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":"五项研究的连续血糖监测指标确定了参与者患1型糖尿病的风险","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":"{\"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}","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
摘要
目的/假设我们旨在评估连续血糖监测（CGM）指标是否可以准确预测胰岛自身抗体（AAb）患者的3期1型糖尿病诊断。方法收集5项研究中AAb型≥1阳性受试者的基线CGM数据：ASK （n=79）、BDR （n=22）、DAISY （n=18）、DIPP （n=8）和TrialNet Pathway to Prevention （n=91）。中位随访时间为2.6年（四分位数：1.5至3.6年）。比较了仅考虑参与者特征的模型、仅考虑CGM指标的模型和结合特征和CGM指标的完整模型。结果全模型在数值上获得了较高的性能预测值(C统计量=0.74；95% CI 0.66, 0.81)预测3期1型糖尿病诊断与仅特征模型相比(C统计量=0.69；95% CI 0.60, 0.77)和cgm模型(C统计量=0.68；95% ci 0.61, 0.75)。7.8 mmol/l （p<0.001）、HbA1c （p=0.02）、一级亲属患有1型糖尿病（p=0.02）和IA-2 AAb检测阳性（p<0.001）的时间百分比越大，1型糖尿病诊断的风险越大。此外，男性（p=0.06）和GAD AAb阴性（p=0.09）也被选择，但没有发现显著性。使用完整模型，被分类为3期1型糖尿病诊断低（n=79）、中（n=98）或高（n=41）风险的参与者在2年内出现症状性疾病的概率分别为5%、13%和48%。结论/解释cgm指标可以帮助预测疾病进展，并结合其他因素对个体1型糖尿病诊断风险进行分类。CGM还可用于更好地评估1型糖尿病进展的风险，并确定潜在预防试验的资格。图形抽象
Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development
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.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
