Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

• DOI: 10.1371/journal.pone.0253125
• Publication Date: 2021-06-24T17:32:34Z
• Authors: William P. T. M. ...; Yuri D. Foreman; Nicolaas C. Schaper; Hans H. C. M. Sav...; Annemarie Koster; Carla J. H. van d...; Anke Wesselius; Miranda T. Schram; Ronald M. A. Henry; Pieter C. Dagnelie; Bastiaan E. de Galan; Otto Bekers; Coen D. A. Stehouwer; Steven J. R. Meex; Martijn C. G. J. ...
• Keywords: Adult; Blood Glucose; Male; Science; Monitoring, Ambulatory; MECHANISMS; Machine Learning; Prediabetic State; 03 medical and health sciences; 0302 clinical medicine; Humans; Internal Medicine - Radboud University Medical Center; Prospective Studies; Exercise; Aged; RISK; Q; R; Middle Aged; Prognosis; Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2; Case-Control Studies; Medicine; Female; Neural Networks, Computer; Radboudumc 6: Metabolic Disorders RIMLS: Radboud Institute for Molecular Life Sciences; Algorithms; Research Article

Background Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

Methods We used data from The Maastricht Study, an observational population‐based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman’s correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).

Results Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).

Conclusions Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.