Abstract
Objectives
The purpose of the study was to compare intake of energy nutrients, physical characteristics, and the prevalence of metabolic syndrome according to protein intake group. Methods: Subjects were 827 men aged 40–65 years. The results presented were based on data from the 2012–2013 National Health and Nutrition Examination Survey and analyzed using SPSS. The odds ratio (OR) of metabolic syndrome was assessed according to the protein intake group and intake pattern of protein-rich foods. Results: The mean of protein intake was 73.96 ± 0.71 g. According to level of protein intake, four groups (deficient, normal, excess 1, excess 2) were created and their percentages were 8.3%, 39.6%, 37.1%, and 15.0% respectively. The mean of daily energy intake was 2,312.33 ± 24.08 kcal. It was higher in excess group 2 than in the deficiency group (p<0.001). Moreover, the intake of all energy nutrients increased significantly with protein intake group (p < 0.001). The main contribution to daily protein included mixed grains (10.96 ± 0.32 g), milled rice (7.14 ± 0.30 g), chicken (3.50 ± 0.21 g), and grilled pork belly (3.04 ± 0.16 g). With regard to physical characteristics, and blood pressure and blood test results, only body mass index increased significantly according to protein intake groups (p<0.05). The prevalence of metabolic syndrome in subjects was 38.5%, and there was no significant correlation with protein intake group. The OR of metabolic syndrome increased with protein intake, and was higher 4.452 times in excess group 2 than in the normal group (p<0.05). Conversely, the OR of metabolic syndrome according to the frequency of protein-rich food intake did not show a significant correlation. Conclusions: The results of this study can be used as significant supporting data to establish guidelines for protein intake in middle-aged men.
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1) Adjusted for energy intake (continuous variable), carbohydrate intake (continuous variable), fat intake (continuous variable) and BMI (continuous variable) in total subjects 2) Odds ratio of deficiency, excessive 1, excessive 2 group based on the risk of moderate group 3) Calculated by Complex Samples Logistic Regression