Persistent urinary metabolic signatures in children with type 1 diabetes

Nakayasu, Ernesto S.; Flores, Javier E.; Bramer, Lisa M.; Chin, Jackson L.; Kim, Young-Mo; Syed, Farooq; Zink, Erika M.; Frohnert, Brigitte I.; Evans-Molina, Carmella; Kaplowitz, Paul B.; Cogen, Fran E.; Pieper, Rembert; Metz, Thomas O.; Webb-Robertson, Bobbie-Jo M.

doi:10.1016/j.nexres.2025.100725

Persistent urinary metabolic signatures in children with type 1 diabetes

Journal Article · Mon Aug 18 00:00:00 EDT 2025 · Next Research

DOI:https://doi.org/10.1016/j.nexres.2025.100725· OSTI ID:2588652

^[1]; ^[1]; ^[1]; Chin, Jackson L. ^[1]; ^[1]; Syed, Farooq ^[2]; ^[1]; Frohnert, Brigitte I. ^[3]; Evans-Molina, Carmella ^[4]; Kaplowitz, Paul B. ^[5]; ^[5]; Pieper, Rembert ^[6]; ^[1]; ^[7]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Beckman Research Institute at City of Hope, Duarte, CA (United States)
University of Colorado, Aurora, CO (United States)
Indiana University School of Medicine, Indianapolis, IN (United States)
Children’s National Medical Center, Washington, DC (United States)
J. Craig Venter Institute, Inc., Rockville, MD (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Colorado, Aurora, CO (United States)

There are an estimated 3.7 million people with undiagnosed type 1 diabetes (T1D), living primarily in poor areas of the globe. Therefore, there is a need for non-invasive, affordable tests to provide accurate diagnosis despite the time post-disease onset and fasting state. Here, we studied persistent urinary T1D biomarkers that can be used to develop such tests. Here, we analyzed the urine metabolomes of three independent cohorts of samples collected within 48 h (from Indiana University), and 1 year (from University of Colorado) and 1–10 years (6 years in average) (from Children’s National Medical Center) post-diagnosis. Samples were submitted to gas chromatography-mass spectrometry and machine learning an0alyses to determine diagnostic metabolite panels. The data were also mapped into a metabolic pathway to understand persistently regulated processes in T1D. Seven metabolites showed consistent increases in all three cohorts: d-glucose, d-mannose, myo-inositol, 3-hydroxyisobutyric acid, gluconolactone, d-gluconic acid, and d-glucuronic acid. A combination of machine learning analysis and metabolite ratios as biomarker candidates diagnosed T1D with high sensitivity and specificity across different cohorts and times. Mapping the regulated metabolites into a pathway showed impairment in glycolysis and overflow of glucose towards other pathways in subjects with T1D that was persistent over time. We identified and cross-validated highly specific and sensitive urinary biomarkers. This opens opportunities to develop affordable, robust, and non-invasive tests. The results also show that most of the biomarkers were signatures of dysregulated glucose metabolism.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2588652

Report Number(s):: PNNL-SA--201388

Journal Information:: Next Research, Journal Name: Next Research Journal Issue: 4 Vol. 2; ISSN 3050-4759

Publisher:: Elsevier BVCopyright Statement

Country of Publication:: United States

Language:: English

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biomarker
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Persistent urinary metabolic signatures in children with type 1 diabetes

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References (45)

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