Ability of Metabolic Score for Insulin Resistance to Detect Insulin Resistance

Metabolic Score for Insulin Resistance

Abstract

Aim: To evaluate the usability of metabolic score for insulin resistance (METS-IR), a novel insulin resistance index, in our country and to determine the optimal cut-off value of this index for detecting insulin resistance.

Material and Methods One thousand five hundred sixty seven individuals who participated in our check-up program between 2020 and 2021 were retrospectively evaluated with the patient files for inclusion in the study. Insulin resistance was accepted when HOMA-IR≥2.7. Subjects were divided into 4 quartiles according to their METS-IR levels. Receiver-operating characteristic curve was used to determine the indices’ predictive performance and the optimal cut-off value of METS-IR to identify insulin resistance. Binary logistic regression model was used to associate insulin resistance with the varying indexes.

Results: Among the 494 participants, 294 (59.5%) were women and the mean age of the subjects was 48.61±12.90 years. As the quartile of METS-IR increased, prevalence of male gender, metabolic syndrome, fatty liver, and levels of age, blood pressure, cigarette smoking, obesity, and insulin resistance indexes, HbA1c increased (all, p<0.001). METS-IR had the highest predictive value for the presence of insulin resistance (AUC = 0.813, p<0.001). The highest sensitivity and specificity were achieved at METS-IR between 39–42. The increase in METS-IR is more significant when compared to other indexes for the prediction of insulin resistance (OR=1.332, p<0.001).

Conclusions: METS-IR can be used as a screening test for insulin resistance in settings such as primary care centers where insulin levels cannot be measured.