Introduction
Metabolic syndrome (MetS), an adverse clustering of cardiovascular risk factors, is a serious public health concern and often results in an increased risk of type 2 diabetes and cardiovascular disease (Ford et al. 2004, Mozumdar & Liguori 2011). The common criteria based on the National Cholesterol Education Program, Adult Treatment Panel III (NCEP-ATP III) defines MetS as the existence of three or more of the following conditions: raised fasting plasma glucose (FPG), raised blood pressure (BP), elevated triglycerides (TG), reduced high density lipoprotein cholesterol (HDL-C), and central obesity (Alberti et al. 2009).
Numerous researchers have demonstrated an increased incidence and prevalence of MetS in women compared with men across a number of countries (Mozumdar & Liguori 2011, Schmitt et al. 2012, Sinclair et al. 2011), including Taiwan (Kuo et al. 2010). MetS is an independent risk factor for all-cause mortality in women (Dai et al. 2010) and that a higher risk for mortality exists in women with MetS compared to men (Wang et al. 2012). Due to its high prevalence and significant health consequences, further research is therefore required to understand the nature and consequences of MetS, especially amongst women.
A range of factors have been identified as being associated with MetS, such as increased age (Sinclair et al. 2011), female gender (Schmitt et al. 2012), lower socioeconomic status (Räikkönen et al. 2007), post-menopausal status (Kwasniewska et al. 2012), poor lifestyle and dietary patterns (Schlundt et al. 1992, Tuomilehto et al. 2001), cigarette smoking (Park et al. 2004), and lower physical activity (PA) levels (Assah et al. 2011, Bianchi et al. 2008, Metzger et al. 2010). Although
these factors have been identified in studies across a range of countries and in various age groups, genders, and social contexts, some gaps in research remain as there have been few large-scale population-based studies that incorporate the wide range of variables necessary to enable detailed multivariable analyses.
Despite the gaps in available research in this field, physical inactivity is one variable that is consistently identified as being an independent predictor of MetS. Specifically, higher levels of cardiorespiratory fitness (CRF) are suggested to be protective against MetS (Hassinen et al. 2010). The MetS-ameliorating effect of PA is mediated by improving CRF, thereby ameliorating the syndrome (Batty 2002, Brage et al. 2004). Such research does not, however, provide consistent information regarding the actual level of PA that is required for the protective effects to be realized. This is in part due to the fact that studies in this field have used various different instruments to measure PA, resulting in a lack of clarity regarding specific PA levels needed to be protective (Assah et al. 2011, Bankoski et al. 2011, Bianchi et al. 2008). In recent years, the World Health Organization (WHO) has developed and validated the International Physical activity questionnaire (IPAQ) to provide a tool that can be used across studies (Bauman et al. 2009, Craig et al. 2003, Guthold et al. 2008). Using this tool, Guthold et al. (2008) reported that physical inactivity was significantly higher for older age groups, urban areas, and women (Guthold et al. 2008). Women have been similarly reported to exhibit much lower CRF compared to men, and this fitness declines more rapidly with age (American College of Sports Medicine [ACSM] 2013).
previous work in the field. In one recent study using the IPAQ, a substantially higher proportion of low PA level was identified in Taiwan, particularly in women, compared to 19 other countries (Bauman et al. 2009). There has, however, been limited research using this instrument in Taiwan, especially in more vulnerable groups, including middle-aged and elderly women from urban communities. There is, therefore, no systematic investigation of the levels of PA amongst middle-and older-aged women in urban areas of Taiwan, middle-and no published data on the relationship between recommended PA levels using the IPAQ guidelines and MetS in this population.
Women exhibit lower PA and CRF as compared with men (Guthold et al. 2008, Bauman et al. 2009). As both PA and CRF inversely correlate with the occurrence of MetS, it can be expected that a higher prevalence of MetS is observed in women, especially in the elderly. In addition to sex and age, menopause is another factor that can exacerbate MetS (Kwasniewska et al. 2012). Because of the combined effect of gender, age and menopause on MetS, middle-aged and elderly women face a greater challenge to avoiding MetS in comparison with other populations. Hence, it is important to identify factors that can play a protective role against MetS in this population.
