Eun Ju Dennis; Minji Kang; Sung Nim Han

doi:10.5720/kjcn.2017.22.5.441

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Relation between Beverage Consumption Pattern and Metabolic Syndrome among Healthy Korean Adults: Eun Ju Dennis, Minji Kang, Sung Nim Han; Korean Journal of Community Nutrition 2017;22(5):441-455.
DOI: https://doi.org/10.5720/kjcn.2017.22.5.441
Published online: October 31, 2017

¹Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul, Korea.

²Center for Gendered Innovations in Science and Technology Researches, Korea Federation of Women's Science & Technology Associations, Seoul, Korea.

³Research Institute of Human Ecology, College of Human Ecology, Seoul National University, Seoul, Korea.

Corresponding author: Sung Nim Han. Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul 08826, Korea. Tel: (02) 880-6836, Fax: (02) 884-0305, snhan@snu.ac.kr

• Received: October 23, 2017 • Revised: October 29, 2017 • Accepted: October 29, 2017

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract
REFERENCES

Abstract

Objectives
The purpose of this study is to describe beverage patterns among healthy Korean adults and investigate their association with prevalence and components of metabolic syndrome.
Methods
Subjects consisted of 6,927 Korean adults, aged 19-64 years in the 6^th Korea National Health and Nutrition Examination Survey (KNHANES, 2013-2015). Beverages were regrouped into twelve groups based on food codes and beverage intake (g/day) was assessed by 24-hour recall. Factor analysis was used to obtain beverage patterns. Waist circumference and body mass index (BMI) were used as anthropometric data; fasting blood glucose, triglyceride, high density lipoprotein (HDL), and blood pressure were used as biochemical indicators. The odds ratio (OR) for prevalence of metabolic syndrome and components of metabolic syndrome was assessed using logistic regression analysis.
Results
Three beverage patterns were identified using factor analysis: 1) carbonated soft drinks 2) coffee (without added sugar or powdered creamer), and 3) alcoholic beverages. Subjects with high scores for the carbonated soft drink and coffee without added sugar or powdered creamer patterns were younger and subjects with high scores for the alcoholic beverage pattern were older. There were significant differences in gender distribution in all three beverage patterns, with men more likely to have high scores for carbonated soft drink and alcoholic beverage patterns. On the other hand, women were more likely to have higher scores for coffee without added sugar or powdered creamer pattern. Within each pattern, there were significant differences in sociodemographic and lifestyle characteristics such as education, household income, frequency of eating out, and smoking status according to the quartile of pattern scores. Alcoholic beverages and carbonated soft drinks patterns were associated with an increased levels of metabolic syndrome components, but coffee without added sugar or powdered creamer was not associated with any of metabolic syndrome components in healthy Korean adults after adjusting for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, and energy intake.
Conclusions
Alcoholic beverages and carbonated soft drinks patterns are associated with increased levels of metabolic syndrome components while coffee without added sugar or powdered creamer pattern is not associated with any of metabolic syndrome components in healthy Korean adults.
Keywords: beverage pattern; metabolic syndrome; Korean adults

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Table 1

Beverage groups

Table 2

Factor loading matric for the three beverage patterns in Korean adults aged 19-64 years (n = 6,927)

Table 3

Sociodemographic and lifestyle characteristics, and BMI of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM

2) Values are percentages and tested by chi square-test

Table 4

Macronutrient intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean ± SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eatingout, energy intake

Table 5

Vitamin and mineral intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake

Table 6

Odd Ratios (ORs) and 95% confidence intervals (CIs) for metabolic syndrome components across quartile (Q) categories of beverage pattern scores

1) Adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake

2) Calculated using the Logistic Regression model

Figure & Data

REFERENCES

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Relation between Beverage Consumption Pattern and Metabolic Syndrome among Healthy Korean Adults

Korean J Community Nutr. 2017;22(5):441-455. Published online October 31, 2017

DOI: https://doi.org/10.5720/kjcn.2017.22.5.441

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Relation between Beverage Consumption Pattern and Metabolic Syndrome among Healthy Korean Adults

Table 1

Beverage groups

Table 2

Factor loading matric for the three beverage patterns in Korean adults aged 19-64 years (n = 6,927)

Table 3

Sociodemographic and lifestyle characteristics, and BMI of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM

2) Values are percentages and tested by chi square-test

Table 4

Macronutrient intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean ± SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eatingout, energy intake

Table 5

Vitamin and mineral intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake

Table 6

Odd Ratios (ORs) and 95% confidence intervals (CIs) for metabolic syndrome components across quartile (Q) categories of beverage pattern scores

1) Adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake

2) Calculated using the Logistic Regression model

Table 1 Beverage groups

Table 2 Factor loading matric for the three beverage patterns in Korean adults aged 19-64 years (n = 6,927)

Table 3 Sociodemographic and lifestyle characteristics, and BMI of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM

2) Values are percentages and tested by chi square-test

Table 4 Macronutrient intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean ± SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eatingout, energy intake

Table 5 Vitamin and mineral intakes of Korean adults according to the quartile (Q) categories of beverage pattern scores

1) Values are Mean±SD and calculated using GLM, data are adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake

Table 6 Odd Ratios (ORs) and 95% confidence intervals (CIs) for metabolic syndrome components across quartile (Q) categories of beverage pattern scores

1) Adjusted for age, sex, education, BMI, weight management, household income, smoking status, frequency of eating out, energy intake