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Bo Young Jang
, So Young Bu
, So Young Bu
Abstract
Purpose
This study investigated major dietary patterns among healthy Korean adults using cluster analysis and analyzed the relationship between energy intake and skeletal muscle mass.
Methods
This study was conducted using the data from the 2008 ~ 2010 Korea National Health and Nutrition Survey. The data of 7,922 subjects aged 30 years and over, without any missing values, were included in the final analysis. K-means cluster analyses were conducted to identify the dietary patterns of the study subjects, which were based on the energy intake from 21 food groups using a 24-h recall method. The changes in energy intake with each dietary pattern, according to quartiles of skeletal muscle mass, were investigated.
Results
Three dietary patterns were identified for both men and women: ‘Flour, Animal fat’, ‘White rice’ and ‘Healthy mixed diet’. The association between energy intake and skeletal muscle mass for both men and women was significant only in the ‘White rice’ dietary pattern. In the ‘White rice’ pattern, the energy intake increased up to > 300 kcal from the lowest to the highest quartile of skeletal muscle mass after adjustment for covariates. Within the ‘White rice’ pattern, skeletal muscle mass was linearly associated with energy intake in all the age groups in men.
Conclusion
Energy intake was significantly associated with changes in skeletal muscle mass only in the ‘White rice’ pattern. Furthermore, the degree of association between the change in skeletal muscle mass and energy intake differed according to gender. These results indicate that the association between skeletal muscle mass and energy intake may be specific to Korean people who are accustomed to a traditional Korean diet.
Figures and Tables
Fig. 1
Estimated change of total energy intake according to quartile increase of the skeletal muscle mass in men of ‘white rice’ dietary pattern. The complex sampling design parameters of the Korea National Health and Nutrition Examination Survey were used. Data were presented by each age group. Data are expressed as estimate mean with the bar of 95% CI. P value for trend is indicated on each age group. For men first quartile (Q1): logSMI < −0.1960, Q2: −0.1960 ≤ logSMI < −0.1138, Q3: −0.1138 ≤ logSMI < −0.0288 and Q4: −0.0288 ≤ logSMI, Regression model was adjusted for age, household income, education, body mass index, systolic blood pressure.
Table 2
Age distribution and lifestyle characteristics of the study subjects by cluster
1) Data for continuous variables are presented as mean ± SE.
2) Numbers of frequency with percent ratio for categorical variables, n (%)
3) The p values are from survey regression for continuous variables and Rao-Scott chi square test for assessing the difference among clusters.
4) A serving size of alcohol intake: one cup (50 cc) of soju or one glass of beer (200 cc), “≥ 1 ~ 2 serving/d” corresponds to ≥ 2 serving/day for men and ≥ 1.5 servings/day for women, “< 1 ~ 2 serving/d” corresponds to < 2 serving/day for men and < 1.5 servings/day for women.
Table 4
Estimated change of total energy intake according to quartile of the skeletal muscle mass within each cluster
1) For men first quartile (Q1): logSMI < −0.1960, Q2: −0.1960 ≤ logSMI < −0.1138, Q3: −0.1138 ≤ logSMI < −0.0288 and Q4: −0.0288 ≤ logSMI, and for women first quartile (Q1): logSMI < −0.5940, Q2: −0.5940 ≤ logSMI < −0.4954, Q3: −0.4954 ≤ logSMI < −0.4054 and Q4: −0.4054 ≤ logSMI
2) Regression model was adjusted for age, household income, education, body mass index, systolic blood pressure.
3) Change of energy intake in each cluster was compared to reference group of skeletal muscle mass index (Q1) within same sex.
4) Beta coefficient with 95% confidence interval is shown.
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