European Journal of Clinical Nutrition (2015), 1–6 © 2015 Macmillan Publishers Limited All rights reserved 0954-3007/15 www.nature.com/ejcn
ORIGINAL ARTICLE
Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women JG Zhang1, ZH Wang1, HJ Wang1, WW Du1, C Su1, J Zhang1, HR Jiang1, FY Zhai2 and B Zhang1 BACKGROUND/OBJECTIVES: Dietary patterns represent the combined effects of foods and efficaciously illustrate the impact of diet on health outcomes. This study identified the dietary patterns and determined their relationships with obesity among young Chinese women. SUBJECTS/METHODS: In 2011, the China Health and Nutrition Survey included 2363 young women aged 18–44 years. Factor analysis of data from three consecutive 24-h dietary recalls identified the dietary patterns. Weight, height and waist circumstance (WC) were measured, and body mass index (BMI) was calculated. General obesity was defined as BMI ⩾ 28 kg/m2 and abdominal obesity as WC ⩾ 85 cm. RESULTS: Four dietary patterns were identified: traditional south; traditional north; snack; and high protein. After adjusting for confounders and energy intake, women in the highest-score quintiles of the traditional south pattern were less likely to have general obesity (odds ratio (OR) = 0.48; 95% confidence interval (CI) 0.29–0.78) and abdominal obesity (OR = 0.64; 95% CI 0.46–0.90). Subjects in the highest-score quintiles of the traditional north pattern had significantly greater risk of general obesity (OR = 2.28; 95% CI 1.38–3.74) and of abdominal obesity (OR = 2.32; 95% CI 1.66–3.24). CONCLUSION: The traditional south pattern of rice as the major staple food with pork and vegetable dishes is associated with lower risk of general and abdominal obesity. The traditional north pattern of high intake of wheat, other cereals and tubers is positively associated with general and abdominal obesity. This provides important information for interventions and policies addressing obesity prevention among young Chinese women. European Journal of Clinical Nutrition advance online publication, 18 February 2015; doi:10.1038/ejcn.2015.8
INTRODUCTION Obesity is a serious public health problem that needs to be addressed urgently, because not only developed countries but also developing ones face an obesity epidemic among both children and adults.1–3 With urbanization and modernization, the epidemic of obesity is rapidly growing among men and women in China.4 Moreover, excessive body weight is an important risk factor for mortality and morbidity from cardiovascular diseases, diabetes, cancers and musculoskeletal disorders, causing nearly 3 million deaths annually worldwide.3 Obesity is caused by a complex interaction between the environment, genetic predisposition and human behavior.5 Diet is an important factor determining obesity. Although dietary habits have been implicated in the development of obesity, this relationship is complex and poorly understood.2 Traditional dietary analyses have had certain limitations because they have focused merely on the relationship between individual nutrients or foods and obesity.6–9 Therefore, dietary pattern analysis has emerged as an alternative, holistic approach.10 Dietary patterns can summarize complex dietary data to render the information more practical and meaningful than individual foods or nutrients for investigating diet–disease relationships given that patterns consider total dietary intake and the colinearity between many foods and nutrients as well as the potentially synergistic effects of foods and nutrients.9–12 China has experienced a rapid nutrition transition during the last few decades.13 The increase in the intake of vegetable oils and animal-source foods has been rapid and appear to be continuing,
and coarse grains, legumes, vegetables and other healthy foods have declined in importance and intake levels.14 Westernization of dietary habits may affect the prevalence of obesity in China. Several studies on dietary patterns have shown the associations of general obesity and abdominal obesity with specific dietary patterns among women, although the results are not consistent.15–20 However, these findings have limited their applicability to Chinese women, as culturally specific dietary patterns are likely to have a role. This study identified the prevailing dietary patterns and examined their associations with general obesity and abdominal obesity among young Chinese women. SUBJECTS AND METHODS Study population We used the data collected by the China Health and Nutrition Survey (CHNS) that was designed to examine how the social and economic transformation in China affects the health and nutritional status of the Chinese population.21,22 The CHNS is an ongoing prospective study initiated in 1989 and followed up in 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011. The project involves 12 provinces that vary in demography, geography, economic development and public resources. A multistage, random cluster sample was used to draw the sample surveyed in each province. The present analysis was based on the 2011 survey, which included 2363 young women aged 18–44 years with complete demographic and dietary data. All subjects gave written informed consent before their participation in the survey. The study was approved by the institutional review
1 National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China and 2Chinese Nutrition Society, Beijing, China. Correspondence: Professor B Zhang, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Beijing 100050, China. E-mail: [email protected] Received 4 April 2014; revised 4 November 2014; accepted 31 December 2014
Dietary patterns associated with obesity JG Zhang et al
2 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.
Dietary measurements Dietary intake was assessed with three consecutive 24-h dietary recalls (2 weekdays and 1 weekend day) in each wave of the CHNS. Trained interviewers used standard forms to administer the 24-h dietary recalls in a household interview. The participants were asked to report the kinds and amount of the food and beverage items (measured in g) that they consume both at home and away from home during the previous 24 h.23 The average intake of the three recalls was used for each individual.
Other relevant variables A general questionnaire collected participants’ age, education, living areas, cigarette smoking habit, alcohol intake, physical activity and annual household income per family member. Well-trained health workers who followed a reference protocol recommended by the World Health Organization collected anthropometrical measurements.24 Body mass index was calculated using height and weight measurements. In the present study, being obese was defined as body mass index ⩾ 28 kg/m2.25 Waist circumference was measured from the midpoint between the lower border of the rib cage and the iliac crest to the nearest 0.1 cm. Abdominal obesity in women was defined as a waist circumference ⩾ 85 cm, a new definition of abdominal obesity that was published by the National Health and Family Planning Commission in 2013 for Chinese adults.
Statistical analysis Factor analysis (principal component) was used to derive food patterns on the basis of 19 foods or food groups of the Chinese Food Composition Table (Table 1).26 The mean intake (g per day) was used as the input value in the analysis. We conducted the analysis using the SAS factor procedure (Statistical Analysis System, SAS Institute, Cary, NC, USA). The factors were rotated by an orthogonal transformation (SAS varian rotation function) to achieve a simpler structure with greater interpretability. In considering the number of factors to retain, we evaluated eigenvalues (41), scree plots and interpretability to determine which set of factors most meaningfully
Table 1.
described distinct food patterns. From these analyses, the four-factor solution was selected. Items were retained in a factor if they had an absolute correlation ⩾ 0.25 with that factor. Factor loadings were calculated for each food group across the four factors. A factor score was calculated for each subject for each of the four factors in which the intake of 19 food groups was weighted by their factor loadings and summed. Quintiles were categorized across the score of each dietary pattern based on the distribution in the whole population. Linear regression analysis and the Mantel–Haenszel χ2 test were used to assess the trend of nutrients and lifestyle factors across increasing quintiles of dietary pattern scores. Logistic regression analysis was used to calculate the odds ratio (OR) and 95% confidence interval (CI) for general obesity and abdominal obesity across the quintile categories of dietary pattern scores. Multivariate adjusted ORs were calculated by adjusting for potential risk factors of obesity, such as age, education, living area, smoking status, drinking status, physical activity, annual household incomes per family member and total energy intake (kcal per day). The linear trend of association was assessed by a logistic regression model assigning median scores to each quintile for each dietary pattern. All statistical analyses were performed using version 9.2 of the SAS software package. A P-valueo0.05 was considered statistically significant.
RESULTS Dietary patterns Factor analysis revealed four dietary patterns. The factor loadings of each pattern after orthogonal rotation are given in Table 2. These four factors explained 34.4% of the variance in total food intake. We observed evidence of factor 1 through its food items (mainly rice, vegetables and pork) and named it the traditional south pattern, which represents a typical traditional diet in South China. The traditional north pattern (factor 2), characterized by high intakes of wheat, other cereals and tubers, represents a typical traditional diet in North China. The third factor, the snack pattern, is characterized by high intakes of fruits, eggs, milk and nuts. The fourth factor is labeled the high-protein pattern because it is high in consumption of fungi, algae, legumes, seafoods and pork, which are all rich in protein.
