Comprehensive and quantitative urinary metabolomic profiling for improved characterization of diabetic nephropathy
Bibliographic record
Abstract
INTRODUCTION: Diabetic nephropathy (DN) is a major cause of chronic kidney disease and end-stage renal failure worldwide. The current diagnostic marker, albuminuria, lacks specificity and often detects renal damage only at advanced stages. OBJECTIVES: This study aimed to characterize urinary metabolic alterations associated with DN and explore metabolite panels with diagnostic potential. METHODS: A targeted urinary metabolomics analysis was performed using the validated TMIC Urine MEGA Assay, quantifying 268 metabolites in 60 participants (20 controls, 20 type 2 diabetes mellitus [DM-2], and 20 DN patients). Data were analyzed by Partial Least Squares Discriminant Analysis (PLS-DA) for visualization, and penalized regression algorithms [Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (EN) with a Genetic Algorithm (GA)] followed by logistic regression (LR) modeling to identify potential discriminative variables. RESULTS: DN patients showed marked alterations in metabolites related to oxidative stress, mitochondrial dysfunction, and inflammation. Twenty-four of 86 quantified uremic toxins differed significantly between DN and comparison groups. The LASSO-derived model identified β-alanine, kynurenine, glucose and argininic acid as key discriminants (AUC = 0.905, 10-fold CV), while inclusion of GFR and additional metabolites (2-hydroxybutyric acid, shikimic acid) improved performance (AUC = 0.96). CONCLUSIONS: Quantitative urinary metabolomics revealed metabolic perturbations reflective of DN pathophysiology and identified candidate metabolite panels with potential for non-invasive disease characterization. These findings, though preliminary, provide a foundation for validation in larger, longitudinal cohorts and for integrating urinary metabolomics into precision diagnostics for diabetic kidney disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".