The effect of coffee consumption on food group intake

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May 3, 2016 - found in cohort studies in the U.S. and Europe [9,10]. ...... (Americano) in relation to dilution rates, Korean J. Food Cookery Sci. ... [17] P.L. Lutsey, L.M. Steffen, J. Stevents, Dietary intake and the development of the met-.
NFS Journal 4 (2016) 9–14

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Original article

The effect of coffee consumption on food group intake, nutrient intake, and metabolic syndrome of Korean adults—2010 KNHANES (V-1) Fangfang Song a, JiEun Oh a, KyungWon Lee b, Mi Sook Cho a,⁎ a b

Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 120-750, Korea Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA

a r t i c l e

i n f o

Article history: Received 9 October 2015 Accepted 27 April 2016 Available online 3 May 2016 Keywords: Coffee consumption Nutrient intake INQ Metabolic syndrome KNHANES

a b s t r a c t Background: Coffee is a popular beverage in Korea recent years. The purpose of this study was to investigate the relationship between coffee consumption and the risk of metabolic syndrome in Korean adults based on the 2010 Korean National Health and Nutrition Examination Survey (KNHANES V-1). Methods: Dietary intake status and the factors of metabolic syndrome were assessed. Three groups (no coffee consumption, moderate intake, and high intake) were divided into tertile according to black coffee cream (include brewed coffee) consumption per day. Results: Our results showed that the Tertile 3 group consumed more calories from fat, and niacin was higher than in the Tertile 1 and Tertile 2 group. INQ for protein and vitamin B1 was significantly higher in no coffee consumption group than the other groups and in Tertile 3 exhibited significantly higher niacin intake. The subjects in Tertile 3 showed significantly higher consumption in grain and oil intake, and Tertile 1 group showed higher consumption in milk and dairy products. In the logistic regression analysis, adjusting for sex, age, energy intake, smoking, and drinking, being in the high coffee consumption group (Tertile 3) was significantly and inversely associated with abdominal obesity (OR = 0.76, CI = 0.71–0.82), hypertension (OR = 0.70, CI = 0.54–0.87), high glucose(OR = 0.71, CI = 0.61–0.86). However, no significant association was found between coffee consumption and metabolic syndrome. Conclusion: Coffee consumption has not a considerably relationship with nutrient intake. Appropriate consumption of coffee may have potentially helpful effects on certain metabolic risk factors, such as abdominal obesity, hypertension, and high glucose. © 2016 The Authors. Published by Elsevier GmbH on behalf of Society of Nutrition and Food Science e.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction In Korea, the culture of coffee consumption has become more common through the adoption of the Westernized diet, and it has continuously and rapidly increased in recent years [1]. In research based on the 2007–2009 (KNHANES V-1), 8056 subjects (52.3%) of the 15,389 subjects included in the analysis reported consuming coffee more than once a day, and 50.7% of the coffee consumers reported that their average coffee consumption was more than twice a day [2]. Recently, coffee was reported to be the most frequently consumed foods among Korean adults after rice (17 times per week), cabbage (14 times per week), and alcoholic beverages (10 times per week) [3]. According to the 2007 KNHANES of the Korea Centers for Disease Control and Prevention, the average daily energy intake among Koreans from coffee/instant coffee mix was 15.3 kcal per day, which ranked 24th for its contribution to

⁎ Corresponding author at: 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, Korea. Tel.: +82 2 3277 2826; fax: +82 2 3277 2862. E-mail address: [email protected] (M.S. Cho).

