EVALUATING FASTING GLUCOSE, INSULIN, AND HOMA-IR IN METABOLIC DISORDERS

Abstract

Objective: Insulin resistance is a metabolic complaint related to the progression of type 2 diabetes and cardiovascular disease, as well as a range of other chronic conditions. The Homeostatic Insulin Resistant Assessment Model ( HOMA-IR ) is widely used as an indicator of this condition, based on measurements of fasting blood glucose sugar and insulin concentrations. However, the nature of the relationship between these indicators can vary depending on different physiological factors and underlying medical condition. Method: A cross-sectional study was conducted with 18 participants, including individuals with difficulty losing weight and patients with type 2 diabetes. Fasting blood glucose levels were estimated by an automated biochemical analyzer, while fasting insulin concentrations were measured using an automated immunoassay system. The HOMA-IR insulin resistance index was calculated using a standardized formula. To assess the strong point and the route of the relationship among the studied variables, with a p-value < 0.05 considered statistically significant, Pearson's correlation coefficient was used. Results: This results showed a strong positive correlation between fasting insulin and the HOMA-IR index (r = 0.945, p < 0.001), indicating a highly statistically significant relationship. In contrast, fasting blood glucose did not show a statistically significant correlation with either fasting insulin (r = 0.145, p = 0.566) or the HOMA-IR index (r = 0.351, p = 0.153). Novelty: The results indicate that fasting insulin concentration is a crucial factor in determining HOMA-IR values ​​and may be a more sensitive indicator for assessing insulin resistance than fasting glucose concentration alone. The lack of a statistically significant correlation between fasting glucose concentration and other variables highlights its limitations as a sole indicator for the early detection of insulin resistance. However, the small size of sample and the nature of cross-sectional in this study limit the broad applicability of these outcomes, necessitating future studies with more samples also more comprehensive analytical approaches. Adding some necessary and complementary tests to the study, such as lipid profile, vitamin D levels, and body mass index, for example.