Although increasing PA has been recommended as a key factor for preventing MetS in the general population, increasing PA can become ineffective for MetS if a specific population loses the adjustability of the physical status of their cardiorespiratory system (Batty 2002, Brage et al. 2004). Some clinical trials even reported no effect of PA on MetS (Andersen et al. 2012), particularly in the elderly (Wang et al. 2012). Thus, it remains unclear whether or not increasing PA can ameliorate MetS in middle-aged and elderly women. If it can, the reference of PA measured by a
standard instrument is not available for the women to determine whether their levels of PA are sufficient to prevent MetS. Moreover, the treatment guidelines by the NCEP and the International Diabetes Federation (IDF) have recommended PA as the most crucial strategy for MetS (ACSM 2013). Although the recommendations relating to the levels of PA for the general adult population have been proposed by the ACSM (Haskell et al. 2007), few studies have examined the relationship between these PA level and MetS, and few have investigated how these recommendations have been adopted in Taiwanese middle-aged and elderly women. As middle-aged and elderly women from urban communities in Taiwan are likely to be a risk group for MetS, we conducted this study to investigate the association between PA level and MetS in this population.
The aim of this study was to investigate (1) whether PA is an independent protective factor against MetS, particularly in middle-aged and elderly women, and, if it is (2) to determine the optimal PA level to reduce the risk of MetS in the population.
Methods
Design
A cross-sectional study was undertaken to determine whether PA level is an independent predictor of MetS in middle-aged and elderly women after adjusting for personal characteristics, socioeconomic status, and menopausal status. In addition, we examined the association between PA level (i.e., low PA, moderate PA, and high PA) and MetS components including raised FPG, raised BP, elevated TG, reduced HDL, and central obesity.
Participants
A sample of 326 participants was recruited. Taking into account the MetS prevalence of 30% (Hwang et al. 2006), type I error of 5%, and absolute accuracy of 3%, this number was deemed sufficient for the proposed analyses. The estimated sample size was responsive to a power of 0.8 based on G-power analysis. We used purposive sampling method. A total of 326 voluntary women participants aged over 40 years were recruited from urban communities in northern Taiwan between January 2012 and December 2012 from a healthcare centre, health department clinics, and primary care practices. All recruited participants were able to speak and understand to Mandarin. Participants were informed and given medical clearance to participate after providing consent. Participants with severe or confirmed psychiatric diseases, a history of cancer, end-stage renal disease with dialysis, or disability resulting from any other diseases were excluded. Moreover, we defined the criteria for MetS based on the definition of NCEP ATP III and modified the criteria according to the definitions of IDF to take account of waist circumference (WC) norms in the Asian population. Thus, female MetS can be defined as having three of the following five risk factors: (i) raised FPG: fasting serum glucose ≧100 mg/dl (5.6mmol/L); (ii) raised BP: systolic blood pressure (SBP) ≧130 mmHg and/or diastolic blood pressure (DBP) ≧85 mmHg; (iii) elevated triglycerides: TG ≧150 mg/dl (1.7mmol/L); (iv) reduced HDL-C: ≦50 mg/dl (1.3mmol/L); and (v) central obesity: waist circumference ≧80 cm.
Measures
We collected data by drawing blood samples and taking anthropometric measurements to determine FPG, SBP/ DBP, TG, HDL-C, and WC. Additionally, all participants underwent a comprehensive assessment including demographic information, lifestyle factors, and PA levels.
Analysis of blood samples and anthropometric measurements
Fasting blood samples were drawn for the measurement of serum glucose, HDL-C, and TG after a 12-hr fast. Plasma glucose was analyzed by an enzymatic assay (Yellow Spring Glucose Analyzer, YSI, Yellow Spring, OH), and TG and HDL-C were measured by the laboratory of metabolism using enzymatic methods under internal and external quality control in a local hospital. The coefficients of variation for the internal quality control of TG, HDL-C and glucose measurements were under 1%, and a check of the external validity revealed that all measurements were in the range of 98–102% of the reference value.
