Abstract P278: 1-year Outcomes of A1c Reduction, Weight Loss, and Lowered QRISK3 Scores in Type 2 Diabetes Remission: Insights From an RCCT Leveraging Whole-Body Digital Twin Technology

Abstract

Background and Aims: The effects of digital twin (DT) technology for remission of T2D on cardiovascular risk reduction is unknown. We evaluated the effectiveness of the DT to improve A1c and body weight and QRISK3 (QResearch Cardiovascular Disease Risk Algorithm 3), which is a prediction algorithm to estimate the 10-year risk for developing cardiovascular disease (CVD), in patients enrolled to achieve remission of T2DM. DT platform uses AI and Internet of Things, to integrate multi-dimensional data to give precision nutrition and health recommendations via the mobile app and by coaches. Materials and Methods: We evaluated the data from prospective cohort of 204 participants who had been on DT for 1 year. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Outcomes included the change in HbA1c, body weight and change in QRISK3 scores. The DT uses a machine learning algorithm to integrate clinical and sensor data to predict personal glucose response. Patients were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using the mobile app. Results: The study participants had a mean diabetes duration of 3.7±2.6 years and a mean age of 44±8.2 years. Based on the ADA criteria, at 1 year, 73% (n=149/204) continued to be under remission. The mean QRISK3 at baseline was 12.6 (±10.7, 95% CI 11.2 to 14) which significantly reduced to 9.5 (±7.9, 95% CI 8.5 to 10.6) at 360 days, p=0.0009. A1c (%) at baseline was 8.9 (±1.8, 95% CI 8.6 to 9.1, minimum 5.5, maximum 16.2) which reduced to 6 (±0.6, 95% CI 5.9 to 6, minimum 4.7, maximum 9.2), p<0.0001. Body weight (kg) at baseline was 78.3 (±14.2, 95% CI 76.4 to 80.2) which reduced to 70.7 (±12.8, 95% CI 69 to 72.4), <0.0001. There was a significant correlation between the reduction in body weight and QRISK3 (Pearson r 0.24 95% CI 0.1 to 0.3, p=0.0004). However, the correlation between the reduction in A1c and QRISK3 (Pearson r 0.04, 95% CI -0.09 to 0.18, p=0.53 ns) was comparable. At baseline, there were 53% of participants who had low risk score (<10), 28% as intermediate risk (10-20), 19% as high risk (>20%) that at 1 year shifted to 67% as low risk, 23% as intermediate risk and 10% as high-risk scores. Conclusion: Incorporating the Digital Twin (DT) technology, which blends AI and IoT for tailored health guidance, our RCCT indicated significant 1-year advancements in A1c, weight, and QRISK3 scores among T2DM patients aiming for remission. The transformative potential of DT is evident, particularly in shifting a majority of participants towards lower cardiovascular risk categories.