Sociodemographic factors associated with multiple cardiovascular risk factors among Malaysian adults
Ghazali et al. BMC Public Health
Sociodemographic factors associated with multiple cardiovascular risk factors among Malaysian adults
Sumarni Mohd Ghazali 0
Zamtira Seman 0
Kee Chee Cheong 0
Lim Kuang Hock 2
Mala Manickam 2
Lim Kuang Kuay 2
Ahmad Faudzi Yusoff 0
Feisul Idzwan Mustafa 1
Amal Nasir Mustafa 0
0 Institute for Medical Research , Kuala Lumpur , Malaysia
1 Disease Control Division, Ministry of Health , Kuala Lumpur , Malaysia
2 Institute for Public Health , Kuala Lumpur , Malaysia
Background: To determine the prevalence and sociodemographic correlates of multiple risk factors for cardiovascular disease (CVD) among Malaysian adults. Methods: We analysed data on 1044 men and 1528 women, aged 24-64 years, participants in the Non Communicable Disease Surveillance 2005/2006, a nationally representative, population-based, cross-sectional study. Prevalence of obesity, high blood pressure, dyslipidaemia, hyperglycemia, physical inactivity, smoking, risky drinking, low vegetable and fruit intake were determined and multivariable logistic regression was used to identify sociodemographic factors associated with having 3 of these cardiovascular disease risk factors. Results: The response rate was 84.6% (2572/3040). Overall, 68.4% (95% CI: 63.2, 73.1) had at least three risk factors. Among men, older age and Indian ethnicity were independently associated with having 3 CVD risk factors; while among women, older age, low education, and housewives were more likely to have 3 CVD risk factors. Conclusion: The prevalence of cardiovascular risk factors clustering among Malaysian adults is high, raising concerns that cardiovascular disease incidence will rise steeply in the near future if no immediate preventive measures are taken. The current national health education and promotion programmes pertaining to modifiable risk factors can be further improved by taking into account the sociodemographic variation in CVD risk factors clustering.
Adult; Prevalence; Cardiovascular disease; Sociodemographic correlates; Lifestyle
Cardiovascular disease remains one of the most
important chronic diseases in developed and developing
countries. Globally, an estimated 17.3 million people died
from cardiovascular disease (CVD) in 2008 alone, by
2030 this figure is estimated to reach 23.6 million .
Although CVDs have decreased in developed countries,
the trend is increasing in developing nations . In
2010, 25.4% (11812) of deaths in Malaysian government
hospitals were due to cardiovascular diseases and it is
also the top cause of premature death, with about 35%
of deaths from CVD occurring in individuals aged
below sixty .
Biological factors like hypertension, hyperglycaemia,
hypercholesterolaemia, obesity and lifestyle factors 
such as physical inactivity, smoking, excessive alcohol
consumption and unhealth dietary behavior are
established risk factors for cardiovascular disease. Increase in
the prevalence of these risk factors is closely related to
the rising prevalence of cardiovascular disease. Having
multiple risk factors is associated with even greater risk
for cardiovascular diseases as compared to having fewer
risk factors . Therefore the prevalence of multiple risk
factors gives a clearer depiction of the burden of CVD
risk in the population. From the public health
perspective, knowledge on the true burden of CVD risk will help
in devising appropriate action in prevention, detection
and treatment of those with multiple CVD risk factors
and ultimately curbing CVD in the population.
The presence of multiple risk factors have been
previously reported, however, biological risk factors [6,7] and
lifestyle risk factors [8,4] have been reported separately
even though a person may have both types of factors
concurrently. In this study we determined the prevalence
of both biological and lifestyle risk factors in the adult
Malaysian population. It is also important to elucidate
the sociodemographic characteristics of those at higher
risk of having multiple CVD risk factors. Therefore, we
examined the sociodemographic factors that are
associated with multiple CVD risk factors.
