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Friday, March 05, 2010

AMERICAN HEART ASSOCIATION

O4
Effect of obesity on total and free insulin-like
growth factor (IGF)-1, and their relationship to
IGF-binding protein (BP)-1, IGFBP-2, IGFBP-3,
insulin, and growth hormone
SY Nam, EJ Lee, KR Kim, BS Cha, YD Song, SK Lim, HC Lee and KB Huh
Division of Endocrinology, Department of Internal Medicine, Yong Dong Severance Hospital, Yonsei University, College of Medicine,
Seoul, Korea
OBJECTIVES: We investigated the effect of obesity on the serum levels of total and free IGF-1 and their relationship to
the circulating levels of insulin and IGF binding proteins (IGFBPs) in age and sex-matched groups.
SUBJECTS: The study included 43 obese subjects (ideal body weight; IBW>120%) and 45 controls (IBW<100%). All of
the subjects were male.
MEASUREMENT: Total IGF-1, free IGF-1, IGFBP-1, IGFBP-2, IGFBP-3, and insulin were measured in obese subjects and
normal control subjects.
RESULTS: No signiŽcant differences in the circulating levels of total and IGFBP-3 were observed between the obese
and control groups. In contrast to total IGF-1, free IGF-1 in obese subjects was signiŽcantly increased compared to
normal controls (P<0.05). Serum total and free IGF-1 were inversely correlated with age (r.˙0.42, P.0.001, and
˙0.44, P.0.001). Fasting serum insulin concentrations were elevated in all the obese subjects (P<0.05) and positively
correlated with IBW (r.0.57, P.0.001). The levels of serum GH and IGFBP-1 were suppressed in all the obese subjects
(P<0.05). IGFBP-1 was inversely correlated with IBW (r.˙0.51, P.0.001) and serum insulin concentrations
(r.˙0.48, P.0.001). The IGFBP-2 concentrations were also suppressed in obese subjects and inversely related to
free IGF-1 (r.˙0.48, P.0.001). Using multiple linear regression analysis, total IGF-1 and insulin concentrations were
positively correlated (r.0.58, P.0.001) and free IGF-1 and IGFBP-1 concentrations were negatively correlated
(r.˙0.57, P.0.001).
CONCLUSION: We conŽrmed that total IGF-1 and IGFBP-3 concentrations were not signiŽcantly different between the
obese and control groups, despite GH hyposecretion in obesity. We also found that free IGF-1 concentrations were
higher in obese subjects than in normal controls. It seems likely that overnutrition and chronic hyperinsulinaemia in
obesity may alter this regulated growth response by insulin stimulation of IGF-1 production and suppression of
hepatic IGFBP-1 and IGFBP-2 production, which may inhibit IGF-1 bioactivity.
Keywords: obesity; total IGF-1; free IGF-1; IGFBPs
Introduction
Obesity is associated with the impairment of normal
GH (growth hormone) secretion and blunted
responses to all stimuli such as GH-releasing hormone
(GHRH), hypoglycaemia, L-dopa, arginine, glucagon,
physical exercise, and sleep.1ą4 Based on the well
known GH hyposecretion in obesity, `GH-dependent'
insulin-like growth factor (IGF)-1 concentrations
would be expected to be subnormal in obesity. However,
the results have been conŻicting,5ą7 due to
clinical differences in age, sex and degree and type
of obesity.
Circulating IGF-1 is predominantly bound in a
ternary 150 kDa complex, consisting of IGF-1, IGFbinding
protein (IGFBP)-3, and an acid-labile subunit.
8,9 Thus IGFBP-3 and IGF-1 have relatively long
half-lives in the circulation,10 and since they are GH
dependent they represent a stable index of long-term
changes in GH availability and action.11 IGFBP-2 is
the second most abundant IGF-binding protein in
serum. The serum concentration of IGFBP-2 is
known to be inversely related to GH secretion,
being high in states of GH deŽciency and low in
states of GH excess.12 These reports have suggested
that measurement of IGFBP-2 might be useful in
detecting GH deŽciency. In contrast, the serum concentrations
of IGFBP-1 are lower than those of
IGFBP-3 and IGFBP-2, and acutely regulated by
food intake and Żuctuations in serum insulin and
glucose concentrations.13ą15 Because of this unique
property, IGFBP-1 is thought to play an important role
in acute regulation of IGF-1 availability.16,17 In previous
reports, IGFBP-1 sequestered free IGF-1 and
Correspondence: Dr SY Nam, Division of Endocrinology,
Department of Internal Medicine, Yong Dong Severance
Hospital, Young Dong PO Box 1217, Seoul, Korea.
Received 21 August 1996; revised 3 January 1997; accepted 16
January 1997
International Journal of Obesity (1997) 21, 355ą359
ß 1997 Stockton Press All rights reserved 0307ą0565/97 $12.00
inhibited its metabolic actions.18,19 A small portion of
circulating IGF-1 is detected in the free or readily
dissociable state, which is thought to be the metabolically
active form. The amount of free/dissociable
IGF-1 in serum is dependent on a complex interplay
between the production rate and concentrations of
IGF-1 and IGFBPs.10
In this study, we investigated the effect of obesity
on the serum concentrations of total and free IGF-1
and their relationship to the concentrations of IGFbinding
proteins and insulin in age and sex-matched
groups.
Subjects and methods
Subjects and protocol
Forty-three obese Korean males aged 20ą49 y (mean -
_s.e.m., 34.9_7.9) and 45 normal Korean male
subjects aged 20ą49 y (33.9_8.3) were studied. The
normal control subjects were within 10% of their ideal
body weight (IBW), and the obese subjects weighed
more than 120% of ideal body weight, as determined
by the Fogarty Center Conference on Obesity. All
subjects were recruited from subjects attending the
medical-checkup programs at the health-care centre of
Yong Dong Severance Hospital.
An oral glucose tolerance test was performed on
each subject to screen out impaired glucose tolerance
or diabetes mellitus. Apart from obesity, no abnormalities
were detected. No subjects had used any
hormonal preparations or drugs within the 60 d prior
to the study.
The study was approved by the Hospital Ethics
Committee, and informed consent was obtained from
each subject.
Blood samples were obtained from each subject by
venepuncture between 08:00 am and 10:00 am after an
overnight fast. Serum was separated by centrifugation,
and stored at ˙20_C until analysis.
Analytical methods
Serum glucose level was measured by the glucoseoxidase
method and insulin level by enzyme-linked
immunoadsorbent assay (ELISA). This ELISA uses
two monoclonal antibodies (Novo Nordisk A/S, Denmark)
directed against human insulin and does not
cross-react with human proinsulin. Serum triglyceride
and cholesterol concentrations were determined in the
overnight fasting state with semi-automated methods
(Boehringer, Mannheim, Germany). Serum GH was
measured by an immunoradiometric assay (IRMA)
from Daiichi (Tokyo, Japan); the sensitivity was
0.1 mg/L and its intra and interassay coefŽcients of
variations were 1.3 and 1.4%, respectively. Serum
IGFBP-1, IGFBP-2, and IGFBP-3 were measured by
an immunoradiometric assay (IRMA) kit (Diagnostic
System Laboratories, Inc. Webster, TX); the sensitivities
were 0.01 ng/mL, 0.5 ng/mL, 0.5 ng/mL, respectively
and their intra and inter-assay coefŽcients of
variations were less than 10%. Serum total IGF-1 was
measured by an IRMA kit (Diagnostic System
Laboratories, Inc. Webster, TX); the sensitivity was
0.3 mg/L and its intra and interassay coefŽcients of
variations were less than 10%. Free/dissociable IGF-1
concentrations were measured by a highly sensitive
two-site IRMA kit (Diagnostic System Laboratories,
Inc. Webster, TX) according to the manufacturer's
suggestions. Assay characteristics have been reported
previously.20 This is a direct detection assay in which
100 mL of serum sample are added to tubes containing
a dense coating of high afŽnity anti-IGF-1 antibody,
which binds IGF-1 that is not bound to or is easily
dissociable from IGFBPs. Samples were then incubated
for two hours at 2ą8_C, washed, and incubated
with an 125I-labeled anti-IGF-1 antibody directed to a
second epitope of IGF-1 for two hours at room
temperature. After decanting and washing 3 times,
the tubes were counted in a g-counter. Assay standards
were recombinant human (rh)IGF˙1 (0.13ą20 mg/L).
The minimal detection limit was 0.05 mg/L or 5 pg/
tube. According to the manufacturer's speciŽcations
and previous characterization of the assay, there was
no cross-reactivity with IGF-II, and no residual
IGFBP-1 or IGFBP-3 was detectable in the assay
tube after the Žrst wash.20 The interassay coefŽcients
of variations were 7.7, 3.6 and 10.7% at 0.26, 5.52 and
13.87 ng/mL, and the intraassay coefŽcients of variations
were 10.3, 5.1, and 3.3% at 0.29, 6.26, and
14.20 ng/mL. All samples from each subject were
analyzed in duplicate at the same time.
