Total and abdominal obesity among rural Chinese women and the association with hypertension

Nutrition 28 (2012) 46–52

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Total and abdominal obesity among rural Chinese women and the association with hypertension Xingang Zhang M.D. a, Shuang Yao B.M. b, Guozhe Sun M.D. a, Shasha Yu M.D. a, Zhaoqing Sun M.D. b, Liqiang Zheng M.D. b, Changlu Xu M.D. b, Jue Li M.D. c, Yingxian Sun M.D., Ph.D a, * a b c

Department of Cardiology, The First Hospital of China Medical University, Shenyang, People’s Republic of China Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China Heart, Lung and Blood Vessel Center, Tongji University, Shanghai, People’s Republic of China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 June 2010 Accepted 1 February 2011

Objective: Obesity increases the risk of hypertension and other chronic diseases, which are little known in rural China. This study aimed to investigate the epidemiologic features and the association with hypertension of obesity in rural Chinese women. Methods: A cross-sectional survey was conducted during 2004 through 2006, which used a multistage cluster sampling method to select a representative sample in Liaoning Province, China. In total 23 178 rural participants at least 35 y of age were examined (the percentage of subjects >64 y old was 14.5%). Data on demographic variables (age, sex, and race), smoking status, use of alcohol, physical activity, and education level were obtained by interview. Overweight and obesity were defined according to the World Health Organization classification. Hypertension was defined according to the criteria established by the Seventh Report of the Joint National Committee, and untreated hypertensive subjects were further classified into three subtypes: isolated systolic hypertension, isolated diastolic hypertension, and systolic and diastolic hypertension. Multivariable models and performed Poisson logistic regression analysis were used to determine associations among body mass index (BMI), waist circumference, and variables. Results: Overall, the prevalences of overweight and obesity were 24.4% and 2.7%, respectively, as defined by BMI, whereas the prevalences were 48.6% and 4.9% as defined by waist circumference. Poisson regression revealed that high levels of physical activity (defined by BMI, moderate: prevalence ratio [PR] 0.976, 95% confidence interval [CI] 0.965–0.988, high: PR 0.985, 95% CI 0.971– 0.999; defined by waist circumference, moderate: PR 0.955, 95% CI 0.944–0.965, high: PR 0.973, 95% CI 0.960–0.985) and current smoking status (defined by BMI, PR 0.950, 95% CI 0.938–0.962; defined by waist circumference, PR 0.966, 95% CI 0.954–0.978) were protective factors and ethnicity was a risk factor (defined by BMI, Mongolian nationality: PR 1.042, 95% CI 1.030–1.054; defined by waist circumference, PR 1.043, 95% CI 1.033–1.054) for overweight or obese participants. There were other risk factors for overweight or obese participants such as high levels of education defined by BMI (PR 1.033, 95% CI 1.010–1.058) and diet score defined by waist circumference (PR 1.004, 95% CI 1.000–1.008). After adjustment, BMI and waist circumference were associated with the greatest likelihood of systolic and diastolic hypertension (for BMI
30 kg/m2, PR 2.455, 95% CI 1.786–3.374; for waist circumference
88 cm, PR 1.517, 95% CI 1.133–2.031). BMI was more related to isolated diastolic hypertension than to isolated systolic hypertension, whereas waist circumference was more related to isolated systolic hypertension than to isolated diastolic hypertension. Conclusion: Although the prevalence of overweight and obesity as defined by BMI was low, it was relatively high as defined by waist circumference in rural Chinese women. High levels of physical activity and current smoking status had negative relations to overweight or obesity, whereas ethnicity, high levels of education, and diet score showed positive relations. Obese women defined by BMI or waist circumference had an increased risk of hypertension. Ó 2012 Elsevier Inc. All rights reserved.

Keywords: Body mass index Waist circumference Hypertension Risk factor Women Rural population Prevalence

Support for this study was provided by grants 2008225003 and 2003225003 from the Key Technology Research and Development program of Liaoning Province, China. 0899-9007/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.nut.2011.02.004

* Corresponding author. Tel.: þ86-24-8328-2688; fax: þ86-24-8328-2688. E-mail addresses: [email protected] or [email protected] (Y. Sun).

