#AI reads Urine# Persistent urinary metabolic signatures in children with type 1 diabetes

To address the need for type 1 diabetes (T1D) diagnosis, this study conducted gas chromatography-mass spectrometry (GC-MS) analysis and machine learning research on children's urine samples from three different post-diagnostic time points (within 48 hours, 1 year, and 1-10 years after diagnosis). Seven persistently upregulated metabolites were identified, including D-glucose and D-mannose. The constructed metabolite combination and metabolite ratio models demonstrated high diagnostic sensitivity and specificity. Additionally, the study revealed the metabolic mechanism in T1D patients involving impaired glycolysis and glucose shunting to other pathways, providing a crucial basis for the development of affordable and non-invasive T1D diagnostic tests. However, the study was limited to child samples, so the conclusions may not apply to adults, and the candidate biomarkers require further clinical validation.

Next Res. 2025 Dec;2(4):100725. doi: 10.1016/j.nexres.2025.100725

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/