Nutritional adequacy of four dietary patterns defined by cluster analysis in Japanese women aged 18-20 years.

Abstract

Information on nutritional adequacy and inadequacy of dietary patterns is useful when making practical dietary recommendations. We examined nutritional inadequacy of dietary patterns among 3756 Japanese female dietetic students aged 18-20 years. Diet was assessed with a validated self-administered diet history questionnaire (DHQ). Dietary patterns were determined from intakes of 33 food groups summarized from 147 foods assessed with DHQ, by cluster analysis. Nutritional inadequacy for the selected 21 nutrients in each dietary pattern was examined using the reference values given in the Dietary Reference Intakes for the Japanese (DRIs) as the gold standard. Four dietary patterns identified were labeled 'fish and vegetables' (n=697), 'meat and eggs' (n=1008), 'rice' (n=1041), and 'bread and confectionaries' (n=1010) patterns. The 'fish and vegetables' pattern, characterized by high intakes of vegetables, potatoes, pulses, fruits, fish, and dairy products, showed significantly the lowest percentage of subjects with inadequate intakes for 15 nutrients, except for the highest prevalence in sodium. In contrast, 'bread and confectionaries' pattern, characterized by high intakes of bread, confectionaries, and soft drinks, showed the highest prevalence of inadequate intakes for nine nutrients. The median number of nutrients not meeting the DRIs as a marker of overall nutritional inadequacy was five in 'fish and vegetables' pattern. It was significantly lower than nine both in 'meat and eggs' and 'rice', and 10 in 'bread and confectionaries' patterns (p<0.001). A dietary pattern high in vegetables, fruits, fish, and some others showed better profile of nutritional adequacy except for sodium in young Japanese women.

Full text

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Asia Pac J Clin Nutr 2010;19 (4):555-563
555

Short Communication

Nutritional adequacy of four dietary patterns defined by
cluster analysis in Japanese women aged 18-20 years

Hitomi Okubo MS1,2, Satoshi Sasaki PhD1,3, Kentaro Murakami PhD3,
Yoshiko Takahashi PhD4, and the Freshmen in Dietetic Course Study II Group

1Department of Social and Preventive Epidemiology, Graduate school of Medicine, the University of Tokyo,
Tokyo, Japan
2Research Fellow of the Japan Society for the Promotion of Science, Japan
3Department of Social and Preventive Epidemiology, School of Public Health, the University of Tokyo, Tokyo,
Japan
4Department of Health and Nutrition, School of Home Economics, Wayo Women’s University, Chiba, Japan


Information on nutritional adequacy and inadequacy of dietary patterns is useful when making practical dietary
recommendations. We examined nutritional inadequacy of dietary patterns among 3756 Japanese female dietetic
students aged 18-20 years. Diet was assessed with a validated self-administered diet history questionnaire (DHQ).
Dietary patterns were determined from intakes of 33 food groups summarized from 147 foods assessed with
DHQ, by cluster analysis. Nutritional inadequacy for the selected 21 nutrients in each dietary pattern was exam-
ined using the reference values given in the Dietary Reference Intakes for the Japanese (DRIs) as the gold stan-
dard. Four dietary patterns identified were labeled ‘fish and vegetables’ (n=697), ‘meat and eggs’ (n=1008),
‘rice’ (n=1041), and ‘bread and confectionaries’ (n=1010) patterns. The ‘fish and vegetables’ pattern, character-
ized by high intakes of vegetables, potatoes, pulses, fruits, fish, and dairy products, showed significantly the
lowest percentage of subjects with inadequate intakes for 15 nutrients, except for the highest prevalence in sodium.
In contrast, ‘bread and confectionaries’ pattern, characterized by high intakes of bread, confectionaries, and soft
drinks, showed the highest prevalence of inadequate intakes for nine nutrients. The median number of nutrients
not meeting the DRIs as a marker of overall nutritional inadequacy was five in ‘fish and vegetables’ pattern. It
was significantly lower than nine both in 'meat and eggs' and ‘rice’, and 10 in ‘bread and confectionaries’ patterns
(p<0.001). A dietary pattern high in vegetables, fruits, fish, and some others showed better profile of nutritional
adequacy except for sodium in young Japanese women.

