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Exploring Covariation between Traditional Markers of Metabolic Health and the Plasma Metabolomic Profile: A Classic Twin Design

  • Kate M. Bermingham
    Kate M. Bermingham
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Lorraine Brennan
    Lorraine Brennan
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Ricardo Segurado
    Ricardo Segurado
    School of Public Health, Physiotherapy, and Sports Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Rebecca E. Barron
    Rebecca E. Barron
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Eileen R. Gibney
    Eileen R. Gibney
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Miriam F. Ryan
    Miriam F. Ryan
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • Michael J. Gibney
    Michael J. Gibney
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
  • , and 
  • Aifric M. O’Sullivan*
    Aifric M. O’Sullivan
    UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
    *E-mail: [email protected]
Cite this: J. Proteome Res. 2019, 18, 6, 2613–2623
Publication Date (Web):May 10, 2019
https://doi.org/10.1021/acs.jproteome.9b00126
Copyright © 2019 American Chemical Society

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    Abstract

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    Novel metabolomic profiling techniques combined with traditional biomarkers provide knowledge of mechanisms underlying metabolic health. Twin studies describe the impact of genes and environment on variation in traits. This study aims to identify relationships between traditional markers of metabolic health and the plasma metabolomic profile using a twin modeling approach and determine whether covariation is caused by shared genetic and environmental factors. Using a classic twin design, this study examined covariation between anthropometric, clinical chemistry, and metabolomic profiles. Cholesky decomposition modeling was used to determine the genetic and environmental path coefficients through successive anthropometric and clinical chemistry traits onto metabolomic derived metabolites. This study shows that WC, TAG, and a metabolomic signature composed of 7 metabolites are inter-related, and that covariation can be attributed to common genetic, shared and unique environmental factors as well as unique environmental factors specific to the metabolite. This quantitative modeling connecting the traditional anthropometry and clinical chemistry traits with the more recent and potentially more sensitive metabolomic profile approach may provide further insight on the pleiotropic genes or modifiable environmental factors influencing variation in metabolic health.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.9b00126.

    • S-1, Classic twin design; S-2, univariate twin modeling; S-3, multivariate twin modeling; Table S-1, selected metabolomic bin regions and associated metabolites; Table S-2, model fitting parameters for anthropometric and clinical chemistry traits; and Table S-3, univariate ACE model and model fitting parameters for the serum metabolomic profile (PDF)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 4 publications.

    1. Kate M. Bermingham, Lorraine Brennan, Ricardo Segurado, Rebecca E. Barron, Eileen R. Gibney, Miriam F. Ryan, Michael J. Gibney, Aifric M. O’Sullivan. Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study. Journal of Proteome Research 2021, 20 (8) , 3992-4000. https://doi.org/10.1021/acs.jproteome.1c00319
    2. Emily Ho, Victoria J. Drake, Alexander J. Michels, Yasmeen M. Nkrumah-Elie, LaVerne L. Brown, Jonathan M. Scott, John W. Newman, Barbara Shukitt-Hale, Amala Soumyanath, Floyd H. Chilton, Stephen R. Lindemann, Andrew Shao, Susan Hazels Mitmesser. Perspective: Council for Responsible Nutrition Science in Session. Optimizing Health with Nutrition—Opportunities, Gaps, and the Future. Advances in Nutrition 2023, 14 (5) , 948-958. https://doi.org/10.1016/j.advnut.2023.05.015
    3. K.M. Bermingham, L. Brennan, R. Segurado, I.J. Gray, R.E. Barron, E.R. Gibney, M.F. Ryan, M.J. Gibney, J.W. Newman, Dr. A.M. O'Sullivan. Genetic and environmental influences on serum oxylipins, endocannabinoids, bile acids and steroids. Prostaglandins, Leukotrienes and Essential Fatty Acids 2021, 173 , 102338. https://doi.org/10.1016/j.plefa.2021.102338
    4. Kate M Bermingham, Lorraine Brennan, Ricardo Segurado, Rebecca E Barron, Eileen R Gibney, Miriam F Ryan, Michael J Gibney, Aifric M O’Sullivan. Genetic and environmental influences on covariation in reproducible diet–metabolite associations. The American Journal of Clinical Nutrition 2021, 113 (5) , 1232-1240. https://doi.org/10.1093/ajcn/nqaa378

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