Kashino et al. Nutrition Journal (2015) 14:58
DOI 10.1186/s12937-015-0046-8

RESEARCH

Open Access

Association of dietary patterns withserum adipokines among Japanese:a cross-sectional study
Ikuko Kashino1*, Akiko Nanri1, Kayo Kurotani1, Shamima Akter1, Kazuki Yasuda2, Masao Sato3,
Hitomi Hayabuchi4 and Tetsuya Mizoue1

Abstract
Background: Diet may influence disease risk by modulating adipokines. Although some foods and nutrients havebeen linked to circulating adipokine levels, little is known about the role of dietary patterns on adipokines. We
investigated the association between major dietary patterns and circulating levels of adiponectin, leptin, resistin,visfatin, and plasminogen activator inhibitor-1 (PAI-1) in a working population.
Methods: The subjects were 509 employees (296 men and 213 women), aged 20 to 65 years, of two municipal
offices. Serum adipokines were measured using a Luminex suspension bead-based multiplexed array. Dietary
patterns were derived by using principal component analysis of the consumption of 52 food and beverage
items, which were ascertained by a validated diet history questionnaire. Multiple regression analysis was
performed to assess the association between dietary pattern scores and adipokine concentrations, withadjustment for potential confounders.
Results: Three major dietary patterns were extracted: a Japanese, a Westernized breakfast, and a meat foodpatterns. Of these, we found significant, inverse associations of the Westernized breakfast pattern, which wascharacterized by higher intake of confectioneries, bread, and milk and yogurt but lower intake of alcoholic
beverages and rice, with serum leptin and PAI-1 concentrations in a fully adjusted model (P for trend = 0.04
for both leptin and PAI-1). The other adipokines were not significantly associated with any dietary pattern.
Conclusion: The Westernized breakfast dietary pattern may be associated with lower circulating levels of
leptin and PAI-1.
Keywords: Cross-sectional study, Dietary pattern, Adipokines, Adiponectin, Leptin, PAI-1, Japanese,
Epidemiology

Introduction
Adipokines such as adiponectin, leptin, resistin, visfatin,
and plasminogen activator inhibitor type-1 (PAI-1) aresecreted from adipose tissues. Adiponectin has been
positively associated with insulin sensitivity [1] and lowered risk of diabetes [2], hypertension [3], and coronary
heart disease (CHD) [4]. In contrast, higher blood leptin
levels have been associated with a higher risk of type 2* Correspondence: kashino@ri.ncgm.go.jp
1
Department of Epidemiology and Prevention, Center for Clinical Sciences,
National Center for Global Health and Medicine, Toyama 1-21-1, Shinjuku-ku,
Tokyo 162-8655, Japan
Full list of author information is available at the end of the article

diabetes [5], hypertension [6], and CHD [7]. Other adipokines such as resistin, visfatin, and PAI-1 have been
identified as pro-inflammatory mediators, and are related to insulin resistance and type 2 diabetes [8–10]. As
adipokines are presumed to be biochemical predictors ofthese diseases [2–9], it is important to identify modifiable factors including diet that may affect adipokines.
Previous studies have demonstrated a correlation between nutrients such omega-3 fatty acid [11] and dietaryfiber [12, 13] and foods or food groups such as vegetables [12, 14], fish [15], soybean [12], sweetened beverages [16], and coffee [17] and blood adipokine levels.
Given that nutrients and foods are consumed in© 2015 Kashino et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://
creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Kashino et al. Nutrition Journal (2015) 14:58

combination, studies that seek to take account of potential
interactions or synergistic effects among nutrients andfoods need to evaluate the diet as a whole. For instance,
the Mediterranean diet and a healthy eating pattern in the
U.S. were associated with higher concentrations of adiponectin [18–20] and lower concentrations of resistin [20],while a Chinese study reported that the Western patterncharacterized by higher intake of milk, meats, cake, andfruits was positively related to serum leptin levels [21]. In
contrast, other studies in the U.S. showed no association
between Western pattern and leptin levels [22, 23]. The
lower age-adjusted mortality rate of cardiovascular disease(CVD) in Japan compared with Western countries such as
the U.K. and U.S. [24] might be ascribable to the favorable
effect of the Japanese diet, which is rich in fish, seaweeds,soybean, and vegetables [25, 26]. It would thus be interesting to determine whether distinctive dietary patterns forJapanese influence circulating adipokine levels, but to ourknowledge only one study has examined the relationshipbetween dietary pattern and blood adiponectin level
among Japanese [27].
Here, we examined the association between major
dietary patterns and serum adipokine levels in a Japaneseworking population by utilizing dietary pattern analysis.

