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

Wirfält, Elisabet LU ; Mattisson, Iréne LU ; Gullberg, Bo LU and Berglund, Göran LU (2000) In Public Health Nutrition 3(2). p.159-173
Abstract
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... (More)
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 Malmo 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 = 28 098). 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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Dietary patterns, Food patterns, Dietary exposure categories, Nutrient density, Standardization, Z-scores, Diet history, Epidemiology
in
Public Health Nutrition
volume
3
issue
2
pages
159 - 173
publisher
Cambridge University Press
external identifiers
  • pmid:10948383
  • scopus:0033860914
ISSN
1475-2727
DOI
10.1017/S1368980000000197
language
English
LU publication?
yes
id
c30a43da-39cb-40a4-bb2a-634abf8f8a63 (old id 1116226)
date added to LUP
2008-07-01 10:37:30
date last changed
2017-07-30 04:33:20
@article{c30a43da-39cb-40a4-bb2a-634abf8f8a63,
  abstract     = {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 Malmo 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 = 28 098). 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.},
  author       = {Wirfält, Elisabet and Mattisson, Iréne and Gullberg, Bo and Berglund, Göran},
  issn         = {1475-2727},
  keyword      = {Dietary patterns,Food patterns,Dietary exposure categories,Nutrient density,Standardization,Z-scores,Diet history,Epidemiology},
  language     = {eng},
  number       = {2},
  pages        = {159--173},
  publisher    = {Cambridge University Press},
  series       = {Public Health Nutrition},
  title        = {Food patterns defined by cluster analysis and their utility as dietary exposure variables: a report from the Malmo Diet and Cancer Study},
  url          = {http://dx.doi.org/10.1017/S1368980000000197},
  volume       = {3},
  year         = {2000},
}