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Physical Activity and Sedentary Behavior Associated with Components of Metabolic Syndrome among People in Rural China

  • Jing Xiao,

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China

  • Chong Shen,

    Affiliation Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 210029, China

  • Min J. Chu,

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China

  • Yue X. Gao,

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China

  • Guang F. Xu,

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China

  • Jian P. Huang,

    Affiliation Department of Chronic Disease and Prevention, Center for Disease Control and Prevention of Nantong, Nantong, Jiangsu, 226007, China

  • Qiong Q. Xu,

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China

  • Hui Cai

    Affiliation Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China



Metabolic syndrome is prevalent worldwide and its prevalence is related to physical activity, race, and lifestyle. Little data is available for people living in rural areas of China. In this study we examined associations of physical activity and sedentary behaviors with metabolic syndrome components among people in rural China.


The Nantong Metabolic Syndrome Study recruited 13,505 female and 6,997 male participants between 2007 and 2008. Data of socio-demographic characteristics and lifestyle were collected. The associations of physical activity and sedentary behaviors with metabolic syndrome components were analyzed.


Prevalence of metabolic syndrome was 21.6%. It was significantly lower in men than in women. Low risks of metabolic syndrome were observed in those who did less sitting and engaged in more vigorous physical activity. The highest tertile of vigorous physical activity was associated with 15–40% decreased odds of metabolic syndrome and all of its components, except for low high-density lipoprotein cholesterol in men. Women with the highest tertile of moderate physical activity had 15–30% lower odds of central obesity, high glucose, and high triglycerides compared with those in the lowest tertile. Sitting time >42 hours per week had a 4%-12% attributable risk of metabolic syndrome, central obesity, and high triglycerides in both genders, and abnormal glucose and diastolic blood pressure in women. Sleeping for more than 8 hours per day was associated with risk of high serum glucose and lipids.


Our data suggested that physical activity has a preventive effect against metabolic syndrome and all its abnormal components, and that longer sitting time and sleep duration are associated with an increased risk of metabolic syndrome components, including central obesity and high triglycerides, glucose, and diastolic blood pressure. This study could provide information for future investigation into these associations. Also, recommendations are developed to reduce prevalence of metabolic syndrome and its components in rural Chinese populations.


Metabolic syndrome (MS) is a cardio-metabolic risk cluster comprising high fasting glucose level, hypertension, high triglyceride level, low high-density lipoprotein cholesterol (HDL-c) level, and high waist circumference [1], and is strongly associated with the development of type II diabetes and risk of cardiovascular morbidity and mortality [2]. MS is highly prevalent in adult populations worldwide, with data suggesting an ethnic predisposition in Asian populations [3]. It is also closely associated with lifestyles, such as smoking, drinking, physical activity (PA), and sedentary behavior.

The World Health Organization estimates that physical inactivity causes approximately 22% of ischemic heart disease, 10–16% of diabetes, and 2 million deaths worldwide, annually [4]. Many epidemiological studies have reported a significantly negative association between the levels of PA and the incidence of MS in both adults [5] and children [6], especially in the association of moderate to vigorous PA with MS and its components [710]. However, the data from these studies are inconsistent. A meta-analysis of cohort studies reported that moderate exercise at leisure time was weakly associated with a decreased risk of MS in men [11]. Other studies found that this exercise had a protective effect on hypertension in Brazilian men [12], little or no effect on Korean adults [13]. It has also been reported that total PA was associated with a lower prevalence of MS in Portuguese adults [14] but was not associated with MS in the Costa Rican population [14, 15]. These inconsistencies could also be found in the association of light PA with MS and its components [16, 17]. In our study PA refers to both leisure time physical activity and occupational physical activity. Sedentary behavior refers to any waking activity characterized by energy expenditure ≤ 1.5 metabolic equivalents and a sitting or reclining posture [18]. Sedentary behavior have been shown to be positively associated with an increased risk of MS [19], and this association between sedentary behavior and the MS is independent of levels of PA [20]. It suggests that sedentary time could be an independent determinant of metabolic dysfunction distinct to that of physical activity. But other report didn’t support this opinion [21]. Also sedentary behavior was associated with increasing body mass index (BMI) and waist circumference in US adults [22], and an increased risk of type 2 diabetes in a systematic review of prospective studies [23]. These inconsistent reports could be due to different populations or relatively small sample size in each study. Also we did not found any data of association of different intensities of PAs or sedentary behaviors with each of the MS components, especially in rural China.

We launched the Nantong Metabolic Syndrome Study in 2007. It is a large population-based cross-sectional study of 20,502 participants (including 6,997 men and 13,505 women), aged 18–74 years, in the rural areas of Nantong, China. The objective of this study was to fully assess the associations of different PA intensity and sedentary behavior with each of the MS components among people in rural China respectively because PA is a protective factor and sedentary behavior is a risk factor in link with MS in previous studies. To our knowledge, this is the largest study in rural China.

