Ashley Y. DuBord

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.

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.

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.

Background: Continuous glucose monitoring (CGM) has transformed diabetes management and research by providing high-frequency data that address many of the limitations of hemoglobin A _1c , enabling more precise clinical treatment targets and responsive trial endpoints. The richness and complexity of high-resolution time-series CGM data have spurred the development of numerous metrics for both clinical care and research applications. Beyond established metrics, there is a growing set of clinical, composite, and research-oriented measures that may support clinical decision support, intervention planning, risk stratification, and discovery-oriented research. This proliferation has created significant challenges in metric selection, interpretation, calculation, and standardization, particularly when metrics are applied across different devices, populations, software packages, and study designs. Objective: The objective is to map the current landscape of CGM metrics and address ongoing challenges in metric selection, clinical and research interpretation, and standardization. We further sought to distinguish between metrics primarily suited for routine clinical interpretation and those designed to explore more granular or multidimensional features of glycemia in research settings. Methods: We identified the literature focusing on the calculation, application, and interpretation of the following categories of CGM metrics: (1) standardized, (2) clinical, (3) emerging, and (4) composite. CGM metrics included in this study were identified from the 27 metrics included in the Diabetes Research Hub platform, additional published standardized and composite metrics, metrics used in established CGM analysis software, and emerging metrics identified during review. We narratively reviewed each metric’s definitions, calculation methods, interpretation, clinical and research utility, and strengths and limitations. In total, 102 articles were reviewed, supporting the synthesis of 36 distinct CGM-derived metrics. Results: The review identifies a fundamental divide in the CGM metric landscape. Standardized and clinical metrics, including time in range, mean glucose, coefficient of variation, and similar, prioritize simplicity and actionability. These metrics facilitate rapid decision-making in clinical settings but potentially mask granular glycemic fluctuations, event patterns, and discordance between average glucose values and variability. Emerging and composite metrics offer deeper insights into glycemic patterns, risk, and variability. However, many rely on specialized software or complex formulas, lack standardized thresholds or clear relationships to clinical outcomes, and do not have consensus methods of calculation and interpretation, limiting their adoption and hindering cross-study comparison. Conclusions: While consensus exists for core clinical metrics, the lack of standardization for complex metrics hinders research replicability and clinical translation. Bridging this gap requires moving toward consensus metric definitions, open-science frameworks, and standardized code libraries. Metric selection should be guided by intended use. Clinical metrics should be well-established, interpretable, and actionable. Research metrics should be clearly described, reproducible, and linked to meaningful outcomes. This review provides a comprehensive resource for navigating the diverse spectrum of CGM metrics, clarifying their applications and limitations to support both research and clinical investigation.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

This article examines the importance of advanced glycation endproducts (AGEs) and summarizes the structure of AGEs, pathological changes associated with AGEs, the contribution of AGEs to metabolic memory, and the value of AGEs as a predictor of diabetic complications and cardiovascular disease in people with and without diabetes. As a practical focus, skin autofluorescence (SAF) is examined as an attractive approach for estimating AGE burden. The measurement of AGEs may be of significant value to specific individuals and groups, including Black and Hispanic/Latino Americans, as they appear to have higher concentrations of hemoglobin A _1c (HbA _1c ) than would be predicted by other metrics of mean glycemia. We hypothesize that if the amount of glycation of HbA _1c is greater than expected from measured glucose levels, and if AGEs are accumulating, then this accumulation of AGEs might account for the increased rate of complications of diabetes in populations with high rates of vascular disease and other complications. Thus, identifying and modifying the burden of AGEs based on measurement of AGEs by SAF may turn out to be a worthwhile metric to determine individuals who are at high risk for the complications of diabetes as well as others without diabetes at risk of vascular disease. We conclude that available evidence supports SAF as both a clinical measurement and as a means of evaluating interventions aimed at reducing the risks of vascular disease and diabetic complications.

Background: The use of continuous subcutaneous insulin infusion (CSII) therapy in pregnancies affected by pregestational diabetes mellitus (DM) has generated mixed outcome data worthy of further investigation. This systematic review and meta-analysis aims to evaluate clinical outcomes associated with CSII versus multiple daily injections (MDIs) in pregnant persons with pregestational DM. Methods: A predefined, systematic, librarian-assisted search of MEDLINE (PubMed), Embase, Cochrane Library, Scopus, ClinicalTrials.gov, and World Health Organization International Clinical Trial Registry Platform (published from 2010 to 2022) yielded 3003 studies describing pregnancy outcomes associated with CSII and/or MDI for pregestational DM. The primary exposure was mode of insulin administration, with cesarean delivery and neonatal hypoglycemia as the primary maternal and neonatal outcomes, respectively. Secondary outcomes included hypertensive disorders of pregnancy, first and third-trimester glycemic control, large-for-gestational age (LGA) neonate, preterm birth, neonatal intensive care unit admission, need for respiratory support, hyperbilirubinemia, 5-minute Apgar <7, shoulder dystocia, and perinatal mortality. We calculated pooled odds ratios (OR) with 95% confidence intervals (CI) using random-effects models. Results: Among 39 eligible studies, 39% of the 5518 pregnancies included were exposed to CSII. Odds of cesarean delivery were higher with CSII (20 studies: 63% vs 56%, odds ratio [OR] 1.3 [95% confidence interval (CI) 1.2-1.5]), but we did not identify a difference in the odds of neonatal hypoglycemia (23 studies: 31% vs 34%, OR 1.1 [95% CI 0.9-1.5]). Among secondary outcomes, only the odds of LGA (20 studies: 47% vs 38%, OR 1.4 [95% CI 1.2-1.6]) were higher in individuals using CSII versus MDI. Conclusions: Use of CSII (vs MDI) for pregestational DM in pregnancy is associated with higher odds of cesarean delivery and delivery of an LGA neonate. Further evaluation of how CSII use may influence neonatal size and delivery route is warranted.

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 3 to November 5, 2022. Meeting topics included (1) the measurement of glucose, insulin, and ketones; (2) virtual diabetes care; (3) metrics for managing diabetes and predicting outcomes; (4) integration of continuous glucose monitor data into the electronic health record; (5) regulation of diabetes technology; (6) digital health to nudge behavior; (7) estimating carbohydrates; (8) fully automated insulin delivery systems; (9) hypoglycemia; (10) novel insulins; (11) insulin delivery; (12) on-body sensors; (13) continuous glucose monitoring; (14) diabetic foot ulcers; (15) the environmental impact of diabetes technology; and (16) spinal cord stimulation for painful diabetic neuropathy. A live demonstration of a device that can allow for the recycling of used insulin pens was also presented.