Metabolomic Identification of Predictive and Early Biomarkers of Cisplatin-induced Acute Kidney Injury in Adult Head and Neck Cancer Patients
Bibliographic record
Abstract
Aim: Cisplatin causes acute kidney injury (AKI) in approximately one-third of patients. Serum creatinine and urinary output are poor markers of cisplatin-induced (AKI). Metabolomics was utilized to identify predictive or early diagnostic biomarkers of cisplatin-induced AKI. Methods: Thirty-one adult head and neck cancer patients receiving cisplatin (dose ≥ 70 mg m2 -1) were recruited for metabolomics analysis. Urine and serum samples were collected prior to cisplatin (pre), 24-48 hours after cisplatin (24-48h), and 5-14 days (post) after cisplatin. Based on serum creatinine concentrations measured at the post timepoint, 11/31 patients were classified with clinical AKI. Untargeted metabolomics was performed using liquid chromatography-mass spectrometry. Results: Metabolic discrimination was observed between “AKI” patients and “no AKI” patients at all timepoints. Urinary glycine, hippuric acid sulfate, 3-hydroxydecanedioc acid, and suberate were significantly different between AKI patients and no AKI patients prior to cisplatin infusion. Urinary glycine and hippuric acid sulfate were lower (-2.22-fold and -8.85-fold), whereas 3-hydroxydecanedioc acid and suberate were higher (3.62-fold and 1.91-fold) in AKI patients relative to no AKI patients. Several urine and serum metabolites were found to be altered 24-48 hours following cisplatin infusion, particularly metabolites involved with mitochondrial energetics. Conclusion: We propose glycine, hippuric acid sulfate, 3-hydroxydecanedioc acid, and suberate as predictive biomarkers of predisposition to cisplatin-induced AKI. Metabolites indicative of mitochondrial dysfunction may serve as early markers of subclinical AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".