Food groups in the factor analysisa
Foods or food groups
Table 2. Factor-loading matrix for the dietary patterns and their foods or food groupsa
Foods included in the group
Foods or food groups
Rice
Round-grained rice, long-grained rice and glutinous rice Wheat Wheat buns and wheat noodles Other cereals Corn, barley and millet Tubers Potato and sweet potato Legumes Soybeans and soybean products Fungi and algae Mushroom, kelp and laver Vegetables Cabbage, eggplant, carrot, pepper, lettuce, rape, tomato and cauliflower Fruits Apple, pear, peach, date, grape, watermelon, orange and other fruits Pork Pork and pork products Other livestock Beef, game, lamb and meat products meats Poultry Chicken, duck and goose Organ meats Organ meats Seafoods Fish, shrimp, crab and shellfish Milk Milk and milk products Eggs Eggs Nuts Nuts Cakes Cakes and ethnic foods Fast foods Convenience foods, hamburgers, pizzas, sandwiches and french fries Soft drinks Carbonated drinks, fruit juices and vegetable juices a
Foods and food Composition Table.
groups
are
based
on
European Journal of Clinical Nutrition (2015) 1 – 6
the
Chinese
Rice Wheat Other cereals Tubers Legumes Fungi and algae Vegetables Fruits Pork Other livestock meats Poultry Organ meats Seafoods Milk Eggs Nuts Cakes Fast foods Soft drinks
Food
Dietary patterns Traditional south
Traditional north
Snack
High protein
0.67835 — — 0.28610 — — 0.66079 — 0.34812 —
− 0.42861 0.80215 0.60660 0.47510 — — — — − 0.37872 —
— — — 0.26653 — — — 0.71429 — —
— — — — 0.49174 0.67243 — — 0.35518 —
— — — − 0.42483 — — − 0.31847 − 0.56631 —
− 0.28471 — — — — — — — —
— — — 0.48733 0.52968 0.37812 — — —
— 0.29819 0.41326 — — — — — —
Absolute values o0.25 are not presented.
a
© 2015 Macmillan Publishers Limited
Dietary patterns associated with obesity JG Zhang et al Abbreviations: Q, quintile; RE, retinol equivalent. aValues are mean ± s.d. for continuous variables and percentage for categorical variables. bP-value for trend was calculated from a linear regression analysis for continuous variables and Mantel–Haenszel χ2 for categorical variables.
o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 2161.7 ± 786.4 45.4 ± 10.8 15.4 ± 3.9 39.0 ± 11.2 15.0 ± 11.0 87.7 ± 68.3 976.1 ± 1405.5 504.8 ± 304.9 26.6 ±15.9 1613.8 ± 617.2 56.7 ± 11.8 11.3 ± 2.5 31.9 ± 12.0 8.5 ± 4.2 65.6 ± 41.8 618.9 ± 799.1 269.1 ± 155.2 15.0 ± 5.9 o 0.001 o 0.001 o 0.001 0.161 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 2092.6 ± 698.5 49.0 ± 10.2 14.6 ± 3.5 36.1 ± 10.2 14.5 ± 12.5 103.7 ± 67.8 1126.7 ± 1687.5 520.0 ± 355.7 22.6 ± 14.5 2108.1 ± 790.6 57.1 ± 10.8 12.7 ± 3.1 29.9 ± 11.1 14.5 ± 7.7 84.7 ± 62.4 555.7 ± 685.1 398.9 ± 257.6 22.4 ± 12.8 2220.7 ± 747.5 52.8 ± 11.9 12.9 ± 3.0 34.1 ± 11.7 13.3 ± 7.7 120.1 ± 63.2 1308.8 ± 1430.5 456.4 ± 258.2 24.1 ± 13.4 1704.0 ± 625.9 48.0 ± 10.1 13.9 ± 3.8 37.9 ± 10.6 12.1 ± 12.7 54.7 ± 51.0 611.3 ± 1357.5 415.6 ± 353.0 18.5 ± 14.0
© 2015 Macmillan Publishers Limited
Dietary intake Energy (kcal per day) Carbohydrate (% of energy) Protein (% of energy) Fat (% of energy) Fiber (g per day) Vitamin C (mg per day) Vitamin A (μg RE per day) Calcium (mg per day) Iron (mg per day)
472 35.2 ± 6.6 20.3 27.3 6.6 0.2 10.8 7.8 22.8 472 32.9 ± 7.4 40.5 67.2 43.4 1.1 18.2 10.6 22.7 N Age (years) Urban (%) North (%) Education (high, %) Current smoker (%) Current drinker (%) General obesity (%) Abdominal obesity (%)
Q5 Q1
o 0.001 o 0.001 o 0.001 o 0.001 0.008 o 0.001 o 0.001 0.019 o 0.001
1965.1 ± 698.5 46.8 ± 12.3 13.6 ± 3.5 39.3 ± 12.2 9.1 ± 4.8 77.4 ± 57.7 1110.3 ± 1141.8 390.8 ± 235.5 19.2 ± 9.1
0.010 o 0.001 o 0.001 o 0.001 o 0.001 0.011 o 0.001 0.999 o 0.001
472 34.2 ± 7.4 23.5 35.4 11.4 1.9 9.3 9.5 26.5 0.086 0.002 o 0.001 o 0.001 0.103 0.002 o 0.001 o 0.001 472 34.4 ± 7.2 23.7 82.4 10.0 1.5 8.3 12.3 30.5 472 33.7 ± 7.1 33.7 5.7 24.8 0.2 14.8 5.3 14.0 o 0.001 o 0.001 o 0.001 o 0.001 0.300 o 0.001 0.142 0.781
Q5 Q1
P-valueb Traditional north P-valueb Traditional south
Characteristics of the participants according to quintiles of four dietary patternsa
Table 3.
1718.4 ± 699.6 52.8 ± 13.4 12.1 ± 3.2 34.9 ± 13.6 9.1 ± 6.8 49.6 ± 37.3 499.1 ± 710.6 284.7 ± 184.3 17.7 ± 10.0
472 34.1 ± 7.4 25.0 58.5 15.7 0.6 8.7 8.3 23.9 0.221 o 0.001 o 0.001 o 0.001 0.236 0.003 0.432 0.176 472 33.3 ± 7.4 39.6 59.8 39.0 0.8 14.8 9.5 22.7
472 33.8 ± 7.0 35.8 35.2 32.2 1.9 17.0 8.5 25.2
Q1 Q1
Snack
P-valueb Q5
High protein
Q5
P-valueb
0.669 o 0.001 o 0.001 o 0.001 0.103 0.006 0.675 0.781
3 Association of dietary patterns with sociodemographic characteristics The characteristics of the young Chinese women across quintile categories of the dietary pattern scores are given in Table 3. One of the four dietary patterns is associated with the participants’ age. Women with high scores for the traditional south pattern (P o 0.001) were older. Women with high scores for the snack pattern (P o 0.001) and the high-protein pattern (P o0.001) were more likely to live in urban areas. Women with high scores for the traditional north pattern (P o0.001) and the snack pattern (P o 0.001) were more likely to live in the north. Education level and alcohol intake were associated with the four dietary patterns. Women with high scores for the snack pattern and the highprotein pattern were more likely to be current drinkers and have better education. General obesity and abdominal obesity increased across the quintile categories of the traditional north pattern (P o 0.001). However, no association was found in between the other three patterns and the prevalence of general obesity and abdominal obesity. Association of dietary patterns with nutrient intakes Women with higher scores for the traditional south pattern had higher intake of energy; higher percentage of energy from carbohydrates, fiber (g per day), vitamin C (mg per day), vitamin A (μg retinol equivalent per day), calcium (mg per day) and iron (mg per day); and lower percentage of energy from proteins and fats (Table 3). The traditional north pattern had higher intake of energy; higher percentage of energy from carbohydrates, vitamin C, fiber and iron; lower intake of vitamin A; and lower percentage of energy from proteins and fats. The snack pattern had higher intake of energy; higher percentage of energy from proteins, fiber, vitamin C, vitamin A, calcium and iron; and lower percentage of energy from carbohydrates. The high-protein pattern had higher intake of energy; higher percentage of energy from proteins and fats, fiber, vitamin C, vitamin A, calcium, and iron; and lower percentage of energy from carbohydrates. Association of dietary patterns with general and abdominal obesity Table 4 reports overall association between the dietary patterns and obesity. After adjusting for all confounders, women in the highest quintiles of the traditional south pattern were less likely to have general obesity (OR = 0.48; 95% CI 0.29–0.78) and abdominal obesity (OR = 0.64; 95% CI 0.46–0.90). The traditional north pattern was positively associated with general obesity (OR = 2.28; 95% CI 1.38–3.74) and abdominal obesity (OR = 2.32; 95% CI 1.66–3.24). The ORs for general obesity and abdominal obesity significantly increased according to the quintile categories of the traditional north pattern after adjustments for age, education, smoking habit, alcohol intake, living area, physical activity, annual household income per family member and energy intake. However, there was no association between the snack pattern or the high-protein pattern and obesity. DISCUSSION The present study identified four distinct dietary patterns among young Chinese women: traditional south; traditional north; snack; and high protein. The traditional south pattern was positively associated with a decreased risk of general and abdominal obesity. On the contrary, subjects who followed the traditional north pattern were found to have a greater risk of general and abdominal obesity. These results suggest that such dietary patterns are associated with general and abdominal obesity after adjusting for all confounders in young Chinese women. European Journal of Clinical Nutrition (2015) 1 – 6
