Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

Nicole Prince, Donghai Liang, Youran Tan, Akram Alshawabkeh, Elizabeth Esther Angel, Stefanie A. Busgang, Su H. Chu, José F. Cordero, Paul Curtin, Anne L. Dunlop, Diane Gilbert-Diamond, Cecilia Giulivi, Anne G. Hoen, Margaret R. Karagas, David Kirchner, Augusto A. Litonjua, Justin Manjourides, Susan McRitchie, John D. Meeker, Wimal PathmasiriWei Perng, Rebecca J. Schmidt, Deborah J. Watkins, Scott T. Weiss, Michael S. Zens, Yeyi Zhu, Jessica A. Lasky-Su, Rachel S. Kelly

Research output: Contribution to journalArticlepeer-review


Introduction: Meta-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. Objective: The 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. Methods: We 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. Results: Only 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 < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10–3), guanidinoacetate (Coefmeta = − 0.28 [− 0.54, − 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = − 0.18 [− 0.33, − 0.04], Pmeta = 0.011), serine (Coefmeta = − 0.18 [− 0.36, − 0.01], Pmeta = 0.034), and lysine (Coefmeta = − 0.16 [− 0.32, − 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. Conclusions: Despite including three cohorts with large sample sizes (N > 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.

Original languageEnglish
Article number16
Issue number1
StatePublished - Feb 2024


  • Childhood obesity
  • Maternal metabolites
  • Metabolomic epidemiology
  • Metabolomic meta-analysis


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