Materials and Methods

Study population

The Nantong Metabolic Syndrome Study recruitment began in July 2007 and was completed in August 2008. The details of it have been described elsewhere [24, 25]. Briefly, 24,519 residents between the ages of 18 and 74 years were recruited from two townships in rural Nantong. Among them, 20,502 participants (6,997 men and 13,505 women) were enrolled in the study, with a response rate of 83.6%. The reasons for non-participation were refusal (3.21%), being out of the area during enrollment (7.21%), and other miscellaneous reasons such as poor health or hearing problems (5.98%). We defined a rural area as an area with a primary administrative unit termed a ‘village’. At the time of interview, most participants (99.5%) lived in a village, and 13,306 (64.9%) were farmers, most of whom were vigorous physical laborers. The Boards of Scientific Research of Nantong University and the Nantong Centers for Disease Control ethics review board approved the original survey protocols and all participants provided their written consent to participate in this study. We assessed the socio-economic factors, dietary intake, alcohol consumption, smoking status, physical activity, sedentary behavior, personal medical history, and family history of several chronic diseases, using the same standard questionnaires for all participants.

Physical activity and sedentary behavior assessment

PA level was assessed in our study using a questionnaire, which is similar to the International Physical Activity Questionnaire, a standardized questionnaire used in epidemiological studies [26]. We collected data on the cumulative hours per day for light, moderate and vigorous intensity PA in two domains (occupational PA and leisure-time PA), and for sitting time, watching TV, and sleeping during the previous 7 days. Intensity Levels of leisure-time PA were defined below: no leisure-time PA (watching TV, reading and writing), light leisure-time PA (Qi Gong and some stretching exercises), moderate leisure-time PA (jogging and dancing) and vigorous leisure-time PA (playing basketball, badminton). Occupational PA was divided into four groups: No job or sedentary work (typists, computer operators), light occupational PA (clerk, teacher), moderate occupational PA (driver, electrician) and vigorous occupational PA (farmer, porter). Furthermore, we defined three categories for each of PA intensities (light, moderate, and vigorous) based on the tertiled of the hours per week of each PA intensity respectively, such as vigorous PA: low (≤ 14 h/w for women and ≤ 7 h/w for men), medium (≤ 42 h/w for both sexes), and high (> 42 h/w for both sexes). What’s more, total PA were calculated by sum of energy expenditure of three PA intensities and were presented as metabolic equivalents-hours per week (MET-h/w). MET-h/w were calculated using hours of each PA intensity per week multiplied by its energy requirement: light intensity = 3.3 METs, (based on walking METs), moderate intensity = 4.0 METs, and vigorous intensity = 8.0 METs. Sedentary behavior refers to activities that involve energy expenditure at the level of 1.0–1.5 metabolic equivalent units (METs) [18]. Sedentary behaviors includes lying down, sitting (reading, or using the computer and other forms of screen-based entertainment) and watching TV rather than physical inactivity. Physical inactivity was defined as not meeting any of the following criteria; (1) at least 30 min of moderate-intensity activity per day on at least 5 days per week (2) at least 20 min of vigorous-intensity activity per day on at least 3 days per week or (3) an equivalent combination by the WHO global health observatory data repository database (web link: Similar categorization was used for total PA in MET-h/w, sitting hours per week, hours of watching TV per week and sleep hours per day.

Other environmental factors

Ever smokers were defined as participants who had smoked at least 100 cigarettes in their life time and they were asked how many cigarettes they consumed per day. All participants were asked about monthly alcohol consumption of grape wine, rice wine, beer, and liquor within the recent year. One drink was defined as consumption of approximately 0.5 ounces of pure alcohol [27]. The intake of meat, including red meat (e.g., pork, beef, and lamb), white meat (e.g., chicken, duck, and goose), and fish were investigated. Data on the amount other foods consumed, such as vegetables, fruits, and soy foods were also collected. We asked the participants how frequently (daily, weekly, monthly, yearly, or never) they consumed these food groups over the past year, followed by a question on the amount consumed in liang (1 liang = 50 grams) per unit of time. We defined tea consumption as drinking tea more than twice per week, for at least six months continuously. Socio-demographic factors, such as age at the time of interview, education (none, elementary school, middle/high school, college, and above), personal income in Yuan/month (≤ 500, 501–1000, ≥ 1001) and occupation, were used as potential confounders in the study.