Dietary patterns associated with obesity JG Zhang et al
4 Table 4.
Association of dietary patterns with general obesity and abdominal obesity across quintiles of dietary pattern scoresa P-valueb
General obesity Q1
Q3
Q5
Traditional south Crude 1.00 Model 1 1.00 Model 2 1.00
0.73 (0.47–1.14) 0.61 (0.38–0.96) 0.61 (0.38–0.96)
0.71 (0.46–1.12) 0.50 (0.31–0.81) 0.48 (0.29–0.78)
Traditional north Crude 1.00 Model 1 1.00 Model 2 1.00
1.08 (0.61–1.89) 1.08 (0.61–1.89) 1.08 (0.61–1.91)
Snack Crude Model 1 Model 2
1.00 1.00 1.00
High protein Crude Model 1 Model 2
1.00 1.00 1.00
P-valueb
Abdominal obesity Q1
Q3
Q5
0.138 0.004 0.002
1.00 1.00 1.00
0.96 (0.70–1.30) 0.78 (0.57–1.08) 0.78 (0.57–1.07)
1.01 (0.74–1.37) 0.70 (0.51–0.98) 0.64 (0.46–0.90)
0.808 0.033 0.007
2.50 (1.53–4.07) 2.28 (1.39–3.75) 2.28 (1.38–3.74)
o 0.001 o 0.001 o 0.001
1.00 1.00 1.00
1.56 (1.11–2.20) 1.57 (1.11–2.23) 1.64 (1.15–2.33)
2.70 (1.95–3.74) 2.36 (1.69–3.30) 2.32 (1.66–3.24)
o0.001 o0.001 o0.001
0.73 (0.46–1.17) 0.80 (0.49–1.28) 0.80 (0.49–1.28)
1.00 (0.64–1.54) 1.22 (0.77–1.92) 1.21 (0.76–1.93)
0.335 0.066 0.079
1.00 1.00 1.00
0.68 (0.50–0.93) 0.78 (0.57–1.06) 0.76 (0.56–1.05)
0.81 (0.60–1.09) 1.03 (0.75–1.41) 0.97 (0.70–1.34)
0.317 0.528 0.841
0.86 (0.53–1.38) 0.94 (0.58–1.53) 0.93 (0.57–1.52)
1.02 (0.64–1.63) 1.16 (0.72–1.87) 1.14 (0.70–1.87)
0.712 0.378 0.453
1.00 1.00 1.00
0.82 (0.60–1.11) 0.88 (0.64–1.21) 0.86 (0.63–1.18)
1.07 (0.79–1.44) 1.27 (0.93–1.73) 1.19 (0.86–1.63)
0.498 0.076 0.215
Abbreviations: CI, confidence interval; OR, odds ratio; Q, quintile. Model 1: adjusted for age (continuous), education (low, medium and high), living area (urban/rural), annual household income per family member (continuous), physical activity (continuous), current smoker (yes/no), current drinker (yes/no). Model 2: model 1 additionally adjusted for total energy intake (continuous). aValues are ORs (95% CI). bP-value for trend was calculated using the median value of each quintile as a continuous variable.
The traditional Chinese diet includes large amounts of cereals and vegetables and small amounts of animal-source foods. Such a diet is low in fat, low in energy density, high in carbohydrates and high in dietary fiber.27 In this study, the traditional south pattern and the traditional north pattern had strong positive correlations with Chinese traditional foods. In northern China, people are more likely to eat staple foods made from flour, such as wheat noodles and steamed buns. The people in southern China prefer meat and vegetable dishes with rice. We therefore labeled these two patterns as traditional, which were similar to the dietary patterns of Chinese adults reported by other research.28,29 The snack pattern in the present study had high loadings mostly for convenience foods, including fruits, milk, eggs and nuts. Similar dietary patterns have been identified elsewhere.30,31 Since 2004, snacking has been rapidly increasing as a dietary component in China. However, to date snacking has not been dominated by savory snacks, sugary beverages and other unhealthy foods as in the West.32 In China, fruit has been one of the more popular snack items.14 The high-protein pattern was a special pattern that is rarely found in other countries. It is characterized by a high consumption of fungi, algae, legumes, seafoods and pork, which are rich in high-quality protein. In this study, we found an inverse relationship between a traditional south pattern and a risk of general and abdominal obesity. This is consistent with a study in Jiangsu, China,33 which showed that the rice-based pattern was negatively associated with weight gain in Chinese adults. It is also consistent with a study from Brazil,34 which showed that a traditional dietary pattern with a high intake of rice and beans was related to a lower risk of obesity. However, it is inconsistent with other rice-based patterns in Asian populations. A traditional Japanese pattern characterized by a high intake of rice, miso soup and soy products was positively associated with obesity among women aged 18–20 years.16 The Korean rice–vegetable dietary pattern characterized by a greater intake of steamed rice, tofu, kimchi, vegetables, dried anchovy and seaweeds was associated with an increased risk of abdominal obesity among women aged 40–69 years.17 Although these discrepancies are attributed to differences in populations, study European Journal of Clinical Nutrition (2015) 1 – 6
designs and analytic methods, the exact explanations have yet to be clarified. One explanation could be the different varieties and cooking methods of rice consumed in these places, which differ in amounts of nutrition and, more importantly, the type of starch, which in turn is related to glycemic index.35,36 The protective role of the Chinese traditional south pattern may be owing to the low glycemic index of this stir-fried food combination, rich micronutrient intakes and greater vegetable intakes. Moreover, the consumption of this diet appears to be a marker of a diet prepared more consistently at home, with lower intakes of fast foods, milk and cakes, and less frequent eating away from home. Interestingly, another traditional dietary pattern, the traditional north pattern, was associated with an increased risk of general and abdominal obesity. This is similar to the noodle dietary pattern in Korean women.17 Unlike the traditional south pattern, the northern diet is less diverse, mainly including wheat foods, such as noodles, dumplings, steamed buns and flat cakes. One characteristic of the traditional north pattern is a high carbohydrate intake. Other studies have also found that a highcarbohydrate diet can increase the risk of general obesity and abdominal obesity.37–39 To date, however, the mechanism underlying a carbohydrate-induced risk for obesity is unclear. It is likely that carbohydrate intake may alter lipid profiles, such as an increase in triglycerides and/or a decrease in high-density lipoprotein cholesterol, leading to increased obesity.40 Further studies on the detailed mechanism are needed. Another characteristic is that the traditional north pattern is comprised of more energy intake and less intake of micronutrients; that is, it is a low-nutrient diet. The micronutrients such as zinc, iron, vitamin C and vitamin A may have an important role in fat deposition and the pathogenesis of obesity.41 The lownutrient diet appears to favor the development of insulin resistance in the central nervous system, which might in part be responsible for leptin resistance and accordingly promotes pleasurable responses to foods.42,43 This study has several limitations. First, the results do not show the causal or resultant relationship between dietary patterns and © 2015 Macmillan Publishers Limited
Dietary patterns associated with obesity JG Zhang et al
5 risk of obesity owing to the cross-sectional data. Second, the statistical methods used to define the dietary patterns are somewhat subjective, including the consolidation of food items into food groups, the number of factors to extract and the labeling of the patterns.44 Third, dietary patterns could be different among studies because of different ethnicity/culture or objectives. It is difficult to compare these findings with other studies. Fourth, the 24-h dietary recall method cannot generally evaluate usual dietary intake. Despite these limitations, this is the first study to reveal the relationships between dietary patterns and risk of general and abdominal obesity in young Chinese women using large survey data. In China, women are in charge of the diet at home. The present study usefully provides a better understanding of the dietary habits of young women, which relates not only to their health but also to that of their family members, especially their children. In conclusion, we identified four unique dietary patterns in young Chinese women: traditional south; traditional north; snack; and high protein. Our findings indicate that the traditional south pattern of rice as the major staple food in conjunction with pork and vegetable dishes is associated with lower risk of general and abdominal obesity. The traditional north pattern of a high intake of wheat, other cereals and tuber is positively associated with general and abdominal obesity. This provides important information for interventions and policies addressing obesity prevention in young Chinese women. Further prospective studies are needed to better understand the relationships. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS This research uses data from the CHNS. We thank the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention; the Carolina Population Center (5 R24 HD050924), University of North Carolina at Chapel Hill, the National Institutes of Health (NIH) (R01-HD30880, DK056350, R24 HD050924, and R01HD38700); and the Fogarty International Center, NIH, for financial support for the CHNS data collection and analysis of files from 1989 to 2011, and for future surveys.
