Background: Non‐insulin dependent diabetes mellitus (NIDDM) represents an independent risk factor for cardiovascular diseases (CVD), being characterized by a continnous low‐grade inflammation and endothelial activation state. Plasma platelet ‐ activating factor ‐ acetylhydrolases (PAF‐AHs) are a subgroup of Ca2+ ‐independent phospholipase A2 family (also known as lipoprotein‐associated phospholipases A2) that hydrolyze and inactivate the lipid mediator platelet‐activating factor (PAF) and/or oxidized phospholipids. This enzyme is considered to play an important role in inflammatory diseases and atherosclerosis. The present study aims to investigate the relations between the levels of PAF‐AH activity and LDL‐cholesterol/HDL‐cholesterol (LDL‐ch/HDL‐ch) ratio in NIDDM patients as compared to controls. Methods: serum PAF‐AH activity was measured in 50 patients with dyslipidemia, in 50 NIDDM patients and in 50 controls (normal lipid and glucose levels). Total cholesterol, LDL‐ch, HDL‐ch, triglyceride and blood glucose were determined in all subjects. Results: All NIDDM patients display hiperlipidemia, with increased LDL‐ch and triglyceride levels. There is a significant correlation between LDL‐ch levels (especially LDL‐ch / HDL‐ch ratio) and PAF‐AH activity in dyslipidemic and NIDDM patients. Conclusion: Diabetic and dyslipidemic patients have an increased plasma PAF‐AH activity correlated with their LDL‐ch levels and mainly with LDL‐ch / HDL‐ch ratio. Plasma PAF‐AH high levels appear to be important as a risk marker for endothelial dysfunction in patients with NIDDM.
Confirming its role as a marker of vascular inflammation, LpPLA2 seems to be a biomarker constantly correlated with HF, regardless of etiology. Elevated plasma values of LpPLA2 in HFpEF are consistent with the exacerbated inflammatory status.
The rate equation for a tight-binding inhibitor of an enzyme-catalysed first-order reversible reaction was used to derive two integrated equations. One of them covers the situations in which competitive, uncompetitive or non-competitive inhibition occurs and the other refers to the special non-competitive case where the two inhibition constants are equal. For these equations, graphical and non-linear regression methods are proposed for distinguishing between types of inhibition and for calculating inhibition constants from progress-curve data. The application of the non-linear regression to the analysis of stimulated progress curves in the presence of a tight-binding inhibitor is also presented. The results obtained are valid for any type of 'dead-end'-complex-forming inhibitor and can be used to characterize an unknown inhibitor on the basis of progress curves.
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