A1 Refereed original research article in a scientific journal
Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development
Authors: Calhoun, Peter; Spanbauer, Charles; Steck, Andrea K.; Frohnert, Brigitte I.; Herman, Mark A.; Keymeulen, Bart; Veijola, Riitta; Toppari, Jorma; Desouter, Aster; Gorus, Frans; Atkinson, Mark; Wilson, Darrell M.; Pietropaolo, Susan; Beck, Roy W.
Publisher: Springer Science and Business Media LLC
Publishing place: NEW YORK
Publication year: 2025
Journal: Diabetologia
Journal name in source: Diabetologia
Journal acronym: DIABETOLOGIA
Number of pages: 10
ISSN: 0012-186X
eISSN: 1432-0428
DOI: https://doi.org/10.1007/s00125-025-06362-1
Web address : https://doi.org/10.1007/s00125-025-06362-1
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).
MethodsBaseline 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.
ResultsThe 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/interpretationCGM 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.
Funding information in the publication:
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.