Persistent urinary metabolic signatures in children with type 1 diabetes.

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. 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 analyses 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.