Anthropometric and biochemical measurements

Anthropometric measurements (weight, height, and waist circumference) were measured twice for each participant and a third measurement was taken if the difference between the two measurements was larger than 1 cm for height and waist circumference or 1 kg for weight, during the in-person interviews. The average value of two closest measurements of height, weight, and waist circumference were used in this study. BMI was calculated using the formula: weight (kg) divided by height2 (m2).

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured three times for each participant using a standardized mercury sphygmomanometer with the cuff on the right upper arm. The average of three measurements was used for analysis. A 10-mL blood sample, following an overnight fast, was drawn into an EDTA vacutainer tube at the time of the in-person interview. Serum samples were obtained by centrifugation of the blood samples. The serum levels of glucose and the lipid profiles of the 20,502 participants were measured using an automated chemistry analyzer (Hitachi 7180, Tokyo, Japan). Reagents from the Shino-Test Corporation in Japan were used to enzymatically analysis in the Nantong Centers for Disease Control. Both the inter- and intra-assay coefficients of variation were less than 3.5% for glucose, triglyceride, and HDL-c.

Criteria for Metabolic syndrome diagnosis

MS criteria were proposed by the most recent Joint Interim Statement of multi-International organizations [28]. A diagnosis of MS was made if three or more of the following metabolic risk factors or cut-off points were present: (1) central obesity: waist circumference ≥85 cm for Chinese men and ≥ 80 cm for Chinese women; (2) high fasting triglyceride: ≥ 1.7 mmol/L or taking medication for abnormal lipid levels; (3) Low HDL-c: < 1.0 mmol/L for Chinese men and < 1.3 mmol/L for Chinese women or undergoing a specific treatment for abnormal HDL-c; (4) high blood pressure: SBP ≥ 130 mmHg or DBP ≥ 85 mmHg or taking hypertension medication; (5) high fasting glucose: ≥ 5.6 mmol/L or taking diabetes medication.

Statistical analysis

Demographic, dietary, and lifestyle characteristics were presented as the mean ± standard deviation (or the median ± inter-quartile range) for continuous variables and as percentages for categorical variables. We compared these characteristics between MS and non-MS subjects using ANOVA for normally distributed variables and a Wilcoxon rank sum test for non-normally distributed variables. A chi-square test was used for categorical variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using a common logistic regression to assess the association of PA intensity with MS and its components. Two dummy variables were created of each of PA intensity (for example: vigorous intensity PA in women, that is ≤ 42 h/w vs ≤ 14 h/w and > 42 h/w vs ≤ 14 h/w). Logistic regression model was used adjusted for potential confounders in the analysis. Ordinal variable (e.g. median hours per week for each category of PA) was used as continuous parameters for test the linear trend. The potentially confounding variables, including age at the time of interview, BMI, personal income, education, marital status, occupation, drinking status, smoking status, tea consumption, and intake of red meat, white meat, fish, soy food, vegetables, and fruits, were applied in the logistic regression model. Additionally light, moderate, and vigorous PA levels were mutually adjusted in the model. All p-values were based on two-tailed test and a p-value < 0.05 was considered statistically significant. All analyses were performed using SAS statistical software (version 9.4; SAS Institute, Cary, NC).


Characteristics of study participants

Differences of selected demographic characteristics, lifestyle factors, anthropometric measurements, and food intake, between MS subjects and non-MS subjects, are presented in Table 1. According to the Joint Interim Statement criteria, the prevalence of MS was 21.6%, and it was 6.2% higher in women (23.7%) than that in men (17.5%) (p < 0.001). MS subjects were older, more likely to be tea consumers, had a higher weight and BMI, and had a higher income, than those non-MS subjects. Among men, MS subjects were more educated, less likely to be farmers, and had a higher marital rate than those non-MS subjects. While in women, MS subjects had less soy food and fruits intake and consumed less alcoholic beverages than non-MS subjects.

Table 1. Characteristics of the study subjects stratified by metabolic syndrome status and genders*.

Association of physical activity and sedentary behavior with metabolic syndrome and its components

Table 2 shows the associations of MS with different PA intensity and sedentary behaviors. Hours of vigorous PA per week were associated with a decreased risk of MS in both men and women (ORs (95% CIs): 0.72 (0.64–0.81) for men and 0.84 (0.77–0.90) for women). An association between hours of moderate PA and MS was observed only in women, with an OR of 0.90 (95% CI: 0.85–0.95). We obtained similar results if we used MET-h/w in the analysis. Increasing MET-h/w of the total PA for both genders was associated with a decreased MS risk (ORs = 0.77 for men and 0.86 for women, both p for trend < 0.001). There was no association between hours of light PA and MS prevalence. Sitting time was associated with an increased prevalence of MS in both genders (ORs = 1.16 for both men and women, both p for trend < 0.001). Sitting time over 42h per week was associated with an increased risk of MS with its attributable risk of 11.38% for men and 10.57% for women, compared with sitting time not more than 7h per week (S1 and S2 Tables). There were interaction effects between sexes and moderate PA, sitting time and sleep duration (all p<0.05).