Methods
Study procedure and subjects

A health survey was conducted during a periodic healthexamination among employees of two municipal offices innortheastern Kyushu, Japan (conducted in July 2009 in
one office and November 2009 in another), as described
elsewhere [17]. In brief, all full-time workers (n = 605)
were invited to participate in the survey and asked to fillout the questionnaires. Of 605 eligible employees, 567(325 men and 242 women) participated in the survey(response rate 94 %). Of these, we excluded 23
subjects with a history of CVD (n = 11) and cancer(n = 13) from analysis of dietary patterns, and a further 20 subjects with a history of diabetes (n = 8),
nephritis (n = 1), chronic hepatitis (n = 3), as well as
pregnant women (n = 8). We also excluded subjects
who had missing data on serum adipokines (n = 20)
and any of the covariates (n = 6). Some of the
excluded subjects had two or more conditions for exclusion. Finally, 509 subjects (296 men and 213
women) aged 20 to 65 years were analysed for thepresent study. Additionally, we analysed 508 subjects
in the analysis of visfatin after excluding one subject
with a visfatin concentration above the upper detection limit. With regard to leptin, analyses were performed among those subjects who participated in the
checkup in the fasting state (at least 8 h) since thelast meal (n = 486). The protocol of the study wasPage 2 of 9

approved by the Ethics Committee of the National
Center for Global Health and Medicine, and written
informed consent was obtained from each participant.
Measurement of adipokines

Blood samples were obtained on the day of the healthcheckup. Serum samples for measurement of adipokines
were stored at -80 °C until biochemical assay. Serum
concentrations of adiponectin, leptin, resistin, visfatin,
and PAI-1 were measured using a Luminex suspension
bead-based multiplexed array using a Bio-Plex 3D suspension array system and Bio-Plex Pro human diabetes
assay panel (Bio-Rad Laboratories, Hercules, CA); intraassay coefficients of variation were 12 % for adiponectin,
11 % for leptin, 8 % for resistin, 19 % for visfatin, and
21 % for PAI-1 [17].
Dietary assessment

Dietary habits during the preceding month were evaluated using a validated brief self-administered diethistory questionnaire (BDHQ), as described elsewhere[28]. In brief, dietary intakes for 58 food and beverage
items commonly consumed in Japan were estimatedusing an ad hoc computer algorithm [29]. According
to a validation study of BDHQ using 16-day semiweight dietary records as gold standard, median
Spearman’s correlation coefficients (range) for 58 foodand beverages items were 0.44 (0.14-0.82) in womenand 0.48 (0.22-0.83) in men [28].
Other variables

The survey questionnaire also enquired about marital
status, smoking status, type of occupation, and nonoccupational physical activity. Occupational physical activity was classified as sedentary work and active work.
Non-occupational physical activity was evaluated fromthe number of minutes per day spent in walking or cycling during commuting to or from work and the numberof hours per week spent in each of five leisure activities(walking, low-, moderate-, and high-intensity activities,
and gardening). Non-occupational physical activities were
estimated from the metabolic equivalent (MET) value andexpressed as the sum of METs multiplied by the time (inhours) spent performing each activity. Body height wasmeasured to the nearest 0.1 cm with subjects standing
without shoes. Body weight in light clothes was measured
to the nearest 0.1 kg. Body mass index (BMI) was calculated by dividing weight by squared height (kg/m2).
Statistical analysis

We performed principal component analysis on the basisof energy-adjusted intakes using the density method(amount of food intake per 1000 kcal of energy) for 52
food and non-alcoholic or alcoholic beverage items,

Kashino et al. Nutrition Journal (2015) 14:58

excluding six items (sugar added to coffee or black tea,salt, oil, sugar, table salt and salt-containing seasoning
added at the table, and soup consumed with noodles).
The factors were rotated by orthogonal transformation(varimax rotation) to maintain uncorrelated factors andgreater interpretability. We determined the number offactors to retain by considering eigenvalues, the scree
test, and the interpretability of factors. Three dietary patterns were identified. Dietary patterns were named according to the food items showing a high load (absolutevalue) in three factors. The factor scores for each dietary
pattern and for each individual were calculated by summing the intakes of food items weighted by their factor
loading. Factor scores were categorized into tertiles.
Participants were classified into tertiles of dietary pattern. Subject characteristics were expressed as means(standard deviation) for continuous variables and percentages for categorical variables. We examined the associations between dietary patterns and serum adipokine
concentrations by multiple regression analysis after firstlog-transforming the adipokine concentrations becauseof their highly skewed distributions. The results were
presented as geometric means and their 95 % confidence
interval (CI). In model 1, we adjusted for age (years,
continuous), sex, and workplace (site A or B). In model
2, we additionally adjusted for marital status (married ornot), BMI (kg/m2, continuous), occupational physical activity (sedentary or active work), non-occupational physical activity (0, 0 < - < 5 MET-hr/week, 5- < 10 MET-hr/
week, ≥10 MET-hr/week), smoking (never smokers, exsmokers, current smokers consuming 1–19 cigarettes/
day, or current smokers consuming ≥20 cigarettes/day),and total energy intake (kcal/day, continuous). The trend
association was tested in each model, with ordinal numbers 0–2 assigned to the tertile categories of each dietary
pattern. In addition, we performed stratified analyses of
dietary patterns and serum leptin and PAI-1 according
to sex and BMI (<23 or ≥23 kg/m2). To assess statisticalinteractions, we created interaction terms by multiplying
dietary patterns (tertile) and the above stratifying variables (dichotomous) and added these to the models.
Two-sided P values <0.05 were regarded as statisticallysignificant. All analyses were performed using the SASstatistical software package version 9.3 (SAS InstituteInc., Cary, NC, USA).

