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Nutrition Journal

, 17:68 | Cite as

Dietary pattern is associated with obesity in Chinese children and adolescents: data from China Health and Nutrition Survey (CHNS)

  • Shihan Zhen
  • Yanan Ma
  • Zhongyi Zhao
  • Xuelian Yang
  • Deliang Wen
Open Access
Research

Abstract

Background

Associations of dietary patterns in Chinese adolescents and children with later obesity have not previously been investigated. The purpose of the present study was to evaluate the associations between dietary patterns and the risk of obesity in Chinese adolescents and children by using a longitudinal design.

Methods

Data from the China Health and Nutrition Survey (CHNS), a nationally representative survey, were used for our analysis. 489 participants 6–14 years of age were followed from 2006 to 2011. Factor analysis was used to identify the dietary patterns in Chinese adolescents and children. Ordered logistic regression models were used to examine the association between dietary patterns and later obesity.

Results

Two dietary patterns were revealed by factor analysis, the traditional Chinese dietary pattern (with high intake of rice, vegetables, poultry, pork and fish and the modern dietary pattern (with high intake of wheat, processed meat and fast food). Children in the highest quartile and the second-highest quartile of the traditional Chinese dietary pattern was inversely associated with later obesity compared with children in the lowest quartile over 5 years (OR = 0.19, 95%CI: 0.09, 0.40 for Q4; OR = 0.47, 95%CI: 0.33, 0.67 for Q3); Children in the highest quartile of the modern dietary pattern was positively associated with later obesity compared with children in the lowest quartile over 5 years (OR = 2.02, 95%CI: 1.17, 3.48).

Conclusions

Dietary patterns in Chinese adolescents and children are associated with later obesity. These findings further confirm the importance of children’s dietary patterns in later obesity and lay groundwork for dietary culture-specific interventions targeted at reducing rates of obesity in children and adolescents.

Keywords

Dietary patterns Obesity China Adolescents Children 

Background

Childhood overweight and obesity have become a worldwide health problem. In developing countries, Childhood overweight and obesity have increased at an alarming rate [1, 2]. In 2014, about 35 million children were affected by overweight or obesity, and the prevalence of overweight and obesity for children in China was 12.2 and 7.3% respectively [1]. These large increases in the prevalence of childhood obesity might greatly increase morbidity in adulthood from causes such as cardiovascular disease, metabolic syndrome and diabetes [3, 4, 5]. Therefore, research identifying risk factors of childhood obesity are needed to address this severe public health problem.

Obesity is caused by an intricacy interaction among the environment, genetics and behavior [6]. Among these factors, diet has been demonstrated to be a determinant in the development of obesity [7]. However, owing to the complexity of diets and the potential associations between dietary components [8], the relationship between diet and obesity is intricate and barely understood [9]. Compared with traditional dietary analyses focusing on individual foods or nutrients [10], dietary pattern analysis might be an alternative holistic and comprehensive approach [11].

Dietary pattern analysis take advantage of complex diet, take into account multiple food groups instead of individual foods or nutrients, and can reveal potential interactions between various nutrients and food [11, 12]. Consequently, dietary pattern analysis might reflect the complexity of dietary intake and provide new insights into what people eat [13, 14]. Thus, dietary pattern analysis has widely been used to determine associations between diet and chronic diseases and to aid in formulating nutritional recommendations [15, 16, 17].

In China, transition of dietary patterns is an ongoing phenomenon [18]. Over the past 20 years, dietary patterns have transitioned from a traditional diet to a modern diet with high intake of refined grains and meat; the abundance of energy-dense food in the modern diet is cause for concern [13]. Dramatic changes in dietary patterns may explain the increase in obesity among children and adolescents [19]. However, to our knowledge, most previous studies have followed a cross-sectional design [15, 20] and have focused primarily on adults [21, 22]. Few studies have analyzed the long-term effects of dietary patterns and obesity in Chinese children or adolescents. Therefore, in the present study, we sought to characterize dietary patterns at baseline and to identify the associations between dietary patterns and later obesity in Chinese children and adolescents by using a longitudinal study design.

Methods

Study design and population

In the present investigation, we used longitudinal data from the China Health and Nutrition Survey (CHNS), an open prospective cohort study. CHNS used a multistage random-cluster sampling process to select samples from 15 provinces in China [23]. CHNS was approved by the Institutional Review Committees of the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety, Chinese Center for Disease Control and Prevention. Details have been described elsewhere [24].

