Dietary patterns and pulmonary function in Korean women: Findings from the Korea National Health and Nutrition Examination Survey 2007–2011

Food and Chemical Toxicology 74 (2014) 177–183

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Food and Chemical Toxicology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / f o o d c h e m t o x

Dietary patterns and pulmonary function in Korean women: Findings from the Korea National Health and Nutrition Examination Survey 2007–2011 Yoonsu Cho a,b, Hye-Kyung Chung c, Seung-Sup Kim b, Min-Jeong Shin a,b,d,* a

Department of Food and Nutrition, Korea University, Seoul 136-703, Republic of Korea Department of Public Health Sciences, Graduate School, Korea University, Seoul 136-703, Republic of Korea c Severance Institute for Vascular and Metabolic Research, Yonsei University College of Medicine, Seoul 120-749, Republic of Korea d Korea University Guro Hospital, Korea University, Seoul 152-703, Republic of Korea b

A R T I C L E

I N F O

Article history: Received 24 April 2014 Accepted 22 September 2014 Available online 5 October 2014 Keywords: Dietary patterns Pulmonary function FEV1 FVC Vitamin A Dietary fat

A B S T R A C T

In the present study, we evaluated the association between dietary patterns and pulmonary functions in Korean women older than 40 years. This study analyzed the data from the Korea National Health and Nutrition Examination Survey IV and V (2007–2010). In total, 7615 women were included in the analysis. Using principal component analysis, two dietary patterns were identified, namely a balanced diet pattern (vegetables, fish, meat, seaweed, and mushrooms) and a refined diet (snacks, bread, milk, dairy products, and fast food). The refined diet pattern was positively associated with energy from fat but negatively associated with vitamin A, β-carotene, niacin, and fiber. After adjusting for potential confounders, the refined diet pattern was negatively associated with levels of predicted forced vital capacity (odds ratio (OR): 0.84, 95% confidence intervals (CIs): 0.70, 0.99) and predicted forced expiratory volume in 1 second (OR: 0.79, 95% CIs: 0.66, 0.93). In conclusion, the refined diet pattern was associated with decreased pulmonary function in Korean women. This information may be useful toward the development of nutritional guidelines for improving pulmonary function in Korean women. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Chronic respiratory diseases constitute a serious public health problem in all countries throughout the world (World Health Organization, 2007), of which the high prevalence and associated medical costs impose large socioeconomic burdens. Among the diseases that are characterized by airflow obstruction and decline in pulmonary function, chronic obstructive pulmonary disease (COPD) and asthma are the most common (Bommart et al., 2014) and especially true in Asian countries due to high rates of smoking, air pollution, and occupational exposure to dusts (Oh et al., 2011). According to data from the 2011 Korea National Health and Nutrition Examination Survey (KNHANES), 12.5% of Koreans reported a

Abbreviations: COPD, chronic obstructive respiratory disease; KNHANES, Korea National Health and Nutrition Examination Survey; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; BMI, body mass index; OR, odds ratio; CI, confidence interval. * Corresponding author. Department of Food and Nutrition, Korea University, Seoul 136-703, Republic of Korea; Department of Public Health Sciences, Graduate School, Korea University, Seoul 136-703, Republic of Korea; Korea University Guro Hospital, Korea University, Seoul 152-703, Republic of Korea. Tel.: +82 2 940 2857; fax: +82 2 940 2850. E-mail address: [email protected] (M.-J. Shin). http://dx.doi.org/10.1016/j.fct.2014.09.014 0278-6915/© 2014 Elsevier Ltd. All rights reserved.

current diagnosis of COPD (Korea Ministry of Health and Welfare, 2012). Pulmonary function is an important predictor of mortality in the general population as well as in the populations with respiratory disease (Hole et al., 1996; Thomason and Strachan, 2000). Several previous studies have reported the relationship between dietary factors, especially antioxidant nutrients and omega-3 fatty acid, and measures of pulmonary function (Kelly, 2005; Romieu, 2005; Spector and Surette, 2003). However, most of the previous studies have focused on the effects of single nutrients or food items on pulmonary function. Considering that nutrients and foods are consumed together rather than in isolation, characterizing dietary patterns within a population and examining whether dietary patterns are related to pulmonary function would be an appropriate approach to analyzing this relationship. Interestingly, recent reports suggested that sexspecific differences exist in the prevalence of chronic respiratory diseases (Po et al., 2011; Han et al., 2007; National Emphysema Treatment Trial Research Group, 2007). It is indicated that women are exposed to the risk of chronic bronchitis and COPD due to household biomass fuel (Po et al., 2011). Women with COPD showed more dyspnea and a lower subjective health status than those in men (Han et al., 2007). Moreover, the pathological nature of respiratory diseases may differ between men and women (Varkey, 2004; National Emphysema Treatment Trial Research Group, 2007), yet further