Key Words: dietary patterns, cluster analysis, dietary reference intakes, nutritional adequacy, Japanese
young women



INTRODUCTION
The dietary requirement for a nutrient is defined as an
intake level that meets specified criteria for adequacy,
thereby minimizing the risk of nutrient deficit or excess.
Traditional nutritional assessment has therefore been fo-
cused on a detailed examination of nutrients. If nutrient
intakes are inadequate or excessive, however, it is neces-
sary to know which foods are mediating the nutrient sup-
ply so that the food supply and nutrition education pro-
grams can be directed effectively toward changing the
dietary pattern.1
More recent evidence suggests that the dietary pattern
approach, which looks at combinations of foods rather
than the traditional single nutrient or food approach, is
useful in examining the relationship between diet and
several health outcomes.2,3 Using this approach, several
studies have evaluated the nutritional quality (adequacy
or inadequacy) of nutrient intakes of dietary patterns by
comparison with the country-specific recommended in-
take levels such as the Dietary Reference Intakes.1,4-6 In a
study of West African immigrants in Madrid,4 two dietary
patterns (‘Healthier’ and ‘Western’ patterns) were identi-
fied by cluster analysis, and the nutritional adequacy of
each nutrient intake were examined by comparison with
the WHO/FAO recommendation. Similar studies were
conducted in Spain and Canada.5,6 However, almost all
these studies have been conducted among Western popu-
lations. No comparable study in Asian countries has been
reported, including Japan, with their different subject
characteristics and culture-specific dietary habits.
Here, we evaluated the nutritional inadequacy of dietary
patterns identified by cluster analysis in a group of Japa-
nese female dietetic students aged 18-20 years using the
reference values given in the Dietary Reference Intakes
for the Japanese (DRIs) as the gold standard.7

Corresponding Author: Dr Satoshi Sasaki, Department of
Social and Preventive Epidemiology, School of Public Health,
the University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-
0033, Japan.
Tel: +81-3-5841-7872; Fax: +81-3-5841-7873
Email: stssasak@m.u-tokyo.ac.jp
Manuscript received 31 March 2010. Initial review completed
20 June 2010. Revision accepted 27 July 2010.

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556 H Okubo, S Sasaki, K Murakami, Y Takahashi and the FDC Study II Group

MATERIALS AND METHODS
Subjects and study procedures
This study was based on a self-administered questionnaire
survey of a wide range of dietary and non-dietary behav-
iors among freshmen who enrolled in the dietetic course
from 54 universities, colleges and technical schools in 33
of 47 prefectures in Japan (n=4679). The survey was con-
ducted from April to May 2005. A detailed description of
the study design and survey procedure has been published
elsewhere.8,9 The study protocol was approved by the
Ethics Committee of the National Institute of Health and
Nutrition, Japan. Participants indicated their informed
consent by completing the survey questionnaires.
In total, 4394 students (4168 women and 226 men)
completed two questionnaires on dietary habits and other
lifestyle behaviors (response rate=93.9%). For the present
analysis, we selected female participants aged 18-20
years (n=4060). We then excluded women who were in
an institution where the survey was not conducted within
two weeks of entry (n=98), those who reported extremely
low or high energy intake (<850 or ≥3375 kcal/d, n=85),
those who were currently receiving dietary counseling
from a doctor or dietitian (n=105), and those with missing
information on the variables used (n=20). As some par-
ticipants were in more than 1 exclusion category, the final
analysis comprised 3756 women in 53 institutions.

