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

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 (LUCID) study participants at baseline (discovery; n=636) and year 1 (internal validation; n=260), and from Frailty Assessment in Renal Disease (FAIR) study participants (external validation; n=355). We used metabolite-wise linear models with empirical Bayesian inference to evaluate the association between metabolites and uremic symptoms' severity, adjusting for key covariates. We accounted for multiple testing using a 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 they 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 44-83%. We identified 627 previously characterized (known) and 35,558 unknown metabolite peaks. No known metabolites were significantly and consistently associated with uremic symptoms' severity across all cohorts. Within cohorts, retinol was negatively associated with nausea/vomiting in LUCID at year 1, and indole-3-propionic acid was negatively associated with anorexia in FAIR. 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, though findings were inconsistent across cohorts. This study highlights the need for further research on uremic toxins and clinical outcomes.