energy intake among Koreans. It was shown that coffee was the only item categorized as a beverage within the top 30 foods ranked in order of their contribution to energy intake [4]. Coffee contains diverse ingredients, such as Café Royal, caffeol, chlorogenic acid, potassium, niacin, and magnesium. Such ingredients as caffeine are known to affect diverse physiological functions in our bodies [5]. Previous research conducted in many countries on the relationship between coffee and health has shown a potential effect on health by frequency of coffee consumption. There was one finding which showed a significantly lower level of metabolic syndrome present in Japanese people who drink coffee frequently [6–8]. However, no significant association between coffee and metabolic syndrome was found in cohort studies in the U.S. and Europe [9,10]. Additionally, the majority of the precedent studies suggested that higher or more frequent consumption of coffee reduces the morbidity rate of diabetes [11–13]. The relationship between coffee consumption and diabetes has been relatively consistent in studies. In comparison to overseas studies, domestic studies on coffee and health are insufficient. Specifically, a general consensus on the risk of metabolic syndrome has not been established to date.

http://dx.doi.org/10.1016/j.nfs.2016.04.002 2352-3646/© 2016 The Authors. Published by Elsevier GmbH on behalf of Society of Nutrition and Food Science e.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

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F. Song et al. / NFS Journal 4 (2016) 9–14

As the consumption of coffee beverages has rapidly grown and the frequency of coffee consumption by adults has become especially high, a majority of studies have been conducted on the physicochemical characteristics of coffee or the product quality and organoleptic properties [14]. However, there have been an inadequate number of studies on food and nutrient intake regarding coffee consumption [15]. There is normally a quantitative correlation between nutrient intake and energy intake. Therefore, an evaluation on the level of nutrient intake should be accompanied by considerations of energy intake [16]. A study by Lutsey et al. [17] on coffee consumption and food intake did not find any significant relationships between the risk of metabolic syndrome and the intake of food groups, including grains, fruits, vegetables, nuts, fish, poultry, and dairy products. While previous studies have focused more on eating patterns followed by coffee consumption or the construction of nutrients and calorie intake among the food groups, there is a gradual trend currently toward the study of the food itself ingested during a meal [18]. However, there is not yet an understanding of the relationship between coffee consumption and chronic diseases. Therefore, nothing much is known about the influence of coffee consumption on the diet of Korean adults, and there is no sufficient nutrient education underway in this field. The 2010 KNHANES was a nationally funded, cross-sectional, nationwide survey that used a multistage sampling design to collect data regarding the health behaviors, nutrition, and socio-demographics of the general Korean population [19,20]. Currently, the nutrition survey has adopted 3 methods, such as health behavior investigation and health examination investigation, food frequency questionnaire and dietary intakes (24 h recall method), and dietary habits research (research on items related to dietary life and eating habits). Additionally, the health behavior survey and checkup survey are underway [21]. Therefore, this study aimed to understand the level of coffee consumption in Korean adults and to compare and analyze the true state of food group and nutrient intake, followed by coffee consumption (Tertile), INQ, and index related to metabolic disease, by utilizing the 2010 KNHANES. We hypothesized that higher coffee consumption affects dietary intake and increase risk of metabolic syndrome. 2. Materials and methods

coffee, calculated by adding up the weight (g) variables. The subjects were classified into 3 groups (Tertile 1, Tertile 2, and Tertile 3) based on their daily coffee consumption by utilizing tertile and by calculating the average coffee intake of each group. 2.3. Dietary assessment This study analyzed and displayed intake per 1000 kcal of each nutrient based on the food intake survey conducted through the 24-h recall method in the nutrient survey part of the data from the 2010 KNHANES (V-1). Nutrient intake was calculated from intake per nutrient of the subjects for analysis using data from the 24-h recall method [2,18]. Nutrients utilized in the analysis were total energy, protein, carbohydrate, fat, fiber, calcium, phosphorus, iron, sodium, vitamin A, vitamin B1, vitamin B2, niacin, and vitamin C. The energy ratio acquired from carbohydrates, fats, and proteins was calculated using the intake of those 3 nutrients. This study calculated daily intake of different food groups by classifying them into the following 6 food groups: grains; meat, fish, egg, and beans; vegetables; fruits; milk and dairy products; and fat, oils, and sugars. These were grouped based on recommendations in the revised edition (2010) of the Nutrition Intake Criteria of Koreans from the Korea Nutrition Society, after examining the food code data which focused on the nutrition code data provided by KNHANES [23]. This study used the Index of Nutritional Quality (INQ) to evaluate the quality of a meal. This index can evaluate the degree of satisfaction in energy intake from each nutrient based on nutrient density. The INQ was used to compare nutrient intake per 1000 kcal of each nutrient and the recommended dietary intake per 1000 kcal. In this study, evaluation of the INQ was conducted on the following 12 nutrients: protein, fiber, calcium, phosphorus, iron, sodium, potassium, vitamin A, vitamin B1, vitamin B2, niacin, and vitamin C. In cases where the INQ exceeds 1, a specific nutrient was sufficiently consumed. In cases where INQ is less than 1, more of that nutrient should be consumed to satisfy the recommended dietary intake [23,24].  INQ ¼