REFERENCES 1 Rokholm B, Baker JL, Sorensen TI. The levelling off of the obesity epidemic since the year 1999--a review of evidence and perspectives. Obes Rev 2010; 11: 835–846. 2 Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012; 70: 3–21. 3 Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr 2012; 10: 22. 4 Xi B, Liang Y, He T, Reilly KH, Hu Y, Wang Q et al. Secular trends in the prevalence of general and abdominal obesity among Chinese adults, 1993-2009. Obes Rev 2012; 13: 287–296. 5 Nguyen DM, El-Serag HB. The epidemiology of obesity. Gastroenterol Clin North Am 2010; 39: 1–7. 6 Bray GA, Paeratakul S, Popkin BM. Dietary fat and obesity: a review of animal, clinical and epidemiological studies. Physiol Behav 2004; 83: 549–555. 7 Boeing H, Bechthold A, Bub A, Ellinger S, Haller D, Kroke A et al. Critical review: vegetables and fruit in the prevention of chronic diseases. Eur J Nutr 2012; 51: 637–663. 8 Anderson JW, Baird P, Davis Jr RH, Ferreri S, Knudtson M, Koraym A et al. Health benefits of dietary fiber. Nutr Rev 2009; 67: 188–205. 9 Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13: 3–9. 10 Van Horn L. Eating pattern analyses: the whole is more than the sum of its parts. J Am Diet Assoc 2011; 111: 203. 11 Tucker KL. Dietary patterns, approaches, and multicultural perspective. Appl Physiol Nutr Metab 2010; 35: 211–218. 12 Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc 2004; 104: 615–635.
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13 Popkin BM. Synthesis and implications: China's nutrition transition in the context of changes across other low- and middle-income countries. Obes Rev 2014; 15 (Suppl 1): 60–67. 14 Zhai FY, Du SF, Wang ZH, Zhang JG, Du WW, Popkin BM. Dynamics of the Chinese diet and the role of urbanicity, 1991-2011. Obes Rev 2014; 15 (Suppl 1): 16–26. 15 Rezazadeh A, Rashidkhani B. The association of general and central obesity with major dietary patterns in adult women living in Tehran, Iran. ARYA Atheroscler 2010; 6: 23–30. 16 Okubo H, Sasaki S, Murakami K, Kim MK, Takahashi Y, Hosoi Y et al. Three major dietary patterns are all independently related to the risk of obesity among 3760 Japanese women aged 18-20 years. Int J Obes (Lond) 2008; 32: 541–549. 17 Kim JH, Lee JE, Jung IK. Dietary pattern classifications and the association with general obesity and abdominal obesity in Korean women. J Acad Nutr Diet 2012; 112: 1550–1559. 18 Quatromoni PA, Copenhafer DL, D'Agostino RB, Millen BE. Dietary patterns predict the development of overweight in women: The Framingham Nutrition Studies. J Am Diet Assoc 2002; 102: 1239–1246. 19 Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB. Dietary patterns and changes in body weight in women. Obesity (Silver Spring) 2006; 14: 1444–1453. 20 Boggs DA, Palmer JR, Spiegelman D, Stampfer MJ, Adams-Campbell LL, Rosenberg L. Dietary patterns and 14-y weight gain in African American women. Am J Clin Nutr 2011; 94: 86–94. 21 Popkin BM, Du S, Zhai F, Zhang B. Cohort Profile: The China Health and Nutrition Survey--monitoring and understanding socio-economic and health change in China, 1989-2011. Int J Epidemiol 2009; 39: 1435–1440. 22 Zhang B, Zhai FY, Du SF, Popkin BM. The China Health and Nutrition Survey, 1989-2011. Obes Rev 2014; 15 (Suppl 1): 2–7. 23 Popkin BM. Reducing meat consumption has multiple benefits for the world's health. Arch Intern Med 2009; 169: 543–545. 24 WHO Expert Committee Physical status: the use and interpretation of anthropometry. Tech Rep Ser 1995, 854: 1–452. 25 Zhou BF. Cooperative Meta-Analysis Group of the Working Group on Obesity in China Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 2002; 15: 83–96. 26 Yang Y, Wang G, Pan X. Chinese Food Composition Tables 2002. Peking University Medical Press: Beijing, China, 2002. 27 Du SF, Wang HJ, Zhang B, Zhai FY, Popkin BM. China in the period of transition from scarcity and extensive undernutrition to emerging nutritionrelated non-communicable diseases, 1949-1992. Obes Rev 2014; 15 (Suppl 1): 8–15. 28 Wang D, He Y, Li Y, Luan D, Yang X, Zhai F et al. Dietary patterns and hypertension among Chinese adults: a nationally representative cross-sectional study. BMC Public Health 2011; 11: 925. 29 Batis C, Sotres-Alvarez D, Gordon-Larsen P, Mendez MA, Adair L, Popkin B. Longitudinal analysis of dietary patterns in Chinese adults from 1991–2009. Br J Nutr 2014; 111: 1441–1451. 30 Gao X, Yao M, McCrory MA, Ma G, Li Y, Roberts SB et al. Dietary pattern is associated with homocysteine and B vitamin status in an urban Chinese population. J Nutr 2003; 133: 3636–3642. 31 Tucker KL, Chen H, Hannan MT, Cupples LA, Wilson PW, Felson D et al. Bone mineral density and dietary patterns in older adults: the Framingham Osteoporosis Study. Am J Clin Nutr 2002; 76: 245–252. 32 Wang Z, Zhai F, Zhang B, Popkin BM. Trends in Chinese snacking behaviors and patterns and the social-demographic role between 1991 and 2009. Asia Pac J Clin Nutr 2012; 21: 253–262. 33 Shi Z, Yuan B, Hu G, Dai Y, Zuo H, Holmboe-Ottesen G. Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu Nutrition Study. Br J Nutr 2011; 105: 1047–1054. 34 Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res 2002; 10: 42–48. 35 Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr 2002; 76: 5–56. 36 Chiu YT, Stewart ML. Effect of variety and cooking method on resistant starch content of white rice and subsequent postprandial glucose response and appetite in humans. Asia Pac J Clin Nutr 2013; 22: 372–379. 37 Park SH, Lee KS, Park HY. Dietary carbohydrate intake is associated with cardiovascular disease risk in Korean: analysis of the third Korea National Health and Nutrition Examination Survey (KNHANES III). Int J Cardiol 2010; 139: 234–240. 38 Lin H, Bermudez OI, Tucker KL. Dietary patterns of Hispanic elders are associated with acculturation and obesity. J Nutr 2003; 133: 3651–3657.
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Dietary patterns associated with obesity JG Zhang et al
6 39 Kim J, Jo I, Joung H. A rice-based traditional dietary pattern is associated with obesity in Korean adults. J Acad Nutr Diet 2012; 112: 246–253. 40 Mittendorfer B, Sidossis LS. Mechanism for the increase in plasma triacylglycerol concentrations after consumption of short-term, high-carbohydrate diets. Am J Clin Nutr 2001; 73: 892–899. 41 Garcia OP, Ronquillo D, Caamano Mdel C, Camacho M, Long KZ, Rosado JL. Zinc, vitamin A, and vitamin C status are associated with leptin concentrations
European Journal of Clinical Nutrition (2015) 1 – 6
and obesity in Mexican women: results from a cross-sectional study. Nutr Metab (Lond) 2012; 9: 59. 42 Isganaitis E, Lustig RH. Fast food, central nervous system insulin resistance, and obesity. Arterioscler Thromb Vasc Biol 2005; 25: 2451–2462. 43 Garcia OP, Long KZ, Rosado JL. Impact of micronutrient deficiencies on obesity. Nutr Rev 2009; 67: 559–572. 44 Martinez ME, Marshall JR, Sechrest L. Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol 1998; 148: 17–19.