Table 2. Associations of MS with PA and other activities: MS subjects vs. non-MS subjects among rural men and women.

Table 3 and Table 4 show association of PA intensity and sedentary behavior with the MS components by gender. In general, increased time spent in moderate and vigorous PA per week and increased MET-h/w of total PA were both protective factors for the MS components, while increased sitting time was a risk factor for MS components in both genders. Increasing the MET-h/w of total PA and the hours of vigorous PA per week were significantly associated with a decreased risk for all MS components in both genders with ORs from 0.77 to 0.93 (all p for trend <0.05), with the exception of low HDL-c in men. And their attributable ‘risks’ (protective effect) of abnormal MS components were 27.60%–11.38% in men and 20.41%–7.04% in women who engaged in total PA over 334.9 MET-h/w, compared with those who engaged in vigorous PA less than 212.2 MET-h/w in men and 216.2 MET-h/w in women (S1 and S2 Tables). Furthermore, we found that over 42 h/w of vigorous PA was associated with a decreased risk of having high waist circumference (OR = 0.59), high triglyceride (OR = 0.63), high glucose (OR = 0.69), high SBP (OR = 0.69), and high DBP (OR = 0.59), compared with those who engaged in not more than 7 h/w vigorous PA in men. Similarly, we found women who engaged in more than 42 h/w of vigorous PA had a decreased risk of having high waist circumference (OR = 0.83), high triglyceride (OR = 0.79), low HDL-c (OR = 0.73), high glucose (OR = 0.59), high SBP (OR = 0.79), and high DBP (OR = 0.70), compared with women who had not more than 14 h/w vigorous PA. Meanwhile, hours of moderate PA was associated with a reduced risk of high waist circumference (ORs = 0.89 for men and 0.88 for women), high triglyceride only in women (OR = 0.91) and high glucose (ORs = 0.84 for men and 0.86 for women). Additionally, light PA was only weakly associated with a decreasing SBP in both genders (ORs = 0.90 for men and 0.94 for women). Increasing the hours of sitting, watching TV per week and sleeping per day were associated with an increased risk of several MS components in both sexes (p for trend <0.05). Sitting time over 42 h/w had 9.19% and 10.71% attributable risks of high waist circumference and 8.06% and 5.75% attributable risks of high triglyceride among men and women, respectively. It also had 11.61% and 7.74% attributable risks of high glucose and high DBP in women, compare with those who had sitting time below 21 h/w (S1 and S2 Tables). Increased time spent watching TV per week was associated with a risk of high waist circumference (OR = 1.20) in men and high triglyceride in women (OR = 1.09). Sleeping 8 h/d or more had 16.83% and 16.69% attributable risks of high glucose, in men and women, respectively, a 20.13% attributable risk of low HDL-c in men, and a 12.02% attributable risk of high triglyceride in women, compare with those who slept less than 7 h/d (S1 and S2 Tables).

Table 3. Associations of MS components with PA and other activities: abnormal MS components vs. normal MS components in men.

Table 4. Associations of MS components with PA and other activities: abnormal MS components vs. normal MS components in women.


In our study we found that women had a higher prevalence of MS than men in rural areas. Moderate and vigorous PA each week (or a high MET-h/w) were important protective factors for abnormal MS components and may result in a 10–30% decrease in the odds, while an increase in sitting time per week and sleep hours per day were risk factors for MS components.

In this cross-sectional study among adults in rural China, there was a lower prevalence of MS in men (17.5%) than in women (23.7%). This difference is consistent with a recent large-scale, population-based study in which a significantly higher prevalence of MS was found in Malay women (30.1%) than in men (24.8%) [29]. However, a recent cross-sectional study of 33,149 employees in Northeast China found a higher prevalence of MS in men (24.5%) than in women (15.4%) [30]. The different prevalence between our study and a study in Northeast China might be due to the fact that most of participants in our study were farmers (some females were housewives), and were living in rural areas, while participants in the study of Northeast China were mainly living in urban China with occupations of professionals, clerks and workers. These two populations had different economic status and lifestyles. Also in urban China, the incidence of MS in men reaches its peak during middle-age and beyond, while the incidence of MS in women increases over age of 60 years [31].

