A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis
Bibliographic record
Abstract
Introduction Uremic toxins contributing to increased risk of death remain largely unknown. We used untargeted metabolomics to identify plasma metabolites associated with mortality in patients receiving maintenance hemodialysis. Methods We measured metabolites in serum samples from 522 Longitudinal US/Canada Incident Dialysis (LUCID) study participants. We assessed the association between metabolites and 1-year mortality, adjusting for age, sex, race, cardiovascular disease, diabetes, body mass index, serum albumin, Kt/Vurea, dialysis duration, and country. We modeled these associations using limma, a metabolite-wise linear model with empirical Bayesian inference, and 2 machine learning (ML) models: Least absolute shrinkage and selection operator (LASSO) and random forest (RF). We accounted for multiple testing using a false discovery rate (pFDR) adjustment. We defined significant mortality-metabolite associations as pFDR < 0.1 in the limma model and metabolites of at least medium importance in both ML models. Results The mean age of the participants was 64 years, the mean dialysis duration was 35 days, and there were 44 deaths (8.4%) during a 1-year follow-up period. Two metabolites were significantly associated with 1-year mortality. Quinolinate levels (a kynurenine pathway metabolite) were 1.72-fold higher in patients who died within year 1 compared with those who did not (pFDR, 0.009), wheras mesaconate levels (an emerging immunometabolite) were 1.57-fold higher (pFDR, 0.002). An additional 42 metabolites had high importance as per LASSO, 46 per RF, and 9 per both ML models but were not significant per limma. Conclusion Quinolinate and mesaconate were significantly associated with a 1-year risk of death in incident patients receiving maintenance hemodialysis. External validation of our findings is needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".