Results
We identified three major dietary patterns by principal
component analysis (Table 1). The first factor wasnamed a Japanese pattern. This pattern was characterized by vegetables, fruits, mushrooms, seaweeds, and soy
products. The second factor was named a Westernized
breakfast pattern, because it had higher scores for confectioneries, bread, and milk and yogurt but lower scores

Page 3 of 9

for alcoholic beverages and rice. The third factor wasnamed a meat food pattern because it represented higher
intakes of meat, processed meat, and mayonnaise anddressing. The first to third dietary patterns accounted for10.3 %, 5.4 %, and 4.3 %, respectively, of the variance infood intake and in total explained 20.0 % of variability.
Table 2 shows the characteristics of the study subjects
according to tertile categories of each dietary patternscore. Subjects with a higher score for the Japanese pattern were more likely to be female and have a lower
BMI, but were less likely to be smokers and engaged insedentary work. Subjects with a higher score for theWesternized breakfast pattern were younger and more
likely to be female and physically active during work,and have a lower BMI, but were less likely to be marriedand smokers, and tended to have lower energy intake.
Regarding the meat food pattern, subjects with a higherscore were younger.
As shown in Table 3, leptin and PAI-I concentrations
decreased with increasing scores of the Westernized
breakfast pattern. In the multivariable-adjusted model(model 2), the adjusted geometric means of leptin concentration for the lowest through highest tertiles of theWesternized breakfast pattern score were 1.82, 1.57, and1.54 ng/ml, respectively (P for trend = 0.04), while thoseof PAI-1 were 31.87, 30.46, and 29.68 ng/ml, respectively(P for trend = 0.04). The Japanese and meat food patterns were not significantly associated with any adipokine; however, those in the middle and highest tertiles
tended to have higher adiponectin levels than those in
the lowest tertile (geometric mean: 4.89 and 4.85 versus
4.44 μg/ml).
In stratified analyses, the inverse associations between
the Westernized breakfast pattern and serum leptin andPAI-1 concentrations were confined to men (P for trend= 0.002 for leptin and 0.01 for PAI-1) and to overweightsubjects (P for trend = 0.02 for leptin and 0.05 for PAI1), although the interaction by sex or BMI was not statistically significant (P for interaction by sex = 0.19 for
leptin and 0.33 for PAI-1, P for interaction by BMI =0.50 for leptin and 0.47 for PAI-1).

Discussion
In this study in Japanese workers, we identified threemajor dietary patterns: a Japanese, a Westernized breakfast, and a meat food dietary pattern. Of these, the Westernized breakfast pattern, characterized by higher intake
of confectioneries, bread, and milk and yogurt but lower
intake of alcoholic beverages and rice, was significantly
inversely associated with serum leptin and PAI-1 concentrations after adjustment for potential confounding
factors, including BMI. These inverse associations were
evident in men and obese subjects. The other adipokines
were not significantly associated with any dietary pattern.

Kashino et al. Nutrition Journal (2015) 14:58

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Table 1 Factor loading matrix for dietary patterns identified by principal component analysisa
Japanese pattern

Westernized breakfast pattern

Meat food pattern

Cabbage/Chinese cabbage

0.75

-

-

Carrots/pumpkin

0.74

-

-

Green leaves vegetables

0.72

-

-

Mushrooms

0.70

−0.15

-

Other root vegetables

0.69

-

-

Japanese radish/turnip

0.65

-

−0.19

Lettuces/cabbage (raw)

0.55

-

0.20

Seaweeds

0.53

−0.22

−0.18

Potatoes

0.45

-

-

Tomatoes

0.42

-

0.15

Tofu/atsuageb

0.35

−0.27

−0.24

Persimmons/strawberries/kiwifruit

0.33

0.17

−0.31

Other fruit

0.31

0.22

−0.24

Pickled green leaves vegetables

0.24

−0.20

−0.23

Lean fish

0.22

−0.18

0.15

c

Natto

0.20

-

−0.16

Green tea

0.20

-

-

Sake

−0.15

-

-

Whisky

−0.17

-

-

Buckwheat noodles

−0.18

−0.15

-

Japanese wheat noodles

−0.18

-

-

Cola drink/soft drink

−0.22

0.17

-

Chinese noodles

−0.34

−0.20

-

Western-type confectioneries

-

0.64

-

Japanese confectioneries

0.15

0.55

−0.19

Rice crackers/rice cake/okonomiyakid

-

0.50

-

Bread

-

0.46

0.24

Milk and yogurt

-

0.33

0.16

Ice cream

−0.23

0.28

-

100 % fruit and vegetable juice

-

0.17

-

Coffee-0.17-

Oily fish

-

−0.21

-

Other pickles

0.18

−0.25

−0.16

Dried fish/salted fish

-

−0.25

-

Liver

-

−0.27

-

Beer

−0.21

−0.30

0.15

Shochu

−0.24

−0.39

-

Pork/beef

-

-

0.49

Chicken

-

−0.15

0.46

Ham/sausage/bacon

-

-

0.38

Mayonnaise/dressing

0.24

-

0.37

Black tea/oolong tea

-

0.15

0.29

Egg

-

-

0.26

Spaghetti and macaroni

-

-

0.26

Kashino et al. Nutrition Journal (2015) 14:58

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Table 1 Factor loading matrix for dietary patterns identified by principal component analysisa (Continued)
Squid/octopus/shrimp/shellfish