Little is known about the association of total PA with MS in rural China. A lower prevalence of MS in the highest tertile of total PA has been reported with ORs of 0.55 for men and 0.63 for women, compared with those in the lowest tertile in Portuguese adults [14]. No such association was found in a study of Costa Rican adults [15]. In our study we found that the highest tertile of total PA was associated with a reduced risk of MS (OR: 0.59 for men and 0.75 for women), compared with those in the lowest tertile of MET. We also found that the highest tertile of hours in vigorous PA was associated with 47% and 30% decreased odds of MS in men and women, respectively. For those in the highest tertile of hours in moderate PA was associated with a 10% decreased odds of MS among women only. These results were consistence with that in Esteghamati’s study in which they concluded that longer durations of moderate and vigorous physical activity are associated with a reduced occurrence of MS [32]. A possible mechanism for this relationship is that the effects of exercise-induced mitochondrial biogenesis in skeletal muscle, which represents about 80–90% of all insulin sensitive tissues and accounts for approximately 50% of basal metabolic rate [33]. The increase in mitochondrial biogenesis (increased volume and functional capacity) is fundamentally important, as it leads to greater rates of oxidative phosphorylation, and an improved capacity to utilize fatty acids during sub-maximal exercise [34]. Additionally, PA also improves insulin sensitivity directly by transporters in both muscle and adipose tissue, which protects against MS.

Previous reports have shown that high levels of PA play a critical role in decreasing the risks of high triglyceride, high glucose, low HDL-c [3539], and abdominal obesity [40]. However, several studies did not find this relationship [10, 41]. In our study we observed that the highest tertile of total PA (> 334.9 MET-h/w for women and 342.7 MET-h/w for men), as well as vigorous PA (> 42 h/w), was associated with a 15–40% decrease in the odds of all these abnormal MS components in both genders, with an exception of HDL-c in men, compared with those in the lowest tertile. We also found that the highest tertile of moderate PA (> 14 h/w for women and 20 h/w for men) was associated with a 20–30% decrease in the odds of high waist circumference and high glucose in both genders, and with a 19% decrease in the odds of high triglyceride only in women. Our data are in line with several studies. Di Loreto reported that constant energy expenditure > 20 METs/h per week decreased waist circumference and triglyceride and increased HDL-c, which therefore, was associated with a 4–5% decrease in the 10-year coronary heart disease risk [42]. Another study reported that higher levels of total PA were associated with a significant decrease in the prevalence of central obesity, dyslipidemia, and dysglycemia [32]. However, we didn’t find any association between light-intensity activity and waist circumference or blood glucose, which were inconsistent with previous findings [15, 43, 44]. It could be that light-intensity level is too week to improve insulin sensitivity and then it is hard to detect its effect on MS components [45]. In addition, different research populations may be another reason for these inconsistencies. Therefore, intervention trials using objective measures of PA are required to validate our results and reveal the physiological and behavioral mechanisms underlying the observations.

Previous studies have also demonstrated that PA has protective effects against hypertension [35, 37, 39, 46]. While this protective effect was not to be found in rural people in South Africa [47], or in Taiwanese adults regardless of PA intensity [10]. However, we observed that increased hours of vigorous PA was associated with a 20–40% reduced odds of high SBP and DBP, and that increasing the hours spent in light PA may be associated with about 10% decreased odds of high SBP in both genders.

It has been shown that lifestyles have changed over the preceding decades in developing countries, especially in China. There has been a trend towards an increase in sedentary lifestyles that likely contribute to an increased incidence of MS [14]. Also it is important to highlight that many observational evidence between sedentary behavior and MS components is complex, depending on the type of sedentary behavior. An overview of systematic reviews reported that there are strong associations between sedentary behavior (based on watching TV) and obesity and blood pressure; and there are strong associations between sedentary behavior (based on watching TV, sitting time) and MS and type 2 diabetes [48]. So we focus on the association of three particular sedentary behavior (sitting, watching TV and sleep duration) on MS and its components among people in rural China and have drawn divergent conclusions. We found more sitting time was associated with the 16% increased odds of MS in both men and women, which were consistent with a meta-analysis that more sitting time has been shown to increase 73% of MS odds [18], but this association cannot be found in a cohort study among American men [49]. But in current study we further found more sitting time was associated with higher waist circumference in men and higher triglyceride in women, which were consistent with the study in American men [49].