X. Zhang et al. / Nutrition 28 (2012) 46–52

Introduction Obesity continues to be a leading public health concern, with an alarming increasing rate worldwide [1–3]. It has long been known that obesity is a high risk factor for cardiovascular diseases such as hypertension, stroke, and coronary heart disease [4–7] and that weight decrease in overweight or obese individuals has beneficial health effects. The usual anthropometric measurements of adiposity are body mass index (BMI) and waist circumference, reflecting total body fat and abdominal fat deposition, respectively, and lower educational attainment, high intakes of energy and carbohydrate, and lower levels of occupational and commuting physical activity are related to a greater incidence of obesity. Few studies have clarified which of these anthropometric parameters has the strongest relation to blood pressure or hypertension. In recent years, the prevalences of obesity and hypertension have increased with the fast socioeconomic developments and associated sociodemographic changes in China. For example, the combined prevalence of overweight and obesity has increased by 49.3%, from 14.6% in 1992 to 21.8% in 2002 [8]. The estimated number of hypertension cases in Chinese adults has increased from 30 million in 1960 to 59 million in 1980 and to 94 million in 1990 [9]. There is a positive association between obesity and hypertension. In the Framingham Study, it was found that a 10% increase in body weight explains a 7-mmHg increase in systolic blood pressure (SBP) in the population at large [10]. It has also been found that every kilogram excess in body weight that is lost is associated with decreases of 0.33 and 0.43 mmHg in SBP and diastolic blood pressure (DBP), respectively [11]. The long-term effect of weight control has demonstrated that decreasing weight can lower the odds of hypertension by 77% [12]. One study found that in countries with a relatively low gross national product, the prevalence of obesity is about 1.5 to 2 times higher in women than in men [13]. However, what is not generally recognized is the fact that obesity also has a greater impact on health outcomes for women than for men and that women appear to be at disproportionate risk for some complications of obesity. This impact is seen in the physical, reproductive, psychological, and social well-being of women compared with men [14]. The reasons women are at risk for obesity and its complications are an important area for research because these can provide insights into the mechanisms that underlie obesity and increase the understanding of obesity. However, little is known about obesity in rural Chinese women. The objectives of the present study were to determine the epidemiologic features of overweight and obesity using BMI and waist circumference and associated factors in rural Chinese women. In addition, overweight and obesity were studied for their association with hypertension subtypes. Materials and methods

47

23 178 individuals from these sampling frames were included in the present survey. All residents selected were at least 35 y old. The response rate was 85.2%. For the sample size calculation, the following parameters were used: a ¼ 0.05, b ¼ 0.10, error margin ¼ 10%, design effect ¼ 2; and the planning rate for possible occasional losses and refusals was 10%. Measurements The baseline survey was conducted by well-trained local doctors using home visits. Before the survey was conducted, the research staff had training, which included the purpose of this research, how to administer the questionnaire, the method of measurement, and the study procedures. After this training, a strict test was conducted, and only those who scored perfectly on the test could become investigators. During data collection, the inspectors had further instructions and support. Further, reliability analyses were used for data collected by these investigators, which revealed higher inter- and intraobserver agreements. During the interview and examination, doctors administered a standard questionnaire including questions related to lifestyle factors. Data on demographic variables (age, sex, and race), smoking status, use of alcohol, physical activity, and education level were obtained by interview. At the study site, local doctors measured height and weight using a standardized protocol [15]. Height was measured to the nearest 0.5 cm, without shoes. Weight was measured with a lever balance to the nearest 100 g, without shoes, in light undergarments. Waist circumferences were measured to the nearest 0.5 cm. BMI was calculated as weight in kilograms divided by the square of the height in meters. The trained and certified observers, using an American Heart Association protocol, performed three BP measurements with the participant in a seated posture after 5 min of rest. The participants were advised to avoid alcohol consumption, cigarette smoking, coffee/tea consumption, and exercise for at least 30 min before these measurements were taken. The research staff used a standardized electronic sphygmomanometer (HEM-741C; Omron, Tokyo, Japan) and one of four cuff sizes (pediatric, regular adult, large, or thigh) chosen on the basis of arm circumference. For most age-related comparisons, participants were separated into four groups according to age (35–44, 45–54, 55–64, and
65 y). For ethnicity, based on self-reported information, participants were grouped as Han nationality, Mongolian, and others. Participants’ reported education levels (no more than primary school, middle school, and at least high school) were used as indicators of socioeconomic status. Physical activity included occupational and leisure-time physical activities. A detailed description of the methods has been presented elsewhere [16]. Occupational and leisure-time physical activities were merged and regrouped into three categories: 1) low was defined as subjects who reported light levels of occupational and leisure-time physical activities; 2) moderate was defined as subjects who reported moderate or high levels of occupational or leisure-time physical activity; and 3) high was defined as subjects who reported a moderate or high level of occupational and leisure-time physical activities. The dietary pattern was assessed as a part of the questionnaire and the evaluation of nutritional habits was based on a validated food-frequency questionnaire [17]. The questionnaire included questions on average consumption during the previous year. All participants were asked to report the average intake (per week) of several food items that they consumed. Then, the frequency of consumption was quantified approximately in terms of grams per week a food was consumed (vegetable consumption: rarely ¼ 3, <250 g ¼ 2, 250–500 g ¼ 1,
500 g ¼ 0; meat consumption including red meat, fish, and poultry: rare ¼ 0, <250 g ¼ 1, 250–1000 g ¼ 2,
1000 g ¼ 3). From the dietary pattern that reported weekly frequency consumption of various food groups, a special diet score (vegetable consumption score plus meat consumption score) was calculated for each participant (range 0–6). Higher values of this diet score indicated higher meat consumption and lower vegetable consumption and greater adherence to a Westernized diet, whereas lower values indicated adherence to the Chinesediet. Similar methods for calculating diet score may be found in ATTICA study [18].

