Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults

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Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9...

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OBJECTIVE Pattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions. DESIGN Analysis was based on the Irish National ...

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BACKGROUND Despite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal component analysis (PCA) and cluster analysis (CA) in older adults and to examine their associations...

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Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults.

OBJECTIVE We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. DESIGN We ...

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ژورنال

عنوان ژورنال: British Journal of Nutrition

سال: 2008

ISSN: 0007-1145,1475-2662

DOI: 10.1017/s0007114508014128