A Bayesian decision support system for automated insulin doses in adults with type 1 diabetes on multiple daily injections: a randomized controlled trial

Alessandra Kobayati, Anas El Fathi, Natasha Garfield, Laurent Legault, Adnan Jafar, Jean-François Yale, Michael A. Tsoukas and Ahmad Haidar ()
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Alessandra Kobayati: McGill University
Anas El Fathi: McGill University
Natasha Garfield: McGill University
Laurent Legault: McGill University
Adnan Jafar: McGill University
Jean-François Yale: McGill University
Michael A. Tsoukas: McGill University
Ahmad Haidar: McGill University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Achieving optimal glycemic control remains challenging for many individuals with type 1 diabetes using multiple daily injections. We report results from a 12-week, open-label, randomized controlled trial evaluating a decision support system (DSS) consisting of a mobile application and a titration algorithm that provides weekly basal and prandial insulin recommendations. Eighty-four adults with type 1 diabetes and suboptimal glycemic control (HbA1c ≥ 7.5%) are randomized 1:1 to receive the DSS or a non-adaptive bolus calculator (control), alongside Freestyle Libre glucose sensors. The primary endpoint is change in HbA1c from baseline; secondary endpoints include additional glycemic and insulin-related metrics. The DSS reduces mean HbA1c from 8.6% (SD 1.1) to 8.1% (0.8) (p = 0.0002), while the control reduces HbA1c from 8.6% (1.0) to 8.5% (1.0) (p = 0.22); yielding a treatment effect of –0.40% (95% CI: –0.75 to –0.051; p = 0.025). There are no reported severe hypoglycemia or diabetic ketoacidosis events. Our DSS improves HbA1c in this population without compromising safety. ClinicalTrials.gov: NCT04123054 .

Date: 2025
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DOI: 10.1038/s41467-025-63671-0

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