Blood pressure was obtained using automated BP monitor devices. A certified biomedical engineering technician used static pressure readings to ensure the accuracy of these automated devices. The BP reading was obtained after participants had been seated quietly for at least 4-5 minutes. Arm circumference measurements were taken to ensure proper cuff selection, and participants were instructed not to talk while the BP measurements were being taken. The average of two measurements was recorded.
between the iliac crest and the lowest rib, usually at the level of the umbilicus, at the end of normal expiration when the participant was standing, using a soft anthropometric tape, with the mean of two measures being used for the analysis.
Demographic information and individual characteristics
Personalized background data obtained from the participants included age, menopausal status, educational level, marital status, personal income, occupation, and lifestyle factors. The lifestyle factors included smoking status, alcohol consumption, and dietary patterns presented by breakfast and late night snacking habit.
International Physical Activity Questionnaire
The type and length of PA over the previous 7 days was evaluated through the 7-item short form International Physical Activity Questionnaire (IPAQ) (Craig et al. 2003). The Taiwanese version of the IPAQ with well-established reliability and validity was developed in 2003 based on the results from the project of international cooperation with PA monitor system of the WHO (Liou et al. 2008). The 7-item IPAQ-Taiwan brief version has been reported to possess reasonable measurement properties for monitoring population levels of PA among ≧18 year-old adults to analyse individuals’ duration of PA including housework, transportation, leisure activity, and moderate to vigorous intensity PA. Weekly PA levels according to the weekly PA recommendation by ACSM (Haskell et al. 2007) are categorized from the IPAQ scores into 3 levels: (1) low, failure
to meet the criteria for categories 2 or 3, (2) moderate PA of at least 600 metabolic equivalent (MET)-minutes/week (3 or more days of vigorous activity of at least 20 minutes per day or 5 or more days of moderate activity or walking at least 30 minutes per day), and (3) high PA of a minimum of 1500 MET-minutes/week (vigorous activity on at least 3 days or 7 days of any combination of walking, moderate or vigorous activities). A continuous IPAQ score expressed as MET-minutes/week was calculated as the MET level multiplied by minutes of activity events per week (Gavin et al. 2011). Additionally, the amount of PA per week including walking, moderate-intensity PA, vigorous-moderate-intensity PA, and total PA (total PA MET-minutes equals to sum of walking, moderate-intensity PA, and vigorous-intensity PA) was also computed for analysis.
Statistical analysis
Results were analyzed using SPSS version 16.0 for Windows. Descriptive data were expressed as means and standard deviations (SD) for continuous variables and percentages (%) for categorical variables. Differences between groups were assessed using the Student’s t-test, Mann-Whitney U test, or chi-square test as appropriate. Multivariable logistic regression analysis appraised the associations among PA levels, MetS, and MetS components after adjustment for socio-demographic variables. All statistical analyses were two-tailed and considered significant at p < .05.
Ethical considerations
written informed consent. Approval to conduct the study was obtained from the local institutional review board in Taiwan. Responses were anonymous and non-traceable to individual participants. Additionally, all instruments were reviewed by members of the investigative team and community advisory board for cultural relevance and acceptability and were self-administered after comprehensive instruction.
Results
Characteristics of the study population
All 326 participants were aged between 40 to 80 years (M= 60.9, SD= 9.0) and provided interviews, blood sample analysis, and anthropometric tests. The baseline characteristics of the study population with and without MetS are summarized in Table 1. There were no significant differences between women with and without MetS in terms of age, marital status, alcohol consumption, smoking habits, and dietary patterns. A significantly higher proportion of women with MetS were post-menopausal (n=132, 93.6%) compared to women without MetS (n=150, 81.1%) (p= .001). Fewer women with MetS reported a higher educational level (n=63, 44.7%), compared with women without MetS (n=125, 67.6%) (p< .001). Moreover, a significantly higher proportion of participants with MetS were without personal income (n=106, 75.2%) (p= .003) and were currently unemployed (n=110, 59.5%) (p= .002) compared to participants without MetS.
Characteristics of MetS components in women with and without MetS
The prevalence of MetS in this sample was 43.3% based on the criteria from the modified NCEP ATP III. The prevalence of MetS components and the absolute values of MetS components in women with and without MetS are presented in Table 2. All values and the prevalence of MetS components were significantly higher in women with MetS than in women without MetS. Furthermore, reduced HDL-C was the most common risk metabolic component in both participants with and without MetS. MetS components with a higher prevalence in participants with MetS were reduced HDL-C (93.6%), raised-FPG (86.5%), high BP (85.8%), and reduced HDL-C (54.1%), elevated-TG (30.8%), and raised-BP (25.9%).
