BackgroundThe 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.MethodsFor 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.ResultsSupport 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].ConclusionsThese 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.Electronic supplementary materialThe online version of this article (10.1186/s12885-017-3877-1) contains supplementary material, which is available to authorized users.

Methylglyoxal (MG) is a highly reactive compound derived mainly from glucose and fructose metabolism. This metabolite has been implicated in diabetic complications as it is a strong AGE precursor. Furthermore, recent studies suggested a role for MG in insulin resistance and beta-cell dysfunction. Although several drugs have been developed in the recent years to scavenge MG and inhibit AGE formation, we are still far from having an effective strategy to prevent MG-induced mechanisms. This review summarizes the mechanisms of MG formation, detoxification, and action. Furthermore, we review the current knowledge about its implication on the pathophysiology and complications of obesity and diabetes.

The effects of metformin, an antidiabetic agent that improves insulin sensitivity, on endothelial function have not been fully elucidated. This study was designed to assess the effect of metformin on impaired endothelial function, oxidative stress, inflammation and advanced glycation end products formation in type 2 diabetes mellitus. EXPERIMENTAL APPROACHGoto-Kakizaki (GK) rats, an animal model of nonobese type 2 diabetes, fed with normal and high-fat diet during 4 months were treated with metformin for 4 weeks before evaluation. Systemic oxidative stress, endothelial function, insulin resistance, nitric oxide (NO) bioavailability, glycation and vascular oxidative stress were determined in the aortic rings of the different groups. A pro-inflammatory biomarker the chemokine CCL2 (monocyte chemoattractant protein-1) was also evaluated. KEY RESULTSHigh-fat fed GK rats with hyperlipidaemia showed increased vascular and systemic oxidative stress and impaired endothelialdependent vasodilatation. Metformin treatment significantly improved glycation, oxidative stress, CCL2 levels, NO bioavailability and insulin resistance and normalized endothelial function in aorta. CONCLUSION AND IMPLICATIONSMetformin restores endothelial function and significantly improves NO bioavailability, glycation and oxidative stress in normal and high-fat fed GK rats. This supports the concept of the central role of metformin as a first-line therapeutic to treat diabetic patients in order to protect against endothelial dysfunction associated with type 2 diabetes mellitus.Abbreviations