nutrient intake per 1; 000 kcal of calorie intake nutrient intake per 1; 000 kcal of recommended calorie intake

2.1. Study population 2.4. Risk factors of metabolic syndrome The analysis in this study was conducted using 2010 KNHANES, V-1 data. Overall, 8958 subjects participated in this research. The following subjects were excluded from the analysis: (1) subjects who were younger than 19 years of age (2171 subjects), (2) subjects whose energy intakes were less than 500 kcal/day or exceeded 5000 kcal/day (1065 subjects), (3) pregnant women (771 subjects), and (4) subjects who had missing values (145 subjects). In total, 4806 individuals were included in the analysis after excluding the subjects who met those criteria. 2.2. Data collection and definitions This study utilized data on sex, age, alcohol consumption, smoking status, body mass index(BMI), and coffee consumption, which were collected through the health inquiry survey of the KNHANE Survey. BMI was divide the subjects into a low weight group (b18.5 kg/m2), normal weight group (18.5 kg/m2 ≤ BMI b 23 kg/m2), overweight group (23 kg/m2 ≤ BMI b 25 kg/m2), and obese group (25 kg/m2 ≥) [22]. To classify the groups according to coffee consumption, this study used variables from the second round of food code to examine the data on food intake of the subjects by using 24-h recall methodused-study in KNHANES. The second round of food code is a classification value to find foods that can be considered the same because their commercial names are identical and their water contents are similar. The coffee intake was analyzed by the black coffee include brewed

This study closely examined the following 8 factors of metabolic risks: BMI, waist circumference, diastolic blood pressure, systolic blood pressure, concentration of blood triglycerides, blood cholesterol concentration, HDL cholesterol concentration, and fasting blood glucose. These factors were used to study the association between coffee consumption and metabolic syndrome. Metabolic syndrome was defined as any Asian, according to the WHO, meeting any 3 out of the following 5 criteria recommended by the 2001 National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III): (1) abdominal obesity: waist circumference for males ≥90 cm and females ≥85 cm; (2) high blood pressure: systolic blood pressure ≥ 133 mmHg or diastolic blood pressure ≥ 85 mmHg, or medication status in the case of a patient who has past medical history of high blood pressure; (3) hypertriglyceridemia: blood triglycerides ≥150 mg/dL or taking medication; (4) low HDL cholesterol: in males b 40 mg/dL, in females b50 mg/dL, or taking medication; and (5) fasting blood glucose ≥100 mg/dL or taking medication. Obesity is defined as BMI ≥ 25.0 kg/m2 according to the criteria of the Asia-Pacific region of the WHO [24,25]. 2.5. Statistical analyses Data analysis was conducted in SAS (Statistical Analysis System, version 9.3, SAS Institute, Cary, NC, USA). Composite sampling based on the outcome data of KNHANES was applied to the data treatment and the

