Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer

Abstract

Breast cancer is the most common malignancy among women worldwide. There is extensive literature on the relationship between body weight and breast cancer risk but some doubts still remain about the role of adipokines per se, the role of insulin and glucose regardless of obesity, as well as the crosstalk between these players. Thus, in this study, we intend to determine the relation between body mass index (BMI), glycaemia, insulinemia, insulin-resistance, blood adipokine levels and tumour characteristics in a Portuguese group of pre- and postmenopausal overweight/obese women with breast cancer. We evaluated clinical and biochemical data in 154 participants, divided in 4 groups: (1) control with BMI <25 kg/m(2), n = 29 (CT); (2) control with BMI >25 kg/m(2), n = 48 (CTOb); (3) breast cancer with BMI <25 kg/m(2), n = 30 (BC); and (4) breast cancer with BMI >25 kg/m(2), n = 47 (BCOb). In women with breast cancer, we also performed tumour characterization. We found that BCOb present increased fasting blood glucose, insulin, resistin and monocyte chemoattractant protein 1, insulin resistance and more aggressive tumours. Notably, this profile is not correlated with BMI, proposing the involvement of other processes than adiposity. Altogether, our results suggest that glucose dysmetabolism, insulin resistance and changes in adipokine secretion, in particular resistin, may be involved in the development and progression of breast cancer in overweight/obese pre- and postmenopausal women.

Classification of Categorical Outcome Variable Based on Logistic Regression and Tree Algorithm

“…In the datasets, the re are 10 independent variables namely, Age (years), Body Mass Index (BMI) (kg/m2), Glucose (mg/dL), Insulin (µU/m L), HOMA, leptin (ng/mL), Adiponectin (µg/mL), Resistin ( ng/mL), Monocyte Chemoattractant Protein 1(MCP-1) (pg/d L) and the dependent variable having labels 1 as healthy cont rols and label 2 as patients or having breast cancer [9]. These parameters are used to predict the person having breast canc er or not [10].…”

The Procedures of Visual Analysis for Multidimensional Data Volumes

“…The following data set is also devoted to the problems of forecasting breast diseases (Crisóstomo et al, 2016), (Patrício et al, 2018). The data set contains 116 points in a 10dimensional attribute space.…”

Machine Learning Models of Breast Cancer Risk Prediction

“…Glucose, age, BMI, resistin, HOMA were 5 predictors of top importance. Actually, these predictors make sense like age, BMI, HOMA are considered common risk factors for breast cancer [5] while glucose, resistin is correlated with breast cancer [8,9]. Additionally, Santillan-Benitez JG, et al [6] reported in 2013; they used BMI, leptin, CA15-3 and the ratio between Leptin and Adiponectin together to assess as a biomarker for breast cancer and achieved model specificity (80%) and the sensitivity (83.3%).…”

“…It is therefore indispensable to get screening each year and do some diagnostic testing including serum insulin, leptin, HOMA, adiponectin, resistin, MCP-1. Crisostomo J et al [5] reported that these variables are responsible for obesity-associated breast cancer. Furthermore, Santillan-Benitez JG, et al [6] were assessed this variable as a biomarker for breast cancer.…”