The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis. For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models. Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91]. These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Context:Adipose tissue is one of the first organs to develop insulin resistance even with moderate BMI. However, the contribution of developing hyperglycaemia and concomitant methylglyoxal increment to tissue dysfunction during type 2 diabetes progression was not addressed before. Methods:Young and aged Wistar and Goto-Kakizaki rats (non-obese model of type 2 diabetes) and a group of MG-treated W rats were used to investigate the chronic effects of hyperglycaemia and ageing and specifically MG-induced mechanisms. Results:Diabetic and aged rats showed decreased adipose tissue irrigation and interstitial hypoxia. Hyperglycaemia of diabetic rats leaded to fibrosis and accumulation of PAS-positive components, exacerbated in aged animals, which also showed decreased hipoadiponectinemia, increased MCP-1 expression and macrophage infiltration to glycated fibrotic regions. MG leaded to increased free fatty acids, hipoadiponectinemia, decreased irrigation, hypoxia and macrophage recruitment for glycated fibrotic regions. Conclusions:MG contributes to dysfunction of adipose tissue during type 2 diabetes progression.

In this study we investigate pyridoxamine (PM) and/or sulforaphane (SFN) as therapeutic interventions to determine whether activators of NFE2-related factor 2 (Nrf2) can be used in addition with inhibitors of advanced glycation end products (AGE) formation to attenuate oxidative stress and improve endothelial dysfunction in type 2 diabetes. Goto-kakizaki (GK) rats, an animal model of non-obese type 2 diabetes, were treated with or without PM and/or SFN during 8 weeks and compared with age-matched Wistar rats. At the end of the treatment, nitric oxide (NO)-dependent and independent vasorelaxation in isolated aorta and mesenteric arteries were evaluated. Metabolic profile, NO bioavailability and vascular oxidative stress, AGE and Nrf2 levels were also assessed. Diabetic GK rats presented significantly lower levels of Nrf2 and concomitantly exhibited higher levels of oxidative stress and endothelial dysfunction. PM and SFN as monotherapy were capable of significantly improving endothelial dysfunction in aorta and mesenteric arteries decreasing vascular oxidative damage, AGE and HbA1c levels. Furthermore, SFN + PM proved more effective reducing systemic free fatty acids levels, normalizing endothelial function, NO bioavailability and glycation in GK rats. Activators of Nrf2 can be used therapeutically in association with inhibitors of AGE and cross-linking formation to normalize endothelial dysfunction in type 2 diabetes.