Factor analysis is more appropriate to identify overall dietary patterns associated with diabetes when compared with Treelet transform analysis.
Treelet transform (TT) is a proposed alternative to factor analysis for deriving dietary patterns. Before applying this method to nutrition data, further analyses are required to assess its validity in nutritional epidemiology. We aimed to compare dietary patterns from factor analysis and TT and their associations with diabetes incidence. Complete data were available for 7349 women (50-55 y at baseline) from the Australian Longitudinal Study on Women's Health. Exploratory factor analysis and TT were performed to obtain patterns by using dietary data collected from an FFQ. Generalized estimating equations analyses were used to examine associations between dietary patterns and diabetes incidence. Two patterns were identified by both methods: a prudent and a Western dietary pattern. Factor analysis factors are a linear combination of all food items, whereas TT factors also include items with zero loading. The Western pattern identified by factor analysis showed a significant positive association with diabetes [highest quintile: OR = 1.94 (95% CI: 1.25, 3.00); P-trend = 0.001). Both factor analysis and TT involve different assumptions and subjective decisions. TT produces clearly interpretable factors accounting for almost as much variance as factors from factor analysis. However, TT patterns include food items with zero loading and therefore do not represent overall dietary patterns. The different dietary pattern loading structures identified by both methods result in different conclusions regarding the relationship with diabetes. Results from this study indicate that factor analysis might be a more appropriate method for identifying overall dietary patterns associated with diabetes compared with TT.
School of Population Health, University of Queensland, Brisbane, Queensland, Australia.
Danielle A J M
DA
Schoenaker
Annette J
AJ
Dobson
Sabita S
SS
Soedamah-Muthu
Gita D
GD
Mishra
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001315:Australia; D016001:Confidence Intervals; D003920:Diabetes Mellitus; D004032:Diet / Q000706:statistics & numerical data*; D005163:Factor Analysis, Statistical; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008875:Middle Aged; D016017:Odds Ratio; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*