Temporal Changes in Blood Metabolome Among Patients on Hemodialysis
Bibliographic record
Abstract
Introduction Hemodialysis (HD) sustains millions of lives but is associated with poor clinical outcomes. Metabolite accumulation undoubtedly accounts for some of the excess morbidity and mortality in this population; specific toxins responsible for this are not well-defined. Methods This prospective study investigates temporal metabolite changes in adults after HD initiation for 60 months or until death or end of study; patients on home or nocturnal HD, peritoneal dialysis, or with a functioning kidney transplant were censored for follow-up. Overall, 267 participants were selected; however, only 241 of these had required samples for analysis at the baseline, 137 at 6 months, 116 at 12 months, and 43 at 60 months. Samples were taken before HD sessions, 80 metabolites isolated from the serum using methanol were quantified using mass spectroscopy techniques, and their concentrations were regressed onto time and participant, using mixed regression in their natural and natural logarithm units. Results Despite maintenance HD treatment, 74 quantified metabolites showed that 43 significantly increased and 4 significantly decreased in serum concentration over the study period. Of the 43 metabolites that increased in serum concentration over the study period, 24 have not been associated with kidney failure previously. In post hoc analyses, we found that lower water solubility appeared more likely to exhibit increases of metabolites concentration in blood ( P = 0.04). Conclusion The metabolome of patients with kidney failure on maintenance HD changes significantly over 60 months. Future work on correlating toxins and clinical outcomes is needed to drive the development of technologies that improve blood purification for people with kidney failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".