Metabolites Associated with Mortality in Hemodialysis Patients
Bibliographic record
Abstract
Background: Uremic toxins contributing to the increased risk of death in hemodialysis patients remain largely unknown. We used untargeted metabolomics profiling to identify plasma metabolite associated with mortality in hemodialysis patients. Methods: We created a cohort of 465 participants from the Longitudinal US/Canada Incident Dialysis (LUCID) study in which we profiled 498 known plasma metabolites measured on an untargeted platform. We assessed the association between metabolites and 1-year mortality adjusting for age, sex, race, cardiovascular disease, diabetes, BMI, albumin, KT/V, dialysis duration, and country. We used limma, a metabolite-wise linear model with empirical Bayesian inference, and two machine learning models, LASSO and random forest (RF), for analysis. We corrected for batch effects in metabolite abundances using the removal of unwanted variation (RUV) method, and we accouted for multiple testing by false discovery rate (q) below 10%. We defined mortality-metabolite associations as robust if significant in the limma model (q<0.1) and at least of medium importance in both LASSO and RF models (metabolite is above the 70th percentile for the variable importance measure). Results: The mean age was 61 years, 88% were male, 54% had diabetes and 48% had cardiovascular disease. There were 44 deaths (9.5%). The mean duration from dialysis initiation to metabolomic profiling was 62 days. We identified two metabolites significantly associated with 1-year mortality; mesaconate (HMDB0000749) and quinolate (HMDB0000232) (q<0.1 and high importance by both LASSO and RF). We identified 29 additional metabolites associated with 1-year mortality (q≥0.1) with high and/or medium importance by both LASSO and RF. Conclusions: We identified two metabolites significantly associated with an increased 1-year mortality risk in incident hemodialysis patients. Mesaconate is an intermediate in the glutamate degradation pathway and has not been previously designated as a uremic toxin. Quinolate is a product in the kynurenine pathway and has been previously considered as a uremic toxin. Our study identifies additional metabolites that could be further investigated for mechanisms of uremic toxicity and potetial targeted interventions to prevent poor outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".