Comparison of PA levels between women with and without MetS
As table 3 demonstrates, women with MetS exhibited significantly less weekly moderate-intensity PA (157 MET-min vs. 216 MET-min, p= .003), walking (955 MET-min vs. 1230 MET-min, p< . 001), and total PA (1280 MET-min vs. 1750 MET-min, p< .001) than did women without MetS. However, there was no significant difference in vigorous-intensity PA (162 MET-min vs. 300 MET-min, p= .164). A higher proportion of low PA levels (24.1% vs. 3.8%) and lower proportion of moderate PA levels (69.5% vs. 85.9%), as well as high PA levels (6.4% vs. 10.3%) were found in MetS compared with non-MetS women (p < .001).
The association among physical activity level, MetS, and MetS risk components
The results from multivariable logistic regression analysis reported in Table 4 revealed that PA level is an independent predictor of MetS and MetS components after an adjustment for menopausal status, educational level, personal income, and occupation. In brief, compared with women with low PA level, women with higher PA levels exhibited a lower probability of having raised-FPG (p= . 006), raised-BP (p= .001), elevated-TG (p= .001), reduced HDL-C (p= .003), and central obesity (p= .002). Moreover, women who participated in at least a moderate PA level were at lower risk for MetS (OR 0.11; 95% CI 0.04-0.26; p< .001) compared with women with a low PA level, particularly for MetS components including raised-FPG (OR 0.29; 95% CI 0.13-0.62; p= .005), raised-BP (OR 0.20; 95% CI 0.09-0.48; p= .031), elevated-TG (OR 0.39; 95% CI 0.19-0.80; p< . 001), reduced-HDL (OR 0.30; 95% CI 0.15-0.61; p= .005), and central obesity (OR 0.30; 95% CI 0.11-0.84; p= .01).
Discussion
The aim of this study was to investigate the association between MetS and PA levels measured by IPAQ. The results indicate that women with MetS participated in significantly less weekly PA, including vigorous-intensity PA, moderate-intensity PA, walking, and total PA compared with women without MetS. Moreover, when PA amounts were categorized into three levels, both moderate and high PA levels were also found to be significantly lower in women with MetS compared with women without MetS. The logistic regression analysis identified that having at least
a moderate PA level was an independent predictor of having MetS and MetS components including raised FPG, raised BP, elevated TG, reduced HDL-C and central obesity. This confirms previous findings that suggested a moderate PA level is effective against MetS.
The association between MetS and PA has been noted in previous studies (Assah et al. 2011, Bianchi et al. 2008, Metzger et al. 2010). Rennie et al. (2003) reported that moderate and vigorous leisure-time PA are associated with a reduced risk of MetS and MetS components. A prospective cohort study demonstrated that PA energy expenditure predicted progression toward the MetS instead of a specific type or intensity of PA (Ekelund et al. 2005). Recently, Assah et al. (2011) also supported that free-living PA energy expenditure is associated with the prevalence of MetS. Moreover, a cross-sectional survey in Taiwan concluded that high PA amounts are inversely correlated with the prevalence of MetS (Dai et al. 2010). Consistent with these studies, our findings revealed significantly different amounts of total PA between women with and without MetS. These findings highlight the importance of PA for middle-aged and elderly women in urban communities. To our knowledge, this is the first study to use the global instrument-IPAQ for the assessment of PA to confirm that a moderate PA level is associated with lower likelihood of developing MetS.
These findings differ from those reported in other studies that have not identified this independent association between PA and MetS (Andersen et al. 2012, Arbey Mesa et al. 2011, Dalacorte et al. 2009, Wang et al. 2012), possibly due to differences in sample characteristics including gender, age group, and sample size. Moreover, the possible explanation for the lack of
this study may be that few participants participate in vigorous-intensity PA compared with those reported in other studies which have included samples recruited through community surveys (Bauman et al. 2009). Moreover, Liou (2004) has suggested that subjects who did not regularly exercise and who had lower educational levels may underestimate the vigorous-intensity PA by IPAQ. MetS women in this study had a higher proportion of low PA level and lower educational levels compared to non-MetS women.
