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. 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. © 2013 American Association for Cancer Research.