Combined Lifestyle Factors and Cardiovascular Disease
Mortality in Chinese Men and Women
The Singapore Chinese Health Study
Andrew O. Odegaard, PhD, MPH; Woon-Puay Koh, PhD; Myron D. Gross, PhD;
Jian-Min Yuan, MD, PhD; Mark A. Pereira, PhD, MPH, MS
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Background—Lifestyle factors directly influence cardiovascular disease (CVD) risk, yet little research has examined the
association of combined lifestyle factors with CVD mortality, especially in Asian populations.
Methods and Results—We examined the association of 6 combined lifestyle factors (dietary pattern, physical activity,
alcohol intake, usual sleep, smoking status, and body mass index) with CVD mortality in 50 466 (44 056 without a
history of diabetes mellitus, CVD, or cancer and 6410 with diabetes mellitus or history of clinical CVD) Chinese men
and women in Singapore who were 45 to 74 years of age during enrollment in the Singapore Chinese Health Study in
1993 to 1998 and followed up through 2009. Each lifestyle factor was independently associated with CVD mortality.
When combined, there was a strong, monotonic decrease in age- and sex-standardized CVD mortality rates with an
increasing number of protective lifestyle factors. Relative to participants with no protective lifestyle factors, the hazard
ratios of CVD mortality for 1, 2, 3, 4, and 5 to 6 protective lifestyle factors were 0.60 (95% confidence interval,
0.45– 0.84), 0.50 (95% confidence interval, 0.38 – 0.67), 0.40 (95% confidence interval, 0.30 – 0.53), 0.32 (95%
confidence interval, 0.24 – 0.43), and 0.24 (95% confidence interval, 0.17– 0.34), respectively, among those without a
history of diabetes mellitus, CVD, or cancer (P for trend ⬍0.0001). A parallel graded inverse association was observed
in participants with a history of CVD or diabetes mellitus at baseline. Results were consistent for coronary heart disease
and cerebrovascular disease mortality.
Conclusion—An increasing number of protective lifestyle factors is associated with a marked decreased risk of
coronary heart disease, cerebrovascular disease, and overall CVD mortality in Chinese men and women. (Circulation.
2011;124:2847-2854.)
Key Words: Asian continental ancestry group 䡲 cardiovascular disease 䡲 life style 䡲 mortality 䡲 risk factors
C
ardiovascular disease (CVD) is the leading cause of
death worldwide.1 The increasing rates in developing
and recently developed populations mirror historic epidemiological data in high-income nations.2 The causes of this
global epidemic have largely been established to originate in
lifestyle factors and their direct impact on established and
novel pathways of clinical cardiovascular risk.3 A rich body
of evidence links lifestyle factors such as diet, alcohol intake,
physical activity, smoking, and relative weight with CVD.4 – 8
A developing body of literature suggests that sleep patterns
are also important.9,10
studies have examined the combined additive associations of
multiple lifestyle factors with CVD mortality.11–16 Additionally, there has been little ethnic variation in the populations
studied. Only 1 study has examined a Chinese population,16
an ethnic group, along with Asian Indians, that represents the
largest swath of the global population. Both south and
southeast Asian populations are experiencing an increasing
prevalence of CVD and many of its risk factors such as
diabetes mellitus as a result of well-documented epidemiological and nutrition transitions.2,17–19
Therefore, we aimed to examine the combined association
of lifestyle factors (dietary pattern, physical activity, alcohol
intake, smoking, usual sleep, and relative weight) with risk of
CVD mortality and the major subgroups of CVD mortality
(coronary heart disease and cerebrovascular disease mortal-
Clinical Perspective on p 2854
Despite the evidence of individual lifestyle factors being
associated with clinical parameters or CVD outcomes, few
Received June 9, 2011; accepted October 19, 2011.
From the University of Minnesota School of Public Health, Division of Epidemiology and Community Health, Minneapolis (A.O.O., M.D.G., M.A.P.);
University of Pittsburgh Division of Cancer Control and Population Sciences Cancer Institute, and Department of Epidemiology, University of Pittsburgh
Graduate School of Public Health, Pittsburgh, PA (J.-M.Y.); and National University of Singapore, Saw Swee Hock School of Public Health, Singapore (W.P.K.).
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.
111.048843/-/DC1.
Correspondence to Andrew Odegaard, PhD, MPH, University of Minnesota, School of Public Health, Division of Epidemiology and Community
Health, 1300 S 2nd St, Ste 300, Minneapolis MN, 55454. E-mail [email protected]
© 2011 American Heart Association, Inc.
Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.111.048843
2847
2848
Circulation
December 20/27, 2011
ity) in a population-based cohort study of Chinese men and
women in Singapore.
