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Association between physical activity and metabolic syndrome among Malay adults in a developing country, Malaysia
Journal of Science and Medicine in Sport 17 (2014) 195–200
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Original research
Association between physical activity and metabolic syndrome among Malay adults in a developing country, Malaysia Anne H.Y. Chu a,∗ , F.M. Moy a,b a b
Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia Julius Centre University of Malaya, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
a r t i c l e
i n f o
Article history: Received 9 May 2012 Received in revised form 8 April 2013 Accepted 13 April 2013 Available online 9 May 2013 Keywords: Blood glucose Central obesity Hypertension Serum lipids Motor activity Malaysia
a b s t r a c t Objectives: Metabolic syndrome is a highly prevalent health problem within the adult population in developing countries. We aimed to study the association of physical activity levels and metabolic risk factors among Malay adults in Malaysia. Design: Cross-sectional. Methods: Body mass index, waist circumference, and systolic/diastolic blood pressure, fasting blood glucose, fasting triglyceride and high-density lipoprotein cholesterol levels were measured in 686 Malay participants (aged 35–74 years). Self-reported physical activity was obtained with the validated International Physical Activity Questionnaire (Malay version) and categorized into low, moderate or high activity levels. Results: Individuals who were classified as overweight and obese predominated (65.6%). On the basis of the modified NCEP ATP III criteria, metabolic syndrome was diagnosed in 31.9% of all participants, of whom 46.1% were men and 53.9% were women. The prevalence of metabolic syndrome among participants with low, moderate or high activity levels was 13.3%, 11.7% and 7.0%, respectively (p < 0.001). Statistically significant negative associations were found between a number of metabolic risk factors and activity categories (p < 0.05). The odds ratios for metabolic syndrome in the moderate and high activity categories were 0.42 (95% CI: 0.27–0.65) and 0.52 (95% CI: 0.35–0.76), respectively, adjusted for gender. Conclusions: Moderate and high activity levels were each associated with reduced odds for metabolic syndrome independent of gender. Although a slightly lower prevalence of metabolic syndrome was associated with high activity than with moderate activity, potential health benefits were observed when moderate activity was performed. © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction Physical inactivity and energy imbalance have resulted in an obesity epidemic. Both developed and developing countries face the challenges of obesity and its comorbidities such as diabetes mellitus and cardiovascular diseases. All of these noncommunicable diseases affect an individual’s physical and social functioning, as well as quality of life. Cardiovascular disease is responsible for one in three deaths worldwide and is the number one cause of mortality.1 Interest is growing in a constellation of cardiovascular risk factors, including visceral obesity, dyslipidaemia, hyperglycaemia and hypertension that constitute another important health problem – the metabolic syndrome. This syndrome is highly prevalent in the
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (A.H.Y. Chu).
adult population worldwide, with a suggested ethnic predisposition in Asians.2 The chance of having metabolic syndrome is closely linked to modifiable lifestyle factors, such as overweight, obesity and physical inactivity. This problem is even more pronounced among middle-aged populations. Malaysia has been identified as a country with an increased prevalence of non-communicable diseases because of high levels of physical inactivity.3 With increasing urbanization and the availability of motorized transportation, people tend to reduce their physical activity levels. According to Malaysia’s Third National Health and Morbidity Survey (NHMS III) conducted in 2006, which used the International Physical Activity Questionnaire (IPAQ), 43.7% of the Malaysian adult population was physically inactive.4 The World Health Survey also found Malaysia to be one of the countries with an outstandingly high prevalence of physical inactivity, the highest among all of Western Pacific Region countries.5 Although inverse associations between moderate and high physical activity levels with obesity and metabolic syndrome have been well-established in a number of Western and Asian countries,
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there is still insufficient evidence in Malaysia. We aimed to fill this gap in our local setting to show the need to combat obesity and metabolic risks through physical activity intervention studies. The primary goal of this study was to explore the associations between different levels of physical activity with obesity and metabolic risk factors in middle-aged Malay adults.
2. Methods This was an analytical cross-sectional study. All eligible employees (aged 35 years and older) of a public university in Kuala Lumpur, Malaysia, were invited to participate in a free annual worksite health screening. A random sample of 1000 employees was selected and invited to participate in our study. Of these employees, 686 agreed to participate, providing a response rate of 68.6%. Study inclusion criteria consisted of the following: (1) self-identified race as Malay (as the IPAQ-M was in the Malay language); (2) absence of physical illness or disabilities that would limit daily physical activities such as walking; and (3) the ability to read and write well enough to record physical activities. Written informed consent was obtained from all participants. Ethics clearance was obtained from the Medical Ethics Committee of the university (Reference Number: MEC 782.18). Data were collected from August 2010 to August 2011. Anthropometric parameters (weight, height and waist circumference), systolic/diastolic blood pressure, fasting blood glucose and fasting lipid profiles were measured. Weight was measured using the SECA digital scale and height with the SECA stadiometer (Hamburg, Germany). Body mass index (kg/m2 ) was calculated using the formula weight (kg) divided by height2 (m2 ). Central obesity was measured with a circumference measurement tape. The waist was measured as the point midway between the iliac crest and the costal margin (lower rib). All measurements were performed by trained staff and quality checks were conducted regularly. Blood pressure was measured using a clinically validated digital automatic blood pressure monitor (Omron HEM-907, Kyoto, Japan). The analysis of biochemical indicators, which included fasting blood glucose and a full lipid profile, was conducted by the Clinical Diagnostic Laboratory of the University Malaya Medical Center. The validated self-administered, long-form Malay version of the IPAQ (IPAQ-M)6 was used to assess the levels of physical activity among participants. The IPAQ-M has previously been tested for test-retest reliability and criterion validity. The intraclass correlation coefficient revealed moderate to good reliability ranging from 0.54 to 0.92 (p < 0.001) and the kappa coefficient of 0.89 showed good validity (95% confidence interval [CI] = 0.79–0.98). The questionnaire included 31 questions on frequency and time spent on walking and moderate and vigorous activities in four domains (work, transportation, home and leisure-time activity), as well as time spent sitting. Participants were asked to recall their activities during the last 7 days. For the analysis of the IPAQ-M data, the following metabolic equivalent of task (MET) values were used: walking = 3.3 METs; moderate physical activity = 4.0 METs; vigorous physical activity = 8.0 METs. The results were presented as the estimation of energy expenditure in metabolic equivalent-minutes per week (MET-min week−1 ). The MET-min week−1 was calculated as minutes of activity/day × days per week × MET level. According to the IPAQ Research Committee, the continuous indicator should be presented as median values and interquartile ranges rather than means. Both continuous and categorical indicators of physical activity were calculated from the data obtained from the IPAQM. A total physical activity score obtained from the IPAQ-M was calculated, as well as separate scores for each of the four physical activity domains and activity levels. On the basis of the IPAQ
guidelines, participants with total physical activity scores of <600 MET-min week−1 were categorized as being in the “low” category, those with 600–2999 MET-min week−1 as being in the “moderate” category and those with ≥3000 MET-min week−1 as being in the “high” category. For the estimation of energy expenditure, the IPAQ scoring guide stated that MET-minute scores are equivalent to kilocalories for a 60-kg person, and that kilocalories may be computed from MET-minutes using the following equation: MET-min × (weight in kilograms/60 kg). Thus these data were also converted to energy expenditure/week adjusted for weight (kcal/week/kg). According to the modified NCEP ATP III criteria that is more suitable for the Malay population,7 the presence of any three or more of the following five factors is required for a working definition of metabolic syndrome: abdominal obesity, hypertriglyceridaemia (triglycerides ≥1.7 mmol/L) or specific treatment for this lipid abnormality; low HDL cholesterol (HDL cholesterol ≤1.03 mmol/L for men and ≤1.29 mmol/L for women) or specific treatment for this lipid abnormality; elevated blood pressure (systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥85 mmHg) or current use of antihypertensive drugs; impaired fasting glucose (fasting plasma glucose ≥ 5.6 mmol/L) or drug treatment for elevated glucose (previously diagnosed type 2 diabetes). The NCEP ATP III criteria suggested the cutoff points of waist circumference should be ethnic specific, with adoption of the Asian criteria for abdominal obesity (waist circumference >90 cm in men and >80 cm in women). Data were entered and analyzed using SPSS for Windows version 16.0. The significance level was set at p < 0.05. Participants’ activity scores (MET-min week−1 ) were presented as medians with 95% CI and interquartile range for each domain and type of activity and further categorized into three categories: low, moderate and high levels of physical activity. Distributions of continuous variables were tested for normality using the Kolmogorov–Smirnov test. Associations between categorical variables were tested using the chi-squared test. The non-parametric Mann–Whitney U test was used for asymmetric continuous variables. Differences in the frequencies of obesity and each metabolic risk factor across categories of physical activity were analyzed using the chi-squared test stratified by gender. Logistic regression was used to estimate the odds ratio (OR) with 95% CI of having metabolic syndrome in each physical activity category. The analyses were further adjusted for gender. We ruled out the presence of an interaction between age and the main independent variables (physical activity categories), which was not statistically significant.
