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Quantitative methods for metabolomic analyses evaluated in the Children’s Health Exposure Analysis Resource (CHEAR)

Journal of Exposure Science & Environmental Epidemiology volume 30, pages 16–27 (2020)Cite this article

Abstract

With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children’s Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQSRS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (1H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).

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References

  1. Noto A, Fanos V, Dessì A. Metabolomics in newborns. Adv. Clin. Chem. 2016;74:35–61.

  2. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13:263–9.

    Article  CAS  Google Scholar 

  3. Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer. 2017;17:692–704.

    Article  CAS  Google Scholar 

  4. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.

    Article  CAS  Google Scholar 

  5. Flanagan JM. Epigenome-wide association studies (EWAS): past, present, and future. Methods Mol Biol. 2015;1238:51–63.

    Article  Google Scholar 

  6. Wu F, Chi L, Ru H, Parvez F, Slavkovich V, Eunus M, et al. Arsenic exposure from drinking water and urinary metabolomics: associations and long-term reproducibility in Bangladesh adults. Environ Health Perspect. 2018;126:17005.

    Article  Google Scholar 

  7. Braun JM, Gennings C, Hauser R, Webster TF. What can epidemiological studies tell us about the impact of chemical mixtures on human health? Environ Health Perspect. 2016;124:A6–9.

    Article  Google Scholar 

  8. Balshaw DM, Collman GW, Gray KA, Thompson CL. The childrenʼs health exposure analysis resource. Curr Opin Pediatr. 2017;29:385–9.

    Article  Google Scholar 

  9. Kile ML, Baccarelli A, Tarantini L, Hoffman E, Wright RO, Christiani DC. Correlation of global and gene-specific DNA methylation in maternal-infant pairs. PLoS ONE. 2010;5:e13730.

    Article  Google Scholar 

  10. Laine JE, Bailey KA, Olshan AF, Smeester L, Drobná Z, Stýblo M, et al. Neonatal metabolomic profiles related to prenatal arsenic exposure. Environ Sci Technol. 2017;51:625–33.

    Article  CAS  Google Scholar 

  11. Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, et al. Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2016;44:D463–70.

    Article  CAS  Google Scholar 

  12. Tibshirani R, Johnstone I, Hastie T, Efron B. Least angle regression. Ann Stat. 2004;32:407–99.

    Article  Google Scholar 

  13. Bach F. Self-concordant analysis for logistic regression. Electron J Stat. 2010;4:384–414.

    Article  Google Scholar 

  14. Chatterjee A, Lahiri SN Asymptotic properties of theresidual bootstrap for LASSO estimators. Proc. Am. Math. Soc. American Math Soc. 2010. p. 4497–509.

    Article  Google Scholar 

  15. Breiman L. Random Forests. Mach. Learn. 2001;45:5–32.

  16. Liaw A, Wiener M. Classification and regression by random forest. R News. 2002;2:18–22.

    Google Scholar 

  17. Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26:1340–7. [cited 2018 Sep 21]

    Article  CAS  Google Scholar 

  18. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. J Agric Biol Environ Stat. 2014;20:100–20.

    Article  Google Scholar 

  19. Curtin P, Kellogg J, Cech N, Gennings C. A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures. Commun. Stat.– Simul. Comput. 2019:1–16. https://doi.org/10.1080/03610918.2019.1577971.

  20. Gennings C, Sabo R, Carney E. Identifying subsets of complex mixtures most associated with complex diseases. Epidemiology. 2010;21:S77–84. [cited 2019 Apr 5]

    Article  Google Scholar 

  21. Carrico C, Gennings C, Wheeler DC. Factor-Litvak P Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. J. Agric. Biol. Environ. Stat. 2015;20:100–20.

    Article  Google Scholar 

  22. Czarnota J, Gennings C, Wheeler DC. Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk. Cancer Inform. 2015;14:159–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Harman HH. Modern factor analysis. Chicago: University of Chicago Press; 1976.

  24. Revelle W. psych: procedures for psychological, psychometric, and personality research. Evanston, Illinois: Northwestern University; 2014.

    Google Scholar 

  25. ten Berge JMF, Krijnen WP, Wansbeek T, Shapiro A. Some new results on correlation-preserving factor scores prediction methods. Linear Algebra Appl. 1999;289:311–8.

