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Compositional analysis of dietary patterns

Authors :
Marina Pollán
Facundo Vitelli-Storelli
Rafael Marcos-Gragera
Adela Castelló
Rocío Capelo
Pilar Amiano
Marta Solans
Gemma Castaño-Vinyals
Mercedes Vanaclocha-Espi
Germà Coenders
M-D Chirlaque
Marcela Guevara
Guillermo Fernández-Tardón
Trinidad Dierssen-Sotos
Amaia Molinuevo
Jesús Castilla
Tania Fernández-Villa
Marc Saez
Beatriz Pérez-Gómez
Manolis Kogevinas
R Barrios
Inés Gómez-Acebo
Yolanda Benavente
Esther Gracia-Lavedan
Nuria Aragonés
Victor Moreno
Source :
STATISTICAL METHODS IN MEDICAL RESEARCH, r-FISABIO. Repositorio Institucional de Producción Científica, instname, Arias Montano. Repositorio Institucional de la Universidad de Huelva, r-FISABIO: Repositorio Institucional de Producción Científica, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Publication Year :
2019
Publisher :
SAGE PUBLICATIONS LTD, 2019.

Abstract

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.<br />The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was partially funded by the ‘‘Accion Transversal del Cancer’’, approved by the Spanish Ministry Council on 11 October 2007; by the Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP); by the Instituto de Salud Carlos III-FEDER (grant nos PI08/1770, PI08/0533, PI08/1359, PS09/00773-Cantabria, PS09/01286-Leo´n, PS09/ 01903-Valencia, PS09/02078-Huelva, PS09/01662-Granada, PI11/01403, PI11/01810, PI11/02213, PI12/00488, PI14/01219, PI14/0613, PI15/00069, PI15/00914, PI15/01032, PI17/01280, PI09/0914, IJCI-2014-20900); by the Spanish Ministry of Health (grant no. CB06/02/1002); by the Spanish Ministry of Economy and Competitiveness (grant no. MTM2015-65016- C2-1-R); by the Catalan Government-Agency for Management of University and Research Grants (AGAUR) (grant nos 2014SGR551, 2017SGR656, 2017SGR733, 2017SGR723, 2017SGR1085); by the University of Girona (grant no. MPCUdG2016/069, GDRCompetUdG2017/19); by the Fundacio´n Marque´s de Valdecilla (grant no. API 10/09); by the Junta de Castilla y Leo´n (grant no. LE22A10-2); by the Consejerı´a de Salud of the Junta de Andalucı´a (grant nos PI-0571- 2009, PI-0306-2011, salud201200057018tra); by the Conselleria de Sanitat of the Generalitat Valenciana (grant no. AP_061/10); by the Regional Government of the Basque Country; by the Consejerı´a de Sanidad de la Regio´n de Murcia; by the European Commission (FOOD-CT-2006-036224-HIWATE); by the Spanish Association Against Cancer (AECC) Scientific Foundation; by the Fundacio´n Caja de Ahorros de Asturias; and by the University of Oviedo. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.

Details

ISSN :
09622802
Database :
OpenAIRE
Journal :
STATISTICAL METHODS IN MEDICAL RESEARCH, r-FISABIO. Repositorio Institucional de Producción Científica, instname, Arias Montano. Repositorio Institucional de la Universidad de Huelva, r-FISABIO: Repositorio Institucional de Producción Científica, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Accession number :
edsair.doi.dedup.....8314443d8f0183872c7395affa113894