Dietary assessment
Dietary habits during the preceding month were assessed
using a self-administered diet history questionnaire
(DHQ).10-12 The DHQ is a structured 16-page question-
naire that asks about the consumption frequency and por-
tion size of selected foods commonly consumed in Japan,
general dietary behaviors, and usual cooking methods.12
Estimates of daily intakes for foods (150 items in total),
energy and nutrients were calculated using an ad hoc
computer algorithm for the DHQ, which was based on the
Standard Tables of Food Composition in Japan.13 A de-
tailed description of the methods used to calculate dietary
intake and the validity of the DHQ have been published
elsewhere.10-12
All self-administered dietary assessment could not avoid
reporting errors, especially under- or over-reporting.14,15 It
may induce bias when comparing the reported nutrient
intake levels and the corresponding DRI values because
the latter does not consider this problem. In order to make
this comparison practically possible, we adjusted the re-
ported nutrient intakes to the energy-adjusted ones in the
assumption that each subject takes her estimated energy
requirement (EER) rather than her reported energy. We
used the EER based on the reported physical activity level
of each subject. The calculation method is as follows:
Energy-adjusted nutrient intake (amount/d) = reported
nutrient intake (amount/d) × EER (kcal/d) / observed en-
ergy intake (kcal/d).

Determination of nutritional quality
Inadequacy of nutrient intake was examined by compar-
ing with each dietary reference value according to the
Japanese DRIs.7 Of the total 34 nutrients presented in the
DRIs, 5 nutrients (biotin, chromium, molybdenum, sele-
nium, iodine) were excluded from this study because of a
lack of the food composition tables in Japan.
For the nutrients with EAR, namely protein, vitamin A
expressed as retinol equivalent (RE), vitamin B1, vitamin
B2, niacin expressed as niacin equivalent, vitamin B6,
vitamin B12, folate, vitamin C, calcium, magnesium, zinc,
and copper, the energy-adjusted intake levels below the
EAR were considered as inadequate.7 For iron, the prob-
ability approach16,17 was used because the EAR cut-point
method cannot be used due to the seriously skewed distri-
bution of the requirement in menstruating women.16-18
Assuming that an iron absorption rate was 15%,7 energy-
adjusted iron intake (mg/d) was converted to usable iron
intake. Each individual's usable iron intake was adjusted
by deducting the median amount of iron required for bas-
al loss (0.77 mg).18 The adjusted individual's usable iron
intake was then log-transformed to improve normality of
the distribution. The probability approach was applied
using the log-normalized mean and standard deviation of
the menstrual iron loss curve (-0.734 and 0.777, respec-
tively)18 and the log-normalized iron intake data adjusted
for basal loss.The probability of inadequacy for iron more
than 50% was considered as inadequate. In the Japanese
DRIs, Tentative Dietary Goal for Preventing Life-style
related Disease (DG) was given for total fat, saturated
fatty acid (SFA), n-3 poly-unsaturated fatty acid (PUFA),
cholesterol, carbohydrate, dietary fiber, and sodium ex-
pressed as salt-equivalent.7 For these nutrients, the en-
ergy-adjusted intake levels outside the range of corre-
sponding DG were considered as inadequate. For the nu-
trients with Adequate Intake (AI) such as n-6 PUFA, vi-
tamin D, vitamin E, expressed as alpha-tocopherol, vita-
min K, pantothenic acid, potassium, phosphorus, and
manganese, the energy-adjusted intake levels at or above
AI were considered as adequate.7,16

Assessment of lifestyle variables
Variables such as geographic area, living status, current
smoking, and whether trying to lose weight were obtained
from the other questionnaire designed for this survey.
Current supplement use, physical activity level, and self-
reported body height (cm) and weight (kg) were obtained
from the DHQ. Body mass index was calculated as body
weight (kg) divided by the square of body height (m2).

Statistical analysis
First, 150 food items in the DHQ were classified into 33
predefined groups with similar nutrient profiles and culi-
nary usage.19 However, nutritional supplement bars, soup
of noodle, and drinking water were difficult to group or
rarely eaten, and were omitted from the study. To remove
the extraneous effect of variables with large variances, we
standardized intake of each energy-adjusted food group to
a mean of zero and standard deviation of one.
Cluster analysis was performed using the FASTCLUS
procedure in SAS.20 This procedure applies the K-means
method to classify subjects into a predetermined number
of mutually exclusive groups by comparing Euclidean
distances between each subject and each cluster center in
an interactive process until no further changes occurred.
To identify the optimal number of clusters, several runs
were conducted varying the number of clusters from 2 to

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Nutritional adequacy of dietary patterns