Methods
Study Population
The design of the Singapore Chinese Health Study has previously
been described.20 Briefly, the cohort was drawn from men and
women 45 to 74 years of age who belonged to one of the major
dialect groups (Hokkien or Cantonese) of Chinese in Singapore.
Between April 1993 and December 1998, 63 257 individuals completed an in-person interview that included questions on usual diet,
demographics, height and weight, use of tobacco, usual physical
activity, menstrual and reproductive history (women only), medical
history, and family history of cancer. The institutional review boards
at the National University of Singapore and the University of
Minnesota approved this study.
Assessment of Lifestyle Risk Factors
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At the baseline interview, all lifestyle factors were collected by
self-report. Height and weight were used to calculate body mass
index (BMI) as weight divided by height squared (kg/m2). Inquiry on
smoking habits included smoking status (never, former, or current
smoker); former and current (ie, ever) smokers were further asked
for age at starting or quitting smoking, number of cigarettes per day,
and number of years of smoking.21 For physical activity, participants
were asked the number of hours per week spent on moderate
activities such as brisk walking, bowling, bicycling on level ground,
and tai chi or chi kung and on strenuous sports such as jogging,
bicycling on hills, tennis, squash, swimming laps, or aerobics. The
physical activity portion of the questionnaire was modeled after the
European Prospective Investigation in Cancer (EPIC) study physical
activity questionnaire, which has been shown to be valid and
repeatable.22 Usual sleep duration was assessed by the following
question: “On average, during the last year, how many hours in a day
did you sleep?” Response categories were ⱕ5, 6, 7, 8, 9, and ⱖ10
hours.
For each of the 4 types of alcoholic beverages (beer, wine,
Western hard liquor, and Chinese hard liquor), participants were
asked to choose from 8 frequency categories: never or hardly, once
a month, 2 to 3 times a month, once a week, 2 to 3 times a week, 4
to 6 times a week, once a day, and ⱖ2 times a day. Consumers were
then asked to choose from 4 defined portion sizes. For beer, the
portion sizes were 1 small bottle (375 mL) or less, 2 small bottles or
1 large bottle (750 mL), 2 large bottles, and 3 large bottles or more.
For wine, the portion sizes were 1 glass (118 mL) or less, 2 glasses,
3 glasses, and 4 glasses or more. For Chinese or Western hard liquor,
the portion sizes were 1 shot (30 mL) or less, 2 shots, 3 shots, and 4
shots or more. One drink was defined as 375 mL beer (13.6 g
ethanol), 118 mL wine (11.7 g ethanol), and 30 mL Western or
Chinese hard liquor (10.9 g ethanol). We expressed levels of alcohol
intake in units of “drinks” per week to facilitate comparison with
Western populations.
A semiquantitative food frequency questionnaire specifically developed for this population assessing 165 commonly consumed food
items was administered during the baseline interview. The questionnaire has been validated against a series of 24-hour dietary recall
interviews20 and selected biomarkers.23,24 A food composition table
was also developed in conjunction with the study.20 Dietary patterns
were derived for this study population through the use of principal
component analysis including all 165 foods and beverages (besides
alcohol) using methods described previously.25 Briefly, the aim of
principal component analysis in nutritional analyses is to account for
the maximal variance of dietary intake by combining the many
different dietary variables into a smaller number of factors based on
the intercorrelations of these variables. A vegetable-, fruit-, and
soy-rich pattern characterized by high intake of those respective
foods and lower intake of meats, dim sum, Western-style fast food,
and sugared soft drinks was included.
Classification of Protective Lifestyle Factors
Lifestyle factors were characterized as protective on the basis of
previously published work examining BMI and CVD mortality,8 as
well as sleep and coronary heart disease mortality, in the cohort.9 For
the other lifestyle factors, protective levels were based on the data
from the Singapore cohort generally aligning with the literature base
on the topic. For alcohol intake, participants who reported no intake
or “high or excessive” intake had a greater risk of dying of CVD
relative to those reporting “light to moderate” intake of alcohol. The
protective definition of dietary intake in the cohort was based on
ranking of the vegetable-, fruit-, and soy-rich dietary pattern score.
Participants without cancer, CVD, or diabetes mellitus whose dietary
pattern was characterized by greater intake of vegetables, fruits and
soy (⬎40th percentile) had a lower risk of CVD mortality relative to
participants with a lower score (⬍40th percentile). This dietary
pattern score identified by principal component analysis represents
usual intake of the study population as captured by a food frequency
questionnaire but does not necessarily reflect the optimal diet in
relation to CVD. However, higher ranking on this dietary pattern is
consistent with historical evidence on diet and risk of CVD because
it represents increasing intake of fruits, vegetables, and soy.26
Physical activity levels were categorized into ⱖ2 h/wk of moderate
activity or any strenuous activity versus lower levels of activity.
