European Journal of Epidemiology

, Volume 17, Issue 6, pp 505–516

Use of multiple correspondence analysis and cluster analysis to study dietary behaviour: Food consumption questionnaire in the SU.VI.MAX. cohort


  • C. Guinot
    • CE.R.I.E.S., Neuilly Sur Seine
  • J. Latreille
    • CE.R.I.E.S., Neuilly Sur Seine
  • D. Malvy
    • INSERM U330University Victor Segalen Bordeaux 2
  • P. Preziosi
    • SU.VI.MAX. co-ordination, ISTNA, CNAM
  • P. Galan
    • SU.VI.MAX. co-ordination, ISTNA, CNAM
  • S. Hercberg
    • SU.VI.MAX. co-ordination, ISTNA, CNAM
  • M. Tenenhaus
    • HEC School of management –
    • Department SIAD, Jouy en Josas

DOI: 10.1023/A:1014586129113

Cite this article as:
Guinot, C., Latreille, J., Malvy, D. et al. Eur J Epidemiol (2001) 17: 505. doi:10.1023/A:1014586129113


Although the effects of individual foods or nutrients on the development of diseases and their risk factors have been investigated in many studies, little attention has been given to the effect of overall dietary patterns. The main objectives of this study were to identify dietary patterns and groups of subjects with similar food consumption habits, i.e. ‘dietary profiles’, using multiple correspondence analysis and cluster analysis. A food frequency questionnaire was sent to a large population-based sample (2923 women and 2180 men), recruited among the ‘SUpplémentation en VItamines et Minéraux AntioXydants’ (SU.VI.MAX.) cohort participants in France. The food items were dichotomised in order to focus the study on the highest levels of consumption. Multiple correspondence analysis allows the construction of principal components, which optimally summarise the data, and enables the construction of graphical displays. An interesting property of these graphical displays is that associations between food items can be observed on various projection planes, each category of each food item being located at the centre of gravity of the subjects corresponding to this category. An ascending hierarchical classification was unsuccessfully tried in order to determine clusters from these principal components. Therefore, a ‘dissection’ of the cloud of points was performed according to the orientation of the axes, providing a readily interpretable eight-dietary profiles typology for each sex. This statistical approach allows identification of particular dietary patterns and dietary profiles, which might be more appropriate in studies of diet-disease associations than the single food or nutrient approach that has dominated past epidemiological research.

Cluster analysisDietary patternsFood consumption habitsFood frequency questionnaireMultiple correspondence analysisNutritional epidemiology
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© Kluwer Academic Publishers 2001