AI-assisted metabolomic profiling identifies candidate metabolites associated with diabetic kidney disease staging in a Korean cohort

Abstract

Summary Diabetic kidney disease (DKD) is commonly staged using albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), yet complementary molecular markers are needed. We profiled urine and serum metabolites from 92 Korean participants (72 with type 2 diabetes and 20 healthy controls) using gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). An exploratory restricted Boltzmann machine framework compared five DKD staging criteria and prioritized candidate metabolites; robustness was assessed against LASSO, linear support vector machine (SVM), and random forest using nested cross-validation. ACR-based staging showed the highest metabolomic discrimination. Urinary adenosine and 5′-methylthioadenosine decreased, whereas serum N2,N2-dimethylguanosine and cis-aconitic acid increased across ACR stages. Integration with a public renal tubular microarray dataset suggested NT5E as a cross-study network hub. These cross-sectional findings define candidate metabolite signatures associated with DKD severity and require external longitudinal validation before clinical translation.