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. CONCLUSION: Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies. IMPACT: We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.
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. CONCLUSION: Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies. IMPACT: We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.
Authors: So-Youn Shin; Ann-Kristin Petersen; Nicole Soranzo; Christian Gieger; Karsten Suhre; Robert P Mohney; David Meredith; Brigitte Wägele; Elisabeth Altmaier; Panos Deloukas; Jeanette Erdmann; Elin Grundberg; Christopher J Hammond; Martin Hrabé de Angelis; Gabi Kastenmüller; Anna Köttgen; Florian Kronenberg; Massimo Mangino; Christa Meisinger; Thomas Meitinger; Hans-Werner Mewes; Michael V Milburn; Cornelia Prehn; Johannes Raffler; Janina S Ried; Werner Römisch-Margl; Nilesh J Samani; Kerrin S Small; H-Erich Wichmann; Guangju Zhai; Thomas Illig; Tim D Spector; Jerzy Adamski Journal: Nature Date: 2011-08-31 Impact factor: 49.962
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Authors: R B Hayes; D Reding; W Kopp; A F Subar; N Bhat; N Rothman; N Caporaso; R G Ziegler; C C Johnson; J L Weissfeld; R N Hoover; P Hartge; C Palace; J K Gohagan Journal: Control Clin Trials Date: 2000-12
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Authors: Majda Haznadar; Padma Maruvada; Eliza Mette; John Milner; Steven C Moore; Holly L Nicastro; Joshua N Sampson; L Joseph Su; Mukesh Verma; Krista A Zanetti Journal: Metabolomics Date: 2014-04-01 Impact factor: 4.290
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