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ORIGINAL ARTICLE
Dietary patterns and their associations with general obesity and abdominal obesity among young Chinese women JG Zhang1, ZH Wang1, HJ Wang1, WW Du1, C Su1, J Zhang1, HR Jiang1, FY Zhai2 and B Zhang1 BACKGROUND/OBJECTIVES: Dietary patterns represent the combined effects of foods and efficaciously illustrate the impact of diet on health outcomes. This study identified the dietary patterns and determined their relationships with obesity among young Chinese women. SUBJECTS/METHODS: In 2011, the China Health and Nutrition Survey included 2363 young women aged 18–44 years. Factor analysis of data from three consecutive 24-h dietary recalls identified the dietary patterns. Weight, height and waist circumstance (WC) were measured, and body mass index (BMI) was calculated. General obesity was defined as BMI ⩾ 28 kg/m2 and abdominal obesity as WC ⩾ 85 cm. RESULTS: Four dietary patterns were identified: traditional south; traditional north; snack; and high protein. After adjusting for confounders and energy intake, women in the highest-score quintiles of the traditional south pattern were less likely to have general obesity (odds ratio (OR) = 0.48; 95% confidence interval (CI) 0.29–0.78) and abdominal obesity (OR = 0.64; 95% CI 0.46–0.90). Subjects in the highest-score quintiles of the traditional north pattern had significantly greater risk of general obesity (OR = 2.28; 95% CI 1.38–3.74) and of abdominal obesity (OR = 2.32; 95% CI 1.66–3.24). CONCLUSION: The traditional south pattern of rice as the major staple food with pork and vegetable dishes is associated with lower risk of general and abdominal obesity. The traditional north pattern of high intake of wheat, other cereals and tubers is positively associated with general and abdominal obesity. This provides important information for interventions and policies addressing obesity prevention among young Chinese women. European Journal of Clinical Nutrition advance online publication, 18 February 2015; doi:10.1038/ejcn.2015.8
INTRODUCTION Obesity is a serious public health problem that needs to be addressed urgently, because not only developed countries but also developing ones face an obesity epidemic among both children and adults.1–3 With urbanization and modernization, the epidemic of obesity is rapidly growing among men and women in China.4 Moreover, excessive body weight is an important risk factor for mortality and morbidity from cardiovascular diseases, diabetes, cancers and musculoskeletal disorders, causing nearly 3 million deaths annually worldwide.3 Obesity is caused by a complex interaction between the environment, genetic predisposition and human behavior.5 Diet is an important factor determining obesity. Although dietary habits have been implicated in the development of obesity, this relationship is complex and poorly understood.2 Traditional dietary analyses have had certain limitations because they have focused merely on the relationship between individual nutrients or foods and obesity.6–9 Therefore, dietary pattern analysis has emerged as an alternative, holistic approach.10 Dietary patterns can summarize complex dietary data to render the information more practical and meaningful than individual foods or nutrients for investigating diet–disease relationships given that patterns consider total dietary intake and the colinearity between many foods and nutrients as well as the potentially synergistic effects of foods and nutrients.9–12 China has experienced a rapid nutrition transition during the last few decades.13 The increase in the intake of vegetable oils and animal-source foods has been rapid and appear to be continuing,
and coarse grains, legumes, vegetables and other healthy foods have declined in importance and intake levels.14 Westernization of dietary habits may affect the prevalence of obesity in China. Several studies on dietary patterns have shown the associations of general obesity and abdominal obesity with specific dietary patterns among women, although the results are not consistent.15–20 However, these findings have limited their applicability to Chinese women, as culturally specific dietary patterns are likely to have a role. This study identified the prevailing dietary patterns and examined their associations with general obesity and abdominal obesity among young Chinese women. SUBJECTS AND METHODS Study population We used the data collected by the China Health and Nutrition Survey (CHNS) that was designed to examine how the social and economic transformation in China affects the health and nutritional status of the Chinese population.21,22 The CHNS is an ongoing prospective study initiated in 1989 and followed up in 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011. The project involves 12 provinces that vary in demography, geography, economic development and public resources. A multistage, random cluster sample was used to draw the sample surveyed in each province. The present analysis was based on the 2011 survey, which included 2363 young women aged 18–44 years with complete demographic and dietary data. All subjects gave written informed consent before their participation in the survey. The study was approved by the institutional review
1 National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China and 2Chinese Nutrition Society, Beijing, China. Correspondence: Professor B Zhang, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, 27 Nanwei Road, Beijing 100050, China. E-mail: [email protected] Received 4 April 2014; revised 4 November 2014; accepted 31 December 2014
Dietary patterns associated with obesity JG Zhang et al
2 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.
Dietary measurements Dietary intake was assessed with three consecutive 24-h dietary recalls (2 weekdays and 1 weekend day) in each wave of the CHNS. Trained interviewers used standard forms to administer the 24-h dietary recalls in a household interview. The participants were asked to report the kinds and amount of the food and beverage items (measured in g) that they consume both at home and away from home during the previous 24 h.23 The average intake of the three recalls was used for each individual.
Other relevant variables A general questionnaire collected participants’ age, education, living areas, cigarette smoking habit, alcohol intake, physical activity and annual household income per family member. Well-trained health workers who followed a reference protocol recommended by the World Health Organization collected anthropometrical measurements.24 Body mass index was calculated using height and weight measurements. In the present study, being obese was defined as body mass index ⩾ 28 kg/m2.25 Waist circumference was measured from the midpoint between the lower border of the rib cage and the iliac crest to the nearest 0.1 cm. Abdominal obesity in women was defined as a waist circumference ⩾ 85 cm, a new definition of abdominal obesity that was published by the National Health and Family Planning Commission in 2013 for Chinese adults.
Statistical analysis Factor analysis (principal component) was used to derive food patterns on the basis of 19 foods or food groups of the Chinese Food Composition Table (Table 1).26 The mean intake (g per day) was used as the input value in the analysis. We conducted the analysis using the SAS factor procedure (Statistical Analysis System, SAS Institute, Cary, NC, USA). The factors were rotated by an orthogonal transformation (SAS varian rotation function) to achieve a simpler structure with greater interpretability. In considering the number of factors to retain, we evaluated eigenvalues (41), scree plots and interpretability to determine which set of factors most meaningfully
Table 1.
described distinct food patterns. From these analyses, the four-factor solution was selected. Items were retained in a factor if they had an absolute correlation ⩾ 0.25 with that factor. Factor loadings were calculated for each food group across the four factors. A factor score was calculated for each subject for each of the four factors in which the intake of 19 food groups was weighted by their factor loadings and summed. Quintiles were categorized across the score of each dietary pattern based on the distribution in the whole population. Linear regression analysis and the Mantel–Haenszel χ2 test were used to assess the trend of nutrients and lifestyle factors across increasing quintiles of dietary pattern scores. Logistic regression analysis was used to calculate the odds ratio (OR) and 95% confidence interval (CI) for general obesity and abdominal obesity across the quintile categories of dietary pattern scores. Multivariate adjusted ORs were calculated by adjusting for potential risk factors of obesity, such as age, education, living area, smoking status, drinking status, physical activity, annual household incomes per family member and total energy intake (kcal per day). The linear trend of association was assessed by a logistic regression model assigning median scores to each quintile for each dietary pattern. All statistical analyses were performed using version 9.2 of the SAS software package. A P-valueo0.05 was considered statistically significant.
RESULTS Dietary patterns Factor analysis revealed four dietary patterns. The factor loadings of each pattern after orthogonal rotation are given in Table 2. These four factors explained 34.4% of the variance in total food intake. We observed evidence of factor 1 through its food items (mainly rice, vegetables and pork) and named it the traditional south pattern, which represents a typical traditional diet in South China. The traditional north pattern (factor 2), characterized by high intakes of wheat, other cereals and tubers, represents a typical traditional diet in North China. The third factor, the snack pattern, is characterized by high intakes of fruits, eggs, milk and nuts. The fourth factor is labeled the high-protein pattern because it is high in consumption of fungi, algae, legumes, seafoods and pork, which are all rich in protein.
