Daily Rhythms in Blood Glucose: Time-of-Day Forecasts in Type 2 Diabetes

Background Accurate and interpretable forecasting of blood glucose levels is critical for effective manage- ment of Type 2 diabetes. While complex machine learning models offer high predictive accuracy, their opacity often limits clinical applicability. This study investigates the perfor- mance of a simple, interpretable reference model: the time-of-day mean forecast.

The proposed approach divides each 24-hour period into discrete time sequences and, for each sequence, computes the mean glucose value across previous days. This methodology captures intra-day regularities in glucose dynamics and implicitly accounts for circadian influences, such as variations in insulin sensitivity and hepatic glucose production.

The model reflects intra-day glucose patterns and identifies clinically relevant periods of elevated variability, such as the postprandial and nocturnal windows. Forecasting performance improves with increased temporal granularity: in 91.84% of the individuals, at least one finer bin size outperformed the naïve baseline. Where, 51% achieved optimal performance using the highest resolution with a 5-minute bin size. Compared to the naïve approach, the 5-minute bin size reduced mean squared error by an average of 12.2%.

We have justified the time-of-day approach using a simple mean forecast model, showing that aligning prediction windows with time-of-day patterns enhances forecast accuracy. Building on this foundation, the time-of-day mean forecast serves as a practical benchmark. Future work should explore more complex models that incorporate individual covariates and dynamic temporal dependencies, while maintaining interpretability using the described temporal structure. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical Approval: the study DiaMont was approved by the Regional Ethical Committee of North Jutland, Denmark (N-20200068). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Contact Information Nicolai Peder Bülow Pedersen Email: npbp19{at}student.aau.dk Tanja Kortsen Bugajski Email: tbugaj17{at}student.aau.dk J. Eduardo Vera-Valdés, PhD Email: eduardo{at}math.aau.dk Stine Hangaard Casper, PhD Email: svh{at}hst.aau.dk Morten Hasselstrøm Jensen, Email: mhj{at}hst.aau.dk Peter Vestergaard, PhD Email: p.vestergaard{at}rn.dk Thomas Kronborg, PhD Email: tkl{at}hst.aau.dk Typo in authors last name Data Availability The data utilized (DiaMont study) in this study are not publicly available due to the inclusion of sensitive patient information, which is subject to strict confidentiality and privacy regulations. Access to the data is restricted to ensure compliance with ethical guidelines and to protect patient privacy. Requests for additional information or collaboration may be considered on a case-by-case basis, subject to appropriate ethical approval and data-sharing agreements. Abbreviations - T2D - Type 2 Diabetes - CGM - Continuous Glucose Monitoring - DiaMonT - Diabetes teleMonitoring of patients in insulin Therapy - ADAPT-T2D - Adherence through Cloud-based Personalized Treatment for Type 2 Diabetes - BGL - Blood Glucose Level - HAC - Heteroskedasticity and Autocorrelation Consistent - LLN - Law of Large Numbers - CLT - Central Limit Theorem - PI - Prediction Interval - HV - High Vaiance - LV - Low Variance - MSE - Mean Squared Error - SD - Standard Deviation