We used the longitudinal data from 2006 to 2011. A total of 736 participants aged 6–14 were involved in 2006. Among them, 489 participants completed all three surveys and were enrolled in the current analysis. We excluded participants with implausibly high or low caloric intakes (ie, < 600 or > 4000 kcal/d), missing dietary pattern data or anthropometry data, or those with a history of metabolic disease before baseline.

Dietary assessment and food grouping

Details of dietary measurements have been provided elsewhere [25]. In brief, dietary were recorded through a 24-h-recall method for three consecutive days for each participant. For children younger than 12 years, parents were asked to recall children’s food consumption. Alcoholic beverages were excluded because of very low consumption in children.

Dietary were based on the food frequency questionnaire using in the survey in 2006. The dietary data were divided into 28 food groups (Table 1) based on their similarity in nutrients and Chinese Food Composition Table [26].
Table 1

Food groupings used in factor analysis

Food group

Examples of food items

Rice

White and brown rice

Wheat noodles

Wheat noodles

Wheat flour

Wheat flour

Wheat buns, breads

Bun, butter bread, salty bread

Cakes, cookies and pastries

Cookies, mooncake, fruit cake, chocolate cake, fruit pie

Deep-fried wheat

Deep-fried dough stick, deep-fried cake with red bean paste

Corn and coarse grain

Corn, corn grits, barley, oats, foxtail millet, sorghum

Starchy roots and tubers

Potato, yam, taro, lotus root, water chestnut, cassava

Fresh legumes

Soybean sprouts, peas with pod, mung bean sprouts

Dried legumes

Soybean flour, dried beans, beans flour, roasted broad bean

Legume products

Tofu, tofu products, red/mung bean paste

Nuts and seeds

Sesame, sunflower, lotus seeds, peanuts, walnuts, almonds, hazelnuts, pine-nuts, pistachios, cashew nuts

Fresh vegetables, non-leafy

Cauliflower, tomatoes, cucumber, zucchini, mushrooms

Fresh vegetables, leafy

Spinach, ‘bok choy’, cabbage

Pickled and salted vegetables

preserved vegetables, vegetables in soy sauce

Seaweed

Fresh or dried seaweed

Fruits

Fresh and canned (no added sugar) fruits

Red meat

beef, lamb, donkey, rabbit

Pork

Pork tenderloin, Pork belly, leg, rib chop

Organ meats

Liver, kidney, large intestine, blood

Processed meats

Sausages, ham, luncheon meat, dried meat, smoked meat

Poultry and game

Chicken, duck, goose

Eggs and eggs and products

Whole eggs, yolk, white, preserved eggs

Fish and seafood

Fresh- and salt-water fish, dried fish, shellfish

Milk and dairy products

Cow milk, goat milk, skim milk, flavored milk, cheese, yogurt

Fast food

Onion rings, potato chips, Western-style fast-food, salty snacks

Candy

Jelly, jam, chocolate, honey, sugar, candies

Sweetened beverages

Fruit or flavored drinks, fruit juice, soft drinks

The traditional Chinese dietary pattern was loaded heavily on rice, red meat, pork, poultry, vegetables (leafy) and fish. The modern dietary pattern was loaded heavily on wheat buns, cakes, legume products, nuts, pickled and salted vegetables, fruit, red meat, processed meats, poultry, eggs, fish, milk and fast food

Outcome variables

Height and weight were measured by using a standardized protocol from the World Health Organization (WHO) [27] in which height is measured to the nearest 0.1 cm, as participants stand with their backs against a wall, with no shoes and with their eyes looking straight ahead. Weight was measured according to the WHO protocol, with a lever balance to the neared 0.1 kg, in participants wearing minimal undergarments and no shoes. Body mass index (BMI) was then calculated as weight (in kilograms) divided by squared height (in meters). Obesity was defined according to age and sex by using the WHO BMI growth reference [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] years.

Covariates

Covariates including age, sex, residency, highest level of parental education, region, physical activity and energy intake were collected by in-home visit interviews and general information questionnaires. Residency was classified into two categories (urban and rural), region was classified into three categories (western, eastern and central), highest level of parental education was classified into five categories (illiterate or primary school; middle school; high school; technical or vocational degree; and college or higher). Physical activity was calculated as duration of total physical activity and expressed in hours per week (hours/week). Energy intake was calculated according to the China Food Composition and expressed in kilocalories per day (kcal/day).