The data used in this study was derived from the Malaysia
Non-Communicable Disease Surveillance-1 (MyNCDS-1)
survey. The MyNCDS-1 was a cross-sectional,
populationbased baseline survey on non-communicable diseases and
its risk factors conducted in 2005 and 2006 . Malaysia is
administratively divided into 13 states and 3 federal
territories. Participants were recruited from all thirteen states
and one of the federal territories (Kuala Lumpur) through
a complex, multistage cluster sampling design using the
year 2000 national household sampling frame with the
assistance of the Malaysian Department of Statistics.
Stratifying variables were state/federal territory and
setting (urban/rural), with enumeration blocks (EBs),
living quarters (LQs) and households as the primary,
secondary, and elementary sampling units respectively.
The numbers of EBs and LQs selected per state were
based on the desired sample size and proportionate to
the 2005 Malaysian adult (age 2564 years) population
size for each state. In all, a total of 398 EBs and 1683
LQs were selected. All household members in all
households in the selected LQs who met the eligibility
criteria were included in the sample. A minimum
required sample size of 2533 calculated based on
prevalence of obesity of 5%, precision of 1.2% and design
effect of 2. The final sample size of 3040 was computed
after adding 20% expected non-response (507). In this
paper, we analysed data comprised of 2572 subjects
(1044 men and 1528 women) which is the final sample
after removing subjects with incomplete information,
yielding an estimated response rate of 84.6%. This final
sample size exceeded the minimum required sample
size (2533), therefore we assume that the missing data
has minimal effect on the generalisability of the
findings and has little effect on the relationship between
sociodemographic factors and multiple cardiovascular
risk factors. The details of the study design and
sampling methods has been reported elsewhere . The
MyNCDS-1 survey was approved by the Medical
Research and Ethics Committee of the Ministry of Health,
Malaysia and is registered with the National Medical
Research Register, National Institutes of Health,
Ministry of Health of Malaysia (NMRR-13-1128-18447).
Collection of data was carried out simultaneously in all
states from September 2005 to February 2008. An
extensive field work manual was used as a practical guide for
training sessions and subsequently during data collection
in the field. Data collection was performed by trained field
survey personnel consisting of nurses, health inspectors
and research assistants under the supervision of medical
doctors. The selected households were visited and eligible
members of the household were interviewed personally
upon giving informed consent. The interview was
conducted using a structured questionnaire printed in English
or Malay language (The questionnaire consisted of
sections on sociodemographic characteristics, medical
history, physical activity, smoking, alcohol consumption and
dietary pattern). Later, appointments were made for
physical examination (blood pressure, pulse rate, waist, height,
weight and hip measurements) and biochemicals (glucose
and lipid profile) measurement at a selected government
health clinic nearest to the respondents residential area.
All selected households were visited, those who were not
at home during the visit were visited again at least two
times before being classified as non-responders.
Selected sociodemographic data on all the study subjects
were analysed. The variables included gender, residential
area (urban or rural), ethnicity, age, marital status,
education, occupation, and monthly household income.
Cardiovascular risk factors
Obesity refers to general and/or abdominal obesity. Data
on height and weight measurements were taken. Height
was measured without footwear to the nearest 0.1
centimetre using a stadiometer. Weight was measured to the
nearest 0.1 kilogram using a balance beam scale or SECA
beam scale with minimal clothing and no shoes. Body
mass index (BMI) (Weight [cm] /height2 [m]) was
calculated and general obesity was defined as BMI 30.0 kg/
m . Waist circumference (WC) was measured directly
over skin or over light clothing to the nearest 0.1 cm at
the smallest circumference below the rib cage and above
the umbilicus while standing with abdominal muscles
relaxed. Abdominal obesity was defined as waist
circumference 90 cm for men and 80 cm for women .
Blood pressure was measured by the auscultatory method
 two or three times (if the first two readings differed
by more than 10 mmHg), at no less than 30 seconds
between measurements, and averaged. Hypertension was
defined as having average systolic pressure 140 mmHg
and/or diastolic pressure 90 mmHg or known case of
Five ml of venous blood samples after overnight fasting
were collected for the measurement of total cholesterol,
HDL-cholesterol (HDL-C), triglycerides and glucose levels.
