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

• Published in: Metabolomics 2024-01, Vol.20 (1), p.16, Article 16
• Main Authors: 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.
• Format: Article
• Language: English
• Subjects: Arabinose; Biochemistry; Biomedical and Life Sciences; Biomedicine; Body Mass Index; Cell Biology; Child; Child, Preschool; Childhood; Children; Data acquisition; Developmental Biology; Epidemiology; Female; Humans; Life Sciences; Linear Models; Lysine; Meta-analysis; Metabolites; Metabolomics; Molecular Medicine; Offspring; Original Article; Pregnancy; Reproducibility; Reproducibility of Results; Statistical analysis
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-023-02082-y

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  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.