Variability of Two Metabolomic Platforms in CKD

Area of Science:

• Metabolomics and biomarker discovery
• Renal disease research
• Biochemical analysis

Background:

• Nontargeted metabolomics identifies thousands of biochemicals but has limitations for chronic kidney disease (CKD) biomarker discovery.
• Key gaps include characterizing technical and intraperson analyte variation, pooling data across platforms, and understanding analyte relationships with estimated glomerular filtration rate (eGFR).

Purpose of the Study:

• To assess the utility of nontargeted metabolomics for biomarker discovery in CKD.
• To characterize technical and intraperson analyte variation across different platforms.
• To investigate the relationship between metabolites and eGFR in CKD patients.

Main Methods:

• Plasma samples from 49 individuals with CKD were analyzed across two study visits, including blind replicates.
• Metabolomic profiling was performed using two distinct platforms (Metabolon and Broad Institute) to enable cross-platform comparison.
• Analysis focused on known metabolites, unnamed compounds, and unknown ion features, assessing coefficients of variation (CVs) for technical and day-to-day variability.

Main Results:

• Both platforms quantified a large number of metabolites (>594 known, thousands of unknown features).
• Low median coefficients of variation (CVs) were observed for known metabolites across blind replicates (6.3%–14.6%) and day-to-day variability (24.9%–29.0%).
• A substantial number of known metabolites (381) were shared across platforms with high correlation (median 0.89), and many showed significant negative correlation with eGFR.

Conclusions:

• Nontargeted metabolomics effectively quantifies over 1000 analytes with low technical variability and excellent agreement between leading platforms.
• Significant intraperson variation and correlations with eGFR were observed for numerous metabolites.
• These findings support the utility of nontargeted metabolomics for biomarker discovery in CKD, highlighting the need to account for analytical and biological variability.