International Journal of Obesity (2010) 34, 340–347
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ORIGINAL ARTICLE
Physical inactivity, abdominal obesity and risk of
coronary heart disease in apparently healthy men
and women
BJ Arsenault1,2, JS Rana3,4, I Lemieux1, J-P Després1,5, JJP Kastelein3, SM Boekholdt3,6, NJ Wareham7
and K-T Khaw8
1
Centre de Recherche de l’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, Canada; 2Faculty of
Medicine, Department of Anatomy and Physiology, Université Laval, Québec, Canada; 3Department of Vascular Medicine,
Academic Medical Center, Amsterdam, The Netherlands; 4Cedars-Sinai Medical Center, Heart Institute, David Geffen School
of Medicine at University of California Los Angeles, CA, USA; 5Division of Kinesiology, Department of Social and Preventive
Medicine, Université Laval, Québec, Canada; 6Academic Medical Center, Department of Cardiology, Amsterdam, The
Netherlands; 7Medical Research Council Epidemiology Unit, Cambridge, UK and 8Department of Public Health and Primary
Care, Institute of Public Health, University of Cambridge, Cambridge, UK
Objective: To test the hypothesis that for any given body mass index (BMI) category, active individuals would have a smaller
waist circumference than inactive individuals. Our second objective was to examine the respective contribution of waist
circumference and physical inactivity on coronary heart disease (CHD) risk.
Design: Prospective, population-based study with an 11.4-year follow-up.
Subjects: A total of 21 729 men and women aged 45–79 years, residing in Norfolk, UK.
Methods: During follow-up, 2191 CHD events were recorded. Physical activity was evaluated using a validated lifestyle
questionnaire that takes into account both leisure-time and work-related physical activity. Waist circumference was measured
and BMI was calculated for each participant.
Results: For both men and women, we observed that within each BMI category (o25.0, 25–30 and X30.0 kg m2), active
participants had a lower waist circumference than inactive participants (Po0.001). In contrast, within each waist circumference
tertile, BMI did not change across physical activity categories (except for women with an elevated waist circumference).
Compared with active men with a low waist circumference, inactive men with an elevated waist circumference had a hazard
ratio (HR) for future CHD of 1.74 (95% confidence interval (CI), 1.34–2.27) after adjusting for age, smoking, alcohol intake and
parental history of CHD. In the same model and after further adjusting for hormone replacement therapy use, compared with
active women with a low waist circumference, inactive women with an elevated waist circumference had an HR for future CHD
of 4.00 (95% CI, 2.04–7.86).
Conclusion: In any BMI category, inactive participants were characterized by an increased waist circumference, a marker of
abdominal adiposity, compared with active individuals. Physical inactivity and abdominal obesity were both independently
associated with an increased risk of future CHD.
International Journal of Obesity (2010) 34, 340–347; doi:10.1038/ijo.2009.229; published online 17 November 2009
Keywords: physical activity; abdominal obesity; coronary heart disease
Introduction
Correspondence: Dr K-T Khaw, Clinical Gerontology Unit, Department of
Public Health and Primary Care, University of Cambridge School of Clinical
Medicine, Addenbrooke’s Hospital, Box 251, Cambridge CB2 2QQ, UK.
E-mail: [email protected]
Received 28 February 2009; revised 21 September 2009; accepted 29
September 2009; published online 17 November 2009
It is now well recognized that obesity, particularly abdominal
obesity, is associated with metabolic alterations that are
associated with an increased risk of both type 2 diabetes and
coronary heart disease (CHD).1–4 Although body mass index
(BMI) has become the established clinical measurement to
quantify obesity as a CHD risk factor, there is increasing
evidence suggesting that abdominal obesity, as estimated by
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
341
waist circumference, could represent a better marker to
estimate CHD risk associated with obesity.3,5,6 Over the past
decades, the prevalence of individuals with an increased
waist circumference has considerably increased, and likely to
have affected age-adjusted CHD mortality rates.7,8
Numerous prospective, population-based studies have also
shown that physical inactivity is an important risk factor for
CHD, independent from obesity and its complications.9–13 It
is generally accepted that physical activity levels and obesity
levels have independent impact on CHD risk. However,
most studies on the topic have used BMI and not waist
circumference. To date, although mounting evidence suggests that waist circumference might be more useful to assess
CHD risk than BMI, no epidemiological study has yet tested
the hypothesis that, independent of obesity, inactive
individuals have a larger waist circumference than active
individuals; moreover, few prospective studies have documented the respective contributions of physical inactivity
and waist circumference with risk of CHD. Moreover, the
magnitude of the association between waist circumference
and physical inactivity with CHD risk is currently poorly
investigated.