There are some limitations to this study that should be noted. First, it should be noted that various instruments for assessing PA have been used in research to date, resulting in differing findings. Many studies have used self-reported PA questionnaires, including various structured questionnaires (Arbey Mesa et al. 2011, Bianchi et al. 2008, Rennie et al. 2003) and semi-structured questionnaires (Dai et al. 2010, Ford et al. 2005, Katariina et al. 2010). More recently, alternative objective measurements of PA has been used, including accelerometers, combined heart rate and movement sensors, or double-labelled water assessments (Assah et al. 2011, Bankoski et al. 2011, Ekelund et al. 2005, Metzger et al. 2010). It is difficult to accurately quantify overall lifestyle PA because it constitutes minutes per day and because much of it is not easy to recall. Although self-report of purposeful PA is sometimes crude and imprecise, it is easier and more efficient to evaluate and compare the outcome of PA and provides a basis for validation in other studies using more objective measures. Moreover, data from self-report tools for assessing PA have a high correlation with that obtained from objective measures of PA such as accelerometers. Recent studies confirm IPAQ as a valid instrument for PA measurement (Bauman et al. 2009, Craig et al.
2003, Guthold et al. 2008). Hence we used this tool to determine the association of MetS and PA level.
Based on the instrument of IPAQ, we found that a higher proportion of MetS women reported a low/insufficient PA level and that lower proportion of MetS women participated in a moderate PA level equivalent to at least 600 MET-minute/week (3 or more days of vigorous activity of at least 20 minutes per day or 5 or more days of moderate activity or walking of at least 30 minutes per day) compared with non-MetS women. Furthermore, our findings confirm the strongly inverse correlation between MetS and PA measured by IPAQ. The NCEP-ATP guidelines propose that increasing PA energy expenditure, including PA level and leisure-time activity, protects against MetS. The proposed mechanisms by which PA is a preventive factor against MetS include increased insulin sensitivity, weight loss, body fat redistribution, lipid metabolism, decreased chronic systematic inflammation, and improved vascular or artery compliance, as well as improved CRF (Caro et al. 2013, Miyaki et al. 2012). These factors provide a sound explanation for our results that a higher PA level results in a lower risk for MetS and MetS components including raised-FPG, raised-BP, elevated-TG, reduced-HDL, and central obesity.
Not surprisingly, in this study, the high prevalence of MetS in middle-aged and elderly women was similar to that from previous studies (Schmitt et al. 2012, Kuo et al. 2010, Lidfeldt et al. 2003). Wyrzykowski et al, for example, reported a substantial increase in the prevalence of MetS (46.3%) in women older than 60 years (Wyrzykowski et al. 2005). Our results also demonstrated the higher
of American Indian adults that reported a high prevalence of MetS in women (53.2%) (Sinclair et al. 2011). Such findings suggest that there gender-specific factors that may predispose women to higher cardiovascular risks due to metabolic disorders.
In line with previous studies, we found that menopause (Kwasniewska et al. 2012) and low socio-economic status (i.e., low educational level, personal income, and unemployed) (Lidfeldt et al. 2003, Räikkönen et al. 2007) are associated with MetS. Such associations may be due to the lowered insulin resistance that may be exaggerated by menopause combined with aging and higher rates of physical inactivity and unhealthy diet amongst those from lower socioeconomic backgrounds (Silventoinen et al. 2005). Although the factors associated with MetS could be numerous, physical inactivity is the most easily changeable factor. PA level was confirmed as a significant independent predictor of MetS, independent of menopausal status, educational level, personal income, and occupation in middle-aged and elderly women.
Dietary pattern is another factor related to MetS. Recent studies have demonstrated that skipping breakfast results in obesity (Kapantais et al. 2011, Veltsista et al. 2010) because breakfast helps reduce dietary fat and minimises impulsive snacking (Schlundt et al. 1992). However, similar dietary patterns were found between study participants with and without MetS. This may be due to the relatively rough evaluation of dietary patterns used in this study. Further research is needed to obtain more detailed dietary information to assess consumption of calories on a daily basis and determine PA levels precisely in relation to MetS independent of the diet-related factors.
due to a sectional design using a convenience sample. The principal limitation of a cross-sectional design is that it cannot infer causality. A more advanced research design such as a cohort, case-control study or a randomized controlled trial is required to determine PA measured by IPAQ and its ability to act as a preventive factor against MetS in middle-aged and elderly women. Further, the generalization of these results to other populations should be made with caution. The study sample included only women aged over 40-years in urban communities of northern Taiwan without known cardiovascular disease. Therefore, these results should not be directly generalized to men or younger women or individuals with diagnosed CVD.