Statistical analysis

Dietary patterns were identified at baseline (in 2006) by factor analysis using the principal component method. The factor scores were orthogonally (varimax) rotated to create less correlation among the patterns and to facilitate interpretability. An eigenvalue is the variance of the factor. In the initial factor solution, the first factor will account for the most variance, the second will account for the next highest amount of variance, and so on. According to the previous study [16, 28], dietary patterns were identified on the basis of the eigenvalue (> 2) and scree plot. Percentages of variances were also calculated. Factor loading of >|0.20| was included to represent the food strongly associated with the identified factor [29]. Pattern-specific factor scores were calculated as the sum of the food factor loading coefficients and the standardized daily consumption of food related to the dietary pattern. Factor scores were divided into four quartiles on the basis of their contribution to each pattern, and an increase from Q1 to Q4 was assumed.

ANOVA tests for continuous variables and chi-square tests for categorical variables were used to compare the different quartiles in dietary patterns at baseline. Ordered logistic regression models were used to estimate the odds ratios (OR) and 95% confidence intervals (95%CI) for later childhood obesity across the quartile categories of dietary pattern score. Model 1 was adjusted for sex and age. Model 2 was additionally adjusted for residency, highest level of parental education, region, physical activity and energy intake. Model 3 additionally used a cluster analysis to reduce the spatial interdependence, which can adversely affect the robustness of regression estimates [30]; we used the classifications of geographical regions as cluster groups to analyze the association.

To gain access to the full sample, we used the multiple imputation technique to fill in the missing values ten times, a procedure suggested to be sufficient in a previous study [31]. Imputation was performed by using multivariate normal regression. A p-value < 0.05 was regarded as statistically significant. All analyses were performed in Stata (version 13.0; StataCorp, College Station, TX, USA).

Results

Examples of food items for each food group are shown in Table 1. The baseline characteristics of participants, according to quartiles for each dietary pattern, are shown in Table 2. At baseline, 37 participants (7.57%) were overweight, while 56 participants (11.45%) had obesity. Participants in the highest quartile of the traditional dietary pattern were more likely to be significantly older, live in the central of China, had a lower prevalence of obesity and higher energy intake than those in the lowest quartile. Conversely, participants in the highest quartile of the modern dietary pattern were more likely to live in urban, eastern of China, had a higher parental education level and higher energy intake than those in the lowest quartile (p < 0.05).
Table 2

Baseline characteristics of participants according to quartiles of dietary patterns

 

Traditional Chinese Dietary pattern scores

p-value

All

Q1

Q2

Q3

Q4

N

489

122

122

123

122

 

Sex (%)

     

0.835

 Boy

249 (50.92)

65 (53.28)

63 (51.64)

58 (47.54)

63 (51.22)

 

 Girl

240 (49.08)

57 (46.72)

59 (48.36)

64 (52.46)

60 (48.78)

 

Age in years (Mean SD)

9.08 (2.76)

8.69 (2.63)

8.20 (2.69)

9.47 (2.83)

9.95 (2.58)

< 0.001

Residency (%)

     

0.075

 Urban

142 (29.04)

33 (27.05)

41 (33.61)

42 (34.43)

26 (21.14)

 

 rural

347 (70.96)

89 (72.95)

81 (66.39)

80 (65.57)

97 (78.86)

 

Highest level of parental Education (%)a

     

0.105

 Illiterate or primary school

101 (20.74)

17 (13.93)

23 (19.17)

29 (23.77)

32 (26.02)

 

 Junior middle school

224 (46.00)

58 (47.54)

62 (51.67)

53 (43.44)

51 (41.46)

 

 High middle school

84 (17.25)

25 (20.49)

22 (18.33)

18 (14.75)

19 (15.45)

 

 Technical or vocational degree

50 (10.27)

18 (14.75)

10 (8.33)

11 (9.02)

11 (8.94)

 

 College or higher

28 (5.75)

4 (3.28)

3 (2.50)

11 (9.02)

10 (8.13)

 

Region (%)

     

< 0.001

 Western

104 (21.27)

10 (8.20)

29 (23.77)

30 (24.59)

35 (28.46)

 

 Eastern

147 (30.06)

