MON-581 Predictive Models For New-onset Diabetes Mellitus In Covid-19: Evaluating Insulin Resistance And Inflammation

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Abstract Disclosure: F. Mohamed: None. S. Gunter: None. B.E. Yirdaw: None. F.J. Raal: None. A.M. Millen: None. I.S. Kalla: None. Background: The association between COVID-19 and new-onset diabetes (DM) is primarily based on retrospective studies, with risk factors in low- and middle-income countries (LMICs) remaining uncertain. This prospective study examines the role of insulin resistance and inflammation in COVID-19 associated new-onset DM. Research Design and Methods: A prospective cohort study was conducted at an academic tertiary hospital and a primary healthcare facility. Participants included patients hospitalised with moderate to severe COVID-19 during the second wave of predominantly the delta variant. Predictors of new-onset DM were assessed using logistic regression analysis. Four predictive models were developed, incorporating combinations of triglyceride-glucose index (TyG index), homeostatic model assessment of insulin resistance (HOMA-IR), body mass index (BMI) and inflammatory cytokines. Model performance and optimal cutoff values were determined using Receiver Operating Characteristic (ROC) analysis and the Youden index. Results: A total of 127 individuals were evaluated, consisting of 84 patients admitted with moderate to severe COVID-19 and 43 healthy controls. Among the 84 COVID-19 participants, 45 were diagnosed with new-onset DM, 20 had no DM, and 19 had pre-existing DM. Those with new-onset DM exhibited significantly higher body mass index (BMI) and IR markers (HOMA-IR, and TyG index) compared to those without. The predictive model for new-onset DM included the TyG index, BMI, IL-10 and IL-1β, achieving an area under the curve (AUC) of 0.91 (95% CI, 0.84-0.98). The TyG index was strongly associated with new-onset DM (odds ratio (OR) 11.25 (95% CI, 2.80-76.28) and showed good predictive accuracy when used with BMI (AUC 0.86; 95% CI, 0.77-0.95), as compared to the TyG index alone (AUC 0.73; 95% CI, 0.59-0.86), indicating its potential efficacy in LMICs with limited resources without the need to measure HOMA-IR which requires insulin quantification. Conclusions: Our study shows that IR, not deficiency, drives new-onset DM in COVID-19. The TyG index is a valuable predictor, especially in resource-limited settings, with BMI and inflammatory markers enhancing model accuracy. Assessing cardiometabolic risk factors is crucial for both acute management and long-term follow-up of patients with COVID-19. Presentation: Monday, July 14, 2025

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