Study population Definitions The procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation of China Medical University. A large-scale cross-sectional survey was conducted from 2004 through 2006 in the rural areas of Fuxin County, Liaoning Province. In this study, a stratified multistage sampling design was used, and selections were made from sampling units based on geographic area, sex, and age. There were 35 primary sampling units (towns), which contained 382 rural villages. Eight sampling frames (83 rural villages) from the primary sampling units were sampled from different geographic areas (according to the number of people, three sampling frames were selected from a southern region, two from an eastern region, but only one small town was selected from other regions), and

According to World Health Organization criteria [1], the BMI is categorized into three groups as normal (<25 kg/m2), overweight (25–<30 kg/m2), and obese (
30 kg/m2). When waist circumference was considered, subjects with waist circumferences of at least 80 cm and smaller than 88 cm were classified as overweight and those with a waist circumference of at least 88 cm were classified as obese [1]. Hypertension was considered present if any of the following conditions were met: SBP at least 140 mmHg, DBP at least 90 mmHg, or reported use of a medication for hypertension and the classification of BP according to the Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [19]; untreated hypertensive

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X. Zhang et al. / Nutrition 28 (2012) 46–52

Table 1 Basic characteristics of study population* Variable

Body mass index (kg/m2) <25

Age (y) Ethnicity (%) Han nationality Mongolian nationality Physical activity (%) Low Moderate High Education (%) Primary school Middle school
High school Current smoking status (%) Current drinking status (%) Height (cm) SBP (mmHg) DBP (mmHg) Diet scorey

Waist circumference (cm)
25

<88

P

51.5 (51.3–51.7)

50.5 (50.2–50.7)

79.2 19.5

74.7 23.9

32.3 43.6 24.1

31.6 43.1 25.4

53.6 42.4 4.0 17.6 6.1 160.6 131.9 80.9 3.7

49.5 45.2 5.2 13.0 5.5 159.2 139.7 86.1 3.7

<0.001 <0.001


88

P

51.3 (51.1–51.6)

51.1 (50.9–51.3)

80.5 18.5

75.9 22.7

30.6 45.6 23.9

33.4 41.7 24.9

53.0 43.0 4.0 17.8 6.0 159.5 130.6 80.6 3.7

52.1 43.3 4.6 15.1 5.9 160.8 136.9 83.8 3.7

0.115 <0.001

<0.001

0.127

<0.001

(160.5–160.6) (131.5–132.2) (80.7–81.1) (3.7–3.8)

<0.001 0.124 <0.001 <0.001 <0.001 0.391

(159.1–159.3) (139.1–140.3) (85.7–86.4) (3.7–3.7)

(159.4–159.6) (130.2–131.1) (80.4–80.8) (3.7–3.7)

0.050

<0.001 0.723 <0.001 <0.001 <0.001 0.189

(160.7–160.9) (136.5–137.4) (83.6–84.1) (3.7–3.8)

DBP, diastolic blood pressure; SBP, systolic blood pressure * Data presented as prevalence ratio (95% confidence interval). y The calculation method was vegetable consumption score plus meat consumption score and its range was 0 to 6. Higher values of this diet score indicate higher meat consumption and lower vegetable consumption and greater adherence to a Westernized diet, whereas lower values indicate adherence to a Chinese diet.

subjects were further classified into three subtypes: isolated systolic hypertension (ISH; SBP
140 and DBP <90 mmHg), isolated diastolic hypertension (IDH; DBP
90 and SBP <140 mmHg), and systolic–diastolic hypertension (SDH; SBP
140 and DBP
90 mmHg) [20]. Drinking habit status was assessed by alcohol consumption; alcohol consumption was defined as the weekly consumption of beer, wine, and hard liquor converted to grams of alcohol. Current drinking was defined as an alcohol consumption of at least 8 g/wk [21]. Smoking was defined as at least one cigarette per day and for at least 1 y [22].

CI 23.4–23.5) and 80.0 cm (95% CI 79.9–80.1), respectively. Subjects with high BMI values had high education and high BP levels in contrast to those with low education levels, short stature, and lower smoking status. Subjects with a large waist circumference had taller stature and high BP levels and lower smoking status. Compared with low levels of BMI and waist circumference, the proportion of Mongolian nationality was higher in people with high BMI and waist circumference values.