Conclusion
We confirmed the high prevalence of MetS in middle-aged and elderly women from urban communities, and concluded that PA level is independently associated with MetS in this population. Urbanization resulting in sedentary lifestyles exaggerates a higher burden of metabolic disorders particularly in middle-aged and elderly women. Therefore, promoting participation in a moderate level of PA for at least 600 MET-minutes/week (3 or more days of vigorous activity of at least 20 minutes per day or 5 or more days of moderate activity or walking of at least 30 minutes per day ) has the potential to protect against MetS and all risk of MetS components in middle-aged and elderly women.
Relevance to clinical practice
PA level among middle-aged and elderly women from urban communities. This information can be used to develop strategies aimed at promoting PA instead of sedentary lifestyles in this population at-risk of MetS. Emphasis should be put on MetS prevention by community healthcare workers. It highlights that middle-aged and elderly women are encouraged to engage in at least a moderate PA level to prevent MetS. Since a moderate PA level means to perform PA of at least 600 MET-minutes/week (3 or more days of vigorous activity of at least 20 minutes per day or 5 or more days of moderate activity or walking at least 30 minutes per day), community nurses can educate individual exercise prescription for this population depends on their physical ability or preferences. Moreover, education efforts for community-dwelling, middle-aged and elderly women, especially those with relatively low educational levels or postmenopausal women should focus on lifestyle changes, known risk factors, and their consequences regardless of MetS to facilitate a long-term lifestyle change.
Acknowledgements
This study was supported by grants from the Tri-Service General Hospital research project (TSGH-C101-175), Taipei, Taiwan. We express our deepest appreciation to Professor H-A Shui, Faculty of the Graduated Institute of Medical Science, National Defense Medical Center, Taiwan, who gave us the recommendation of data interpretation and manuscript writing. No potential conflicts of interest relevant to this article were reported.
Contributions
Study design: CHL, LCC; data collection: SLC, YJH; data analysis: CHL, MSL and manuscript preparation: CHL, PY, WCT, LCC.
References
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM & Smith SC (2009) 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 120, 1640-1645.
American College of Sports Medicine (2013) ACSM's guidelines for exercise testing and prescription (9th. ed.). Williams & Wilkins, Lippincott
Andersen E, Hostmark AT & Anderssen SA (2012) Effect of a physical activity intervention on the metabolic syndrome in Pakistani immigrant men: A randomized controlled trial. Journal of Immigrant and Minority Health 14, 738-746.
Arbey Mesa J, Fabian Suarez M, Arbelaez A, Mosquera M, Pradilla A, Ramirez-Velez R & de Plata AC (2011) Lack of relationship of physical activity level with cardiovascular risk factors and metabolic syndrome in apparently healthy men. Endocrinología y Nutrición 58, 68-74.
Assah FK, Ekelund U, Brage S, Mbanya JC &Wareham NJ (2011) Urbanization, physical activity, and metabolic health in sub-saharan Africa. Diabetes Care 34, 491-496.
Batty GD (2002) Physical activity and coronary heart disease in older adults. A systematic review of epidemiological studies. European Journal of Public Health 12, 171-176.
Bankoski A, Harris TB, McClain JJ, Brychta RJ, Caserotti P, Chen KY, Berrigan D, Troiano RP & Koster A (2011) Sedentary activity associated with metabolic syndrome independent of physical activity. Diabetes Care 34, 497-503.
Bauman A, Bull F, Chey T, Craig CL, Ainsworth BE, Sallis JF, Bowles HR, Hagstromer M, Sjostrom M & Pratt M (2009) The International Prevalence Study on Physical Activity: results from 20 countries. International Journal of Behavioral Nutrition and Physical Activity 6, 21-31.
