Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

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 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 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."

Results: Participant mean estimated glomerular filtration rate was 43mL/min/1.73m², with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 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 exhibited at least a moderate correlation with estimated glomerular filtration 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 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.

Plain-language summary: Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

Keywords: Chronic Renal Insufficiency Cohort (CRIC); Chronic kidney disease; machine learning; metabolomics; multivariable model; uremic symptoms.

Figure 1.

Analysis plan for identifying metabolites associated with uremic symptom severity. Lasso and random forest models were chosen as complementary approaches to account for multicollinearity among metabolites. Metabolites selected by 2 or more of the 3 modeling approaches were considered overall significant. The statistical significance for repeated linear regression models was determined using Benjamini-Hochberg method to control for a false discovery rate (FDR) threshold of 0.05. Stability selection with Lasso regression was applied to 100 bootstrapped samples with replacement and the importance factor (IF) was then calculated as the proportion of bootstrapped samples in which the variable (i.e., metabolite) was selected by the Lasso. Variable importance in random forest models was determined by quantifying the decrease in model accuracy as a result of excluding a particular variable. Significant metabolites were defined as those included in the top 5% most important features in ≥5/10 of the training iterations.

Figure 2.

Prevalence of uremic symptom severity stratified by eGFR category among 1,761 CRIC Study participants. Uremic symptom severity was assessed by single items on the symptom subscale of the KDQOL-36 instrument using a Likert scale from 1 (none) to 5 (extremely bothered). Mild symptom severity was defined by a response of “somewhat bothered” and ≥moderate symptom severity was defined as a response of “moderately bothered,” “very much bothered,” or “extremely bothered.” All symptoms demonstrated a statistically significant association with eGFR category (p<0.001). A small number of participants were missing data on uremic symptom severity and not included in the histogram for that symptom (N; fatigue 1, pruritus 3, nausea 2, paresthesia 2, pain 2).

Figure 3.

Scatter plots of unadjusted relationship between eGFR and metabolite level (log-transformed and standardized). Panel A depicts metabolites positively associated with uremic symptom severity, e.g., a higher metabolite level was associated with worse symptom severity. Panel B depicts metabolites for which a lower level was associated with higher uremic symptom severity. Pearson correlation coefficients are shown. For this plot, metabolite levels less than or greater than 5 times the standard deviation of the metabolite distribution were omitted (n=24).

Figure 3.

Scatter plots of unadjusted relationship between eGFR and metabolite level (log-transformed and standardized). Panel A depicts metabolites positively associated with uremic symptom severity, e.g., a higher metabolite level was associated with worse symptom severity. Panel B depicts metabolites for which a lower level was associated with higher uremic symptom severity. Pearson correlation coefficients are shown. For this plot, metabolite levels less than or greater than 5 times the standard deviation of the metabolite distribution were omitted (n=24).