A Metabolomics Approach To Identify Metabolites Associated with Uremic Symptoms in Patients Receiving Maintenance Hemodialysis

Key Points Uremic symptoms are common in patients with kidney failure, but the chemicals causing them are unknown. We used state-of-the-art untargeted metabolomics and rigorous analyses to search for metabolites associated with uremic symptoms. We identified metabolite-symptom associations but could not replicate findings and offer recommendations for future research. Background The specific toxins causing uremic symptoms (nausea, vomiting, pruritus, fatigue, difficulty concentrating, and pain) in kidney failure remain unknown. We used untargeted metabolomics to identify plasma metabolites associated with uremic symptoms in patients receiving hemodialysis. Methods We measured metabolites in plasma samples from Longitudinal US/Canada Incident Dialysis study participants at baseline (discovery; n =636) and year 1 (internal validation; n =260) and from Frailty Assessment in Renal Disease study participants (external validation; n =355). We used metabolite-wise linear models with empirical Bayesian inference to evaluate the association between metabolites and uremic symptom severity, adjusting for key covariates. We accounted for multiple testing using a false discovery rate ( P values adjusted for false discovery rate [pFDR]) for linear models and used two machine learning models to evaluate the association consistency. We defined association as significant if pFDR < 0.1 and consistent if it had medium or high importance in both machine learning models. Results Participants had a mean age of 63 years, with uremic symptom prevalence ranging from 44% to 83%. We identified 627 previously characterized (known) and 35,558 unknown metabolite peaks. No known metabolites were significantly and consistently associated with uremic symptom severity across all cohorts. Within cohorts, retinol was negatively associated with nausea/vomiting in Longitudinal US/Canada Incident Dialysis at year 1, and indole-3-propionic acid was negatively associated with anorexia in Frailty Assessment in Renal Disease. Several unknown metabolites were associated with symptoms (lowest pFDR, 0.0004), but none were consistent across cohorts. Conclusions We identified metabolites associated with uremic symptom severity, although findings were inconsistent across cohorts. This study highlights the need for additional research on uremic toxins and clinical outcomes.

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Publication Details

Journal
Kidney360
Published
2025-09-23
DOI
https://doi.org/10.34067/kid.0000000838
Primary Topic
Dialysis and Renal Disease Management
Type
article
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article

A Metabolomics Approach To Identify Metabolites Associated with Uremic Symptoms in Patients Receiving Maintenance Hemodialysis

Mara McAdams‐DeMarco, Solaf Al Awadhi, Neil R. Powe, Sahir Kalim et al.
Kidney360
Dialysis and Renal Disease Management
article

A Metabolomics Approach To Identify Metabolites Associated with Uremic Symptoms in Patients Receiving Maintenance Hemodialysis

Mara McAdams‐DeMarco, Solaf Al Awadhi, Neil R. Powe, Sahir Kalim, Ranjani N. Moorthi, Katherine P. Liao, Leslie Myint, Dorry Lidor Segev, Marcello Tonelli, Tariq Shafi, Clary B. Clish, Kendra E. Wulczyn, Jonathan Himmelfarb, Sharon M. Moe, Eliseo Güallar, Ravi Thandhani
article en

Abstract

Key Points Uremic symptoms are common in patients with kidney failure, but the chemicals causing them are unknown. We used state-of-the-art untargeted metabolomics and rigorous analyses to search for metabolites associated with uremic symptoms. We identified metabolite-symptom associations but could not replicate findings and offer recommendations for future research. Background The specific toxins causing uremic symptoms (nausea, vomiting, pruritus, fatigue, difficulty concentrating, and pain) in kidney failure remain unknown. We used untargeted metabolomics to identify plasma metabolites associated with uremic symptoms in patients receiving hemodialysis. Methods We measured metabolites in plasma samples from Longitudinal US/Canada Incident Dialysis study participants at baseline (discovery; n =636) and year 1 (internal validation; n =260) and from Frailty Assessment in Renal Disease study participants (external validation; n =355). We used metabolite-wise linear models with empirical Bayesian inference to evaluate the association between metabolites and uremic symptom severity, adjusting for key covariates. We accounted for multiple testing using a false discovery rate ( P values adjusted for false discovery rate [pFDR]) for linear models and used two machine learning models to evaluate the association consistency. We defined association as significant if pFDR < 0.1 and consistent if it had medium or high importance in both machine learning models. Results Participants had a mean age of 63 years, with uremic symptom prevalence ranging from 44% to 83%. We identified 627 previously characterized (known) and 35,558 unknown metabolite peaks. No known metabolites were significantly and consistently associated with uremic symptom severity across all cohorts. Within cohorts, retinol was negatively associated with nausea/vomiting in Longitudinal US/Canada Incident Dialysis at year 1, and indole-3-propionic acid was negatively associated with anorexia in Frailty Assessment in Renal Disease. Several unknown metabolites were associated with symptoms (lowest pFDR, 0.0004), but none were consistent across cohorts. Conclusions We identified metabolites associated with uremic symptom severity, although findings were inconsistent across cohorts. This study highlights the need for additional research on uremic toxins and clinical outcomes.

Kidney360Vol. 7(2)
Broad Institute (US), Emory Healthcare (US), Johns Hopkins University (US), Emory University (US), University of Calgary (CA), University of California, San Francisco (US), NYU Langone Health (US), Massachusetts General Hospital (US), Methodist Hospital (US), First Solar (United States) (US), Baylor Scott & White Health (US), Indiana University School of Medicine, Macalester College (US), Indiana University – Purdue University Indianapolis (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 72%
Dialysis and Renal Disease Management
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