Predicting Type 2 Diabetes Remission After Bariatric Surgery: The Role of Homeostatic Model Assessment of Insulin Resistance (Homa-IR), Visceral Adiposity Index (Vai) and Triglyceride-Glucose (TyG) Index

<b>Objective:</b> This study aimed to evaluate the prognostic value of changes in the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Visceral Adiposity Index (VAI), and Triglyceride-Glucose (TyG) index in predicting type 2 diabetes mellitus (T2DM) remission following bariatric surgery. <b>Methods:</b> This retrospective cohort study analyzed anthropometric, biochemical, and metabolic parameters from 66 T2DM patients who underwent bariatric surgery between 2021 and 2024. Data from the preoperative and 6-month postoperative periods were classified for diabetes remission using American Society for Metabolic and Bariatric Surgery (ASMBS) criteria. <b>Results:</b> The mean participant age was 49.9 ± 9.3 years; 72.7% were female. Post-surgery, 51.5% achieved complete remission, 24.25% partial remission, and 24.25% no remission. Only one patient continued insulin, and 83.8% discontinued oral antidiabetics. Significant postoperative improvements were observed in BMI, waist circumference, fasting glucose, HbA1c, triglycerides, HOMA-IR, VAI, and TyG indices, with increased HDL levels (<i>p</i> < 0.001). However, preoperative HOMA-IR, VAI, and TyG did not differ significantly across remission groups in univariate analyses. Multivariate logistic regression identified only younger age, higher preoperative BMI, and elevated postprandial insulin as independent predictors of complete remission. Other preoperative biochemical markers were not significantly related to remission outcomes. <b>Conclusions:</b> This study indicates that preoperative HOMA-IR, VAI, and TyG have limited standalone value for predicting diabetes remission after bariatric surgery. While they reflect postoperative metabolic improvements, their individual utility in pre-surgical risk stratification is insufficient. More large-scale, prospective studies are needed to determine if these markers could enhance future personalized predictive models.