The objective of this study was to test the hypothesis that
for any given BMI category, active individuals would have a
smaller waist circumference than inactive individuals. Our
second objective was to investigate whether waist circumference and physical inactivity independently contribute to
CHD risk.
Materials and methods
Study design
The European Prospective Investigation into Cancer and
Nutrition (EPIC)-Norfolk study is a population-based study
of 25 639 men and women aged between 45 and 79 years,
resident in Norfolk, UK, who completed a baseline questionnaire survey and attended a clinic visit.14 Participants
were recruited from age–sex registers of general practices in
Norfolk as part of the 10-country collaborative EPIC study
designed to investigate dietary and other determinants of
cancer. Additional data were obtained in EPIC-Norfolk to
enable the assessment of determinants of other diseases.
The study cohort was closely similar to the UK population
samples with regard to many characteristics, including
anthropometry, blood pressure and lipids, but with a lower
proportion of smokers. In the initial cohort of 25 639
participants, 1115 have had previous myocardial infarction
or stroke and we therefore excluded from the present
analyses, leaving 24 524 participants without cardiovascular
disease history. We excluded 2795 participants with missing
data on diabetes, smoking, blood pressure, physical activity,
waist circumference, BMI or blood lipids, leaving 21 729
individuals for the final analyses. As these participants were
excluded from the present analyses because they had missing
data, it is not possible to compare them with the participants
who were included in our analyses.
Study population
The design and methods of the study have been described in
detail.14 In short, eligible participants were recruited by mail.
At the baseline survey conducted between 1993 and 1997,
participants completed a detailed health and lifestyle
questionnaire. At the clinic visit, trained research nurses
took anthropometric measurements on individuals in light
clothing without shoes. Height was measured to the nearest
0.1 cm with a freestanding stadiometer. Weight was measured to the nearest 100 g with digital scales (Salter Industrial
Measurement Ltd., West Bromwich, UK). We used a D-loop
nonstretch fiberglass tape to measure waist circumference
(measured at the smallest circumference between the ribs
and iliac crest). We calculated BMI as weight per height2
(kg m2). Blood was taken by venipuncture into plain and
citrate tubes. Blood samples were processed for various assays
at the Department of Clinical Biochemistry, University of
Cambridge, or stored at 80 1C. All individuals have been
flagged for mortality at the UK Office of National Statistics,
with vital status ascertained for the entire cohort. Death
certificates for all decedents were coded by trained nosologists according to the International Classification of Diseases
(ICD), 9th revision. Death was attributed due to CHD if the
underlying cause was coded as ICD 410–414. These codes
encompass the clinical spectrum of CHD, that is, unstable
angina, stable angina and myocardial infarction. Previous
validation studies in our cohort indicated high specificity for
such case ascertainment.15 In addition, participants admitted
to hospital were identified by their unique National Health
Service number by data linkage with ENCORE (East Norfolk
Health Authority database), which identifies all hospital
contacts throughout England and Wales for Norfolk residents.
Participants were identified as having CHD during follow-up
if they had a hospital admission and/or died with CHD as an
underlying cause. The Norwich District Health Authority
Ethics Committee approved the study, and all participants
gave signed informed consent. In this paper, we report results
obtained from an average follow-up of 11.4 years.
Habitual physical activity was assessed using two questions
referring to activity during the past year. The first question
was about physical activity at work, the second about the
amount of time spent in hours per week in activities: cycling
and leisure time physical activities such as jogging or
swimming, in winter and summer separately. A simple
physical activity index was devised to allocate individuals
into four categories of usual physical activity: inactive,
moderately inactive, moderately active and active (Table 1).