Statistical analysis To account for the clustering and stratification of the survey design and to adjust for non-response, the data were weighted to match the age and gender distribution of the 2000 China population census data of residents at least 35 y old, unless otherwise stated. The weighting factor was based on the probability of selection in each cluster. Therefore, all prevalences related to the total 2000 China population at least 35 y old. All data analyses were conducted by use of PASW Statistics 18.0 (SPSS Inc., Chicago, IL, USA). Continuous variables were presented as mean values and 95% confidence intervals (95% CIs). Categorical variables were presented as frequencies and a test for trend was used to analyze the significance of an increase or decrease in prevalence across BMI and waist circumference groups. Comparisons of continuous variables between groups were performed by analysis of t test. To evaluate the association between overweight and obesity and associated factors, Poisson regression analysis was applied to estimate the prevalence ratio (PR) of overweight and obesity through levels of various explanatory factors. The adjusted PR was presented with a 95% CI. To estimate the effect of combined lifestyle variables on hypertension, some low-risk factors were defined as the presence of any of the following components: no current smoking, no current drinking, at least moderate levels of physical activity, and diet scores no higher than 2. Assuming there was a causal relation between risk factors and hypertension, the results of combined lifestyle variables with hypertension were analyzed as adjusted by BMI and waist circumference, respectively. For all comparisons, P < 0.05 was considered statistically significant.

Results Basic characteristics of study population The characteristics of the survey participants in this study, as stratified by BMI and waist circumference, are listed in Table 1. All subjects selected were 35 to 99 y old and the average ages of the selected rural Chinese women were 51.2 y (95% CI 51.1–51.4). Mean BMI and mean waist circumference were 23.4 kg/m2 (95%

Prevalence and associated factors of overweight and obesity As presented in Table 2, the prevalence estimates of overweight and obesity categories were calculated by age-specific proportions. There were 35.8% subjects 35 to 44 y old, 29.6% subjects 45 to 54 y old, 20.1% subjects 55 to 64 y old, and 14.5% subjects older than 65 y. Using BMI criteria, approximately 27.1% of rural Chinese women had overweight or obesity. The corresponding figure was 53.5% by waist circumference criteria. Furthermore, considerable age differences were observed. Using different criteria, the highest prevalence of overweight was in subjects 45 to 54 y old; however, at 55 y and older, the prevalence decreased as age increased. For obesity, the highest prevalence was in subjects 55 to 64 y old. Table 3 presents the results of Poisson regression analyses of overweight and obesity defined by BMI and waist circumference. Table 2 Prevalence of overweight and obesity using different criteria by age group in rural Chinese women* Age (y)

Body mass indexy Overweight

Obesity

Overweight

Obesity

Total 35–44 45–54 55–64
65

5653 1952 1917 1218 566

620 221 163 156 80

11 273 3955 3520 2324 1474

1126 334 327 303 162

* y z

(24.4) (23.5) (28.0) (26.1) (16.8)

Waist circumferencez

(2.7) (2.7) (2.4) (3.3) (2.4)

(48.6) (47.7) (51.3) (49.8) (43.8)

Data presented as number of subjects (percentage). Overweight defined as 25 to <30 kg/m2, and obesity as
30 kg/m2. Overweight defined as 80 to <88 cm, obesity as
88 cm.

(4.9) (4.0) (4.8) (6.5) (4.8)

X. Zhang et al. / Nutrition 28 (2012) 46–52

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Table 3 Poisson regression analysis of overweight and obesity and associated factors in rural Chinese women* Variables

Age (y) 35–44 45–54 55–64
65 Ethnicity Han nationality Mongolian nationality Others Physical activity Low Moderate High Education Primary school Middle school
High school Smoking status No Yes Drinking status No Yes Diet score (per 1 unit)

Body mass index

Waist circumference

PR (95% CI)

P

PR (95% CI)

P

1.000 1.038 1.034 0.949

(reference) (1.026–1.050) (1.019–1.049) (0.933–0.965)

<0.001 <0.001 <0.001

1.000 1.029 1.026 0.964

(reference) (1.018–1.040) (1.013–1.039) (0.948–0.980)

<0.001 <0.001 <0.001

1.000 (reference) 1.042 (1.030–1.054) 1.035 (0.994–1.078)

<0.001 0.095

1.000 (reference) 1.043 (1.033–1.054) 1.071 (1.034–1.109)

<0.001 <0.001

1.000 (reference) 0.976 (0.965–0.988) 0.985 (0.971–0.999)

<0.001 0.033

1.000 (reference) 0.955 (0.944–0.965) 0.973 (0.960–0.985)

<0.001 <0.001

1.000 (reference) 1.016 (1.005–1.027) 1.033 (1.010–1.058)

0.004 0.006

1.000 (reference) 1.006 (0.966–1.016) 1.011 (0.990–1.032)

0.244 0.323

1.000 (reference) 0.950 (0.938–0.962)

<0.001

1.000 (reference) 0.966 (0.954–0.978)

<0.001

1.000 (reference) 1.007 (0.986–1.027) 0.999 (0.995–1.003)

0.524 0.748

1.000 (reference) 1.009 (0.990–1.029) 1.004 (1.000–1.008)

0.332 0.035

CI, confidence interval; PR, prevalence ratio * Adjusted for age, ethnicity, physical activity, education, smoking status, drinking status, and diet.