Statistical analysis
The results were expressed as the mean_s.e.m.
Statistical comparisons were made using the Student's
unpaired t-Test between obese and control groups.
Correlation between different parameters was calculated
by linear regression analysis. Factors related to
total and free IGF-1 were analyzed using stepwise
multiple regression. P<0.05 was accepted as the
signiŽcance level.
Results
Percentage of ideal body weight (138.2_11.7%,
mean_s.e.m.), BMI (30.0_2.5 kg/m2), and waist to
hip ratio (0.93_0.2) in obese subjects were signiŽ-
cantly (P<0.001) higher than those of normal controls
(97.6_6.4%, 21.3_1.4 kg/m2, and 0.84_0.0).
Fasting serum glucose (FSG), total cholesterol, triglyceride,
insulin and GH concentrations are summarized
in Table 1.
The FSG, triglyceride and insulin concentrations in
obese subjects were signiŽcantly greater than those of
normal controls. In all obese subjects, fasting GH
IGF-1 and IGFBPs in obesity
SY Nam et al
356
concentrations were suppressed as compared with
control subjects (P<0.05).
No signiŽcant differences in the circulating concentrations
of the total IGF-1 and IGFBP-3 were
observed between the control and obese subjects
(Figure 1). Serum total IGF-1 was weakly correlated
with IGFBP-3 (r.0.28, P.0.05). The free IGF-1
concentrations were signiŽcantly elevated in obese
subjects (1.46_1.1 mg/L vs 0.91_0.9 mg/L, P<0.05;
Figure 1). The ratio of free to total IGF-1 also was
higher in obese subjects (0.63% vs 0.39%, P<0.05;
Figure 1).
The IGFBP-1 concentrations were signiŽcantly
lower in the obese subjects (Figure 1) and negatively
correlated with percent of IBW (r.˙0.51, P.0.001)
and serum insulin (r.˙0.48, P.0.001).
The IGFBP-2 concentrations were also suppressed
in obese subjects (P<0.05; Figure 1) and inversely
related to free IGF-1 (r.˙0.48, P.0.001). Additionally,
the ratio of IGFBP-2 to IGF-1 was decreased
(1.6_0.5 vs 2.2_1.4, P<0.005) in obese subjects.
Using multiple linear regression analysis, the total
IGF-1 and free IGF-1 were analyzed by age, IBW,
WHR, fasting serum insulin, GH, IGFBP-1, IGFBP-2,
and IGFBP-3 in all subjects. Of all variables, age and
serum insulin concentrations were associated with
serum total IGF-1. The total IGF-1 was inversely
correlated with age (r.˙0.42, P<0.001) and positively
correlated with serum insulin (r.0.58,
P<0.001). The free IGF-1 was negatively correlated
with age (r.˙0.44, P<0.001) and IGFBP-1
(r.˙0.57, P<0.001).
Discussion
Using age and sex matched subjects, we conŽrmed
that GH levels were reduced in the obese while `GHdependent'
total IGF-1 levels were not signiŽcantly
different between obese and control subjects. Similar
results have been reported previously,21ą23 but the
contradictory Žndings of increased or decreased
IGF-1 levels found by others, although due in part
to important clinical differences in age, sex and
degree of obesity in the different studies, could be
profoundly inŻuenced by nutritional factors.24,25 In
addition, the discordant relationship between GH and
IGF-1 in obesity may be also explained by the
hyperinsulinaemia present in obesity:26 insulin may
increase hepatic IGF-1 production.27 We found a
positive correlation between the serum total IGF-1
and insulin concentrations.
A small portion of circulating IGF-1 is detected in
the free or readily dissociable state, which is thought
to be the metabolically active form.28,29 In the present
study, free/dissociable IGF-1 was assessed by a twosite
immunoradiometric assay. The absolute values for
free/dissociable IGF-1 in both obese and control
groups were higher than those reported by Frystyk
et al30 using RIA after separation by centrifugal
ultraŽltration. The difference between our results
and theirs are mostly likely due to the ages of
subjects; older subjects have lower total and free
IGF-1.30 However, the ratios of free to total IGF-1
in both groups are similar between the two assay
systems.
The amount of free/dissociable IGF-1 in serum is
dependent on a complex interplay between the production
rate and the concentrations of IGF-1 and
IGFBPs. IGFBP-3 serves as major stabilizer of the
circulating IGF pool. Circulating IGFBP-3 might
undergo limited proteolysis, which results in smaller
Table 1 Metabolic and hormonal data in control and obese
groups
Control Obesity
Fasting serum glucose (mmol/L) 5.0_0.5 5.3_0.6*
Total cholesterol (mg/dL) 185.8_34.5 191.8_39.1
Triglyceride (mg/dL) 114.8_49.3 176.3_38.1*
Insulin (pmol/L) 23.5_12.0 49.2_23.8*
GH (ng/mL) 2.0_0.8 0.8_1.1*
* P<0.05 control vs obesity.
Figure 1 Levels of total IGF-1, free IGF-1, ratio of free to total
IGF-1, IGFBP-1, IGFBP-2, and IGFBP-3 in control (u) and obese
subjects (j). *P<0.05 control vs obesity.
IGF-1 and IGFBPs in obesity
SY Nam et al
357
molecular weight IGFBP-3 fragments that have lower
afŽnity for IGF-1 than the intact binding protein.
Consequently, the levels of free/dissociable IGF-1
are elevated in situations where IGFBP-3 proteolysis
is increased.31 Bereket et al32 showed that acute mealrelated
hyperinsulinaemia did not increase IGFBP-3
proteolysis. However, it is not known whether IGFBP-
3 proteolysis increases in obese subjects.
In contrast, IGFBP-1 exhibits marked diurnal variation
in relation to meals.32 Insulin is the principal
regulator of IGFBP-1 and is reported to inhibit hepatic
IGFBP-1 synthesis.33 Because of this property,
IGFBP-1 is thought to play an important role in the
regulation of IGF bioactivity. Therefore, the obesityrelated
hyperinsulinaemia may decrease circulating
concentrations of IGFBP-1, which can result in elevated
free IGF-1 in obesity. In our study, circulating
IGFBP-1 concentrations in obese subjects were lower
than normal controls. There were also negative correlations
between free IGF-1 and IGFBP-1.
IGFBP-2 is the second most abundant IGF-binding
protein in serum.34 Several reports have shown that
IGFBP-2 is differentially regulated by GH and IGF-
1.12,34 Administration of GH causes a reduction in
IGFBP-2, while the serum concentration of IGFBP-2
is high in states of GH deŽciency.34 In normal adults,
the infusion of IGF-1 results in suppression of GH and
an increase in IGFBP-2.38 The dissociation of the
effect of GH and IGF-1 in regulating IGFBP-2 suggests
that IGFBP-2 and the ratio of IGFBP-2 to IGF-1
may be useful in detecting GH deŽciency. Thus, based
on the well-known GH hyposecretion in obesity, the
levels of IGFBP-2 and the ratio of IGFBP-2 to IGF-1
would be expected to be high. However, we found that
IGFBP-2 concentrations and the ratios of IGFBP-2 to
IGF-1 were suppressed in obese subjects. These Žndings
indicate that factors other than GH may be
regulating serum IGFBP-2 in obesity. Clemmons et
al34 showed that acute stimulation of insulin did not
suppress IGFBP-2, but IGFBP-2 concentrations were
elevated after prolonged fasting. This suggests that
prolonged changes in insulin secretion may result in a
signiŽcant change of IGFBP-2. It appears, therefore,
that the obesity-related chronic hyperinsulinaemia
may result in suppression of IGFBP-2 in obesity.
We also found a negative correlation between
IGFBP-2 and free IGF-1 concentrations. This indicates
that IGFBP-2 can quantitatively inŻuence IGF-1
transport and may have some role in regulating IGF-1
bioavailability in obesity, because serum IGFBP-2 are
more abundant than IGFBP-1 in adults.34
Conclusions
We conŽrmed that `GH-dependent' IGF-1 and
IGFBP-3 concentrations were not signiŽcantly different
between obese and control groups, despite the GH
hyposecretion in obesity. We also found that free IGF-
1 concentrations were increased in obesity compared
to normal controls. It seems likely that overnutrition
and chronic hyperinsulinaemia in obesity may alter
this regulated growth response: insulin may stimulate
production of IGF-1 and suppress production of
IGFBP-1 and IGFBP-2, which are thought to be
inhibitory IGFBPs.
