Empirical transition probability modeling of continuous glucose monitoring data
- 1Northwestern University Feinberg School of Medicine, Department of Preventive Medicine, Division of Biostatistics and Informatics, Chicago, Illinois, United States of America
- 2Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, United States of America
Abstract
Introduction: Continuous glucose monitoring (CGM) produces rich time series data. Standard metrics such as time-in-range summarize glycemia but miss the sequence of changes. We aimed to characterize glucose dynamics using empirical transition probability techniques. Materials and Methods: Using publicly available data for 244 participants who enrolled in the Insulin-Only Bionic Pancreas (IOBP2) randomized clinical trial, we mapped each CGM reading to one of five states: < 54, 54–69, 70–180, 181–250, or >250 mg/dL. We counted state changes between consecutive readings collected every 5 minutes. For each participant, we built a 5 × 5 transition probability matrix and averaged matrices within comparator groups. Statistical inference using estimated transition probability matrices was compared to inferences based on standard metrics including mean glucose, standard deviation, coefficient of variation, time-in-range metrics, Glycemia Risk Index (GRI), Mean of Daily Differences (MODD), and Mean Amplitude of Glycemic Excursions (MAGE). Results: Transition probability analysis showed more frequent moves from hyperglycemia (181–250 or >250 mg/dL) to the in-range (70–180 mg/dL) state for the bionic pancreas that was evaluated in the IOBP2 trial compared to control. This pattern appeared in both adults and in children and persisted through the trial. Conventional CGM metrics also showed treatment differences in mean glucose, GRI, and time in range; MODD, MAGE, and coefficient of variation showed less consistent differences between treatment groups. Conclusion: Transition probability matrices provide an informative summary of glucose dynamics that complements established CGM metrics. In IOBP2, this approach highlighted improved patterns of recovery from hyperglycemia with the bionic pancreas compared with control. Future studies should determine whether transition-based summaries are associated with clinically meaningful outcomes.
Related
- Knowledge, attitude and practice on continuous glucose monitoring and self-monitoring of blood glucose among healthcare professionals across India: A cross-sectional study
- Continuous Glucose Monitoring for Metabolic Phenotyping and Precision Nutrition: Current Evidence, Clinical Applications and Future Perspectives
- Continuous glucose monitoring after discontinuation of incretin-based obesity pharmacotherapy: indirect evidence and a research framework for metabolic stability and weight maintenance
- Intraoperative Neurophysiological Monitoring During Continuous Radiofrequency Treatment of Morton’s Neuroma: Complete Ablation
- Performance and Safety of a Continuous Glucose Monitoring System in Pregnant Women With Gestational Diabetes Mellitus: A Multicenter, Open-Label Trial
BibTeX
@article{yu2026empirical,
title = {{Empirical transition probability modeling of continuous glucose monitoring data}},
author = {Yuanzhi Yu and Margaret Banker and Jennifer Sherr and Denise M. Scholtens},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358361},
url = {https://doi.org/10.1371/journal.pone.0358361},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Empirical transition probability modeling of continuous glucose monitoring data
AU - Yuanzhi Yu
AU - Margaret Banker
AU - Jennifer Sherr
AU - Denise M. Scholtens
PY - 2026
DA - 2026/10/06
JO - PLOS One
DO - 10.1371/journal.pone.0358361
UR - https://doi.org/10.1371/journal.pone.0358361
AB - Introduction: Continuous glucose monitoring (CGM) produces rich time series data. Standard metrics such as time-in-range summarize glycemia but miss the sequence of changes. We aimed to characterize glucose dynamics using empirical transition probability techniques. Materials and Methods: Using publicly available data for 244 participants who enrolled in the Insulin-Only Bionic Pancreas (IOBP2) randomized clinical trial, we mapped each CGM reading to one of five states: < 54, 54–69, 70–180, 181–250, or >250 mg/dL. We counted state changes between consecutive readings collected every 5 minutes. For each participant, we built a 5 × 5 transition probability matrix and averaged matrices within comparator groups. Statistical inference using estimated transition probability matrices was compared to inferences based on standard metrics including mean glucose, standard deviation, coefficient of variation, time-in-range metrics, Glycemia Risk Index (GRI), Mean of Daily Differences (MODD), and Mean Amplitude of Glycemic Excursions (MAGE). Results: Transition probability analysis showed more frequent moves from hyperglycemia (181–250 or >250 mg/dL) to the in-range (70–180 mg/dL) state for the bionic pancreas that was evaluated in the IOBP2 trial compared to control. This pattern appeared in both adults and in children and persisted through the trial. Conventional CGM metrics also showed treatment differences in mean glucose, GRI, and time in range; MODD, MAGE, and coefficient of variation showed less consistent differences between treatment groups. Conclusion: Transition probability matrices provide an informative summary of glucose dynamics that complements established CGM metrics. In IOBP2, this approach highlighted improved patterns of recovery from hyperglycemia with the bionic pancreas compared with control. Future studies should determine whether transition-based summaries are associated with clinically meaningful outcomes.
SN - 1932-6203
ER -