3. Results A total of 686 employees (39.7% men, 60.3% women) were recruited. Their demographic, clinical and anthropometric characteristics are presented in Table 1. Participants were 35–74 years old (mean age: 45.9 ± 6.5 years). There was no significant difference in age groups by gender. Most participants were in the moderate activity level category (46.1%), followed by those in the low activity level (27.1%) and the high activity level (26.8%). Individuals who were classified as overweight and obese predominated (65.6%). Male participants were significantly heavier than female participants; had a larger waist circumference; had higher systolic/diastolic blood pressure, triglyceride and fasting glucose levels; and had lower HDL cholesterol levels (p < 0.001). From the modified NCEP ATP III definition, metabolic syndrome was diagnosed in 31.9% of participants. There was a significant difference in the prevalence of metabolic syndrome between men (37.1%) and women (24.2%) (p < 0.05).
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Table 1 Characteristics of participants.
Mean age (years ± SD) Less than 40 years 40–49 years 50–59 years 60 years and above Educational level Primary Secondary Tertiary Levels of physical activity Low Moderate High Body mass index (kg/m2 )b Underweight < 18.5 Normal 18.6 to 24.9 Overweight 25 to 29.9 Obese ≥ 30 Waist circumference (cm) Systolic BP (mmHg) Diastolic BP (mmHg) Triglycerides (mmol/L) HDL cholesterol (mmol/L) Fasting glucose (mmol/L) Metabolic syndrome
Total (n = 686)
Men (n = 272)
Women (n = 414)
p value
45.9 ± 6.5 139 (20.3) 349 (50.9) 194 (28.3) 4 (0.5)
46.4 ± 6.9 52 (19.1) 131 (48.2) 86 (31.6) 3 (1.1)
45.5 ± 6.0 87 (21.0) 218 (52.7) 108 (26.1) 1 (0.2)
0.14
142 (20.7) 247 (36.0) 297 (43.3)
66 (24.3) 80 (29.4) 126 (46.3)
76 (18.4) 167 (40.3) 171 (41.3)
186 (27.1) 316 (46.1) 184 (26.8)
59 (21.7) 116 (42.6) 97 (35.7)
127 (30.7) 200 (48.3) 87 (21.0)
10 (1.5) 226 (32.9) 269 (39.2) 181 (26.4) 86.5 ± 10.7 126.4 ± 15.3 79.2 ± 10.8 1.5 ± 0.9 1.3 ± 0.3 5.4 ± 1.9 219 (31.9)
3 (1.1) 86 (31.6) 131 (48.2) 52 (19.1) 89.1 ± 9.7 130.8 ± 13.5 81.6 ± 9.8 1.8 ± 1.0 1.2 ± 0.2 5.6 ± 2.1 101 (37.1)
7 (1.7) 140 (33.8) 138 (33.3) 129 (31.2) 84.9 ± 11.1 123.6 ± 15.8 77.7 ± 11.1 1.2 ± 0.6 1.5 ± 0.4 5.2 ± 1.8 118 (24.2)
0.01a
<0.001a
<0.001a
<0.001a <0.001a <0.001a <0.001a <0.001a 0.001a 0.02a
The data represent means ± SD, number (%). BP = blood pressure; HDL = high-density lipoprotein. a Significant difference between men and women by the Mann–Whitney U test for continuous variables and chi-square test for categorical variables. b Classification according to the World Health Organization.8
The median (95% CI) of total reported physical activity was 1710.5 (930.8, 3177.0) MET-min week−1 (data not shown). Domestic physical activity was the domain in which participants were most active (567.5 [510.0, 630.0] MET-min week−1 , p < 0.001), followed by occupational (297.0 [245.0, 330.0] MET-min week−1 ), transportation (165.0 [132.0, 198.0] MET-min week−1 ) and leisuretime domains (165.0 [99.0, 198.0] MET-min week−1 ). Activity in the domestic domain was significantly higher than that in the transportation and leisure-time domains (p < 0.001), while activity in the transportation domain did not differ significantly from that in the leisure-time domain (p = 0.72). Moderate activity (720.0 [642.5, 765.0] MET-min week−1 ) was significantly higher than walking activity (585.8 [495.0, 660.0] MET-min week−1 ) and high activity (0 [0, 480.0] MET-min week−1 , p < 0.001). There was no significant difference between walking activity and high activity (p = 0.07). Physical activity based on activity in all domains and all activity levels was analyzed separately for men and women (Table 2). Men reported significantly higher total physical activity scores (2155.3 [1780.0, 2533.5] MET-min week−1 ) than women (1475.0 [1330.5, 1683.0] MET-min week−1 , p < 0.001). The overall energy expenditure of the participants was 1898.4 (1757.8, 2098.7) kcal/kg/week; men were significantly more active than women (men: 2600.68 [2154.9, 2993.6] kcal/kg/week vs. women: 1624.40[1438.2, 1825.4] kcal/kg/week, p < 0.05). A total of 396 (57.7%) participants fulfilled the physical activity guidelines recommended by the American College of Sports Medicine and the American Heart Association9 for improvement of health and wellness (data not shown). Obesity and risk factors for metabolic syndrome at different physical activity levels among men and women are shown in Table 2. The prevalence of metabolic syndrome among all participants was significantly higher in those with a low activity level (13.3%) than in those with a moderate (11.7%, p = 0.003) or a high activity level (7.0%, p = 0.001) (data not shown). However, there was no significant difference between participants categorized as having moderate activity or high activity levels (p = 0.88). Metabolic syndrome was significantly associated with physical activity levels among women only (p = 0.002). Statistically significant negative associations were found between a number of metabolic risk
factors and activity categories (p < 0.05). Central obesity was the most prevalent metabolic risk factor among all participants (54.3%). We further examined the associations between physical activity levels and the prevalence of obesity, metabolic syndrome and its components through logistic regression analysis (Table 3). Participants with a low activity level were defined as the reference group. Increasing moderate and high activity levels each showed statistically significant protective effects against metabolic syndrome (p < 0.05). Specifically, being moderately active (OR: 0.47; 95% CI: 0.30–0.73) offered a stronger significant protective effect against metabolic syndrome, obesity, central obesity and hypertension than being highly active did (OR: 0.54; 95% CI: 0.37–0.78). After adjusting for gender, those with moderate and high activity levels had significantly lower odds of having metabolic syndrome, hypertriglyceridaemia, hypertension and hyperglycaemia (ranging from 0.38 to 0.78, p < 0.05). 