Acknowledgement
We thank the Ewon Reference Laboratory for the
measurement of serum IGF-1, IGFBPs, GH and insulin.
This paper was presented at the 15th annual
meeting of the Korean Association of Endocrinology
held in Seoul 10ą11th May, 1996.
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IGF-1 and IGFBPs in obesity
SY Nam et al
359

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C57BL/6J Characterization Data
Figures 1, 2, and 3 below compare typical average weights, plasma glucose levels, and percent body fat among groups of male B6 mice fed 10%, 45%, or 60% fat diets (Table 1).
500
450
400
350
300
250
200
150
100
50
0
45
40
35
30
25
20
15
Blood Glucose (mg/dl)
Body Weight (g)
D12492i (60 kcal% fat)
D12451i (45 kcal% fat)
D12450Bi (10 kcal% fat)
0 30 60 90 120
Time Post Administration (minutes)
4 6 8 10 12 14 16 18
10 kcal% fat
45 kcal% fat
60 kcal% fat
Age (Weeks)
Figure 1. Mean body weights (g +/- SEM) for groups of B6 males (n = 20-44) fed 10%, 45%, and 60% fat diets from 6 to 17 weeks of age.
Figure 2. Mean blood glucose levels (mg/dl +/- SEM) for groups of 17- to 19-week old B6 males (n = 10-33) fed 10%, 45%, and 60% fat diets for 11-13 weeks (data obtained by glucose tolerance test).
Percent Fat
Table 1. Macro-nutrient Composition of Commonly Used High Fat Diets from Research Diets, Inc.
45
40
35
30
25
20
15
10
5
0
Formula # D12450B D12451 D12492
gm% kcal% gm% kcal% gm% kcal%
Protein 19 20 24 20 26 20
Carbohydrate 67 70 41 35 26 20
Fat 4 10 24 45 35 60
Total 100 100 100
kcal/gm 3.8 4.7 5.2
Note that the source of the increased fat is from lard.
60 kcal% fat
45 kcal% fat
10 kcal% fat
Diet Group
Figure 3. Mean % body fat (+/- SEM) for groups of B6 males (n = 10-17) fed 10%, 45%, and 60% fat diets for 11-13 weeks (data obtained by DEXA).
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07
AMERICAN JOURNAL OF HYPERTENSION | VOLUME 21 NUMBER 1 | 17-22 | janu ary 2008 17
nature publishing group articles
See COMMENTARY page 8
Bac kgro und
The metabolic syndrome is a predictor of diabetes and coronary
events. We hypothesized that it also predicts hypertension.
Met hodS
A total of 1,944 subjects (901 men and 1,043 women;
age 46 ą 12 years) from the Hong Kong Cardiovascular Risk Factor
Prevalence Survey were recruited in 1995–1996 and restudied in
2000–2004. The prevalence of hypertension and factors predicting
its development were determined.
Results
In 2000–2004, hypertension was found in 23.2% of the men and
17.2% of the women. Of the 1,602 subjects who were normotensive
at baseline, 258 subjects developed hypertension after a median
interval of 6.4 years. According to the National Cholesterol Education
Program (NCEP) and International Diabetes Federation (IDF)
criteria, the hazard ratios associated with the metabolic syndrome
were 1.89 (95% confidence interval (CI): 1.41–2.54) and
1.72 (95% CI: 1.24–2.39), respectively. The positive and negative
predictive values of the metabolic syndrome for identifying
subjects who will develop hypertension in this population were
34.7 and 85.4% (NCEP criteria), and 33.1 and 85.5% (IDF criteria),
respectively. The development of hypertension was related to
the number of components of the metabolic syndrome
(other than raised blood pressure), present in men (P = 0.003)
and in women (P = 0.001). Using multivariate analysis, age,
baseline systolic blood pressure (SBP), body mass index (BMI),
and the triglycerides/high-density lipoprotein (HDL) ratio were found
to be significant predictors of the development of hypertension.
Compared with optimal blood pressure, the hazards of developing
hypertension associated with normal or high-normal blood
pressure were 2.31 (95% CI: 1.68–3.17) and 3.48 (95% CI: 2.52–4.81),
respectively.
Conclusions
Blood pressure, when not optimal, is the predominant predictor
of hypertension. The metabolic syndrome contributes to the risk,
especially when blood pressure is optimal.
Am J Hypertens 2008; 21:17-22 Š 2008 American Journal of Hypertension, Ltd.
Relationship Between the Metabolic Syndrome
and the Development of Hypertension in the
Hong Kong Cardiovascular Risk Factor Prevalence
Study-2 (CRISPS2)
Bernard M.Y. Cheung1, Nelson M.S. Wat1, Y. B. Man1, Sidney Tam2, C. H. Cheng1, Gabriel M. Leung3,
Jean Woo4, Edward D. Janus5, C. P. Lau1, T. H. Lam3 and Karen S.L. Lam1
1Department of Medicine, University of Hong Kong, Hong Kong; 2Clinical
Biochemistry Unit, Queen Mary Hospital, Hong Kong; 3Department of
Community Medicine, University of Hong Kong, Hong Kong; 4Department
of Medicine and Therapeutics, Chinese University of Hong Kong, Shatin,
Hong Kong; 5Department of General Medicine, University of Melbourne,
Western Hospital, Footscray, Australia. Correspondence: Bernard M.Y. Cheung
(b.cheung@bham.ac.uk)
Received 5 June 2007; first decision 5 July 2007; accepted 26 July 2007.
doi:10.1038/ajh.2007.19
Š 2008 American Journal of Hypertension, Ltd.
Hypertension is an important cardiovascular risk factor and a
major health problem worldwide. In the United States, 29.3% of
the population has hypertension, but only 36.8% of people with
hypertension have good blood pressure control.1 There is a need
for improvement because controlling blood pressure markedly
reduces cardiovascular risk.
Although there are many drugs that effectively lower blood
pressure, it is not a cure. Curable forms of hypertension are of
low prevalence. In the vast majority of hypertensive patients,
the cause of hypertension is multifactorial. Therefore, there is a
need to identify modifiable factors that lead to the development
of hypertension.
The metabolic syndrome is a cluster of cardiovascular risk factors
including central obesity, high blood pressure, raised blood
glucose and triglyceride levels, and low high-density lipoprotein
(HDL) cholesterol levels.2,3 In recent years, the syndrome has
been found to be useful in identifying people predisposed to
diabetes and coronary heart disease.4–6 It is not known whether
the syndrome also predicts the development of hypertension.
As high blood pressure is a component of this syndrome, we
set out to examine the association of the metabolic syndrome
and its components with the development of hypertension in a
Chinese population.
Met hods
The Hong Kong Cardiovascular Risk Factor Prevalence Study is
a unique and comprehensive study of cardiovascular risk factors


08
ORIGINAL ARTICLE
The associations between current recommendation
for physical activity and cardiovascular risks
associated with obesity
M Akbartabartoori1, MEJ Lean2 and CR Hankey2
1Nutrition and Biochemistry Department, School of Public Health, Yasuj University of Medical Sciences, Yasuj, Iran and 2Human
Nutrition at Glasgow, Division of Developmental Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, Scotland
Objective: To examine associations between current recommended physical activity levels and body mass index (BMI) with
some cardiovascular disease (CVD) risk factors (total cholesterol, high-density lipoprotein cholesterol (HDL-C), non-HDLcholesterol
(non-HDL-C), C-reactive protein (CRP), fibrinogen, and blood pressure), general health score (GHQ12) and
predicted coronary heart disease (CHD) risk.
Design: Further analysis of the cross-sectional Scottish Health Survey 1998 data.
Subjects: Five thousand four hundred and sixty adults 16–74 years of age.
Results: After controlling for some confounding factors, obesity was significantly associated with higher odds ratio (OR) for
elevated cholesterol, CRP, systolic blood pressure, non-HDL-C and lower HDL-C (Po0.001), and with greater predicted CHD risk
compared to BMI o25 kg/m2. Regular self-reported physical activity was associated with smaller OR of lower HDL-C and higher
CRP, and average predicted 10-year CHD risk in obese subjects, but did not eliminate the higher risk of the measured CVD risk
factors in this group. The OR of these two risk factors were still high 4.39 and 2.67, respectively, when compared with those who
were inactive with BMI o25 kg/m2 (Po0.001). Those who reported being physically active had better GHQ scores in all BMI
categories (Po0.001).
Conclusion: Reporting achievement of recommended physical activity levels may reduce some CVD risk factors, predicted CHD
risk and improve psychosocial health, but may not eliminate the extra risk imposed by overweight/obesity. Therefore, increasing
physical activity and reducing body weight should be considered to tackle CVD risk factors.