This index was validated against heart rate monitoring in
173 individuals over 1 year.16 This validation study showed
that the repeatability of the physical activity index was high
(weighted ¼ 0.6, Po0.001). There were positive associations
between the physical activity index from the questionnaire
International Journal of Obesity
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
342
Table 1
Absolute levels of physical activity in each physical activity category
Label
Description
Inactive
Moderately
inactive
Moderately
active
Sedentary job and no recreational physical activity
Sedentary job with o0.5 h recreational physical activity per
day or standing job with no recreational physical activity
Sedentary job with 0.5–1 h recreational physical activity per
day or standing job with 0.5 h recreational physical activity or
physical job with no recreational physical activity
Sedentary job with 41 h recreational physical activity per day
or standing job with 40.5 h recreational physical activity or
physical job with at least some recreational physical activity or
heavy manual job
Active
and the objective measures of the ratio of daytime energy
expenditure to resting metabolic rate and cardiorespiratory
fitness (CRF).
Biochemical analyses
Nonfasting serum levels of total cholesterol, high-density
lipoprotein cholesterol and triglycerides were measured in
fresh samples with the RA 1000 (Bayer Diagnostics,
Basingstoke, UK), and low-density lipoprotein cholesterol
levels were calculated with the Friedewald formula.17
Statistical analyses
Mean levels of cardiovascular risk factors were calculated for
four categories, according to their physical activity levels. We
performed sex-specific analyses to obtain the mean waist
circumference of participants classified on the basis of BMI
and physical activity levels as well as mean BMI in
participants classified on the basis of waist circumference
and physical activity levels and examined the trends before
and after adjusting for age. Cox regression analyses were
used to calculate hazard ratios (HR) and corresponding 95%
confidence interval (95% CI) for the risk of future CHD in
men and women classified on the basis of physical activity
categories and sex-specific waist circumference tertile before
adjustment for age, smoking, alcohol intake, family history
of CHD and hormone replacement therapy use (women).
Statistical analyses were performed using SPSS software
(Version 12.0.1, Chicago, IL, USA). A P-value o0.05 was
considered as statistically significant.
Results
The sex-specific baseline characteristics of this study population have previously been published.6,18 Baseline characteristics of men and women classified into physical activity
categories are presented in Table 2. In both sexes, active
participants were younger than inactive participants. They
were also less likely to smoke, to have diabetes, to be obese
and to have an elevated blood pressure. Active participants
International Journal of Obesity
also showed the lowest total and low-density lipoprotein
cholesterol as well as triglyceride levels.
Table 3 presents mean waist circumference in men and
women classified according to BMI categories (o25.0,
25.0–30.0 and X30.0 kg m2) and physical activity categories. In every BMI category, waist circumference was
higher in inactive individuals compared with active individuals (P for trend o0.001). In men, the difference between
inactive and active men ranged from 2.2 to 3.3 cm depending on the BMI category. In women, this difference ranged
from 1.9 to 4.1 cm. Similarly, Table 4 presents mean BMI in
men and women classified according to waist circumference
tertiles and physical activity categories. Unlike waist circumference that decreased with increasing physical activity
categories, within each waist circumference tertile, BMI
values did not vary across physical activity categories. On
the contrary, among men, BMI tended to be lower in inactive
men (at least in the lowest and middle waist circumference
tertiles). Among women, there were no differences in BMI
across physical activity levels in the lowest and middle
tertiles of waist circumference, and BMI was slightly higher
in inactive women of the highest waist circumference tertile
(P ¼ 0.04). In both sexes, adjusting mean values for age did
not impact the relationship of BMI and physical activity on
waist circumference and the relationship of waist circumference and physical activity on BMI.
