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Food patterns defined by cluster analysis and their utility as dietary exposure variables: a report from the Malmö Diet and Cancer Study

Published online by Cambridge University Press:  02 January 2007

Elisabet Wirfält ,
Irene Mattisson ,
Bo Gullberg  and
Göran Berglund
Elisabet Wirfält*
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
Irene Mattisson
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
Bo Gullberg
Affiliation:
Department of Community Medicine, Lund University, SE-20502: Malmö, Sweden
Göran Berglund
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
*
*Corresponding author: Email elisabet.wirfalt@smi.mas.lu.se
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Abstract

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Objective

To explore the utility of cluster analysis in defining complex dietary exposures, separately with two types of variables.

Design

A modified diet history method, combining a 7-day menu book and a 168-item questionnaire, assessed dietary habits. A standardized questionnaire collected information on sociodemographics, lifestyle and health history. Anthropometric information was obtained through direct measurements. The dietary information was collapsed into 43 generic food groups, and converted into variables indicating the per cent contribution of specific food groups to total energy intake. Food patterns were identified by the QUICK CLUSTER procedure in SPSS, in two separate analytical steps using unstandardized and standardized (Z-scores) clustering variables.

Setting

The Malmö Diet and Cancer (MDC) Study, a prospective study in the third largest city of Sweden, with baseline examinations from March 1991 to October 1996.

Subjects

A random sample of 2206 men and 3151 women from the MDC cohort (n=28098).

Results

Both variable types produced conceptually well separated clusters, confirmed with discriminant analysis. ‘Healthy’ and ‘less healthy’ food patterns were also identified with both types of variables. However, nutrient intake differences across clusters were greater, and the distribution of the number of individuals more even, with the unstandardized variables. Logistic regression indicated higher risks of past food habit change, underreporting of energy and higher body mass index (BMI) for individuals falling into ‘healthy’ food pattern clusters.

Conclusions

The utility in discriminating dietary exposures appears greater for unstandardized food group variables. Future studies on diet and cancer need to recognize the confounding factors associated with ‘healthy’ food patterns.

Keywords

Dietary patternsFood patternsDietary exposure categoriesNutrient densityStandardizationZ-scoresDiet historyEpidemiology
Type
Research Article
Information
Public Health Nutrition , Volume 3 , Issue 2 , June 2000 , pp. 159 - 173
Copyright
Copyright © CABI Publishing 2000

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