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- Fahey, Michael T., et al.
(författare)
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Identifying dietary patterns using a normal mixture model: application to the EPIC study
- 2012
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Ingår i: Journal of Epidemiology and Community Health. - : BMJ. - 1470-2738 .- 0143-005X. ; 66:1, s. 89-94
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Tidskriftsartikel (refereegranskat)abstract
- Background Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption.
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| 2. |
- Freisling, Heinz, et al.
(författare)
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Region-Specific Nutrient Intake Patterns Exhibit a Geographical Gradient within and between European Countries
- 2010
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Ingår i: Journal of Nutrition. - : Elsevier BV. - 1541-6100 .- 0022-3166. ; 140:7, s. 1280-1286
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Tidskriftsartikel (refereegranskat)abstract
- Until recently, the study of nutrient patterns was hampered at an international level by a lack of standardization of both dietary methods and nutrient databases. We aimed to describe the diversity of nutrient patterns in the European Prospective Investigation into Cancer and Nutrition (EPIC) study at population level as a starting point for future nutrient pattern analyses and their associations with chronic diseases in multi-center studies. In this cross-sectional study, 36,034 persons aged 35-74 y were administered a single, standardized 24-h dietary recall. Intake of 25 nutrients (excluding intake from dietary supplements) was estimated using a standardized nutrient database. We used a graphic presentation of mean nutrient intakes by region and sex relative to the overall EPIC means to contrast patterns within and between 10 European countries. In Mediterranean regions, including Greece, Italy, and the southern centers of Spain, the nutrient pattern was dominated by relatively high intakes of vitamin E and monounsaturated fatty acids (MUFA), whereas intakes of retinol and vitamin D were relatively low. In contrast, in Nordic countries, including Norway, Sweden, and Denmark, reported intake of these same nutrients resulted in almost the opposite pattern. Population groups in Germany, The Netherlands, and the UK shared a fatty acid pattern of relatively high intakes of PUFA and SFA and relatively low intakes of MUFA, in combination with a relatively high intake of sugar. We confirmed large variability in nutrient intakes across the EPIC study populations and identified 3 main region-specific patterns with a geographical gradient within and between European countries. J. Nutr. 140: 1280-1286, 2010.
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