By different criteria, a positive association was observed between age and overweight or obesity when age was younger than 65 y, whereas there was an inverse association when age at least 65 y. Mongolian nationality appeared to indicate a 1.0-fold higher risk of gaining weight than Han nationality. When levels of physical activity were considered, an inverse relation between levels of physical activity and overweight and obesity was observed. Subjects with higher levels of education were more likely to overweight and obese compared with subjects with a low education level only by BMI criteria. Overweight or obesity was found to be less in current smokers than in those who did not smoke. A Westernized diet seemed to be a risk factor for overweight or obesity by waist circumference alone.

Prevalence and association with overweight and obesity of hypertension subtypes The prevalence of hypertension was 38.6%, and among untreated hypertensive women, the prevalences of ISH, IDH, and

SDH were 10.6%, 5.8%, and 14.9%, respectively. With increasing of levels of BMI and waist circumference, the prevalence of hypertension and hypertension subtypes increased. The IDH, SDH, and hypertension statuses seemed to be more prevalent in overweight or obese subjects defined by BMI; however, an ISH status was more prevalent in obese people defined by waist circumference (Table 4). Of the untreated hypertensive women with total overweight or obesity (Fig. 1A), 27.7% had ISH, 18.6% IDH, and 53.7% SDH overall across all age groups; for untreated hypertensive women with abdominal overweight or obesity (Fig. 1B), there were comparable distributions of hypertensive subtypes by age, with ISH in 32.2%, IDH in 17.8%, and SDH in 50.0% overall across all age groups. Table 5 lists the estimates of the relative risk of hypertension for the combined effect of the low-risk factors (low-risk diet, high physical activity, no current smoking, and no current drinking). Subjects with low-risk behaviors had a lower risk of hypertension compared with the high-risk group (women who were smoking, drinking, less physically active, and had high

Table 4 Prevalence of ISH, IDH, and hypertension by body mass index and waist circumference in rural Chinese women*

Body mass index (kg/m2) <25 25–29.9
30 P for trend Waist circumference (cm) <80 80–87.9
88 P for trend

ISH

IDH

SDH

Hypertension

1616 (10.4) 533 (11.2) 55 (12.1) 0.065

815 (5.3) 354 (7.4) 40 (8.8) <0.001

1945 (12.5) 978 (20.6) 161 (35.3) <0.001

5765 (34.1) 2763 (48.9) 420 (67.7) <0.001

978 (9.8) 1108 (11.2) 118 (14.1) <0.001

532 (5.3) 626 (6.3) 51 (6.1) 0.007

1183 (11.9) 1681 (17.0) 220 (26.4) <0.001

3492 (32.4) 4775 (42.4) 681 (60.5) <0.001

IDH, isolated diastolic hypertension; ISH, isolated systolic hypertension; SDH, systolic and diastolic hypertension * Data presented as number of subjects (percentage).

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X. Zhang et al. / Nutrition 28 (2012) 46–52

Fig. 1. Frequency distribution of hypertension subtypes by age in untreated individuals with total overweight or obesity (A) and those with abdominal overweight or obesity (B). Numbers at the tops of bars represent the overall percentage distribution of all subtypes of untreated hypertension in the age group. No shading, isolate systolic hypertension (systolic blood pressure
140 mmHg and diastolic blood pressure <90 mmHg); gray shading, isolated diastolic hypertension (systolic blood pressure <140 mmHg and diastolic blood pressure
90 mmHg); black shading, systolic hypertension (systolic blood pressure
140 mmHg and diastolic blood pressure
90 mmHg).

scores for a healthy pattern). Further, with the decrease of risk behaviors, the risks of hypertension were gradually decreased. Nearly 40% of the risk for hypertension was decreased in subjects with four low-risk factors. Table 6 presents the association among BMI, waist circumference, and hypertension subtypes from a predictive Poisson regression model. Compared with normal-weight subjects, overweight and obese subjects had a significantly higher risk to develop hypertension. Furthermore, in the untreated hypertensive women, different associations between overweight and obesity and hypertension subtypes were observed. After adjustment, BMI and waist circumference were associated with greatest likelihood of SDH. BMI was more related to IDH than to ISH, whereas waist circumference was more related to ISH to IDH.