Bianchi G, Rossi V, Muscari A, Magalotti D & Zoli M (2008) Physical activity is negatively associated with the metabolic syndrome in the elderly. Quarterly Journal of Medicine 101, 713-721.
Brage S, Wedderkopp N, Ekelund U, Franks PW, Wareham NJ, Andersen LB, Froberg K &
European Youth Heart Study (2004) 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 27, 2141-2148.
Caro J, Navarro I, Romero P, Lorente RI, Priego MA, Martinez-Hervas S, Real JT & Ascaso JF (2013) Metabolic effects of regular phisical exercice in healthy population. Endocrinología y Nutrición 60, 167-172.
Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF & Oja P (2003) International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise 35, 1381-1395.
Dai DF, Hwang JJ, Chen CL, Chiang FT, Lin JL, Hsu KL, Tseng CD & Tseng YZ (2010) Effect of physical activity on the prevalence of metabolic syndrome and left ventricular hypertrophy in apparently healthy adults. Journal of The Formosan Medical Association 109, 716-724.
Dalacorte RR, Reichert CL & Vieira JL (2009) Metabolic syndrome and physical activity in southern Brazilian community-dwelling elders: a population-based, cross-sectional study. BMC Public Health 9, 25.
Ekelund U, Brage S, Franks PW, Hennings S, Emms S & Wareham NJ (2005) Physical activity energy expenditure predicts progression toward the metabolic syndrome independently of aerobic fitness in middle-aged healthy Caucasians. Diabetes Care 28, 1195.
Ford ES, Giles WH & Mokdad AH (2004) Increasing prevalence of the metabolic syndrome among U.S. adults. Diabetes Care 27, 2444-2449.
Ford ES, Kohl, HW, Mokdad AH & Ajani UA (2005) Sedentary Behavior, Physical Activity, and the Metabolic Syndrome among US Adults. Obesity Research 13, 608-614.
Gavin JR, Fox KM & Grandy S (2011) Race/Ethnicity and gender differences in health intentions and behaviors regarding exercise and diet for adults with type 2 diabetes: a cross-sectional analysis. BMC Public Health 11, 533.
Guthold R, Ono T, Strong KL, Chatterji S & Morabia A (2008) Worldwide variability in physical inactivity a 51-country survey. American Journal of Preventive Medicine 34, 486-494.
Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD & Bauman A (2007) Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Medicine and Science in Sports and Exercise 39, 1423-1434.
Hassinen M, Lakka TA, Hakola L, Savonen K, Komulainen P, Litmanen H, Kiviniemi V, Kouki R, Heikkilá H & Rauramaa R (2010) Cardiorespiratory Fitness and Metabolic Syndrome in Older Men and Women: The Dose Responses to Exercise Training (DR's EXTRA) study. Diabetes Care 33, 1655-1657.
Hwang LC, Bai CH & Chen CJ (2006) Prevalence of obesity and metabolic syndrome in Taiwan. Journal of the Formosan Medical Association 105, 626-635.
Kapantais E, Chala E, Kaklamanou D, Lanaras L, Kaklamanou M & Tzotzas T (2011) Breakfast skipping and its relation to BMI and health-compromising behaviours among Greek adolescents. Public Health Nutrition 14, 101-108.
Katariina K, Arja H, Hannu K, Hannu K, Markku P, Pekka M, Heikki O, Olli K & Mauno V (2010) Leisure-time physical activity and metabolic syndrome plus depressive symptoms in the
FIN-D2D survey. Preventive Medicine: An International Journal Devoted to Practice and Theory
51, 466-470.
Kuo CF, Yu KH, Chen YM, Hwang JS, Li HY, Shen YM & See LC (2010) Gender differences in the prevalence of metabolic syndrome. Formosan Journal of Medicine 14, 384-392.
Kwasniewska M, Pikala M, Kaczmarczyk-Chalas K, Piwonnska A, Tykarski A, Kozakiewicz K, Pajak A, Zdrojewski T & Drygas W (2012) Smoking status, the menopausal transition, and metabolic syndrome in women. Menopause 19, 194-201.
Lidfeldt J, Nyberg P, Nerbrand C, Samsioe G, Scherstén B & Agardh CD (2003) Socio-demographic and psychosocial factors are associated with features of the metabolic syndrome. The Women's Health in the Lund Area (WHILA) study. Diabetes, Obesity & Metabolism 5, 106-112.