557

6. The final cluster solution was selected by comparing
the ratio of between-cluster variance to within-cluster
variance divided by the number of clusters. Based on
these determinations, we selected the four-cluster solution
as the most appropriate.
The median differences in intakes of energy-adjusted
food group and nutrient across clusters were examined by
the Kruskal-Wallis test. To examine the nutritional inade-
quacy of nutrient intakes of each dietary pattern, we esti-
mated the percentage of subjects whose intake was below
the EAR or outside the range of DG. The nutrients set for
AI were excluded from calculation of the prevalence of
inadequacy because no firm conclusion can be drawn on
inadequacy if usual intakes are less than AI.7,16 The chi-
square test was used to examine the difference of vari-
ables expressed as proportion such as lifestyle variables
and the prevalence of inadequacy.
To assess the overall nutritional inadequacy of each


Table 1. Daily energy-adjusted intake of 33 food groups (g/d) assessed with a self-administered diet history
questionnaire across the four dietary patterns identified among 3756 Japanese women aged 18-20 years †

Dietary pattern‡
Food group
All (n = 3756)
Fish and vegeta-
bles (n = 697)
Meat and eggs
(n = 1008)
Rice
(n = 1041)
Bread and
confectionaries
(n = 1010)
221 (165, 284)Rice 276 (201, 362) 252(188, 328) 241(187, 297)401* (338, 472)
Bread 42 (21, 69) 32(15, 55) 38(20, 58) 27(12, 46) 73* (49, 100)
Noodles 55 (25, 95) 49(23, 83) 59(28, 95) 54(23, 96) 59 (27, 102)
Potatoes
Nuts
Pulses
22 (15, 34)
0 (0, 1)
30 (16, 53)
35*
1*
62*
(23, 54)
(0, 2)
(39, 86)
24(16, 36)
(0, 2)
(16, 47)
18(12, 27)
(0, 1)
(16, 48)
20 (14, 28)
0 (0, 2)
21 (12, 34)
00
2828
Sugar 10 (7, 14) 13*(10, 16) 10(8, 13) 8(6, 11) 10 (7, 14)
Confectioneries 73 (51, 102) 62(43, 83) 72(52, 95) 57(40, 76) 109* (82, 144)
Butter 0 (0, 1) 0 (0, 1) 1* (0, 2) 0(0, 1) 0 (0, 1)
Vegetable oil 22 (16, 29) 22 (16, 28) 29*(23, 36) 18(14, 23) 19 (14, 25)
Fruits 36 (19, 67) 61* (35, 110) 33(19, 58) 29 (15, 53) 35 (19, 65)
Green and yellow vegetables 62 (37, 98) 127* (96, 171) 64 (44, 90) 49 (28, 73) 44 (28, 69)
White vegetables 82 (55, 118) 147* (109, 194)89 (66, 117) 67 (45, 95) 62 (42, 90)
Pickled vegetables 5 (2, 13) 11*(4, 22) 5 (3, 13) 5 (2, 12) 4 (2, 10)
Mushrooms 6 (4, 15) 19* (11, 31) 7 (4, 15) 5(3, 9) 5 (3, 9)
Seaweeds 7 (4, 17) 21* (13, 34) 7(4, 14) 6(4, 15) 5 (3, 10)
Alcoholic beverages 0 (0, 0) 0 (0, 0) 0 (0, 0) 0(0, 0) 0 (0, 0)
Fruit and vegetable juice 26 (0, 82) 26(0, 95) 26(0, 82) 15 (0, 58) 38* (0, 101)
Japanese and Chinese tea 431 (178, 737) 557* (323, 908) 426 (201, 740)416 (163, 738) 360 (140, 630)
Tea 16 (0, 58) 21*(0, 75) 20 (0, 64) 0(0, 31) 19 (0, 61)
Coffee and cocoa 0 (0, 63) 12(0, 73) 13(0, 58) 0(0, 42) 22* (0, 94)
Soft drinks 17 (0, 70) 7 (0, 36) 25 (3, 80) 12(0, 51) 36* (4, 93)
Dairy products 98 (37, 179) 127* (57, 203) 84(34, 155) 92(30, 183) 105 (39, 188)
Fish 22 (14, 34) 34* (23, 53) 24(16, 37) 20(12, 28) 18 (10, 26)
Shellfish 11 (6, 16) 14*(9, 20) 13(7, 18) 9 (4, 14) 9 (5, 14)
Sea products 12 (7, 21) 20*(12, 31) 14 (9, 23) 10(6, 17) 9 (5, 15)
Chicken 12 (8, 21) 14 (9, 24) 18* (10, 28) 10(7, 14) 10 (7, 15)
Beef and pork 33 (22, 49) 32(22, 46) 50* (36, 67) 26 (18, 36) 26 (19, 38)
Processed meat 6 (3, 11) 6(3, 11) 9*(5, 15) 5 (3, 9) 5 (3, 8)
Eggs 29 (12, 48) 36(16, 51) 39*(23, 55) 25(10, 46) 21 (8, 37)
Miso soup 83 (24, 150) 121(66, 177) 66(18, 122) 125* (50, 206) 45 (3, 102)
Other soup 0 (0, 8) 0(0, 9) 0(0, 8) 0 (0, 0) 0 (0, 8)
Salt-containing seasonings 12 (9, 17) 17* (13, 23) 15(11, 19) 10(8, 13) 10 (7, 14)