Finally, participants were classified as never or ever smokers. All
factors were defined the same for persons with a history of clinical
diabetes mellitus or CVD at enrollment in the study except the
dietary pattern score and BMI. For this subpopulation, only the top
20% of vegetable-, fruit-, and soy-rich dietary pattern was associated
with lower risk with CVD mortality, perhaps because those at high
risk need to adhere to a dietary pattern that is more prudent than the
rest of the population to observe potential benefit. Furthermore, only
those who were underweight (⬍18.5 kg/m2) were at elevated risk of
dying of CVD. Each lifestyle factor was coded as 1 (protective) or 0
(referent).
Assessment of Mortality, Diabetes Mellitus,
and CVD
Information on date and cause of death was obtained through linkage
analysis with the nationwide registry of birth and death in Singapore.
Up to 6 different International Classification of Disease, version 9,
codes were recorded in the registry. Primary cause of death was used
for analysis. Vital status for cohort participants was updated through
December 31, 2009. Only 27 persons were lost to follow-up as a
result of migration out of Singapore, suggesting that emigration of
the cohort participants was negligible and that vital statistics
follow-up was virtually complete. The end points in the analyses
were deaths resulting from CVD (codes 394.0 – 459.0), coronary
heart disease (410.0 – 414.9, 427.5), and cerebrovascular disease
(430.0 – 438.0).
Diabetes status was assessed by the following question: “Have you
been told by a doctor that you have diabetes (high blood sugar)?” If
the answer was “yes,” the next question was, “Please also tell me the
age at which you were first diagnosed.” A validation study of the
self-report of diabetes mellitus cases used 2 different methods and
was previously reported.27,28 Similarly, CVD was assessed by the
following questions: “Have you been told by a doctor that you have
had a heart attack or angina (chest pain or exertion that is relieved by
medication)? ” and “Have you been told by a doctor that you have
had a stroke”? If the answer was “yes,” the next question was,
“Please tell me the age you were first diagnosed.”
Statistical Analysis
Of the original 63 257 participants, we excluded 1936 with a history
of invasive cancer (except nonmelanoma skin cancer) or superficial,
papillary bladder cancer at baseline because they did not meet study
inclusion criteria and 10 070 participants who were missing measurements of height, weight, or both. We also excluded participants
who reported extreme sex-specific energy intake (⬍600 or ⬎3000
kcal for women; ⬍700 or ⬎3700 kcal for men). The present analyses
included 50 466 participants: 44 056 who were “healthy” at enroll-
Odegaard et al
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ment with no reported clinical history of CVD, diabetes mellitus, or
cancer and 6410 who reported a history of clinical CVD or diabetes
mellitus at enrollment and at high risk. Those with a history of CVD
or diabetes mellitus at study enrollment were combined because of
their highly similar magnitude of increased risk and rates of CVD
mortality.
For each study subject, person-years were counted from the date
of baseline interview to the date of death, date of last contact (for the
few who migrated out of Singapore), or December 31, 2009,
whichever occurred first. Baseline characteristics were calculated for
participants across the overall number of protective lifestyle factors.
Age- and sex-standardized mortality rates were calculated from the
person-year weight of the entire cohort during the follow-up by the
following age categories: ⬍50, 50 to 54, 55 to 59, 60 to 64, 65 to 69,
and ⱖ70 years.
Proportional hazards (Cox) regression methods were used to
examine the associations between combined lifestyle factors and
hazard risk of death owing to CVD among healthy participants (who
had no history of CVD, diabetes mellitus, or cancer) and participants
with a history of clinical diabetes mellitus or CVD at baseline. All
regression analyses were conducted with SAS statistical software
version 9.2 (SAS Institute, Cary, NC). We estimated the hazard ratio
of death resulting from CVD according to the number of protective
lifestyle factors and the corresponding 95% confidence interval.
There was no evidence that proportional hazards assumptions were
violated, as indicated by the lack of significant interaction between
the combined lifestyle factor variable and a function of survival time
in the models.
In the analysis considering individual lifestyle factors, the models
included all the lifestyle factors simultaneously, as well as age (⬍50,
50 –54, 55–59, 60 – 64, ⱖ65 years), sex, year of interview (1993–
1995 and 1996 –1998), dialect (Hokkien versus Cantonese), level of
education (no formal schooling, primary school, secondary school
and above), marital status (married, separated/divorced, widowed,
never married), and total energy intake (kcal/d). The same adjustments were made in the analysis of the combined protective lifestyle
factors. We further adjusted for the age at diagnosis of diabetes
mellitus or CVD in participants with a reported history at baseline.