4. Discussion In this cross-sectional study, we obtained a relatively low response rate for an Asian country. This may be attributed to sociodemographic variables such as gender, age and level of education among our study population. In an international study on the prevalence of physical activity from 20 countries across Europe, America and Asia, a median country-level response rate of 61.0% was obtained. Hence, our response rate of 68.6% was considered acceptable in comparison. The lower response rate among men in our study was consistent with that in a study done in Japan (37.1% men, 62.9% women).10 The gender difference is probably due to the difference in awareness of the importance of physical activity between men and women; in addition, work responsibilities may have restricted the men’s attendance. Our results showed a higher prevalence of metabolic syndrome in men than in women. However, a recent large-scale, population-based survey of all major ethnic groups in Malaysia found a significantly higher prevalence of metabolic syndrome in Malay women (51.5%) compared with men (38.5%) using the same diagnostic criteria.11 This could be due to the difference in the
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Table 2 Characteristics of participants’ involvement in physical activity and frequencies of metabolic syndrome by gender. Physical activity parameters
Men (n = 272) Median (95% CI); IQR
Women (n = 414) Median (95% CI); IQR
p valuea
Total Occupational Transportation Domestic Leisure time Walking Moderate Vigorous Sitting
2155.3 (1780.0, 2533.5); 2631.8 480.0 (330.0, 495.0); 933.0 231.0 (198.0, 297.0); 643.5 520.0 (420.0, 630.0); 953.8 315.5 (231.0, 396.0); 897.8 693.0 (577.5, 825.0); 1113.8 720.0 (625.0, 780.0); 1200.0 240.0 (0, 240.0); 960.0 2370.0 (2220.0, 2520.0); 1620.0
1475.0 (1330.5, 1683.0); 1848.4 231.0 (169.5, 268.5); 645.0 99.0 (66.0, 165.0); 495.0 600.0 (540.0, 720.0); 967.5 54.8 (0, 99.0); 360.0 486.8 (396.0, 594.0); 858.0 720.0 (600.0, 840.0); 1080.0 0 (0, 180.0); 180.0 2760.0 (2640.0, 2880.0); 1215.0
<0.001 <0.001 0.001 0.01 <0.001 <0.001 0.24 <0.001 0.001
Physical activity levels Low n (%) Metabolic syndrome components Obesityb 18 (35.3) 28 (24.8) Central obesityc d 38 (25.3) Hypertriglyceridaemia e Low HDL cholesterol 23 (27.7) 39 (22.2) Hypertensionf 16 (25.0) Hyperglycaemiag 29 (28.7) Metabolic syndrome
Physical activity levels
Moderate n (%)
High n (%)
p valuea
Low n (%)
Moderate n (%)
High n (%)
p valuea
22 (43.1) 52 (46.0) 63 (23.2) 30 (36.1) 83 (47.2) 27 (42.2) 43 (42.6)
11 (21.6) 33 (29.2) 49 (32.7) 30 (36.1) 54 (30.7) 21 (32.8) 29 (28.7)
0.01 0.16 0.23 0.20 0.05 0.74 0.05
59 (47.2) 100 (38.5) 38 (34.9) 52 (35.6) 77 (41.4) 26 (37.7) 51 (43.2)
54 (43.2) 122 (46.9) 46 (42.2) 64 (43.8) 81 (43.5) 27 (39.1) 48 (40.7)
12 (9.6) 38 (14.6) 25 (22.9) 30 (20.5) 28 (15.1) 16 (23.2) 19 (16.1)
<0.001 <0.001 0.33 0.25 <0.001 0.23 0.002
The data represent number (%). CI = confidence interval; HDL = high density lipoprotein; IQR = interquartile range. a Significant difference between men and women by the Mann–Whitney U test for continuous variables and chi-square test for categorical variables. b Obesity was calculated as body mass index ≥ 30 kg/m2 . c Central obesity was defined as a waist circumference >90 cm in men and >80 cm in women. d Hypertriglyceridaemia was ≥ 1.7 mmol/L or on treatment. e HDL cholesterol was ≤ 1.03 mmol/L for men and ≤ 1.29 mmol/L for women or treatment. f Blood pressure was ≥ 130/85 mmHg or on treatment. g Hyperglycaemia was ≥ 5.6 mmol/L or on treatment.
female participants’ working status. All of our female participants were working, whereas the women in the previous study included both working and non-working women/housewives. According to the Department of Statistics, Malaysia, 46.1% of women in the whole country were unemployed/housewives in 2010. The NHMS III showed that the prevalence of obesity among housewives was
20.3%; they were also the second most physically inactive group compared with other occupational categories.4 Therefore, this may have contributed to a higher prevalence of metabolic syndrome among female participants in the previous study.11 Unhealthy dietary intake, sedentary lifestyle and stress were also found to increase the prevalence of metabolic syndrome
Table 3 Crude and adjusted OR (95% CI) for risk factors of metabolic syndrome at different physical activity levels. Physical activity levels
Obesityb Crude OR (95% CI) Adjusted OR (95% CI)a Central obesityc Crude OR (95% CI) Adjusted OR (95% CI)a Hypertriglyceridaemiad Crude OR (95% CI) Adjusted OR (95% CI)a Low HDL cholesterole Crude OR (95% CI) Adjusted OR (95% CI)a Hypertensionf Crude OR (95% CI) Adjusted OR (95% CI)a Hyperglycaemiag Crude OR (95% CI) Adjusted OR (95% CI)a Metabolic syndrome Crude OR (95% CI) Adjusted OR (95% CI)a
Low (n = 186)
Moderate (n = 316)
High (n = 184)
p value
1 1
0.20 (0.12–0.34) 0.22 (0.13–0.37)
0.45 (0.30–0.66) 0.46 (0.31–0.67)
<0.001 0.01
1 1
0.29 (0.19–0.44) 0.32 (0.21–0.50)
0.56 (0.38–0.81) 0.57 (0.39–0.84)
<0.001 <0.001
1 1
0.98 (0.64–0.15) 0.73 (0.47–1.14)
0.76 (0.53–1.11) 0.70 (0.47–1.03)
0.26 <0.001
1 1
0.72 (0.47–1.1) 0.75 (0.49–1.15)
0.63 (0.43–0.92) 0.63 (0.43–0.93)
0.05 0.24
1 1
0.49 (0.32–0.74) 0.38 (0.25–0.59)
0.65 (0.45–0.94) 0.61 (0.42–0.89)
0.003 <0.001
1 1
0.86 (0.52–1.42) 0.78 (0.47–1.30)
0.71 (0.45–1.11) 0.69 (0.44–1.08)
0.31 0.02
1 1
0.47 (0.30–0.73) 0.42 (0.27–0.65)
0.54 (0.37–0.78) 0.52 (0.35–0.76)
0.001 0.004
CI, confidence interval; HDL, high density lipoprotein; OR, odds ratio. a Adjusted for gender. b Obesity was defined as body mass index ≥30 kg/m2 . c Central obesity was defined as a waist circumference >90 cm in men and >80 cm in women. d Hypertriglyceridaemia was ≥1.7 mmol/L or on treatment. e HDL cholesterol was ≤1.03 mmol/L for men and ≤1.29 mmol/L for women or on treatment. f Blood pressure was ≥130/85 mmHg or on treatment. g Hyperglycaemia was ≥5.6 mmol/L or on treatment.