remains a major concern of public health issues. Discussions about the epidemic of obesity had often used the future tense for rural Chinese. We previously showed that current rates of overweight and obesity were already unacceptably high in rural Chinese women, although that was much lower than in other countries such as Australia, where the prevalence of overweight or obesity was almost 60% defined by BMI or waist circumference [23]. In South Korea, the prevalences of overweight or obesity were 28.3% and 38.5% as defined by BMI and waist circumference, respectively [24]. The discrepancies between the present study and others may due to different related factors. As has been pointed out, the global epidemic of obesity has resulted mainly from societal factors that promote sedentary lifestyles and the consumption of high-fat, energy-dense diets [25]. A previous study in China also showed that high intakes of energy and carbohydrate and lower levels of occupational and commuting physical activity were related to a greater incidence of being overweight [26]. In this study, we also found that increased physical activity decreased the risk of total and abdominal obesity, but that a Westernized diet increased the risk of abdominal obesity alone. From a large

Discussion The results of this study indicated that the prevalence of overweight and obesity was extremely high by waist circumference criteria. Moreover, using the BMI threshold, although the prevalence of overweight and obesity was slightly lower, it

Table 5 Effect of combined low-risk behaviors in relation to risk of hypertension by body mass index and waist circumference in rural Chinese women Adjusted for body mass index*

Variables

1 2 3 4

low-risk low-risk low-risk low-risk

factor factors factors factors

Adjusted for waist circumference*

PR (95% CI)

P

PR (95% CI)

P

0.956 0.847 0.763 0.635

0.590 0.036 0.001 <0.001

0.953 0.858 0.772 0.655

0.564 0.053 0.001 <0.001

(0.812–1.126) (0.726–0.989) (0.652–0.892) (0.518–0.780)

(0.809–1.123) (0.735–1.002) (0.660–0.903) (0.534–0.804)

CI, confidence interval; PR, prevalence ratio * The model was also adjusted for age, ethnicity, and education; a low-risk factor was defined as the presence of any of the following components: No current smoking, no current drinking, at moderate level of physical activity, and diet scores 2.

X. Zhang et al. / Nutrition 28 (2012) 46–52

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Table 6 Association among ISH, IDH, SDH, hypertension, and gaining weight in rural Chinese women* ISH

BMI (kg/m2) <25 25–29.9
30 WC (cm) <80 80–87.9
88

IDH

SDH

Hypertension

PR (95% CI)

P

PR (95% CI)

P

PR (95% CI)

P

PR (95% CI)

P

1.000 (reference) 1.395 (1.262–1.542) 1.733 (1.324–2.268)

<0.001 <0.001

1.000 (reference) 1.597 (1.406–1.814) 2.455 (1.786–3.374)

<0.001 <0.001

1.000 (reference) 1.797 (1.661–1.945) 2.993 (2.536–3.531)

<0.001 <0.001

1.000 (reference) 1.493 (1.425–1.564) 1.975 (1.785–2.184)

<0.001 <0.001

1.000 (reference) 1.300 (1.191–1.419) 1.872 (1.543–2.271)

<0.001 <0.001

1.000 (reference) 1.273 (1.132–1.432) 1.517 (1.133–2.031)

<0.001 0.005

1.000 (reference) 1.510 (1.399–1.629)) 2.293 (1.977–2.659)

<0.001 <0.001

1.000 (reference) 1.313 (1.256–1.373) 1.781 (1.638–1.937)

<0.001 <0.001

BMI, body mass index; CI, confidence interval; IDH, isolated diastolic hypertension; ISH, isolated systolic hypertension; PR, prevalence ratio; SDH, systolic and diastolic hypertension; WC, waist circumference * Adjusted for age, ethnicity, physical activity, education, smoking status, drinking status, and diet.

body of evidence, lower educational attainment showed the most consistent relation to obesity [27–29]; however, the present study found that subjects with higher levels of education had a risk to gain weight. Although the exact reasons were not clear, it may be due to differences in income and dietary pattern. In rural China, well-educated people usually have a good income and thus can pay for foods that are linked to obesity, such as meat, edible oil, and sugar. Similar to our study, the global epidemic of obesity varies markedly by ethnicity [30–32], and the differences among races may be caused by genetic differences and differences in lifestyle. In contrast, we found that with increasing age, the risk of gaining weight decreases; after age 65 y, there was an inverse association between becoming obese and aging. As has been pointed out, one reason could be the deceased lean mass that occurs with increasing age [33]. Another explanation may come from survivor bias. Further researches will be done for more possible reasons. Obesity is an established risk factor for hypertension. Epidemiologic studies have found a progressive increase in the prevalence of increase BP with increasing adipose tissue [34–36]. However, the relation between anthropometric measurements and BP or hypertension was different. In the United States, compared with BMI, waist circumference had a stronger association with SBP and DBP in men and women [36]. Similar results have been reported in Australian and Guadeloupian women [37, 38]. However, a Japanese study showed that among BMI, waist circumference, waist-to-hip ratio, and waist-to-height ratio, BMI had the strongest association with BP and hypertension in women [39]. Another study also found that BMI is a more important index for BP and waist circumference somewhat less so for women [40]. Our study confirmed that compared with normal-weight women, overweight or obese women according to BMI had a higher risk to develop hypertension. Furthermore, different associations between overweight and obesity and hypertension subtypes were observed in the present study. However, unlike the present results, a previous study in Chinese showed that waist circumference and BMI were strongly associated with SBP and DBP and had coefficients of similar magnitude for SBP and DBP [41]. In contrast, Ledoux et al. [42] found that measurements of body fat (BMI) and abdominal fat distribution (waist circumference and waist-to-hip ratio) played an approximately equal role in assessing the concurrent presence of high BP in adults. Because BMI is mostly a measurement of weight and therefore does not discriminate between body fat and lean mass, some investigators have claimed that waist circumference could replace BMI and waist-to-hip ratio as a simple indicator of need for weight management as a health-promotion activity [43]. However, this view is not

