OBJECTIVES: To characterize dietary patterns using two different cluster analysis strategies. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years, in rural Pennsylvania. STATISTICAL ANALYSIS: Cluster analysis was performed. RESULTS: The methods differed in the food subgroups that clustered together. Both methods produced clusters that had significant differences in overall diet quality as assessed by Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly higher amounts of most micronutrients. Both methods consistently clustered subgroups with high energy contribution (eg, fats and oils and dairy desserts) with a lower HEI score. Clusters resulting from the percent energy method were less likely to differentiate fruit and vegetable subgroups. The higher diet quality dietary pattern derived from the number of servings method resulted in more favorable weight status. CONCLUSIONS: Cluster analysis of food subgroups using two different methods on the same data yielded similarities and dissimilarities in dietary patterns. Dietary patterns characterized by the number of servings method of analysis provided stronger association with weight status and was more sensitive to fruit and vegetable intake with regard to a more healthful dietary pattern within this sample. Public health recommendations should evaluate the methodology used to derive dietary patterns.
OBJECTIVES: To characterize dietary patterns using two different cluster analysis strategies. DESIGN: In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and anthropometric data were collected via home visit. SUBJECTS: One hundred seventy-nine community-dwelling adults, aged 66 to 87 years, in rural Pennsylvania. STATISTICAL ANALYSIS: Cluster analysis was performed. RESULTS: The methods differed in the food subgroups that clustered together. Both methods produced clusters that had significant differences in overall diet quality as assessed by Healthy Eating Index (HEI) scores. The clusters with higher HEI scores contained significantly higher amounts of most micronutrients. Both methods consistently clustered subgroups with high energy contribution (eg, fats and oils and dairy desserts) with a lower HEI score. Clusters resulting from the percent energy method were less likely to differentiate fruit and vegetable subgroups. The higher diet quality dietary pattern derived from the number of servings method resulted in more favorable weight status. CONCLUSIONS: Cluster analysis of food subgroups using two different methods on the same data yielded similarities and dissimilarities in dietary patterns. Dietary patterns characterized by the number of servings method of analysis provided stronger association with weight status and was more sensitive to fruit and vegetable intake with regard to a more healthful dietary pattern within this sample. Public health recommendations should evaluate the methodology used to derive dietary patterns.
Authors: Maree G Thorpe; Catherine M Milte; David Crawford; Sarah A McNaughton Journal: Int J Behav Nutr Phys Act Date: 2016-02-29 Impact factor: 6.457
Authors: Ana Amélia Freitas-Vilela; Rebecca M Pearson; Pauline Emmett; Jon Heron; Andrew D A C Smith; Alan Emond; Joseph R Hibbeln; Maria Beatriz Trindade Castro; Gilberto Kac Journal: Matern Child Nutr Date: 2017-03-02 Impact factor: 3.092
Authors: Ana Amélia Freitas-Vilela; Andrew D A C Smith; Gilberto Kac; Rebecca M Pearson; Jon Heron; Alan Emond; Joseph R Hibbeln; Maria Beatriz Trindade Castro; Pauline M Emmett Journal: Matern Child Nutr Date: 2016-10-09 Impact factor: 3.092
Authors: Shi Yan; Benjamin Jun Jie Seng; Yu Heng Kwan; Chuen Seng Tan; Joanne Hui Min Quah; Julian Thumboo; Lian Leng Low Journal: BMC Fam Pract Date: 2019-04-23 Impact factor: 2.497