Surrogate indices as tools for investigating the dynamics ofinsulin resistance in different body mass index categories
Introduction: Insulin resistance (IR) is a complex pathophysiological condition multifactorial etiology characterized by diminished responsiveness of insulin target tissues. Today, various diagnostic approaches involving different laboratory parameters are available, but simple and non-invasive indices based on mathematical models are increasingly used in practice. This study aims to assess the effectiveness of various clinical surrogate indices in predicting IR across a population with varying body weights. Methods: The matched case-control study was conducted between January 2021 and December 2022. Secondary data extracted from the medical records of 129 subjects was analyzed, including demographic characteristics (age and gender), anthropometric measures (height and weight), and biochemical laboratory test results. y further divided into two subgroups based on body mass index (BMI): overweight (BMI between 25 and 29.9 kg/m2) and obese (BMI of 30 kg/m2 or higher). Using laboratory data values for six widely used clinical surrogate markers were calculated: Homeostatic model assessment for IR (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), Mcauley index (MCAi), metabolic score for IR (METS-IR), Triglyceride to Glucose Index (TyG), and TyG to BMI (TyG-BMI). Results: Significant differences in HOMA-IR levels were observed between the groups (p < 0.001). A similar pattern was found for the TyG-BMI, with notable differences (p < 0.001). The obese participants had the highest mean levels for METS-IR and the TyG index while the control group had the highest mean values for the QUICKI and MCAi indices (p < 0.001). According to the analysis, three indices showed statistical significance in predicting IR independent of BMI (p < 0.05). Sensitivity and specificity were higher in the obese group (0.704 and 0.891) than in the overweight group (0.631 and 0.721). Conclusion: Given that IR is a multifactorial disease, using derived indices based on a combination of biochemical parameters and anthropometric indicators can significantly aid in predicting and mitigating numerous complications.