Longitudinal plasma metabolome patterns and relation to kidney function and proteinuria in pediatric chronic kidney disease.

INTRODUCTION: Understanding plasma metabolome patterns in relation to changing kidney function in pediatric chronic kidney disease (CKD) is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There is a limited number of studies of longitudinal metabolomics, and virtually none in pediatric CKD.

METHODS: The Chronic Kidney Disease in Children (CKiD) study is a multi-institutional, prospective cohort that enrolled children aged six-months to 16-years with estimated glomerular filtration rate (eGFR) 30-90ml/min/1.73m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, two-, and four-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements.To identify metabolite associations with eGFR or urine protein:creatinine (UPCR) among all three timepoints, we applied linear mixed effects (LME) models. To identify metabolites associated with time, we applied LME models to the two- and four-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance based on both the False Discovery Rate (FDR) <0.05 and p<0.05.

RESULTS: There were 1156 person-visits (N: baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three timepoints. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR<0.05 respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR<0.05. For participants with complete data at both follow-up visits (N=123), we report 35 metabolites with ∆level∼∆eGFR associations significant at FDR<0.05. There were no metabolites with significant ∆level∼∆UPCR associations at FDR<0.05. We report 16 metabolites with ∆level∼∆UPCR associations at p<0.05 and associations with UPCR in LME modeling at FDR<0.05.

CONCLUSION: We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.