European Journal of Clinical Nutrition (2008) 62, 1–9; doi:10.1038/sj.ejcn.1602693; published online 7 March 2007
Keywords: physical activity; overweight/obesity; cardiovascular disease risk factors
Introduction
Overweight, obesity and inactivity are major risk factors for
cardiovascular disease and all cause mortality (WHO, 2003a).
Physical activity has benefits for physical, mental and social
health (WHO, 2003b), which is a major determinant of
energy expenditure and an essential factor to maintain
energy balance and weight control. Apart from the effect of
physical activity on controlling body weight, evidences show
it is also associated with reduced risk for cardiovascular
disease, diabetes and metabolic syndrome (Katzmarzyk et al.,
2004, 2005; Hu et al., 2004b).
Physical activity and physical fitness can modify obesityrelated
chronic diseases and mortality, and evidence suggests
that overweight or obese people who are active and fit have
less cardiovascular disease and lower all cause mortality than
normal weight unfit people (Blair and Brodney, 1999;
Church et al., 2004; Wessel et al., 2004). Katzmarzyk et al.
(2005) have reported that cardiorespiratory fitness,
assessed by a maximal treadmill exercise test, modifies the
relationships between obesity, metabolic status and mortality
in men and can protect against premature mortality
Received 25 May 2006; revised 2 January 2007; accepted 4 January 2007;
published online 7 March 2007
Correspondence: Dr CR Hankey, Human Nutrition at Glasgow, Division of
Developmental Medicine, University of Glasgow, Glasgow Royal Infirmary,
Glasgow G31 2ER, Scotland.
E-mail: crh3d@clinmed.gla.ac.uk
Guarantor: Dr M Akbartabartoori.
Contributors: MA designed the study, completed the statistical analysis and
prepared the draft under supervision of both CRH and MEJL. Both CRH and
MEJL participated in developing the study design. All authors approved the
final version.
European Journal of Clinical Nutrition (2008) 62, 1–9
& 2008 Nature Publishing Group All rights reserved 0954-3007/08 $30.00
www.nature.com/ejcn
regardless of body weight status on the presence of metabolic
syndrome.
In contrast, in a prospective study on Russian and US men
aged 40–59 years, Stevens et al. (2004) concluded that the
effects of fitness might be more robust across populations
than are the effects of fatness. In a previous study, Stevens
et al. (2002) reported that both fitness and fatness are
opposing risk factors for mortality, but being fit does not
completely reverse the increased risk associated with excess
adiposity.
Similarly, Meyer et al. (2002) found that even among men
who reported a high level of physical activity during leisure
time, estimated by questionnaire, obesity was associated
with an increased total mortality. In women participating in
the nurses's health study both body mass index (BMI) and
the level of physical activity significantly and independently
predicted mortality, but a high physical activity level did not
eliminate the excess of deaths associated with obesity (Hu
et al., 2004a). A study of healthy men showed that fatness
was more strongly and consistently associated with cardiovascular
disease risk than aerobic fitness, assessed by
maximal oxygen consumption (Christou et al., 2005).
Weinstein et al. (2004) found that both BMI and physical
activity were important for the development of type 2
diabetes in women. However, BMI was a better predictor
than recreational physical activity, which was measured by a
validated questionnaire, in predicting the incidence of type 2
diabetes.
There is thus still debate as to the magnitude of influence
these two factors have in combination on health outcomes,
perhaps because of differences among study populations,
methods and outcomes (Blair and Church, 2004) and
recommendations for physical activity vary.
This study aimed to clarify the associations between
currently recommended physical activity levels and BMI
with some clinical and biochemical health indicators in a
representative sample of Scottish adults with a very high rate
of CHD.
Subjects and methods
Sample
The Scottish Health Survey is a cross-sectional nationally
representative survey programme that was designed to
provide a comprehensive picture of the health of the Scottish
population and to document the prevalence of health risk
factors as well as monitor progress toward health targets. Full
details of the survey methods have been published elsewhere
(Shaw et al., 2000).
Of the total 9047 adults aged 16–74 years (3941 men and
5106 women), who participated in the 1998 survey, total
numbers of valid samples that have been used in this analysis
were total plasma cholesterol 5924, high-density lipoprotein
cholesterol (HDL-C) 5891, c-reactive protein (CRP) 5988,
fibrinogen 5460, systolic blood pressure 6221 and general
health questionnaire (GHQ12) 8045.
Anthropometric measures
Weight and height were measured using standard techniques
by trained staff (Shaw et al., 2000). Height was measured in a
standing position with a portable stadiometer. Body weight
was measured to the nearest 0.1 kg in bare feet and light
clothes with the Soehlne scales. BMI was calculated as weight
divided by height squared (kg/m2). Overweight and obesity
were defined as a BMI of 25–29.9 and X30 kg/m2, respectively
(WHO, 1998).
Cigarette smoking status was classified as follows: regular
cigarette smokers, those who said they smoked cigarette at
all at the time of the interview; ex-smokers, those who
smoked cigarettes regularly in the past but not currently; and
nonsmokers, those who had never smoked cigarettes
regularly and were not current smokers. Reported levels of
physical activity were measured by a questionnaire that
asked about the frequency, duration and intensity of four
major types of activity: activity at home, walks of 15 min or
more, sports and exercise activities, and activity at work in
the 4 weeks before the interview (Shaw et al., 2000). These
activities were then summed up to calculate estimated total
physical activity, which was divided into five categories
based on different levels of physical activity recommendations
(American College of Sports Medicine, 1990; Blair and
Connelly, 1995). Categories were inactive, low activity, at
least 30 min moderate activity on at least 5 days a week, at
least 20 min vigorous activity on at least 3 days a week, and
30 min moderate activity on 5 days a week plus 20 min
vigorous activity on 3 days a week. These five categories were
collapsed into three main categories: active, those who
reached at least one of the two guideline levels (either three
occasions of 20 min vigorous activity per week or five
occasions of moderate activity per week or both); less active,
those were not active enough to meet either guideline level
but were active on at least 1 day a week; and inactive, those
respondents who reported less than 1 day per week of
moderate or vigorous activity of at least 20 min duration.
Habitual alcohol consumption over the previous 12
months was assessed with questions on frequency, type,
average number of days per week on which alcohol was
drunk, the usual quantity consumed on any 1 day and finally
the 'usual' weekly units of alcohol consumed calculated. This
was then divided into four groups of weekly alcohol intake
for both men and women. For men, these quantities were
under 1, 1–10, 10–21 and above 21 units. For women these
were under 1 unit, 1–7, 7–14 and above 14 units.
Information on dietary habits was obtained by a short
dietary questionnaire, which included questions relating to
type and frequency of major food items (Lean et al., 2003). In
this study, total fruit and vegetable consumption was
accessed using a categorical variable divided in three
groups: low consumers (o200 g /day), moderate consumers
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
2
European Journal of Clinical Nutrition
(200–o400 g /day) and achievers of current targets (X400 g/
day) (The Scottish Office, 1996).
Social class was based on the Register General's Standard
Occupation Classification using the current or last occupation
of the chief income earner within informant's household,
in one of four categories: professional and
intermediate, skilled (nonmanual), skilled (manual), partly
skilled and unskilled (Shaw et al., 2000).
Blood pressure was measured by using an automated
device, the Dinamap 8100 monitor. Three blood pressure
readings were taken on the right arm in a seated position
after 5 min rest. The mean of the second and third readings
were used as the blood pressure (Shaw et al., 2000).
The GHQ12 has been used to assess the psychosocial
health of participants (Goldberg and Williams, 1988; Shaw
et al., 2000). Participants were asked to complete a selfcompletion
booklet, which comprised 12 questions about
general levels of happiness, anxiety, depression, stress and
sleep disturbance over the past few weeks before the
interview. An overall GHQ12 score X4 has been used to
identify subjects with a poor psychological health.
Metabolic syndrome and predicted coronary heart disease risk
Metabolic syndrome was defined using available data in
Scottish health Survey database any three of waist circumference
4102cm in men and 488cm in women, blood
pressure X130/85mm Hg, HDL-C p1 mmol/l in men and
o1.3 mmol/l in women, non-HDL-C 44 mmol/l and a
medical diagnosis of diabetes (NCEP, 2001). Ten-year total
coronary heart disease risk was calculated in 1877 men and
2323 women aged 30–74 years by using the 1998 Framingham
sex-specific risk equations based on total cholesterol (Wilson
et al., 1998). Our choice of equation was guided by the data
already provided by the SHS. Subjects with three major
existing cardiovascular conditions (angina, heart attack or
stroke) were excluded before calculating the risk.
Blood samples analyses
Nonfasting venous blood samples were obtained and
analysis for CRP, total cholesterol, HDL-C and fibrinogen
carried out using standardized methods (Shaw et al., 2000).