-

−0.21

0.23

Canned tuna

-

-

0.20

Low-fat milk and yogurt

-

-

−0.16

Small fish with bones

0.18

−0.22

−0.25

Miso soup

-

−0.24

−0.36

Citrus fruit

0.29

0.19

−0.36

Rice

−0.34

−0.28

−0.48

a

Factor loading less than ±0.15 is represented by a dash for simplicity. The table omits food items with factor loadings less than ±0.15 for all dietary
patterns (wine)b
Deep-fried tofucFermented soybeansdMeat/fish and vegetable pancakewith PAI-1 in obese rats [34]. In the present study population, higher consumption of coffee, which modestly
contributed to the Westernized breakfast pattern (factor
loading score = 0.17), was associated with lower levels of
leptin and PAI-1 [17]. The present finding is also compatible with those of our previous studies in the samesetting that examined associations between dietary pattern and other related outcomes. Specifically, scores of
dietary patterns characterized by the frequent intake ofbread and dairy products but infrequent intake of alcohol, similar to the present Westernized breakfast pattern,have been inversely associated with the prevalence of
metabolic syndrome [30], high blood pressure [30], andglucose tolerance abnormalities [32] and lower concentrations of C-peptide [31]. These abnormalities have also
been linked to leptin [6, 35, 36] and PAI-I levels [37].
Therefore, the Westernized breakfast pattern may
decrease risks of these metabolic diseases by lowering
circulating leptin and PAI-1levels.
Guo et al. reported that a Japanese dietary pattern,which was characterized by a higher intake of vegetables,
seaweeds, tofu, and fish, was significantly associated with

To our knowledge, this study is one of only a few studies
to examine the associations between dietary patterns andadipokines in an Asian population.
The dietary patterns identified in the present study
were similar to those in previous Japanese and Westernstudies: a Japanese pattern (similar to the Japanese pattern in previous studies [25, 27, 30, 31]); a Westernized
breakfast pattern (similar to the sweets-fruits pattern[27, 30–32]); and a meat food pattern (similar to theWestern pattern [21–23, 25]). Of these patterns, theWesternized breakfast pattern showed significant inverse
associations with leptin and PAI-1 levels. We are notaware of any study that has reported associations between similar dietary patterns and these adipokines. Thisfinding might be explained by some foods and nutrientsthat contributed to the Westernized breakfast pattern.
With regard to dairy food, a prospective case-control
study reported a significant inverse correlation between
the sum of pentadecanoic acid and heptadecanoic acidin serum phospholipids (biomarkers of milk fat intake)and leptin and PAI-1 [33]. Conjugated linoleic acid,
which is rich in dairy products, was inversely associated
Table 2 Subject characteristics by dietary pattern (n = 509)
Japanese pattern
T1 (low)

T2

T3 (high)

Westernized breakfast pattern
T1 (low)

T2

T3 (high)

Meat food pattern
T1 (low)

T2

T3 (high)

No. of subjects

169

170

170

170

170

169

169

170

170

Age (years), mean ± SD

43.4 ± 10.9

43.5 ± 11.1

43.4 ± 10.8

46.6 ± 11.7

42.5 ± 10.1

41.2 ± 10.4

47.9 ± 10.9

43.2 ± 10.9

39.2 ± 9.3

Men (%)

84.6

58.2

31.8

74.6

60.6

39.4

58.6

63.5

52.4

Workplace (site A) (%)

32.5

31.8

24.1

31.4

25.3

32.0

18.3

34.1

35.9

Married (%)

74.0

72.9

70.6

80.5

70.0

67.1

75.7

72.9

68.8

BMI (kg/m2), mean ± SD

22.9 ± 3.1

22.3 ± 3.3

21.8 ± 3.5

22.9 ± 3.3

22.4 ± 3.4

21.7 ± 3.2

22.3 ± 3.2

22.6 ± 3.4

22.1 ± 3.3

Sedentary work (%)

87.6

80.6

75.3

84.7

81.8

77.1

76.9

82.4

84.1

Non-job physical activity(≥5 MET-hr/week) (%)

34.9

37.1

35.3

42.6

37.1

27.7

39.1

35.3

32.9

Current smoker (%)

39.1

24.1

12.4

38.5

24.1

12.9

24.9

26.5

24.1

Total energy intake (kcal/day)

1827 ± 503

1752 ± 529

1773 ± 491

1890 ± 539

1786 ± 526

1676 ± 433

1747 ± 522

1861 ± 517

1743 ± 478

T1 to T3 tertiles 1 to 3, BMI body mass index

Kashino et al. Nutrition Journal (2015) 14:58

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Table 3 Geometric means (95 % CI) of serum adipokine concentrations by tertile of dietary pattern score
No. of subjects

Tertile categories of dietary pattern scores
T1 (low)

T2

T3 (high)

Trend Pa

Adiponectin (μg/ml)
Japanese pattern
Model 1509

4.46 (4.00–4.98)

4.91 (4.45–5.43)

4.84 (4.36–5.37)

0.30

Model 2509

4.44 (3.89–5.08)

4.89 (4.31–5.54)

4.85 (4.26–5.53)

0.26

Westernized breakfast pattern
Model 1509

4.64 (4.18–5.16)

4.70 (4.24–5.21)

4.87 (4.41–5.39)

0.51

Model 2509

4.68 (4.12–5.31)

4.71 (4.13–5.36)

4.83 (4.23–5.51)

0.69

Model 1509

4.88 (4.38–5.44)

4.74 (4.28–5.23)

4.64 (4.20–5.13)

0.50

Model 2509

4.80 (4.20–5.49)

4.73 (4.17–5.36)

4.68 (4.12–5.33)

