An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression

Abstract

Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier “GSE22309”. The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.