It was placed into two vacuum test tubes; 2 ml blood into
a test tube with NAF oxalate anticoagulant for blood sugar
measurement and another 3 ml blood was filled into the
test tube without anticoagulant for lipid profile. All the test
tubes were properly labelled with respondents
identification and date of blood collection. All blood-taking
procedures was carried out under aseptic technique using a 5 ml
syringe with 0.55 mm (21G) needle. All blood samples
were transported in a cool box packed with dry ice to the
central coordinating centre before being sent to the
laboratory. All biochemical measurements were carried out
according to the standard protocol of the WHO STEPwise
approach to chronic disease risk factor surveillance .
Fasting lipid levels
The concentrations of HDL-cholesterol and triglycerides
were measured using enzymatic assay kits (Automated
HDL Cholesterol Flex reagent cartridge and
Triglyceride Flex reagent cartridge). Serum total cholesterol was
determined using enzymatic colorimetric tests with
cholesterol esterase, cholesterol oxidase and glycerol
phosphate oxidase respectively. Respondents were classified
as having hypercholesterolemia if their total cholesterol
was more than 5.2 mmol/L or were known cases of
dyslipidemia or hypercholesterolemia . Fasting plasma
triglyceride >2.3 mmol/L was used as the cut-off point
for presence of hypertriglyceridemia .
Those with no known diabetes were screened for
diabetes mellitus using the two-hour post prandial glucose
tolerance test. Glucose levels were measured using an
enzymatic assay kit (Glucose Flex reagent cartridge).
Both those with fasting plasma glucose 7.0 mmol/L
 or known case of diabetes mellitus were classified
as diabetes mellitus in our study.
Inadequate physical activity
Physical activity was assessed using the Global Physical
Activity Questionnaire (GPAQ) recommended by the
WHO STEPwise approach to chronic disease risk factor
surveillance . The amount of energy accumulated
from work, travelling and leisure time-related activities
were quantified in terms of Metabolic Equivalent to
Task (METs) minutes per week. One MET is defined as
1 cal/kg/hour or 3.5 ml/kg/min oxygen consumed,
which is equivalent to sitting quietly. Accumulation of
less than 600 METs per week is considered as being
physically inactive .
Inadequate vegetable and fruit intake
Vegetable and fruit intake refers to consumption of all
types of vegetables and fruits whether raw, cooked,
dried or frozen. A serving of fruit is defined as one
medium piece or two small pieces of fruit or one cup of
diced pieces, a serving of vegetables is defined as half
cup cooked vegetables or one cup of salad vegetables.
Inadequate intake refers to consumption of less than
five servings of vegetables and/or fruits daily .
Smoking refers to current smoking which was defined as
smoking any tobacco product daily at least once a day
(Daily smoker), or smoked but not every day (Occasional
smoker) at the time of the survey .
Risky drinking is defined as consuming 14 standard
alcoholic drinks per week for men and 7 standard drinks
per week for women .
Multiple cardiovascular risk factors
Multiple cardiovascular risk factors were defined as
having three or more cardiovascular risk factors whether
biological (obesity, hypertension, hypertriglyceridaemia,
hypercholesterolemia, Type II diabetes mellitus) or
lifestyle (physical inactivity, smoking, risky drinking,
inadequate fruit and vegetable intake).
We described the sociodemographic characteristics of the
study sample, the prevalence of each CVD risk factor and
prevalence of having one to nine CVD risk factor/s by
gender. Multivariable logistic regression was conducted
separately for men and women to determine
sociodemographic factors associated with clustering of 3 CVD
risk factors. All the analyses were performed using SPSS
software version 19.0 (SPSS Inc, Chicago). Sample weights
were used in the analysis to adjust for the possible
differences in the probability of EB and LQ selection or by
nonresponse at the subjects level. Post stratification weights
which took into account the population locality, gender
and age-group stratification in 2005 were also applied.
The sociodemographic characteristics of the study
participants are presented in Table 1. A majority of the
respondents were Malay (55.4%), married (86.7%), with
income less than RM1000 (Approximately USD290).