*The highest median values.
† Values are medians (interquartile ranges). Intakes of food group were energy-adjusted as follows: energy-adjusted intake (g/d) = observed
intake (g/d) × Estimated Energy Requirement (kcal/d)/observed energy intake (kcal/d).
‡ All food group intakes were significantly different across the four dietary patterns (p < 0.001; Kruskal-Wallis test).

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558 H Okubo, S Sasaki, K Murakami, Y Takahashi and the FDC Study II Group

subject, we counted the number of nutrients which did not
meet the DRIs among 14 and 7 nutrients with EAR and
DG, respectively.5,6 The nutrients with AI were excluded
from this analysis because of the reason mentioned above.
Therefore, this number ranged from 0 (meeting all the 21
DRIs) to 21 (meeting none of the 21 DRIs).
All statistical analyses were performed using SAS v.
9.1 (SAS Institute Inc., Cary, NC, USA). A two-sided p-
value of 0.05 was considered significant.

RESULTS
Four clusters of dietary pattern were identified (Table 1).
We descriptively labeled them as ‘fish and vegetables’,
‘meat and eggs’, ‘rice’ and ‘bread and confectionaries’
patterns, based on the food groups predominant in each
cluster. The ‘fish and vegetables’ pattern was character-
ized by higher median intakes of potatoes, nuts, pulses,
sugar, fruits, green and yellow vegetables, white vegeta-
bles, pickled vegetables, mushrooms, seaweeds, Japanese
and Chinese tea, dairy products, fish, shellfish, sea prod-
ucts, and salt-containing seasonings other than those in
the other three patterns. The ‘meat and eggs’ pattern was
characterized by higher median intakes of chicken, beef
and pork, processed meat, eggs, butter, and vegetable oil.
The ‘rice’ pattern was characterized by higher median
intakes of rice and miso soup. The ‘bread and confection-
aries’ pattern was characterized by higher median intakes
of bread, confectioneries, fruit and vegetable juice, coffee
and cocoa, and soft drinks.
Table 2 shows the subject characteristics for lifestyle
variables across the four dietary patterns. The subjects in
the ‘fish and vegetables’ pattern were more likely to be
non-smokers, supplement users, physically active, lived
with family members, and tried to lose weight than those
in other patterns. The subjects in the ‘rice’ pattern were
likely to be few supplement users, physically inactive,
and not trying to lose weight. The subjects in the ‘bread
and confectionaries’ pattern were more likely to be cur-
rent smokers and lived with someone other than their
family members.
Table 3 shows median of the energy-adjusted nutrient
intakes and the prevalence of subjects who did not meet

Table 2. Subject characteristics across the four dietary patterns identified among 3756 Japanese women aged 18-20
years†

