Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May;68(5):930-939.
doi: 10.1007/s00125-025-06362-1. Epub 2025 Feb 11.

Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development

Peter Calhoun  1 , Charles Spanbauer  2 , Andrea K Steck  3 , Brigitte I Frohnert  3 , Mark A Herman  4 , Bart Keymeulen  5   6   7 , Riitta Veijola  8 , Jorma Toppari  9 , Aster Desouter  5   6   7 , Frans Gorus  5   6   7 , Mark Atkinson  10 , Darrell M Wilson  11 , Susan Pietropaolo  4 , Roy W Beck  2
Affiliations

Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development

Peter Calhoun et al. Diabetologia. 2025 May.

Abstract

Aims/hypothesis: We aimed to assess whether continuous glucose monitor (CGM) metrics can accurately predict stage 3 type 1 diabetes diagnosis in those with islet autoantibodies (AAb).

Methods: Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (n=79), BDR (n=22), DAISY (n=18), DIPP (n=8) and TrialNet Pathway to Prevention (n=91). Median follow-up time was 2.6 years (quartiles: 1.5 to 3.6 years). A participant characteristics-only model, a CGM metrics-only model and a full model combining characteristics and CGM metrics were compared.

Results: The full model achieved a numerically higher performance predictor estimate (C statistic=0.74; 95% CI 0.66, 0.81) for predicting stage 3 type 1 diabetes diagnosis compared with the characteristics-only model (C statistic=0.69; 95% CI 0.60, 0.77) and the CGM-only model (C statistic=0.68; 95% CI 0.61, 0.75). Greater percentage of time >7.8 mmol/l (p<0.001), HbA1c (p=0.02), having a first-degree relative with type 1 diabetes (p=0.02) and testing positive for IA-2 AAb (p<0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (p=0.06) and having a negative GAD AAb (p=0.09) were selected but not found to be significant. Participants classified as having low (n=79), medium (n=98) or high (n=41) risk of stage 3 type 1 diabetes diagnosis using the full model had a probability of developing symptomatic disease by 2 years of 5%, 13% and 48%, respectively.

Conclusions/interpretation: CGM metrics can help predict disease progression and classify an individual's risk of type 1 diabetes diagnosis in conjunction with other factors. CGM can also be used to better assess the risk of type 1 diabetes progression and define eligibility for potential prevention trials.

Keywords: Continuous glucose monitoring; Measurement; Prediction of type 1 diabetes; Prevention; Type 1 diabetes.

PubMed Disclaimer

Conflict of interest statement

Acknowledgements: Selected data were presented at the 84th meeting of the American Diabetes Association, 21–24 June 2024, Orlando, FL, USA. Data availability: The datasets analysed during the current study are not publicly available due to participant confidentiality. Funding: Research reported in this publication was supported by Breakthrough T1D (formerly JDRF; Award number: 2-SRA-2022-1156-S-B). The content is solely the responsibility of the authors and does not necessarily represent the official views of Breakthrough T1D. Authors’ relationships and activities: BK, AD and FG report no personal financial disclosures but their institution has received study supplies from Medtronic and Dexcom. DMW reports funding from the NIH and Breakthrough T1D. RWB reports no personal financial disclosures but reports that his institution has received funding on his behalf as follows: grant funding and study supplies from Tandem Diabetes Care, Beta Bionics, and Dexcom; study supplies from Medtronic, Ascencia and Roche; consulting fees and study supplies from Eli Lilly and Novo Nordisk; and consulting fees from Insulet, Bigfoot Biomedical, vTv Therapeutics and Diasome. PC, CS, AKS, BIF, MAH, RV, JT, MA and SP declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: PC researched and interpreted the data and wrote the manuscript. CS performed statistical analysis and contributed to writing and reviewed the manuscript. AKS, BIF, MAH, BK, RV, JT, AD, FG, MA, DMW, SP and RWB researched data, contributed to the discussion and reviewed/edited the manuscript. All authors reviewed the work critically for important intellectual content and approved the final version submitted for publication. PC is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

References

    1. Insel RA, Dunne JL, Atkinson MA et al (2015) Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38(10):1964–1974. https://doi.org/10.2337/dc15-1419 - DOI - PubMed - PMC
    1. American Diabetes Association Professional Practice Committee (2024) 2. Diagnosis and classification of diabetes: standards of care in diabetes-2024. Diabetes Care 47(Suppl 1):S20–S42. https://doi.org/10.2337/dc24-S002 - DOI
    1. Gorus FK, Balti EV, Messaaoui A et al (2017) Twenty-year progression rate to clinical onset according to autoantibody profile, age, and HLA-DQ genotype in a registry-based group of children and adults with a first-degree relative with type 1 diabetes. Diabetes Care 40(8):1065–1072. https://doi.org/10.2337/dc16-2228 - DOI - PubMed
    1. Ziegler AG, Rewers M, Simell O et al (2013) Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA 309(23):2473–2479. https://doi.org/10.1001/jama.2013.6285 - DOI - PubMed - PMC
    1. Jensen ET, Stafford JM, Saydah S et al (2021) Increase in prevalence of diabetic ketoacidosis at diagnosis among youth with type 1 diabetes: the SEARCH for diabetes in youth study. Diabetes Care 44(7):1573–1578. https://doi.org/10.2337/dc20-0389 - DOI - PubMed - PMC

LinkOut - more resources