Non-HDL-C, which contains cholesterol in low-density
lipoprotein and very low-density lipoprotein, was calculated
by subtracting HDL-C from total cholesterol (Grundy, 2002).
Data analyses
Analysis was carried out using the statistical package, SPSS
11.0 (SPSS Inc., Chicago, IL, USA). As data were not normally
distributed, log-transformed values of dependent variables
were used for improving the normality of distributions.
To evaluate the combined impact of physical activity and
BMI, physical activity status and BMI were combined into
nine categories and inactive subjects with BMI below 25 kg/m2
were defined as the reference category. A general linear
model incorporating Bonferroni post hoc test was used to
compare the adjusted geometric means of the risk factors
within a combined BMI and physical activity variable in a
stratified analysis by sex. In this analysis age was used as a
covariate, and social class, cigarette smoking, combined
activity and BMI, alcohol consumption and fruit and
vegetable consumption as fixed factors. The logistic regression
model was used to compute the odds ratio (OR) for the
probability of having high value for CRP (X3 mg/l), fibrinogen
(X3 g/l), total cholesterol (X6.2 mmol/l), low HDL-C
(p1 mmol/l), systolic blood pressure (X130mm Hg) and
GHQ12 (X4) and metabolic syndrome among the subgroups
of the combined physical activity and BMI with the reference
category (inactive subjects with a BMI below 25 kg/m2). A
value of Po0.05 was used for statistical significance.
Results
General characteristics of the study population are presented
in Table 1. Approximately 34% male and 33% female
subjects were current smokers, 63% of men and 54% of
women were either overweight or obese and around 20% of
total sample were obese. The proportion of the total subjects
who achieved the recommended physical activity levels
(either 3*20 vigorous or 5*30 moderate or both of them)
were 33%, 38% men and 29% women. Around 31% of men
consumed more than 21 units of alcohol per week and
among women almost 14% more than 14 units per week.
Only 16.6% of men and 28.7% of women reached the
current targets for total daily fruit and vegetables consumption.
Almost 22% of the population was classified as partly
skilled and unskilled by the social class of chief income
earners.
Figures 1–3 show the adjusted geometric mean values of
the risk factors among different categories of combined
physical activity and BMI in men and women. Initial
analysis used three separate categories for people who
achieved the recommended activity levels (either 3*20
vigorous or 5*30 moderate or both). These have not been
presented in detail due to small numbers, for example seven
men and 13 women with BMI X30 kg/m2 had reported 3*20
vigorous activity level. The patterns of cardiovascular risk for
these three categories were similar, so they were collapsed
into a single 'active' category. Overweight and obese subjects
had significantly lower mean HDL-C concentrations regardless
of physical activity levels when compared with inactive
subjects with BMI o25 kg/m2 in both sexes (Po0.001;
Figure 1). Mean cholesterol and non-HDL-C were significantly
higher in overweight and obese subjects (Po0.01) and
activity levels did not change the results significantly. Mean
CRP concentration was highest in obese inactive subjects.
Although mean CRP concentrations were lower in active
subjects in all BMI categories compared with inactive people,
particularly in active subjects with BMI o30 kg/m2, the
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
3
European Journal of Clinical Nutrition
mean CRP was still significantly higher in obese active
subjects in both sexes compared with reference categories
(Po0.05; Figure 2). Mean systolic blood pressure rose with
increasing BMI in different activity levels; however, it was
significantly higher in female obese subjects in all activity
levels compared with inactive BMI o25 kg/m2 (Po0.001).
Female obese subjects had significantly higher mean fibrinogen
levels across the physical activity levels than the
reference group (Po0.001). Active male and less active
female with BMI o25 kg/m2 had significantly lower mean
fibrinogen compared with inactive subjects with BMI
o25 kg/m2 (Po0.05; Figure 3).
Table 2 shows adjusted OR of having the cardiovascular
disease risk factors and GHQ scores above the cut-off points
by physical activity status and BMI. After controlling for age,
gender, social class, smoking, alcohol intake, and fruit and
vegetable consumption, inactivity, overweight and obesity
were associated significantly with higher OR for elevated
cholesterol, CRP, systolic blood pressure, non-HDL-C and
lower HDL-C than inactive with BMI o25 kg/m2 (Po0.05).
Physical activity improved GHQ scores in all BMI categories
(Po0.001). Physical activity reduced the likelihood of lower
Table 1 General characteristics of the study population aged 16–74 by
sex
Male Female Total
% % n %
Smoking
Nonsmokers 41.8 48.5 2476 45.5
Ex-smokers 23.9 18.3 1134 20.8
Current smokers 34.3 33.2 1835 33.7
BMI (kg/m2)
o25 36.7 45.8 2129 41.7
25–30 44.3 32.8 1945 38.1
X30 18.9 21.3 1034 20.2
Physical activity
Active 38.5 28.6 1799 33.1
Less active 39.3 50.4 2463 45.3
Inactive 22.2 21.0 1173 21.6
Alcohol (u/w)
Male 0–1 13.2 323
1–10 30.7 753
10–21 25.0 614
421 31.1 763
Female 0–1 27.6 818
1–7 40.5 1199
7–14 18.0 532
414 13.9 410
Social class
I and II 34.9 34.2 1838 34.5
IIINM 11.5 20.7 879 16.5
IIIM 33.5 22.4 1458 27.4
IV and V 20.1 22.7 1145 21.5
Fruit and vegetables
Achievers of target 16.6 28.7 1272 23.2
Moderate consumer 35.4 39.4 2062 37.6
Low consumer 48.0 31.9 2155 39.3
n, sample size based on total fibrinogen, the lowest valid sample among the
variables; u/w, units per week. Social class, I and II: professional and
intermediate, IIINM, skilled (nonmanual); IIIM, skilled (manual); IV and V,
partly skilled and unskilled.
Geometric mean HDL-cmmol/l
1.8
1.6
1.4
1.2
1
0.8 BMI<25 BMI BMI>=30 BMI<25 BMI>=30 BMI<25
25-30
BMI
25-30
BMI BMI>=30
25-30
Inactive Low activity Active
*
*
*
*
*
*
* *
*
+
+
*
Figure 1 Adjusted geometric mean HDL-C by physical activity and
BMI categories and sex. Reference group inactive BMI
o25,ţPo0.05,* Po0.001. ', male and &, female.
Geometric mean CRP mg/l
4.5
3.5
2.5
1.5
0.5
4
3
2
*
*
*
*
*
*
+
+
+
1
0 BMI<25 BMI BMI>=30 BMI<25 BMI>=30 BMI<25
25-30
BMI
25-30
BMI BMI>=30
25-30
Inactive Low activity Active
Figure 2 Adjusted geometric mean C-reactive protein by physical
activity and BMI categories and sex. Reference group inactive BMI
o25,ţPo0.05,* Po0.001. ', male and &, female.
Geometric mean fibrinogen g/l
3.2
* * *
+
+
3.1
3
2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2
BMI<25 BMI BMI>=30 BMI<25 BMI>=30 BMI<25
25-30
BMI
25-30
BMI BMI>=30
25-30
Inactive Low activity Active
Figure 3 Adjusted geometric mean fibrinogen by physical activity
and BMI categories and sex. Reference group inactive BMI
o25,ţPo0.05,* Po0.001. ', male and &, female.
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
4
European Journal of Clinical Nutrition
HDL-C and higher CRP in obese subjects, but it did not
eliminate the higher risk of the measured cardiovascular
disease risk factors in this group and OR for these two risk
factors were still high, 4.39 and 2.67, respectively, compared
with the reference group (Po0.001). Increasing physical
activity did not change OR of having higher systolic blood
pressure values, but overweight and obesity significantly
increased the OR across different physical activity levels.
Overweight and obese subjects had significantly higher OR
for higher non-HDL-C in different activity levels (Po0.001).
The OR of having higher fibrinogen decreased in active
subjects with BMI o30 kg/m2 (Po0.001); however, it did not
change in obese participants.
Table 3 shows OR for subjects who felt within criteria for
metabolic syndrome (NCEP, 2001). Fasting glucose and
triglyceride values were not available; therefore, medically
diagnosed diabetes and non-HDL-C values were used to
estimate metabolic syndrome. With this definition, almost
20% of men and women were categorized with metabolic
syndrome and the OR of having metabolic syndrome was
significantly higher in overweight and obese subjects within
each category. Being physically active had a protective effect
with metabolic syndrome lower in the obese active compared
with obese inactive subjects particularly in men;
however, prevalence of metabolic syndrome was still high
among obese active subjects.