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investigation is required. In addition, limited studies have examined the associations between dietary patterns and pulmonary disease mainly conducted in western population. For example, previous observational studies suggested that adults following a “prudent” diet (high in fruit, vegetables, fish, and whole grains) are positively associated to forced expiratory volume in 1 second (FEV1), with reduction in the OR for COPD (Shaheen et al., 2010), and asthma (Varraso et al., 2009). Compared to Western people, Asian population including Koreans has a unique diet represented as high in refined carbohydrate (noodle, and rice cake), soyfoods, and fermented vegetables; therefore, it is interesting to characterize the dietary pattern predicting pulmonary function especially in this population. In the present study, we aimed to construct dietary patterns in Korean women using factor analysis. Then, we sought to examine the associations between dietary patterns and pulmonary function (i.e. FEV1 and forced vital capacity (FVC)) among Korean women over 40 years. 2. Subjects and methods 2.1. Study population This study was based on data from the KNHANES IV and V, 2007– 2010. Details of the KNHANES are available (Kweon et al., 2014). The KNHANES is a cross-sectional survey conducted nationwide by the Ministry of Health and Welfare. The KNHANES is composed of the following three sections: a health interview, health examination, and nutrition survey. A nationally representative sample was chosen from the Korean population using household records that were provided by the 2005 Population and Housing Census in Korea. Twenty households were selected from each survey section using a stratified, multistage probability cluster sampling method that considers each participant’s geographical area, age, and sex. In the KNHANES IV (2007–2009) and V (2010–2011), 42,347 individuals participated in the examination (response rate: higher than 80% for age ≥ 1 year). We limited our analyses to women older than 40 years because lung function tests were only implemented in those older than 40. We excluded subjects with missing data for pulmonary function levels and dietary intake. Finally, 7615 women were included in the statistical analysis. The institutional review board of the Centers for Disease Control and Prevention in Korea approved the KNHANES. All the participants in the survey provided informed written consent. 2.2. General characteristics of the subjects We obtained data from KNHANES IV and V, including demographic, anthropometric, and biochemical measurement data. Trained experts obtained anthropometric measurements by following standardized protocols. The body weights and heights of the subjects were measured to the nearest 0.1 kg and 0.1 cm, respectively. Body mass index (BMI) was calculated as weight (kg)/height squared (m2). Demographic variables that were potential confounders included age, smoking status, secondhand smoking status, physical activity, education, monthly income, occupation, and residence area. Smoking status was defined based on the questionnaire: “Do you currently smoke?” Subjects who answered “everyday” or “sometimes” were regarded as current smokers and subjects who answered “smoked in the past but not currently smoking” were regarded as past smokers. The mean number of cigarette packs smoked per day was also determined. Secondhand smokers were defined as subjects exposed to indirect cigarette smoke at their workplace or home. Physical exercise was divided into two categories, either as “practice” or as “do not practice,” according to whether or not the individual participated in any of the following at least 5 days within each week: intense physical activity for at least 20 minutes, moderate physical activity for at least 30 minutes, or walking for at least

