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  • 标题:Gender Differences in Dietary Patterns and Their Association with the Prevalence of Metabolic Syndrome among Chinese: A Cross-Sectional Study
  • 本地全文:下载
  • 作者:Shu-Hong Xu
  • 期刊名称:Nutrients
  • 电子版ISSN:2072-6643
  • 出版年度:2016
  • 卷号:8
  • 期号:4
  • 页码:180
  • DOI:10.3390/nu8040180
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:Few studies have investigated gender differences in dietary intake. The objective of this cross-sectional study was to examine gender differences in dietary patterns and their association with the prevalence of metabolic syndrome. The food intakes of 3794 subjects enrolled by a two-stage cluster stratified sampling method were collected using a valid semi-quantitative food frequency questionnaire (FFQ). Metabolic syndrome (MetS) was defined according to the International Diabetes Federation (IDF) and its prevalence was 35.70% in the sample (37.67% in men and 24.67% in women). Dietary patterns were identified using factor analysis combined with cluster analysis and multiple group confirmatory factor analysis was used to assess the factorial invariance between gender groups. The dominating dietary pattern for men was the “balanced” dietary pattern (32.65%) and that for women was the “high-salt and energy” dietary pattern (34.42%). For men, the “animal and fried food” dietary pattern was related to higher risk of MetS (odds ratio: 1.27; 95% CI: 1.01–1.60), after adjustment for age, marital status, socioeconomic status and lifestyle factors. For women, the “high-salt and energy” dietary pattern was related to higher risk of MetS (odds ratio: 2.27; 95% CI: 1.24–4.14). We observed gender differences in dietary patterns and their association with the prevalence of MetS. For men, the “animal and fried food” dietary pattern was associated with enhancive likelihood of MetS. For women, it was the “high-salt and energy” dietary pattern.
  • 关键词:dietary patterns; metabolic syndrome; factor analysis; invariance; cluster analysis dietary patterns ; metabolic syndrome ; factor analysis ; invariance ; cluster analysis
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