Food groups in the factor analysisa
Foods or food groups
Table 2. Factor-loading matrix for the dietary patterns and their foods or food groupsa
Foods included in the group
Foods or food groups
Rice
Round-grained rice, long-grained rice and glutinous rice Wheat Wheat buns and wheat noodles Other cereals Corn, barley and millet Tubers Potato and sweet potato Legumes Soybeans and soybean products Fungi and algae Mushroom, kelp and laver Vegetables Cabbage, eggplant, carrot, pepper, lettuce, rape, tomato and cauliflower Fruits Apple, pear, peach, date, grape, watermelon, orange and other fruits Pork Pork and pork products Other livestock Beef, game, lamb and meat products meats Poultry Chicken, duck and goose Organ meats Organ meats Seafoods Fish, shrimp, crab and shellfish Milk Milk and milk products Eggs Eggs Nuts Nuts Cakes Cakes and ethnic foods Fast foods Convenience foods, hamburgers, pizzas, sandwiches and french fries Soft drinks Carbonated drinks, fruit juices and vegetable juices a
Foods and food Composition Table.
groups
are
based
on
European Journal of Clinical Nutrition (2015) 1 – 6
the
Chinese
Rice Wheat Other cereals Tubers Legumes Fungi and algae Vegetables Fruits Pork Other livestock meats Poultry Organ meats Seafoods Milk Eggs Nuts Cakes Fast foods Soft drinks
Food
Dietary patterns Traditional south
Traditional north
Snack
High protein
0.67835 — — 0.28610 — — 0.66079 — 0.34812 —
− 0.42861 0.80215 0.60660 0.47510 — — — — − 0.37872 —
— — — 0.26653 — — — 0.71429 — —
— — — — 0.49174 0.67243 — — 0.35518 —
— — — − 0.42483 — — − 0.31847 − 0.56631 —
− 0.28471 — — — — — — — —
— — — 0.48733 0.52968 0.37812 — — —
— 0.29819 0.41326 — — — — — —
Absolute values o0.25 are not presented.
a
© 2015 Macmillan Publishers Limited
Dietary patterns associated with obesity JG Zhang et al Abbreviations: Q, quintile; RE, retinol equivalent. aValues are mean ± s.d. for continuous variables and percentage for categorical variables. bP-value for trend was calculated from a linear regression analysis for continuous variables and Mantel–Haenszel χ2 for categorical variables.
o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 2161.7 ± 786.4 45.4 ± 10.8 15.4 ± 3.9 39.0 ± 11.2 15.0 ± 11.0 87.7 ± 68.3 976.1 ± 1405.5 504.8 ± 304.9 26.6 ±15.9 1613.8 ± 617.2 56.7 ± 11.8 11.3 ± 2.5 31.9 ± 12.0 8.5 ± 4.2 65.6 ± 41.8 618.9 ± 799.1 269.1 ± 155.2 15.0 ± 5.9 o 0.001 o 0.001 o 0.001 0.161 o 0.001 o 0.001 o 0.001 o 0.001 o 0.001 2092.6 ± 698.5 49.0 ± 10.2 14.6 ± 3.5 36.1 ± 10.2 14.5 ± 12.5 103.7 ± 67.8 1126.7 ± 1687.5 520.0 ± 355.7 22.6 ± 14.5 2108.1 ± 790.6 57.1 ± 10.8 12.7 ± 3.1 29.9 ± 11.1 14.5 ± 7.7 84.7 ± 62.4 555.7 ± 685.1 398.9 ± 257.6 22.4 ± 12.8 2220.7 ± 747.5 52.8 ± 11.9 12.9 ± 3.0 34.1 ± 11.7 13.3 ± 7.7 120.1 ± 63.2 1308.8 ± 1430.5 456.4 ± 258.2 24.1 ± 13.4 1704.0 ± 625.9 48.0 ± 10.1 13.9 ± 3.8 37.9 ± 10.6 12.1 ± 12.7 54.7 ± 51.0 611.3 ± 1357.5 415.6 ± 353.0 18.5 ± 14.0
© 2015 Macmillan Publishers Limited
Dietary intake Energy (kcal per day) Carbohydrate (% of energy) Protein (% of energy) Fat (% of energy) Fiber (g per day) Vitamin C (mg per day) Vitamin A (μg RE per day) Calcium (mg per day) Iron (mg per day)
472 35.2 ± 6.6 20.3 27.3 6.6 0.2 10.8 7.8 22.8 472 32.9 ± 7.4 40.5 67.2 43.4 1.1 18.2 10.6 22.7 N Age (years) Urban (%) North (%) Education (high, %) Current smoker (%) Current drinker (%) General obesity (%) Abdominal obesity (%)
Q5 Q1
o 0.001 o 0.001 o 0.001 o 0.001 0.008 o 0.001 o 0.001 0.019 o 0.001
1965.1 ± 698.5 46.8 ± 12.3 13.6 ± 3.5 39.3 ± 12.2 9.1 ± 4.8 77.4 ± 57.7 1110.3 ± 1141.8 390.8 ± 235.5 19.2 ± 9.1
0.010 o 0.001 o 0.001 o 0.001 o 0.001 0.011 o 0.001 0.999 o 0.001
472 34.2 ± 7.4 23.5 35.4 11.4 1.9 9.3 9.5 26.5 0.086 0.002 o 0.001 o 0.001 0.103 0.002 o 0.001 o 0.001 472 34.4 ± 7.2 23.7 82.4 10.0 1.5 8.3 12.3 30.5 472 33.7 ± 7.1 33.7 5.7 24.8 0.2 14.8 5.3 14.0 o 0.001 o 0.001 o 0.001 o 0.001 0.300 o 0.001 0.142 0.781
Q5 Q1
P-valueb Traditional north P-valueb Traditional south
Characteristics of the participants according to quintiles of four dietary patternsa
Table 3.
1718.4 ± 699.6 52.8 ± 13.4 12.1 ± 3.2 34.9 ± 13.6 9.1 ± 6.8 49.6 ± 37.3 499.1 ± 710.6 284.7 ± 184.3 17.7 ± 10.0
472 34.1 ± 7.4 25.0 58.5 15.7 0.6 8.7 8.3 23.9 0.221 o 0.001 o 0.001 o 0.001 0.236 0.003 0.432 0.176 472 33.3 ± 7.4 39.6 59.8 39.0 0.8 14.8 9.5 22.7
472 33.8 ± 7.0 35.8 35.2 32.2 1.9 17.0 8.5 25.2
Q1 Q1
Snack
P-valueb Q5
High protein
Q5
P-valueb
0.669 o 0.001 o 0.001 o 0.001 0.103 0.006 0.675 0.781
3 Association of dietary patterns with sociodemographic characteristics The characteristics of the young Chinese women across quintile categories of the dietary pattern scores are given in Table 3. One of the four dietary patterns is associated with the participants’ age. Women with high scores for the traditional south pattern (P o 0.001) were older. Women with high scores for the snack pattern (P o 0.001) and the high-protein pattern (P o0.001) were more likely to live in urban areas. Women with high scores for the traditional north pattern (P o0.001) and the snack pattern (P o 0.001) were more likely to live in the north. Education level and alcohol intake were associated with the four dietary patterns. Women with high scores for the snack pattern and the highprotein pattern were more likely to be current drinkers and have better education. General obesity and abdominal obesity increased across the quintile categories of the traditional north pattern (P o 0.001). However, no association was found in between the other three patterns and the prevalence of general obesity and abdominal obesity. Association of dietary patterns with nutrient intakes Women with higher scores for the traditional south pattern had higher intake of energy; higher percentage of energy from carbohydrates, fiber (g per day), vitamin C (mg per day), vitamin A (μg retinol equivalent per day), calcium (mg per day) and iron (mg per day); and lower percentage of energy from proteins and fats (Table 3). The traditional north pattern had higher intake of energy; higher percentage of energy from carbohydrates, vitamin C, fiber and iron; lower intake of vitamin A; and lower percentage of energy from proteins and fats. The snack pattern had higher intake of energy; higher percentage of energy from proteins, fiber, vitamin C, vitamin A, calcium and iron; and lower percentage of energy from carbohydrates. The high-protein pattern had higher intake of energy; higher percentage of energy from proteins and fats, fiber, vitamin C, vitamin A, calcium, and iron; and lower percentage of energy from carbohydrates. Association of dietary patterns with general and abdominal obesity Table 4 reports overall association between the dietary patterns and obesity. After adjusting for all confounders, women in the highest quintiles of the traditional south pattern were less likely to have general obesity (OR = 0.48; 95% CI 0.29–0.78) and abdominal obesity (OR = 0.64; 95% CI 0.46–0.90). The traditional north pattern was positively associated with general obesity (OR = 2.28; 95% CI 1.38–3.74) and abdominal obesity (OR = 2.32; 95% CI 1.66–3.24). The ORs for general obesity and abdominal obesity significantly increased according to the quintile categories of the traditional north pattern after adjustments for age, education, smoking habit, alcohol intake, living area, physical activity, annual household income per family member and energy intake. However, there was no association between the snack pattern or the high-protein pattern and obesity. DISCUSSION The present study identified four distinct dietary patterns among young Chinese women: traditional south; traditional north; snack; and high protein. The traditional south pattern was positively associated with a decreased risk of general and abdominal obesity. On the contrary, subjects who followed the traditional north pattern were found to have a greater risk of general and abdominal obesity. These results suggest that such dietary patterns are associated with general and abdominal obesity after adjusting for all confounders in young Chinese women. European Journal of Clinical Nutrition (2015) 1 – 6