The overall number of protective lifestyle factors was the sum of the
risk score (0, 1) over all 6 lifestyle factors. Tests for trend were
performed by entering the combined lifestyle factor variable into the
model as a continuous variable.
Effect modification of the associations was considered by age, sex,
educational attainment, and self-reported physician-diagnosed hypertensive status at study enrollment. To reduce potential bias caused
by nonreported preexisting disease, underlying illness, or severity of
disease among those with a reported history of diabetes mellitus or
CVD, participants who died within 3 and 5 years were excluded from
sensitivity analyses.
Results
During ⬇593 118 person-years of follow-up, there were 1971
deaths resulting from CVD in individuals free of diabetes
mellitus, CVD, and cancer. Table 1 gives the definition of and
association of 6 different independent lifestyle factors with
CVD mortality. Each individual factor (dietary pattern, physical activity, alcohol intake, usual sleep, smoking, and relative weight) was associated with CVD mortality, but the
prevalence of the protective level of each factor differed.
Table 2 presents participant characteristics at baseline
according to the number of protective lifestyle factors in
healthy (n⫽44 056) participants. There was a somewhat
statistically normal distribution of protective lifestyle factors in the study population, with the greatest frequency of
participants having 2 or 3 protective lifestyle factors and
the fewest participants with 0 or 6 protective lifestyle
factors. As the number of protective lifestyle factors
Lifestyle and CVD Mortality
2849
Table 1. Definition of Lifestyle Factors and the Independent
Association With Cardiovascular Disease Mortality
(International Classification of Disease, Version 9, Codes
390 – 459)
Definition of Lifestyle Factors
Referent
Protective
Dietary pattern
Lowest 40% of dietary
pattern score
characterized by high
intake of vegetables,
fruits, and soy
Upper 60% of dietary
pattern score
characterized by high
intake of vegetables,
fruits, and soy
Physical activity
⬍2 h/wk of moderate or
no strenuous activity
ⱖ2 h/wk of moderate
or any strenuous activity
⬍6 or ⱖ 9 h/d
6–8 h/d
Sleep
BMI
Age ⬍65 y
⬍18.5 and ⬎21.5 kg/m2
18.5–21.5 kg/m2
Age ⱖ65 y
⬍18.5 and ⬎24.5 kg/m
18.5–24.5 kg/m2
None or ⬎2 drinks/d
Light to moderate intake
(1–14 drinks/wk)
Ever smoked
Never smoked
Alcohol intake
Smoking
2
Independent Association of Lifestyle Factors With Cardiovascular Disease
Mortality
Factor
Percent With Protective
Protective: HR (95% CI)
Dietary pattern
60.0
0.81 (0.74–0.89)
Physical activity
23.6
0.85 (0.76–0.95)
Sleep
85.0
0.83 (0.75–0.93)
BMI
32.7
0.77 (0.70–0.85)
Alcohol intake
19.2
0.82 (0.73–0.92)
Smoking
70.6
0.68 (0.61–0.75)
BMI indicates body mass index; HR, hazard ratio; and CI, confidence interval.
Model includes all factors simultaneously and adjusted for age, sex, dialect,
year enrolled, education, marital status, and energy intake. n⫽44 056 healthy
participants.
increased, participants at baseline were younger, more
educated, more likely to be currently married, and less
hypertensive and had a lower BMI.
Table 3 displays the age- and sex-standardized rates and
hazard ratios with 95% confidence intervals for overall CVD,
coronary heart disease, and cerebrovascular disease mortality
according to number of protective lifestyle factors in the
healthy population. A graded monotonic decrease in age- and
sex-standardized rate and risk of coronary heart disease,
cerebrovascular disease, and overall CVD mortality was
observed with an increasing number of protective lifestyle
factors. Individuals with 5 to 6 protective lifestyle factors had
approximately one fourth the risk of CVD mortality relative
to those with no protective lifestyle factors. Of note, in the
289 participants who reported 6 protective lifestyle factors,
only 2 deaths were recorded as caused by CVD (none by
coronary heart disease). The hazard ratio of CVD mortality
for those participants was only 0.06 (95% confidence interval, 0.02– 0.25) relative to those with no protective factors.
Table I in the online-only Data Supplement reports the
sex-stratified results for overall CVD mortality and displays a
similar monotonic decrease in risk between sexes, although
greater absolute rates of CVD mortality occur in men.