Watching TV, another sedentary behaviors, was positively associated with high triglyceride and high glucose, (ORs of 2.59 and 13.9 respectively for watching TV ≥ 21h/week vs. 0–5 h/week) in Taiwanese adults [10] and associated with central obesity in rural Indian women [50]. We found that watching TV over 14h/w was associated with a risk of high waist circumference in men (OR = 1.20) and high triglyceride in women (OR = 1.09), compared with people watching TV 7 hours per week or less. A potential mechanisms of this association could be that a significant reduction lipoprotein lipase activity in the microvascular beds in muscle, a key enzyme regulating lipid metabolism, has been shown to occur during sedentary activity [51]. Additionally, sedentary behavior, simply on the basis of its low expenditure, may result in being overweight or obese [52]. We found that sleeping for more than 8 hours was associated with a 20.13% attributable risk of low HDL-c, a 16.83% attributable risk of high glucose in men, and a 16.69% attributable risk of high glucose, a 12.02% attributable risk of high triglyceride in women, compare to those sleeping less than 7 hours per day. A previous study in Costa Rica also got similar conclusions [14]. More studies need to be granted for further investigation in association between sleeping and MS components and underline mechanisms.

The strengths of our study include a population-based design, a large sample size, and a comprehensive assessment of associations of a wide range of PA and other lifestyle factors with MS and its components. However, limitations should be taken into account. First, our data on PA and other lifestyles were obtained by self-report, which could bias the results. However, PA and sitting time were assessed with International Physical Activity Questionnaire, a valid and reliable instrument, and may control errors to some extent. Second, the cross-sectional design cannot get any conclusions on the causality of the observed associations. Thus, longitudinal studies should be used in the future to evaluate potentially causal relationships.


In conclusion, we found there were an independent preventive effect of total PA, moderate and vigorous PA and a risk effect of sitting time on MS and its components. A long sleep duration was also shown to be related to abnormal serum glucose and lipids. Our findings suggest that participation in PA and decrease of sedentary time should be encouraged to reduce the prevalence of the MS and its abnormal components, even in rural China. In the absence of contraindications, more vigorous PA should also be considered to obtain additional health benefits.

Supporting Information

S1 Table. Attributable risk (CI, %) of metabolic syndrome and its components by PA and other activities among rural men.


S2 Table. Attributable risk (CI, %) of metabolic syndrome and its components by PA and other activities among rural women.



We deeply appreciate the participants of the Nantong Metabolic Syndrome Study in Nantong, China.

Author Contributions

Conceived and designed the experiments: JX CS HC. Performed the experiments: JX CS GFX JPH. Analyzed the data: JX YXG QQX MJC. Wrote the paper: JX CS HC MJC.