Dietary patterns associated with obesity JG Zhang et al
4 Table 4.
Association of dietary patterns with general obesity and abdominal obesity across quintiles of dietary pattern scoresa P-valueb
General obesity Q1
Q3
Q5
Traditional south Crude 1.00 Model 1 1.00 Model 2 1.00
0.73 (0.47–1.14) 0.61 (0.38–0.96) 0.61 (0.38–0.96)
0.71 (0.46–1.12) 0.50 (0.31–0.81) 0.48 (0.29–0.78)
Traditional north Crude 1.00 Model 1 1.00 Model 2 1.00
1.08 (0.61–1.89) 1.08 (0.61–1.89) 1.08 (0.61–1.91)
Snack Crude Model 1 Model 2
1.00 1.00 1.00
High protein Crude Model 1 Model 2
1.00 1.00 1.00
P-valueb
Abdominal obesity Q1
Q3
Q5
0.138 0.004 0.002
1.00 1.00 1.00
0.96 (0.70–1.30) 0.78 (0.57–1.08) 0.78 (0.57–1.07)
1.01 (0.74–1.37) 0.70 (0.51–0.98) 0.64 (0.46–0.90)
0.808 0.033 0.007
2.50 (1.53–4.07) 2.28 (1.39–3.75) 2.28 (1.38–3.74)
o 0.001 o 0.001 o 0.001
1.00 1.00 1.00
1.56 (1.11–2.20) 1.57 (1.11–2.23) 1.64 (1.15–2.33)
2.70 (1.95–3.74) 2.36 (1.69–3.30) 2.32 (1.66–3.24)
o0.001 o0.001 o0.001
0.73 (0.46–1.17) 0.80 (0.49–1.28) 0.80 (0.49–1.28)
1.00 (0.64–1.54) 1.22 (0.77–1.92) 1.21 (0.76–1.93)
0.335 0.066 0.079
1.00 1.00 1.00
0.68 (0.50–0.93) 0.78 (0.57–1.06) 0.76 (0.56–1.05)
0.81 (0.60–1.09) 1.03 (0.75–1.41) 0.97 (0.70–1.34)
0.317 0.528 0.841
0.86 (0.53–1.38) 0.94 (0.58–1.53) 0.93 (0.57–1.52)
1.02 (0.64–1.63) 1.16 (0.72–1.87) 1.14 (0.70–1.87)
0.712 0.378 0.453
1.00 1.00 1.00
0.82 (0.60–1.11) 0.88 (0.64–1.21) 0.86 (0.63–1.18)
1.07 (0.79–1.44) 1.27 (0.93–1.73) 1.19 (0.86–1.63)
0.498 0.076 0.215
Abbreviations: CI, confidence interval; OR, odds ratio; Q, quintile. Model 1: adjusted for age (continuous), education (low, medium and high), living area (urban/rural), annual household income per family member (continuous), physical activity (continuous), current smoker (yes/no), current drinker (yes/no). Model 2: model 1 additionally adjusted for total energy intake (continuous). aValues are ORs (95% CI). bP-value for trend was calculated using the median value of each quintile as a continuous variable.
The traditional Chinese diet includes large amounts of cereals and vegetables and small amounts of animal-source foods. Such a diet is low in fat, low in energy density, high in carbohydrates and high in dietary fiber.27 In this study, the traditional south pattern and the traditional north pattern had strong positive correlations with Chinese traditional foods. In northern China, people are more likely to eat staple foods made from flour, such as wheat noodles and steamed buns. The people in southern China prefer meat and vegetable dishes with rice. We therefore labeled these two patterns as traditional, which were similar to the dietary patterns of Chinese adults reported by other research.28,29 The snack pattern in the present study had high loadings mostly for convenience foods, including fruits, milk, eggs and nuts. Similar dietary patterns have been identified elsewhere.30,31 Since 2004, snacking has been rapidly increasing as a dietary component in China. However, to date snacking has not been dominated by savory snacks, sugary beverages and other unhealthy foods as in the West.32 In China, fruit has been one of the more popular snack items.14 The high-protein pattern was a special pattern that is rarely found in other countries. It is characterized by a high consumption of fungi, algae, legumes, seafoods and pork, which are rich in high-quality protein. In this study, we found an inverse relationship between a traditional south pattern and a risk of general and abdominal obesity. This is consistent with a study in Jiangsu, China,33 which showed that the rice-based pattern was negatively associated with weight gain in Chinese adults. It is also consistent with a study from Brazil,34 which showed that a traditional dietary pattern with a high intake of rice and beans was related to a lower risk of obesity. However, it is inconsistent with other rice-based patterns in Asian populations. A traditional Japanese pattern characterized by a high intake of rice, miso soup and soy products was positively associated with obesity among women aged 18–20 years.16 The Korean rice–vegetable dietary pattern characterized by a greater intake of steamed rice, tofu, kimchi, vegetables, dried anchovy and seaweeds was associated with an increased risk of abdominal obesity among women aged 40–69 years.17 Although these discrepancies are attributed to differences in populations, study European Journal of Clinical Nutrition (2015) 1 – 6
designs and analytic methods, the exact explanations have yet to be clarified. One explanation could be the different varieties and cooking methods of rice consumed in these places, which differ in amounts of nutrition and, more importantly, the type of starch, which in turn is related to glycemic index.35,36 The protective role of the Chinese traditional south pattern may be owing to the low glycemic index of this stir-fried food combination, rich micronutrient intakes and greater vegetable intakes. Moreover, the consumption of this diet appears to be a marker of a diet prepared more consistently at home, with lower intakes of fast foods, milk and cakes, and less frequent eating away from home. Interestingly, another traditional dietary pattern, the traditional north pattern, was associated with an increased risk of general and abdominal obesity. This is similar to the noodle dietary pattern in Korean women.17 Unlike the traditional south pattern, the northern diet is less diverse, mainly including wheat foods, such as noodles, dumplings, steamed buns and flat cakes. One characteristic of the traditional north pattern is a high carbohydrate intake. Other studies have also found that a highcarbohydrate diet can increase the risk of general obesity and abdominal obesity.37–39 To date, however, the mechanism underlying a carbohydrate-induced risk for obesity is unclear. It is likely that carbohydrate intake may alter lipid profiles, such as an increase in triglycerides and/or a decrease in high-density lipoprotein cholesterol, leading to increased obesity.40 Further studies on the detailed mechanism are needed. Another characteristic is that the traditional north pattern is comprised of more energy intake and less intake of micronutrients; that is, it is a low-nutrient diet. The micronutrients such as zinc, iron, vitamin C and vitamin A may have an important role in fat deposition and the pathogenesis of obesity.41 The lownutrient diet appears to favor the development of insulin resistance in the central nervous system, which might in part be responsible for leptin resistance and accordingly promotes pleasurable responses to foods.42,43 This study has several limitations. First, the results do not show the causal or resultant relationship between dietary patterns and © 2015 Macmillan Publishers Limited
Dietary patterns associated with obesity JG Zhang et al
5 risk of obesity owing to the cross-sectional data. Second, the statistical methods used to define the dietary patterns are somewhat subjective, including the consolidation of food items into food groups, the number of factors to extract and the labeling of the patterns.44 Third, dietary patterns could be different among studies because of different ethnicity/culture or objectives. It is difficult to compare these findings with other studies. Fourth, the 24-h dietary recall method cannot generally evaluate usual dietary intake. Despite these limitations, this is the first study to reveal the relationships between dietary patterns and risk of general and abdominal obesity in young Chinese women using large survey data. In China, women are in charge of the diet at home. The present study usefully provides a better understanding of the dietary habits of young women, which relates not only to their health but also to that of their family members, especially their children. In conclusion, we identified four unique dietary patterns in young Chinese women: traditional south; traditional north; snack; and high protein. Our findings indicate that the traditional south pattern of rice as the major staple food in conjunction with pork and vegetable dishes is associated with lower risk of general and abdominal obesity. The traditional north pattern of a high intake of wheat, other cereals and tuber is positively associated with general and abdominal obesity. This provides important information for interventions and policies addressing obesity prevention in young Chinese women. Further prospective studies are needed to better understand the relationships. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS This research uses data from the CHNS. We thank the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention; the Carolina Population Center (5 R24 HD050924), University of North Carolina at Chapel Hill, the National Institutes of Health (NIH) (R01-HD30880, DK056350, R24 HD050924, and R01HD38700); and the Fogarty International Center, NIH, for financial support for the CHNS data collection and analysis of files from 1989 to 2011, and for future surveys.
