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

Peter Calhoun; Charley Spanbauer; Andrea Steck; Brigitte Frohnert; Mark  Herman; Bart Keymeulen; Rita Veijola; Jorma Toppari; Aster Desouter; Frans Gorus; Mark A Atkinson; Darrell Wilson; Susan Pietropaulo; Roy Beck

doi:10.1007/s00125-025-06362-1

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

Peter Calhoun, Charley Spanbauer, Andrea Steck, Brigitte Frohnert, Mark Herman, Bart Keymeulen, Rita Veijola, Jorma Toppari, Aster Desouter, Frans Gorus, Mark A Atkinson, Darrell Wilson, Susan Pietropaulo, Roy Beck

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	930-939
Number of pages	10
Journal	Diabetologia
Volume	68
DOIs	https://doi.org/10.1007/s00125-025-06362-1
Publication status	Published - 11 Feb 2025

Bibliographical note

Funding Information:
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.

Funding Information:
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.

Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Access to Document

10.1007/s00125-025-06362-1

Cite this

Calhoun, P., Spanbauer, C., Steck, A., Frohnert, B., Herman, M., Keymeulen, B., Veijola, R., Toppari, J., Desouter, A., Gorus, F., Atkinson, M. A., Wilson, D., Pietropaulo, S., & Beck, R. (2025). Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development. Diabetologia, 68, 930-939. https://doi.org/10.1007/s00125-025-06362-1

@article{17b5e8c2aef04ac398eaad047242f92d,

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abstract = "Aims/hypothesisWe 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{\textquoteright}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.",

author = "Peter Calhoun and Charley Spanbauer and Andrea Steck and Brigitte Frohnert and Mark Herman and Bart Keymeulen and Rita Veijola and Jorma Toppari and Aster Desouter and Frans Gorus and Atkinson, {Mark A} and Darrell Wilson and Susan Pietropaulo and Roy Beck",

note = "Funding Information: 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. Funding Information: 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. Publisher Copyright: {\textcopyright} The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.",

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doi = "10.1007/s00125-025-06362-1",

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Calhoun, P, Spanbauer, C, Steck, A, Frohnert, B, Herman, M, Keymeulen, B, Veijola, R, Toppari, J, Desouter, A , Gorus, F, Atkinson, MA, Wilson, D, Pietropaulo, S & Beck, R 2025, 'Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development', Diabetologia, vol. 68, pp. 930-939. https://doi.org/10.1007/s00125-025-06362-1

TY - JOUR

T1 - Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development

AU - Calhoun, Peter

AU - Spanbauer, Charley

AU - Steck, Andrea

AU - Frohnert, Brigitte

AU - Herman, Mark

AU - Keymeulen, Bart

AU - Veijola, Rita

AU - Toppari, Jorma

AU - Desouter, Aster

AU - Gorus, Frans

AU - Atkinson, Mark A

AU - Wilson, Darrell

AU - Pietropaulo, Susan

AU - Beck, Roy

N1 - Funding Information: 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. Funding Information: 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. Publisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

PY - 2025/2/11

Y1 - 2025/2/11

N2 - Aims/hypothesisWe 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.

AB - Aims/hypothesisWe 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.

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

Abstract

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

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