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ARS Home » Northeast Area » Boston, Massachusetts » Jean Mayer Human Nutrition Research Center On Aging » Research » Publications at this Location » Publication #166709
Title: DIETARY PATTERNS OF OLDER ADULTS: A COMPARISON CLUSTER ANALYSIS METHODS

Author
item BAILEY, REGAN - PENN STATE UNIVERSITY
item DAVIS, MELISSA - PENN STATE UNIVERSITY
item MITCHELL, DIANE - PENN STATE UNIVERSITY
item MILLER, CARLA - PENN STATE UNIVERSITY
item SMICIKLAS-WRIGHT, HELEN - PENN STATE UNIVERSITY

Submitted to: Experimental Biology
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/2003
Publication Date: 4/1/2004
Citation: Bailey RL, Davis MS, Mitchell DC, Smiciklas-Wright H, Miller CK. Dietary Patterns of older adults: a comparision of cluster analysis methods. FASEB Journal 18(4) 108.1,2004

Interpretive Summary:

Technical Abstract: Dietary patterns have been increasingly important in examining diet and disease relationships in epidemiological studies. Dietary patterns of older adults were examined using 2 different cluster analysis strategies: by number of servings (SVGS) and % energy contribution (KCAL) from food subgroups. Data were part of a cohort from the Geisinger Rural Aging Study (GRAS), a longitudinal study of older adults in rural Pennsylvania. Demographic, health, and anthropometric data were collected via home visit; dietary intake was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. The methods differed in the food subgroups that 'clustered' together. Both methods produced clusters that had significant differences in Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly greater amounts of most micronutrients. Waist circumference was significantly lower in the cluster with higher HEI scores using the SVGS method but not with the KCAL method. Both methods were able to cluster food subgroups with high energy contribution (e.g., fats and oils, processed meat and dairy desserts). KCAL was not sensitive to food subgroups with minimal energy contribution (e.g., fruits and vegetables). The two methods employed yielded very different solutions; the commonality of the two methods provides specific food targets for nutrition intervention.