REFERENCES 1 Rokholm B, Baker JL, Sorensen TI. The levelling off of the obesity epidemic since the year 1999--a review of evidence and perspectives. Obes Rev 2010; 11: 835–846. 2 Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012; 70: 3–21. 3 Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr 2012; 10: 22. 4 Xi B, Liang Y, He T, Reilly KH, Hu Y, Wang Q et al. Secular trends in the prevalence of general and abdominal obesity among Chinese adults, 1993-2009. Obes Rev 2012; 13: 287–296. 5 Nguyen DM, El-Serag HB. The epidemiology of obesity. Gastroenterol Clin North Am 2010; 39: 1–7. 6 Bray GA, Paeratakul S, Popkin BM. Dietary fat and obesity: a review of animal, clinical and epidemiological studies. Physiol Behav 2004; 83: 549–555. 7 Boeing H, Bechthold A, Bub A, Ellinger S, Haller D, Kroke A et al. Critical review: vegetables and fruit in the prevention of chronic diseases. Eur J Nutr 2012; 51: 637–663. 8 Anderson JW, Baird P, Davis Jr RH, Ferreri S, Knudtson M, Koraym A et al. Health benefits of dietary fiber. Nutr Rev 2009; 67: 188–205. 9 Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13: 3–9. 10 Van Horn L. Eating pattern analyses: the whole is more than the sum of its parts. J Am Diet Assoc 2011; 111: 203. 11 Tucker KL. Dietary patterns, approaches, and multicultural perspective. Appl Physiol Nutr Metab 2010; 35: 211–218. 12 Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc 2004; 104: 615–635.
© 2015 Macmillan Publishers Limited
13 Popkin BM. Synthesis and implications: China's nutrition transition in the context of changes across other low- and middle-income countries. Obes Rev 2014; 15 (Suppl 1): 60–67. 14 Zhai FY, Du SF, Wang ZH, Zhang JG, Du WW, Popkin BM. Dynamics of the Chinese diet and the role of urbanicity, 1991-2011. Obes Rev 2014; 15 (Suppl 1): 16–26. 15 Rezazadeh A, Rashidkhani B. The association of general and central obesity with major dietary patterns in adult women living in Tehran, Iran. ARYA Atheroscler 2010; 6: 23–30. 16 Okubo H, Sasaki S, Murakami K, Kim MK, Takahashi Y, Hosoi Y et al. Three major dietary patterns are all independently related to the risk of obesity among 3760 Japanese women aged 18-20 years. Int J Obes (Lond) 2008; 32: 541–549. 17 Kim JH, Lee JE, Jung IK. Dietary pattern classifications and the association with general obesity and abdominal obesity in Korean women. J Acad Nutr Diet 2012; 112: 1550–1559. 18 Quatromoni PA, Copenhafer DL, D'Agostino RB, Millen BE. Dietary patterns predict the development of overweight in women: The Framingham Nutrition Studies. J Am Diet Assoc 2002; 102: 1239–1246. 19 Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB. Dietary patterns and changes in body weight in women. Obesity (Silver Spring) 2006; 14: 1444–1453. 20 Boggs DA, Palmer JR, Spiegelman D, Stampfer MJ, Adams-Campbell LL, Rosenberg L. Dietary patterns and 14-y weight gain in African American women. Am J Clin Nutr 2011; 94: 86–94. 21 Popkin BM, Du S, Zhai F, Zhang B. Cohort Profile: The China Health and Nutrition Survey--monitoring and understanding socio-economic and health change in China, 1989-2011. Int J Epidemiol 2009; 39: 1435–1440. 22 Zhang B, Zhai FY, Du SF, Popkin BM. The China Health and Nutrition Survey, 1989-2011. Obes Rev 2014; 15 (Suppl 1): 2–7. 23 Popkin BM. Reducing meat consumption has multiple benefits for the world's health. Arch Intern Med 2009; 169: 543–545. 24 WHO Expert Committee Physical status: the use and interpretation of anthropometry. Tech Rep Ser 1995, 854: 1–452. 25 Zhou BF. Cooperative Meta-Analysis Group of the Working Group on Obesity in China Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 2002; 15: 83–96. 26 Yang Y, Wang G, Pan X. Chinese Food Composition Tables 2002. Peking University Medical Press: Beijing, China, 2002. 27 Du SF, Wang HJ, Zhang B, Zhai FY, Popkin BM. China in the period of transition from scarcity and extensive undernutrition to emerging nutritionrelated non-communicable diseases, 1949-1992. Obes Rev 2014; 15 (Suppl 1): 8–15. 28 Wang D, He Y, Li Y, Luan D, Yang X, Zhai F et al. Dietary patterns and hypertension among Chinese adults: a nationally representative cross-sectional study. BMC Public Health 2011; 11: 925. 29 Batis C, Sotres-Alvarez D, Gordon-Larsen P, Mendez MA, Adair L, Popkin B. Longitudinal analysis of dietary patterns in Chinese adults from 1991–2009. Br J Nutr 2014; 111: 1441–1451. 30 Gao X, Yao M, McCrory MA, Ma G, Li Y, Roberts SB et al. Dietary pattern is associated with homocysteine and B vitamin status in an urban Chinese population. J Nutr 2003; 133: 3636–3642. 31 Tucker KL, Chen H, Hannan MT, Cupples LA, Wilson PW, Felson D et al. Bone mineral density and dietary patterns in older adults: the Framingham Osteoporosis Study. Am J Clin Nutr 2002; 76: 245–252. 32 Wang Z, Zhai F, Zhang B, Popkin BM. Trends in Chinese snacking behaviors and patterns and the social-demographic role between 1991 and 2009. Asia Pac J Clin Nutr 2012; 21: 253–262. 33 Shi Z, Yuan B, Hu G, Dai Y, Zuo H, Holmboe-Ottesen G. Dietary pattern and weight change in a 5-year follow-up among Chinese adults: results from the Jiangsu Nutrition Study. Br J Nutr 2011; 105: 1047–1054. 34 Sichieri R. Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res 2002; 10: 42–48. 35 Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr 2002; 76: 5–56. 36 Chiu YT, Stewart ML. Effect of variety and cooking method on resistant starch content of white rice and subsequent postprandial glucose response and appetite in humans. Asia Pac J Clin Nutr 2013; 22: 372–379. 37 Park SH, Lee KS, Park HY. Dietary carbohydrate intake is associated with cardiovascular disease risk in Korean: analysis of the third Korea National Health and Nutrition Examination Survey (KNHANES III). Int J Cardiol 2010; 139: 234–240. 38 Lin H, Bermudez OI, Tucker KL. Dietary patterns of Hispanic elders are associated with acculturation and obesity. J Nutr 2003; 133: 3651–3657.
European Journal of Clinical Nutrition (2015) 1 – 6
Dietary patterns associated with obesity JG Zhang et al
6 39 Kim J, Jo I, Joung H. A rice-based traditional dietary pattern is associated with obesity in Korean adults. J Acad Nutr Diet 2012; 112: 246–253. 40 Mittendorfer B, Sidossis LS. Mechanism for the increase in plasma triacylglycerol concentrations after consumption of short-term, high-carbohydrate diets. Am J Clin Nutr 2001; 73: 892–899. 41 Garcia OP, Ronquillo D, Caamano Mdel C, Camacho M, Long KZ, Rosado JL. Zinc, vitamin A, and vitamin C status are associated with leptin concentrations
European Journal of Clinical Nutrition (2015) 1 – 6
and obesity in Mexican women: results from a cross-sectional study. Nutr Metab (Lond) 2012; 9: 59. 42 Isganaitis E, Lustig RH. Fast food, central nervous system insulin resistance, and obesity. Arterioscler Thromb Vasc Biol 2005; 25: 2451–2462. 43 Garcia OP, Long KZ, Rosado JL. Impact of micronutrient deficiencies on obesity. Nutr Rev 2009; 67: 559–572. 44 Martinez ME, Marshall JR, Sechrest L. Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol 1998; 148: 17–19.
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