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among Asians.12 Some studies showed that although Asians were less obese than Caucasians, they were more vulnerable to metabolic risks.13,14 These variations suggest that metabolic syndrome as a whole may correspond to different clinical and metabolic circumstances in the population studied. Genetic make-up, racial/ethnic differences and developmental environment, as well as interactions among these factors, may have affected individuals differently. We demonstrated a graded negative association between metabolic syndrome and moderate to high activity levels. There was a significant positive association among women with a low activity level and metabolic syndrome (p = 0.002), similar to that reported elsewhere.12 Understanding the role of gender in relation to physical activity is crucial because women typically exhibit lower levels of physical activity than men do. Disparity between physical activity and types of physical activity was observed between men and women, in accordance with the findings of previous studies.15,16 It is believed that while women have taken a larger share of household chores and family care, men usually perform a relatively wider range of activities in each domain and are more heavily engaged in heavy leisure-time activities. Thus, domestic activities may be more clearly associated with fitness and cardioprotective effects in women. Individuals classified in the moderate to high activity levels in our study had significantly lower odds for metabolic syndrome compared with those classified in the low activity level, and these trends remained significant after adjustment for gender. Our results coincide with those reported by Rennie et al., which showed an inverse association between physical activity and metabolic syndrome after adjustment for age and gender.17 In addition, the ATTICA project18 suggested that even light-to-moderate physical activity was associated with significantly lower odds of having metabolic syndrome. One difference between our study and the ATTICA study that should be emphasized, however, is that eating habits were included in the ATTICA study and were associated with metabolic syndrome, whereas dietary information was not collected in our study. The association between physical activity and individual components of metabolic syndrome was explored separately in our study. Increasing levels of physical activity showed protective effects against central obesity and hypertension (p < 0.05). Similarly, some previous studies also demonstrated that, after adjusting for gender, higher physical activity levels remained significantly associated with central obesity19,20 and hypertension.21,22 There was also a significant decrease in the prevalence of hypertriglyceridaemia and hyperglycaemia in our study, in agreement with other studies.23,24 Notably, increased physical activities were inversely associated with body mass index, similar to the findings reported among a Bahrain population.25 It could be hypothesized that physical activity exerts its beneficial effects through a variety of mechanisms that work simultaneously either on multiple metabolic risk factors, or on the common underlying mechanisms of the disease. Our results demonstrate that a moderate activity level has protective effects against metabolic risk factors, as also seen in other populations.26,27 This finding supports the ideas of Churilla and Zoeller28 that regular, moderate-intensity physical activity may prevent metabolic syndrome. Thus, physical activity does not need to be vigorous to yield positive health benefits and improve metabolic factors. In contrast, it was noted in a study of a middleaged French population that the likelihood of having metabolic syndrome decreased 10–30% in participants who were vigorously active compared with those who were moderately active.29 Hence, it was suggested that when it is feasible and safe for the participants, substantial vigorous activity may be included in everyday life as a useful strategy to prevent metabolic syndrome. In addition, a previous study showed that structured high-intensity exercise
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program improved metabolic risk factors in middle-aged adults with metabolic syndrome or those who were overweight or obese.30 These findings indicate that a more intense exercise program may improve cardiovascular health. The need to implement lifestyle interventions into people’s daily activities is highlighted by the fact that both the high-intensity exercise program and moderate physical activity elicit efficacious metabolic effects through distinct mechanisms. Our study has a few limitations. Given the complex nature of physical activity, recall bias may be encountered with the use of self-reported data for recording physical activity. In addition, the results presented are not generalizable to the Malay population as a whole, but may represent the middle-aged urban Malay population; thus these findings should be interpreted with caution. As dietary patterns were not studied, their effect on physical activity and metabolic syndrome cannot be determined. These findings are also limited by the use of a cross-sectional design, which is unable to establish potential causal relationships. Therefore, intervention studies should be done in future and the exact dose–response relationship needs to be addressed. Despite these limitations, the present study has helped us to understand the association between physical activities and metabolic syndrome. We suggest that moderate to high levels of activity may have a protective effect against the risk of metabolic syndrome in populations that are similar to this cohort. 5. Conclusion Moderate and high activity levels were each associated with reduced odds of developing metabolic syndrome independent of gender. Although a slightly lower prevalence of metabolic syndrome was associated with a high activity level in comparison with a moderate activity level, potential beneficial health effects were observed when moderate activity was performed. 6. Practical implications All benefits of physical activity are important for the middleaged population, which is at higher risk for chronic diseases. Increased physical activity is beneficially associated with reduced odds for obesity and metabolic risk factors among adults; even a modest increase in daily activity is worthwhile. Substantial health benefits can be obtained by a moderate amount of activity (e.g., at least 30 minutes of brisk walking, on 5 or more days of the week), especially for working adults. Acknowledgements This research was supported by a MOHE HIR grant (STeMME000010-20001) and a post-graduate research grant from the University of Malaya (PV065/2011A). We also thank all participants and colleagues for their support in this study. References 1. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks, Geneva, Switzerland, World Health Organization, 2009. 2. Yusuf S, Reddy S, Ôunpuu S et al. Global burden of cardiovascular diseases. Circulation 2001; 104(23):2855–2864. 3. Wan Rabiah WO, Patterson I, Pegg S. Healthy lifestyle: promoting walking behaviour in Kuala Lumpur, Malaysia. World J Manage 2011; 3(1):109–123. 4. Institute for Public Health (IPH). The Third National Health and Morbidity Survey (NHMS III) 2006, Kuala Lumpur, Malaysia, Ministry of Health, 2008. 5. Guthold R, Ono T, Strong KL et al. Worldwide variability in physical inactivity: a 51-country survey. Am J Prev Med 2008; 34(6):486–494. 6. Chu AHY, Moy FM. Reliability and validity of the Malay International Physical Activity Questionnaire (IPAQ-M) among a Malay population in Malaysia. Asia Pac J Public Health 2012.
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7. Moy FM, Bulgiba A. The modified NCEP ATP III criteria maybe better than the IDF criteria in diagnosing metabolic syndrome among Malays in Kuala Lumpur. BMC Public Health 2010; 10(1):678. 8. WHO. Obesity: preventing and managing the global epidemic, report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894(1–7):1–253. 9. Haskell WL, Lee I-M, Pate RR et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007; 39(8):1423–1434. 10. Kim J, Tanabe K, Yokoyama N et al. Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study. BMC Public Health 2011; 11:624. 11. Rampal S, Mahadeva S, Guallar E et al. Ethnic differences in the prevalence of metabolic syndrome: results from a multi-ethnic population-based survey in Malaysia. PLoS ONE 2012; 7(9):e46365. 12. Katulanda P, Ranasinghe P, Jayawardana R et al. Metabolic syndrome among Sri Lankan adults: prevalence, patterns and correlates. Diabetol Metab Syndr 2012; 4(1):24. 13. Araneta MR, Wingard DL, Barrett-Connor E. Type 2 diabetes and metabolic syndrome in Filipina-American women: a high-risk nonobese population. Diabetes Care 2002; 25(3):494–499. 14. Fujimoto WY, Bergstrom RW, Boyko EJ et al. Type 2 diabetes and the metabolic syndrome in Japanese Americans. Diabetes Res Clin Pract 2000; 50(Suppl. 2):S73–S76. 15. Dan Jr SP, Mohd Nasir MT, Zalilah MS. Sex and ethnic differentials in physical activity levels of adolescents in Kuantan. Malays J Nutr 2007; 13(2):109–120. 16. Poh BK, Safiah M, Tahir A et al. Physical activity pattern and energy expenditure of Malaysian adults: findings from the Malaysian Adult Nutrition Survey (MANS). Malays J Nutr 2010; 16(1):13–37. 17. Rennie K, McCarthy N, Yazdgerdi S et al. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003; 32(4):600–606. 18. Panagiotakos DB, Pitsavos C, Chrysohoou C et al. Impact of lifestyle habits on the prevalence of the metabolic syndrome among Greek adults from the ATTICA study. Am Heart J 2004; 147(1):106–112.
19. Mohan V, Gokulakrishnan K, Deepa R et al. Association of physical inactivity with components of metabolic syndrome and coronary artery disease – the Chennai Urban Population Study (CUPS no. 15). Diabetic Med 2005; 22(9):1206–1211. 20. Theorell-Haglow J, Berne C, Janson C et al. Associations between short sleep duration and central obesity in women. Sleep 2010; 33(5):593–598. 21. Dunstan DW, Salmon J, Owen N et al. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia 2005; 48(11):2254–2261. 22. Pitsavos C, Panagiotakos DB, Chrysohoou C et al. The associations between physical activity, inflammation, and coagulation markers, in people with metabolic syndrome: the ATTICA study. Eur J Cardiovasc Prev Rehabil 2005; 12(2):151–158. 23. Esteghamati A, Khalilzadeh O, Rashidi A et al. Association between physical activity and insulin resistance in Iranian adults: national surveillance of risk factors of non-communicable diseases (SuRFNCD-2007). Prev Med 2009; 49(5):402–406. 24. Li CL, Lin JD, Lee SJ et al. Associations between the metabolic syndrome and its components, watching television and physical activity. Public Health 2007; 121(2):83–91. 25. Al-Mahroos F, Al-Roomi K. Obesity among adult Bahraini population: impact of physical activity and educational level. Ann Saudi Med 2001; 21(3–4):183–187. 26. Katano S, Nakamura Y, Nakamura A et al. Relationship among physical activity, smoking, drinking and clustering of the metabolic syndrome diagnostic components. J Atheroscler Thromb 2010; 17(6):644–650. 27. Klein S, Sheard NF, Pi-Sunyer X et al. Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies. Diabetes Care 2004; 27(8):2067–2073. 28. Churilla JR, Zoeller RF. Physical activity: physical activity and the metabolic syndrome: a review of the evidence. Am J Lifestyle Med 2008; 2(2):118–125. 29. Bertrais S, Beyeme-Ondoua JP, Czernichow S et al. Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obes Res 2005; 13(5):936–944. 30. Tjonna AE, Lee SJ, Rognmo O et al. Aerobic interval training versus continuous moderate exercise as a treatment for the metabolic syndrome: a pilot study. Circulation 2008; 118(4):346–354.
Contents lists available at ScienceDirect
Journal of Science and Medicine in Sport journal homepage: www.elsevier.com/locate/jsams
Original research
Association between physical activity and metabolic syndrome among Malay adults in a developing country, Malaysia Anne H.Y. Chu a,∗ , F.M. Moy a,b a b
Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia Julius Centre University of Malaya, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
a r t i c l e
i n f o
Article history: Received 9 May 2012 Received in revised form 8 April 2013 Accepted 13 April 2013 Available online 9 May 2013 Keywords: Blood glucose Central obesity Hypertension Serum lipids Motor activity Malaysia
a b s t r a c t Objectives: Metabolic syndrome is a highly prevalent health problem within the adult population in developing countries. We aimed to study the association of physical activity levels and metabolic risk factors among Malay adults in Malaysia. Design: Cross-sectional. Methods: Body mass index, waist circumference, and systolic/diastolic blood pressure, fasting blood glucose, fasting triglyceride and high-density lipoprotein cholesterol levels were measured in 686 Malay participants (aged 35–74 years). Self-reported physical activity was obtained with the validated International Physical Activity Questionnaire (Malay version) and categorized into low, moderate or high activity levels. Results: Individuals who were classified as overweight and obese predominated (65.6%). On the basis of the modified NCEP ATP III criteria, metabolic syndrome was diagnosed in 31.9% of all participants, of whom 46.1% were men and 53.9% were women. The prevalence of metabolic syndrome among participants with low, moderate or high activity levels was 13.3%, 11.7% and 7.0%, respectively (p < 0.001). Statistically significant negative associations were found between a number of metabolic risk factors and activity categories (p < 0.05). The odds ratios for metabolic syndrome in the moderate and high activity categories were 0.42 (95% CI: 0.27–0.65) and 0.52 (95% CI: 0.35–0.76), respectively, adjusted for gender. Conclusions: Moderate and high activity levels were each associated with reduced odds for metabolic syndrome independent of gender. Although a slightly lower prevalence of metabolic syndrome was associated with high activity than with moderate activity, potential health benefits were observed when moderate activity was performed. © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction Physical inactivity and energy imbalance have resulted in an obesity epidemic. Both developed and developing countries face the challenges of obesity and its comorbidities such as diabetes mellitus and cardiovascular diseases. All of these noncommunicable diseases affect an individual’s physical and social functioning, as well as quality of life. Cardiovascular disease is responsible for one in three deaths worldwide and is the number one cause of mortality.1 Interest is growing in a constellation of cardiovascular risk factors, including visceral obesity, dyslipidaemia, hyperglycaemia and hypertension that constitute another important health problem – the metabolic syndrome. This syndrome is highly prevalent in the
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (A.H.Y. Chu).
adult population worldwide, with a suggested ethnic predisposition in Asians.2 The chance of having metabolic syndrome is closely linked to modifiable lifestyle factors, such as overweight, obesity and physical inactivity. This problem is even more pronounced among middle-aged populations. Malaysia has been identified as a country with an increased prevalence of non-communicable diseases because of high levels of physical inactivity.3 With increasing urbanization and the availability of motorized transportation, people tend to reduce their physical activity levels. According to Malaysia’s Third National Health and Morbidity Survey (NHMS III) conducted in 2006, which used the International Physical Activity Questionnaire (IPAQ), 43.7% of the Malaysian adult population was physically inactive.4 The World Health Survey also found Malaysia to be one of the countries with an outstandingly high prevalence of physical inactivity, the highest among all of Western Pacific Region countries.5 Although inverse associations between moderate and high physical activity levels with obesity and metabolic syndrome have been well-established in a number of Western and Asian countries,
1440-2440/$ – see front matter © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jsams.2013.04.003
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there is still insufficient evidence in Malaysia. We aimed to fill this gap in our local setting to show the need to combat obesity and metabolic risks through physical activity intervention studies. The primary goal of this study was to explore the associations between different levels of physical activity with obesity and metabolic risk factors in middle-aged Malay adults.