59 (48.36)

45 (36.89)

33 (27.05)

10 (8.13)

 

 Central

238 (48.67)

53 (43.44)

48 (39.34)

59 (48.36)

78 (63.41)

 

Obesity status (%)

     

0.01

 Non-overweight or obesity

396 (80.98)

84 (68.85)

102 (83.61)

107 (87.70)

103 (83.74)

 

 Overweight

37 (7.57)

15 (12.30)

7 (5.74)

7 (5.74)

8 (6.50)

 

 Obesity

56 (11.45)

23 (18.85)

13 (10.66)

8 (6.56)

12 (9.76)

 

Physical activity (Mean SD)

7.06 (5.32)

6.67 (5.17)

7.28 (5.22)

7.39 (5.51)

7.03 (5.54)

0.265

Energy (Mean SD)b

1462.43 (530.01)

1285.73 (455.83)

1243.43 (415.33)

1454.80 (495.97)

1862.476 (511.79)

< 0.001

 

Modern Dietary pattern scores

 

All

Q1

Q2

Q3

Q4

p-value

N

489

122

122

122

123

 

Sex (%)

     

0.204

 Boy

249 (50.92)

65 (53.28)

52 (42.62)

67 (54.92)

65 (52.85)

 

 Girl

240 (49.08)

57 (46.72)

70 (57.38)

55 (45.08)

58 (47.15)

 

Age in years (Mean SD)

9.08 (2.76)

8.80 (2.63)

9.16 (2.79)

9.11 (2.95)

9.24 (2.67)

0.6044

Residency (%)

     

< 0.001

 Urban

142 (29.04)

25 (20.49)

28 (22.95)

35 (28.69)

54 (43.90)

 

 rural

347 (70.96)

97 (79.51)

94 (77.05)

87 (71.31)

69 (56.10)

 

Highest level of parental Education (%)a

     

< 0.001

 Illiterate or primary school

101 (20.74)

39 (32.50)

36 (29.50)

11 (9.02)

15 (12.20)

 

 Junior middle school

224 (46.00)

59 (49.17)

58 (47.53)

61 (50.00)

46 (37.40)

 

 High middle school

84 (17.25)

17 (14.17)

19 (15.57)

22 (18.03)

26 (21.14)

 

 Technical or vocational degree

50 (10.27)

3 (2.50)

7 (5.74)

19 (15.57)

21 (17.07)

 

 College or higher

28 (5.75)

2 (1.67)

2 (1.64)

9 (7.38)

15 (12.20)

 

Region (%)

     

< 0.001

 Western

104 (21.27)

60 (49.18)

32 (26.23)

7 (5.74)

5 (4.07)

 

 Eastern

147 (30.06)

10 (8.20)

20 (16.39)

39 (31.97)

78 (63.41)

 

 Central

238 (48.67)

52 (42.62)

70 (57.38)

76 (62.3)

40 (32.52)

 

Obesity status (%)

     

0.208

 Non-overweight or obesity

396 (80.98)

108 (88.52)

99 (81.15)

94 (77.05)

95 (77.24)

 

 Overweight

37 (7.57)

3 (2.46)

10 (8.20)

12 (9.84)

12 (9.76)

 

 Obesity

56 (11.45)

11 (9.02)

13 (10.66)

16 (13.11)

16 (13.01)

 

Physical activity (Mean SD)

7.06 (5.32)

7.33 (5.78)

6.90 (5.39)

7.02 (5.18)

7.01 (5.03)

0.797

Energy (Mean SD)b

1462.43 (530.01)

1325.28 (461.8118)

1386.38 (488.41)

1461.66 (552.026)

1674.66 (550.73)

< 0.001

p-value was calculated from χ2 test for binary variables or by ANOVA for continuous variables. Dietary patterns were identified at baseline (in 2006). p-values< 0.05 are bold. a: n = 487 cause of the missing data. b: n = 435 cause of the missing data

Two dietary patterns were determined in the present study. The factor loading of each pattern is shown in Table 3. The traditional Chinese dietary pattern (Eigenvalue = 2.36) was loaded heavily on rice, red meat, pork, poultry, vegetables (leafy) and fish, and was inversely loaded on wheat flour, wheat buns, vegetables (non-leafy), corn and eggs. The modern dietary pattern (Eigenvalue = 2.22) was loaded heavily on wheat buns, cakes, legume products, nuts, pickled and salted vegetables, fruit, red meat, processed meats, poultry, eggs, fish, milk and fast food, and was inversely loaded on wheat noodles. The two patterns together explained 16.36% of the dietary intake variance.
Table 3