30 minutes. Education level was divided into four categories as elementary school, middle school, high school, or university, according to the subject’s highest achieved level. Monthly income was divided into quartiles and reported in the South Korean currency won as follows: lowest (≤1 million won), lower middle (1 million won ≤ 2 million won), upper middle (2 million won ≤ 3 million won), or highest (>3 million won). The area of residence was classified as urban or rural. Occupation type was categorized into four groups according to a previous study (Lee et al., 2013), with slight modifications, as an office worker (administrator, professional, salesperson, or service worker), manual worker (agriculture, fishing, or simple laborer), technician, or unemployed. 2.3. Dietary assessment Dietary data were obtained from the nutrition survey in the KNHANES. To identify dietary patterns, all food items in the food frequency questionnaire were categorized into 18 groups based on the Korean nutrient database (The Korean Nutrition Society, 2010; Fig. 1). The food frequency questionnaire reflects how often subjects consumed each particular food over the prior 12-month period according to a 10-point scale (9 = 3 times per day, 8 = 2 times per day, 7 = once per day, 6 = 4–6 times per week, 5 = 2–3 times per week, 4 = once per week, 3 = 2–3 times per month, 2 = once per month, 1 = 6–11 times per year, and 0 = almost never). The content and amount of food consumed were obtained by the 1-day 24-hour recall method; the nutrient intake was then analyzed using the database from the food composition table published by the Rural Development Administration (2006). Nutrient intake data included total energy (kcal), carbohydrate (g), protein (g), fat (g), fiber (g), calcium (mg), phosphorous (mg), iron (mg), sodium (mg), potassium (mg), vitamin A (μg RE), vitamin B1 (mg), vitamin B2 (mg), niacin (mg NE), and vitamin C (mg). The ratio of energy intake from each macronutrient to the total energy was calculated as the percentage of energy intake from carbohydrates, fats, and protein. The intake of other nutrients was also calculated as a ratio per 1000 kcal of total energy. 2.4. Pulmonary function assessment Trained technicians measured pulmonary function, including FVC and FEV1, using a rolling-dry seal spirometer (model 2130, SensorMedics, Yorba Linda, CA, USA), according to the guidelines of the American Thoracic Society and European Respiratory Society (ATS/ERS Task Force, 2005). Pulmonary function measured by spirometry is a good predictor of mortality in the general population and in patients with respiratory disease, including asthma, dyspnea, COPD, and bronchial disorder (Petty, 2005). Spirometry was performed at least three times in adults older than 40, those without severe lung disease (tuberculosis, pneumothorax, and pulmonary emphysema), surgery history within the 3 months, and experience of stroke or myocardial infarction within the 3 months. All spirometry results were examined as to whether the results met the ATS/ERS criteria for acceptability and repeatability (Korean Academy of Tuberculosis and Respiratory Diseases, 2005). To obtain the acceptable data, subjects should not be interfered by cough or obstruction of mouthpiece during the first second of expiration. Also, the spirometry test must properly initiate and terminate without hesitation during the expiration. Finally, two largest spirograms were obtained, satisfying the following conditions: two acceptable measurements must be within 0.15 L, with initial expiration volume of less than 5% of the FVC or 0.15 L. 2.5. Statistical analyses Statistical analyses were performed using SPSS version 21.0 (SPSS, Inc., Chicago, IL, USA). Dietary patterns were analyzed using the

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Food groups

Food item

Processed grains

White rice

Whole grains

Unpolished rice, Barley, Glutinous rice

Noodles

Instant noodle (Ramyon), Chinese noodle, wheat noodle

Rice cakes

Korean traditional steamed rice food

Snacks

Crackers, Cookies

Bread

Loaf bread, Cakes, Sandwich

Potatoes

Potatoes, Sweet potatoes

Legumes

Soybeans, Soybean curd, Soybean paste, Soybean milk

Vegetables

Cabbage, White radish, Soybean sprout, Spinach, Cucumber, Hot pepper, Carrot, Pumpkin, Tomatoes

Mushrooms

Mushrooms

Fruits

Mandarin orange, Orange, Persimmon, Apple, Pear, Watermelon, Strawberry, Oriental melon, Grapes, Peach, Banana, Pineapple

Meat

Beef, Chicken, Pork, ham, Sausage, Bacon

Eggs

Egg

Fish

Mackerel, Tuna, Yellow corbina, Pollock, Anchovy, Fish cakes, Squid, Shellfish

Seaweeds

Seaweed, Laver

Milk and dairy products

Milk, Yogurt, Ice cream, Cheese

Beverages

Carbonated beverages, Coffee, Green tea,

Alcohol

Beer, Soju, Makgeolli (Traditional rice wine)

Fast foods

Hamburger, Pizza, Fried foods

Kimchi

Traditional salted vegetables Fig. 1. Food groups from the food frequency questionnaire used in dietary pattern analysis.

