20-OR: Digital Twin (DT) Technology in Type 2 Diabetes Remission and Glycemic Improvement—Eighteen Months Results from a Randomized Controlled Trial

This study assesses the impact of Digital Twin (DT) technology, utilizing AI for postprandial glucose prediction, on T2D remission. Of the 233 participants using DT, 18 months' results were available for 218, showing significant improvements. Key metrics included reductions in HbA1c levels, body weight, HOMA2-IR, and HOMA2B. The mean age and duration of diabetes were 44 years (±8.6) and 3.7 years (±2.7), respectively. Notable changes from baseline to eighteen months in these 218 participants included HbA1c (from 8.9 ± 1.9, 95% CI 8.7-9.2 to 6.4 ± 0.9, 95% CI 6.3-6.5), body weight (from 78.1 kg ± 14.3 to 72.8 kg ± 13.7), BMI (from 27.2 ± 4.3 to 25.2 ± 4.1), waist circumference (from 97.4 cm ± 11 to 88.1 cm ± 10.5), HOMA2-IR (from 1.9 ± 0.9 to 1.5 ± 0.8), and HOMA2B (from 51.9 ± 30.8 to 70.9 ± 32.9). Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Out of the initial 233 DT participants, 207 completed a minimum of six months of the intervention, qualifying them for remission evaluation. Of these, 64.3% (133/207) achieved sustained remission at eighteen months. The standard care participants showed minimal HbA1c change (from 8.5 ± 1.8 to 8.3 ± 1.6). These findings underscore the potential of DT as a transformative tool in diabetes management, with significant correlations observed between reductions in A1c, body weight, and HOMA2B.