Factor loadings for dietary patterns derived from exploratory factor analysis

 

Traditional pattern

Modern pattern

Rice

0.7401

 

Wheat noodles

 

−0.2918

Wheat flour

−0.4377

 

Wheat buns, breads

−0.5472

0.2077

Cakes, cookies and pastries

 

0.4780

Deep-fried wheat

  

Corn and coarse grain

−0.3337

 

Starchy roots and tubers

  

Fresh legumes

  

Dried legumes

  

Legume products

 

0.3734

Nuts and seeds

 

0.4711

Fresh vegetables, non-leafy

−0.2290

 

Fresh vegetables, leafy

0.6119

 

Pickled and salted vegetables

 

0.2529

Seaweed

  

Fruits

 

0.5154

Red meat

0.2667

0.2245

Pork

0.4855

 

Organ meats

  

Processed meats

 

0.3149

Poultry and game

0.2555

0.3335

Eggs and eggs products

−0.3168

0.3282

Fish and seafood

0.2524

0.4346

Milk and dairy products

 

0.4770

Fast food

 

0.4726

Candy

  

Sweetened beverages

  

Dietary patterns were identified at baseline (in 2006). Absolute values< 0.20 are excluded from the table for simplicity

The relationship between dietary patterns and later obesity in each wave, as assessed with the ordered logistic model, is displayed in Tables 4 and 5, respectively. In Table 4, dietary patterns were identified at baseline (in 2006), whereas participants’ obesity status was measured in 2009. In 2009, after cluster analysis and adjustment for confounders, children in the highest quartile (Q4) of the traditional Chinese dietary pattern had lower odds of obesity (OR = 0.29, 95%CI: 0.16, 0.51 for Q4) than those in the lowest (Q1), whereas children in Q4, Q3 and Q2 of the modern dietary pattern had greater odds of obesity than those in Q1 (OR = 3.22, 95%CI: 1.66, 6.23 for Q4; OR = 2.46, 95%CI: 2.17, 2.80 for Q3; OR = 2.22, 95%CI: 1.42, 3.46 for Q2). Moreover, in Table 5, participants’ obesity status were measured in 2011, children in Q4 and Q3 of the traditional Chinese dietary pattern still had lower odds of obesity (OR = 0.19, 95%CI: 0.09, 0.40 for Q4; OR = 0.47, 95%CI: 0.33, 0.67 for Q3) than those in Q1, whereas children in Q4 of the modern dietary pattern still had greater odds of obesity (OR = 2.02, 95%CI: 1.17, 3.48) than those in Q1.
Table 4

Multivariable adjusted ORs and 95%CI for obesity in 2009 across quartiles of dietary pattern scores

 

Traditional Chinese Dietary pattern

 

Q1

Q2

Q3

Q4

P for trend

Model1

1

0.87 (0.43, 1.75)

0.60 (0.29, 1.28)

0.34 (0.14, 0.82)

0.010

Model2

1

1.04 (0.52, 2.10)

0.66 (0.31, 1.41)

0.29 (0.10, 0.78)

0.015

Model3

1

1.04 (0.54, 2.00)

0.66 (0.31, 1.40)

0.29 (0.16, 0.51)

0.002

 

Modern Dietary Pattern

 
 

Q1

Q2

Q3

Q4

P for trend

Model1

1

2.09 (0.84, 5.20)

2.47 (1.01, 6.07)

3.18 (1.33, 7.64)

0.009

Model2

1

2.22 (0.87, 5.63)

2.46 (0.99, 6.14)

3.22 (1.26, 8.18)

0.018

Model3

1

2.22 (1.42, 3.46)

2.46 (2.17, 2.80)

3.22 (1.66, 6.23)

0.006

Model 1: adjusted for sex and age. Model 2: additionally adjusted for residency, highest level of parental education, region, physical activity and energy intake. Model 3: additionally included use of classifications of geographical regions as cluster groups to analyze the association. Dietary patterns were identified at baseline (in 2006). p-value for trend was obtained by adjusting the quartiles of the pattern scores as a continuous variable. p-values< 0.05 are bold

