Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Mader, JK; Wong, JC; Freckmann, G; Garcia-Tirado, J; Hirsch, IB; Johnson, SB; Kerr, D; Kim, SH; Lal, R; Montaser, E; O'Donnell, H; Pleus, S; Shah, VN; Ayers, AT; Ho, CN; Biester, T; Dovc, K; Farrokhi, F; Fleming, A; Gillard, P; Heinemann, L; López-Díez, R; Maahs, DM; Mathieu, C; Quandt, Z; Rami-Merhar, B; Wolf, W; Klonoff, DC.
The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes.
J Diabetes Sci Technol. 2025; 19322968251333441
Doi: 10.1177/19322968251333441
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Leading authors Med Uni Graz
- Mader Julia
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- 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.
- Find related publications in this database (Keywords)
- CGM
- teplizumab
- stage 2 T1D
- artificial intelligence
- machine learning