and Atherogenic Coefficient with microvascular complications of T2DM. Etio-pathogenesis of cardiovascular risk factors and lipid biomarkers speaks of the thrombotic events of cerebrovascular accidents and also the reno-vascular mechanisms of renal arterial thrombotic events. Documentary evidence have proved that the micro albuminuria is a "cutting edge" to assess the microvascular complications of renal and retina. Uncontrolled diabetes is known to alter the triglycerides, lower HDL-cholesterol and elevate LDL-cholesterol. Alteration of lipid profile mimics a major link between diabetes and the increased cardiovascular risk in diabetic patients.

Visualising structural and functional characteristics distinguishing between newly diagnosed high-tension and low-tension glaucoma patients.

PURPOSE: To determine whether there are quantifiable structural or functional differences that can distinguish between high-tension glaucoma (HTG; intraocular pressure [IOP] > 21 mm Hg) and low-tension glaucoma (LTG; IOP ≤ 21 mm Hg) at diagnosis. METHOD: This was a retrospective, cross-sectional study. Clinical results of one eye from 90 newly diagnosed HTG and 319 newly diagnosed LTG patients (117 with very-low-tension glaucoma [vLTG; ≤15 mm Hg] and 202 with middling LTG [mLTG; >15 mm Hg, ≤21 mm Hg]) were extracted, which included relevant demographic covariates of glaucoma, quantitative optical coherence tomography (including the optic nerve head, retinal nerve fibre layer and ganglion cell-inner plexiform layer) measurements and standard automated perimetry global metrics. We used binary logistic regression analysis to identify statistically significant clinical parameters distinguishing between phenotypic groups for inclusion in principal component (PC) (factor) analysis (PCA). The separability between each centroid for each cohort was calculated using the Euclidean distance (d(x,y)). RESULTS: The binary logistic regression comparing HTG and all LTG identified eight statistically significant clinical parameters. Subsequent PCA results included three PCs with an eigenvalue >1. PCs 1 and 2 accounted for 21.2% and 20.2% of the model, respectively, with a d(x,y) = 0.468, indicating low separability between HTG and LTG. The analysis comparing vLTG, mLTG and HTG identified 15 significant clinical parameters, which were subsequently grouped into five PCs. PCs 1 and 2 accounted for 24.1% and 17.8%, respectively. The largest separation was observed between vLTG and HTG (d(x,y) = 0.581), followed by vLTG and mLTG (d(x,y) = 0.435) and lastly mLTG and HTG (d(x,y) = 0.210). CONCLUSION: Conventional quantitative structural or functional parameters could not distinguish between pressure-defined glaucoma phenotypes at the point of diagnosis and are therefore not contributory to separating cohorts. The overlap in findings highlights the heterogeneity of the primary open-angle glaucoma clinical presentations among pressure-defined groups at the cohort level.