Dietary pattern
Meat and
eggs
(n = 1008)
18.1 ± 0.3


All
(n = 3756)
Fish and
vegetables
(n = 697)
18.1 ± 0.3
Rice
(n = 1041)
Bread and
confectionaries
(n = 1010)
18.1 ± 0.3
p-value‡
Age (years) 18.1 ± 0.3 18.1 ± 0.4 0.77
Body height (cm) 157.9 ± 5.3 157.8 ± 5.4 157.7 ± 5.2 157.8 ± 5.4 158.1 ± 5.4 0.30
Body weight (kg)
Body mass index (kg/m2)
52.2 ± 7.6 51.8 ± 7.3 52.2 ± 7.8 52.4 ± 7.6 52.4 ± 7.8 0.35
21.0 ± 2.8 20.8 ± 2.7 21.0 ± 2.9 21.1 ± 2.8 20.9 ±2.8 0.33
Geographic area (%)

Hokkaido and Tohoku 10.0 10.6 7.0 13.4 8.9 <0.001
Kanto 34.6 36.3 34.6 31.4 36.6

Hokuriku and Tokai 13.7 14.8 12.9 15.7 11.6

Kinki 20.0 18.9 22.6 15.2 23.1

Chugoku and Shikoku 10.5 6.7 10.4 12.2 11.6

Kyushu 11.3 12.6 12.4 12.2 8.2

Living status (%)

Living alone 5.9 3.2 4.2 7.8 7.4 <0.001
Living with family 88.8 92.0 91.0 86.9 86.2

Living with others 5.4 4.9 4.9 5.3 6.3

Current smoker (%) 1.4
18.3
0.6
24.7
1.8
17.2
0.9
14.2
2.1
19.4
0.02
<0.001
Current dietary supplement user (%)
Subjects trying to lose weight (%)
Physical activity level (%)§
35.6 45.3 36.0 28.5 35.7 <0.001



Level I (low) 62.2 47.1 59.8 72.8 64.1 <0.001
Level II (moderate) 35.8 49.2 37.6 26.4 34.6

Level III (high) 2.0 3.7 2.6 0.8 1.4


† Values are mean ± standard deviation or percentage of subjects.
‡ For continuous variables, ANOVA was used; for categorical variables, chi-square test was used to test differences across the dietary pat-
terns.
§ Categorization was according to the Dietary Reference Intakes for Japanese, 2010 (reference 7).

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Nutritional adequacy of dietary patterns


559

Table 3. Daily energy-adjusted nutrient intakes assessed with a self-administered diet history questionnaire and prevalence of subjects with inadequate nutrient intakes compared with
Dietary Reference Intakes (DRIs) for the Japanese, 2010, using the cut-point method across the four dietary patterns identified among 3,756 Japanese women aged 18-20 years†

Dietary pattern
All
(n = 3756)
Fish and vegetables
(n = 697)
Meat and eggs
(n = 1008)
Rice
(n = 1041)
Bread and confectionaries
(n = 1010)
Nutrient
Refer-
ence
value‡
Median IQR
Prevalence of
inadequacy
(%)§
-

Median IQR (%)§Median IQR (%)§MedianIQR (%)§
MedianIQR (%)§
p-
value¶¶
Energy intake
Nutrient with DG
Total fat
1700 kcal1736 (1459, 2054)

1809 (1513, 2106)

-

1872* (1604, 2203)

-

1560 (1345, 1807)

-

1765 (1468, 2087)

-

-

20-30% of
energy
4.5-7.0%
of energy
≥1.8 g
<600 mg
50-70% of
energy
≥17 g
29.5 (25.6, 33.3) 51.4 29.7 (26.8, 33.0) 50.233.8* (30.9, 36.7) 81.9 25.2 (22.0, 28.4) 27.329.3 (25.8, 32.5) 46.8 <0.001
SFA
8.1 (6.8, 9.5) 73.1 7.8 (6.7, 9.1) 70.7 9.0* (7.9, 10.2) 91.06.8 (5.7, 8.1) 52.38.4 (7.1, 10.1) 78.3 <0.001
n-3 PUFA
Cholesterol
Carbohydrate
2.3 (1.9, 2.8)
284 (208, 370)
21.1
1.3
2.8* (2.4, 3.3)
325 (251, 402)
3.2
1.9
2.7 (2.4, 3.1)
345* (476, 412)
2.4
2.4
2.0 (1.7, 2.4)
240 (170, 320)
35.2
0.8
2.0 (1.6, 2.3)
243 (188, 314)
37.5 <0.001
0.2 <0.001
55.8 (51.4, 60.3) 20.4 54.2 (50.9, 58.0) 21.750.6 (47.4, 53.7) 44.2 60.5* (57.1, 64.3) 7.6 57.2 (53.5, 60.8) 9.1 <0.001
Dietary fiber
Sodium
equivalent)¶
Nutrient with EAR
Protein
Vitamin A††
Vitamin B1
Vitamin B2
Niacin ‡‡