exploratory principal component factor analysis based on the Korean nutrition database. The Kaiser criterion (eigenvalues > 1.0) was used to identify the number of factors to be retained. The retained scree plots were rotated by an orthogonal transformation (Varimax) to obtain a more condensed structure with advanced interpretability. After the Varimax rotation, factor scores for each dietary pattern and individual were calculated by summing the intake of each food group using factor loading. Continuous and categorical variables were described as mean ± standard error values or number and percentages of subjects, respectively. Associations between dietary pattern scores and nutrient intake were examined by correlation analysis with and without adjustment for covariates. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for levels of pulmonary function across the tertiles of dietary patterns, with the lowest tertile group as the reference. The first model was unadjusted, and the second was adjusted for age, BMI, smoking status, number of cigarette packs smoked per day, secondhand smoking, exercise, education, income, occupation, and residence area. 3. Results 3.1. Dietary patterns and characteristics of the population Two different types of dietary patterns were derived from the factor analysis: a balanced diet or a refined diet (Table 1). A balanced diet was characterized as the consumption of various foods including vegetables, fish, meat, seaweeds, and mushrooms. A refined diet was characterized by high intakes of snacks, breads, dairy

Table 1 Factor loading matrix for the dietary patterns of study population. Food groups

Processed grains Whole grains Noodles Bread Rice cakes Snacks Legumes Potatoes Vegetables Mushrooms Fruit Meat Eggs Fish Seaweeds Dairy products Beverages Alcohol Fast foods Kimchi Total % of variance explained

Componenta Factor 1 Balanced diet

Factor 2 Refined diet

−0.09 0.05 0.03 0.12 0.21 0.02 0.17 0.53 0.73 0.64 0.66 0.37 0.42 0.67 0.64 0.43 0.13 0.03 0.13 0.15 15.8

−0.12 0.01 0.20 0.68 0.49 0.68 −0.01 0.17 −0.05 0.02 0.19 0.44 0.27 0.09 0.04 0.30 0.13 −0.29 0.45 −0.07 9.8

Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. a Factor loadings are only displayed for values ≤ −0.30 or ≥0.30.

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Table 2 Basic characteristics by quartile of dietary pattern score in the population. Pattern 1: Balanced diet T1 (n = 2538)

T2 (n = 2539)

T3 (n = 2538)

P-value

T1 (n = 2538)

T2 (n = 2539)

T3 (n = 2538)

P-value

56.9 ± 0.1 24.2 ± 0.04

60.9 ± 0.2a 24.3 ± 0.1a

56.0 ± 0.2b 24.3 ± 0.1a

53.9 ± 0.2c 24.0 ± 0.1b

<0.001 0.006

59.3 ± 0.2a 24.4 ± 0.1a

58.7 ± 0.2a 24.4 ± 0.1a

52.7 ± 0.2b 23.9 ± 0.1b

<0.001 <0.001

7005 (92.3) 91 (1.2) 492 (6.5) 0.69 ± 0.04 648 (8.7)

2278 (90.0) 37 (1.5) 216 (8.5) 0.98 ± 0.08 196 (7.9)

2359 (93.3) 23 (0.9) 147 (5.8) 0.56 ± 0.06 225 (9.0)

2368 (93.7) 31 (1.2) 129 (5.1) 0.54 ± 0.05 227 (9.2)

<0.001

2285 (90.4) 31 (1.2) 213 (8.4) 0.90 ± 0.07 9.5

2354 (93.0) 29 (1.1) 148 (5.8) 0.60 ± 0.06 7.1

2366 (93.6) 31 (1.2) 131 (5.2) 0.57 ± 0.06 8.1

<0.001

2180 (28.6) 743 (9.8) 1028 (13.5)

693 (27.3) 238 (9.4) 274 (10.8)

718 (28.3) 261 (10.3) 341 (13.4)

769 (30.2) 244 (9.6) 413 (16.3)

<0.001

759 (29.9) 285 (11.2) 343 (13.5)

739 (29.1) 233 (9.2) 303 (11.9)

682 (26.9) 225 (8.9) 382 (15.1)

<0.001

1682 (22.2) 1768 (23.4) 246 (3.3) 3868 (51.1)

409 (16.2) 745 (29.6) 75 (3.0) 1290 (51.2)

621 (24.6) 608 (24.1) 96 (3.8) 1199 (47.5)

652 (25.9) 415 (16.5) 75 (3.0) 1379 (54.7)

<0.001

443 (17.6) 710 (28.1) 67 (2.7) 1304 (51.7)

476 (18.9) 645 (25.6) 92 (3.7) 1303 (51.8)

763 (30.2) 413 (16.4) 87 (3.4) 1261 (50.0)

<0.001

307 (4.0) 370 (4.9) 41 (0.5) 6 (0.1)

135 (5.3) 116 (4.6) 15 (0.6) 2 (0.1)

87 (3.4) 122 (4.8) 14 (0.6) 2 (0.1)

85 (3.4) 132 (5.2) 12 (0.5) 2 (0.1)

<0.001 0.030 <0.001 0.406

113 (4.5) 126 (5.0) 19 (0.8) 1 (0.0)

110 (4.3) 120 (4.7) 12 (0.5) 4 (0.2)

84 (3.3) 124 (4.9) 10 (0.4) 1 (0.0)

0.097 0.468 <0.001 0.136

2.88 ± 0.01 92.9 ± 0.1 2.29 ± 0.01 94.5 ± 0.2 0.800 ± 0.001

2.74 ± 0.01 c 92.4 ± 0.3b 2.17 ± 0.01 c 94.0 ± 0.3 0.790 ± 0.001b

Pattern 2: Refined diet

2.90 ± 0.01b 92.8 ± 0.2ab 2.32 ± 0.01b 93.3 ± 0.3 0.800 ± 0.001a

3.00 ± 0.01a 93.6 ± 0.2a 2.40 ± 0.01a 93.2 ± 0.3 0.800 ± 0.001a

<0.001 0.202

<0.001 0.002 <0.001 0.083 <0.001

2.82 ± 0.01b 93.1 ± 0.3a 2.23 ± 0.01b 94.2 ± 0.3a 0.790 ± 0.001b

2.81 ± 0.01b 92.2 ± 0.3b 2.23 ± 0.01b 93.5 ± 0.3ab 0.790 ± 0.001b

3.01 ± 0.01a 93.3 ± 0.2a 2.42 ± 0.01a 92.9 ± 0.2b 0.800 ± 0.001a

<0.001 0.003

<0.001 0.003 <0.001 0.004 <0.001

The values of age, body mass index (BMI), amount of daily smoke, forced vital capacity (FVC), predicted FVC (FVCp), forced expiratory volume in 1 second (FEV1), and predicted FEV1 (FEV1p) are represented as mean ± S.E. The values of smoking status, secondary smoking, physical activity, occupation, and physician diagnosis are represented as the percentage of total subjects. Differences in the tertiles of dietary pattern were determined using oneway ANOVA with Bonferroni’s post hoc test for continuous variables, and chi square test for categorical variables (P-value <0.05). Sharing the same alphabet indicates no significant difference between groups. 1 Smoking status: Subjects who smoked during the survey period were regarded as current smokers. 2 Smoking (pack-day): the mean number of cigarette packs smoked per day. 3 Secondary smoking: subject who exposed to cigarettes by second hand at work place or home. 4 Physical activity: doing exercise like walking for over 30 minutes and at least five times or more a week or swimming, tennis, volley ball or badminton for over 30 minutes and at least five times or more a week.

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Age (year) BMI (kg/m2) Smoking status (n, %)1 Nonsmokers Ex-smokers Current smokers Smoking (pack-day)2 Secondary smoking (n, %)3 Physical activity (n, %)4 Walking Moderate Severe Occupation (n, %) Office worker Manual worker Technician Unemployed Physician diagnosis (n, %) Asthma Tuberculosis COPD Lung cancer Pulmonary function indices FVC (L) FVCp (%) FEV1 (L) FEV1p (%) FEV1/FVC

Total subject (n = 7615)

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Table 3 Pearson’s coefficient between dietary pattern scores and nutrient intakes.

