%0 Journal Article
%T Gender Differences in Dietary Patterns and Their Association with the Prevalence of Metabolic Syndrome among Chinese: A Cross-Sectional Study
%A Chen-Ming Sun
%A Cong Wang
%A Hai-Xia Zhang
%A Hui Wang
%A Jian-Jun Huang
%A Jie Liang
%A Nan Qiao
%A Qing Lu
%A Shu-Hong Xu
%A Shuang-Shuang Tian
%A Tong Wang
%A Xiao-Meng Liu
%A Yan Cui
%J Archive of "Nutrients".
%D 2016
%R 10.3390/nu8040180
%X 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¨C1.60), 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¨C4.14). 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
%K dietary patterns
%K metabolic syndrome
%K factor analysis
%K invariance
%K cluster analysis
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848649/