0.75

Model 1486

1.70 (1.47–1.97)

1.68 (1.46–1.93)

1.76 (1.53–2.03)

0.72

Model 2486

1.64 (1.42–1.90)

1.64 (1.42–1.88)

1.67 (1.45–1.93)

0.85

Meat food pattern

Leptin (ng/ml)
Japanese pattern

Westernized breakfast pattern
Model 1486

1.88 (1.63–2.17)

1.65 (1.44–1.90)

1.64 (1.43–1.88)

0.17

Model 2486

1.82 (1.58–2.09)

1.57 (1.37–1.81)

1.54 (1.33–1.78)

0.04

Model 1486

1.61 (1.39–1.87)

1.78 (1.56–2.04)

1.74 (1.51–2.00)

0.48

Model 2486

1.66 (1.44–1.93)

1.68 (1.46–1.92)

1.61 (1.39–1.86)

0.68

Model 1509

3.43 (3.14–3.75)

3.39 (3.12–3.68)

3.13 (2.88–3.41)

0.15

Model 2509

3.59 (3.21–4.00)

3.59 (3.23–3.98)

3.32 (2.98–3.70)

0.24

Meat food pattern

Resistin (ng/ml)
Japanese pattern

Westernized breakfast pattern
Model 1509

3.32 (3.04–3.62)

3.20 (2.94–3.48)

3.40 (3.13–3.69)

0.67

Model 2509

3.48 (3.14–3.87)

3.37 (3.03–3.76)

3.63 (3.25–4.05)

0.51

Model 1509

3.45 (3.16–3.77)

3.32 (3.06–3.60)

3.18 (2.93–3.46)

0.18

Model 2509

3.64 (3.26–4.07)

3.50 (3.16–3.89)

3.36 (3.02–3.74)

0.20

Model 1508

0.86 (0.74–1.01)

0.84 (0.73–0.97)

1.04 (0.89–1.20)

0.10

Model 2508

0.93 (0.77–1.13)

0.91 (0.75–1.09)

1.12 (0.92–1.36)

0.11

Meat food pattern

Visfatin (ng/ml)
Japanese pattern

Westernized breakfast pattern
Model 1508

0.92 (0.80–1.07)

1.00 (0.86–1.15)

0.84 (0.73–0.97)

0.35

Model 2508

0.98 (0.81–1.18)

1.07 (0.89–1.30)

0.91 (0.75–1.10)

0.49

Model 1508

0.85 (0.73–0.99)

0.93 (0.81–1.07)

0.95 (0.82–1.10)

0.30

Model 2508

0.92 (0.76–1.12)

0.99 (0.83–1.20)

1.02 (0.85–1.23)

0.36

509

30.41 (28.92–31.98)

29.50 (28.17–30.89)

28.71 (27.37–30.12)

0.11

Meat food pattern

PAI-1 (ng/ml)
Japanese pattern
Model 1Kashino et al. Nutrition Journal (2015) 14:58

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Table 3 Geometric means (95 % CI) of serum adipokine concentrations by tertile of dietary pattern score (Continued)
Model 2509

31.48 (29.60–33.47)

30.73 (29.00–32.57)

30.08 (28.32–31.96)

0.21

Westernized breakfast pattern
Model 1509

30.96 (29.50–32.50)

29.27 (27.92–30.68)

28.45 (27.17–29.80)

0.01

Model 2509

31.87 (30.07–33.78)

30.46 (28.70–32.33)

29.68 (27.93–31.54)

0.04

Model 1509

29.81 (28.36–31.33)

29.75 (28.40–31.16)

28.99 (27.67–30.38)

0.42

Model 2509

31.42 (29.54–33.41)

31.03 (29.29–32.89)

29.88 (28.16–31.71)

0.15

Meat food pattern

CI confidence interval, PAI-1 plasminogen activator inhibitor-1aBased on multiple regression analysis, with ordinal numbers 0 to 2 assigned to the tertile categories of each dietary pattern
Model 1: adjusted for age (years, continuous), sex, workplace (site A or B)
Model 2: additionally adjusted for married (yes or no), body mass index (kg/m2, continuous), occupational physical activity (sedentary and active), non-occupational
physical activity (0, 0 < - < 5 MET-hr/week, 5- < 10 MET-hr/week, or ≥10 MET-hr/week), smoking (never smokers, ex-smokers, current smokers consuming 1–19
cigarettes/day, or current smokers consuming ≥20 cigarettes/day), and total energy intake (kcal/day, continuous)