  1. 1. Huffman KM, Sun JL, Thomas L, Bales CW, Califf RM, Yates T, et al. Impact of baseline physical activity and diet behavior on metabolic syndrome in a pharmaceutical trial: results from NAVIGATOR. Metabolism. 2014; 63: 554–61. pmid:24559843
  2. 2. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, et al. Metabolism syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol. 2007; 49: 403–14. pmid:17258085
  3. 3. Yusuf S, Reddy S, Ounpuu S, Anand S. Global burden of cardiovascular diseases: part II: variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention stragegies. Circulation. 2001; 104: 2855–64. pmid:11733407
  4. 4. Available:
  5. 5. Laaksonen DE, Lakka HM, Salonen JT, Niskanen LK, Rauramaa R, Lakka TA. Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome. Diabetes Care. 2002; 25: 1612–8. pmid:12196436
  6. 6. Beage S, Wedderkopp N, Ekelund U, Franks PW, Wareham NJ, Andersen LB, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in danish children: the European youth heart study(EYHS). Diabetes Care. 2004; 27: 2141–8. pmid:15333475
  7. 7. Dunstan DW, Salmon J, Healy GN, Shaw JE, Jolley D, Owen N. Association of television viewing with fasting and 2-h postchallenge palsma glucose levels in adults without diagnosed diabetes. Diabetes Care. 2007; 30: 516–22. pmid:17327314
  8. 8. Dunstan DW, Salmon J, Owen N. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia. 2005; 48: 2254–61. pmid:16211373
  9. 9. Healy GN, Dunstan DW, Shaw JE, Zimmet PZ, Owen N. Beneficial associations of physical activity with 2-h but not fasting blood glucose in Australian adults. Diabetes Care. 2006; 29: 2598–604. pmid:17130191
  10. 10. Li CL, Lin JD, Lee SJ, Tesng RF. Associations between the metabolic syndrome and its components, watching television and physical activity. Public Health. 2007; 121: 83–91. pmid:17188724
  11. 11. He D, Xi B, Xue J, Huai P, Zhang M, Li J. Association between leisure time physical activity and metabolic syndrome: a meta-analysis of prospective cohort studies. Endocrine. 2014; 6: 231–40.
  12. 12. Perez LG, Pratt M, Simoes EJ, de Moura L, Malta DC. Association between leisure-time physical activity and self-reported hypertension among Brazilian adults, 2008. Prev Chronic Dis. 2013; 10: E172. pmid:24157075
  13. 13. Yoon JH, So WY. Association between leisure-time physical activity and hypertension status in Korean adults. Salud Publica Mex. 2013; 55: 492–7. pmid:24626620
  14. 14. Santos AC, Ebrahim S, Barros H. Alcohol intake, smoking, sleeping hours, physical activity and the metabolic syndrome. Prev Med. 2007; 44: 328–34. pmid:17239432
  15. 15. Hastert TA, Gong J, Campos H, Baylin A. Physical activity patterns and metabolic syndrome in Costa Rica. Prev Med. 2015; 70: 39–45. pmid:25445330
  16. 16. Hamasaki H, Noda M, Moriyama S, Yoshikawa R, Katsuyama H, Sako A, et al. Daily Physical Activity Assessed by a Triaxial Accelerometer Is Beneficially Associated with Waist Circumference, Serum Triglycerides, and Insulin Resistance in Japanese Patients with Prediabetes or Untreated Early Type 2 Diabetes. J Diabetes Res. 2015; pmid:26064983
  17. 17. Levine JA, Eberhardt NL, Jensen MD. Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science. 1999; 283: 212–4. pmid:9880251
  18. 18. Sedentary Behaviour Research Network. Standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 2012; 37: 540–542. pmid:22540258
  19. 19. Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, Wilmot EG, et al. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS One. 2012; 7: e34916. pmid:22514690
  20. 20. Bankoski A, Chen KY, Harris TB, Berrigan D, McClain JJ, Troiano RP, et al. Sedentary activity associated with metabolic syndrome indepentdent of physical activity. Diabetes Care. 2011; 34: 497–503. pmid:21270206
  21. 21. Helmerhorst HJ, Wijndaele K, Brage S, Wareham NJ, Ekelund U. Objectively measured sedentary time may predict insulin resistance independent of moderate- and vigorous- intensity physical activity. Diabetes. 2009; 58: 1776–1779. pmid:19470610
  22. 22. Ladabaum U, Mannalithara A, Myer PA, Singh G. Obesity, abdominal obesity, physical activity, and caloric intake in US adults: 1988–2010 Am J Med. 2014; 127: 717–27. pmid:24631411
  23. 23. Proper KI, Singh AS, van Mechelen W, Chinapaw MJM. Sedentary behaviors and health outcomes among adults. A systematic review of prospective studies. Am J Prev Med. 2011; 40: 174–182. pmid:21238866
  24. 24. Cai H, Huang JP, Xu GF, Yang ZL, Liu M, Mi YP, et al. Prevalence and determinants of metabolic syndrome among women in Chinese rural areas. PLoS One. 2012; 7: e36936. pmid:22590636
  25. 25. Xiao J, Huang JP, Xu GF, Chen DX, Wu GY, Zhang M, et al. Association of alcohol consumption and components of metabolic syndrome among people in rural China. Nutr Metab. 2015; 12: 5. pmid:25745507
  26. 26. Committee IR. Guideline for data procssing and analysis of the international physical activity questionnaire (IPAQ), short form. Available: http://wwwipadkise. Accessed 18 January 2010.
  27. 27. Shu XO, Hatch MC, Mills J, Clemens J, Susser M. Maternal smoking, alcohol drinking, caffeine consumption, and fetal growth: results from a prospective study. Epidemiology. 1995; 6: 115–20. pmid:7742395
