Metabolites Associated With Uremic Symptoms in Hemodialysis Patients
Bibliographic record
Abstract
Background: The specific substances causing uremic symptoms are unknown. We used untargeted metabolomics to identify metabolite markers associated with uremic symptoms in hemodialysis patients. Methods: We profiled 29,591 plasma metabolites (Broad Institute) in 517 Longitudinal US/Canada Incident Dialysis (LUCID) study participants at baseline (discovery) and subset at year 1 (validation). We concurrently assessed uremic symptoms (KDQOL-36; fatigue, pruritus, anorexia, nausea/vomiting, daytime sleepiness, difficulty concentrating, and pain) and investigated demographic- and clinical covariate-adjusted associations using: a) metabolite-wise linear models with empirical Bayesian inference, accounting for multiple testing; b) LASSO; c) random forest (RF) models. We defined robust symptom-metabolite associations if significant in linear models and at least medium importance in both LASSO and RF models. Results: The mean age was 61 years, 80% were male, and mean duration from dialysis initiation was 62 days. We identified several metabolites robustly associated with uremic symptoms; 3 metabolites associated with anorexia, 8 with pruritus, and 1 each with pain, sleepiness, and concentration (Table). Higher levels of 2-hydroxy-3-methylpentanoate/hydroxyisocaproate were linked to higher severity of anorexia, bodily pain, and difficulty concentrating. Lower levels of indoxyl sulfate were associated with higher severity of daytime sleepiness. No metabolites were significantly associated with fatigue or nausea/vomiting. Conclusions: We identified several metabolites associated with uremic symptoms, which could be targeted for future interventions if replicated in other studies. Funding: Other NIH Support - NINRMetabolites robustly associated with uremic symptoms in the LUCID study
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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 | 0.000 |
| 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".