G. Alexander Fleming

Continuous glucose monitoring (CGM) provides information about real-time glycemic exposure that is not captured by measurements of fasting plasma glucose (FPG), changes in glycemia during oral glucose tolerance testing (OGTT), or Hemoglobin A1c (HbA1c). We propose that (1) CGM can identify dysglycemia earlier than existing approaches, (2) CGM may serve as an adjunctive diagnostic tool, and (3) CGM may eventually support independent diagnostic criteria. The strongest case is for Stage 2 type 1 diabetes (T1D), because this stage represents a critical opportunity for intervention before progression to Stage 3 disease. CGM as a diagnostic tool could identify individuals at risk of progression to Stage 3 T1D, particularly those with islet autoantibodies, by detecting progression of dysglycemia, supporting disease staging, and identifying candidates for confirmatory testing, closer monitoring, or future prevention trials. Beyond T1D, CGM might also prove to be a reliable diagnostic test for type 2 diabetes (T2D) in selected populations. Overall, there is a need for prospective clinical studies to compare the value of using CGM-derived metrics directly with existing diagnostic standards, including OGTT and HbA1c. For both T1D and T2D, earlier diagnosis by CGM has the potential to improve patient outcomes by creating opportunities for prevention and treatment early in the natural history of the disease. This article presents an outcome-based framework for deriving and validating CGM-based diagnostic thresholds. The framework emphasizes that future diagnostic criteria should be based on clinically meaningful outcomes, rather than concordance with existing tests.

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.

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM’s diagnostic accuracy and screening protocols. They concluded that while CGM cannot yet replace blood-based diagnostics, it holds promise as a screening tool that could lead to earlier, more effective intervention. Metrics such as time above range >140 mg/dL could indicate progression risk, and artificial intelligence (AI)-based modeling may enhance predictive capabilities. Further research is needed to establish CGM-based diagnostic criteria and refine screening strategies to improve T1D detection and intervention.

Background: Clinical interpretation of continuous glucose monitoring (CGM) data for people without diabetes has not been well established. This study aimed to investigate concordance among CGM experts in recommending clinical follow-up for individuals without diabetes, based upon their independent review of CGM data. Methods: We sent a survey out to expert clinicians ( n = 18) and asked them to evaluate 20 potentially challenging Dexcom G6 Pro CGM reports (and hemoglobin A1c [HbA1c] and fasting venous blood glucose levels) from individuals without diabetes. Clinicians reported whether they would recommend follow-up and the reasoning for their decision. We performed Fleiss Kappa interrater reliability to determine agreement among clinicians. Results: More than half of expert clinicians (56-100%, but no clear consensus) recommended follow-up to individuals who spent >2% time above range (>180 mg/dL), even if HbA1c <5.7% and fasting glucose <100 mg/dL. There were no observed trends for recommending follow-up based on mean glucose or glucose management indicator. Overall, we observed poor agreement in recommendations for who should receive follow-up based on their CGM report (Fleiss Kappa = 0.36). Conclusions: High discordance among expert clinicians when interpreting potentially challenging CGM reports for people without diabetes highlights the need for more research in developing normative data for people without diabetes. Future work is required to develop CGM criteria for identifying potentially high-risk individuals who may progress to prediabetes or type 2 diabetes.

The advanced glycation end products (AGEs) Webinar was co-hosted by Diabetes Technology Society and Kitalys Institute on August 8, 2024, with the goal of reviewing progress made in the measurement and use of AGEs in clinical practice. Meeting topics included (1) AGEs as predictors of diabetic nephropathy (DKD), (2) hemoglobin glycation index (HGI) and the glycation gap (GG), (3) formation and structure of AGEs, (4) AGEs as a risk factor of cardiovascular disease (CVD), and (5) approaches to limit or prevent AGE formation.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool—the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Background: Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased healthspan. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity, and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand, and meet the healthcare, workforce, wellbeing, and socioeconomic needs of ageing populations, while supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies. Methods: The foundation for developing such classification and staging systems is to define the scope of what constitutes an ageing-related pathology, disease or syndrome. To this end, a consensus meeting was hosted by the International Consortium to Classify Ageing-Related Pathologies (ICCARP), on February 19 ^th , 2024, in Cardiff, UK, and was attended by 150 recognised experts. Discussions and voting were centred on provisional criteria that had been distributed prior to the meeting. The participants debated and voted on these. Each criterion required a consensus agreement of ≥70% for approval. Results: The accepted criteria for an ageing-related pathology, disease or syndrome were: Develops and/or progresses with increasing chronological age. Should be associated with, or contribute to, functional decline, or an increased susceptibility to functional decline. Evidenced by studies in humans. Conclusions: Criteria for an ageing-related pathology, disease or syndrome have been agreed by an international consortium of subject experts. These criteria will now be used by the ICCARP for the classification and ultimately staging of ageing-related pathologies, diseases and syndromes.

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.

The Biomarkers for the Diagnosis of Heart Failure in Diabetes webinar was hosted by Diabetes Technology Society on September 20, 2023, with the objective to review current evidence and management practices of biomarker screening for heart failure in people with diabetes. The webinar discussed (1) the four stages of heart failure, (2) diabetes and heart failure, (3) natriuretic peptide and troponin for diagnosing heart failure in diabetes, (4) emerging composite and investigational biomarkers for diagnosing heart failure, and (5) prevention of heart failure progression. Experts in heart failure from the fields of clinical chemistry, cardiology, and diabetology presented data about the importance of screening for heart failure as an often-unnoticed complication of people with type 1 and type 2 diabetes.