Table 3 Odds ratio for metabolic syndrome according to physical activity and BMI
BMI (kg/m2/activity) categories Men Women
n MS (%) OR (95% CI) n MS (%) OR (95% CI)
Inactive
BMIo25 154 3 (1.9) 1 202 11 (5.4) 1
25–30 206 48 (23.3) 14.7 (4.4–48.6)* 174 51 (29.3) 6.3 (3.4–12.9)*
X30 128 90 (70.3) 132.0 (39.1–445.4)* 188 126 (67.0) 35.7 (17.4–73.1)*
Less active
BMI o25 322 20 (6.2) 4.7 (1.3–15.8)ţ 604 26 (4.3) 1.3 (0.6–2.7)
25–30 413 69 (16.7) 12.5 (3.8–40.9)* 488 82 (16.8) 5.3 (2.7–10.4)*
X30 200 109 (54.5) 79.9(24.2–263.9)* 275 144 (52.4) 37.2 (18.5–74.7)*
Active
BMI o25 345 13 (3.8) 3.3 (0.9–12.1) 400 8 (2.0) 0.8 (0.3–2.0)
25–30 386 47 (12.2) 11.2 (3.4–37.2)* 230 31 (13.5) 4.9 (2.3–10.5)*
X30 124 52 (41.9) 56.4 (16.7–190.6)* 119 50 (42.0) 33.1 (15.4y71.2)*
MS; metabolic syndrome: defined using available data in SHS database any three of WC4102 cm (M) and488 cm (F), BPX130/85mm Hg, HDL-Cp1 mmol/l (M)
and o1.3 mmol/l (F), non-HDL-C 44 mmol/l and diagnosed diabetes. Results adjusted for age, social class, cigarette smoking, alcohol consumption, and fruit and
vegetable intake. Significantly different from the reference category: ţPo0.05, *Po0.001.
Table 2 Adjusted odds ratio of cardiovascular risk factors and GHQ by activity and BMI
BMI (kg/m2/activity
categories)
HDL-C p1 mmol/l
(nź5434)
Cholesterol
X6.2 mmol
(nź5463)
CRP X3mg/l
(nź5525)
Fibrinogen X3 g/l
(nź5057)
Systolic BP
X130mm Hg
(nź6221)
GHQ X4 score
(nź8045)
Inactive
BMIo25 1 1 1 1 1 1
25–30 3.13* 1.61w 1.77* 1.01 1.50w 0.83
X30 6.40* 2.30* 4.86* 1.18 2.77* 0.98
Less active
BMI o25 1.40 1.13 0.60w 0.49* 0.98 0.52*
25–30 2.48* 2.22* 1.21 0.80 1.52w 0.40*
X30 5.98* 2.50* 3.08* 1.34 2.32* 0.47*
Active
BMI o25 1.04 1.10 0.62w 0.44* 0.96 0.40*
25–30 1.73w 2.10* 0.91 0.58* 1.86* 0.31*
X30 4.39* 2.44* 2.67* 1.29 2.49* 0.40*
Active, people who performed at least 3 days 20min vigorous activity or 5 days 30 min moderate activity or both of them; BP, blood pressure; CRP, C-reactive
protein; HDL-C, HDL-cholesterol; GHQ, general health questionnaire; Low active, people who were still active but less than current recommendations.
Results adjusted for age, gender, social class, cigarette smoking, alcohol consumption and fruit and vegetables.
Significantly different from reference category: *Po0.001, wPo0.05.
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
5
European Journal of Clinical Nutrition
About 30% of men and 9% of women had predicted 10-
year coronary heart disease risk X15% in which obese
inactive subjects had the highest proportion of the risk
(57.6% in men and 26.5% in women). The median coronary
heart disease risk was highest among obese inactive (16.6%
in men and 10.3% in women) and lowest among active
subjects with BMI o25 kg/m2 (5.4% in men and 1.8% in
women). Geometric mean of coronary heart disease risk was
significantly lower in obese active and active subjects with
BMI o25 kg/m2 compared with their counterparts
(Po0.001). The mean coronary heart disease risk was not
significantly different between obese active and inactive
subjects with BMIo25 kg/m2. However, it should be noted
that inactive groups were older than active groups and
because age was part of the risk equations, it was not
controlled for in this analysis. The distribution of predicted
coronary heart disease risk among BMI/activity categories
across ages 30–74 years has been shown in Figures 4 and 5. As
Figure 4 shows, in men, average coronary heart disease risk
was highest in obese inactive and lowest in active subjects
with BMI o25 kg/m2. Obese active men had lower average
coronary heart disease risk than obese inactive, but higher
than inactive group with BMI o25 kg/m2. In women
(Figure 5), obese groups had higher average coronary heart
disease risk than group with BMI o25 kg/m2; however,
physical activity reduced the risk slightly only in older obese
subjects.
Discussion
Many studies of different types have demonstrated that
physical activity has protective effects for chronic diseases,
including coronary heart diseases, hypertension, diabetes,
osteoporosis, colon cancer, and anxiety and depression (Pate
et al., 1995). Apart from the effects of physical activity on
obesity and obesity-related diseases, it is reported that
physical activity or physical fitness has additional health
benefits independent of BMI.
Physical activity is defined as 'any bodily movement
produced by skeletal muscles that results in energy expenditure'
and physical fitness is a 'set of attributes that people
have or achieve that relates to the ability to perform physical
activity' (Caspersen et al., 1985; Pate et al., 1995). The
amount of physical activity necessary for preventing cardiovascular
disease risk is not clear; however, different amounts
and types of physical activity have been recommended
(American College of Sports Medicine, 1990; Blair and
Connelly, 1995).
Cardiorespiratory fitness, assessed with maximal treadmill
exercise to calculate the maximal oxygen uptake, is stronger
and more accurate than self-reported physical activity as a
predictor of health outcome (Blair et al., 2001). However the
most accurate methods of measuring fitness, such as VmaxO2,
are often unavailable and are not feasible for large population
studies. Although more accurate methods are needed to
measure total physical activity, a physical activity questionnaire
is the most practical and widely used instrument for
measuring physical activity in population studies. An
evaluation on recommendations for physical activity levels
for public health on cardiovascular disease risk factors would
be beneficial to clarify the health effects of physical activity
along with BMI. In the present study, we have assumed that
reported physical activity reflects fitness, and evaluated the
associations between recommended levels of physical activity,
in combination with overweight and obesity, and
cardiovascular risk factors.
Being active in this study was defined as at least 30 min
moderate activity on at least 5 days a week or at least 20 min
active BMI>=30
inactive BMI>=30
inactive BMI<25
active BMI<25
20
Age (years)
2.0
1.5
1.0
0.5
Predicted CHD risk over 10 yr (log)
0.0
30 40 50 60 70 80
Figure 4 : Predicted 10-year CHD risk distribution by age
according to physical activity and BMI categories in men.
active BMI?=30
inactive BMI?=30
inactive BMI?25
active BMI?25
20
Age (years)
2.0
1.5
1.0
0.5
–0.5 Predicted CHD risk over 10 yr (
log)
–1.0
0.0
30 40 50 60 70 80
Figure 5 Predicted 10-yr CHD risk distribution by age according to
physical activity and BMI categories in women.
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
6
European Journal of Clinical Nutrition
vigorous activity on at least 3 days a week or both.
Overweight and obesity were strongly linked with raised
risk factors and predicted coronary heart disease risk.
Subjects who were overweight or obese had greater mean
and OR for most of the cardiovascular risk factors, metabolic
syndrome and predicted coronary heart disease risk than
subjects with BMI o25 kg/m2. Recommended physical
activity levels reduced the risk-associated overweight and
obesity for CRP, HDL-C, predicted coronary heart disease risk
and metabolic yndrome particularly in men when compared
with the reference group of inactive subjective with BMI
o25 kg/m2. However, this level of activity could not
eliminate the health risks associated with obesity and those
who achieved this level still were at elevated risk compared
with the nonobese reference group. The results showed that
physical activity was associated with better self-assessed
health scores in all BMI categories.
Our finding that BMI was a more important factor than
physical activity in association with cardiovascular disease
risk factors and predicted coronary heart disease risk is in
agreement with some other studies (Meyer et al., 2002;
Weinstein et al., 2004; Hu et al., 2004a; Christou et al., 2005).
Meyer et al., (2002) found that in all categories (sedentary,
moderate, intermediate and intensive) of self-reported
physical activity during leisure time, obese men had a
similar increased relative risk of death compared with
normal weight individuals in the same category of physical
activity. Weinstein et al. (2004) examined the combined
relationship of BMI and physical activity (self-reported
recreational activity during the past years) in women and
found that increasing physical activity had a modest
reduction in the risk of diabetes compared to a large increase
in the risk with increasing BMI. In a cross-sectional study of
135 healthy men, fatness was a better and stronger predictor
of 18 established cardiovascular disease risk factors including
total cholesterol, HDL-C, systolic blood pressure and fibrinogen
than aerobic fitness assessed by VmaxO2 (Christou et al.,
2005). Stevens et al. (2002) studied a cohort of the Lipid
Research Clinics Study of American men and women and
reported that both high levels of fatness and low levels of
fitness (assessed using a treadmill test) increased mortality
from all cause and from cardiovascular disease. This
suggested that to reduce the mortality risk, a combination
of both a moderate level of fitness and low fatness were
required.
