Compositional analysis of dietary patterns

  • M Solans
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • G Coenders
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • R Marcos-Gragera
    Research Group on Statistics, Econometrics and Health (GRECS), Universitat de Girona, Girona, Spain
  • A Castelló
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • E Gràcia-Lavedan
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • Y Benavente
    Unit of molecular and genetic epidemiology in infections and cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain
  • V Moreno
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • B Pérez-Gómez
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • P Amiano
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • T Fernández-Villa
    Instituto de Biomedicina, Universidad de León, León, Spain
  • M Guevara
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • I Gómez-Acebo
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • G Fernández-Tardón
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • M Vanaclocha-Espi
    Cancer and Public Health Area, FISABIO – Public Health, Valencia, Spain
  • MD Chirlaque
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • R Capelo
    Centro de Investigación en Recursos Naturales, Salud y medio Ambiente (RENSMA), Universidad de Huelva, Huelva, Spain
  • R Barrios
    Departamento de Medicina Preventiva y Salud Pública, Facultad de Medicina, Universidad de Granada, Granada, Spain
  • N Aragonés
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • A Molinuevo
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • F Vitelli-Storelli
    Instituto de Biomedicina, Universidad de León, León, Spain
  • J Castilla
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • T Dierssen-Sotos
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • G Castaño-Vinyals
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • M Kogevinas
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • M Pollán
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • M Saez
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain

抄録

<jats:p> Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others. </jats:p>

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