Half of the female respondents were housewives.
Table 1 Sociodemographic characteristics of respondents
No formal education
Public sector worker
Private sector employee
Monthly income level
Low (<RM 1000)
Middle (RM 1000-RM3999)
High (RM 4000)
The estimated prevalence of 3 CVD risk factors was
62.5% (95% CI: 58.5, 66.3), 64.3% (95% CI: 57.7, 70.4)
among men and 60.6% (95% CI: 56.9, 64.2) among
women. 1.5% had no risk factor (95% CI: 0.8, 2.7) 10.5%
(95% CI: 8.7, 12.1) had biological risk factors only, 16.8%
(95% CI: 14.9, 18.9) had lifestyle risk factors only and
71.5% (95% CI: 68.4, 74.3) had at least one biological
and one lifestyle risk factor.
Among the biological risk factors,
hypercholesterolemia (53.5% (95% CI: 47.3, 59.7)) and obesity (48.8
(95% CI: 45.4, 52.2)) were the most common, while the
lifestyle risk factor with the highest prevalence was
inadequate vegetable and fruit intake (72.8 (95% CI: 9.5,
75.9)) followed by physical inactivity (41.3 (95% CI:
37.4, 45.3)). Smoking and risky drinking were significantly
higher among men while physical inactivity and obesity
were significantly higher among women (Table 2).
Among men, the odds of having 3 CVD risk factors
were higher among Indians and those aged 45 years.
Among women, 3 CVD risk factors were more likely to
be present among housewives, age 35 and with low
education attainment (Table 3).
Table 2 Overall and gender-specific prevalence of cardiovascular risk factors
General and/or abdominal obesity
Inadequate vegetable and fruit intake
Inadequate physical activity
Total number of biological and/or lifestyle risk factors
3 biological/lifestyle risk factors
53.5 (47.3, 59.7)
48.8 (45.4, 52.2)
27.8 (20.8, 36.0)
25.8 (23.2, 28.6)
11.0 (9.2, 13.2)
72.8 (69.5, 75.9)
41.3 (37.4, 45.3)
25.5 (23.0, 28.2)
10.5 (8.6, 12.7)
25.5 (22.3, 29.0)
26.2 (23.4, 29.2)
20.9 (19.0, 23.0)
10.2 (8.8, 11.7)
62.5 (58.5, 66.3)
53.2 (44.7, 61.5)
40.9 (36.3, 45.8)
31.9 (26.4, 38.1)
26.2 (22.5, 30.2)
9.8 (7.3, 12.9)
70.3 (65.6, 74.5)
36.8 (32.9, 40.8)
46.5 (42.3, 50.8)
9.9 (7.4, 13.3)
24.5 (19.8, 30.0)
24.7 (20.8, 29.1)
22.5 (19.0, 26.4)
10.8 (8.8, 13.2)
64.3 (57.7, 70.4)
53.9 (49.2, 58.5)
57.2 (53.4, 60.9)
23.3 (14.7, 34.8)
25.4 (22.5, 28.5)
12.4 (10.6, 14.4)
75.5 (72.4, 78.4)
46.2 (41.0, 51.4)
0.1 (0.02, 0.5)
11.1 (8.4, 14.3)
26.6 (24.0, 29.3)
27.8 (24.7, 31.2)
19.2 (16.8, 22.0)
9.5 (7.7, 11.5)
60.6 (56.9, 64.2)
The results suggest that taking into account various
cardiovascular risk factors both biological and lifestyle, a
vast majority (62.5%) of Malaysian adults would have
three or more risk factors for cardiovascular disease.
Data from the International Collaborative Study of
Cardiovascular Disease in Asia (InterAsia), a cross-sectional
nationwide survey conducted between 20002001, was
analysed to determine the prevalence of CVD risk factor
clustering among Chinese adults age 3574 years old.
They reported that 17.2% of Chinese adults age 35 to 74
had at least three of the following risk factors:
dyslipidemia, hypertension, diabetes, smoking and overweight.
