One-year outcomes of a digital twin intervention for type 2 diabetes: a retrospective real-world study - Paramesh Shamanna; Ravi Sankar Erukulapati; Ashutosh Shukla; Lisa Shah; Bree Willis; Mohamed Thajudeen; Rajiv Kovil; Rahul Baxi; Mohsin Wali; Suresh Damodharan; Shashank Joshi (Scientific Reports)
Abstract This retrospective observational study, building on prior research that demonstrated the efficacy of the Digital Twin (DT) Precision Treatment Program over shorter follow-up periods​​, aimed to examine glycemic control and reduced anti-diabetic medication use after one-year in a DT commercial program. T2D patients enrolled had adequate hepatic and renal function and no recent cardiovascular events. DT intervention powered by artificial intelligence utilizes precision nutrition, activity, sleep, and deep breathing exercises. Outcome measures included HbA1c change, medication reduction, anthropometrics, insulin markers, and continuous glucose monitoring (CGM) metrics. Of 1985 enrollees, 132 (6.6%) were lost to follow-up, leaving 1853 participants who completed one-year. At one-year, participants exhibited significant reductions in HbA1c [mean change: -1.8% (SD 1.7%), p < 0.001], with 1650 (89.0%) achieving HbA1c below 7%. At baseline, participants were on mean 1.9 (SD 1.4) anti-diabetic medications, which decreased to 0.5 (SD 0.7) at one-year [change: -1.5 (SD 1.3), p < 0.001]. Significant reductions in weight [mean change: -4.8 kg (SD 6.0 kg), p < 0.001], insulin resistance [HOMA2-IR: -0.1 (SD 1.2), p < 0.001], and improvements in β-cell function [HOMA2-B: +21.6 (SD 47.7), p < 0.001] were observed, along with better CGM metrics. These findings suggest that DT intervention could play a vital role in the future of T2D care.