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

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Abstract

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.

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Data availability

Data sharing is not applicable in this study as no datasets were generated or analyzed during the current study. Only summary statistics were utilized.

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Acknowledgements

The authors wish to thank our ECHO Colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohorts. We also acknowledge the contribution of the following ECHO Program collaborators: ECHO Components—Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith PB, Newby LK; Data Analysis Center: Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland: Jacobson LP; Research Triangle Institute, Durham, North Carolina: Catellier DJ; Person-Reported Outcomes Core: Northwestern University, Evanston, Illinois: Gershon R, Cella D. ECHO Awardees and Cohorts—VDAART; AEC; NHBCS; MARBLES; PROTECT, Boston University Medical Center, Boston, MA: O’Connor G; Kaiser Permanente, Southern California, San Diego, CA: Zeiger R; Washington University of St. Louis, St Louis, MO: Bacharier L; AJ Drexel Autism Institute, Philadelphia, PA: Lyall K; John Hopkins Bloomberg School of Public Health, Baltimore, MD: Volk H. We also wish to thank the NIH Children's Health Exposure Analysis Resource (CHEAR) Program and the Human Health Exposure Analysis Resource (HHEAR) Program for their support.

Funding

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) Program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (PRO Core), U2CES026544 RTI Children’s Health Exposure Analysis Resource (CHEAR) Exposure Assessment Untargeted Hub (NIEHS, Fennell, Sumner), U2CES03085 North Carolina Human Health Exposure Analysis Resource (HHEAR) Hub (NIEHS, Fennell, Sumner, Du), UH3OD023318 (Dunlop), UH3OD023275 (Karagas), UH3OD023268 (Weiss), UH3OD023342 (Lyall), R01HL141826 (Lasky-Su), R01HL123915 (Lasky-Su), K01 HL146980 (Kelly).

Author information

Authors and Affiliations

  1. Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Nicole Prince, Su H. Chu, Scott T. Weiss, Jessica A. Lasky-Su & Rachel S. Kelly

  2. Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA

    Donghai Liang & Youran Tan

  3. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA

    Akram Alshawabkeh

  4. Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, 95616, USA

    Elizabeth Esther Angel & Rebecca J. Schmidt

  5. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Stefanie A. Busgang & Paul Curtin

  6. Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA

    José F. Cordero

  7. Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, USA

    Anne L. Dunlop

  8. Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA

    Diane Gilbert-Diamond, Anne G. Hoen, Margaret R. Karagas & Michael S. Zens

  9. Department of Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA

    Diane Gilbert-Diamond

  10. Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA

    Diane Gilbert-Diamond

  11. Department of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, CA, 95616, USA

    Cecilia Giulivi

  12. Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA

    David Kirchner, Susan McRitchie & Wimal Pathmasiri

  13. Division of Pediatric Pulmonary Medicine, Golisano Children’s Hospital at Strong, University of Rochester Medical Center, Rochester, NY, USA

    Augusto A. Litonjua

  14. Department of Health Sciences, Northeastern University, Boston, MA, USA

    Justin Manjourides

  15. Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA

    John D. Meeker & Deborah J. Watkins

  16. Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Wei Perng

  17. MIND Institute, School of Medicine, University of California Davis, Davis, CA, 95616, USA

    Rebecca J. Schmidt

  18. Kaiser Permanente Northern California Division of Research, Oakland, CA, USA

    Yeyi Zhu

  19. Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA

    Rachel S. Kelly

Authors
  1. Nicole Prince
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  2. Donghai Liang
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  3. Youran Tan
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  4. Akram Alshawabkeh
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  5. Elizabeth Esther Angel
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  6. Stefanie A. Busgang
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  7. Su H. Chu
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  8. José F. Cordero
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  9. Paul Curtin
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  10. Anne L. Dunlop
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  11. Diane Gilbert-Diamond
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  12. Cecilia Giulivi
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  13. Anne G. Hoen
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  14. Margaret R. Karagas
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  15. David Kirchner
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  16. Augusto A. Litonjua
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  17. Justin Manjourides
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  18. Susan McRitchie
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  19. John D. Meeker
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  20. Wimal Pathmasiri
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  21. Wei Perng
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  22. Rebecca J. Schmidt
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  23. Deborah J. Watkins
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  24. Scott T. Weiss
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  25. Michael S. Zens
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  26. Yeyi Zhu
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  27. Jessica A. Lasky-Su
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  28. Rachel S. Kelly
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Contributions

NP and RSK: wrote the main manuscript text and conducted the meta-analysis. DL, YT, SB, and MSZ: conducted analysis in individual cohorts. AA, EEA, SAB, SHC, JFC, PC, ALD, DGD, CG, AGH, MRK, DK, AAL, JM, SM, JDM, WP, WP, RJS, DJW, STW, YZ, and JALS: provided cohort-specific information on demographics and details of study design, contributed to reporting of data collection procedures, assisted in design of statistical models, and critically reviewed the manuscript.

Corresponding author

Correspondence to Rachel S. Kelly.

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Submitted on behalf of program collaborators for Environmental influences on Child Health Outcomes; See Acknowledgments for full listing of collaborators.

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Prince, N., Liang, D., Tan, Y. et al. Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium. Metabolomics 20, 16 (2024). https://doi.org/10.1007/s11306-023-02082-y

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