In addition, they also reported 35.9% of US adults had
these risk factors using data from the National Health &
Nutrition Examination Survey of 19992000 . The
inclusion of more risk factors in our study may have
contributed to the higher prevalence of multiple risk
factors in our study than in China and US adults.
Data from the 1996 Malaysian National Health and
Morbidity Survey showed that 61% of Malaysian adults
age 30 and above had at least one cardiovascular risk
factor and 27% had at least two or more risk factors out
of four CVD risk factors investigated (hypertension,
abnormal glucose tolerance, hypercholesterolemia and
overweight) . Selvarajah et al.  reported only 14%
prevalence of 3 out of 4 biological risk factors
(hypertension, hyperglycaemia, hypercholesterolaemia, central
obesity) among adults age 18 and above, based on data
from the 2006 National Health and Morbidity Survey. In
addition to fewer number of risk factors, these two
studies did not include lifestyle risk factors. In fact, lifestyle
risk factors such as smoking have been identified as
independent factors for CVD. And these factors are very
prevalent in this country, for instance, prevalence of
smoking alone among male adults was 46.5%  and
physical inactivity was 43.7% (50.5% among female and
35.3% among male adults) . Therefore, if we
overlook these lifestyle factors we might underestimate the
burden of CVD risk factors.
Among men, the odds of having 3 CVD risk factors
were higher among Indian men and age 45 and above. A
high proportion (70.6%, data not shown) of male Indians
were obese, therefore, obesity is the major contributor to
the multiple CVD risk factors among Indians in our
study. Likewise, the 2006 Malaysian National Health and
Private sector employee
No formal education
53.6 (43.7, 63.2)
62.0 (52.1, 71.1)
75.4 (68.2, 81.5)
79.8 (72.9, 85.3)
62.1 (52.0, 71.2)
69.5 (60.2, 77.4)
80.8 (68.2, 89.2)
59.2 (51.5, 66.5)
57.3 (47.7, 66.4)
65.1 (57.7, 71.8)
71.1 (42.1, 89.3)
62.2 (47.1, 75.2)
63.3 (53.9, 71.8)
65.3 (58.7, 71.3)
60.9 (51.9, 69.2)
61.3 (52.3, 69.6)
67.1 (57.8, 75.2)
78.6 (68.1, 86.3)
51.3 (33.4, 68.9)
64.7 (58.5, 70.4)
69.8 (62.8, 75.9)
73.8 (57.6, 85.3)
63.3 (53.6, 72.1)
66.0 (61.0, 70.7)
1.25 (0.77, 2.02)
2.43 (1.53, 3.84)**
2.68 (1.39, 5.18)*
1.47 (0.85, 2.52)
2.85 (1.31, 6.23)*
0.89 (0.55, 1.43)
1.02 (0.56, 1.83)
1.09 (0.29, 4.18)
0.97 (0.46, 2.08)
0.85 (0.34, 2.12)
0.85 (0.58, 1.23)
1.50 (0.90, 2.50)
1.91 (0.87, 4.21)
2.05 (0.89, 4.70)
1.97 (0.86, 4.52)
2.58 (0.82, 8.18)
1.32 (0.83, 2.10)
42.8 (36.5, 49.3)
62.2 (56.8, 67.4)
74.5 (68.3, 79.8)
83.1 (76.7, 88.0)
58.4 (53.4, 63.3)
62.2 (54.1, 69.7)
68.4 (59.4, 76.2)
60.8 (53.7, 67.5)
45.5 (35.1, 56.4)
61.2 (57.1, 65.3)
70.9 (59.1, 80.5)
59.4 (42.1, 74.7)
58.4 (53.9, 62.7)
60.7 (56.2, 65.0)
44.7 (37.1, 52.5)
60.3 (49.9, 69.8)
70.2 (66.2, 73.9)
51.2 (38.8, 63.3)
46.2 (36.1, 56.7)
39.0 (29.6, 49.3)
58.6 (53.8, 63.2)
71.6 (66.6, 76.2)
70.2 (61.8, 77.5)
60.1 (55.1, 65.0)
61.5 (57.1, 65.7)
Adjusted OR (95% CI)
1.81 (1.29, 2.34)*
3.10 (2.18, 4.41)**
5.76 (3.63, 9.15)**
0.93 (0.61, 1.41)
1.43 (0.82, 2.50)
1.06 (0.74, 1.54)
0.91 (0.51, 1.62)
1.24 (0.60, 2.60)
0.75 (0.38, 1.48)
0.68 (0.32, 1.45)
1.29 (0.79, 2.10)
2.16 (1.49, 3.14)**
1.24 (0.65, 2.38)
0.69 (0.34, 1.43)
1.78 (1.13, 3.17)*
1.91 (1.16, 3.17)*
1.97 (1.08, 3.59)*
0.98 (0.73, 1.32)
Adjusted OR, adjusted for all other variables in the model; *p-value < 0.05; **p-value <0.001.