In contrast, a number of other published studies support
the theory that physical fitness is more important than
fatness. Katzmarzyk et al. (2004) in a follow-up study
revealed that cardiorespiratory fitness had a protective effect
against all-cause and cardiovascular mortality in healthy
men and men with the metabolic syndrome. In this study,
body weight status was not an important modifier of
mortality risk when cardiorespiratory fitness was taken into
account. In Russian men, fitness assessed by a treadmill test
but not fatness was associated with all-cause and cardiovascular
disease mortality, but in US men, both fatness and
fitness were associated with all-cause and cardiovascular
disease mortality (Stevens et al., 2004). Katja et al. (2006) in a
cross-sectional study of Finnish adults after adjusting for
confounding factors including waist-to-hip ratio as a measure
of obesity showed that self-assessed fitness and aerobic
fitness measured by questionnaire were inversely associated
with CRP. Another study in adults that examined cardiorespiratory
fitness and its association with metabolic syndrome
in a prospective design showed that cardiorespiratory
fitness was inversely associated with the incidence of
metabolic syndrome (LaMonte et al., 2005). Their data also
showed that second and third cardiorespiratory fitness
tertiles were significantly associated with lower risk of
developing metabolic syndrome even in those men who
are overweight or obese (BMI X25 kg/m2). This association
was not significant in women, possibly because of the small
numbers. Katzmarzyk et al. (2005) reported that obesity and
metabolic syndrome were associated with an increased risk
of all-cause and cardiovascular disease mortality, but these
risks were largely related to low cardiorespiratory fitness.
Around 450 million people worldwide suffer from mental
or behavioral disorders (WHO, 2001) such as depression and
anxiety and mental health is essential to the overall wellbeing
of individuals and societies. Physical activity has been
shown to have a positive association with mental health and
psychological well-being (Stephens, 1988). The results of a
study using a large data set from the US population showed
that obesity was negatively associated with health-related
quality of life including mental health. Individuals who used
exercise alone or with diet to lose weight reported better
health-related quality of life scores (Hassan et al., 2003).
Schmitz et al. (2004) in the German National Health
Interview and Examination Survey found that self-reported
physical activity was associated with a better quality of life
and higher levels of physical activity were associated with
higher health-related quality of life among persons with
mental disorders. Dunn et al. (2005) in a randomized placebo
control study found that aerobic exercise at a dose compatible
with public health recommendations (17.5 kcal/kg/
week) was effective in the treatment of mild to moderate
major depressive disorder compared with a lower dose of
exercise (7.0 kcal/kg/week) or control. Our results support
these findings and indicated that active subjects may have
suffered less current psychological problems than inactive
subjects in all BMI categories. The mechanisms that explain
the beneficial effects of physical activity on mental health
are unclear. However, various psychological hypotheses such
as improvements in distraction, self-efficacy and social
interaction, and physiological hypotheses like increased
monoamines and endorphins have been proposed (Peluso
and Andrade, 2005).
The main limitations of the present study are self-reported
rather than measured physical activity and lack of fasting
blood samples necessary to conclusively define metabolic
syndrome. Both shortcomings represent practical limitations
with large-scale representative survey.
Current recommendation for physical activity and cardiovascular risks
M Akbartabartoori et al
7
European Journal of Clinical Nutrition
Our results show that reporting achieving recommended
physical activity levels can be beneficial, reduce some
cardiovascular disease risk factors and improve psychosocial
health but that cannot eliminate the extra health risk
imposed by overweight/obesity. Our data cannot be used to
suggest that a higher recommendation for physical activity
in obese people might be necessary to reverse their increased
cardiovascular disease risk, but a more active population
would appear to be healthier one from these data.
Conclusion
Overweight and obesity were associated with significantly
greater mean and/or OR for elevated total cholesterol, CRP,
systolic blood pressure, lower HDL-C, higher prevalence of
metabolic syndrome and predicted 10-year coronary heart
disease risk than BMI o25 kg/m2. Current recommended
physical activity levels for cardiovascular health were
associated with modestly reduced risk of lower HDL-C,
higher CRP concentrations and predicted coronary heart
disease risk. The higher cardiovascular disease risk factors in
active obese subjects were not eliminated when compared to
inactive subjects with BMI o25 kg/m2. Physical activity was
associated with better general health scores across all BMI
categories, therefore obese active subjects reported feeling
better according to their GHQ score. These data confirm the
importance of both physical activity and reducing body
weight in obese subjects to challenge cardiovascular disease
risk.
Acknowledgements
Mehdi Akbartabartoori was funded by Islamic Republic of
Iran. The authors thank the Scottish Health Survey Group for
access to their data.
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9
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09
ORIGINAL ARTICLE
Metabolic syndrome risk for cardiovascular disease
and diabetes in the ARIC study
CM Ballantyne1,2, RC Hoogeveen1,2, AM McNeill3,4, G Heiss4, MI Schmidt4,5, BB Duncan4,5
and JS Pankow6
1Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, USA;
2Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart Center, Houston, TX, USA; 3Department of
Epidemiology, Merck Research Labs, North Wales, PA, USA; 4Department of Epidemiology, School of Public Health,
University of North Carolina, Chapel Hill, NC, USA; 5Graduate Studies Program in Epidemiology, School of Medicine,
Federal University of Rio Grande do Sul, Porto Alegre, Brazil and 6Division of Epidemiology and Community Health, School
of Public Health, University of Minnesota, Minneapolis, MN, USA
Objective: The metabolic syndrome is associated with increased risk for cardiovascular disease and diabetes. Several analyses
from the Atherosclerosis Risk in Communities (ARIC) study have been performed to examine the role of the metabolic syndrome
and its components in predicting risk for cardiovascular disease and diabetes.
Design and subjects: The large, biracial, population-based ARIC study enrolled 15 792 middle-aged Americans in four
communities in the United States and has followed them for the development of cardiovascular disease and diabetes.
Measurements: Outcome parameters included prevalence of the metabolic syndrome and its individual components, carotid
intima-media thickness, incident coronary heart disease, incident ischemic stroke and incident diabetes.
Results and conclusion: Several analyses from the ARIC study have shown that the metabolic syndrome, as well as individual
metabolic syndrome components, is predictive of the prevalence and incidence of coronary heart disease, ischemic stroke,
carotid artery disease and diabetes.
International Journal of Obesity (2008) 32, S21–S24; doi:10.1038/ijo.2008.31
Keywords: metabolic syndrome; coronary heart disease; cardiovascular disease; diabetes; risk factors
Introduction
The metabolic syndrome has been shown to increase risk for
cardiovascular disease (CVD) and diabetes and was established
as a secondary target of therapy in the Adult
Treatment Panel III (ATP III) guidelines of the US National
Cholesterol Education Program.1 According to the ATP III
definition, the metabolic syndrome is diagnosed by the
presence of three or more of the following characteristics:
waist circumference 440 inches (102 cm) in men, 435
inches (88 cm) in women; triglycerides X107mmol l1; highdensity
lipoprotein cholesterol (HDL-C) o100mmol l1 in
men, o103mmol l1 in women; blood pressure X130/
X85mmHg; and fasting glucose 601–609mmol l1.2 Data
from the US National Health and Nutrition Examination
Survey indicate that age-adjusted prevalence of the metabolic
syndrome, as defined by the ATP III guidelines, is 27% in
adults and has been increasing.3
The role of the metabolic syndrome and its individual
components in predicting risk for CVD and diabetes has
been examined in the Atherosclerosis Risk in Communities
(ARIC) study, a large, prospective, biracial cohort study of
cardiovascular risk factors in 15 792 middle-aged (aged 45–64
years) Americans.4 Analyses have evaluated the influence of
the metabolic syndrome on both prevalence and incidence
of CVD as well as on the prediction of diabetes.
Metabolic syndrome and prevalence of CVD
In a cross-sectional analysis of 14 502 ARIC participants,
using data from 1987 to 1989, prevalence of the metabolic
syndrome as defined by ATP III was 30%, with considerable
variation across subgroups defined by sex and race.5 The
highest metabolic syndrome prevalence was in black women
(38%), and the lowest was in black men (26%); 31% of white
men and 28% of white women had the metabolic syndrome.
Correspondence: Dr CM Ballantyne, Department of Medicine, Baylor College
of Medicine, 6565 Fannin, MS A-601, Houston, TX 77030, USA.