  28. 28. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation. 2009; 20: 1640–5.
  29. 29. Rampal S, Mahadeva S, Guallar E, Bulqiba A, Mohamed R, Rahmat R, et al. Ethnic differences in the prevalence of metabolic syndrome: results from a multi-ethnic population-based survey in Malaysia. PLoS One. 2012; 7: e46365. pmid:23029497
  30. 30. Wang X, Yang F, Bots ML, Guo WY, Zhao B, Hoes AW, et al. Prevalence of the metabolic syndrome among employees in Northeast China. Chin Med J(Engl). 2015; 128 (15): 1989–93. pmid:26228207
  31. 31. Wu YF, Ma GS, Hu YH, Li YP, Li X, Cui ZH, et al. The current prevalence status of body overweight and obesity in China: Data from the China National Nutrition and Health Survey. Chin J Pre Med. 2005; 39: 316–20. pmid:16266540
  32. 32. Esteghamati A, Khalilzadeh O, Rashidi A, Meysamie A, Haghazali M, Abbasi M, et al. Association between physicial activity and metabolic syndrome in Iranian adults: national surveillance of risk factors of noncommunicable diseases (SuRFNCD-2007). Metabolism. 2009; 58: 1347–55. pmid:19501857
  33. 33. Zurlo F, Larson K, Bogardus C, Ravussin E. Skeletal muscle metabolism is a major determinant of resting energy expenditure. J Clin Invest. 1990; 86: 1423–7. pmid:2243122
  34. 34. Holloszy JO, Coyle EF. Adaptations of skeletal muscle to endurance exercise and their metabolic consequences. J Appl Physiol Respir Environ Exerc Physiol. 1984; 56: 831. pmid:6373687
  35. 35. Ekelund U, Brage S, Franks PW, Hennings S, Emms S, Wareham N. Physical activity energy expenditure predicts progression toward the metabolic syndrome independently of aerobic fitness in middle-aged healthy Caucasians: the Medical Research Council Ely Study. Diabetes Care. 2005; 28: 1195–200. pmid:15855588
  36. 36. Lindstrom J, Ilanne-Parikka P, Peltonen M, Aunola S, Eriksson JG, Hemio K, et al. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet. 2006; 368: 1673–9. pmid:17098085
  37. 37. Mohan V, Gokulakrishnan K, Deepa R, Shanthirani CS, Datta M. Association of physicial inactivity with components of metabolic syndrome and coronary artery disease-the Chennai Urban Population Study (CUPS No. 15). Diabetes Med. 2005; 22: 1206–11. pmid:16108850
  38. 38. Pischon T, Hankinson S, Hotamisligil G, Rifai N, Rimm E. Leisure-time physical activity and reduced plasma levels of obesity-related inflammatory markers. Obes Res. 2003; 11: 1055–64. pmid:12972675
  39. 39. Simmons RK, Griffin SJ, Steele R, Wareham NJ, Ekelund U. Increasing overall pyhsical activity and aerobic fitness is associated with improvements in metabolic risk: cohort analysis of the ProActive trial. Diabetologia. 2008; 51: 787–94. pmid:18317727
  40. 40. Rennie K, McCarthy N, Yazdgerdi S, Marmot M, Brunner E. Association of the metabolic syndrome with both vigorous and modetate physical activity. Int J Epidemiol. 2003; 36: 600–6.
  41. 41. Bianchi G, Rossi V, Muscari A, Magalotti D, Zoli M. Physical activity is negatively associated with the metabolic syndrome in the elderly. QJM. 2008; 101: 713–21. pmid:18650227
  42. 42. Di Loreto CD, Fanelli C, Lucide P, Murdolo G, De Cicco A, Parlanti N, et al. Validation of a counselling strategy to promote the adoption and the maintenance of physicial activity by type 2 diabetic subjects. Diabetes Care. 2003; 26: 404–8. pmid:12547870
  43. 43. Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose. Diabetes Care. 2007; 30: 1384–9. pmid:17473059
  44. 44. Healy GN, Wijndaele K, Dunstan DW, Shaw JE, Salmon J, Zimmet PZ, et al. Objectively measured sedentary time, physicial activity, and metabolic risk: the Australian Diabetes, Obesity and Life style study(AusDiab). Diabetes Care. 2008; 31: 369–71. pmid:18000181
  45. 45. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med. 2003; 37: 197–206. pmid:12782543
  46. 46. Mandarim-de-Lacerda CA. Beneficial effects of physical exercise on hypertension and cardiovascular adverse remodeling of diet-induced obese rats. Nutr Metab Cardiovasc Dis. 2007; 17: 365–75. pmid:17008074
  47. 47. Mkhonto SS, Labadarios D, Mabaso M. Association of body weight and physical activity with blood pressure in a rural population in the Dikgale village of Limpopo Province in South Africa. BMC Res Notes. 2012; 5: 118. pmid:22361366
  48. 48. de Rezende LF, Rodrigues Lopes M, Rey-López JP, Matsudo VK, Luiz Odo C. Sedentary behavior and health outcomes: an overview of systematic reviews. PLoS ONE. 2014; 9:e105620. pmid:25144686
  49. 49. Shuval K, Finley CE, Barlow CE, Gabriel KP, Leonard D, Kohl HW 3rd. Sedentary behavior, Cardiorespiratory fitness, Physicial activity, and Cardiometabolic risk in men: the cooper center longitudinal study. Mayo Clin Proc. 2014; 89: 1052–62. pmid:25034308
  50. 50. Ghosh A, Bhaqat M. Association of television viewing time with central obesity status in rural Asian Indian women: Santiniketan women study. Am J Hum Biol. 2014; 26: 427–30. pmid:24590684
  51. 51. Zderic TW, Hamilton MT. Physical inactivity amplifies the sensitivity of skeletal muscle to the lipid-induced downregulation of lipoprotein lipase activity. J Appl Physiol. 2006; 100: 249–57. pmid:16195388
  52. 52. Parsons TJ, Manor O, Power C. Television viewing and obesity: a prospective study in the 1958 British birth cohort. Eur J Clin Nutr. 2008; 62: 1355–63. pmid:17717536