Energy (kcal) % Carbohydratea % Proteina % Fata Fiber (g)b Calcium (mg)b Phosphorous (mg)b Iron (mg)b Sodium (mg)b Potassium (mg)b Vitamin A (μgRE)b Carotene (μg)b Retinol (μg)b Vitamin B1 (mg)b Vitamin B2 (mg)b Niacin (mg)b Vitamin C (mg)b

Balanced diet rc

P-valued

Refined diet rc

P-valued

0.052 −0.034 0.112 0.061 0.078 0.123 0.157 0.064 0.030 0.166 0.085 0.082 0.012 0.092 0.167 0.101 0.166

<0.001 0.003 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.010 <0.001 <0.001 <0.001 0.305 <0.001 <0.001 <0.001 <0.001

0.099 0.007 −0.016 0.104 −0.023 0.001 −0.026 −0.029 −0.060 −0.016 −0.026 −0.041 0.041 −0.013 0.022 −0.028 −0.003

<0.001 0.522 0.162 <0.001 0.049 0.905 0.026 0.015 <0.001 0.172 0.027 <0.001 <0.001 0.262 0.066 0.019 0.798

a Macronutrients to energy was calculated as the ratio of energy from each macronutrient to total energy. b Micronutrients intake calculated as value per 1000 calories. c Coefficients (r) were derived from partial correlation analysis adjusting for age, BMI, smoking status, smoking pack-year, secondary smoking, exercise, education, income, occupation, and residence area (urban/rural). d Differences were tested using partial correlation analysis adjusting for age, BMI, smoking status, smoking pack-day, secondary smoking, exercise, education, income, occupation, and residence area (urban/rural) (P-value < 0.005).

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3.2. Nutrient intake according to dietary pattern score For each derived dietary pattern, we examined whether nutrient intakes were related to each dietary pattern score (Table 3) and found that the balanced diet pattern was positively correlated with all nutrient intake indices (all p < 0.05), except for retinol. The refined diet pattern was positively correlated with total energy intake, energy from fat, and retinol intake but was negatively correlated with fiber, phosphorous, iron, sodium, vitamin A, carotene, and niacin intakes. 3.3. Relationship between pulmonary function levels and dietary pattern scores To examine the effect of dietary patterns on lung function, we conducted multivariate logistic analyses. The ORs for lung function by the tertiles of dietary patterns are presented in Table 4. A balanced diet was positively associated with FVC before (OR: 3.81, 95% CI: 3.23–4.48) and after covariate adjustment (OR: 1.33, 95% CI: 1.09–1.64). The ORs for predicted FVC and FEV1 were significantly higher before adjustment in the highest tertile than in the lowest tertile for the balanced diet pattern, but this relationship disappeared after adjustment. A refined diet was negatively associated with predicted FVC and FEV1 before and after adjustment (OR: 0.84, 95% CI: 0.70–0.99; OR: 0.79, 95% CI: 0.66–0.93, respectively). The ORs for FVC and FEV1 increased from the lowest to the highest tertile of the refined diet pattern before adjustment; however, this relationship was reversed in the adjusted model. 4. Discussion

products, and fast foods. Balanced and refined diets accounted for a variance of 15.8% and 9.8% in food intakes, respectively. The subjects were divided into tertile groups according to the dietary pattern scores (Table 2). The mean age of the total study population was 56.9 ± 0.1 years (range, 40–90 years), and 6.5% of the total subjects were current smokers. Subjects with high scores for each dietary pattern were younger, had lower BMI levels, smoked fewer cigarettes, and had a lower percentage of current smokers than the subjects with low scores for each dietary pattern (all p < 0.05). In addition, the subjects with high scores were more likely to hold an office job (p < 0.001). The subjects with a high score in the balanced diet pattern were more likely to exercise, and this pattern was reversed for those in the refined diet pattern (p < 0.001). In addition, the high scores for balanced diet pattern were associated with higher pulmonary function levels compared with low scores for balanced diet pattern (all p < 0.05). The subjects with high scores for refined diet pattern showed decreased levels of FEV1p (p < 0.05).