2. Methods This was an analytical cross-sectional study. All eligible employees (aged 35 years and older) of a public university in Kuala Lumpur, Malaysia, were invited to participate in a free annual worksite health screening. A random sample of 1000 employees was selected and invited to participate in our study. Of these employees, 686 agreed to participate, providing a response rate of 68.6%. Study inclusion criteria consisted of the following: (1) self-identified race as Malay (as the IPAQ-M was in the Malay language); (2) absence of physical illness or disabilities that would limit daily physical activities such as walking; and (3) the ability to read and write well enough to record physical activities. Written informed consent was obtained from all participants. Ethics clearance was obtained from the Medical Ethics Committee of the university (Reference Number: MEC 782.18). Data were collected from August 2010 to August 2011. Anthropometric parameters (weight, height and waist circumference), systolic/diastolic blood pressure, fasting blood glucose and fasting lipid profiles were measured. Weight was measured using the SECA digital scale and height with the SECA stadiometer (Hamburg, Germany). Body mass index (kg/m2 ) was calculated using the formula weight (kg) divided by height2 (m2 ). Central obesity was measured with a circumference measurement tape. The waist was measured as the point midway between the iliac crest and the costal margin (lower rib). All measurements were performed by trained staff and quality checks were conducted regularly. Blood pressure was measured using a clinically validated digital automatic blood pressure monitor (Omron HEM-907, Kyoto, Japan). The analysis of biochemical indicators, which included fasting blood glucose and a full lipid profile, was conducted by the Clinical Diagnostic Laboratory of the University Malaya Medical Center. The validated self-administered, long-form Malay version of the IPAQ (IPAQ-M)6 was used to assess the levels of physical activity among participants. The IPAQ-M has previously been tested for test-retest reliability and criterion validity. The intraclass correlation coefficient revealed moderate to good reliability ranging from 0.54 to 0.92 (p < 0.001) and the kappa coefficient of 0.89 showed good validity (95% confidence interval [CI] = 0.79–0.98). The questionnaire included 31 questions on frequency and time spent on walking and moderate and vigorous activities in four domains (work, transportation, home and leisure-time activity), as well as time spent sitting. Participants were asked to recall their activities during the last 7 days. For the analysis of the IPAQ-M data, the following metabolic equivalent of task (MET) values were used: walking = 3.3 METs; moderate physical activity = 4.0 METs; vigorous physical activity = 8.0 METs. The results were presented as the estimation of energy expenditure in metabolic equivalent-minutes per week (MET-min week−1 ). The MET-min week−1 was calculated as minutes of activity/day × days per week × MET level. According to the IPAQ Research Committee, the continuous indicator should be presented as median values and interquartile ranges rather than means. Both continuous and categorical indicators of physical activity were calculated from the data obtained from the IPAQM. A total physical activity score obtained from the IPAQ-M was calculated, as well as separate scores for each of the four physical activity domains and activity levels. On the basis of the IPAQ
guidelines, participants with total physical activity scores of <600 MET-min week−1 were categorized as being in the “low” category, those with 600–2999 MET-min week−1 as being in the “moderate” category and those with ≥3000 MET-min week−1 as being in the “high” category. For the estimation of energy expenditure, the IPAQ scoring guide stated that MET-minute scores are equivalent to kilocalories for a 60-kg person, and that kilocalories may be computed from MET-minutes using the following equation: MET-min × (weight in kilograms/60 kg). Thus these data were also converted to energy expenditure/week adjusted for weight (kcal/week/kg). According to the modified NCEP ATP III criteria that is more suitable for the Malay population,7 the presence of any three or more of the following five factors is required for a working definition of metabolic syndrome: abdominal obesity, hypertriglyceridaemia (triglycerides ≥1.7 mmol/L) or specific treatment for this lipid abnormality; low HDL cholesterol (HDL cholesterol ≤1.03 mmol/L for men and ≤1.29 mmol/L for women) or specific treatment for this lipid abnormality; elevated blood pressure (systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥85 mmHg) or current use of antihypertensive drugs; impaired fasting glucose (fasting plasma glucose ≥ 5.6 mmol/L) or drug treatment for elevated glucose (previously diagnosed type 2 diabetes). The NCEP ATP III criteria suggested the cutoff points of waist circumference should be ethnic specific, with adoption of the Asian criteria for abdominal obesity (waist circumference >90 cm in men and >80 cm in women). Data were entered and analyzed using SPSS for Windows version 16.0. The significance level was set at p < 0.05. Participants’ activity scores (MET-min week−1 ) were presented as medians with 95% CI and interquartile range for each domain and type of activity and further categorized into three categories: low, moderate and high levels of physical activity. Distributions of continuous variables were tested for normality using the Kolmogorov–Smirnov test. Associations between categorical variables were tested using the chi-squared test. The non-parametric Mann–Whitney U test was used for asymmetric continuous variables. Differences in the frequencies of obesity and each metabolic risk factor across categories of physical activity were analyzed using the chi-squared test stratified by gender. Logistic regression was used to estimate the odds ratio (OR) with 95% CI of having metabolic syndrome in each physical activity category. The analyses were further adjusted for gender. We ruled out the presence of an interaction between age and the main independent variables (physical activity categories), which was not statistically significant.
3. Results A total of 686 employees (39.7% men, 60.3% women) were recruited. Their demographic, clinical and anthropometric characteristics are presented in Table 1. Participants were 35–74 years old (mean age: 45.9 ± 6.5 years). There was no significant difference in age groups by gender. Most participants were in the moderate activity level category (46.1%), followed by those in the low activity level (27.1%) and the high activity level (26.8%). Individuals who were classified as overweight and obese predominated (65.6%). Male participants were significantly heavier than female participants; had a larger waist circumference; had higher systolic/diastolic blood pressure, triglyceride and fasting glucose levels; and had lower HDL cholesterol levels (p < 0.001). From the modified NCEP ATP III definition, metabolic syndrome was diagnosed in 31.9% of participants. There was a significant difference in the prevalence of metabolic syndrome between men (37.1%) and women (24.2%) (p < 0.05).
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Table 1 Characteristics of participants.
Mean age (years ± SD) Less than 40 years 40–49 years 50–59 years 60 years and above Educational level Primary Secondary Tertiary Levels of physical activity Low Moderate High Body mass index (kg/m2 )b Underweight < 18.5 Normal 18.6 to 24.9 Overweight 25 to 29.9 Obese ≥ 30 Waist circumference (cm) Systolic BP (mmHg) Diastolic BP (mmHg) Triglycerides (mmol/L) HDL cholesterol (mmol/L) Fasting glucose (mmol/L) Metabolic syndrome
Total (n = 686)
Men (n = 272)
Women (n = 414)
p value
45.9 ± 6.5 139 (20.3) 349 (50.9) 194 (28.3) 4 (0.5)
46.4 ± 6.9 52 (19.1) 131 (48.2) 86 (31.6) 3 (1.1)