During follow-up, 2191 CHD events were reported. The HR
for future CHD according to waist circumference tertiles and
physical activity categories are presented in Table 5 before
and after adjusting for age, smoking, alcohol intake, parental
history of CHD and hormone replacement use therapy use
in women. In both men and women, after adjusting for
traditional CHD risk factors, the relationship between
physical activity, abdominal obesity and CHD risk was
substantially lowered, although it remains statistically
significant. In the adjusted model, compared with active
men with a low waist circumference, inactive men with an
elevated waist circumference had an HR for future CHD of
1.74 (95% CI, 1.34–2.27). Compared with active women with
a low waist circumference, inactive women with an elevated
waist circumference had an HR for future CHD of 4.00 (95%
CI, 2.04–7.86). Among individuals with a low waist circumference, inactive individuals were at increased risk compared
with active individuals (HR ¼ 1.44 (95% CI, 1.07–1.94) and
HR ¼ 2.35 (95% CI, 1.14–4.86), respectively for men and
women). Among active individuals, those with an elevated
waist circumference were also at increased CHD risk
compared with individuals with a low waist circumference
(HR ¼ 1.37 (95% CI, 1.00–1.86) and HR ¼ 3.63 (95% CI, 1.72–
7.68), respectively for men and women). To assess the
potential bias due to reverse causality, we have repeated
analyses excluding individuals who had an event within the
first 2 years of follow-up and found no significant difference
(data not shown). Moreover, to assess the validity of the Cox
model, Kaplan–Meier survival curves were assessed. These
analyses revealed that both the relationship between waist
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
343
Table 2
Baseline characteristics of the 9564 men and 12 165 women classified on the basis of physical activity levels in EPIC-Norfolk
Men
Number of participants
Age, years
Smoking
Current
Past
Never
History of diabetes
Body mass index, kg m2
Waist circumference, cm
Systolic blood pressure, mm Hg
Diastolic blood pressure, mm Hg
Total cholesterol, mmol l1
LDL cholesterol, mmol l1
HDL cholesterol, mmol l1
Triglycerides, mmol l1
Women
Number of participants
Age, years
Smoking
Current
Past
Never
History of diabetes
Body mass index, kg m2
Waist circumference, cm
Systolic blood pressure, mm Hg
Diastolic blood pressure, mm Hg
Total cholesterol, mmol l1
LDL cholesterol, mmol l1
HDL cholesterol, mmol l1
Triglycerides, mmol l1
Inactive
Moderately inactive
Moderately active
Active
P for trend
2772
62±9
2356
59±10
2253
58±9
2183
57±9
o0.001
13.9 (385)
56.9 (1577)
29.2 (810)
3.7 (103)
26.8±3.4
98±10
140±18
85±12
6.0±1.1
4.0±1.0
1.2±0.3
1.8 (1.3–2.5)
10.6 (250)
52.1 (1227)
37.3 (879)
2.3 (55)
26.3±3.2
95±9
137±17
85±11
6.0±1.1
3.9±1.0
1.2±0.3
1.7 (1.2–2.4)
11.8 (266)
52.9 (1191)
35.3 (796)
2.1 (48)
26.1±3.0
94±9
136±17
83±11
6.0±1.0
3.9±0.9
1.2±0.3
1.7 (1.2–2.3)
11.3 (246)
51.4 (1121)
37.4 (816)
1.5 (32)
26.2±3.1
94±9
135±17
84±10
5.9±1.0
3.9±1.0
1.2±0.3
1.6 (1.1–2.2)
o0.001
o0.001
o0.001
o0.001
o0.001
0.01
0.01
o0.001
o0.001
3523
62±9
3938
58±9
2779
56±9
1925
55±8
o0.001
12.3 (435)
33.5 (1179)
54.2 (19909)
2.0 (70)
26.9±4.5
84±11
138±19
83±11
6.5±1.2
4.2±1.1
1.5±0.4
1.5 (1.1–2.1)
11.0 (434)
30.7 (1210)
58.3 (2294)
1.3 (50)
26.1±4.1
82±10
133±18
81±11
6.2±1.2
4.0±1.1
1.6±0.4
1.4 (1.0–1.9)
11.4 (317)
29.9 (831)
58.7 (1631)
0.7 (19)
25.7±4.0
80±10
131±18
80±11
6.1±1.2
3.9±1.1
1.6±0.4
1.3 (0.9–1.8)
9.2 (178)
33.9 (653)
56.8 (1094)
0.6 (12)
25.5±3.7
79±10
129±18
79±11
6.1±1.1
3.8±1.0
1.6±0.4
1.2 (0.9–1.7)
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
Abbreviations: HDL, high-density lipoprotein; LDL, low-density lipoprotein. Data are presented as mean (±s.d.) or % (n) or median (interquartile range).
circumference and CHD risk and the relationship between
physical activity levels and CHD risk were proportional over
the study follow-up (data not shown).
Discussion
In this European cohort, we observed that when controlling
for BMI, inactive men and women were characterized by
higher levels of abdominal obesity as quantified by waist
circumference, than active individuals. We also observed
that although CHD risk associated with both an elevated
waist circumference and low physical activity levels appeared
to be explained in part by traditional CHD risk factors,
sedentary individuals with an elevated waist circumference
were at increased risk for future CHD compared with active
individuals with a low waist circumference. Moreover, our
results show that individuals with a low waist circumference
remained at increased CHD risk if they had a sedentary
lifestyle. Conversely, irrespective of their level of physical
activity, abdominally obese individuals were at increased risk
for CHD.
The independent effects of physical inactivity and obesity
are well documented. In the Nurses’ Health Study that
followed 116 564 women for an average of 24 years, Hu
et al.11 reported that higher levels of physical activity
appeared to be beneficial at any adiposity level. However,
irrespective of physical activity levels, obese women had
higher risk of total mortality. On the other hand, women
reporting being sedentary were at increased risk at any given
BMI category, a finding that underlines the independent and
strong effects of both obesity and physical inactivity as
important cardiovascular risk factors. In that study, these
two risk factors accounted for 59% of cardiovascular deaths.
Physical inactivity and abdominal obesity were also predictive of CHD and type 2 diabetes risk in that cohort.13,19 A
Finnish study also investigated the respective contributions
of abdominal obesity and physical inactivity to coronary
events.20 Sedentary and abdominally obese men and women
had a 2.16- and a 1.77-fold increased cardiovascular disease
risk, respectively. Although the results of these studies are
International Journal of Obesity
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
344
Table 3
Mean waist circumference in men and women classified on the basis of physical activity and body mass index (BMI) categories
Active
Moderately active
Moderately inactive
Inactive
P for linear trend
86.3±5.7
86.4 (0.21)
86.9±6.0
87.0 (0.22)
87.3±6.0
87.4 (0.22)
88.5±6.5
88.6 (0.22)
o0.001
o0.001
BMI 25.0–29.9 kg m2
Adjusted for age
96.1±6.0
96.1 (0.18)
96.6±6.5
96.5 (0.18)
97.3±6.1
97.3 (0.17)
98.5±6.2
98.5 (0.17)
o0.001
o0.001
BMI X30.0 kg m2
Adjusted for age
108.0±7.9
108.0 (0.40)
109.4±6.9
109.2 (0.42)
110.0±7.2
110.0 (0.38)
111.3±8.0
111.3 (0.31)
o0.001
o0.001
P for linear trend
o0.001
o0.001
o0.001
o0.001
Women
BMI o25.0 kg m2
Adjusted for age
73.6±5.4
73.7 (0.20)
73.7±5.7
73.9 (0.17)
74.5±5.8
74.7 (0.15)
75.5±6.1
75.7 (0.20)
o0.001
o0.001
BMI 25.0–29.9 kg m2
Adjusted for age
82.7±6.6
82.5 (0.24)
83.7±6.4
83.5 (0.20)
84.0±6.3
83.8 (0.17)
85.2±6.7
85.0 (0.19)
o0.001
o0.001
BMI X30.0 kg m2
Adjusted for age
94.7±8.9
94.6 (0.40)
96.4±9.3
96.3 (0.33)
97.0±9.4
97.0 (0.27)
98.8±9.6
98.8 (0.26)
o0.001
o0.001
o0.001
o0.001
o0.001
o0.001
Men
BMI o25.0 kg m2
Adjusted for age
P for linear trend
Data are presented as mean±s.d. for the unadjusted model and as mean (s.e.) for the adjusted model.
Table 4
Mean body mass index in men and women classified on the basis of physical activity and waist circumference tertiles
Active
Moderately active
Moderately inactive
Inactive
23.8±1.8
23.8 (0.07)
23.6±1.9
23.6 (0.07)
23.5±1.9
23.5 (0.08)
23.6±2.0
23.5 (0.10)
0.002
0.007
Waist 91.0–97.9 cm
Adjusted for age
26.2±1.8
26.2 (0.08)
26.0±1.7
26.0 (0.08)
25.9±1.6
25.9 (0.08)
25.8±1.7
25.8 (0.09)
o0.001
0.01
Waist 498.0 cm
Adjusted for age
29.1±2.8
29.2 (0.08)
29.1±2.6
29.1 (0.08)
29.0±2.8
29.0 (0.07)
29.2±3.0
29.2 (0.05)
0.7
0.02
P for linear trend
o0.001
o0.001
o0.001
o0.001
22.7±1.9
22.7 (0.09)
22.6±1.9
22.6 (0.09)
22.6±1.9
22.6 (0.08)
22.7±2.0
22.5 (0.11)
0.80
0.59
Waist 76.0–84.9 cm
Adjusted for age
25.5±2.2
25.5 (0.10)
25.3±2.3
25.3 (0.09)
25.4±2.2
25.5 (0.08)
25.4±2.2
25.4 (0.09)
0.40
0.21
Waist 485.0 cm
Adjusted for age
29.8±3.5
29.9 (0.12)
30.0±3.8
30.0 (0.10)
30.0±4.1
30.1 (0.08)
30.3±4.3
30.4 (0.08)
0.04
0.01
P for linear trend
o0.001
o0.001
o0.001
o0.001
Men
Waist o91.0 cm
Adjusted for age
Women
Waist o76.0 cm
Adjusted for age
P for linear trend
Data are presented as mean±s.d. for the unadjusted model and as mean (s.e.) for the adjusted model.
consistent with our observations, it is important to mention
that the reported measures of association with CHD risk were
stronger in this study, especially in women. This discrepancy
may be explained by the fact that we used a measure of
physical activity that took into account both leisure time
and work-related physical activity.
International Journal of Obesity
In an observational cohort study of 21 925 men followed
for an average of 8 years, Lee et al.21 reported that body
fatness and CRF had independent contributions to all-cause
mortality and cardiovascular risk. However, interestingly,
within every waist circumference category examined, men
with a higher level of CRF were not found to be at increased
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
345
Table 5
Hazard ratios for future coronary heart disease in men and women classified on the basis of physical activity and waist circumference tertiles
Active
Moderately active
Moderately inactive
Inactive
1.00
1.00
1.04 (0.75–1.45)
0.92 (0.66–1.28)
1.08 (0.77–1.51)
0.89 (0.63–1.24)
2.18 (1.62–2.93)
1.44 (1.07–1.94)
Waist 91.0–97.9 cm
Adjusted
1.39 (1.01–1.93)
1.22 (0.88–1.69)
1.58 (1.16–2.15)
1.29 (0.95–1.75)
1.50 (1.10–2.05)
1.16 (0.85–1.58)
2.05 (1.54–2.72)
1.31 (0.98–1.75)
Waist 498.0 cm
Adjusted
1.71 (1.26–2.33)
1.37 (1.00–1.86)
2.32 (1.74–3.10)
1.72 (1.29–2.29)
2.25 (1.71–2.98)
1.61 (1.21–2.13)
3.04 (2.35–3.94)
1.74 (1.34–2.27)
1.00
1.00
2.70 (1.29–5.66)
2.49 (1.19–5.21)
2.58 (1.24–5.36)
1.92 (0.93–4.00)
4.48 (2.18–9.19)
2.35 (1.14–4.86)
Waist 76.0–84.9 cm
Adjusted
4.51 (2.17–9.37)
3.47 (1.67–7.23)
4.09 (2.00–8.38)
2.77 (1.35–5.69)
4.37 (2.19–8.73)
2.47 (1.24–4.96)
9.35 (4.75–18.4)
3.69 (1.86–7.30)
Waist 485.0 cm
Adjusted
5.34 (2.53–11.3)
3.63 (1.72–7.68)
6.34 (3.14–12.79)
3.74 (1.85–7.56)
9.44 (4.81–18.56)
4.86 (2.47–9.58)
11.38 (5.83–22.20)
4.00 (2.04–7.86)
Men
Waist o91.0 cm
Adjusted
Women
Waist o76.0 cm
Adjusted
Adjusted for age, smoking, alcohol intake, parental history of coronary heart disease and hormone replacement therapy use in women.
mortality risk. Moreover, waist circumference was not
associated with risk of mortality in individuals with a high
CRF. Cardiovascular mortality was not reported in that
subanalysis. These findings seem to be in contrast with the
results of this study in which we found that, even
after adjusting for potential confounders, the presence of
abdominal obesity was associated with an increased CHD
risk, even in active individuals. Moreover, the relationship of
physical activity with CHD risk inside each waist circumference category seemed to be limited to individuals with the
lowest levels of abdominal obesity, thereby suggesting that
both physical activity and abdominal obesity are important
for CHD risk prediction. However, the fact that we reported
CHD events rather than all-cause mortality and that we
measured physical activity using a lifestyle questionnaire,
which is a different exposure than a direct measurement of
CRF through a maximal treadmill exercise test, might be two
important factors that explain this discrepancy.
We also observed several sex-specific associations between
body composition, physical activity and CHD risk that we
believe deserve attention, as the risk associated with
abdominal obesity and inactivity was much stronger in
women than in men. These observations highlight the
importance of maintaining low levels of abdominal obesity,
and being physically active to reduce risk of CHD, especially
among women. We are not aware of other studies showing
such sex-specific differences in the relationship between
body composition, physical activity and CHD risk. However,
in a recent investigation conducted in a nested case–control
sample of the EPIC-Norfolk population, we have shown that
the effect of physical inactivity and abdominal obesity on
plasma levels of several inflammatory markers associated
with an increased CHD risk were more pronounced in
women than in men.22
Our results also show that in every BMI category inactive
participants have a higher waist circumference than those
who reported being active, a finding that provides additional
support from a population-based study to the notion that
when controlling for BMI, active individuals have a more
favorable body composition, which may explain their more
favorable cardiometabolic risk profile.23,24 Meanwhile, we
have chosen to study the respective contributions of physical
activity and waist circumference on CHD risk rather than
those of physical activity and BMI, because waist circumference is more closely associated with CHD risk than BMI.3 In
this regard, Janssen et al.25 have shown that waist circumference, and not BMI explained obesity-associated health
hazard such as hypertension, dyslipidemia and the metabolic syndrome in the third National Health and Nutrition
Examination Survey.
In our study, participants who reported being physically
inactive were on average older than active participants. It is
well recognized that within the age range of our study
population, older individuals may be characterized by
different patterns of body composition than younger
individuals as they may show lower fat-free mass levels and
higher total fat mass accumulation, which may translate into
a higher waist circumference.26 However, adjusting for age
did not impact mean waist circumference in participants
classified on the basis of BMI and physical activity levels
(Table 3). Consequently, although we cannot rule out the
possibility that inactive individuals may show higher total
fat mass levels, irrespective of BMI, than active individuals,
our results show that these differences are not likely to be
directly caused by the age differences across subgroups.
Certain other aspects of this study merit further consideration. First, we used only surrogate indicators of body
composition and could not delineate separate associations
International Journal of Obesity
Physical inactivity, abdominal obesity and CHD
BJ Arsenault et al
346
for different fat depots. We believe that further prospective
studies are required to validate the results of this study with
specific measurement of visceral obesity with computed
tomography. In addition, our study sample included mostly
Caucasians. Therefore, these findings cannot be extrapolated
to other ethnic groups. Further studies should document the
respective contributions of physical activity and abdominal
obesity in other ethnic groups such as South Asians who are
more prone to central obesity and related consequences.27
Furthermore, we studied a composite measure of leisure time
and work-related physical activity. Such a combined measure
of exposure may be more appropriate than leisure time
physical activity alone, as used in numerous other studies.
However, participants were categorized according to their
self-reported physical activity habits. Measurement errors in
self-reported physical activity are inevitable, and random
misclassification may have underestimated the association
of physical activity with risk of CHD. Nevertheless, this is
unlikely to affect the analyses stratified according to physical
activity levels substantially. Finally, CHD events were not
validated by an independent outcome committee. However,
validation studies have shown that the outcome definition
had high specificity for case ascertainment.15
In conclusion, in a cohort representative of a contemporary Western population, we found that when controlling for
BMI, sedentary individuals had a higher waist circumference
than active individuals. Moreover, as abdominally obese
and inactive individuals had a considerably increased CHD
risk, these results underline that the measurement of
waist circumference is important to better assess the CHD
risk associated with obesity and physical inactivity.
Physical activity should be promoted at the population level
to carefully manage the risk associated with abdominal
obesity.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
We thank the participants, general practitioners and staff in
EPIC-Norfolk. EPIC-Norfolk was supported by program
grants from the Medical Research Council UK and Cancer
Research UK and with additional support from the European
Union, Stroke Association, British Heart Foundation, and
Research into Ageing. None of the study sponsors have had
any role in study design, collection analysis and interpretation of data, writing of the report or decision to submit the
paper for publication. Jean-Pierre Després is Scientific
Director of the International Chair on Cardiometabolic Risk,
which is supported by an unrestricted grant awarded to
Université Laval by Sanofi Aventis.
International Journal of Obesity
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