Liou YM (2004) Development and Verification of Validity and Reliability of the International Physical Activity Questionnaire Taiwan Version. Institute of Nursing in National Taiwan University, Taipei.
Liou YM, Jwo CJ, Yao KG, Chiang LC & Huang LH 2008. Selection of appropriate Chinese terms to represent intensity and types of physical activity terms for use in the Taiwan version of IPAQ. The Journal of Nursing Research 16, 252-263.
Metzger JS, Catellier DJ, Evenson KR, Treuth MS, Rosamond WD & Siega-Riz AM (2010) Associations between patterns of objectively measured physical activity and risk factors for the metabolic syndrome. American Journal of Health Promotion 24, 161-169.
Miyaki A, Maeda S, Choi Y, Akazawa N, Tanabe Y & Ajisaka R (2012) Habitual aerobic exercise increases plasma pentraxin 3 levels in middle-aged and elderly women. Applied Physiology, Nutrition, and Metabolism 37, 907-911.
Mozumdar A & Liguori G (2011) Persistent Increase of Prevalence of Metabolic Syndrome Among U.S. Adults: NHANES III to NHANES 1999-2006. Diabetes Care 34, 216-219.
Park HS, Oh SW, Cho SI, Choi WH & Kim YS (2004) The metabolic syndrome and associated lifestyle factors among South Korean adults. International Journal of Epidemiology 33, 328-336.
Räikkönen K, Matthews KA & Kuller LH (2007) Depressive symptoms and stressful life events predict metabolic syndrome among middle-aged women: a comparison of World Health Organization, Adult Treatment Panel III, and International Diabetes Foundation definitions. Diabetes Care 30, 872-877.
Rennie K, McCarthy N, Yazdgerdi S, Marmot M & Brunner E (2003) Association of the metabolic syndrome with both vigorous and moderate physical activity. International Journal of Epidemiology 32, 600.
Schlundt DG, Hill JO, Sbrocco T, Pope-Cordle J & Sharp T (1992) The role of breakfast in the treatment of obesity: a randomized clinical trial. The American Journal of Clinical Nutrition
55, 645-651.
Schmitt AC, Cardoso MR, Lopes H, Pereira WM, Pereira EC, de Rezende DA, Guarizi RG, Dellu, MC, de Moura Oliveira J, Flauzino E, Blumel JE & Aldrighi JM (2013) Prevalence of
metabolic syndrome and associated factors in women aged 35 to 65 years who were enrolled in a family health program in Brazil. Menopause 20, 470-476.
Silventoinen K, Pankow J, Jousilahti P, Hu G & Tuomilehto J (2005) Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women. International Journal of Epidemiology 34, 327-334.
Sinclair KA, Bogart A, Buchwald D & Henderson JA (2011) The prevalence of metabolic syndrome and associated risk factors in northern plains and southwest american indians. Diabetes Care 34, 118-120.
Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V & Uusitupa M (2001) Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. The New England Journal of Medicine 344, 1343-1350.
Veltsista A, Laitinen J, Sovio U, Roma E, Järvelin MR & Bakoula C (2010) Relationship between eating behavior, breakfast consumption, and obesity among Finnish and Greek adolescents. Journal of Nutrition Education and Behavior 42, 417-421.
Wang WS, Wahlqvist ML, Hsu CC, Chang HY, Chang WC & Chen CC (2012) Age- and gender-specific population attributable risks of metabolic disorders on all-cause and cardiovascular mortality in Taiwan. BMC Public Health 12, 111.
Wang X, Hsu FC, Isom S, Walkup MP, Kritchevsky SB, Goodpaster BH, Church TS, Pahor M, Stafford RS & Nicklas BJ (2012) Effects of a 12-month physical activity intervention on
prevalence of metabolic syndrome in elderly men and women. Journal of Gerontology 67, 417-424.
Wyrzykowski B, Zdrojewski T, Sygnowska E, Biela U, Drygas W, Tykarski A, Tendera M & Broda G (2005) Epidemiology of metabolic syndrome in Poland. Results of the WOBASZ program. Kardiologia Polska 63, S641-644.
