Ó³»­´«Ã½

RATIONALE & OBJECTIVE: The toxins contributing to uremic symptoms in patients with CKD are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD.

STUDY DESIGN: Cross-sectional.

SETTING & PARTICIPANTS: 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not on dialysis.

PREDICTORS: Measurement of 448 known plasma metabolites.

OUTCOMES: The uremic symptoms fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 (KDQOL) instrument.

ANALYTICAL APPROACH: Multivariable adjusted linear regression, Lasso 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 two of the three modeling approaches were deemed "overall" significant.

RESULTS: Participant mean eGFR was 43 mL/min/1.73 m, with 44% self-identifying as female and 41% Non-Hispanic Black. The prevalence of uremic symptoms ranged from 22 - 55%. We identified 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 demonstrated at least a moderate correlation with eGFR (Pearson's r ≥ 0.5), and some were also associated with 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 findings.

CONCLUSIONS: Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed.