Eur J Nutr DOI 10.1007/s00394-014-0713-0

ORIGINAL CONTRIBUTION

Dietary patterns and cognitive function in Korean older adults Jihye Kim • Areum Yu • Bo Youl Choi • Jung Hyun Nam • Mi Kyung Kim • Dong Hoon Oh • Kirang Kim • Yoon Jung Yang

Received: 8 January 2014 / Accepted: 5 May 2014 Ó Springer-Verlag Berlin Heidelberg 2014

Abstract Purpose The objectives of this study were to identify major dietary patterns and to investigate the association between dietary patterns and cognitive function in older adults. Methods This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study, which is a part of the Korean Genome Epidemiology Study, were used. There were 806 (340 men and 466 women) subjects aged C60 years. Usual dietary intake was assessed using a quantitative food frequency questionnaire with 106 food items. Cognitive function was assessed using the Korean version Mini-Mental State Examination (MMSE-KC). We

J. Kim
A. Yu Department of Clinical Nutrition, Graduate School of Health Sciences, Dongduk Women’s University, 23-1 Wolgok-dong, Sungbuk-gu, Seoul 136-714, Republic of Korea B. Y. Choi
M. K. Kim Department of Preventive Medicine, College of Medicine, Hanyang University, 17 Haengdang Dong, Sungdong Gu, Seoul 133-791, Republic of Korea J. H. Nam
D. H. Oh Department of Psychiatry, College of Medicine, Hanyang University, 17 Haengdang Dong, Sungdong Gu, Seoul 133-791, Republic of Korea

conducted factor analysis using the principal component analysis method to identify the major dietary patterns. The association between major dietary patterns and cognitive function was investigated by logistic regression analysis. Results Three major dietary patterns were identified and assigned descriptive names based on the food items with high loadings: ‘‘prudent’’ pattern, ‘‘bread, egg, and dairy’’ pattern, and ‘‘white rice only’’ pattern. As the white rice only pattern scores increased, a significant decreasing trend for MMSE-KC scores was observed after adjusting for covariates. The bread, egg, and dairy pattern was inversely related to the risk of cognitive impairment, and the white rice only pattern was positively associated with the risk of cognitive impairment. Conclusions This study suggests that specific dietary patterns were significantly associated with cognitive impairment in older adults. In particular, like the white rice only pattern, a rice-centered diet without well-balanced meals may increase the risk of cognitive impairment. However, since our study is a cross-sectional design, the possibility of reverse causality should be considered. Keywords Dietary patterns
Cognitive function
Older adults
Cognitive impairment

Introduction K. Kim Department of Food Science and Nutrition, College of Natural Science, Dankook University, 119, Dandae-ro, Dangnam-gu, Cheonan-si, Chungnam 330-714, Republic of Korea Y. J. Yang (&) Department of Foods and Nutrition, College of Natural Sciences, Dongduk Women’s University, 23-1 Wolgok-dong, Sungbuk-gu, Seoul 136-714, Korea e-mail: [email protected]

The number of elderly is growing rapidly due to longer life span. As a result, the prevalence of dementia and agingassociated diseases is also increased rapidly. According to the Delphi Consensus Study, 24.3 million people had dementia in 2001, and it was estimated that 81.1 million people will have dementia worldwide in 2040, which is an increase of about 4.6 million new cases every year since

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2001 [1]. The prevalence of Korean citizens with dementia and mild cognitive impairment (MCI) aged C65 years were 8.1 and 24.1 % in 2008 [2]. It has been estimated that the number of patients with dementia will be 2.13 million in 2050 in Korea [2]. Because MCI constitutes a high-risk factor for dementia [3], it is important to prevent MCI. A ‘‘healthy’’ diet has been suggested among preventative efforts against dementia and MCI. Studies have investigated associations between cognitive function and nutrients or foods. The protective effects of antioxidants [4], omega-3 fatty acids [5], and vegetables [6] have been reported, but some studies did not find any associations between cognitive function and particular nutrients [7, 8]. Many studies focused on a single or a few nutrients or foods, whereas more efficient health outcomes occur by altering dietary patterns [9, 10]. Because people eat combinations of foods instead of a single nutrient, these complex combinations of nutrients are likely to interact and synergize the functions of each nutrient in the body [11]. Therefore, it is valuable to examine the relationship between dietary pattern and MCI. There are a priori and a posteriori approaches to extract dietary patterns. A priori approaches are based on knowledge of a predefined ‘‘healthy’’ diet [12, 13], and ‘‘healthy’’ dietary patterns derived by these approaches were related to cognitive function in the elderly [14–16]. In contrast, a posteriori approaches generate dietary patterns by summarizing overall dietary behaviors using statistical techniques without an a priori hypothesis [12, 13]. Limited information is available about the association between dietary pattern extracted by the a posteriori method and cognitive function in the elderly. A few studies have investigated the relationship between cognitive function and dietary patterns derived by a posteriori approaches. ‘‘Healthy’’ [17, 18], ‘‘whole food’’ [19], and ‘‘Mediterranean-style’’ [20] dietary patterns were associated with significantly better cognitive function. Asian countries have their unique dietary patterns [21–23]. However, to our knowledge, no study has reported the association between Korean dietary pattern and MCI. Thus, the objective of this study was to extract dietary patterns of Korean older adults and to investigate the association between dietary pattern and cognitive function.

Subjects and methods Study population Data from the Korean Multi-Rural Communities Cohort Study (MRCohort), a part of the Korean Genome Epidemiology Study, were used. The MRCohort has been carried out since 2004 to determine the risk factors for cardiovascular disease in adults aged C40 years living in the rural

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community (Yangpyeong, Gyeonggi, South Korea). The major jobs of the subjects were farmers and housewives. The MRCohort study consisted of a health interview, health examination, and clinical examination. Subjects surveyed in July 2009 and August 2010 were included in this study (n = 1,638). Among the 1,638 individuals aged C40 years, only subjects aged C60 years (n = 808) completed the Korean version of the Mini-Mental State Examination (MMSE-KC). We excluded subjects who reported implausible energy intake of \500 kcal (n = 2). The final participants included 806 subjects (340 men and 466 women) aged C60 years. The Declaration of Helsinki and all procedures involving human subjects were approved by the Institutional Review Board of Hanyang University. Written informed consent was obtained from all subjects. General characteristics, anthropometrics variables, and cognitive function examination Data from a health interview and health examination survey were used to obtain information on smoking, alcohol intake, education, regular exercise, presence of disease (hypertension, hyperlipidemia, diabetes, cardiovascular disease, or stroke, which were identified as diseases influencing cognitive function in the literature) as well as anthropometric measurements. Participants were weighed in kg, and height was measured within 0.1 cm using an anthropometer. Body mass index (BMI) was calculated as weight (kg)/height (m2). The Mini-Mental State Examination (MMSE) is one of the most frequently used screening tools to assess cognitive function [24]. In this study, cognitive function was assessed by the MMSE-KC (Mini-Mental State Examination-Korean version) which was developed as part of the Korean version of the Consortium to Establish a Registry of Alzheimer’s Disease (CERAD) Assessment and has proven to be equally reliable and valid as the English version of the CERAD [25]. The range of possible scores is 0–30 points, and higher scores indicate better cognitive function [26]. The MMSEKC was administered by trained interviewers using a standard protocol. We classified the subjects by the MMSE-KC criteria according to age, sex, and education [26]: ‘‘normal,’’ MMSE-KC scores C-1.5 standard deviations (SD) of the mean score or ‘‘cognitive impairment,’’ MMSE-KC scores \-1.5 SD of the mean score. Several studies have used the MMSE-KC to assess cognitive function in Koreans [27]. Dietary data A nutrition survey was conducted using a quantitative food frequency questionnaire (FFQ) with 106 food items.

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Validation and reproducibility of this FFQ have been reported previously [28]. Nutrient intake was calculated by multiplying consumption frequency per day, a portion size in grams of 106 food items, and nutrients per gram. Nutrients per gram were obtained from CAN-PRO 4.0 (Computer-Aided Nutritional Analysis Program, the Korea Nutrition Society). The daily nutrient intakes of the study participants were the sum of the nutrient intakes of the 106 food items.

value of the NAR values. A MAR value of 100 % indicates that intake is equivalent to the requirement. As the assessment of cognitive impairment from the MMSE-KC is based on age, sex, and education, the three variables were not included in the model. All statistical analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC, USA).

Results Statistical analysis The 106 food items were grouped into 23 food groups according to similarity of intake and nutrient profiles to identify the dietary patterns: white rice, multigrain rice, flour-based foods, rice cakes, cereals, breads, sweet foods, nuts, beans, eggs, potatoes, salty vegetables, vegetables, meats, soups, fish, seafood, dairy foods, soymilk, coffee, green tea, soft drinks, and fruits and fruit juices. Food group variables were log-transformed prior to analysis to improve normality of the distribution. Dietary patterns were identified using factor analysis with the consumption frequency of the 23 food groups. The principal component analysis method and varimax rotation (orthogonal rotation) were used to extract factor loadings. The number of dietary patterns was determined by eigenvalues [1.25, a plot of the eigenvalues (scree plot), and interpretability of the factors [29]. Food group variables with factor loadings of \0.30 and [-0.20 were not included in the calculation of pattern scores because these items represent the foods most strongly associated with the identified factor [30, 31]. Factor loadings for each food group were calculated across the three factors. Factor scores were calculated for each participant by summing the consumption frequency of the 23 food groups weighted by their factor loadings. The participants were categorized into quartiles by factor score. The linear trend across quartiles of dietary pattern scores was tested by the general linear model for continuous variables and the Chi-square test for categorical variables. The association between major dietary patterns and cognitive function was investigated by multivariate logistic regression analysis after adjusting for potential confounders. Potential confounders were identified by examining the linear trends across quartiles of dietary pattern scores. Nutrient adequacy ratios (NAR) and Mean adequacy ratio (MAR) were calculated for each protein and micronutrients (vitamin A, vitamin C, thiamine, riboflavin, niacin, vitamin B6, folate, vitamin B12, calcium, phosphorus, magnesium, iron, and zinc) based on the recommended nutritional intake (RNI) of the Dietary Reference Intake for Koreans (KDRIs) to assess nutrient adequacy of the diet according to dietary pattern [32]. The MAR is the mean

General characteristics of the study participants are shown in Table 1. Men made up 42.2 % of the total subjects, and the proportions of male and female subjects aged 60–69 years were 62.1 and 65.9 %, respectively. Men were more highly educated than women. The major job of the male subjects was farmer (61.2 %), and 22.9 % of male subjects did not have occupation. The major job of the female subjects was farmer (49.8 %). The mean body mass index of male and female subjects were 23.6 and 25.0 kg/m2, respectively.

Table 1 General characteristics of the study subjects Men (n = 340)

Women (n = 466)

60–69

211 (62.1)b

307 (65.9)

70–79

120 (35.3)

146 (31.3)

80?

9 (2.7)

13 (2.8)

Uneducated

25 (7.4)

144 (30.9)

Elementary

154 (45.6)

255 (54.7)

Middle school

59 (17.5)

35 (7.5)

High school

71 (21.0)

21 (4.5)

College or higher

29 (8.6)

11 (2.4)

Office work

11 (3.2)

4 (0.9)

Non-office work

23 (6.8)

21 (4.5)

Service industry

8 (2.4)

26 (5.6)

Farmer

208 (61.2)

232 (49.8)

Housework Unemployed

0 (0) 78 (22.9)

99 (21.2) 77 (16.5)

Others

Age (years)

Pa 0.497

\0.0001

Education

\0.0001

Occupation

12 (3.5)

7 (1.5)

Height (cm)

163.7 ± 5.8c

151.0 ± 5.7

\0.0001

Weight (kg)

63.3 ± 9.0

57.0 ± 8.6

\0.0001

BMI

23.6 ± 2.8

25.0 ± 3.3

\0.0001

BMI body mass index a

T test for continuous variables and Chi-square test for categorical variables b n (%) c

Mean ± SD

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Thirty-six percent of the subjects were classified into cognitive impairment group. We identified three major dietary patterns using factor analysis and assigned descriptive names based on the food items with high loadings: the ‘‘prudent’’ pattern (higher consumption of beans, potatoes, vegetables, fish, seafood, and fruits and fruit juices), the ‘‘bread, egg, and dairy’’ pattern (higher consumption of breads, eggs, flour-based foods, dairy foods, meats, and fruits and fruit juices), and the ‘‘white rice only’’ pattern (higher consumption of white rice and lower consumption of multigrain rice). The three factors were selected and explained 33.6 % of the total variance of the 23 food group variables. Factor loading matrices for the three dietary patterns are shown in Table 2. The general characteristics of the study participants according to quartiles of the three dietary patterns are shown in Table 3. Regular exercise and supplement intake increased but age decreased across quartiles of dietary pattern scores for the prudent and the bread, egg, and dairy patterns. When educational level was divided by higher or lower educational level, a significant increasing trend for educational level was observed for the prudent and the Table 2 Rotated factor loading matrix for dietary patterns Foods and food groups

Prudent

Bread, egg, and dairy

White rice only

White rice

–

–

0.95a

Multigrain rice

–

–

-0.93

Flour-based foods

–

0.51

–

Rice cakes

–

0.26

–

Cereals

–

0.29

–

Breads

–

0.69

–

Sweet foods

–

0.40

–

Nuts

0.36

0.35

–

Beans

0.62

–

–

Eggs

–

0.53

–

Potatoes

0.48

–

–

Salty vegetables Vegetables

0.60 0.79

– –

– –

Meats

0.29

0.48

–

Soups

0.40

–

–

Fish

0.62

0.29

–

Seafood

0.51

0.25

–

Dairy foods

0.24

0.49

–

Soymilk

0.22

–

–

Coffee

–

–

0.22

Green tea

0.40

–

–

Soft drinks

0.33

0.23

–

Fruits and fruit juices

0.49

0.38

–

Absolute values \0.2 and [-0.2 are not listed for simplicity a

Absolute values C0.3 are bolded

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bread, egg, and dairy dietary patterns. The percentage of men decreased across quartiles of dietary pattern scores in the prudent pattern but increased in the bread, egg, and dairy and white rice only patterns. Alcohol intake increased but regular exercise decreased across the quartiles of dietary pattern scores for the white rice only pattern. Variables showing significant trends across the quartiles of dietary pattern scores in Table 3 were adjusted as potential confounders in Fig. 1 and Table 5. The MMSE-KC scores according to quartiles of dietary pattern score are presented in Fig. 1. No clear trends in MMSE-KC scores were observed according to the quartiles of dietary pattern scores in the prudent and bread, egg, and dairy patterns after adjusting for age, sex, education, regular exercise, alcohol intake, and supplement intake. However, a significant decreasing trend in the MMSE-KC scores was observed for the white rice only pattern after adjusting for age, sex, education, regular exercise, alcohol intake, and supplement intake (P for trend = 0.003). A clear increasing trend was associated with all nutrient intakes and with the prudent and bread, egg, and dairy patterns after adjusting for age and sex. Significant decreasing trends in the intake of total energy, carbohydrate, protein, fat, vitamin C, E, and B6, b-carotene, and folate were observed for the white rice only diet pattern after adjusting for sex (Table 4). MAR score, an index of overall diet quality, increased according to the quartiles of the prudent and the bread, egg, and dairy patterns but decreased according to the quartiles of the white rice only diet pattern. The relationships between the dietary patterns and cognitive impairment are shown in Table 5. The prudent pattern was not associated with cognitive impairment in the crude model (P for trend = 0.272) or in model 1 (P for trend = 0.788). The bread, egg, and dairy pattern was inversely related to the risk of cognitive impairment after adjusting for covariates (4th v. 1st quartile, odds ratio [OR], 0.57; 95 % confidence interval [CI], 0.37–0.87; P for trend = 0.014). The white rice only pattern was positively associated with the risk of cognitive impairment after adjusting for covariates (4th vs. 1st quartile; OR 2.13; 95 % CI 1.38–3.29; P for trend = 0.004).

Discussion We identified prudent, bread, egg, and dairy, and white rice only diet patterns among MRCohort participants aged [60 years. The bread, egg, and dairy pattern was negatively associated with the risk of cognitive impairment independent of age, sex, education, exercise, alcohol intake, and supplement intake. In contrast, the white rice only diet pattern was positively associated with the risk of

Eur J Nutr Table 3 Characteristics of the subjects in the lowest (Q1) and highest (Q4) quartiles of each pattern Prudent Q1

Bread, egg, and dairy

a

Q4

P

b

Q1

White rice only

Q4

P

Q1

Q4

P

n

201

201

202

201

202

201

Age

69.2 ± 5.8c

66.6 ± 5.0 \0.0001

68.1 ± 4.9

67.3 ± 5.5

0.007

67.1 ± 5.0

68.4 ± 6.1

0.891

BMI

24.1 ± 3.4

24.6 ± 3.1

0.208

24.5 ± 3.2

24.1 ± 3.1

0.224

24.5 ± 3.1

24.1 ± 3.1

0.353

Men (%)

67.7

55.2

0.009

33.3

57.7

28.9

59.2

\0.0001

\0.0001d

Education (%)

\0.0001

\0.0001

0.764

Uneducated

31.5

13.5

29.0

13.4

18.9

26.0

Elementary Middle school

53.5 7.0

47.5 13.5

59.0 8.5

37.8 15.9

51.2 10.5

44.0 12.0

High school

7.0

17.0

3.0

21.9

13.9

14.0

College or higher

1.0

8.5

0.5

11.0

5.5

4.0 \0.0001

Current drinker (%)

36.8

39.3

0.540

36.8

41.3

0.458

28.4

49.3

Current smoker (%)

1.5

2.0

1.000

0.5

3.5

0.105

1.5

3.5

0.068

Regular exerciser (%)

20.4

40.3

\0.0001

15.9

35.3

\0.0001

34.3

15.4

\0.0001

Dietary supplement user (%)

7.5

14.9

0.010

6.5

17.9

0.0001

14.9

8.0

0.243

Disease (%)e

42.3

39.3

0.750

42.8

37.3

0.372

44.8

37.3

0.074

BMI body mass index a

Quartile

b

P values for linear trend. General linear model for continuous variables and Chi-square test for categorical variables

c

Mean ± SD P for the trend was calculated after educational level was divided by higher or lower educational level

d e

Presence of disease (hypertension, hyperlipidemia, diabetes, cardiovascular disease, or stroke)

Fig. 1 Mini-Mental State Examination-Korean version (MMSE-KC) score according to quartiles of dietary pattern score. All dietary patterns were adjusted by age, sex, education, exercise, alcohol intake, and supplement intake

Prudent P for trend =0.903 MMSE-KC score

25.5 25.0 24.5 24.0 23.5 23.0 22.5 22.0 Q1

Q2

Q3

Q4

Factor score White rice only P for trend =0.003

25.5

25.5

25.0

25.0

MMSE-KC score

MMSE-KC score

Bread, egg, and dairy P for trend =0.590

24.5 24.0 23.5 23.0 22.5 22.0

24.5 24.0 23.5 23.0 22.5 22.0

Q1

Q2

Q3

Factor score

Q4

Q1

Q2

Q3

Q4

Factor score

123

123 4,302.8 ± 102.4

1,120.1 ± 104.1 1.2 ± 0.1

0.5 ± 0.01

MAR

e

d

c

b

a

0.8 ± 0.01

3.9 ± 0.1

1.8 ± 0.2 2.0 ± 0.2 1.4 ± 0.1 0.6 ± 0.01

\0.0001 \0.0001 \0.0001

4.8 ± 0.5

\0.0001 \0.0001

1.2 ± 0.1 395.4 ± 11.6

\0.0001 \0.0001

1.0 ± 0.03

2,193.4 ± 130.3

\0.0001 \0.0001

5.4 ± 0.2

\0.0001

Least squares mean ± SE

P values for linear trend and general linear model for continuous variables

Quartile

Adjusted for sex

Adjusted for age and sex

MAR mean adequacy ratio

1.1 ± 0.1

Polyunsaturated fatty acid (g)

6.8 ± 0.2

6.1 ± 0.2

2.8 ± 0.2 2.7 ± 0.2

Saturated fatty acid (g)

14.8 ± 0.5

580.1 ± 8.3

3.7 ± 0.1

1.6 ± 0.02

6.5 ± 0.5

Monounsaturated fatty acid (g)

Total fatty acid (g)

252.6 ± 8.5

Vitamin B12 (ug)

Folate (ug)

0.7 ± 0.02

Vitamin B6 (mg)

b-carotene (ug)

9.7 ± 0.2

3.9 ± 0.2

Vitamin E (mg)

11.6 ± 0.7 68.8 ± 3.4

\0.0001 \0.0001

257.6 ± 5.1 36.7 ± 1.2

28.4 ± 0.8 125.8 ± 2.6

13.1 ± 0.8 36.7 ± 2.7

Fat (g)

Vitamin C (mg)

1,281.4 ± 27.6

\0.0001

322.5 ± 4.7 62.1 ± 1.0

231.8 ± 4.8 31.1 ± 1.0

Carbohydrate (g) Protein (g)

\0.0001 \0.0001

1,774.9 ± 26.2

1,181.4 ± 26.6e

Total energy (kcal)

0.8 ± 0.01

3.6 ± 0.1

7.3 ± 0.2

7.1 ± 0.2

16.4 ± 0.5

461.6 ± 11.5

3.4 ± 0.1

1.3 ± 0.03

3,060.9 ± 129.0

8.5 ± 0.2

94.1 ± 3.4

30.6 ± 0.7

309.5 ± 5.0 57.1 ± 1.2

1,733.8 ± 27.3

Q4

Q1

Q4

Q1c

Pd

Bread, egg, and dairya

Prudenta

Table 4 Dietary intake of subjects in the lowest (Q1) and highest (Q4) quartiles of each pattern

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001

\0.0001 \0.0001

\0.0001

P

0.7 ± 0.01

2.5 ± 0.1

4.3 ± 0.3

4.3 ± 0.3

10.1 ± 0.6

442.1 ± 12.1

2.3 ± 0.1

1.2 ± 0.03

2,763.8 ± 135.3

7.1 ± 0.2

88.3 ± 3.6

20.4 ± 0.9

308.9 ± 5.2 48.9 ± 1.3

1,610.6 ± 30.4

Q1

White rice onlyb

0.6 ± 0.01

2.2 ± 0.1

4.2 ± 0.3

3.9 ± 0.3

9.2 ± 0.6

364.0 ± 11.9

2.2 ± 0.1

1.0 ± 0.03

2,284.3 ± 132.8

6.2 ± 0.2

71.9 ± 3.5

17.8 ± 0.9

270.3 ± 5.1 40.5 ± 1.3

1,408.5 ± 29.9

Q4

\0.0001

0.159

0.503

0.481

0.343

\0.0001

0.611

\0.0001

0.022

0.008

0.001

0.030

\0.0001 0.0001

\0.0001

P

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Eur J Nutr Table 5 Risk of cognitive impairment across quartiles of dietary pattern scores adjusted for covariates Model 1a

Crude OR

95 % CI

OR

95 % CI

Prudent Q1

1.00

Q2 Q3

0.73 0.81

0.48, 1.10 0.54, 1.22

0.73 0.91

0.48, 1.10 0.60, 1.37

Q4

0.76

0.50, 1.14

0.88

0.58, 1.34

P for trend

0.272

1.00

0.788

Bread, egg, and dairy Q1

1.00

Q2

0.66

0.44, 0.99

0.71

0.47, 1.07

Q3

0.64

0.43, 0.96

0.71

0.47, 1.07

Q4

0.49

0.33, 0.75

0.57

0.37, 0.87

P for trend

0.001

1.00

0.014

White rice only Q1

1.00

Q2

1.53

0.10, 2.35

1.47

0.95, 2.28

Q3

1.66

1.08, 2.54

1.75

1.13, 2.70

Q4

2.25

1.48, 3.43

2.13

1.38, 3.29

P for trend

1.00

0.004 0.009

a

All dietary patterns were adjusted by exercise, alcohol intake, and supplement intake

cognitive impairment independent of age, sex, education, exercise, alcohol intake, and supplement intake. Several studies have investigated the relationship between overall dietary patterns by extracting an a posteriori method and cognitive function in elderly populations. A ‘‘healthy’’ dietary pattern (fruits, whole grains, fresh dairy products, vegetables) in a middle-aged French population [17], ‘‘healthy’’ dietary pattern (fish in men and fruits and vegetables in women) in the elderly French [18], ‘‘whole food’’ dietary pattern (vegetables, fruits, dried legumes, and fish) in middle-aged British population [19], and a ‘‘Mediterranean-style’’ dietary pattern (vegetables, fish, tomato-based sauces, oil and vinegar dressing, beans) in elderly British [20] were associated with significantly better cognitive function. A ‘‘processed food’’ dietary pattern (fried foods, snacks, takeaway, chocolate and sweets, processed meats and fish) in Australians aged C65 years [33] and a ‘‘traditional’’ dietary pattern (tinned vegetables, peas or beans, carrots, baked beans, bottled sauces) in elderly British [20] were associated with poorer cognitive function. Prudent and bread, egg, and dairy dietary patterns are similar to the ‘‘healthy’’ [17], ‘‘whole food’’ [19], and ‘‘Mediterranean-style’’ dietary patterns [20]. No pattern similar with the white rice only dietary pattern was observed among Western studies.

When the dietary patterns of the present study were compared to the dietary patterns of previous studies conducted among Koreans, the prudent and bread, egg, and dairy patterns in our study were similar to the ‘‘dairy and fruit’’ pattern (higher consumption of legumes, milk and dairy foods, flour and breads, fruits and nuts) identified by Shin et al. (64 years on average) [21] and the ‘‘healthy dietary’’ pattern (higher consumption of flour products, fish and other seafood, red meats, eggs, legumes, green and yellow vegetables, mushrooms, starchy vegetables, seaweeds, fruits, dairy products) identified by Baik et al. (aged 40–69 years) [22]. The white rice only pattern of our study was similar to the ‘‘white rice, kimchi, and seaweed’’ pattern (white rice, seaweeds, kimchi, and fish and shellfish) identified by Shin et al. [21], the ‘‘unhealthy dietary’’ pattern (higher consumption of white rice, meats, sweetened carbonated beverages, and noodles and lower consumption of mixed grain rice and legumes) identified by Baik et al. [22], the ‘‘white rice and kimchi’’ pattern (higher consumption of white rice, kimchi, mushroom) identified by Kim et al. (aged C19 years) [23], and the ‘‘rice-oriented’’ pattern (higher consumption of white rice) identified by Song et al. (aged C20 years) [34]. The white rice only pattern in our study was positively associated with the risk of cognitive impairment, the ‘‘white rice, kimchi, and seaweed’’ dietary pattern was negatively associated with bone health [21], the ‘‘white rice and kimchi’’ dietary pattern was positively associated with obesity [23], and the ‘‘rice-oriented’’ dietary pattern was positively associated with hypertriglyceridemia and low- and high-density lipoprotein cholesterol [34]. We determined that the white riceoriented unbalanced dietary pattern may be a risk factor for several health problems for Koreans. The positive association between the white rice only dietary pattern and cognitive impairment could be explained by the pattern’s unhealthy components, which may include inadequate overall dietary intake and low intakes of micronutrients such as vitamin C, vitamin E, bcarotene, vitamin B6, and folate [35–37]. In the present study, decreasing intake of antioxidant vitamins (vitamin E, vitamin C, and b-carotene) and B vitamins (vitamin B6 and folate) were observed according to the quartiles of the white rice only dietary pattern score. Several epidemiological studies have demonstrated relationships between blood concentrations or dietary intake of antioxidants and cognitive impairment [36, 38, 39]. Studies suggest that as levels of endogenous antioxidants in brain tissue are low and brain tissue is particularly vulnerable to free radical damage, adequate intake of antioxidant vitamins may protect brain tissue from damage by free radicals. Hyperhomocysteinemia was observed in patients who have diseases of the central nervous system [40]. Many studies have indicated associations between cognitive impairment

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and hyperhomocysteinemia and/or B vitamin (folate, B6, B12) deficiency [40, 41]. Homocysteine is normally remethylated to methionine depending on folate and vitamin B12 or can be trans-sulfurated to cystathionine depending on vitamin B6. However, hyperhomocysteinemia can develop when intake of folate, vitamin B12, and/or vitamin B6 is insufficient [40]. Therefore, insufficient intake of such antioxidant vitamins or B vitamins in the white rice only dietary pattern may explain the high risk of cognitive impairment. We evaluated overall adequacy of dietary intake using the MAR. As a result, a clear increasing trend in MAR score was observed according to the quartiles of dietary pattern scores in the prudent and the bread, egg, and dairy patterns after adjusting for age and sex. However, in the white rice only pattern, a decreasing trend in the MAR score was observed according to the quartiles of dietary pattern scores. Several studies on the defined dietary patterns such as the Mediterranean diet (MeDi) score, the Healthy Eating Index (HEI), and the Recommended Food Score (RFS) have reported that subjects adhering to a healthy diet had lower cognitive decline [14–16, 42]. Higher quality diets seem to protect against cognitive decline in the elderly. Both the prudent and bread, egg, and dairy dietary patterns showed similar decreasing trends in their associations with MCI but a significant inverse relationship was only found in the bread, egg, and dairy dietary pattern. Compared to the prudent dietary pattern, the bread, egg, and dairy dietary pattern was highly correlated with dairy food intake. Lee et al. [43] reported that poor cognitive function was related to low consumption of milk and dairy products in Korean women aged 60–83 years. Crichton et al. [44] also observed that frequent dairy food intake was related to better cognitive function. Rahman et al. [45] and Park et al. [46] reported that cheese intake was associated with cognitive function, whereas milk intake was not related to cognitive impairment. In contrast, other studies have shown that consuming full-cream milk or saturated fatty acid from dairy foods was associated with an increased risk of cognitive impairment [47, 48]. As few studies have been conducted to investigate the relationship between dairy food intake and cognitive function, it is difficult to derive potential mechanisms, but constituents of milk such as whey protein, bioactive peptides, a-lactalbumin, calcium, vitamin B12, and probiotics are thought to exert a favorable influence on several diseases [49]. Further studies of potential mechanisms are necessary. The limitations of the study need to be considered when interpreting the findings. As this is a cross-sectional study, we cannot conclude causality of the dietary patterns with cognitive function. The possibility of reverse causality

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must also be considered because the old adults who already have been experiencing cognitive decline may lose their ability to purchase healthy foods and prepare adequate meals. Second, even though the factor analysis is an extensively used technique to define the dietary patterns in nutrition epidemiology, it is somewhat subjective such as how to group foods and food groups, the methods of rotation, the number of factors, and labeling of the factors [50]. Despite these limitations, our results showed similar dietary patterns compared with previous studies in Koreans [21–23, 34] and confirmed that subjects adhering to a healthy diet have less cognitive decline [17–20, 33]. In conclusion, this is the first study to investigate the relationships between dietary patterns of Korean older adults and cognitive function and suggests that adhering to a healthy diet may prevent cognitive impairment in Korean elderly. In particular, like white rice only pattern, ricebased diet without well-balanced meals may increase the risk of cognitive impairment. However, since our study is a cross-sectional design, further investigations including clinical trials and longitudinal studies are required to confirm these findings. Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2012R1A1A1041792). Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

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