F. Song et al. / NFS Journal 4 (2016) 9–14

weighted-value application system, as recommended by the Korean Centers for Disease Control and Prevention. The general aspects and data of the subjects were shown in the form of frequency and percentile, which were analyzed using a chi-square test. Analysis of variance (ANOVA) was performed to compare averages among the 3 groups (Tertile 1, Tertile 2, and Tertile 3) regarding their nutrients intake, food group intake, and evaluation on meal quality. The post hoc comparison was performed using Turkey's multiple range test on variables showing a significant difference as a result of the analysis of variance. To analyze the relationship between metabolic syndrome and relevant risk factors taking into account the composite sampling, the following models were used: a logistic regression analysis model, which did not adjust for disturbance factors related to metabolic syndrome (Model 1: unadjusted); an analysis model, which adjusted for the factors of age, sex, and energy intake (Model 2: age, sex, energy intakeadjusted); and an analysis model, which adjusted for the factors of age, sex, energy intake, smoking, and drinking (Model 3: age, sex, energy intake, smoking, drinking adjusted). Every statistical analysis used in this study tested significance at the level of α = 0.05. 3. Results Analysis on a total of 4806 individuals (male: 1960 and female: 2846) was conducted, and their general characteristics are presented in Table 1 with grouping according to coffee intake. The age group between 30 years old and 50 years old was the largest group accounting for 44.07% of participants; 77.85% were drinkers and 22.15% were smokers. In the category of BMI, 42.05% were of standard weight, the highest of all groups; the proportion of subjects who were obese was 31.25% and overweight was 22.36%. Results of analysis on food group intake by coffee consumption are shown in Table 2. Significant differences were found for the following groups: grains; milk and dairy products; and fat, oils, and sugars. Tertile 1 group had the highest average daily intake of the grains food group, as well as milk and dairy product food group (p b 0.001). The oil and sugar food group was consumed the most by Tertile 3 (p b 0.01). The average daily nutrient intake of subjects by coffee consumption is shown in Table 3. A statistically significant difference was shown in the intake of fat from energy (p b 0.01), total energy (p b 0.001), protein (p b 0.05), vitamin B1 (p b 0.05), and niacin (p b 0.01) by coffee consumption. Protein accounted for the highest proportion of calories in the no coffee consumption group (p b 0.05), which also had the lowest intake of fat (p b 0.01). The Tertile 3 group, which had the highest coffee consumption, also had the highest level of average daily energy intake (p b 0.001). The Tertile 1 group had the highest average daily protein and vitamin B1 intake (p b 0.05), whereas had the lowest average daily niacin intake (p b 0.01).

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Table 2 Food groups intake status of all subjects by coffee consumption group. Coffee consumption group

Grains (g) Meat, fish, eggs and beans (g) Vegetables (g) Fruits (g) Milk and dairy products (g) Oil and sugars (g)

Tertile 1

Tertile 2

Tertile 3

p-value

359.29 ± 5.21a 216.01 ± 4.91

349.03 ± 4.95a 215.61 ± 5.72

326.54 ± 4.71b 205.85 ± 5.21

b;.0001 0.2476

357.10 ± 7.22 354.74 ± 7.77 358.34 ± 7.16 219.18 ± 12.17 223.46 ± 17.97 204.25 ± 13.66 a a 79.29 ± 5.04 66.31 ± 5.43b 99.05 ± 6.58

0.9333 0.3574 0.0017

13.47 ± 0.57c

16.31 ± 0.59b

26.19 ± 0.93a

b.0001

Values are expressed as mean ± SD adjusted for sex, age, and energy intake. Mean values in the row on the coffee intake quartile are significantly different (*p b 0.05, **p b 0.01, ***p b 0.001) by ANOVA. a, b, Mean values with different superscripts are significantly different by Tukey's multiple rang test.

The results of analysis of INQ, which evaluates the quality of a meal by using the proportion of nutrient content in diet by the recommended intake of each nutrient per 1000 kcal, are shown in Table 4. The INQ index of protein (p b 0.05), vitamin B1 (p b 0.01), and niacin (p b 0.001) showed statistically significant results. The Tertile 1 group showed a significantly higher results in protein (p b 0.05), and vitamin B1 (p b 0.01) compared to the Tertile 3 group; it also showed a significantly lower level of niacin (p b 0.001). To estimate the relationship between coffee consumption and metabolic syndrome, this study performed a logistic repercussion analysis and obtained odds ratios (OR) and confidence intervals (95% CI), which are shown in Table 5. Among the index related to abdominal obesity, high blood pressure, and high fasting blood glucose were associated with coffee consumption. Analysis of the risk factors for metabolic syndrome was performed by comparing the no coffee consumption group. Model 2, in which confounding factors (sex, age, energy intake) have been adjusted, showed that the risk of abdominal obesity was lower in the Tertile 3 group (0.77; 95% CI = 0.72–0.82) (p b 0.05). The risk of hypertension was lower in the Tertile 3 group (0.69; 95% CI = 0.64–0.76) (p b 0.05), and the high fasting blood glucose in Tertile 3 group was also lower (0.71; 95% CI = 0.60–0.86) (p b 0.05). In Model 1 and Model 3, where diverse confounding factors (sex, age, energy intake, smoking status, and drinking status) were adjusted, the risk of abdominal obesity was found to be lower in Tertile 3 (0.76; 95% CI = 0.71–0.81) (p b 0.05). Compared to Tertile 1, the risk of blood pressure increasing was shown to be lower in the Tertile 3 group, and the risk of high blood pressure decreased significantly (0.70; 95% CI = 0.54–0.87) (p b 0.001) as coffee consumption increased. The risk of high glucose was lower (0.71; 95% CI 0.61–0.86) (p b 0.05) in the Tertile 3 group. According to these results, risks for abdominal obesity, high blood pressure, and high fasting blood

Table 1 General characteristics of all subjects (N, %). Characteristic

Tertile 1

Tertile 2

Tertile 3

Total (N = 4806 )

Gender Age

Drinking Smoking

BMI (kg/m2)

Male Female 19 ≤ age b 30 30 ≤ age b 50 50 ≤ age b 65 ≥65 No Yes Never Former Current b18.5 18.5 ≤ BMI b 23 23 ≤ BMI b 25 ≥25

620(37.17) 1048(62.83) 295(17.69) 496(29.74) 454(27.22) 423(25.36) 579(34.71) 1092(65.29) 1109(66.49) 335(20.08) 224(13.43) 79(4.74) 703(42.15) 380(22.78) 506(30.34)

495(33.56) 980(66.44) 86(5.83) 543(36.81) 488(33.08) 358(24.27) 429(29.08) 1046(71.92) 985(66.78) 294(19.93) 196(13.29) 49(3.32) 620(42.03) 340(23.05) 466(28.02)

845(50.81) 818(49.19) 162(9.74) 812(44.83) 463(27.84) 236(14.19) 339(20.38) 1324(79.62) 828(49.79) 353(21.23) 482(28.98) 68(4.09) 697(41.91) 378(22.73) 520(31.27)

1960(49.84) 2846(50.16) 543(19.79) 1851(44.07) 1395(24.32) 1017(11.83) 1344(22.15) 3462(77.85) 2922(53.75) 982(20.57) 902(25.68) 196(4.34) 2020(42.05) 1098(22.36) 1492(31.25)

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Table 3 Nutrition status of all subjects by coffee consumption group. Coffee consumption group

% Energy Carbohydrate1 Protein Fat Energy (kcal)2 Carbohydrate (g) Protein (g) Fat (g) Fiber (g) Calcium (mg) Phosphorus (mg) Iron (mg) Sodium (mg) Potassium (mg) Vitamin A (μg RE) Vitamin B1 (mg) Vitamin B2 (mg) Niacin (mg) Vitamin C (mg)

Tertile 1

Tertile 2

Tertile 3

p-value

68.82 ± 0.31 14.69 ± 0.12 16.39 ± 0.26b 1924.11 ± 23.35b 324.02 ± 2.35 72.50 ± 0.65a 38.24 ± 0.62 8.02 ± 0.17 537.53 ± 11.13 1202.71 ± 10.94 15.62 ± 0.31 5075.66 ± 80.6 3094.09 ± 39.26 839.79 ± 28.24 1.36 ± 0.04a 1.25 ± 0.04 16.41 ± 0.20b 112.44 ± 3.02

