Sources of variability in metabolite measurements from urinary samples.
BACKGROUND
The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.


METHODS
We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2-3 samples per person from 17 male subjects (age 38-70) over 2-10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies.


RESULTS
Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of "usual" levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations.


CONCLUSIONS
The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Qian
Q
Xiao
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Steven C
SC
Moore
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Simina M
SM
Boca
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Charles E
CE
Matthews
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Nathaniel
N
Rothman
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Rachael Z
RZ
Stolzenberg-Solomon
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Rashmi
R
Sinha
Imperial College London, London, United Kingdom.
Amanda J
AJ
Cross
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America.
Joshua N
JN
Sampson
D000236:Adenoma / Q000652:urine; D000328:Adult; D000368:Aged; D016022:Case-Control Studies; D002853:Chromatography, Liquid / Q000592:standards; D003110:Colonic Neoplasms / Q000652:urine; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000592:standards; D055432:Metabolomics / Q000379:methods / Q000592:standards; D008875:Middle Aged; D015203:Reproducibility of Results; D012307:Risk Factors; D016482:Urinalysis / Q000379:methods / Q000592:standards