45.5 ± 6.0 87 (21.0) 218 (52.7) 108 (26.1) 1 (0.2)
0.14
142 (20.7) 247 (36.0) 297 (43.3)
66 (24.3) 80 (29.4) 126 (46.3)
76 (18.4) 167 (40.3) 171 (41.3)
186 (27.1) 316 (46.1) 184 (26.8)
59 (21.7) 116 (42.6) 97 (35.7)
127 (30.7) 200 (48.3) 87 (21.0)
10 (1.5) 226 (32.9) 269 (39.2) 181 (26.4) 86.5 ± 10.7 126.4 ± 15.3 79.2 ± 10.8 1.5 ± 0.9 1.3 ± 0.3 5.4 ± 1.9 219 (31.9)
3 (1.1) 86 (31.6) 131 (48.2) 52 (19.1) 89.1 ± 9.7 130.8 ± 13.5 81.6 ± 9.8 1.8 ± 1.0 1.2 ± 0.2 5.6 ± 2.1 101 (37.1)
7 (1.7) 140 (33.8) 138 (33.3) 129 (31.2) 84.9 ± 11.1 123.6 ± 15.8 77.7 ± 11.1 1.2 ± 0.6 1.5 ± 0.4 5.2 ± 1.8 118 (24.2)
0.01a
<0.001a
<0.001a
<0.001a <0.001a <0.001a <0.001a <0.001a 0.001a 0.02a
The data represent means ± SD, number (%). BP = blood pressure; HDL = high-density lipoprotein. a Significant difference between men and women by the Mann–Whitney U test for continuous variables and chi-square test for categorical variables. b Classification according to the World Health Organization.8
The median (95% CI) of total reported physical activity was 1710.5 (930.8, 3177.0) MET-min week−1 (data not shown). Domestic physical activity was the domain in which participants were most active (567.5 [510.0, 630.0] MET-min week−1 , p < 0.001), followed by occupational (297.0 [245.0, 330.0] MET-min week−1 ), transportation (165.0 [132.0, 198.0] MET-min week−1 ) and leisuretime domains (165.0 [99.0, 198.0] MET-min week−1 ). Activity in the domestic domain was significantly higher than that in the transportation and leisure-time domains (p < 0.001), while activity in the transportation domain did not differ significantly from that in the leisure-time domain (p = 0.72). Moderate activity (720.0 [642.5, 765.0] MET-min week−1 ) was significantly higher than walking activity (585.8 [495.0, 660.0] MET-min week−1 ) and high activity (0 [0, 480.0] MET-min week−1 , p < 0.001). There was no significant difference between walking activity and high activity (p = 0.07). Physical activity based on activity in all domains and all activity levels was analyzed separately for men and women (Table 2). Men reported significantly higher total physical activity scores (2155.3 [1780.0, 2533.5] MET-min week−1 ) than women (1475.0 [1330.5, 1683.0] MET-min week−1 , p < 0.001). The overall energy expenditure of the participants was 1898.4 (1757.8, 2098.7) kcal/kg/week; men were significantly more active than women (men: 2600.68 [2154.9, 2993.6] kcal/kg/week vs. women: 1624.40[1438.2, 1825.4] kcal/kg/week, p < 0.05). A total of 396 (57.7%) participants fulfilled the physical activity guidelines recommended by the American College of Sports Medicine and the American Heart Association9 for improvement of health and wellness (data not shown). Obesity and risk factors for metabolic syndrome at different physical activity levels among men and women are shown in Table 2. The prevalence of metabolic syndrome among all participants was significantly higher in those with a low activity level (13.3%) than in those with a moderate (11.7%, p = 0.003) or a high activity level (7.0%, p = 0.001) (data not shown). However, there was no significant difference between participants categorized as having moderate activity or high activity levels (p = 0.88). Metabolic syndrome was significantly associated with physical activity levels among women only (p = 0.002). Statistically significant negative associations were found between a number of metabolic risk
factors and activity categories (p < 0.05). Central obesity was the most prevalent metabolic risk factor among all participants (54.3%). We further examined the associations between physical activity levels and the prevalence of obesity, metabolic syndrome and its components through logistic regression analysis (Table 3). Participants with a low activity level were defined as the reference group. Increasing moderate and high activity levels each showed statistically significant protective effects against metabolic syndrome (p < 0.05). Specifically, being moderately active (OR: 0.47; 95% CI: 0.30–0.73) offered a stronger significant protective effect against metabolic syndrome, obesity, central obesity and hypertension than being highly active did (OR: 0.54; 95% CI: 0.37–0.78). After adjusting for gender, those with moderate and high activity levels had significantly lower odds of having metabolic syndrome, hypertriglyceridaemia, hypertension and hyperglycaemia (ranging from 0.38 to 0.78, p < 0.05). 4. Discussion In this cross-sectional study, we obtained a relatively low response rate for an Asian country. This may be attributed to sociodemographic variables such as gender, age and level of education among our study population. In an international study on the prevalence of physical activity from 20 countries across Europe, America and Asia, a median country-level response rate of 61.0% was obtained. Hence, our response rate of 68.6% was considered acceptable in comparison. The lower response rate among men in our study was consistent with that in a study done in Japan (37.1% men, 62.9% women).10 The gender difference is probably due to the difference in awareness of the importance of physical activity between men and women; in addition, work responsibilities may have restricted the men’s attendance. Our results showed a higher prevalence of metabolic syndrome in men than in women. However, a recent large-scale, population-based survey of all major ethnic groups in Malaysia found a significantly higher prevalence of metabolic syndrome in Malay women (51.5%) compared with men (38.5%) using the same diagnostic criteria.11 This could be due to the difference in the
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Table 2 Characteristics of participants’ involvement in physical activity and frequencies of metabolic syndrome by gender. Physical activity parameters
Men (n = 272) Median (95% CI); IQR
Women (n = 414) Median (95% CI); IQR
p valuea
Total Occupational Transportation Domestic Leisure time Walking Moderate Vigorous Sitting
2155.3 (1780.0, 2533.5); 2631.8 480.0 (330.0, 495.0); 933.0 231.0 (198.0, 297.0); 643.5 520.0 (420.0, 630.0); 953.8 315.5 (231.0, 396.0); 897.8 693.0 (577.5, 825.0); 1113.8 720.0 (625.0, 780.0); 1200.0 240.0 (0, 240.0); 960.0 2370.0 (2220.0, 2520.0); 1620.0
1475.0 (1330.5, 1683.0); 1848.4 231.0 (169.5, 268.5); 645.0 99.0 (66.0, 165.0); 495.0 600.0 (540.0, 720.0); 967.5 54.8 (0, 99.0); 360.0 486.8 (396.0, 594.0); 858.0 720.0 (600.0, 840.0); 1080.0 0 (0, 180.0); 180.0 2760.0 (2640.0, 2880.0); 1215.0
<0.001 <0.001 0.001 0.01 <0.001 <0.001 0.24 <0.001 0.001
Physical activity levels Low n (%) Metabolic syndrome components Obesityb 18 (35.3) 28 (24.8) Central obesityc d 38 (25.3) Hypertriglyceridaemia e Low HDL cholesterol 23 (27.7) 39 (22.2) Hypertensionf 16 (25.0) Hyperglycaemiag 29 (28.7) Metabolic syndrome
Physical activity levels
Moderate n (%)
High n (%)
p valuea
Low n (%)
Moderate n (%)
High n (%)
p valuea
22 (43.1) 52 (46.0) 63 (23.2) 30 (36.1) 83 (47.2) 27 (42.2) 43 (42.6)
11 (21.6) 33 (29.2) 49 (32.7) 30 (36.1) 54 (30.7) 21 (32.8) 29 (28.7)
0.01 0.16 0.23 0.20 0.05 0.74 0.05
59 (47.2) 100 (38.5) 38 (34.9) 52 (35.6) 77 (41.4) 26 (37.7) 51 (43.2)
54 (43.2) 122 (46.9) 46 (42.2) 64 (43.8) 81 (43.5) 27 (39.1) 48 (40.7)
12 (9.6) 38 (14.6) 25 (22.9) 30 (20.5) 28 (15.1) 16 (23.2) 19 (16.1)
<0.001 <0.001 0.33 0.25 <0.001 0.23 0.002
The data represent number (%). CI = confidence interval; HDL = high density lipoprotein; IQR = interquartile range. a Significant difference between men and women by the Mann–Whitney U test for continuous variables and chi-square test for categorical variables. b Obesity was calculated as body mass index ≥ 30 kg/m2 . c Central obesity was defined as a waist circumference >90 cm in men and >80 cm in women. d Hypertriglyceridaemia was ≥ 1.7 mmol/L or on treatment. e HDL cholesterol was ≤ 1.03 mmol/L for men and ≤ 1.29 mmol/L for women or treatment. f Blood pressure was ≥ 130/85 mmHg or on treatment. g Hyperglycaemia was ≥ 5.6 mmol/L or on treatment.
