A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis.

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 LASSO, 46 RF, and 9 both ML models but were not significant 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.