Untargeted metabolomics reveals N, N, N-trimethyl-L-alanyl-L-proline betaine (TMAP) as a novel biomarker of kidney function
Bibliographic record
Abstract
The diagnosis and prognosis of chronic kidney disease (CKD) currently relies on very few circulating small molecules, which can vary by factors unrelated to kidney function. In end-stage renal disease (ESRD), these same small molecules are used to determine dialysis dose and dialytic clearance. Therefore, we aimed to identify novel plasma biomarkers to estimate kidney function in CKD and dialytic clearance in ESRD. Untargeted metabolomics was performed on plasma samples from patients with a single kidney, non-dialysis CKD, ESRD and healthy controls. For ESRD patients, pre- and post-dialysis plasma samples were obtained from several dialysis modalities. Metabolomics analysis revealed over 400 significantly different features in non-dialysis CKD and ESRD plasma compared to controls while less than 35 features were significantly altered in patients with a single kidney. N,N,N-trimethyl-L-alanyl-L-proline betaine (TMAP, AUROC = 0.815) and pyrocatechol sulfate (AUROC = 0.888) outperformed creatinine (AUROC = 0.745) in accurately identifying patients with a single kidney. Several metabolites accurately predicted ESRD; however, when comparing pre-and post-hemodialysis, TMAP was the most robust biomarker of dialytic clearance for all modalities (AUROC = 0.993). This study describes TMAP as a novel potential biomarker of kidney function and dialytic clearance across several hemodialysis modalities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".