female participants’ working status. All of our female participants were working, whereas the women in the previous study included both working and non-working women/housewives. According to the Department of Statistics, Malaysia, 46.1% of women in the whole country were unemployed/housewives in 2010. The NHMS III showed that the prevalence of obesity among housewives was
20.3%; they were also the second most physically inactive group compared with other occupational categories.4 Therefore, this may have contributed to a higher prevalence of metabolic syndrome among female participants in the previous study.11 Unhealthy dietary intake, sedentary lifestyle and stress were also found to increase the prevalence of metabolic syndrome
Table 3 Crude and adjusted OR (95% CI) for risk factors of metabolic syndrome at different physical activity levels. Physical activity levels
Obesityb Crude OR (95% CI) Adjusted OR (95% CI)a Central obesityc Crude OR (95% CI) Adjusted OR (95% CI)a Hypertriglyceridaemiad Crude OR (95% CI) Adjusted OR (95% CI)a Low HDL cholesterole Crude OR (95% CI) Adjusted OR (95% CI)a Hypertensionf Crude OR (95% CI) Adjusted OR (95% CI)a Hyperglycaemiag Crude OR (95% CI) Adjusted OR (95% CI)a Metabolic syndrome Crude OR (95% CI) Adjusted OR (95% CI)a
Low (n = 186)
Moderate (n = 316)
High (n = 184)
p value
1 1
0.20 (0.12–0.34) 0.22 (0.13–0.37)
0.45 (0.30–0.66) 0.46 (0.31–0.67)
<0.001 0.01
1 1
0.29 (0.19–0.44) 0.32 (0.21–0.50)
0.56 (0.38–0.81) 0.57 (0.39–0.84)
<0.001 <0.001
1 1
0.98 (0.64–0.15) 0.73 (0.47–1.14)
0.76 (0.53–1.11) 0.70 (0.47–1.03)
0.26 <0.001
1 1
0.72 (0.47–1.1) 0.75 (0.49–1.15)
0.63 (0.43–0.92) 0.63 (0.43–0.93)
0.05 0.24
1 1
0.49 (0.32–0.74) 0.38 (0.25–0.59)
0.65 (0.45–0.94) 0.61 (0.42–0.89)
0.003 <0.001
1 1
0.86 (0.52–1.42) 0.78 (0.47–1.30)
0.71 (0.45–1.11) 0.69 (0.44–1.08)
0.31 0.02
1 1
0.47 (0.30–0.73) 0.42 (0.27–0.65)
0.54 (0.37–0.78) 0.52 (0.35–0.76)
0.001 0.004
CI, confidence interval; HDL, high density lipoprotein; OR, odds ratio. a Adjusted for gender. b Obesity was defined as body mass index ≥30 kg/m2 . c Central obesity was defined as a waist circumference >90 cm in men and >80 cm in women. d Hypertriglyceridaemia was ≥1.7 mmol/L or on treatment. e HDL cholesterol was ≤1.03 mmol/L for men and ≤1.29 mmol/L for women or on treatment. f Blood pressure was ≥130/85 mmHg or on treatment. g Hyperglycaemia was ≥5.6 mmol/L or on treatment.
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among Asians.12 Some studies showed that although Asians were less obese than Caucasians, they were more vulnerable to metabolic risks.13,14 These variations suggest that metabolic syndrome as a whole may correspond to different clinical and metabolic circumstances in the population studied. Genetic make-up, racial/ethnic differences and developmental environment, as well as interactions among these factors, may have affected individuals differently. We demonstrated a graded negative association between metabolic syndrome and moderate to high activity levels. There was a significant positive association among women with a low activity level and metabolic syndrome (p = 0.002), similar to that reported elsewhere.12 Understanding the role of gender in relation to physical activity is crucial because women typically exhibit lower levels of physical activity than men do. Disparity between physical activity and types of physical activity was observed between men and women, in accordance with the findings of previous studies.15,16 It is believed that while women have taken a larger share of household chores and family care, men usually perform a relatively wider range of activities in each domain and are more heavily engaged in heavy leisure-time activities. Thus, domestic activities may be more clearly associated with fitness and cardioprotective effects in women. Individuals classified in the moderate to high activity levels in our study had significantly lower odds for metabolic syndrome compared with those classified in the low activity level, and these trends remained significant after adjustment for gender. Our results coincide with those reported by Rennie et al., which showed an inverse association between physical activity and metabolic syndrome after adjustment for age and gender.17 In addition, the ATTICA project18 suggested that even light-to-moderate physical activity was associated with significantly lower odds of having metabolic syndrome. One difference between our study and the ATTICA study that should be emphasized, however, is that eating habits were included in the ATTICA study and were associated with metabolic syndrome, whereas dietary information was not collected in our study. The association between physical activity and individual components of metabolic syndrome was explored separately in our study. Increasing levels of physical activity showed protective effects against central obesity and hypertension (p < 0.05). Similarly, some previous studies also demonstrated that, after adjusting for gender, higher physical activity levels remained significantly associated with central obesity19,20 and hypertension.21,22 There was also a significant decrease in the prevalence of hypertriglyceridaemia and hyperglycaemia in our study, in agreement with other studies.23,24 Notably, increased physical activities were inversely associated with body mass index, similar to the findings reported among a Bahrain population.25 It could be hypothesized that physical activity exerts its beneficial effects through a variety of mechanisms that work simultaneously either on multiple metabolic risk factors, or on the common underlying mechanisms of the disease. Our results demonstrate that a moderate activity level has protective effects against metabolic risk factors, as also seen in other populations.26,27 This finding supports the ideas of Churilla and Zoeller28 that regular, moderate-intensity physical activity may prevent metabolic syndrome. Thus, physical activity does not need to be vigorous to yield positive health benefits and improve metabolic factors. In contrast, it was noted in a study of a middleaged French population that the likelihood of having metabolic syndrome decreased 10–30% in participants who were vigorously active compared with those who were moderately active.29 Hence, it was suggested that when it is feasible and safe for the participants, substantial vigorous activity may be included in everyday life as a useful strategy to prevent metabolic syndrome. In addition, a previous study showed that structured high-intensity exercise
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program improved metabolic risk factors in middle-aged adults with metabolic syndrome or those who were overweight or obese.30 These findings indicate that a more intense exercise program may improve cardiovascular health. The need to implement lifestyle interventions into people’s daily activities is highlighted by the fact that both the high-intensity exercise program and moderate physical activity elicit efficacious metabolic effects through distinct mechanisms. Our study has a few limitations. Given the complex nature of physical activity, recall bias may be encountered with the use of self-reported data for recording physical activity. In addition, the results presented are not generalizable to the Malay population as a whole, but may represent the middle-aged urban Malay population; thus these findings should be interpreted with caution. As dietary patterns were not studied, their effect on physical activity and metabolic syndrome cannot be determined. These findings are also limited by the use of a cross-sectional design, which is unable to establish potential causal relationships. Therefore, intervention studies should be done in future and the exact dose–response relationship needs to be addressed. Despite these limitations, the present study has helped us to understand the association between physical activities and metabolic syndrome. We suggest that moderate to high levels of activity may have a protective effect against the risk of metabolic syndrome in populations that are similar to this cohort. 5. Conclusion Moderate and high activity levels were each associated with reduced odds of developing metabolic syndrome independent of gender. Although a slightly lower prevalence of metabolic syndrome was associated with a high activity level in comparison with a moderate activity level, potential beneficial health effects were observed when moderate activity was performed. 6. Practical implications All benefits of physical activity are important for the middleaged population, which is at higher risk for chronic diseases. Increased physical activity is beneficially associated with reduced odds for obesity and metabolic risk factors among adults; even a modest increase in daily activity is worthwhile. Substantial health benefits can be obtained by a moderate amount of activity (e.g., at least 30 minutes of brisk walking, on 5 or more days of the week), especially for working adults. Acknowledgements This research was supported by a MOHE HIR grant (STeMME000010-20001) and a post-graduate research grant from the University of Malaya (PV065/2011A). We also thank all participants and colleagues for their support in this study. References 1. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks, Geneva, Switzerland, World Health Organization, 2009. 2. Yusuf S, Reddy S, Ôunpuu S et al. Global burden of cardiovascular diseases. Circulation 2001; 104(23):2855–2864. 3. Wan Rabiah WO, Patterson I, Pegg S. Healthy lifestyle: promoting walking behaviour in Kuala Lumpur, Malaysia. World J Manage 2011; 3(1):109–123. 4. Institute for Public Health (IPH). The Third National Health and Morbidity Survey (NHMS III) 2006, Kuala Lumpur, Malaysia, Ministry of Health, 2008. 5. Guthold R, Ono T, Strong KL et al. Worldwide variability in physical inactivity: a 51-country survey. Am J Prev Med 2008; 34(6):486–494. 6. Chu AHY, Moy FM. Reliability and validity of the Malay International Physical Activity Questionnaire (IPAQ-M) among a Malay population in Malaysia. Asia Pac J Public Health 2012.
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