Vitamins Reverse Endothelial Dysfunction Through Regulation of eNOS and NAD(P)H Oxidase Activities

Antioxidant vitamins C and E have protective properties in genetic hypertension associated with enhanced oxidative stress. This study investigated whether vitamins C and/or E modulate vascular function by regulating enzymatic activities of endothelial nitric oxide synthase (eNOS) and NAD(P)H oxidase using thoracic aortas of 20- to 22-week-old male spontaneously hypertensive rats (SHR) and their matched normotensive counterparts, Wistar-Kyoto rats (WKY). SHR aortas had impaired relaxant responses to acetylcholine but not to sodium nitroprusside, despite an 2-fold increase in eNOS activity and NO release. The levels of superoxide anion (O2 ), a potent NO scavenger, and NAD(P)H oxidase activity were also 2-fold higher in SHR aortas. Mechanical but not pharmacological inactivation of endothelium (by rubbing and 100 mol/L L-NAME, respectively) significantly abrogated O2 in both strains. Treatments of SHR aortas with NAD(P)H oxidase inhibitors, namely diphenyleneiodinium and apocynin, significantly diminished O2 production. The incubation of SHR aortas with different concentrations of vitamin C (10 to 100 mol/L) and specifically with high concentrations of vitamin E (100 mol/L) improved endothelial function, reduced superoxide production as well as NAD(P)H oxidase activity, and increased eNOS activity and NO generation in SHR aortas to the levels observed in vitamin C- and E-treated WKY aortas. Our results reveal endothelial NAD(P)H oxidase as the major source of vascular O2 in SHR and also show that vitamins C and E are critical in normalizing genetic endothelial dysfunction through regulation of eNOS and NAD(P)H oxidase activities.

Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two “mixed” predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.