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Abstract Disclosure: P. Shamanna: None. M. Dharmalingam: None. A. Vadavi: None. A. Keshavamurthy: None. S. Bhonsley: None. M. Thajudeen: None. A. Balasubramanian: None. S.R. Joshi: None. Background and aims: Digital twin (DT) platform uses Artificial Intelligence (AI) and Internet of Things, to integrate multi-dimensional data for precision nutrition and health recommendations via the mobile app. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Evidence suggests that remission is more likely in individuals with early type 2 diabetes (T2D) who are not using insulin therapy and who can engage in significant weight loss. We evaluated the predictive value of baseline parameters on T2D remission, including duration of diabetes, HbA1c levels, diabetic medications as drug count, and CGM metrics, with the objective to improvise the patient selection strategies for remission Methods We analyzed T2D patients (n=209) who completed 18 months of digital twin intervention. Potential predictors were examined by logistic regression analyses, with model performance evaluated by accuracy, precision, recall, and Area under the Receiver Operating Characteristic Curve (AUC-ROC) metrics. Results The model achieved an accuracy of 78.57%, precision of 79.41%, recall of 93.10%, and AUC-ROC of 0.745. Significant inverse associations with remission were observed for the duration of diabetes (OR: 0.74), baseline HbA1c (OR: 0.76), and the number of anti-diabetic medications as oral drugs (OR: 0.77), indicating decreased odds of remission with increasing values. Conversely, positive associations were found for Time Below Range 2 at baseline (OR: 1.22) and HOMA2IR at baseline (OR: 1.24), suggesting increased odds of remission with higher values in these metrics. We describe the Receiver Operating Characteristic (ROC) curve for the diabetes remission prediction model, with the area under the curve (AUC) indicating the model's discriminatory ability. The duration of diabetes emerged as the most significant negative predictor, followed by baseline HbA1c and the number of diabetic drugs, while counterintuitive findings like the positive association of baseline HOMA2IR with remission suggest areas for further investigation. Conclusion We identified baseline parameters significantly influencing the likelihood of T2D remission in people undergoing digital twin intervention. The odds ratios indicate the effect sizes of these predictors, highlighting the importance of personalized, data-driven treatment approaches that potentially enhance remission outcomes. Further validation of these findings in larger external cohorts would be useful. Presentation: 6/1/2024
Shamanna et al. (Tue,) studied this question.