Table 5

Multivariable adjusted ORs and 95%CI for obesity in 2011 across quartiles of dietary pattern scores

 

Traditional Chinese Dietary Pattern

 

Q1

Q2

Q3

Q4

P for trend

Model1

1

1.00 (0.52, 1.95)

0.52 (0.24, 1.12)

0.28 (0.11, 0.70)

0.002

Model2

1

1.06 (0.54, 2.08)

0.47 (0.21, 1.02)

0.19 (0.07 0.49)

< 0.001

Model3

1

1.06 (0.30, 3.69)

0.47 (0.33, 0.67)

0.19 (0.09, 0.40)

< 0.001

 

Modern Dietary Pattern

 
 

Q1

Q2

Q3

Q4

P for trend

Model1

1

1.30 (0.56, 3.06)

1.67 (0.75, 3.77)

2.17 (0.99, 4.77)

0.039

Model2

1

1.28 (0.54, 3.01)

1.58 (0.69, 3.65)

2.02 (0.88, 4.65)

0.086

Model3

1

1.28 (0.48, 3.44)

1.58 (0.74, 3.39)

2.02 (1.17, 3.48)

0.078

Model 1: adjusted for sex and age. Model 2: additionally adjusted for residency, highest level of parental education, region, physical activity and energy intake. Model 3: additionally included use of classifications of geographical regions as cluster groups to analyze the association. Dietary patterns were identified at baseline (in 2006). p-value for trend was obtained by adjusting the quartiles of the pattern scores as a continuous variable. p-values< 0.05 are bold

Discussion

In the present study, we report analysis of dietary patterns among Chinese children aged 6–14 at baseline and the association of dietary patterns with later obesity. Two dietary patterns were identified in the multicenter longitudinal study that satisfactorily captured eating habits (16.36% of dietary intake variance explained). The first pattern, the traditional Chinese dietary pattern, was characterized mainly by high consumption of rice, vegetables, pork, red meat, poultry and fish. The second pattern, the modern dietary pattern, consisted of a combination of wheat buns, bread, cake, cookies, legumes, pickled (salted) vegetables, fruits, nuts, red meat, processed meat, poultry, eggs, fish, milk and fast food. The identified “traditional” and “modern” dietary patterns are similar to those reported previously conducted among adults in China [16, 17, 21].

In the present study, the traditional Chinese dietary pattern was inversely associated with later obesity, whereas the modern dietary pattern was positively associated with later obesity. Our results are comparable to those of previous studies. The Dutch Lifelines cohort study indicated that the healthier dietary pattern which have high intake of fruit, vegetables and fish were inversely associated with obesity, whereas the modern dietary pattern which have high intake of bread, potatoes and sweet snacks had a significantly increased risk of obesity [32]. In a 6-year prospective study conducted in UK, Ambrosini et al. [33] also reported that a dietary pattern high in meat, energy density and fat might be positively associated with obesity. However, previous studies have mainly focused on adults [22, 23] and cross-sectional designed [16, 21]. Some findings relating dietary patterns and obesity may not be applicable to Chinese adolescents and children, owing to cultural factors affecting intake.

Overall, the present results suggested that the traditional Chinese dietary pattern may be protective against later obesity. The protective effect may be attributed to its healthful components. For example, the benefit of consuming whole grains, vegetables and legumes as part of the traditional Chinese dietary pattern may explain the protective effect in our study [16, 22]. First, rice, an essential staple food in China, is a low-energy food that constitutes the bulk of the traditional Chinese diet. Energy density of rice is lower than that of wheat [15]. Different methods are used in cooking rice and wheat. Steamed rice contains twice the amount of water and half the energy content as an equivalent amount of bread [22]. Second, instead of organ and processed meats, the traditional Chinese dietary pattern includes a high intake of white meat, fish and seafood, which are all associated with a low prevalence of obesity. Fish has been found to be protective against obesity because of its omega-3 polyunsaturated fatty acid content [34]. White meat has less fat than red meat, and the fats present are primarily healthful unsaturated fats [31, 35]. Another potential explanation for our findings may be vegetable intake. Vegetables that provide large amounts of dietary fiber, antioxidants (e.g., vitamins C and E) and water may contribute to decreasing the risk of later obesity [16, 36]. However, in the present study, fresh leafy and non-leafy vegetables showed opposite effects in factor loadings. We assumed that the different cooking methods using in preparing vegetables might explain the opposite effects. For instance, stir-frying non-leafy vegetables in vegetable oil may increase energy density [22, 37].

