Impact of antidiabetic medications on inflammatory markers and adiponectin levels: a cross-sectional study in Iraqi patients with type2 diabetes

Abstract

Abstract Purpose This study explores the impact of various antidiabetic medications on modulating inflammatory markers and adiponectin levels, providing insights into their potential therapeutic benefits. Methods We conducted a cross-sectional study at (name blinded for peer-review), including 360 patients with type 2 diabetes mellitus (T2DM). Participants were divided into four groups according to treatment regimen. Clinical and biochemical parameters were measured, including fasting glucose, HbA1c, lipid profile, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and adiponectin. Statistical analysis included the Kruskal–Wallis test with Dunn’s post-hoc analysis (Bonferroni correction), and multivariable regression adjusted for age, sex, BMI, and disease duration. Results The cohort ( n = 360) had a median age of 50–56 years, with 48.1% males. Post hoc power analysis using G-power indicated the study had sufficient statistical power (96%). Insulin therapy was associated with lower inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) ( p < 0.001), indicating its anti-inflammatory effects. Metformin monotherapy was significantly associated with higher adiponectin levels ( p < 0.001) compared to metformin combination therapies. Additionally, the combination of metformin and glimepiride was associated with the most favorable lipid profile, including cholesterol and triglycerides ( p < 0.001). Conclusion In Iraqi patients with type 2 diabetes, insulin therapy is associated with reduced inflammatory markers and showed a partial elevation in adiponectin levels. Metformin enhanced both adiponectin and glycemic control, whereas the metformin–glimepiride combination yielded the most favorable lipid profile. These findings underscore the metabolic advantages particular to each regimen and emphasize the importance of tailoring therapy based on patient profiles.