higher serum adiponectin concentrations in men [27]. In
our study, serum adiponectin levels in the middle andhighest tertiles of the Japanese pattern, characterized byvegetables, fruits, mushrooms, seaweeds, and soy products, tended to be higher than those in the lowest levelof this pattern, although the trend association was not statistically significant. The lack of a significant association in
the present population may be ascribed to the lower factor
loading scores of fish and shellfish and soy products compared to those of the previous study (fish and shellfish,
0.47 in the previous study vs 0.02 to 0.22 in our study; soy
products, 0.31 to 0.60 in the previous study vs -0.03 to
0.35 in our study) [27]. A review of studies in humans
concluded that a fish-based diet and an omega-3 fatty
acid-rich diet may increase circulating adiponectin levels[11]. Additionally, rats fed with soy protein diet showed
higher plasma adiponectin concentrations than rats fed
with a casein diet [38]. A dietary pattern with higher loadings of both fish and soy foods may show a clearer association with adiponectin concentrations.
We found no association between the meat food pattern, characterized by higher intake of meat, processed
meat, and mayonnaise and dressing, and serum levels of
adipokines, including leptin. A few previous studies reported an association between meat food patterns andleptin. A study in the U.S. found no association between
a pattern characterized by higher intakes of red meats,
processed meats, high-energy drinks, and refined grains
and serum leptin [23]. Another U.S. study showed that a
dietary pattern characterized by higher intakes of redmeats, processed meats, french fries, and eggs was positively related to leptin levels, but that the association lost
statistical significance after additional adjustment for
BMI [22]. In contrast, a dietary pattern characterized byhigher intake of milk, meats, cake, and fruits was positively related to leptin levels in Chinese men and women[21]. The majority of studies to date do not support anindependent role of diet with a high meat intake in theregulation of circulating leptin.

Our study has a number of strengths, including its
high response rate (94 %), use of a validated dietary
questionnaire, and adjustment for potentially important
confounding factors. This study has also several limitations. First, dietary patterns extracted based on principal
component analysis involves subjective decisions in determining the number of factors to retain, in choosing
the method of rotation of the initial factors, and in labeling the dietary patterns. However, we identified major
dietary patterns that have previously been reported inJapanese as well as Western populations. Second, somefood items demonstrated relatively low validities. For instance, Spearman’s rank correlation coefficients for seaweeds and potatoes were less than 0.2 in men. Thismight partly account for the lack of clear associations
for the Japanese dietary pattern. Third, we measured adipokine levels in serum that was obtained at a single
point in time, and the measured data may not be adequately reflect long term status. Nevertheless, plasma
adipokine levels have been shown to be relatively stable
across season [39]. Fourth, because the present study
was cross-sectional, we were unable to conclude that theobserved associations were causal. To minimize the possibility of reverse causality, however, we excluded subjects with chronic diseases that might affect dietary habit
and adipokines, including cardiovascular disease, cancer,
and diabetes. Fifth, our sample size was relatively small(n = 509), which lack sufficient power to detect moderateassociations with statistical significance. Finally, thepresent findings were derived from only employees of
two municipal offices in northeastern Kyushu, Japan,
which may not be the representative of general Japanese
population.

Conclusion
We found that a Westernized breakfast pattern was inversely related to serum leptin and PAI-1 concentrations.
This cross-sectional observation awaits prospective
confirmation.

Kashino et al. Nutrition Journal (2015) 14:58

Abbreviations
BDHQ: Brief self-administered diet history questionnaire; BMI: Body massindex; CHD: Coronary heart disease; CI: Confidence interval;
CVD: Cardiovascular disease; MET: Metabolic equivalent; PAI-1: Plasminogenactivator inhibitor type-1.

Page 8 of 9

9.

10.

Competing interests
The authors declare that they have no competing interests.

11.

Authors’ contributions
TM and IK designed the research; AN, MS, HH, and TM arranged the fieldsurvey; KY measured serum adipokine levels; IK performed statistical analysis,
wrote the manuscript, and had primary responsibility for final content; andIK, AN, KK, SA, KY, MS, HH, and TM were involved in revision of themanuscript and the final version of the manuscript. All authors read andapproved the final manuscript.

12.

13.

Acknowledgments
We are grateful to the study participants for their cooperation andparticipation. We also thank Seiko Miyazaki and Yasutaka Horiuchi (Kyushu
University); Emi Tanaka, Youko Tsuruda, Misaki Hirose, Meishu Sai, Miho
Isayama, Midori Sasaki, Mie Shimomura and Azumi Uehara (Fukuoka
Women’s University); Yaeko Nagano (retired nurse); and Akiko Hayashi, Yu
Teruyama, Kae Saito, Kayoko Washizuka and Yuho Mizoue (National Center
for Global Health and Medicine) for their help in data collection. We thankKazuko Nagase and Dai Suzuki (Department of Metabolic Disorder, Diabetes
Research Center, National Center for Global Health and Medicine) for theircontribution to the measurement of serum adipokines. This study was
supported by a Grant-in-Aid for Scientific Research (B) (21390213) from theJapan Society for the Promotion of Science (to Dr. Mizoue), a Grant-in-Aid forYoung Scientists (B) (21790598) from the Ministry of Education, Culture,
Sports, Science and Technology and Grants of National Center for Global
Health and Medicine (to Drs. Nanri and Yasuda) and a Grant from National
Institute of Biomedical Innovation (to Dr. Yasuda).

14.

Author details
1
Department of Epidemiology and Prevention, Center for Clinical Sciences,
National Center for Global Health and Medicine, Toyama 1-21-1, Shinjuku-ku,
Tokyo 162-8655, Japan. 2Department of Metabolic Disorder, Diabetes
Research Center, National Center for Global Health and Medicine, Tokyo,
Japan. 3Department of Applied Biological Chemistry, Graduate School of
Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka,
Japan. 4Graduate School of Nutrition and Health Science, Fukuoka Women’s
University, Fukuoka, Japan.

19.

15.

16.

17.

18.

20.

