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Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method

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Abstract

The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by 1H NMR analysis on 807 serum samples originating from a nested liver cancer case–control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.

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Acknowledgments

The authors would like to acknowledge Dr. Jianying Li (National Institute of Environmental Health Sciences, Research Triangle Park, USA) and Dr. Martyn Plummer (IARC, Lyon, France) for helpful discussions on PVCA analysis. We thank Dr. Augustin Scalbert and Pr. Lyndon Emsley for fruitful comments to the manuscript.

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Authors and Affiliations

  1. Institut des Sciences Analytiques, Centre de RMN à très hauts champs, CNRS/ENS Lyon/UCB Lyon-1, Université de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France

    Anne Fages & Bénédicte Elena-Herrmann

  2. International Agency for Research in Cancer (IARC-WHO), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France

    Pietro Ferrari, Mattias Johansson & Mazda Jenab

  3. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Stefano Monni

  4. Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team, 94805, Villejuif, France

    Laure Dossus

  5. Université Paris Sud, UMRS 1018, 94805, Villejuif, France

    Laure Dossus

  6. Institut Gustave-Roussy (IGR), 94805, Villejuif, France

    Laure Dossus

  7. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany

    Anna Floegel

  8. Department of Community Medicine Systems Epidemiology, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway

    Nicolle Mode

  9. Department of Biobank Research, Umeå University, Umeå, Sweden

    Mattias Johansson

  10. Cancer Epidemiology Unit, University of Oxford, Oxford, UK

    Ruth C Travis

  11. WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece

    Christina Bamia

  12. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    María-José Sánchez-Pérez

  13. Andalusian School of Public Health, Instituto de Investigación Biosanitaria de Granada, Granada, Spain

    María-José Sánchez-Pérez

  14. Department of Mental Health and Preventive Medicine, Second University of Naples, Naples, Italy

    Paolo Chiodini

  15. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

    Hendriek C Boshuizen

  16. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Marc Chadeau-Hyam & Elio Riboli

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  1. Anne Fages
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  2. Pietro Ferrari
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  16. Bénédicte Elena-Herrmann
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Corresponding authors

Correspondence to Pietro Ferrari or Bénédicte Elena-Herrmann.

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Anne Fages and Pietro Ferrari contributed equally to this work.

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Fages, A., Ferrari, P., Monni, S. et al. Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method. Metabolomics 10, 1074–1083 (2014). https://doi.org/10.1007/s11306-014-0647-9

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