MIRA: myocardial insulin resistance app for clinical practice

Abstract

Background and Objective. Type 2 diabetes (T2D) is a prevalent disease characterized by insulin resistance (IR), leading to energy disruptions in myocardial cells and increasing cardiovascular (CV) risk. Current diagnostic methods are either systemic, thus lacking tissue-specific information, or invasive. The hyperinsulinemic euglycemic clamp (HEC) combined with [18F]FDG-PET images, only used in clinical trials, allow to assess regional IR and identify phenotypes within T2D, linking myocardial IR to higher CV risk. However, phenotyping is not easily accessible, creating a need for alternative assessment tools. Thus, we propose a myocardial IR model to address these gaps and improve T2D management. Methods. The study included forty-two patients with T2D who enrolled in a clinical trial (NCT02248311) and who underwent biochemical analyses, anthropometric measurements and [18F]FDG PET/CT imaging before and after HEC with. Patients were phenotyped into mIR and mIS according to poor or good uptake after HEC, respectively. The proposed predictive model was based on stepwise regression including feature selection to provide an estimate of myocardial IS and thus IR=1/IS by using biochemical parameters in T2D. A software application, the myocardial IR app (MIRA), was developed using MATLAB. Results. MIRA was developed as a myocardial IR estimator (R2=0.97, p=7.1 × 10-7, error=1.24) for patients with T2D. Moreover, since HEC is not allowed in rutinary clinical practice, the application includes a prediction of the expected myocardial HEC [18F]FDG uptake from baseline uptake (r=0.52, p=5 × 10-4, R2=0.60). The app also yields the patient's phenotype, either mIR or mIS, according to poor or good uptake after HEC. Enhanced CV risk exposure due to altered T2D biomarkers and associated to mIR is also provided with highlighted features. Conclusions. We hereby present MIRA, a myocardial IR calculation app to manage myocardial-specific affectation in T2D, as well as to provide with patient phenotyping and CV risk assessment.