  26. Hellmuth C, Lindsay KL, Uhl O, Buss C, Wadhwa PD, Koletzko B, et al. Association of maternal prepregnancy BMI with metabolomic profile across gestation. Int J Obes. 2017;41:159–69.

    Article  CAS  Google Scholar 

  27. Ferdous F, Ma E, Raqib R, Wagatsuma Y. Birth weight influences the kidney size and function of Bangladeshi children. J. Dev. Orig. Health Dis. 2018;9:386–94.

    Article  Google Scholar 

  28. Ahearne CE, Denihan NM, Walsh BH, Reinke SN, Kenny LC, Boylan GB, et al. Early cord metabolite index and outcome in perinatal asphyxia and hypoxic-ischaemic encephalopathy. Neonatology. 2016;110:296–302.

    Article  CAS  Google Scholar 

  29. Bahado-Singh RO, Syngelaki A, Mandal R, Graham SF, Akolekar R, Han B, et al. Metabolomic determination of pathogenesis of late-onset preeclampsia. J Matern Fetal Neonatal Med. 2017;30:658–64.

    Article  CAS  Google Scholar 

  30. Denihan NM, Walsh BH, Reinke SN, Sykes BD, Mandal R, Wishart DS, et al. The effect of haemolysis on the metabolomic profile of umbilical cord blood. Clin Biochem. 2015;48:534–7.

    Article  CAS  Google Scholar 

  31. Braun JM, Kalkbrenner AE, Just AC, Yolton K, Calafat AM, Sjödin A, et al. Gestational exposure to endocrine-disrupting chemicals and reciprocal social, repetitive, and stereotypic behaviors in 4- and 5-year-old children: the HOME study. Environ Health Perspect. 2014;122:513–20.

    Article  CAS  Google Scholar 

  32. Patel CJ, Bhattacharya J, Butte AJ. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE. 2010;5:e10746.

    Article  Google Scholar 

  33. Van den Berg M, Birnbaum LS, Denison M, De Vito M, Farland W, Feeley M, et al. The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol Sci. 2006;93:223–41.

    Article  Google Scholar 

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Acknowledgements

This study was supported by funding from the National Institute of Environmental Health Sciences (R01ES015533, ES000002, U2C ES026544-01 (CHEAR RTI Lab Hub) and U2C ES036555-01 (CHEAR Data Center)).

CHEAR Metabolomics Analysis Team

Maya A. Deyssenroth1, Elena Colicino1, Paul Curtin1, Megan M. Niedzwiecki1

Author information

Author notes

  1. These authors contributed equally: Maya A. Deyssenroth, Elena Colicino, Paul Curtin, Megan M. Niedzwiecki

Authors and Affiliations

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

    Maya A. Deyssenroth, Elena Colicino, Paul Curtin, Megan M. Niedzwiecki, Matthew Mazzella & Chris Gennings

  2. Department of Nutrition, School of Public Health, University of North Carolina-Chapel Hill, Kannapolis, NC, 28081, USA

    Susan J. Sumner & Wimal Pathmasiri

  3. Harvard T.H.Chan School of Public Health and Harvard Medical School, 665 Huntington Avenue, Building I Room 1401, Boston, MA, 02115, USA

    Shangzhi Gao, Li Su, Nancy Diao & David C. Christiani

  4. Dhaka Community Hospital, Dhaka, Bangladesh

    Golam Mostofa & Qazi Qamruzzaman

  5. RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC, 27709, USA

    Timothy Fennell

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CHEAR Metabolomics Analysis Team

  • Maya A. Deyssenroth
  • , Elena Colicino
  • , Paul Curtin
  •  & Megan M. Niedzwiecki

Corresponding author

Correspondence to Maya A. Deyssenroth.

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CHEAR Metabolomics Analysis Team., Mazzella, M., Sumner, S.J. et al. Quantitative methods for metabolomic analyses evaluated in the Children’s Health Exposure Analysis Resource (CHEAR). J Expo Sci Environ Epidemiol 30, 16–27 (2020). https://doi.org/10.1038/s41370-019-0162-1

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