Agatha F. Scheideman

Objective: To evaluate the completeness of methodological reporting in continuous glucose monitoring (CGM)-based clinical trials, and based on our findings, to propose a standardized reporting framework to improve reproducibility and transparency. Research Design and Methods: We conducted a search to assemble a study sample of CGM-based clinical trials published between 2019 and 2024. Articles were identified via PubMed and screened based on predefined eligibility criteria. We measured the presence or absence of reporting elements across seven core methodological features essential for reproducibility, spanning CGM device characteristics, data platforms, data extraction processes, preprocessing and transformation methods, handling of missing data, CGM metrics, and analytic approaches. Data were manually extracted by trained reviewers, with quality assurance performed on a subset of records. Descriptive statistics were used to summarize reporting completeness. Results: A total of 248 studies met inclusion criteria. On average, studies reported 4.39 (SD 1.22) of the seven core methodological features. Reporting was highest for CGM metrics (100%), analytic methods (100%), and device make/model (96%). In contrast, key elements of the CGM data pipeline were frequently underreported, including data extraction processes (56%), preprocessing methods (43%), handling of missing data (32%), and CGM data platform (30%). Only 6% of studies reported all seven features. These findings demonstrate substantial heterogeneity and consistent gaps in reporting practices across CGM clinical research within the literature sampling frame. Conclusions: Incomplete reporting of CGM data pipelines limits reproducibility, comparability, and the clinical and computational utility of CGM-derived outcomes. We propose a preliminary, candidate CGM-specific reporting checklist to standardize documentation of data acquisition, processing, and analysis. Adoption of a refined, consensus-based version of such a framework may strengthen methodological rigor, support regulatory and clinical interpretation, and enhance the reliability of CGM data for emerging digital health and artificial intelligence applications.

We read with interest a recent article in Diabetology titled “Enhancing Insulin Therapy Adherence Through Technology: Which Needles Do People with Diabetes Prefer

The adoption of artificial intelligence (AI) tools for hospital insulin management is currently limited by data fragmentation and difficult integration into clinical workflows. This commentary examines the data infrastructure requirements for safe AI deployment in clinical settings. Device-mediated and clinician-administered dosing are the two methods by which insulin is managed in hospitals. In device-mediated dosing, glucose and insulin data often remain siloed within proprietary device ecosystems outside the electronic health record (EHR). In clinician-administered dosing, relevant data elements typically exist within the EHR but are distributed across workflows in ways that limit their usefulness for decision support. The Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE) initiative is a standard for integrating device-generated diabetes data into clinical systems, which can lay the foundation for organizing hospital data in support of the development of trustworthy AI. A staged roadmap for hospitals building towards AI-ready insulin management infrastructure is presented along with governance requirements for trustworthy deployment. The value of iCoDE is that it helps define the conditions under which such AI can become clinically meaningful, trustworthy, and scalable.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale—lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

Artificial Intelligence (AI) has the potential to impact healthcare across multiple domains. In diabetes, a complex chronic disease affecting 600 million people globally, AI is already being used from primary care to tertiary specialist care to reduce patient and clinician burden. However, for medical AI to be widely implemented and applied specifically to diabetes, such stakeholders as patients, clinicians, healthcare administrators, regulators, and AI developers will need to establish trust in this technology. Building trust is a balancing act depending on individual priorities of stakeholders which may not necessarily align. Both probabilistic outputs and “top-choice only” outputs are used in medical AI. To achieve trust in AI for diabetes care, it will be necessary to move beyond expecting only single, deterministic outputs and to establish clear standards for medical AI provenance and performance. This article presents priorities for each of the various stakeholders if they are to develop trust in medical AI and their responsibilities for contributing to the establishment of trust in medical AI. For a medical AI system to be trustworthy, six key attributes must be incorporated including accuracy, reproducibility, privacy/security, transparency, human oversight, and fairness. We present practical methods to achieve each of these six attributes of trustworthy medical AI prioritizing diabetes that are important for all stakeholders.

The postprandial glycemic response (PPGR) is associated with diabetes and cardiovascular disease and is highly individualized. The PPGR is affected by both physiological and behavioral factors. Attention to the PPGR has dramatically increased recently with the widespread use of continuous glucose monitors. It is expected that individualized control of PPGRs will be important in the prevention of diabetes and its associated complications. In this article, we discuss six modifiable factors associated with the PPGRs, including (1) the glucoregulatory hormones, (2) gastric emptying, (3) salivary or pancreatic amylase, (4) diet, (5) physical exercise, and (6) sleep and circadian rhythm. Modifying these factors may allow for personalized intervention strategies to control the PPGR—to reduce the risk for cardiovascular disease in individuals with varying degrees of glycemia.