In the present study, we evaluated the association between dietary patterns and pulmonary function in a large sample of Korean women older than 40 years. In this population, a factor analysis indicated that dietary patterns were divided into “balanced diet pattern” and “refined diet pattern”. In particular, a refined diet pattern characterized by high intakes of bread, snacks, rice cakes, fast food, and meat was significantly associated with pulmonary function measured by predicted FVC and FEV1. Consistent with the previous findings in other populations (McKeever et al., 2010; Shaheen et al., 2010; Varraso et al., 2007a, 2007b), the refined diet pattern showed negative associations with predicted FEV1 and FVC. A 5-year longitudinal study of the general population suggested that higher intakes of meat and potatoes and lower intakes of soy and cereal appear to have adverse effects on FEV1 and COPD symptoms (McKeever et al., 2010). In addition, dietary intake pattern with an increased consumption of refined

Table 4 Multivariate adjusted odds ratio (95% confidence intervals) for pulmonary function index according to tertile of dietary pattern scores in the study population. Model

FVC (L) FVCp (%) FEV1 (L) FEV1p (%) FEV1/FVC

Unadjusted Adjusteda Unadjusted Adjusteda Unadjusted Adjusteda Unadjusted Adjusteda Unadjusted Adjusteda

Pattern 1: Balanced diet

Pattern 2: Refined diet

T1 (n = 2538) OR (95% CI)

T2 (n = 2539) OR (95% CI)

T3 (n = 2538) OR (95% CI)

T1 (n = 2538) OR (95% CI)

T2 (n = 2539) OR (95% CI)

T3 (n = 2538) OR (95% CI)

1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref)

2.07 (1.77, 2.42) 1.02 (0.84, 1.24) 1.10 (0.94, 1.28) 0.95 (0.81, 1.12) 2.28 (1.95, 2.67) 1.08 (0.88, 1.33) 0.88 (0.75, 1.03) 0.92 (0.79, 1.09) 1.58 (1.35–1.85) 1.01 (0.84–1.20)

3.81 (3.23, 4.48) 1.33 (1.09, 1.64) 1.31 (1.12, 1.53) 1.07 (0.90, 1.27) 3.75 (3.19, 4.42) 1.16 (0.94, 1.44) 0.85 (0.73, 0.99) 0.95 (0.80, 1.13) 1.79 (1.15–2.09) 0.91 (0.76–1.10)

1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref)

0.96 (0.82, 1.12) 0.84 (0.70, 1.02) 0.91 (0.83, 1.14) 0.81 (0.69, 0.95) 0.91 (0.83, 1.14) 0.81 (0.66, 0.99) 0.87 (0.75, 1.01) 0.84 (0.72, 0.99) 1.17 (1.01–1.37) 1.10 (0.93–1.30)

2.67 (2.27, 3.14) 0.97 (0.79, 1.19) 1.04 (0.89, 1.22) 0.84 (0.70, 0.99) 2.72 (2.32, 3.20) 0.84 (0.68, 1.04) 0.72 (0.62, 0.85) 0.79 (0.66, 0.93) 1.62 (1.38–1.89) 0.94 (0.78–1.13)

Values are expressed as odds ratios (confidence intervals). Level of pulmonary function was defined as the highest quartiles group. a Adjusted for age, BMI, smoking status, smoking pack-day, secondary smoking, exercise, education, income, occupation, and residence area (urban/rural).