2850
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December 20/27, 2011
Table 2. Baseline Participant Characteristics According to the Number of Protective Lifestyle Factors in Healthy Participants (Free
of Diabetes Mellitus and History of Cardiovascular Disease)
Protective Lifestyle Factors
0
n
1
415
Age, y
2
3589
57.8 (7.4)
15 998
4
9764
6
289
55.6 (7.6)
55.1 (7.6)
20.7
32.7
50.8
61.6
59.5
52.0
43.3
Education, %
21.9
21.0
26.7
31.2
41.1
50.5
58.9
Married, %
76.5
83.2
84.1
85.5
86.5
87.6
88.9
21.4
BMI, kg/m2
21.6
23.1 (4.4)
23.6 (4.1)
21.9
21.1
23.6 (3.7)
23.2 (3.5)
55.0 (7.9)
5
2648
Female sex, %
Hypertension, %
56.5 (7.6)
3
11 353
18.4
22.2 (3.0)
55.2 (8.2)
54.2 (8.1)
15.7
21.5 (2.4)
10.0
20.5 (1.1)
Percent reporting protective level
of lifestyle factor
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BMI
NA
7.4
16.9
29.0
54.3
75.9
100
Dietary pattern
NA
9.5
30.5
69.8
88.5
95.9
100
Physical activity
NA
1.9
6.7
16.7
45.7
81.3
100
Sleep
NA
54.3
77.9
90.3
95.5
98.9
100
Smoking
NA
23.2
57.2
78.0
87.4
94.1
100
Alcohol intake
NA
3.7
10.7
16.3
28.6
53.9
100
Data for age and body mass index (BMI) are given as mean (SD).
without cancer or reported history of major chronic disease
at study enrollment, the 6410 participants with a reported
history of clinical CVD or diabetes mellitus at enrollment
experienced a marked stepwise decrease in rate and risk of
dying of CVD with an increasing number of protective
lifestyle factors, as shown in the Figure. Comparable
results for coronary heart disease and cerebrovascular
disease were observed (data not shown).
The results from the parallel analysis of 6410 participants with a history of clinical CVD or diabetes mellitus at
enrollment were analogous across the individual lifestyle
factors as presented in Table 4. With an increasing number
of protective lifestyle factors, participants were younger,
had a younger age of diagnosis of diabetes mellitus or
CVD, were more educated, and had higher BMIs (data not
presented). Similar to the results of the 44 056 participants
Table 3. Age- and Sex-Adjusted Cardiovascular Disease Mortality Rates and Hazard Ratio and 95% Confidence Interval of
Cardiovascular Disease Mortality According to the Number of Protective Lifestyle Factors in Healthy Chinese Men and Women:
Singapore Chinese Health Study
Protective Lifestyle Factors
0
n
1
2
3
4
5/6
415
3589
11 353
15 998
9764
2937
55
264
616
630
325
81
110
57
41
29
24
20
P for
Trend
Total CVD mortality
(ICD-9 codes 390 – 459)
CVD deaths, n
Standardized rate*
HR (95% CI)
1.00
0.60 (0.45–0.84)
0.50 (0.38–0.67)
0.40 (0.30–0.53)
0.32 (0.24–0.43)
0.24 (0.17–0.34)
⬍0.0001
CHD mortality (ICD-9 codes
410.0–414.9, 427.5)
CHD deaths, n
29
152
335
324
158
41
Standardized rate*
58
33
22
15
12
10
HR (95% CI)
1.00
0.67 (0.45–0.99)
0.53 (0.36–0.78)
0.40 (0.27–0.59)
0.30 (0.20–0.45)
0.23 (0.14–0.37)
⬍0.0001
CERE mortality (ICD-9 430–438)
CERE deaths, n
18
61
193
180
103
Standardized rate*
36
13
13
8
8
HR (95% CI)
1.00
0.41 (0.24–0.69)
0.46 (0.28–0.74)
0.33 (0.20–0.54)
0.30 (0.18–0.50)
28
7
0.25 (0.14–0.46)
⬍0.0001
CVD indicates cardiovascular disease; ICD-9, International Classification of Disease, version 9; HR, hazard ratio; CI, confidence interval; CHD, coronary heart disease;
and CERE, cerebrovascular disease. Model was adjusted for age, sex, year of enrollment, dialect, education, marital status, and energy intake.
*Age- and sex-standardized CVD/CHD/CERE mortality rate per 10 000 person-years using person-year time, age, and sex distributions of the Singapore Chinese
Health Study.