Morbidity Survey reported Indians had the highest
prevalence of abdominal obesity compared to Malays
and Chinese . Another Malaysian study reported a
similarly high 68.8% prevalence of abdominal obesity
and 44.8% metabolic syndrome among Indians, the
highest across the ethnic groups . The high
prevalence among Indians suggests that other than
behavioural and dietary factors, genetics may play a role .
A review comparing obesity-related diseases between
South Asians and white Caucasians found that South
Asians had higher body fat, high truncal, subcutaneous
and intra-abdominal fat, and low muscle mass, were
different in terms of biochemical parameters
(hyperinsulinemia, hyperglycemia, dyslipidemia,
hyperleptinemia, low levels of adiponectin and high levels of
C-reactive protein), procoagulant state and endothelial
dysfunction, from white Caucasians . These findings
suggest risk factors other than lifestyle behaviours may
contribute to multiple CVD risk in the Indian population.
Previous studies have reported on the increase in the
prevalence of diabetes, hypertension, dyslipidaemia and
obesity with age [7,24,27]. This indicates that there is an
increasing trend in risk factors as the population ages.
Our data showed older age was associated with multiple
risk factors in both men and women. But, among men,
the odds of having 3 risk factors was higher from middle
age and above, while among women, significantly higher
prevalence was observed at a younger age group (3544).
A probable explanation for this disparity is women
become more sedentary and obese at a younger age
compared to men.
In women, among the occupation groups, multiple risk
factors was more likely to be found among housewives.
This may be attributed to higher prevalences of obesity
(60.3%), low fruit and vegetable consumption (82.7%)
and physical inactivity (57.5%) among housewives in our
study (Data not shown). Housewives had significantly
higher odds of abdominal obesity  and physical
inactivity  in the 2006 National Health and Morbidity
Survey. A cross-sectional study in a rural Malay
population in Malaysia showed being unemployed or a
housewife is associated with metabolic syndrome .
Lower socioeconomic status (SES) which consists of
the trio of education, income and occupation, is usually
correlated with poor health. Our data showed women
with lower education had significantly higher
prevalence of multiple CVD risk factors. In a
populationbased study of 11247 Australian adults aged 25 years
conducted in 19992000, women with lower education
were associated with hyperinsulinemia,
hypertriglyceridemia, abdominal obesity and hypertension . It is
known that lower education is associated with poorer
health because of their poorer access, utilisation and
understanding of health information . We presume
that this is because women with lower education may
lack knowledge on healthy food and of healthy lifestyle
The nations rapid economic growth accompanied by
technological advancement and urbanization have led to
changes towards a lifestyle of convenience and luxury
with smoking, increasingly sedentary lifestyle and
unhealthy dietary practices. These are modifiable risk
factors that contributed to the rise in cardiovascular
diseases. There is a need to introduce more creative and
innovative health education and promotion programmes
that result in behavioural modification. The Ministry of
Health of Malaysia has recommended workplace-based
and community-based programmes to empower
individuals at high risk or with chronic diseases to develop
health literacy, take responsibility for their own health
and be actively involved in promoting health in their
One limitation in this study is worthy of note, this
being a cross-sectional study, as such a definitive causal
association between risk factors such as obesity and
physical activity is not possible. Our sample size is
relatively small compared to other national health surveys,
but it was nationally representative.