68.74 ± 0.39 14.51 ± 0.16 16.75 ± 0.29b 1913.80 ± 25.45b 325.19 ± 3.03 71.40 ± 0.78a 37.99 ± 0.70 7.92 ± 0.18 525.61 ± 9.73 1194.23 ± 10.37 15.35 ± 0.35 5069.86 ± 94.28 3112.43 ± 39.65 864.95 ± 36.80 1.32 ± 0.04a 1.22 ± 0.04 16.47 ± 0.19b 113.89 ± 2.92

67.89 ± 0.35 14.29 ± 0.15 17.82 ± 0.25a 2105.51 ± 22.41a 327.65 ± 2.67 69.49 ± 0.75b 39.04 ± 0.65 7.86 ± 0.22 522.70 ± 10.74 1184.62 ± 10.12 15.63 ± 0.39 5096.09 ± 84.21 3119.69 ± 42.10 852.04 ± 34.78 1.26 ± 0.04b 1.20 ± 0.05 17.45 ± 0.21a 108.33 ± 2.74

0.0632 0.1038 0.0002 b.0001 0.5933 0.0108 0.5133 0.7446 0.5634 0.4488 0.7886 0.9762 0.0621 0.8360 0.0052 0.3089 0.0010 0.2864

a, b, mean values with different superscripts are significantly different by Tukey's multiple rang test. 1 Values are expressed as mean ± SD adjusted for sex, age, and energy intake. 2 Mean values in the row on the coffee intake group are significantly different (*pb 0.05, **p b 0.01, ***p b 0.001) by ANOVA.

glucose decreased significantly as coffee intake increased. However, no significant relationship was shown between the coffee intake and risk for metabolic syndrome. 4. Discussion In the present study, we evaluated the relationship between coffee consumption and dietary intake status and metabolic syndrome using the KNHANES data (2010). We found that coffee consumption has not very clear relationship with food and nutrition intake, whereas has potentially helpful effects on some of metabolic risk factors. Lutsey et al.'s research [17] determined no significant relationship between risk of metabolic syndrome and intake of food groups, such as grains, fruits, vegetables, nuts, poultry, dairy products, and coffee intake. Among the high coffee consumption group, when controlling for sex, age, and energy intake, the daily energy intake was shown to be at the highest level. In research by Christina et al. [26], the highest coffee intake group also had the highest energy intake. In the lowest coffee consumption group, the average daily intake of protein and vitamin B1 was the highest; however, the average daily intake of niacin was significantly lower. This result coincided with a study by Bae, Kim [18], and Table 4 Index of Nutrition Quality (INQ) of all subjects by coffee consumption group. Coffee Consumption Group

Protein (g) Fiber (g) Calcium (mg) Phosphorus (mg) Iron (mg) Sodium (mg) Potassium (mg) Vitamin A (μg RE) Vitamin B1(mg) Vitamin B2(mg) Niacin (mg) Vitamin C (mg)

Tertile 1

Tertile 2

Tertile 3

p-value

1.49 ± 0.03a 0.38 ± 0.02 0.80 ± 0.03 1.77 ± 0.03 1.66 ± 0.08 3.73 ± 0.11 0.91 ± 0.02 1.29 ± 0.10 1.21 ± 0.03a 0.95 ± 0.03 1.11 ± 0.02b 1.17 ± 0.06

1.49 ± 0.03a 0.38 ± 0.02 0.79 ± 0.03 1.75 ± 0.03 1.59 ± 0.06 3.68 ± 0.11 0.92 ± 0.02 1.29 ± 0.07 1.17 ± 0.03ab 0.94 ± 0.04 1.12 ± 0.03b 1.17 ± 0.06

1.45 ± 0.03b 0.37 ± 0.02 0.78 ± 0.03 1.73 ± 0.03 1.59 ± 0.08 3.67 ± 0.13 0.94 ± 0.02 1.28 ± 0.08 1.12 ± 0.03b 0.93 ± 0.04 1.19 ± 0.03a 1.12 ± 0.06

0.0161 0.4008 0.4867 0.1491 0.2159 0.7224 0.1502 0.9940 0.0011 0.5472 0.0002 0.3399

Values are expressed as mean ± SD adjusted for sex, age, and energy intake. Mean values in the row on the coffee intake quartile are significantly different (*p b 0.05, **p b 0.01, ***p b 0.001) by ANOVA. a, b, mean values with different superscripts are significantly different by Tukey's multiple rang test.