21.
Received: 25 February 2015 Accepted: 28 May 201522.
References
1. Tschritter O, Fritsche A, Thamer C, Haap M, Shirkavand F, Rahe S, et al.
Plasma adiponectin concentrations predict insulin sensitivity of bothglucose and lipid metabolism. Diabetes. 2003;52(2):239–43.
2. Li S, Shin HJ, Ding EL, van Dam RM. Adiponectin levels and risk of type 2
diabetes: a systematic review and meta-analysis. JAMA. 2009;302(2):179–88.
doi:10.1001/jama.2009.976.
3. Kim DH, Kim C, Ding EL, Townsend MK, Lipsitz LA. Adiponectin levels andthe risk of hypertension: a systematic review and meta-analysis. Hypertension. 2013;62(1):27–32. doi:10.1161/HYPERTENSIONAHA.113.01453.
4. Zhang H, Mo X, Hao Y, Huang J, Lu X, Cao J, et al. Adiponectin levels andrisk of coronary heart disease: a meta-analysis of prospective studies. Am J
Med Sci. 2013;345(6):455–61. doi:10.1097/MAJ.0b013e318262dbef.
5. Chen GC, Qin LQ, Ye JK. Leptin levels and risk of type 2 diabetes: genderspecific meta-analysis. Obes Rev. 2014;15(2):134–42. doi:10.1111/obr.12088.
6. Shankar A, Xiao J. Positive relationship between plasma leptin level andhypertension. Hypertension. 2010;56(4):623–8. doi:10.1161/
HYPERTENSIONAHA.109.148213.
7. Sattar N, Wannamethee G, Sarwar N, Chernova J, Lawlor DA, Kelly A, et al.
Leptin and coronary heart disease: prospective study and systematic review.
J Am Coll Cardiol. 2009;53(2):167–75. doi:10.1016/j.jacc.2008.09.035.
8. Matsuzawa Y. Adipocytokines and metabolic syndrome. Semin Vasc Med.
2005;5(1):34–9. doi:10.1055/s-2005-871744.

23.

24.
25.

26.

27.

28.

Meigs JB, Dupuis J, Liu C, O’Donnell CJ, Fox CS, Kathiresan S, et al. PAI-1
Gene 4G/5G polymorphism and risk of type 2 diabetes in a populationbased sample. Obesity. 2006;14(5):753–8. doi:10.1038/oby.2006.85.
Rajala MW, Qi Y, Patel HR, Takahashi N, Banerjee R, Pajvani UB, et al.
Regulation of resistin expression and circulating levels in obesity, diabetes,and fasting. Diabetes. 2004;53(7):1671–9.
Gray B, Steyn F, Davies PS, Vitetta L. Omega-3 fatty acids: a review of the effects on adiponectin and leptin and potential implications for obesity management. Eur J Clin Nutr. 2013;67(12):1234–42. doi:10.1038/ejcn.2013.197.
Murakami K, Sasaki S, Takahashi Y, Uenishi K, Yamasaki M, Hayabuchi H,
et al. Nutrient and food intake in relation to serum leptin concentration
among young Japanese women. Nutrition. 2007;23(6):461–8. doi:10.1016/
j.nut.2007.04.006.
Kuroda M, Ohta M, Okufuji T, Takigami C, Eguchi M, Hayabuchi H, et al.
Frequency of soup intake and amount of dietary fiber intake are inversely
associated with plasma leptin concentrations in Japanese adults. Appetite.
2010;54(3):538–43. doi:10.1016/j.appet.2010.02.010.
Tsukinoki R, Morimoto K, Nakayama K. Association between lifestyle factors
and plasma adiponectin levels in Japanese men. Lipids Health Dis.
2005;4:27. doi:10.1186/1476-511X-4-27.
Kondo K, Morino K, Nishio Y, Kondo M, Fuke T, Ugi S, et al. Effects of afish-based diet on the serum adiponectin concentration in young,non-obese, healthy Japanese subjects. J Atheroscler Thromb.
2010;17(6):628–37.
de Koning L, Malik VS, Kellogg MD, Rimm EB, Willett WC, Hu FB. Sweetened
beverage consumption, incident coronary heart disease, and biomarkers ofrisk in men. Circulation. 2012;125(14):1735–41. doi:10.1161/
CIRCULATIONAHA.111.067017. S1.
Pham NM, Nanri A, Yasuda K, Kurotani K, Kuwahara K, Akter S, et al. Habitualconsumption of coffee and green tea in relation to serum adipokines: across-sectional study. Eur J Nutr. 2014. doi:10.1007/s00394-014-0701-4.
Mantzoros CS, Williams CJ, Manson JE, Meigs JB, Hu FB. Adherence to theMediterranean dietary pattern is positively associated with plasma
adiponectin concentrations in diabetic women. Am J Clin Nutr.
2006;84(2):328–35.
Fragopoulou E, Panagiotakos DB, Pitsavos C, Tampourlou M, Chrysohoou C,
Nomikos T, et al. The association between adherence to the Mediterranean
diet and adiponectin levels among healthy adults: the ATTICA study. J Nutr
Biochem. 2010;21(4):285–9. doi:10.1016/j.jnutbio.2008.12.013.
Fargnoli JL, Fung TT, Olenczuk DM, Chamberland JP, Hu FB, Mantzoros CS.
Adherence to healthy eating patterns is associated with higher circulating
total and high-molecular-weight adiponectin and lower resistin concentrations in women from the Nurses’ Health Study. Am J Clin Nutr.
2008;88(5):1213–24.
Zuo H, Shi Z, Dai Y, Yuan B, Wu G, Luo Y, et al. Serum leptin concentrations
in relation to dietary patterns in Chinese men and women. Public HealthNutr. 2014;17(7):1524–30. doi:10.1017/S1368980013001535.
Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, Willett WC, et al.
Association between dietary patterns and plasma biomarkers of obesity andcardiovascular disease risk. Am J Clin Nutr. 2001;73(1):61–7.
Ganji V, Kafai MR, McCarthy E. Serum leptin concentrations are not related
to dietary patterns but are related to sex, age, body mass index, serum
triacylglycerol, serum insulin, and plasma glucose in the US population. Nutr
Metab (Lond). 2009;6:3. doi:10.1186/1743-7075-6-3.
World Health Organization. WHO Mortality Database. http://apps.who.int/
healthinfo/statistics/mortality/whodpms/. Accessed: September 21, 2014
Shimazu T, Kuriyama S, Hozawa A, Ohmori K, Sato Y, Nakaya N, et al. Dietary
patterns and cardiovascular disease mortality in Japan: a prospective cohort
study. Int J Epidemiol. 2007;36(3):600–9. doi:10.1093/ije/dym005.
Yamagishi K, Iso H, Date C, Fukui M, Wakai K, Kikuchi S, et al. Fish, omega-3
polyunsaturated fatty acids, and mortality from cardiovascular diseases in anationwide community-based cohort of Japanese men and women the
JACC (Japan Collaborative Cohort Study for Evaluation of Cancer Risk) Study.
J Am Coll Cardiol. 2008;52(12):988–96. doi:10.1016/j.jacc.2008.06.018.
Guo H, Niu K, Monma H, Kobayashi Y, Guan L, Sato M, et al. Association ofJapanese dietary pattern with serum adiponectin concentration in Japanese
adult men. Nutr Metab Cardiovasc Dis. 2012;22(3):277–84. doi:10.1016/
j.numecd.2010.06.006.
Kobayashi S, Murakami K, Sasaki S, Okubo H, Hirota N, Notsu A, et al.
Comparison of relative validity of food group intakes estimated bycomprehensive and brief-type self-administered diet history questionnaires

