Abstract
Koreans have undergone many changes in dietary patterns with economic growth. The purpose of this research was to examine changes in dietary patterns using data from the 1998, 2001, and 2005 Korean National Health and Nutrition Examination Survey. The study included 21,525 subjects (8,295 from 1998, 6,880 from 2001, and 6,350 from 2005) who were 20 years or older and who participated in a 24-h diet recall. The percentage energy intake from 22 food groups was calculated, and a cluster analysis was applied to identify dietary patterns. Two dietary patterns emerged; the first pattern was characterized by high intake of white rice, legumes, vegetables, kimchi, and seaweeds, the so-called "traditional" pattern (53%), whereas the other pattern was characterized by high intake of other grains, noodle dumplings, floured bread, pizza, hamburgers, cereals and snacks, potatoes, sugared sweets, nuts, fruits, meat·its products, eggs, fish, milk and dairy products, oils, beverages and seasoning, or the so-called "modified" pattern. The modified pattern comprised a higher proportion of younger aged, metropolitan residents with more education and higher incomes. However, the gender distribution was not significantly different. The modified pattern had a significantly higher intake of all nutrients except carbohydrates and had a higher proportion of energy from fat and protein. No association with a risk for metabolic syndrome was found for either dietary pattern. After age was standardized, the traditional pattern included 52% of the respondents in 1998, 54% in 2001, and 50% in 2005. However, the modified pattern was significantly more prevalent in the younger age group (20-29 yr), whereas the traditional pattern increased significantly in the older age group (≥ 65 yr). In conclusion, a secular trend was found for dietary pattern by age group, suggesting that it is necessary to monitor the changes in dietary pattern by age group and to develop appropriate dietary education and guidelines.
Figures and Tables
Table 5
All the models were tested using general linear regression models by cluster group (*: p < 0.05, *: p < 0.01, ***: p < 0.001) Multivariate models were included with age, gender, education level, income, region, and study year. Logistic analyses used age, gender, region, education level, income, and study year as covariates. OR: odds ratio
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