Kim [27], which also found high intake of protein and vitamin B1 and low intake of niacin in the low coffee consumption group. Such a result can be observed because the high coffee intake group consumes more foods from the meat group and vegetable group that have high protein and vitamin B1 contents. For food group intake according to coffee

Table 5 Crude and adjusted odds ratios (95% confidence interval) of the risk of metabolic syndrome with coffee consumption group of all subjects. Tertile 1

Tertile 2

Obesity (BMI ≥ 25 kg/m2) Model 1a 1.0 0.90 (0.74–1.09) Model 2 1.0 0.92 (0.76–1.12) Model 3 1.0 0.92 (0.76–1.12)

Tertile 3

p for trend

0.96 (0.79–1.17) 0.91 (0.75–1.12) 0.92 (0.75–1.13)

0.4150 0.8841 0.9466

Abdominal obesity (waist circumstances ≥90 cm in men, ≥85 cm in women) Model 1 1.0 0.84 (0.70–1.02) 0.68 (0.57–0.81) 0.0025 Model 2 1.0 1.04 (0.86–1.26) 0.77 (0.72–0.82) 0.0139 Model 3 1.0 1.03 (0.79–1.27) 0.76 (0.71–0.81) 0.0133 Hypertension (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg) Model 1 1.0 1.01 (0.84–1.19) 0.81 (0.68–0.96) Model 2 1.0 0.72 (0.66–0.88) 0.69 (0.64–0.76) Model 3 1.0 0.72 (0.66–0.88) 0.70 (0.54–0.87)

0.0144 0.0006 0.0011

Hypertriglyceridemia (serum TG ≥ 150 mg/dL) Model 1 1.0 1.02 (0.82–1.28) 1.10 (0.89–1.36) Model 2 1.0 1.09 (0.86–1.38) 1.05 (0.84–1.31) Model 3 1.0 1.11 (0.88–1.41) 1.02 (0.81–1.28)

0.4348 0.7180 0.3182

Low HDL-cholesterol (HDL b 40 mg/dL in men, b50 mg/dL in women) Model 1 1.0 0.95 (0.78–1.16) 0.81 (0.66–0.99) 0.1093 Model 2 1.0 1.11 (0.91–1.35) 1.06 (0.85–1.32) 0.6323 Model 3 1.0 1.12 (0.92–1.35) 1.04 (0.83–1.29) 0.4956 High glucose (plasma glucose ≥100 mg/dL) Model 1 1.0 1.15 (0.84–1.58) Model 2 1.0 0.78 (0.67–0.91) Model 3 1.0 0.78 (0.67–0.91)

0.67 (0.47–0.87) 0.71 (0.60–0.85) 0.71 (0.61–0.86)

0.0094 0.0232 0.0266

Metabolic syndrome Model 1 1.0 Model 2 1.0 Model 3 1.0

1.01(0.81–1.26) 1.14 (0.91–1.42) 1.12 (0.89–1.40)

0.9471 0.7764 0.5917

1.02(0.82–1.27) 1.17 (0.94–1.46) 1.18 (0.95–1.47)

OR (95% CI), ORs from the Tertile 2, Tertile 3 relative to the Tertile 1. p from multiple logistic regression analysis p b 0.05. a Model 1: crude; Model 2: adjusted for age, sex, and energy intake; Model 3: adjusted for age, sex, energy intake, smoking, and drinking.

