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

IF 8.4 1区医学 Q1 ENDOCRINOLOGY & METABOLISM

Diabetologia Pub Date : 2025-02-11 DOI:10.1007/s00125-025-06362-1

Peter Calhoun, Charles Spanbauer, Andrea K. Steck, Brigitte I. Frohnert, Mark A. Herman, Bart Keymeulen, Riitta Veijola, Jorma Toppari, Aster Desouter, Frans Gorus, Mark Atkinson, Darrell M. Wilson, Susan Pietropaolo, Roy W. Beck

{"title":"Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development","authors":"Peter Calhoun, Charles Spanbauer, Andrea K. Steck, Brigitte I. Frohnert, Mark A. Herman, Bart Keymeulen, Riitta Veijola, Jorma Toppari, Aster Desouter, Frans Gorus, Mark Atkinson, Darrell M. Wilson, Susan Pietropaolo, Roy W. Beck","doi":"10.1007/s00125-025-06362-1","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Aims/hypothesis</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (<i>n</i>=79), BDR (<i>n</i>=22), DAISY (<i>n</i>=18), DIPP (<i>n</i>=8) and TrialNet Pathway to Prevention (<i>n</i>=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.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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 (<i>p</i><0.001), HbA<sub>1c</sub> (<i>p</i>=0.02), having a first-degree relative with type 1 diabetes (<i>p</i>=0.02) and testing positive for IA-2 AAb (<i>p</i><0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (<i>p</i>=0.06) and having a negative GAD AAb (<i>p</i>=0.09) were selected but not found to be significant. Participants classified as having low (<i>n</i>=79), medium (<i>n</i>=98) or high (<i>n</i>=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.</p><h3 data-test=\"abstract-sub-heading\">Conclusions/interpretation</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\n","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":"58 1","pages":""},"PeriodicalIF":8.4000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetologia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00125-025-06362-1","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}

引用次数: 0

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), HbA_1c (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.

Graphical Abstract

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Diabetologia 医学-内分泌学与代谢

CiteScore

18.10

自引率

2.40%

发文量

193

审稿时长

1 months

期刊介绍： Diabetologia, the authoritative journal dedicated to diabetes research, holds high visibility through society membership, libraries, and social media. As the official journal of the European Association for the Study of Diabetes, it is ranked in the top quartile of the 2019 JCR Impact Factors in the Endocrinology & Metabolism category. The journal boasts dedicated and expert editorial teams committed to supporting authors throughout the peer review process.