The prevalence of cardiovascular risk factors clustering
among Malaysian adults is high, raising concerns that
cardiovascular disease incidence will rise steeply in the
near future if traditional approaches that target single
risk factors continue to be used. It is time that a
comprehensive, integrated approach that target all CVD risk
factors be developed especially for those at high risk
(housewives, low-educated and the elderly).
CVD: Cardiovascular disease; WC: Waist circumference; BMI: Body Mass Index;
HDL-C: High Density Lipoprotein-Cholesterol; MET: Metabolic Equivalent of
The authors declare they have no competing interests.
SMG, ZS, KCC, LKH and FIM contributed to acquisition of the data, conducted
the analysis and interpretation of the data and drafted the manuscript. MM,
LKK, AFY, FIM, ANM contributed in drafting the manuscript and critically
reviewing the content. All authors revised and approved the final manuscript.
The authors express their gratitude to the Director-General of Health,
Malaysia for granting permission to publish this paper, the Director of the
Institute for Medical Research for her support and the Non Communicable
Disease Section, Disease Control Division, Ministry of Health, Putrajaya for
providing data from Malaysia NCD Surveillance-1.
1. World Health Organization. Global atlas on cardiovascular disease prevention and control: Policies, strategies and interventions . Geneva: WHO ; 2011 .
2. World Health Organization . World Health Report 2003 - Shaping the future . Geneva: WHO; 2003 .
3. Ministry of Health Malaysia . Annual Report 2010 . Putrajaya: Ministry of Health Malaysia ; 2011 .
4. Carlsson AC , Wndell PE , Gigante B , Leander K , Hellenius ML , de Faire U. Seven modifiable lifestyle factors predict reduced risk for ischemic cardiovascular disease and all-cause mortality regardless of body mass index: A cohort study . Int J Cardiol . 2013 ; 168 ( 2 ): 946 - 52 .
5. Pearson TA , Blair SN , Daniels SR , Eckel RH , Fair JM , Fortmann SP , et al. AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke : 2002 Update: Consensus Panel Guide to Comprehensive Risk Reduction for Adult Patients Without Coronary or Other Atherosclerotic Vascular Diseases . American Heart Association Science Advisory and Coordinating Committee. Circulation . 2002 ; 106 ( 3 ): 388 - 91 .
6. Ebrahim S , Montaner D , Lawlor DA . Clustering of risk factors and social class in childhood and adulthood in British women's heart and health study: cross sectional analysis . BMJ . 2004 ; 328 ( 7444 ): 861 .
7. Selvarajah S , Haniff J , Kaur G , Guat Hiong T , Chee Cheong K , Lim CM , et al. Clustering of cardiovascular risk factors in a middle-income country: a call for urgency . Eur J Prev Cardiol . 2013 ; 20 ( 2 ): 368 - 75 .
8. Schuit AJ , van Loon AJ , Tijhuis M , Ock M. Clustering of lifestyle risk factors in a general adult population . Prev Med . 2002 ; 35 ( 3 ): 219 - 24 .
9. Disease Control Division , Ministry of Health . NCD risk factors in Malaysia . Putrajaya: Ministry of Health Malaysia ; 2006 . p. 2006 .
10. World Health Organization/International Association for the Study of Obesity/International Obesity Task Force: The Asia-Pacific perspective: redefining obesity and its treatment . Available at URL: http://www.wpro. who.int/nutrition/documents/Redefining_obesity/en/ 2000. Accessed: November 1, 2013 .
11. O'Brien E , Asmar R , Beilin L , Imai Y , Mallion JM , Mancia G , et al. European Society of Hypertension recommendations for conventional, ambulatory and home blood pressure measurement . J Hypertens . 2003 ; 21 ( 5 ): 821 - 48 .
12. Chobanian AV , Bakris GL , Black HR , Cushman WC , Green LA , Izzo JL , et al. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension . 2003 ; 42 : 1206 - 52 .
