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

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• 作者：Anne Fages (1)
  Pietro Ferrari (2)
  Stefano Monni (3)
  Laure Dossus (4) (5) (6)
  Anna Floegel (7)
  Nicolle Mode (8)
  Mattias Johansson (2) (9)
  Ruth C Travis (10)
  Christina Bamia (11)
  María-José Sánchez-Pérez (12) (13)
  Paolo Chiodini (14)
  Hendriek C Boshuizen (15)
  Marc Chadeau-Hyam (16)
  Elio Riboli (16)
  Mazda Jenab (2)
  Bénédicte Elena-Herrmann (1)
  
• 关键词：Metabolomics ; Principal component analysis ; Nuclear magnetic resonance ; Epidemiology ; Systematic variation ; European prospective investigation on cancer and nutrition
• 刊名：Metabolomics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：10
• 期：6
• 页码：1074-1083
• 全文大小：610 KB
• 参考文献：1. Athersuch, T. J. (2012). The role of metabolomics in characterizing the human exposome. / Bioanalysis, / 4, 2207-212. class="external" href="http://dx.doi.org/10.4155/bio.12.211" target="_blank" title="It opens in new window">CrossRef
  2. Beckonert, O., Keun, H. C., Ebbels, T. M. D., Bundy, J. G., Holmes, E., Lindon, J. C., et al. (2007). Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. / Nature Protocols, / 2, 2692-703. class="external" href="http://dx.doi.org/10.1038/nprot.2007.376" target="_blank" title="It opens in new window">CrossRef
  3. Bernini, P., Bertini, I., Luchinat, C., Nincheri, P., Staderini, S., & Turano, P. (2011). Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. / Journal of Biomolecular NMR, / 49, 231-43. class="external" href="http://dx.doi.org/10.1007/s10858-011-9489-1" target="_blank" title="It opens in new window">CrossRef
  4. Blaise, B. J., Giacomotto, J., Elena, B., Dumas, M. E., Toullhoat, P., Segalat, L., et al. (2007). Metabotyping of Caenorhabditis elegans reveals latent phenotypes. / Proceedings of the National Academy of Sciences of the United States of America, / 104, 19808-9812. class="external" href="http://dx.doi.org/10.1073/pnas.0707393104" target="_blank" title="It opens in new window">CrossRef
  5. Bollard, M. E., Stanley, E. G., Lindon, J. C., Nicholson, J. K., & Holmes, E. (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. / NMR in Biomedicine, / 18, 143-62. class="external" href="http://dx.doi.org/10.1002/nbm.935" target="_blank" title="It opens in new window">CrossRef
  6. de Haan, J. R., Wehrens, R., Bauerschmidt, S., Piek, E., van Schaik, R. C., & Buydens, L. M. (2007). Interpretation of ANOVA models for microarray data using PCA. / Bioinformatics, / 23, 184-90. class="external" href="http://dx.doi.org/10.1093/bioinformatics/btl572" target="_blank" title="It opens in new window">CrossRef
  7. Dumas, M. E., Maibaum, E. C., Teague, C., Ueshima, H., Zhou, B. F., Lindon, J. C., et al. (2006). Assessment of analytical reproducibility of H-1 NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP study. / Analytical Chemistry, / 78, 2199-208. class="external" href="http://dx.doi.org/10.1021/ac0517085" target="_blank" title="It opens in new window">CrossRef
  8. Ebbels, T. M. D., Keun, H. C., Beckonert, O. P., Bollard, M. E., Lindon, J. C., Holmes, E., et al. (2007). Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: The consortium on metabonomic toxicology screening approach. / Journal of Proteome Research, / 6, 4407-422. class="external" href="http://dx.doi.org/10.1021/pr0703021" target="_blank" title="It opens in new window">CrossRef
  9. Ellis, J. K., Athersuch, T. J., Thomas, L. D., Teichert, F., Perez-Trujillo, M., Svendsen, C., et al. (2012). Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population. / BMC Medicine, / 10, 61. class="external" href="http://dx.doi.org/10.1186/1741-7015-10-61" target="_blank" title="It opens in new window">CrossRef
  10. Eriksson, L., Antti, H., Gottfries, J., Holmes, E., Johansson, E., Lindgren, F., et al. (2004). Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm). / Analytical and Bioanalytical Chemistry, / 380, 419-29. class="external" href="http://dx.doi.org/10.1007/s00216-004-2783-y" target="_blank" title="It opens in new window">CrossRef
  11. Fages, A., Pontoizeau, C., Jobard, E., Lévy, P., Bartosch, B., & Elena-Herrmann, B. (2013). Batch profiling calibration for robust NMR metabolomic data analysis. / Analytical and Bioanalytical Chemistry, / 405, 8819-827. class="external" href="http://dx.doi.org/10.1007/s00216-013-7296-0" target="_blank" title="It opens in new window">CrossRef
  12. Fallani, M., Young, D., Scott, J., Norin, E., Amarri, S., Adam, R., et al. (2010). Intestinal microbiota of 6-week-old infants across Europe: geographic influence beyond delivery mode, breast-feeding, and antibiotics. / Journal of Pediatric Gastroenterology and Nutrition, / 51, 77-4. class="external" href="http://dx.doi.org/10.1097/MPG.0b013e3181d1b11e" target="_blank" title="It opens in new window">CrossRef
  13. Floegel, A., Stefan, N., Yu, Z., Muhlenbruch, K., Drogan, D., Joost, H. G., et al. (2012). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. / Diabetes, / 62, 639-48. class="external" href="http://dx.doi.org/10.2337/db12-0495" target="_blank" title="It opens in new window">CrossRef
  14. Fonville, J. M., Richards, S. E., Barton, R. H.,
• 作者单位：Anne Fages (1)
  Pietro Ferrari (2)
  Stefano Monni (3)
  Laure Dossus (4) (5) (6)
  Anna Floegel (7)
  Nicolle Mode (8)
  Mattias Johansson (2) (9)
  Ruth C Travis (10)
  Christina Bamia (11)
  María-José Sánchez-Pérez (12) (13)
  Paolo Chiodini (14)
  Hendriek C Boshuizen (15)
  Marc Chadeau-Hyam (16)
  Elio Riboli (16)
  Mazda Jenab (2)
  Bénédicte Elena-Herrmann (1)
  
  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
  2. International Agency for Research in Cancer (IARC-WHO), 150 Cours Albert Thomas, 69372, Lyon CEDEX 08, France
  3. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  4. Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team, 94805, Villejuif, France
  5. Université Paris Sud, UMRS 1018, 94805, Villejuif, France
  6. Institut Gustave-Roussy (IGR), 94805, Villejuif, France
  7. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
  8. Department of Community Medicine Systems Epidemiology, Faculty of Health Sciences, University of Troms?, Troms?, Norway
  9. Department of Biobank Research, Ume? University, Ume?, Sweden
  10. Cancer Epidemiology Unit, University of Oxford, Oxford, UK
  11. WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece
  12. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
  13. Andalusian School of Public Health, Instituto de Investigación Biosanitaria de Granada, Granada, Spain
  14. Department of Mental Health and Preventive Medicine, Second University of Naples, Naples, Italy
  15. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
  16. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
  
• ISSN：1573-3890

文摘

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.