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consumption, the results were identical to YY et al.'s research [15], which reported a higher intake of meat, sugar, fat, and oils by the high coffee intake group. However, there is a need for more diet surveys on choice and preference of food consumed. Additionally, INQ evaluates the quality of a meal using a ratio dividing nutrient content in the body by a recommended intake per 1000 kcal of that nutrient. The results were similar to the results of YJ & MH's research [18], where a high INQ of vitamin B1 and vitamin B2 was found in a low coffee consumption group. Additionally, a low INQ of protein was found in a low coffee consumption group in YY et al.'s study [15]. This result can be observed because the no coffee intake group consumed high of grains, milk, and dairy products; therefore, intake of protein and vitamin B1, which are contained in those food groups, also increased. This study analyzed the influence of coffee consumption on metabolic syndrome and risk factors of metabolic syndrome through crosssectional study. Although prior research has studied the relevance of coffee intake and index related to metabolic disease, a definite association between coffee consumption and BMI could not be found in the existing literature [10,28] related to coffee consumption and metabolic syndrome. Lisanne and et al.'s research [29] showed a lower level of waist circumference was associated with higher coffee consumption, although results were not significant. In this study, a significant relationship between coffee intake and BMI was not found as it was in the previous study, but the relationship between coffee consumption and abdominal obesity was found. Several studies have reported that there is no clear relationship between coffee consumption and blood pressure [30,31], and there is also research suggesting the opposite. The analysis of results from this study showed that there is lower risk of high blood pressure if the level of coffee consumption is higher. Coffee drinking increased BP in non-habitual drinkers but not in habitual coffee drinkers [32]. In meta-analyses conducted by Arthur et al. [33] and Steffen [34], longterm coffee consumption was not associated with a clear increase in BP or risk of hypertension. In this study, results also showed that the risk of high fasting blood glucose was lower in the high coffee consumption group, and the risk of high fasting blood glucose decreased significantly as coffee intake increased. Recently published research found that consuming 5 cups of coffee per day decreased insulin resistance [35], and that coffee consumption may reduce the risk of type 2 diabetes mellitus and hypertension [36]. It was suggested in previous studies that there is a visible preventive effect on diabetes, which is closely related to metabolic syndrome [7,13,37]. The research showed that the risk of high fasting blood decreased if coffee intake was higher, which was also consistent with this study. In a meta-analysis [38,39], the intake of coffee, especially unfiltered coffee, contributed significantly to the increase in TC, LDL-C, and TG. However, in this study, we cannot find a significant association between coffee consumption and LDL-C or TG. A significant negative (beneficial) association between coffee consumption and prevalence of metabolic syndrome was observed both in men and women [39], while in studies conducted by Lisanne et al. [29] and Lutsey et al. [17], no significant relationship between coffee intake and incidence of metabolic syndrome was determined. Similarly, no significant relationship between coffee consumption and risk of metabolic syndrome was shown in this study. In a meta-analysis study [40], coffee consumption may have an effect to be favorably related with health such like to reduce risk of diabetes and to protect by metabolic impairment. Our study found that, more grains and milk, dairy products were consumed in the no coffee consumption group, and coffee intake have not a very clear association with food group and nutrition intake. No clear relationship was found between that and the risk of metabolic syndrome or intake of coffee can potentially help in preventing the occurrence of chronic diseases, such as abdominal obesity, high blood pressure, and high fasting blood glucose.

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In conclusion, coffee consumption has not a clear association with food group intake, nutrient intake, INQ. Appropriate consumption of coffee may have potentially helpful effects on some of the metabolic risk factors, such as abdominal obesity, hypertension and high glucose. However, in this study, we did not find a significant relationship between coffee consumption and metabolic syndrome. As a widely consumed beverage in South Korea, coffee can be included as part of a healthy diet for the general public and also for those with increased metabolic syndrome risk factors. Due to methodological limitations, we cannot exclude that the observed associations on coffee consumption and metabolic syndrome are due to other healthy lifestyle behaviors, and further prospective studies are needed that better adjust for potential confounding factors. In the future, we need to collect data on dietary intake and conduct research using the cohort method.

Conflict of interest The authors declare that they have no conflict of interest.

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