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grains, cured and red meats, desserts, and French fries have shown to be associated with an increased development of COPD and its mortality, and decreased FEV1 in both US men and women (Varraso et al., 2007a, 2007b). In addition, a diet rich in meats, sodium, and refined carbohydrates was shown to appear to increase the risk frequency of asthma attacks (Varraso et al., 2009), and cough with phlegm (Butler et al., 2006). Also, a cross-sectional study conducted with Japanese women reported that meals oriented to high intakes of fast food were associated with the prevalence of wheezing symptom (Takaoka and Norback, 2008). Based on the previous results from western population together with ours from Korean population, dietary patterns with a refined food appeared to be associated with deterioration of pulmonary function as well as with the increase in the risk of COPD. Although the exact mechanism by which a refined diet may negatively influence pulmonary function remains unclear, we speculate that differences in intakes of several nutrients may influence this relationship. Earlier studies have reported positive associations between FEV1 and vitamins C, A, E, and beta-carotene intakes, due to their anti-oxidative effect (Schünemann et al., 2002). A direct effect of nutrients that have antioxidant and anti-inflammatory properties is readily plausible since oxidant stress is an important risk factor in lung function impairment (Schünemann et al., 1997). In the present study, the subjects with a high score for the refined diet pattern showed relatively low vitamin A and beta-carotene intakes but high energy intake from fat. For the relationship between dietary fat intake and pulmonary health, several studies have suggested that dietary fat may be related to bronchial hyperresponsiveness (Soutar et al., 1997), incidence of asthma (Strom et al., 1996), and a higher risk for COPD (Varraso et al., 2007b). In addition, it is shown that high saturated fat intake from a “meat and dimsum” dietary pattern was associated with an increased risk for incident cough with phlegm (Butler et al., 2006). An animal study also showed that a high fat diet may cause lung inflammation (Kim et al., 2013) and incident lung fibrosis by increasing specific profibrotic factors (Ge et al., 2013) that potentially aggravate lung function. In this study, the proportion of fat intake from total energy was 14.2 ± 0.1% of total calorie intake, which is within the reference range and relatively lower compared with that in Western societies. Therefore, a high-fat accompanied by lowantioxidant dietary pattern might increase the risk of lipid peroxidation. Subjects with a refined diet pattern have enhanced production of oxygen free radicals from excessive fat intake, which cannot be removed effectively due to insufficient antioxidant vitamin levels, therefore negatively impacting respiratory function; however, this was not experimentally proven in the present study. The results of this study also confirm previous findings that dietary fiber intake is associated with pulmonary function (Butler et al., 2004; Kan et al., 2008; Park et al., 2011; Varraso et al., 2010). Previous data suggest beneficial effects of fiber intake on chronic respiratory disease in adults independent of antioxidant vitamin intake (Park et al., 2011). Research from the Atherosclerosis Risk in Communities study revealed that subjects in the highest quintile of fiber intake had higher levels of predicted FVC and FEV1 than those in the lowest quintile (Kan et al., 2008). In addition, a recent longitudinal study on a US population reported that the cumulative average intake of fiber was negatively associated with risk for newly diagnosed COPD after adjustment for confounders, especially in women (Varraso et al., 2010). We observed that the refined diet pattern was negatively correlated with niacin intake, which raises the possibility that inadequate niacin intake may be associated with reduced respiratory function. Recently, niacin was reported to be helpful in the reduction of pulmonary fibroblasts and lung inflammation (Ji et al., 2009; Kwon et al., 2011). Taken together, our results suggest that the refined diet pattern with a high fat intake but low vitamin C, niacin, and fiber intakes is associated with reduced

pulmonary function in Korean women. In contrast to the previous studies (McKeever et al., 2010; Varraso et al., 2007a, 2007b), the balanced diet pattern was weakly associated with only FVC in the present study. Therefore, the refined diet pattern seems to be the predominant contributor to the pulmonary health of Korean women. This study had some limitations. First, this study was crosssectional; therefore, a causal relationship could not be fully determined. However, “reverse causation” is an unlikely explanation for the main findings since individuals developing worse lung function would choose healthier diet. Second, selection toward subjects with a better pulmonary function may have occurred, since subjects who fail to perform an adequate pulmonary function measurement were excluded. Accordingly, our results may not be applied for the population with poor pulmonary function. Third, we used pre-bronchodilator spirometry which is difficult, considering minor symptoms of the restrictive disease. Fourth, indoor air pollutant such as indoor cooking was not considered since the information was not available. Finally, KNHANES does not contain detailed information on the occupations and residences of participants. There are various environmental sources influencing pulmonary function including air pollution and toxic working environment. Although we considered the effects of occupations and regional air quality on the pulmonary function by using six classified occupations and 16 classified residence areas, we cannot exclude the possibility of being environmentally exposed to air pollutants. Several strengths should be noted in this study. First, we analyzed a large scale dataset, which provided statistical power to explore the association between dietary patterns and pulmonary functions in Korea. Second, instead of a single nutrient, we constructed a dietary pattern and examined its association with pulmonary function, for the first time in Asia. Strength of our study can provide insight toward the promotion of healthy eating patterns. In conclusion, this study found that a refined diet pattern characterized by high intakes of bread, snacks, rice cakes, fast food, and meat was associated with decreased pulmonary function, as determined by FVC and FEV1, in Korean women older than 40 years. The observed effects on pulmonary function might be of clinical significance, and this information may be used toward the development of nutritional guidelines for improving pulmonary function.

Transparency document The Transparency document associated with this article can be found in the online version.

Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF2013R1A1A2A10006101). The authors certify that there is no conflict of interest.

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