The present study also found a higher risk of obesity associated with the modern dietary pattern. Previous studies have shown positive associations between modern dietary patterns abundant in saturated fat and cholesterol [16, 38, 39] and a higher risk of obesity. The associations between the modern dietary pattern and later obesity may be attributed to the high amounts of suboptimal foods together with the lower fiber intake [29, 33, 40]. Wheat gluten may promote weight gain, partly by decreasing the thermogenic capacity of adipose tissue [21]. Second, high consumption of red meat and processed meat, containing amounts of saturated fat and cholesterol, is directly associated with an increased risk of obesity [16, 35, 41]. Another characteristic of the modern pattern is the use of the unhealthful cooking method deep-frying [37]. Consumption of deep-fried fast food, such as potato chips and deep-fried dough, is of serious concern. A prospective cohort study has suggested that greater consumption of fried food is associated with a higher risk of obesity in follow-up [42]. Additionally, in this study, we found that milk and dairy products were positively associated with the modern pattern score. A previous study has suggested that milk intake associated with risk of childhood obesity, and the insulinotropic and IGF-1-raising effects of milk and dairy products may have further adverse effects on obesity [43]. Moreover, excess energy intake may play a role in the development of obesity [44]. After adjustment for energy intake, we found that the associations for each dietary pattern remained significant.

To our knowledge, this is the first study reporting the relations between dietary patterns and later obesity risk among the children and adolescents in China. Our findings provided valuable information for the primary prevention of childhood obesity through the dietary modifications in a Chinese children and adolescents. However, it is important to note some potential limitations. Firstly, the present study was limited by old data source and small sample sizes (here we report 489 participants) which may not necessarily representative of the general Chinese populations. Second, there was a high rate of loss of subjects to follow-up because of migration and city construction. However, there was no significant difference between those lost in follow-up and those retained. Thus, the association between dietary patterns and the risk of later obesity might not have been biased. Thirdly, dietary data were collected through three 24-h dietary recalls, which might not represent long-term usual intake, and using a baseline dietary data might provide a biased estimate. Fourthly, some subjective and arbitrary decisions should be considered, including the number of factors retained and the labeling of dietary patterns. However, these aspects are common to factor analyses, and the methodology has been validated and found satisfactory [22]. Finally, even though we controlled for many covariates, there are still some potential socioeconomic and genetic factors that may have confounded our estimation.

Conclusion

In conclusion, the present study identified two dietary patterns among Chinese adolescents and children aged 6–14 years at baseline: the traditional Chinese pattern and the modern pattern. The traditional Chinese dietary pattern was inversely associated with later obesity, whereas the modern dietary pattern was positively associated with later obesity. These findings further confirmed the importance of dietary patterns among children in later obesity and lay a groundwork for culture-specific interventions targeted at reducing rates of childhood obesity. Interventions aiming at discouraging the adoption of the modern dietary pattern, such as consumption of fried food, processed meat, and energy-dense foods, while encouraging the traditional Chinese dietary pattern, should be developed.

Notes

Acknowledgements

This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924, and R01-HD38700) and the NIH Fogarty International Center (D43 TW009077, D43 TW007709) for financial support for the CHNS data collection and analysis files from 1989 to 2015 and future surveys, and the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009, Chinese National Human Genome Center at Shanghai since 2009, and Beijing Municipal Center for Disease Prevention and Control since 2011.

Funding

This work was supported by the National Natural Science Foundation of China [71774173].

Availability of data and materials

The data that support the findings of this study are available from The China Health and Nutrition Survey, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of The China Health and Nutrition Survey.

Authors’ contributions

SZ analyzed and interpreted the data, and was a major contributor in writing the manuscript. YM explained the data regarding the relationship between dietary pattern and obesity. ZZ analyzed the data regarding the relationship between dietary pattern and obesity. XY analyzed the data regarding the relationship between dietary pattern and obesity. DW made substantial contributions to design the study. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Data of this study was from the China Health and Nutrition Survey (CHNS). CHNS was approved by the Institutional Review Committees of the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety, Chinese Center for Disease Control and Prevention.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors and Affiliations

  1. 1.China Medical UniversityShenyangChina

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