The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes
dc.contributor.author | Mader, Julia K. | |
dc.contributor.author | Wong, Jenise C. | |
dc.contributor.author | Freckmann, Guido | |
dc.contributor.author | Garcia-Tirado, Jose | |
dc.contributor.author | Hirsch, Irl B. | |
dc.contributor.author | Johnson, Suzanne Bennett | |
dc.contributor.author | Kerr, David | |
dc.contributor.author | Kim, Sun H. | |
dc.contributor.author | Lal, Rayhan | |
dc.contributor.author | Montaser, Eslam | |
dc.contributor.author | O'Donnell, Holly | |
dc.contributor.author | Pleus, Stefan | |
dc.contributor.author | Shah, Viral N. | |
dc.contributor.author | Ayers, Alessandra T. | |
dc.contributor.author | Ho, Cindy N. | |
dc.contributor.author | Biester, Torben | |
dc.contributor.author | Dovc, Klemen | |
dc.contributor.author | Farrokhi, Farnoosh | |
dc.contributor.author | Fleming, Alexander | |
dc.contributor.author | Gillard, Pieter | |
dc.contributor.author | Heinemann, Lutz | |
dc.contributor.author | López-Díez, Raquel | |
dc.contributor.author | Maahs, David M. | |
dc.contributor.author | Mathieu, Chantal | |
dc.contributor.author | Quandt, Zoe | |
dc.contributor.author | Rami-Merhar, Birgit | |
dc.contributor.author | Wolf, Wendy | |
dc.contributor.author | Klonoff, David C. | |
dc.contributor.department | Medicine, School of Medicine | |
dc.date.accessioned | 2025-06-17T17:44:43Z | |
dc.date.available | 2025-06-17T17:44:43Z | |
dc.date.issued | 2025-05-30 | |
dc.description.abstract | This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM's diagnostic accuracy and screening protocols. They concluded that while CGM cannot yet replace blood-based diagnostics, it holds promise as a screening tool that could lead to earlier, more effective intervention. Metrics such as time above range >140 mg/dL could indicate progression risk, and artificial intelligence (AI)-based modeling may enhance predictive capabilities. Further research is needed to establish CGM-based diagnostic criteria and refine screening strategies to improve T1D detection and intervention. | |
dc.eprint.version | Final published version | |
dc.identifier.citation | Mader JK, Wong JC, Freckmann G, et al. The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes. J Diabetes Sci Technol. Published online May 30, 2025. doi:10.1177/19322968251333441 | |
dc.identifier.uri | https://hdl.handle.net/1805/48832 | |
dc.language.iso | en_US | |
dc.publisher | Sage | |
dc.relation.isversionof | 10.1177/19322968251333441 | |
dc.relation.journal | Journal of Diabetes Science and Technology | |
dc.rights | Publisher Policy | |
dc.source | PMC | |
dc.subject | CGM | |
dc.subject | Artificial intelligence | |
dc.subject | Machine learning | |
dc.subject | Stage 2 T1D | |
dc.subject | Teplizumab | |
dc.title | The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes | |
dc.type | Article | |
ul.alternative.fulltext | https://pmc.ncbi.nlm.nih.gov/articles/PMC12125016/ |