OBJECTIVE: This study explored the usefulness of cluster analysis in identifying food choice patterns of three groups of adults in relation to their energy intake. DESIGN: Food frequency data were converted to percentage of total energy from 38 food groups and entered into a cluster analysis procedure. Subjects in the emerging food group patterns were compared in terms of weight status, demographics, and the nutrition composition of their usual diet. SETTING: Data were collected as part of three studies in two US metropolitan areas using identical protocols. Participants were university employees (103 women and 99 men) who volunteered for a reliability study of health behavior questionnaires and moderately obese volunteers (223 women and 101 men) to two weight-loss studies who were recruited by newspaper advertisements. STATISTICAL ANALYSIS PERFORMED: Subjects were clustered according to food energy sources using the FASTCLUS procedure in the Statistical Analysis System. One-way analysis of variance and chi 2 analysis were then performed to compared the weight status, nutrient intakes, and demographics of the food patterns. RESULTS: Six food pattern clusters were identified. Subjects in the two clusters associated with high consumption of pastry and meat had significantly higher fat intakes (P = .0001). Subjects in two other clusters, those associated with high intake of skim milk and a broad distribution of energy sources had significantly higher micronutrient levels (P = .0001). Body mass index and the distribution of gender were also significantly different across clusters. CONCLUSIONS: The success of cluster analysis in identifying dietary exposure categories with unique demographic and nutritional correlates suggests that the approach may be useful in epidemiologic studies that examine conditions such as obesity, and in the design of nutrition interventions.
OBJECTIVE: This study explored the usefulness of cluster analysis in identifying food choice patterns of three groups of adults in relation to their energy intake. DESIGN: Food frequency data were converted to percentage of total energy from 38 food groups and entered into a cluster analysis procedure. Subjects in the emerging food group patterns were compared in terms of weight status, demographics, and the nutrition composition of their usual diet. SETTING: Data were collected as part of three studies in two US metropolitan areas using identical protocols. Participants were university employees (103 women and 99 men) who volunteered for a reliability study of health behavior questionnaires and moderately obese volunteers (223 women and 101 men) to two weight-loss studies who were recruited by newspaper advertisements. STATISTICAL ANALYSIS PERFORMED: Subjects were clustered according to food energy sources using the FASTCLUS procedure in the Statistical Analysis System. One-way analysis of variance and chi 2 analysis were then performed to compared the weight status, nutrient intakes, and demographics of the food patterns. RESULTS: Six food pattern clusters were identified. Subjects in the two clusters associated with high consumption of pastry and meat had significantly higher fat intakes (P = .0001). Subjects in two other clusters, those associated with high intake of skim milk and a broad distribution of energy sources had significantly higher micronutrient levels (P = .0001). Body mass index and the distribution of gender were also significantly different across clusters. CONCLUSIONS: The success of cluster analysis in identifying dietary exposure categories with unique demographic and nutritional correlates suggests that the approach may be useful in epidemiologic studies that examine conditions such as obesity, and in the design of nutrition interventions.
Authors: Linda Van Horn; Lu Tian; Marian L Neuhouser; Barbara V Howard; Charles B Eaton; Linda Snetselaar; Nirupa R Matthan; Alice H Lichtenstein Journal: J Nutr Date: 2011-12-21 Impact factor: 4.798
Authors: Stephanie A Navarro Silvera; Susan T Mayne; Harvey A Risch; Marilie D Gammon; Thomas Vaughan; Wong-Ho Chow; Joel A Dubin; Robert Dubrow; Janet Schoenberg; Janet L Stanford; A Brian West; Heidrun Rotterdam; William J Blot Journal: Ann Epidemiol Date: 2011-03-23 Impact factor: 3.797
Authors: Enju Liu; Nicola M McKeown; P K Newby; James B Meigs; Ramachandran S Vasan; Paula A Quatromoni; Ralph B D'Agostino; Paul F Jacques Journal: Br J Nutr Date: 2009-02-16 Impact factor: 3.718
Authors: Dolores M Wolongevicz; Lei Zhu; Michael J Pencina; Ruth W Kimokoti; P K Newby; Ralph B D'Agostino; Barbara E Millen Journal: J Obes Date: 2010-01-05
Authors: Dolores M Wolongevicz; Lei Zhu; Michael J Pencina; Ruth W Kimokoti; P K Newby; Ralph B D'Agostino; Barbara E Millen Journal: Br J Nutr Date: 2009-11-24 Impact factor: 3.718
Authors: Sinéad Boylan; Ailsa Welch; Hynek Pikhart; Sofia Malyutina; Andrzej Pajak; Ruzena Kubinova; Oksana Bragina; Galina Simonova; Urszula Stepaniak; Aleksandra Gilis-Januszewska; Lubomíra Milla; Anne Peasey; Michael Marmot; Martin Bobak Journal: BMC Public Health Date: 2009-12-01 Impact factor: 3.295