Skip to main content Accessibility help
×
Home
Hostname: page-component-768ffcd9cc-w9xp6 Total loading time: 1.019 Render date: 2022-11-29T21:41:48.029Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Dietary patterns and their associations with health behaviours in Korea

Published online by Cambridge University Press:  19 October 2010

Eo Rin Cho
Affiliation:
Cancer Epidemiology Branch, Division of Cancer Epidemiology and Management, Research Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Republic of Korea Department of Public Health, The Graduate School, Yonsei University, Republic of Korea Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, Republic of Korea
Aesun Shin*
Affiliation:
Cancer Epidemiology Branch, Division of Cancer Epidemiology and Management, Research Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Republic of Korea
Sun-Young Lim
Affiliation:
Cancer Epidemiology Branch, Division of Cancer Epidemiology and Management, Research Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Republic of Korea
Jeongseon Kim
Affiliation:
Cancer Epidemiology Branch, Division of Cancer Epidemiology and Management, Research Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Republic of Korea
*
*Corresponding author: Email shina@ncc.re.kr
Rights & Permissions[Opens in a new window]

Abstract

Objective

Dietary habits, including dietary patterns, have been associated with the risk of chronic diseases, including cancer. The objective of the present study was to evaluate Korean dietary patterns as assessed by using an FFQ and associations of dietary patterns with lifestyle risk factors.

Design

Dietary patterns were analysed by factor analysis using a sixteen-group FFQ. The associations between dietary patterns and lifestyle risk factors were investigated by logistic regression analysis.

Setting

The National Cancer Center in South Korea.

Subjects

The study population included 11 440 participants aged ≥30 years who were recruited between 2002 and 2007.

Results

Compared with the lowest quartile intake of each dietary pattern, current smoking was positively associated with the Western pattern (OR = 1·55 for the highest quartile, 95 % CI 1·27, 1·88; P < 0·001) and the traditional pattern (OR = 1·34, 95 % CI 1·11, 1·62; P = 0·002) in men, but was inversely associated with the healthy pattern in both genders (P < 0·001) and the traditional pattern (OR = 0·52, 95 % CI 0·36, 0·75; P = 0·001) in women. Alcohol consumption was positively associated with all patterns in both genders, while no association was observed with the healthy pattern in women. Physical activity and dietary supplement use were positively associated with all patterns in both genders, with the exception of physical activity in women, which showed an inverse association with the traditional pattern.

Conclusions

Dietary patterns are strongly associated with health behaviours. The possible confounding effect of other risk behaviours should be appropriately considered when conducting nutritional epidemiological studies.

Type
Research paper
Copyright
Copyright © The Authors 2010

Although traditionally nutritional research has focused primarily on single nutrients or individual foods, interest is growing in dietary patterns that consider the complexity of the overall diet(Reference Zarraga and Schwarz1, Reference Hu2). An exploratory approach using factor or cluster analysis empirically identifies patterns that represent actual eating behaviours of the study population. Typically, analyses extract two to six patterns that reflect different dietary compositions(Reference Newby and Tucker3).

Recently, several experimental, clinical and epidemiological studies have examined dietary patterns and found that patterns reflecting certain eating habits are associated with the risk of chronic diseases(Reference Hu2, Reference Cai, Zheng and Xiang4Reference van Dam, Rimm and Willett7), including cancer(Reference Terry, Hu and Hansen8, Reference Terry, Suzuki and Hu9). Dietary habits are also closely related to other health-related behaviours. Current smoking is generally inversely related to a healthy diet(Reference Bamia, Orfanos and Ferrari10Reference Schulze, Hoffmann and Kroke13); alcohol consumption is positively related to diets with high intakes of fat and meat, but inversely related to a prudent diet(Reference Park, Murphy and Wilkens14Reference Imamura, Lichtenstein and Dallal17); and dietary supplement use is often associated with a healthy diet that reflects high intakes of skimmed milk, yoghurt, juice, cereals, rice, chicken, fruit and cod liver oil(Reference McNaughton, Mishra and Stephen11, Reference Beitz, Mensink and Hintzpeter18, Reference Kerver, Yang and Bianchi19). However, most studies of these factors have been conducted in North America or Europe, whereas only a few studies have investigated Asian populations(Reference Kim, Sasaki and Sasazuki15, Reference Shin, Oh and Park20), and the results may not apply to the Korean population whose dietary habits are different from patterns in Western countries.

Thus, the purpose of the present study was to evaluate Korean dietary patterns by using an FFQ and to assess the association of dietary patterns with lifestyle risk factors, such as smoking, alcohol consumption, physical activity and dietary supplement use.

Experimental methods

Study participants

The source population comprised 14 531 men and women who underwent cancer screening examinations at the Center for Cancer Prevention and Detection at the National Cancer Center in South Korea from August 2002 to May 2007. Participants who failed to complete information for all sixteen food groups in the FFQ or who were <30 years of age were excluded. After these exclusions, 11 440 participants (6434 men and 5006 women) remained for the analyses. Written informed consent was obtained from all participants, and all procedures were approved by the Institutional Review Board of the National Cancer Center.

Data collection

All participants were asked to complete a self-administered questionnaire about their sociodemographic characteristics (e.g. age, education and household income), cigarette smoking habit, alcohol consumption habit, physical activity, dietary supplement use, personal medical history and medication use. The average alcohol consumption amount (g/d) was calculated by summing the beverage-specific amount consumed as reported on the FFQ, average consumption frequency and the volume of one standard drink for each type of alcoholic beverage (beer, hard liquor, wine and traditional drinks, including soju and Korean rice wine).

At the time of screening, for each participant, height and weight were measured using the InBody 3·0 (Biospace, Seoul, Korea) body composition analyser, and BMI was calculated as weight (kg) divided by the square of height (m2). Three categories were constructed for smoking habits (non-, ex- and current) and alcohol consumption (non-, light and heavy), and two categories each were constructed for participation in leisure-time physical activity, dietary supplement use and past medical history of hypertension and diabetes (yes or no).

Dietary pattern derivation

Dietary information was collected using an FFQ. The FFQ consisted of sixteen food groups: cereals, salted vegetables and seafood, light-coloured vegetables, green-yellow vegetables, seaweeds, fruits, grilled meat and seafood, healthy protein foods, dairy products, bonefish, fried foods, cholesterol-rich foods, animal fat-rich foods, sweet foods, fast foods and caffeinated drinks (see Appendix). All participants were asked to record their current intake frequency for each food group according to the following categories: consumed rarely, once monthly, 2–3 times/month, once weekly, 2–3 times/week, 4–6 times/week, once daily or >2 times/d. In our previous study, the validity of this FFQ was compared to the 3 d dietary record method as a reference standard for 1401 participants from the source cohort. The cross-classification of the tertile categories by using these two methods showed good agreement (range: 38–96 %, depending on the food group). Generally, there was a trend that participants who reported a high frequency of intake on the brief FFQ also reported a high amount of intake of food in the 3 d dietary records. The highest agreement was observed for cereals (68·7 %), dairy foods (59·1 %), fruits (55·6 %) and caffeinated drinks (61·8 %)(Reference Shin, Lim and Sung21).

Statistical analyses

Dietary patterns were derived by using factor analysis, with the sixteen food groups entered into the analysis by the frequency of intake. The PROC FACTOR procedure in the SAS statistical software package version 9·1 (SAS Institute, Cary, NC, USA) was applied to perform the analysis. This procedure uses factor analysis and orthogonal rotation, using the Varimax option in SAS, to derive non-correlated factors and to render the results more easily interpretable. To determine which number of factors to retain, we examined both the scree plots and the factors themselves to see which set of factors most meaningfully described distinct food intake patterns. We considered components with an eigenvalue of >1·0. This served to limit the number of factors, as well as to better identify the three most meaningful factors. Factor loadings were calculated for each food group across the three factors. Factors were thereby interpreted as dietary patterns and named after the food groups with a loading of >0·2. A dietary factor score for each individual was then calculated by summing the consumption of food groups weighted by the factor loadings.

Dietary factor scores were categorized into quartiles separately for men and women based on the distribution of the study population, and linear regression analysis was performed to evaluate the association between dietary pattern categories and health behaviour variables with adjustments for age. P for trend was calculated from generalized linear models with adjustments for age as a continuous variable and from Mantel–Haenszel χ 2 tests with adjustments for age group as a categorical variable. To assess associations between dietary patterns and health behaviours, polytomous multiple logistic regression models were used to calculate the OR and 95 % CI for each quartile. P for trend in the OR was calculated with the dietary pattern categories as a continuous variable. Analyses were conducted using the SAS software version 9·1 (SAS Institute). All analyses were performed separately for men and women, and a two-sided P value of <0·05 was considered to be statistically significant.

Results

Among men and women aged ≥30 years who were recruited between 2002 and 2007 from the National Cancer Center in South Korea, 11 440 participants were included in the final analysis (6434 men and 5006 women). Table 1 shows the three main dietary patterns and the factor-loading matrix between food groups. The larger the loading of a given food group to the factor, the greater the contribution of that food group to a specific factor. Dietary pattern 1 was characterized by high consumption of fast foods, animal fat-rich foods, fried foods, grilled meat and seafood (barbecue), cholesterol-rich foods, sweet foods and caffeinated drinks. We named this pattern the ‘Western pattern’. Dietary pattern 2 was characterized by high consumption of green-yellow vegetables, seaweeds, healthy protein foods, bonefish, fruit and dairy products, and we named this pattern the ‘healthy pattern’. Dietary pattern 3 was characterized by high consumption of salted vegetables and seafood, cereals and light-coloured vegetables, and we named this pattern the ‘traditional pattern’. The major dietary patterns identified separately for men and women proved to be similar. Each of the three patterns explained 15·3 %, 13·0 % and 7·8 % of variance of food frequency consumption in men and 15·3 %, 12·2 % and 8·3 % in women, respectively.

Table 1 Factor-loading matrix for the major dietary patterns identified by factor analysis

For the sake of simplicity, factor loadings of <0·20 are not listed.

Demographic characteristics and lifestyle factors are presented as means (sd) or numbers and percentages stratified by quartiles of factor scores for each dietary pattern (Table 2). Among both men and women, participants with a higher healthy dietary pattern score tended to have a higher educational level and household income, to smoke less and to report more physical activity and dietary supplement use. Participants with a higher Western dietary pattern score were younger and more likely to smoke and drink, and men were more likely to have a higher BMI. Participants with a higher traditional dietary pattern score were more likely to have a higher educational level and household income and to be involved in regular leisure-time physical activity.

Table 2 Characteristics of study participants according to quartiles of factor scores for each pattern

*P for trend was calculated from a generalized linear model with adjustments for age for continuous variables, and a Mantel–Haenszel χ 2 test with adjustments for the age group for categorical variables.

†Age and BMI data are mean and standard deviation.

‡Unit is thousand won ()/month.

§Hypertension was defined as the use of antihypertensive medication or a history of hypertension. Diabetes was defined as the use of any diabetes medication or a history of diabetes.

The results of multivariate logistic regression analyses of the association between dietary patterns and health behaviours such as smoking, alcohol consumption, regular exercise and dietary supplement use are presented in Table 3 for men and in Table 4 for women. Current smoking was positively associated with the Western dietary pattern (P for trend <0·001) and the traditional dietary pattern (P for trend = 0·002) in men. However, current smoking was inversely associated with the healthy dietary pattern in both genders (P for trend <0·001) and with the traditional dietary pattern (P for trend = 0·001) in women. Alcohol consumption was positively associated with all patterns in men, with the exception of light drinking, which was inversely associated with the Western dietary pattern (P for trend = 0·044). In women, heavy drinking was positively associated with the Western dietary pattern (P for trend = 0·007), and light drinking was positively associated with the traditional dietary pattern (P for trend = 0·001), while there was no association observed with the healthy pattern. Physical activity and dietary supplement use were positively associated with all patterns in both genders, with the exception of physical activity, which was inversely associated with the traditional dietary pattern in women.

Table 3 Dietary patterns according to lifestyle risk factors (smoking, alcohol consumption, physical activity and dietary supplement use status), by polytomous logistic regression analysis, in men

Ref., reference category.

*Adjusted for age.

†Non-drinkers (0 g/d); light drinkers (<12 g/d); and heavy drinkers (≥12 g/d).

Table 4 Dietary patterns according to lifestyle risk factors (smoking, alcohol consumption, physical activity, dietary supplement use status), by polytomous logistic regression analysis, in women

Ref., reference category.

*Adjusted for age.

†Non-drinkers (0 g/d); light drinkers (<12 g/d); and heavy drinkers (≥12 g/d).

Discussion

In the present study of Korean adults, we identified three major dietary patterns, Western, healthy and traditional, and we evaluated their relationship with health behaviours. The three dietary patterns identified in the present study were similar to the dietary patterns found by previous studies conducted among Asian and Western populations using factor analysis(Reference Bamia, Orfanos and Ferrari10, Reference Park, Murphy and Wilkens14, Reference Kim, Sasaki and Sasazuki15, Reference Yang, Kerver and Song22).

The Western pattern and the healthy pattern (or ‘prudent pattern’) have been reported by many other studies. The main contributors to the Western dietary pattern are typically meats, fats, fast food, sweets, grains, butter, eggs, potatoes and sugar-containing foods(Reference Terry, Hu and Hansen8, Reference Slattery, Boucher and Caan23, Reference Fung, Hu and Fuchs24). The main contributors to the healthy pattern are vegetables, fruit, fish and poultry(Reference Terry, Suzuki and Hu9, Reference Pryer, Cook and Shetty12, Reference Slattery, Boucher and Caan23Reference Chen, Ward and Graubard26). Several studies have also reported a traditional dietary pattern(Reference Pryer, Cook and Shetty12, Reference Kim, Sasaki and Sasazuki15, Reference Shin, Oh and Park20, Reference Yang, Kerver and Song22, Reference van Dam, Grievink and Ocke27, Reference Engeset, Alsaker and Ciampi28). In a study of 1441 Korean children, in which thirty-three food groups were created and entered into a factor analysis, the traditional (Korean) pattern included vegetables, seaweeds, beans, fruits, milk and dairy products(Reference Shin, Oh and Park20). A study of the dietary pattern of 637 Korean Americans found that the traditional Korean pattern was characterized by high intake of traditional Korean dishes, such as soyabean paste stew, anchovies, Korean-style grains, tofu, vegetables, kimchi, salted/fermented fish, seaweeds, Korean-style soups, red meat, other seafood (shrimp, squid, clams, oyster, etc) and fish(Reference Yang, Kerver and Song22). In Japan, the traditional pattern includes pickled vegetables, salted fish and roe, fish, rice and miso soup(Reference Kim, Sasaki and Sasazuki15). In contrast, in the Netherlands, the traditional pattern is characterized by higher consumption of red meat, potatoes, highly saturated added fats, coffee and beer and lower consumption of soya products, low-fat dairy, breakfast cereals, tea and fruits(Reference van Dam, Grievink and Ocke27). A traditional Norwegian dietary pattern consists mainly of two or three similar cold meals, usually open sandwiches, and one hot meal with either fish or meat served with potatoes and vegetables(Reference Engeset, Alsaker and Ciampi28). The traditional dietary pattern in the present study included salted foods, such as pickled vegetables, kimchi and rice.

Studies of dietary patterns conducted in other populations have noted the importance of evaluating associations of dietary patterns with health behaviours, such as smoking, alcohol consumption, physical activity and dietary supplement use. For smoking, our study found that current smoking was strongly associated with the traditional and Western dietary patterns in men, but was inversely associated with the healthy pattern in both genders. In general, previously conducted studies have found that current smoking is inversely related to a healthy dietary pattern(Reference McNaughton, Mishra and Stephen11, Reference Pryer, Cook and Shetty12), whereas former smokers tend to have a healthier dietary pattern(Reference Knudsen, Rasmussen and Haraldsdottir29). For example, in a population-based Dutch study of 4244 men, men who smoked >20 cigarettes/d had significantly lower intake of β-carotene and especially ascorbic acid compared with men who never smoked, which was attributed to an almost 60 % lower fruit intake among smokers(Reference Zondervan, Ocke and Smit30). The European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study found that the ‘fruit and vegetable’ dietary pattern was negatively associated with smoking (β = −0·188 in men and −0·225 in women)(Reference Bamia, Orfanos and Ferrari10). The Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) population survey of 976 men aged 45–64 years also found a negative association of smoking status (P < 0·001) with a healthy dietary pattern(Reference Perrin, Dallongeville and Ducimetiere31). Finally, the Multiethnic Cohort Study of 195 298 participants residing in Hawaii and Los Angeles reported that current smokers showed a positive association with the fat and meat pattern (OR = 1·67, 95 % CI 1·62, 1·72) and inverse associations with the vegetable (OR = 0·66, 95 % CI 0·64, 0·68) and fruit and milk patterns (OR = 0·53, 95 % CI 0·52, 0·55)(Reference Park, Murphy and Wilkens14). Consistent with these findings, another study has shown that smokers are less likely than non-smokers to consume vegetables and milk or dairy foods. Smokers may consume more caffeinated or alcoholic beverages and meat products in order to enhance the taste of smoking(Reference McClernon, Westman and Rose32).

For alcohol consumption, we observed that heavy alcohol consumption was positively associated with the Western pattern in both genders, whereas light alcohol consumption was inversely associated with the Western pattern in men. Among 15 073 university graduates enrolled in the Seguimiento Universidad de Navarra study in Spain, higher adherence to the Western dietary pattern was less likely to decrease participants’ alcohol consumption during follow-up, whereas participants with higher adherence to the Mediterranean (healthy) dietary pattern were less likely to increase their alcohol consumption (OR = 0·66, 95 % CI 0·46, 0·95)(Reference Sanchez-Villegas, Toledo and Bes-Rastrollo16). In contrast, the Multiethnic Cohort Study in Hawaii found that the fat and meat dietary pattern was positively associated with BMI, smoking and alcohol consumption (≥1 drink/week; OR = 1·40, 95 % CI 1·37, 1·43)(Reference Park, Murphy and Wilkens14).

For physical activity and dietary supplement use, our study found both of these factors to be positively associated with all dietary patterns in both genders, with the exception of physical activity, which was inversely associated with the traditional dietary pattern in women. The EPIC–Potsdam study found that physical activity level was positively associated with the ‘fruit and vegetable’ dietary pattern in men (β = 0·182) and was negatively associated with the ‘bread and sausage’ pattern in women (β = 0·099)(Reference Bamia, Orfanos and Ferrari10). The Multiethnic Cohort Study found that physical activity (≥3 times/week) was positively associated with the vegetable (OR = 1·73, 95 % CI 1·69, 1·77) and fruit and milk patterns (OR = 1·44, 95 % CI 1·40, 1·47), but not with the fat and meat pattern (OR = 0·98, 95 % CI 0·96, 1·01)(Reference Park, Murphy and Wilkens14). In addition, participants with the healthy pattern have been reported to have higher physical activity levels and to be more likely to take dietary supplements daily(Reference van Dam, Grievink and Ocke27). Likewise, in our study, physical activity was more strongly associated with the healthy dietary pattern than other dietary patterns in men (OR = 2·16, 95 % CI 1·86, 2·53) and women (OR = 2·03, 95 % CI 1·70, 2·41). Among 64 252 women in the French E3N-EPIC cohort, supplement use was positively associated with the fruit/vegetable pattern and inversely associated with the processed meat/starchy foods and alcohol/meat products patterns (P for trend for all associations <0·001)(Reference Touvier, Niravong and Volatier33). In the German Nutrition Survey, comprising 7124 men and women, significant differences in food consumption between regular vitamin and mineral supplement users and non-users were observed, indicating a tendency towards a healthier diet among regular users of supplements(Reference Kerver, Yang and Bianchi19). In a study by McNaughton et al.(Reference McNaughton, Mishra and Stephen11), dietary supplement use was positively associated with the fruit/vegetable pattern and inversely associated with a Western dietary pattern, and dietary supplement users tended to have a healthier lifestyle and diet than non-users.

Most dietary pattern studies have been conducted in North America or Europe, and only a few studies have been conducted in Asia and Korea. Thus, the results of the present study are important as they reflect dietary habits common in Asian countries. It is important to note that our study participants were volunteers recruited at one cancer screening centre; therefore, the results may not be generalizable to other Korean populations. However, the relatively high socio-economic status of our study population may contribute to more accurate responses regarding risk factors and may have helped to reduce errors related to internal validity. Yet the present study has a limitation. The FFQ focused on eating habits rather than actual food intake amounts. The FFQ did not ask about portion size, and therefore information on nutrient intake was not available. In addition, the use of an FFQ containing only sixteen food groups may restrict the number of food categories used to characterize the usual dietary intake. It is not clear to what degree this FFQ represents habitual food intake.

In summary, the present study found three different dietary patterns in a middle-aged Korean population and indicates that these dietary patterns are strongly associated with health behaviours. The healthy dietary patterns were mainly associated with other healthy lifestyle behaviour factors, including not smoking, low alcohol consumption, participating in physical activity and dietary supplement use. The possible confounding effect of other risk behaviours should be appropriately considered when conducting nutritional epidemiological studies of the association between dietary patterns and disease outcomes.

Acknowledgements

The present study was supported by grants from the National Cancer Center, Korea (Grant numbers 0610552 and 0910222). There are no conflicts of interest. A.S. and J.K. designed the study; E.R.C. and S.-Y.L. conducted the statistical analysis; and E.R.C. wrote the manuscript. All authors critically reviewed and approved the manuscript.

Appendix

Food list for the sixteen food groups in the FFQ of the present study

References

1.Zarraga, IG & Schwarz, ER (2006) Impact of dietary patterns and interventions on cardiovascular health. Circulation 114, 961973.CrossRefGoogle ScholarPubMed
2.Hu, FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13, 39.CrossRefGoogle ScholarPubMed
3.Newby, PK & Tucker, KL (2004) Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 62, 177203.CrossRefGoogle ScholarPubMed
4.Cai, H, Zheng, W, Xiang, YB et al. (2007) Dietary patterns and their correlates among middle-aged and elderly Chinese men: a report from the Shanghai Men’s Health Study. Br J Nutr 98, 10061013.CrossRefGoogle ScholarPubMed
5.Heidemann, C, Schulze, MB, Franco, OH et al. (2008) Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women. Circulation 118, 230237.CrossRefGoogle Scholar
6.Osler, M, Heitmann, BL, Gerdes, LU et al. (2001) Dietary patterns and mortality in Danish men and women: a prospective observational study. Br J Nutr 85, 219225.CrossRefGoogle ScholarPubMed
7.van Dam, RM, Rimm, EB, Willett, WC et al. (2002) Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med 36, 201209.CrossRefGoogle Scholar
8.Terry, P, Hu, FB, Hansen, H et al. (2001) Prospective study of major dietary patterns and colorectal cancer risk in women. Am J Epidemiol 154, 11431149.CrossRefGoogle ScholarPubMed
9.Terry, P, Suzuki, R, Hu, FB et al. (2001) A prospective study of major dietary patterns and the risk of breast cancer. Cancer Epidemiol Biomarkers Prev 10, 12811285.Google ScholarPubMed
10.Bamia, C, Orfanos, P, Ferrari, P et al. (2005) Dietary patterns among older Europeans: the EPIC–Elderly study. Br J Nutr 94, 100113.CrossRefGoogle ScholarPubMed
11.McNaughton, SA, Mishra, GD, Stephen, AM et al. (2007) Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr 137, 99105.CrossRefGoogle ScholarPubMed
12.Pryer, JA, Cook, A & Shetty, P (2001) Identification of groups who report similar patterns of diet among a representative national sample of British adults aged 65 years of age or more. Public Health Nutr 4, 787795.CrossRefGoogle ScholarPubMed
13.Schulze, MB, Hoffmann, K, Kroke, A et al. (2001) Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study. Br J Nutr 85, 363373.CrossRefGoogle ScholarPubMed
14.Park, SY, Murphy, SP, Wilkens, LR et al. (2005) Dietary patterns using the Food Guide Pyramid groups are associated with sociodemographic and lifestyle factors: the Multiethnic Cohort Study. J Nutr 135, 843849.CrossRefGoogle ScholarPubMed
15.Kim, MK, Sasaki, S, Sasazuki, S et al. (2004) Prospective study of three major dietary patterns and risk of gastric cancer in Japan. Int J Cancer 110, 435442.CrossRefGoogle ScholarPubMed
16.Sanchez-Villegas, A, Toledo, E, Bes-Rastrollo, M et al. (2009) Association between dietary and beverage consumption patterns in the SUN (Seguimiento Universidad de Navarra) cohort study. Public Health Nutr 12, 351358.Google ScholarPubMed
17.Imamura, F, Lichtenstein, AH, Dallal, GE et al. (2009) Confounding by dietary patterns of the inverse association between alcohol consumption and type 2 diabetes risk. Am J Epidemiol 170, 3745.CrossRefGoogle ScholarPubMed
18.Beitz, R, Mensink, GB, Hintzpeter, B et al. (2004) Do users of dietary supplements differ from nonusers in their food consumption? Eur J Epidemiol 19, 335341.CrossRefGoogle ScholarPubMed
19.Kerver, JM, Yang, EJ, Bianchi, L et al. (2003) Dietary patterns associated with risk factors for cardiovascular disease in healthy US adults. Am J Clin Nutr 78, 11031110.CrossRefGoogle ScholarPubMed
20.Shin, KO, Oh, SY & Park, HS (2007) Empirically derived major dietary patterns and their associations with overweight in Korean preschool children. Br J Nutr 98, 416421.CrossRefGoogle ScholarPubMed
21.Shin, A, Lim, SY, Sung, J et al. (2009) Dietary intake, eating habits, and metabolic syndrome in Korean men. J Am Diet Assoc 109, 633640.CrossRefGoogle ScholarPubMed
22.Yang, EJ, Kerver, JM & Song, WO (2005) Dietary patterns of Korean Americans described by factor analysis. J Am Coll Nutr 24, 115121.CrossRefGoogle ScholarPubMed
23.Slattery, ML, Boucher, KM, Caan, BJ et al. (1998) Eating patterns and risk of colon cancer. Am J Epidemiol 148, 416.CrossRefGoogle ScholarPubMed
24.Fung, T, Hu, FB, Fuchs, C et al. (2003) Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med 163, 309314.CrossRefGoogle ScholarPubMed
25.Newby, PK, Muller, D, Hallfrisch, J et al. (2003) Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr 77, 14171425.CrossRefGoogle ScholarPubMed
26.Chen, H, Ward, MH, Graubard, BI et al. (2002) Dietary patterns and adenocarcinoma of the esophagus and distal stomach. Am J Clin Nutr 75, 137144.CrossRefGoogle ScholarPubMed
27.van Dam, RM, Grievink, L, Ocke, MC et al. (2003) Patterns of food consumption and risk factors for cardiovascular disease in the general Dutch population. Am J Clin Nutr 77, 11561163.CrossRefGoogle ScholarPubMed
28.Engeset, D, Alsaker, E, Ciampi, A et al. (2005) Dietary patterns and lifestyle factors in the Norwegian EPIC cohort: the Norwegian Women and Cancer (NOWAC) study. Eur J Clin Nutr 59, 675684.CrossRefGoogle ScholarPubMed
29.Knudsen, VK, Rasmussen, LB, Haraldsdottir, J et al. (2002) Use of dietary supplements in Denmark is associated with health and former smoking. Public Health Nutr 5, 463468.CrossRefGoogle ScholarPubMed
30.Zondervan, KT, Ocke, MC, Smit, HA et al. (1996) Do dietary and supplementary intakes of antioxidants differ with smoking status? Int J Epidemiol 25, 7079.CrossRefGoogle ScholarPubMed
31.Perrin, AE, Dallongeville, J, Ducimetiere, P et al. (2005) Interactions between traditional regional determinants and socio-economic status on dietary patterns in a sample of French men. Br J Nutr 93, 109114.CrossRefGoogle Scholar
32.McClernon, FJ, Westman, EC, Rose, JE et al. (2007) The effects of foods, beverages, and other factors on cigarette palatability. Nicotine Tob Res 9, 505510.CrossRefGoogle ScholarPubMed
33.Touvier, M, Niravong, M, Volatier, JL et al. (2009) Dietary patterns associated with vitamin/mineral supplement use and smoking among women of the E3N-EPIC cohort. Eur J Clin Nutr 63, 3947.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Factor-loading matrix for the major dietary patterns identified by factor analysis

Figure 1

Table 2 Characteristics of study participants according to quartiles of factor scores for each pattern

Figure 2

Table 3 Dietary patterns according to lifestyle risk factors (smoking, alcohol consumption, physical activity and dietary supplement use status), by polytomous logistic regression analysis, in men

Figure 3

Table 4 Dietary patterns according to lifestyle risk factors (smoking, alcohol consumption, physical activity, dietary supplement use status), by polytomous logistic regression analysis, in women

Figure 4

You have Access
16
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Dietary patterns and their associations with health behaviours in Korea
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Dietary patterns and their associations with health behaviours in Korea
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Dietary patterns and their associations with health behaviours in Korea
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *