Metabolites Associated with Uremic Symptoms in Patients with CKD: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study
AMERICAN JOURNAL OF KIDNEY DISEASES(2024)
Abstract
Rationale & Objective: The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling fi ling to identify solutes associated with uremic symptoms in patients with CKD. Study Design: Cross-sectional. Setting & Participants: 1,761 Chronic Renal Insufficiency i ciency Cohort (CRIC) participants with CKD not treated with dialysis. Predictors: Measurement of 448 known plasma metabolites. Outcomes: The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. Analytical Approach: Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." fi cant." Results: Participant mean estimated glomerular fi ltration rate was 43 mL/min/1.73 m2, 2 , with 44% self-identifying as female and 41% as non- Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified fi ed 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular fi ltration rate (Pearson's r >= 0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. Limitations: Lack of a second independent cohort for external validation of our fi ndings. Conclusions: Metabolomic profiling fi ling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed.
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Key words
chronic kidney disease,metabolomics,uremic symptoms,Chronic Renal Insufficiency Cohort (CRIC),multivariable model,machine learning
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