학술논문
Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium
Document Type
article
Author
Prince, Nicole; Liang, Donghai; Tan, Youran; Alshawabkeh, Akram; Angel, Elizabeth Esther; Busgang, Stefanie A; Chu, Su H; Cordero, José F; Curtin, Paul; Dunlop, Anne L; Gilbert-Diamond, Diane; Giulivi, Cecilia; Hoen, Anne G; Karagas, Margaret R; Kirchner, David; Litonjua, Augusto A; Manjourides, Justin; McRitchie, Susan; Meeker, John D; Pathmasiri, Wimal; Perng, Wei; Schmidt, Rebecca J; Watkins, Deborah J; Weiss, Scott T; Zens, Michael S; Zhu, Yeyi; Lasky-Su, Jessica A; Kelly, Rachel S
Source
Metabolomics. 20(1)
Subject
Language
Abstract
IntroductionMeta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.ObjectiveThe objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.MethodsWe used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.ResultsOnly 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.