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Clin J Am Soc Nephrol. 2019 Jan 7;14(1):40-48. doi: 10.2215/CJN.07070618. Epub 2018 Dec 20.

Variability of Two Metabolomic Platforms in CKD.

Author information

1
Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts; eprhee@partners.org coresh@jhu.edu.
2
Renal Division, Brigham and Women's Hospital, Boston, Massachusetts.
3
Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland.
4
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
5
Department of Biostatistics, Epidemiology, and Informatics.
6
Metabolon, Inc., Durham, North Carolina.
7
Metabolite Profiling, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
8
Division of Nephrology, The Children's Hospital of Philadelphia, and.
9
Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; and.
10
Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
11
Division of Kidney Urologic and Hematologic Diseases, National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.
12
Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland; eprhee@partners.org coresh@jhu.edu.

Abstract

BACKGROUND AND OBJECTIVES:

Nontargeted metabolomics can measure thousands of low-molecular-weight biochemicals, but important gaps limit its utility for biomarker discovery in CKD. These include the need to characterize technical and intraperson analyte variation, to pool data across platforms, and to outline analyte relationships with eGFR.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:

Plasma samples from 49 individuals with CKD (eGFR<60 ml/min per 1.73 m2 and/or ≥1 g proteinuria) were examined from two study visits; 20 samples were repeated as blind replicates. To enable comparison across two nontargeted platforms, samples were profiled at Metabolon and the Broad Institute.

RESULTS:

The Metabolon platform reported 837 known metabolites and 483 unnamed compounds (selected from 44,953 unknown ion features). The Broad Institute platform reported 594 known metabolites and 26,106 unknown ion features. Median coefficients of variation (CVs) across blind replicates were 14.6% (Metabolon) and 6.3% (Broad Institute) for known metabolites, and 18.9% for (Metabolon) unnamed compounds and 24.5% for (Broad Institute) unknown ion features. Median CVs for day-to-day variability were 29.0% (Metabolon) and 24.9% (Broad Institute) for known metabolites, and 41.8% for (Metabolon) unnamed compounds and 40.9% for (Broad Institute) unknown ion features. A total of 381 known metabolites were shared across platforms (median correlation 0.89). Many metabolites were negatively correlated with eGFR at P<0.05, including 35.7% (Metabolon) and 18.9% (Broad Institute) of known metabolites.

CONCLUSIONS:

Nontargeted metabolomics quantifies >1000 analytes with low technical CVs, and agreement for overlapping metabolites across two leading platforms is excellent. Many metabolites demonstrate substantial intraperson variation and correlation with eGFR.

KEYWORDS:

EGFR protein; Epidermal Growth Factor; Molecular Weight; Receptor; Renal Insufficiency, Chronic; biomarker; chronic kidney disease; glomerular filtration rate; human; metabolomics; proteinuria

PMID:
30573658
PMCID:
PMC6364529
[Available on 2020-01-07]
DOI:
10.2215/CJN.07070618

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