Kashino et al. Nutrition Journal (2015) 14:58

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

Page 9 of 9against 16 d dietary records in Japanese adults. Public Health Nutr.
2011;14(7):1200–11. doi:10.1017/S1368980011000504.
Sience and Technology Agency. Standard Tables of Food Composition in
Japan, Fifth Revised and Enlarged Edition. Tokyo, Japan: Printing Bureau of
the Ministry of Finance; 2005.
Akter S, Nanri A, Pham NM, Kurotani K, Mizoue T. Dietary patterns andmetabolic syndrome in a Japanese working population. Nutr Metab (Lond).
2013;10(1):30. doi:10.1186/1743-7075-10-30.
Akter S, Nanri A, Yi S, Pham NM, Kurotani K, Kimura Y, et al. Dietary patterns
and C-peptide concentrations in a Japanese working population. Nutrition.
2012;28(9):e29–35. doi:10.1016/j.nut.2012.01.018.
Mizoue T, Yamaji T, Tabata S, Yamaguchi K, Ogawa S, Mineshita M, et al.
Dietary patterns and glucose tolerance abnormalities in Japanese men. J
Nutr. 2006;136(5):1352–8.
Warensjo E, Jansson JH, Berglund L, Boman K, Ahren B, Weinehall L, et al.
Estimated intake of milk fat is negatively associated with cardiovascular risk
factors and does not increase the risk of a first acute myocardial infarction.
A prospective case-control study. Br J Nutr. 2004;91(4):635–42. doi:10.1079/
BJN20041080.
Martins SV, Lopes PA, Alfaia CM, Rodrigues PO, Alves SP, Pinto RM, et al.
Serum adipokine profile and fatty acid composition of adipose tissues areaffected by conjugated linoleic acid and saturated fat diets in obese Zucker
rats. Br J Nutr. 2010;103(6):869–78. doi:10.1017/S000711450999256X.
Yun JE, Kimm H, Jo J, Jee SH. Serum leptin is associated with metabolic
syndrome in obese and nonobese Korean populations. Metabolism.
2010;59(3):424–9. doi:10.1016/j.metabol.2009.08.012.
Zuo H, Shi Z, Yuan B, Dai Y, Wu G, Hussain A. Association between serum
leptin concentrations and insulin resistance: a population-based study from
China. PLoS One. 2013;8(1):e54615. doi:10.1371/journal.pone.0054615.
Alessi MC, Juhan-Vague I. PAI-1 and the metabolic syndrome: links, causes,
and consequences. Arterioscler Thromb Vasc Biol. 2006;26(10):2200–7.
doi:10.1161/01.ATV.0000242905.41404.68.
Nagasawa A, Fukui K, Kojima M, Kishida K, Maeda N, Nagaretani H, et al.
Divergent effects of soy protein diet on the expression of adipocytokines.
Biochem Biophys Res Commun. 2003;311(4):909–14.
Lee SA, Kallianpur A, Xiang YB, Wen W, Cai Q, Liu D, et al. Intra-individual
variation of plasma adipokine levels and utility of single measurement ofthese biomarkers in population-based studies. Cancer Epidemiol Biomarkers
Prev. 2007;16(11):2464–70. doi:10.1158/1055-9965.EPI-07-0374.

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