comprehensive. Men are more muscular with greater lean mass and more visceral and hepatic adipose tissue, whereas women have more peripheral or subcutaneous adipose tissue [44]. This highlights the importance of BMI-defined obesity in BP in women instead of waist circumference. Our study has several limitations. The major limitation is the cross-sectional design, which cannot establish causal relations but can only generate hypotheses about the associations between obesity and sociodemographic and lifestyle of the participants. Although this is a population-based study with a large sample, the estimation may not exactly represent the entire population of Liaoning; hence, the findings limit generalizability. Moreover, China is a vast country with diverse lifestyles. Our findings cannot be extrapolated to other provinces in the country. In addition, although the researchers had been trained according to a standardized protocol of measurements, measurements at a single visit may lead to incorrect values for BP, height, weight, and waist circumference. Other limitations include the possible misclassification of recall bias and confounding factors. Conclusions The present study demonstrated that the prevalences of overweight and obesity were already unacceptably high in rural Chinese women. The associations between total or abdominal obesity and subtypes of hypertension were different. The overweight and obesity defined by BMI may be most closely related to an increased BP. To prevent hypertension and decrease the increasing burden of cardiovascular disease in rural areas within the undeveloped economy of China, the urgent aim may be to decrease obesity and other related factors. References [1] World Health Organization. Obesity-preventing and managing the global epidemic: report of a WHO consultation on obesity. Geneva: World Health Organization; 1998. [2] Friedrich MJ. Epidemic of obesity expands its spread to developing countries. JAMA 2002;287:1382–6. [3] Wang H, Du S, Zhai F, Popkin BM. Trends in the distribution of body mass index among Chinese adults, aged 20–45 years (1989–2000). Int J Obesity 2007;31:272–8. [4] Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: A 26-year follow up of participants in the Framingham Heart Study. Circulation 1983;67:968–77. [5] Eckel RH, Krauss RM. American Heart Association call to action: obesity as a major risk factor for coronary heart disease. Circulation 1998;97:2099–100. [6] Havlik RJ, Hubert HB, Fabsitz RR, Feinleib M. Weight and hypertension. Ann Intern Med 1983;98:855–9. [7] Hubert HB, Eaker ED, Garrison RJ, Castelli WP. Life-style correlates of risk factor change in young adults: an eight-year study of coronary heart

52

[8]

[9] [10] [11]

[12]

[13] [14] [15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

X. Zhang et al. / Nutrition 28 (2012) 46–52 disease risk factors in the Framingham offspring. Am J Epidemiol 1987;125:812–31. Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obesity 2007;31:177–88. Wu X, Duan X, Gu D, Hao J, Tao S, Fan D. Prevalence of hypertension and its trends in Chinese populations. Int J Cardiol 1995;52:39–44. Ashley FW Jr, Kannel WB. Relation of weight change to changes in atherogenic traits: Framinghan Study. J Chron Dis 1974;27:103–4. Stevens VJ, Corrigan SA, Obarzanek E, Bernauer E, Cook NR, Hebert P, et al. Weight loss Intervention in phase 1 of the trials of hypertension prevention. The TOHP Collaborative Research Group. Arch Intern Med 1993;153:849. He J, Whelton PK, Appel LJ, Charleston J, Klag MJ. Long-term effects of weight loss and dietary sodium reduction on incidence of hypertension. Hypertension 2000;35:544–9. Scidell JC. Epidemiology of obesity. Semin Vasc Med 2005;5:3–14. Ryan D. Obesity in women: a life cycle of medical risk. Int J Obesity (Lond) 2007;31(suppl 2):S3–7. Pajak A, Kuulasmaa K, Tuomilehto J, Ruokokoski E. Geographical variation in the major risk factors of coronary heart disease in men and women aged 35– 64 years. The WHO MONICA Project. World Health Stat Q 1988;41:115–40. Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur Heart J 2004;25:2183–4. Katsouyanni K, Rimm EB, Gnardellis C, Trichopoulos D, Polychronopoulos E, Trichopoulou A. Reproducibility and relative validity of an extensive semiquantitative food frequency questionnaire using dietary records and biochemical markers among Greek schoolteachers. Int J Epidemiol 1997;26:S118–27. Panagiotakos DB, Pitsavos C, Chrysohoou C, Risvas G, Kontogianni MD, Zampelas A, et al. Epidemiology of overweight and obesity in a Greek adult population: the ATTICA Study. Obes Res 2004;12:1914–20. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003;42:1206–52. Franklin SS, Jacobs MJ, Wong ND, L’Italien GJ, Lapuerta P. Predominance of isolated systolic hypertension among middle-aged and elderly US hypertensives: analysis based on National Health and Nutrition Examination Survey (NHANES) III. Hypertension 2001;37:869–74. National Institute on Alcohol Abuse and Alcoholism. Health risks and benefits of alcohol consumption. Tenth Special Report to the US Congress on Alcohol and Health. Alcohol Res Health 2000;24:5–11. Flanders WD, Lally CA, Zhu BP, Henley SJ, Thun MJ. Lung cancer mortality in relation to age, duration of smoking, and daily cigarette consumption: results from Cancer Prevention Study II. Cancer Res 2003;63:6556–62. Cameron AJ, Welborn TA, Zimmet PZ, Dunstan DW, Owen N, Salmon J, et al. Overweight and obesity in Australia: The 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Med J Aust 2003;178:427–32. Park HS, Yun YS, Park JY, Kim YS, Choi JM. Obesity, abdominal obesity, and clustering of cardiovascular risk factors in South Korea. Asia Pac J Clin Nutr 2003;12:411–8. World Health Organization. Obesity: preventing and managing the global epidemicdreport of a WHO consultation. WHO technical report series no. 894. Geneva: World Health Organization; 2000.