13. World Health Organization. The WHO STEPwise approach to chronic disease risk factor surveillance . Geneva: WHO ; 2001 .
14. Ministry of Health Malaysia. Clinical Practice Guidelines: Management of Dyslipidemia (4th Edition) . Putrajaya: Ministry of Health Malaysia ; 2011 .
15. World Health Organization. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications: Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus . Geneva: WHO ; 1999 .
16. Global Physical Activity Questionnaire (GPAQ) Analysis Guide . Available at URL: http://www.who. int/chp/steps/resources/GPAQ_Analysis_Guide.pdf. Accessed November 20 , 2014 .
17. World Health Organization. Guidelines for controlling and monitoring the tobacco epidemic . Geneva: WHO ; 1998 .
18. Fine LJ , Philogene GS , Gramling R , Coups EJ , Sinha S. Prevalence of multiple chronic disease risk factors: 2001 National Health Interview Survey . Am J Prev Med . 2004 ; 27 Suppl 2 : 18 - 24 .
19. Gu D , Gupta A , Muntner P , Hu S , Duan X , Chen J , et al. Prevalence of cardiovascular disease risk factor clustering among the adult population of China: Results from the International Collaborative Study of Cardiovascular Disease in Asia (Inter Asia) . Circulation . 2005 ; 112 ( 5 ): 658 - 65 .
20. Lim TO , Ding LM , Zaki M , Merican I , Kew ST , Maimunah AH , et al. Clustering of hypertension, abnormal glucose tolerance, hypercholesterolaemia and obesity in Malaysian adult population . Med J Malaysia . 2000 ; 55 ( 2 ): 196 - 208 .
21. Lim HK , Ghazali SM , Kee CC , Lim KK , Chan YY , Teh HC , et al. Epidemiology of smoking among Malaysian adult males: prevalence and associated factors . BMC Public Health 2013 . [http://www.biomedcentral.com/1471- 2458 /13/8]
22. Institute for Public Health . The Third National Health and Morbidity Survey (NHMS III) 2006 , Vol II . Kuala Lumpur: Ministry of Health Malaysia ; 2008 .
23. Kee CC , Jamaiyah H , Noor Safiza MN , Khor GL , Suzana S , Jamalludin AR , et al. Abdominal obesity in Malaysian adults: National Health and Morbidity Survey III (NHMS III , 2006 ). Malays J Nutr . 2008 ; 14 ( 2 ): 125 - 35 .
24. Mohamud WN , Ismail AA , Sharifuddin A , Ismail IS , Musa KI , Kadir KA , et al. Prevalence of metabolic syndrome and its risk factors in adult Malaysians: results of a nationwide survey . Diabetes Res Clin Pract . 2011 ; 91 ( 2 ): 239 - 45 .
25. Misra A , Vikram NK , Gupta R , Pandey RM , Wasir JS , Gupta VP . Waist circumference cutoff points and action levels for Asian Indians for identification of abdominal obesity . Int J Obes . 2006 ; 30 ( 1 ): 106 - 11 .
26. Misra A , Khurana L. Obesity-related non-communicable diseases: South Asians vs White Caucasians . Int J Obes . 2011 ; 35 ( 2 ): 167 - 87 .
27. Jan Mohamed HJ , Mitra AK , Zainuddin LR , Leng SK , Wan Muda WM . Women are at a higher risk of metabolic syndrome in rural Malaysia . Women Health . 2013 ; 53 ( 4 ): 335 - 48 .
28. Kavanagh A , Bentley RJ , Turrell G , Shaw J , Dunstan D , Subramanian SV . Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes . Soc Sci Med . 2010 ; 71 ( 6 ): 1150 - 60 .
29. Disease Control Division , Ministry of Health. National strategic plan for non communicable diseases: Medium term strategic plan to further strengthen the cardiovascular diseases & diabetes prevention & control program in Malaysia (2010-2014) . Putrajaya: Ministry of Health Malaysia ; 2010 .