A Comparative Metabolomics Study of Multiple Urological Diseases by Highly Sensitive Dansylation Isotope Labeling LC-MS
Bibliographic record
Abstract
Urine analysis is a straightforward, non-invasive testing method that, when integrated with metabolomics, shows great potential for detecting small-molecule metabolites as biomarkers of abnormal metabolic activity in the urinary tract, including drug interactions, toxicity, and diseases. However, integrated and comparative analyses of multiple urinary tract pathologies are currently limited. In this study, 12C2/13C2-chemical dansylation labeling was used to explore the urinary amine/phenol-metabolome profiles of eight urological conditions compared with normal profiles. We obtained ten samples for each condition (disease and normal) from a total of 90 participants, pooling them as representative samples, and constructed metabolite panels to differentiate various urological conditions. We discovered nine metabolites that were dysregulated between urine samples from patients with and without cancer. Another seven metabolites were differentially expressed between the benign prostatic hyperplasia group and the prostate cancer group. Among 1854 peak pairs of metabolites in an amine/phenol submetabolome analyzed by dansyl chloride derivatization coupled with LC–MS/MS, 1747 (94.2%) were detectable in urine specimens from all nine groups. Notably, 18 identified metabolites showed substantial stability across all urological conditions. Given the considerable variability in urine metabolite composition, these metabolites could potentially be used for normalization in urine metabolome analysis, addressing the need for stably expressed molecules as internal standards in the development of urinary biomarkers. Our findings provide the preliminary insights into the stability of urinary metabolomics and the metabolic perturbations associated with different urinary tract-related pathologies.
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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 | 0.000 |
| 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 | 0.000 | 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".