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

dc.contributor.authorAl Awadhi, Solaf
dc.contributor.authorMyint, Leslie
dc.contributor.authorGuallar, Eliseo
dc.contributor.authorClish, Clary B.
dc.contributor.authorWulczyn, Kendra E.
dc.contributor.authorKalim, Sahir
dc.contributor.authorThadhani, Ravi
dc.contributor.authorSegev, Dorry L.
dc.contributor.authorMcAdams DeMarco, Mara
dc.contributor.authorMoe, Sharon M.
dc.contributor.authorMoorthi, Ranjani N.
dc.contributor.authorHostetter, Thomas H.
dc.contributor.authorHimmelfarb, Jonathan
dc.contributor.authorMeyer, Timothy W.
dc.contributor.authorPowe, Neil R.
dc.contributor.authorTonelli, Marcello
dc.contributor.authorRhee, Eugene P.
dc.contributor.authorShafi, Tariq
dc.contributor.departmentMedicine, School of Medicine
dc.date.accessioned2024-10-30T13:58:33Z
dc.date.available2024-10-30T13:58:33Z
dc.date.issued2024-06-29
dc.description.abstractIntroduction: 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 per LASSO, 46 per RF, and 9 per both ML models but were not significant per 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.
dc.eprint.versionFinal published version
dc.identifier.citationAl Awadhi S, Myint L, Guallar E, et al. A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis. Kidney Int Rep. 2024;9(9):2718-2726. Published 2024 Jun 29. doi:10.1016/j.ekir.2024.06.039
dc.identifier.urihttps://hdl.handle.net/1805/44357
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isversionof10.1016/j.ekir.2024.06.039
dc.relation.journalKidney International Reports
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcePMC
dc.subjectArtificial intelligence
dc.subjectHemodialysis
dc.subjectMetabolomics
dc.subjectMortality
dc.titleA Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis
dc.typeArticle
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