Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment.
While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by "fast lane" LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.
Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, California, United States of America.
Kyoungmi
K
Kim
Division of Nephrology, Department of Internal Medicine, University of California, Davis, Davis, California, United States of America.
Christine
C
Mall
Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, California, United States of America.
Sandra L
SL
Taylor
Division of Nephrology, Department of Internal Medicine, Emory University, Atlanta, Georgia, United States of America.
Stacie
S
Hitchcock
Department of Chemistry, Michigan State University, East Lansing, Michigan, United States of America.
Chen
C
Zhang
Division of Nephrology, Department of Internal Medicine, University of California, Davis, Davis, California, United States of America.
Hiromi I
HI
Wettersten
Department of Chemistry, Michigan State University, East Lansing, Michigan, United States of America ; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America.
A Daniel
AD
Jones
Division of Nephrology, Department of Internal Medicine, Emory University, Atlanta, Georgia, United States of America.
Arlene
A
Chapman
Division of Nephrology, Department of Internal Medicine, University of California, Davis, Davis, California, United States of America ; Cancer Center, University of California, Davis, Davis, California, United States of America ; Medical Service, Sacramento VA Medical Center, Sacramento, California, United States of America.
Robert H
RH
Weiss
D000328:Adult; D015415:Biomarkers / Q000097:blood* / Q000652:urine*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008297:Male; D055442:Metabolome; D055432:Metabolomics / Q000379:methods; D016891:Polycystic Kidney, Autosomal Dominant / Q000097:blood / Q000652:urine; D015203:Reproducibility of Results; D012307:Risk Factors; D055815:Young Adult
D015415:Biomarkers