title: Metabolomics in epidemiology: sources of variability in metabolite measurements and implications.
abstract: BACKGROUND
  Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies.
  
  
  METHODS
  Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study.
  
  
  RESULTS
  Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of "usual" levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability.
  
  
  CONCLUSIONS
  Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies.
  
  
  CONCLUSIONS
  We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Rockville, MD 20852, USA. joshua.sampson@nih.gov
authors: Joshua N
authors: JN
authors: Sampson
authors: Simina M
authors: SM
authors: Boca
authors: Xiao Ou
authors: XO
authors: Shu
authors: Rachael Z
authors: RZ
authors: Stolzenberg-Solomon
authors: Charles E
authors: CE
authors: Matthews
authors: Ann W
authors: AW
authors: Hsing
authors: Yu Ting
authors: YT
authors: Tan
authors: Bu-Tian
authors: BT
authors: Ji
authors: Wong-Ho
authors: WH
authors: Chow
authors: Qiuyin
authors: Q
authors: Cai
authors: Da Ke
authors: DK
authors: Liu
authors: Gong
authors: G
authors: Yang
authors: Yong Bing
authors: YB
authors: Xiang
authors: Wei
authors: W
authors: Zheng
authors: Rashmi
authors: R
authors: Sinha
authors: Amanda J
authors: AJ
authors: Cross
authors: Steven C
authors: SC
authors: Moore
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis*; D016022:Case-Control Studies; D002849:Chromatography, Gas; D002853:Chromatography, Liquid; D015331:Cohort Studies; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D055432:Metabolomics / Q000592:standards*; D008875:Middle Aged; D009369:Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000378:metabolism; D011379:Prognosis; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor
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