Odegaard et al
Table 4. Definition of Lifestyle Factors and the Independent
Association With Cardiovascular Disease Mortality
(International Classification of Disease, Version 9, Codes
390 – 459) in Participants With Clinical Cardiovascular Disease
or Diabetes Mellitus at Baseline (nⴝ6410)
Definition of Lifestyle Factors
Dietary pattern
Physical activity
Sleep
BMI
Alcohol intake
Protective
Upper 20% of dietary
pattern score
characterized by high
intake of vegetables,
fruits, and soy
⬍2 h/wk of moderate
or no strenuous
activity
ⱖ2 h/wk of
moderate or any
strenuous activity
⬍6 h or ⱖ9 h/d
6–8 h/d
⬍18.5 kg/m2
ⱖ18.5 kg/m2
None or ⬎2 drinks/d
Light to moderate
intake (1–14
drinks/wk)
Ever smoked
Never smoked
Independent Association of Lifestyle Factors With Cardiovascular Disease
Mortality
Factor
Percent With Protective
Protective: HR (95% CI)
Dietary pattern
20.0
0.84 (0.71–0.98)
Physical activity
29.8
0.82 (0.72–0.94)
Sleep
77.9
0.82 (0.72–0.94)
BMI
95.5
0.75 (0.58–0.96)
Alcohol intake
11.4
0.76 (0.62–0.93)
Smoking
64.3
0.73 (0.64–0.84)
BMI indicates body mass index; HR, hazard ratio; and CI, confidence interval.
Model includes all factors simultaneously and is adjusted for age, age at
diagnoses of diabetes mellitus or cardiovascular disease, sex, dialect, year
enrolled, education, marital status, and energy intake.
There was no evidence that the nature of the strong
inverse association varied by age, sex, educational attainment, or hypertensive status at baseline in the 44 056
participants in the main analysis or in those with diabetes
mellitus or a history of CVD at baseline. Indeed, the
pattern of relative risk was similar across the multiple
2851
stratified analyses, but the absolute rates were greater in
participants who were older, male, hypertensive, and less
educated (data not presented by age, hypertensive status,
and education level). The exclusion of deaths within 3 or
5 years of enrollment did not materially change the nature
of the results for those who were healthy or had a history
of diabetes mellitus or CVD.
Discussion
Chinese men and women free of major chronic disease
with protective lifestyle factors represented by a dietary
pattern plentiful in vegetables, fruit, and soy; higher
relative levels of physical activity; light to moderate
alcohol consumption; average usual sleep of 6 to 8 h/d; no
history of smoking; and healthy relative weight were at a
significantly reduced risk of dying of CVD relative to their
peers who did not adhere to these protective lifestyle
factors. In combination, a marked decrease in rate and risk
of dying of CVD was observed with each additional
protective lifestyle factor. The nature of the association did
not differ materially among population subgroups or length
of follow-up. Very similar results were observed in participants with a history of diabetes mellitus and CVD at
study enrollment, enhancing the generalizability to both
primary and secondary prevention.
Few studies have addressed the combination of different
lifestyle factors with death resulting from CVD despite the
large body of evidence linking them with CVD.4 –10
Knoops et al11 examined the combination of a Mediterranean diet index, moderate alcohol consumption, physical
activity, and nonsmoking in 2339 European men and
women 70 to 90 years of age and observed a decreasing
risk of death caused by coronary heart disease and overall
CVD with each increasing protective factor. In the EPICNorfolk study, ⬎20 000 men and women 45 to 79 years of
age who were free of CVD and cancer had 4 combined
healthy lifestyle factors defined as plasma vitamin C as a
marker of a fruit/vegetable-rich diet, noncurrent smoker,
light to moderate alcohol intake, and physical activity.12
Participants experienced a significant increase in risk of
CVD mortality with each poor lifestyle factor. Comparable
results were observed in 2057 participants with CVD or
cancer at study baseline, similar to our study.
1.20
1.00
1.00
HR (95% CI)
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Smoking
Referent
Lowest 80% of dietary
pattern score
characterized by high
intake of vegetables,
fruits, and soy
Lifestyle and CVD Mortality
0.80
0.77
0.60
0.60
0.47
0.41
0.40
0.20
0.00
0-1
2
3
4
# Protective Lifestyle Factors
5-6
Figure. Hazard ratios (HRs) and 95% confidence
intervals (CIs) of cardiovascular disease (CVD)
mortality (International Classification of Disease,
version 9, codes 390 – 459) according to number of
protective lifestyle factors in individuals with a history of clinical CVD or diabetes mellitus at enrollment (n⫽6410; CVD deaths, 1184; P for trend
⬍0.0001). Respective n (CVD deaths) for 0/1, 2, 3,
4, and 5 to 6 low-risk lifestyle factors: 358 (104),
1602 (370), 2605 (460), 1451 (203), and 394 (47).
Age- and sex-standardized CVD mortality rates
across categories: 311, 214, 149, 113, and 94.
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December 20/27, 2011
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In the Nurses’ Health Study (NHS), 77 782 women 34 to
59 years of age who were free of CVD and cancer, had a
lower healthy eating index score, were less physically active,
ever smoked, drank heavily or abstained, and carried excess
weight (BMI ⱖ25.0 kg/m2) were at a marked increased risk
of dying of CVD during follow-up relative to women who
adhered to healthy levels of these lifestyle factors.13 Similar
results were seen in 4886 men and women in the United
Kingdom who were ⱖ18 years of age and had low fruit/
vegetable intake, had low physical activity levels, currently
smoked, or excessively consumed alcohol.15 In 38 110 men
20 to 84 years of age who were free of CVD and cancer, a
combination of a high level of cardiorespiratory fitness,
moderate or higher levels of physical activity, light to
moderate alcohol intake, normal relative weight, and not
currently smoking was associated with a decreasing risk of
CVD death.14
The only other study to examine combined lifestyle factors
with CVD mortality in an Asian population was the Shanghai
Women’s Study, which included 71 243 women 40 to 70
years of age who did not smoke or drink alcohol.16 Women
who had a BMI between 18.5 and 25 kg/m2, had a low
waist-to-hip ratio, were physically active, reported a high
level of fruit and vegetable intake, and had a spouse who did
not report smoking experienced a significant decrease in risk
of dying of CVD.
In summary, our study and the other noted research
suggest that a plant-based dietary pattern, greater levels of
physical activity, avoidance of smoking, maintenance of a
healthy weight range, and light to moderate alcohol consumption all contribute independently to the prevention of
death resulting from CVD. Our study also raises the point
that sleep patterns are another important lifestyle factor to
consider for CVD risk. Sleep deprivation results in adverse
physiological changes central to CVD risk,29 whereas
longer sleep duration may be a marker of underlying CVD,
mental health status, or poor thyroid function, all of which
affect CVD risk.30,31
The results from our study examining participants with a
history of CVD or diabetes mellitus and thus at significantly
increased risk for CVD mortality reaffirm prior or ongoing
randomized trials focused on lifestyle factors in similar
high-risk populations32,33 and provide the important message
that delaying or preventing death caused by CVD is possible
with an increasing number of protective lifestyle factors in
individuals with a history of diabetes mellitus or CVD. The
results observed in our study are without information on
clinical treatment. However, a previous study found that an
unhealthy lifestyle increases the risk of CVD independently
of pharmacological treatment.34 Another aspect to consider in
the interpretation of these results is that lifestyle at enrollment
may be a marker of severity of disease. However, the
consistency with healthy participants in the cohort, other
aforementioned studies, and no material difference in the
results relative to follow-up time suggest that disease severity
or some other bias is not the driving factor in the association.
With the increasing global prevalence of type 2 diabetes
mellitus35 and the increase in rates of CVD in developing and
recent developed populations,2 the results of a protective
lifestyle, concomitant with usual medical treatment in this
high-risk population, suggest a potential for significant
benefit.
An important facet to consider in studies examining lifestyle factors in relation to CVD mortality is that the statistical
distribution of combined protective lifestyle factors is a
somewhat normal bell shape across populations.11–16 For
example, in this study, participants with 0 or 5 to 6 combined
protective lifestyle factors were the least common, whereas
participants with 2 or 3 combined protective lifestyle factors
were most numerous and contributed the majority of deaths
resulting from CVD. This distribution of combined lifestyle
factors is consistent with the distribution of clinical data (eg,
lipids, blood pressure) from previous studies.36 Thus, efforts
to increase the prevalence of protective lifestyle factors will
likely have a significant influence on lowering the rates of
CVD incidence and mortality.37,38 Indeed, evidence from
surveillance, initiatives, and policy efforts suggests significant potential benefit related to increasing population levels
of healthy lifestyle factors in relation to lessening CVD.39 – 41
There are a number of strengths to consider in the interpretation of this study. The large prospective design with a
non-Western population uniquely contributes to the literature.
Furthermore, there are few long-term prospective data on
lifestyle factors in individuals with a history of diabetes
mellitus or CVD and risk of CVD mortality. Other strengths
include the high participant response rate, detailed collection
of data through face-to-face interviews, thorough adjustment
for confounders, very low level of participants lost to followup, and nearly complete mortality assessment with objectively obtained records on time and cause of death.
Limitations also need to be considered. The self-report of
lifestyle data may result in some misclassification and residual confounding while underestimating the associations assuming nondifferential misclassification. Despite the face
validity for the results of all the different lifestyle factors,
objective measures other than self-report would be ideal.
Furthermore, the lifestyle factors were dichotomized, which
may lead to underestimation of risk because there are graded
associations among some of the lifestyle factors. However,
our results are consistent with the previous published
studies on the topic in strength and gradient. Repeated
assessments of the lifestyle factors would have allowed us
to examine change in lifestyle in relation to CVD mortality
and would have complemented our data. We also relied on
self-report of physician-diagnosed diabetes mellitus and
CVD for categorization of individuals, which results in
some misclassification.
In short, an increasing number of protective lifestyle
factors in healthy Chinese men and women is associated
with marked decreasing rates and risk of dying of CVD. A
similar association occurred in high-risk participants with
a history of diabetes mellitus or CVD at study enrollment.
Overall, our study highlights in a large cohort study of
Chinese adults that multiple modifiable lifestyle factors are
of paramount importance in improving population-wide
cardiovascular risk reduction in both primary and secondary prevention.
Odegaard et al
Acknowledgments
We thank Siew-Hong Low of the National University of Singapore
for supervising the field work of the Singapore Chinese Health Study
and Kazuko Arakawa and Renwei Wang for the development and
maintenance of the cohort study database. Finally, we acknowledge
the founding, longstanding principal investigator of the Singapore
Chinese Health Study, Mimi C. Yu.
Sources of Funding
This work was supported by the National Institutes of Health (NCI
RO1 CA055069, R35 CA053890, R01 CA080205, R01 CA098497,
and R01 CA144034).
Disclosures
None.
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CLINICAL PERSPECTIVE
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Lifestyle factors directly affect established and novel pathways of cardiovascular risk. Despite this evidence, few studies
have examined the combined association of multiple lifestyle factors with cardiovascular disease (CVD) mortality.
Furthermore, there has been little ethnic variation in the populations studied or examination of this topic in patients with
a history of diabetes mellitus and/or CVD. Therefore, this study examined the combined association of lifestyle factors with
CVD mortality and its major subgroups in healthy Chinese men and women and a high-risk subcohort with a history of
diabetes mellitus and CVD during study enrollment in the Singapore Chinese Health Study (1993–98) and followed up
through 2009. In 50 466 healthy and high-risk Chinese men and women, protective lifestyle factors were represented by
a diet plentiful in vegetables, fruit, and soy; higher relative levels of physical activity; light to moderate alcohol
consumption; average usual sleep of 6 to 8 h/d; no history of smoking, and normal weight (ie, not underweight or
overweight). In combination, a marked decrease in rate and risk of dying as a result of CVD was observed with each
additional protective lifestyle factor in healthy and high-risk participants. Overall, data from this large cohort of Chinese
adults highlight that multiple modifiable lifestyle factors are of paramount importance in improving population-wide
cardiovascular risk reduction in both primary and secondary prevention. Efforts to increase the prevalence of protective
lifestyle factors in healthy and high-risk populations should significantly influence CVD mortality.
Combined Lifestyle Factors and Cardiovascular Disease Mortality in Chinese Men and
Women: The Singapore Chinese Health Study
Andrew O. Odegaard, Woon-Puay Koh, Myron D. Gross, Jian-Min Yuan and Mark A. Pereira
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Circulation. 2011;124:2847-2854; originally published online November 21, 2011;
doi: 10.1161/CIRCULATIONAHA.111.048843
Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2011 American Heart Association, Inc. All rights reserved.
Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circ.ahajournals.org/content/124/25/2847
Data Supplement (unedited) at:
http://circ.ahajournals.org/content/suppl/2011/11/18/CIRCULATIONAHA.111.048843.DC1
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
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SUPPLEMENTAL MATERIAL
Table S1: Sex Stratified Results for Total Cardiovascular Disease Mortality (ICD-9 390-459): Age Adjusted Rates and Hazard
Ratios According to Number of Protective Lifestyle Factors in Healthy Chinese Women and Men: SCHS
Number of
Protective
Lifestyle Factors
0
1
2
N CVD Deaths / N
*Standardized rate
HR (95% CI)
11 / 86
22
1.00
67 / 1,174
15
0.51 (0.27-0.96
218 / 5,764
14
0.39 (0.21-0.72)
N CVD Deaths / N
*Standardized rate
HR (95% CI)
44 / 329
88
1.00
197 / 2,415
43
0.67 (0.45-0.99)
398 / 5,589
26
0.53 (0.36-0.78)
3
4
5/6
277 / 9,860
13
0.31 (0.17-0.56)
151 / 5,807
11
0.28 (0.15-0.52)
30 / 1,502
7
0.20 (0.10-0.41)
P Trend
<0.0001
353 / 6,138
16
0.40 (0.27-0.59)
174 / 3,957
13
0.30 (0.20-0.45)
51 / 1,435
13
0.23 (0.14-0.37)
P Trend
<0.0001
Women
Men
SCHS= Singapore Chinese Health Study
*Standardized rate=Age standardized CVD mortality rate per 10,000 person years using person year time, and age distributions of SCHS by sex
HR (95% CI) = Hazard Ratio; 95 % confidence interval: Model adjusted for age, year of enrollment, dialect, education, marital status, and energy intake