Using cluster analysis to examine dietary patterns: Nutrient intakes, gender, and weight status differ across food pattern clusters
Using cluster analysis to examine dietary patterns: Nutrient intakes, gender, and weight status differ across food pattern clusters
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DOI：
10.1016/s0002-8223(97)00071-0
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发表时间：
1997-03-01
影响因子：
--
通讯作者：
Jeffery, RW
中科院分区：
文献类型：
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作者：
Wirfalt, AKE;Jeffery, RW
目的 本研究探讨聚类分析在确定三组成年人与其能量摄入相关的食物选择模式方面的有用性。设计 食物频率数据被转换为 38 个食物组总能量的百分比，并进入聚类分析程序。对新兴食物组模式中的受试者的体重状况、人口统计数据和日常饮食的营养成分进行了比较。作为在美国两个大都市地区使用相同方案进行的三项研究的一部分，收集了设置数据。参与者是自愿参加健康行为问卷可靠性研究的大学雇员（103 名女性和 99 名男性），以及通过报纸广告招募的参加两项减肥研究的中度肥胖志愿者（223 名女性和 101 名男性）。 进行统计分析 使用统计分析系统中的 FASTCLUS 程序，根据食物能量来源对受试者进行聚类。然后进行单向方差分析和chi(2) 分析，以比较体重状况、营养摄入量和食物模式的人口统计数据。结果 确定了六个食物模式簇。与大量食用糕点和肉类相关的两个组中的受试者的脂肪摄入量显着较高 (P=.0001)。另外两个组中的受试者，即那些与大量摄入低脂牛奶和广泛分布能源相关的受试者，其微量营养素水平显着较高 (P=.0001)。不同聚类之间的体重指数和性别分布也存在显着差异。 结论 聚类分析在识别具有独特的人口统计和营养相关性的饮食暴露类别方面取得的成功表明，该方法可能有助于检查肥胖等疾病的流行病学研究以及营养干预措施的设计。
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 compare 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 slim 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 maybe useful in epidemiologic studies that examine conditions such as obesity, and in the design of nutrition interventions.