[26] Hu G, Pekkarinen H, Hanninen O, Tian H, Jin R. Comparison of dietary and non-dietary risk factors in overweight and normal-weight Chinese adults. Br J Nutr 2002;88:91–7. [27] Panagiotakos DB, Pitsavos C, Chrysohoou C, Risvas G, Kontogianni MD, Zampelas A, et al. Epidemiology of overweight and obesity in a Greek adult population: the ATTICA Study. Obes Res 2004;12:1914–20. [28] Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA 2002;288:1723–7. [29] Macdonald SM, Reeder BA, Chen Y, Despres JP. Obesity in Canada: a descriptive analysis. Canadian Heart Health Surveys Research Group. CMAJ 1997;157(suppl 1):S3–9. [30] Sundquist J, Johansson S. The influence of socioeconomic status, ethnicity and lifestyle on body mass index in a longitudinal study. Int J Epidemiol 1998;27:57–63. [31] Stern MP, Gaskill SP, Hazuda HP, Gardner LI, Haffner SM. Does obesity explain excess prevalence of diabetes among Mexican Americans? Results of the San Antonio Heart Study. Diabetologia 1983;24:272–7. [32] Stern MP, Patterson JK, Mitchell BD, Haffner SM, Hazuda HP. Overweight and mortality in Mexican Americans. Int J Obes Relat Metab Disord 1990;14:623–9. [33] Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord 1998;22:39–47. [34] Mark AL, Correia M, Morgan DA, Shaffer RA, Haynes WG. Obesity-induced hypertension. New concepts from the emerging biology of obesity. Hypertension 1999;33:537–41. [35] Bell AC, Adair LS, Popkin BM. Ethnic differences in the association between body mass index and hypertension. Am J Epidemiol 2002;155:346–53. [36] Zhu SK, Wang ZM, Heshka S, Heo M, Faith MS, Heymsfield SB. Waist circumference and obesity-associated risk factors among whites in the Third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 2002;76:743–9. [37] Dalton M, Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risks in Australian adults. J Intern Med 2003;254:555–63. [38] Foucan L, Hanley J, Deloumeaux J, Suissa S. Body mass index and waist circumference as screening tools for cardiovascular risk factors in Guadeloupian women. J Clin Epidemiol 2002;55:990–6. [39] Sakurai M, Miura K, Takamura T, Ota T, Ishizaki M, Morikawa Y, et al. Gender differences in the association between anthropometric indices of obesity and blood pressure in Japanese. Hypertens Res 2006;29:75–80. [40] Yalcin BM, Sahin EM, Yalcin E. Which anthropometric measurements are most closely related to elevated blood pressure? J Fam Pract 2005;22: 541–7. [41] Wildman RP, Gu D, Reynolds K, Duan X, Wu X, He J. Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults? Am J Clin Nutr 2005;82:1195–202. [42] Ledoux M, Lambert J, Reeder BA, Despres JP. Correlation between cardiovascular disease risk factors and simple anthropometric measures. Canadian Heart Health Surveys Research Group. CMAJ 1997;157(suppl 1):S46–53. [43] Lean MEJ, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158–61. [44] Geer EB, Shen W. Gender differences in insulin resistance, body composition, and energy balance. Gender Med 2009;6(suppl 1):60–75.