IQR = interquartile ranges, DG = tentative dietary goal for preventing life-style related disease, SFA = saturated fatty acids, PUFA = poly-unsaturated fatty acids, EAR = estimated average requirement, RE = retinol
equivalents, NE = niacin equivalents, AI = adequate intake.
*The highest median values.
† Values are medians (interquartile ranges). Nutrients expressed as amount per day were energy-adjusted by using the following equation: energy-adjusted intake (unit/d) = observed intake (unit/d) × Estimated Energy
Requirement (EER, kcal/d) / observed energy intake (kcal/d). All nutrient intakes were significantly different across the dietary patterns (P < 0.001; Kruskal-Wallis test).
‡ Dietary reference intakes (units/d) for non-pregnant Japanese females aged 18-29 years old. The EER of physical activity level I, II, and III are 1700, 1950, and 2250 kcal/d, respectively.
§ Percentage of subjects whose intake was outside the range of DG or below the EAR. Each energy-adjusted nutrient intake (unit/d) was compared with each DRI value (unit/d), using the cut-point method. Nutrients
with AI were not examined.
¶ Considering the convenience of the use, the DG of sodium are expressed as salt-equivalent [salt (g) = 58.5/23 × sodium (g)] (reference 7).
†† 1 μgRE = retinol (μg) + beta-carotene (μg) × 1/12 + alpha-carotene (μg) × 1/24 + beta-cryptoxantin (μg) × 1/24 + other provitamin A carotenoides (μg) × 1/24 (reference 7).
‡‡ Niacin equivalents were computed as niacin (mg) + protein (mg)/6000 according to the Dietary Reference Intake for the Japanese, 2010 (reference 7).
§§ Probability approach was used to assess inadequacy for iron intake.
¶¶ Chi-square test was used to test differences of the prevalence of subjects with inadequate nutrient intake across the dietary patterns.

11.0 (9.1, 13.5) 90.8 16.2* (14.1, 18.7) 57.110.6 (9.0, 12.3) 99.110.0 (8.3, 11.7) 98.8 10.4 (8.9, 12.2) 97.6 <0.001
(salt-
<7.5 g 9.5 (8.0, 11.2) 81.6 11.5* (10.1, 13.4) 96.810.1 (8.9, 11.5) 90.18.6 (7.3, 10.1) 72.3 8.6 (7.4, 9.9) 72.1 <0.001



40 g
450 μg RE
0.9 mg
1.0 mg
9 mg NE
59.2 (52.5, 66.3)
419 (294, 605)
0.71 (0.61, 0.83)
1.19 (0.99, 1.42)
22.0 (18.9, 25.6)
1.4
55.9
84.7
25.9
0
69.9* (63.1, 76.4)
658* (507, 977)
0.85* (0.77, 0.96)
1.46* (1.27, 1.7)
26.9* (24.2, 30.3)
0 62.6 (57.6, 68.8)
432 (326, 606)
0.76 (0.68, 0.87)
1.21 (1.06, 1.42)
24.0 (21.6, 26.9)
0.6
55.3
80.5
17.9
0
54.3 (48.7, 59.8)
340 (244, 477)
0.62 (0.54, 0.71)
1.07 (0.87, 1.27)
19.5 (17.2, 22.0)
2.3
71.4
93.8
40.6
0
55.0 (49.5, 60.2)
358 (255, 492)
0.65 (0.57, 0.74)
1.11 (0.93, 1.30)
19.8 (17.6, 22.1)
2.4 <0.001
68.7 <0.001
94.2 <0.001
34.5 <0.001
0
15.4
63.7
3.3
0-