title: A posteriori dietary patterns: how many patterns to retain?
abstract: Principal component analysis (PCA) and cluster analysis are used frequently to derive dietary patterns. Decisions on how many patterns to extract are primarily based on subjective criteria, whereas different solutions vary in their food-group composition and perhaps association with disease outcome. Literature on reliability of dietary patterns is scarce, and previous studies validated only 1 preselected solution. Therefore, we assessed reliability of different pattern solutions ranging from 2 to 6 patterns, derived from the aforementioned methods. A validated food frequency questionnaire was administered at baseline (1993-1997) to 39,678 participants in the European Prospective Investigation into Cancer and Nutrition-The Netherlands (EPIC-NL) cohort. Food items were grouped into 31 food groups for dietary pattern analysis. The cohort was randomly divided into 2 halves, and dietary pattern solutions derived in 1 sample through PCA were replicated through confirmatory factor analysis in sample 2. For cluster analysis, cluster stability and split-half reproducibility were assessed for various solutions. With PCA, we found the 3-component solution to be best replicated, although all solutions contained ≥1 poorly confirmed component. No quantitative criterion was in agreement with the results. Associations with disease outcome (coronary heart disease) differed between the component solutions. For all cluster solutions, stability was excellent and deviations between samples was negligible, indicating good reproducibility. All quantitative criteria identified the 2-cluster solution as optimal. Associations with disease outcome were comparable for different cluster solutions. In conclusion, reliability of obtained dietary patterns differed considerably for different solutions using PCA, whereas cluster analysis derived generally stable, reproducible clusters across different solutions. Quantitative criteria for determining the number of patterns to retain were valuable for cluster analysis but not for PCA. Associations with disease risk were influenced by the number of patterns that are retained, especially when using PCA. Therefore, studies on associations between dietary patterns and disease risk should report reasons to choose the number of retained patterns.
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: Heidi P
authors.initials: HP
authors.lastname: Fransen
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Anne M
authors.initials: AM
authors.lastname: May
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Martin D
authors.initials: MD
authors.lastname: Stricker
authors.affiliation: National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: Jolanda M A
authors.initials: JM
authors.lastname: Boer
authors.affiliation: Department of Statistical Science, University College London, London, UK; and.
authors.forename: Christian
authors.initials: C
authors.lastname: Hennig
authors.affiliation: Department of Data Analysis, Ghent University, Ghent, Belgium.
authors.forename: Yves
authors.initials: Y
authors.lastname: Rosseel
authors.affiliation: National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
authors.forename: Marga C
authors.initials: MC
authors.lastname: Ocké
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Petra H M
authors.initials: PH
authors.lastname: Peeters
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands J.Beulens@umcutrecht.nl.
authors.forename: Joline W J
authors.initials: JW
authors.lastname: Beulens
mesh_terms: D000328:Adult; D000368:Aged; D002908:Chronic Disease; D016000:Cluster Analysis; D003327:Coronary Disease / Q000453:epidemiology*; D004032:Diet; D002149:Energy Intake; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D015994:Incidence; D008019:Life Style; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D009426:Netherlands; D015596:Nutrition Assessment; D025341:Principal Component Analysis; D011446:Prospective Studies; D015203:Reproducibility of Results; D012307:Risk Factors; D011795:Surveys and Questionnaires; D055815:Young Adult