E-mail: cmb@bcm.tmc.edu
International Journal of Obesity (2008) 32, S21–S24
& 2008 Nature Publishing Group All rights reserved 0307-0565/08 $30.00
www.nature.com/ijo




15
Obesity and Metabolic syndrome
Obesity is an increasingly significant U.S. health problem. Over 4 decades, the prevalence of obesity (body mass index [BMI] > 30) has increased from 13 percent to 31 percent in adults and the prevalence of overweight (BMI 25-29.9 kg/m2) has increased from 31 percent to 34 percent.1 Concurrent increases occurred in adolescents and children. Obesity is especially common in African Americans, some Hispanic populations, and Native Americans and some health sequelae reflect similar ethnic differences. Obesity is more common in women, and overweight is more common in men. Obesity is a risk factor for major causes of death, including cardiovascular disease, numerous cancers, and diabetes, and is linked with markedly diminished life expectancy. Osteoarthritis, gall bladder disease, sleep apnea, respiratory impairment, diminished mobility, and social stigmatization are associated with obesity.
Steps for Treating overweight and Obesity in the Primary care setting
Step 1: Measure height and weight so that you can estimate your patient's BMI
If pounds and inches are used
BMI = weight (pounds) x 703 / height squared (inches2)
http://www.nhlbisupport.com/bmi/
Classification of BMI:
??Underweight <18.5 kg/m2 ??Normal weight 18.5–24.9 kg/m2 ??Overweight 25–29.9 kg/m2 ??Obesity (Class 1) 30–34.9 kg/m2 ??Obesity (Class 2) 35–39.9 kg/m2 ??Extreme obesity (Class 3) 40 kg/m2
Step 2: Measure waist circumference
??Excess abdominal fat is an independent risk factor for diabetes, dyslipidemia,
hypertension, and cardiovascular disease. ??It is particularly useful in patients who are categorized as normal or overweight. ??It is not necessary to measure waist circumference in individuals with BMIs 35 kg/m2
since it adds little to the predictive power of the disease risk classification of BMI. ??High Risk waist circumference :
o Men = > 40 inches, and
o Women = > than 35 inches.

Classification of Overweight and Obesity by BMI, Waist Circumference and Associated Disease Risk
BMI Obesity Class Disease Risk*
(kg/m2) (Relative to Normal Weight
and Waist Circumference)
Men =40 in (= > 40 in (> 102
102 cm) cm)
Women = 35 in
(= 88 cm) > 35 in (> 88 cm)
Underweight ight < 18.5
Normal† 18.5–24.9
Overweight 25.0–29.9 Increased High
Obesity
30.0–34.9 I High Very High
35.0–39.9 II Very High Very High
Risk*
Extreme = 40 III Extremely High Extremely High
Obesity

* Disease risk for type 2 diabetes, hypertension, and CVD.
† Increased waist circumference can also be a marker for increased risk even in persons of normal weight. Adapted from "Preventing and Managing the Global Epidemic of Obesity. Report of the World Health Organization Consultation of Obesity." WHO, Geneva, June 1997.26 Table
Step 3: Assess co morbidities for the "Assessment of Risk Status."
The following diseases/risk factors place patient at a high absolute risk for subsequent mortality, and they require aggressive treatment: 1) Established coronary heart disease, other atherosclerotic diseases, DM-2, and sleep apnea 2) Three or more of the following are considered high risk,
a. hypertension,
b. cigarette smoking,
c. high low-density lipoprotein cholesterol (LDL-C),
d. low high-density lipoprotein cholesterol (HDL-C),
e. impaired fasting glucose,
f. family history of early cardiovascular disease, and age (male 45 years, female 55 years).


3) Other disease/conditions that denote high absolute risk but are not generally life threatening are:
a. Osteoarthritis,
b. gallstones,
c. stress incontinence, and
d. gynecological abnormalities such as amenorrhea and menorrhagia also increase risk.

Step 4: Decide if the patient should be treated?
Weight loss therapy is recommended for patients: 1) With a BMI > 30 and 2) For BMI between 25-29.9 3) Or a high risk waist circumference and 2 or more risk factors.
Step 5: Is the patient ready and motivated to lose weight? Evaluation of readiness should include the following:
1) reasons and motivation for weight loss, 2) previous attempts at weight loss, 3) support expected from family and friends, 4) understanding of risks and benefits, 5) attitudes toward physical activity, 6) time availability, and 7) Potential barriers to the patient's adoption of change.
Step 6:
If the answer is "yes" to treatment, decide which treatment is best using the following table.
A Guide to Selecting Treatment
BMI Category
Treatment
25-26.9 27-29.9 30-34.9 35-39.9 > 40
Diet, physical With With
activity and behavior comorbidities comorbidities + + +
therapy
Pharmacotherapy With
comorbidities + + +
Surgery With
comorbidities

Goals of therapy:
Goals of therapy are to reduce body weight and maintain a lower body weight for the long term; the prevention of further weight gain is the minimum goal. 1) An initial weight loss of 10 percent of body weight achieved over 6 months is a

recommended target. 2) The rate of weight loss should be 1 to 2 pounds per week.
3) Greater rates of weight loss do not achieve better long-term results.
4) After the first 6 months of weight loss therapy, the priority should be weight maintenance achieved through combined changes in diet, physical activity, and behavior.
5) Further weight loss can be considered after a period of weight maintenance.
Dietary Therapy:
1) Caloric intake should be reduced by 500 to 1,000 calories per day (kcal/day) from the current level this will produce the recommended weight loss of 1-2 pounds per week..
2) The diet should be low in calories, but it should not be too low (less than 800 kcal/day). Diets lower than 800 kcal/day have been found to be no more effective than low-calorie diets in producing weight loss. They should not be used routinely, especially not by providers untrained in their use.
3) In general, diets containing 1,000 to 1,200 kcal/day should be selected for most women; 4) A diet between 1,200 kcal/day and 1,600 kcal/day should be chosen for men and may be appropriate for women who weigh 165 pounds or more, or who exercise regularly. 5) If the patient can stick with the 1,600 kcal/day diet but does not lose weight you may want to try the 1,200 kcal/day diet. 6) If a patient on either diet is hungry, you may want to increase the calories by 100 to 200 per day. Included in Appendix D are samples of both a 1,200 and 1,600 calorie diet.
7) Long-term changes in food choices are more likely to be successful when the patient's preferences are taken into account and when the patient is educated about food composition, labeling, preparation, and portion size.
8) Although dietary fat is a rich source of calories, reducing dietary fat without reducing calories will not produce weight loss. 9) Frequent contact with practitioners during the period of diet adjustment is likely to improve compliance.
Physical Therapy
Increasing physical activity has direct and indirect effects: 1) Increases the energy expenditure and helps in weight loss. 2) Reduces the risk of heart disease more than that achieved by weight loss. 3) Physical activity should be increased gradually in an obese individual to avoid any
injury. 4) All adults should set a long term goal to accumulate at least 30 minutes or more of moderate intensity physical activity on most and preferably all, days of the week. Check Guide to physical activity appendix -1
Behavior Therapy
Strategies, based on learning principles such as reinforcement, that provide tools for overcoming barriers to compliance with dietary therapy and/or increased physical activity are helpful in achieving weight loss and weight maintenance.
Specific strategies include 1) self-monitoring of both eating habits and physical activity, 2) stress management, 3) stimulus control, 4) problem solving, 5) contingency management, 6) cognitive restructuring, and7) social support.
Check guide to behavior change appendix 2
Pharmacotherapy


Drugs can be used as an adjunct to diet, physical activity changes and behavior therapy in some patients. The decision to add a drug should be made after consideration of all potential riska and benefits and only after all behavioral options have been exhausted. Currently, there use is limited to those patients:
1) who have a BMI of > 30, or 2) >27 if concomitant obesity related disease or risk factors exist. 3) It should be considered in patients in whom life style changes of 6 months
duration did not promote any weight loss. Drugs currently approved by FDA are:
Drug Action Adverse Effects
Sibutramine Norepinephrine, dopamine, and Increase in heart rate and
serotonin reuptake inhibitor blood pressure
Orlistat Inhibits pancreatic lipase, Decrease in absorption of fat-
Decreases fat absorption soluble vitamins; soft stools
and anal leakage

Surgery
Weight loss surgery should be reserved for patients in whom efforts at medical therapy have failed and who are suffering from the complications of extreme obesity. Gastrointestinal surgery (gastric restriction [vertical gastric banding] or gastric bypass [Roux-en Y]) is an intervention weight loss option for motivated subjects with acceptable operative risks. An integrated program must be in place to provide guidance on diet, physical activity, and behavioral and social support both prior to and after the surgery. Surgery is an option for the following patients:
1) BMIs >= 40 or 2) >= 35 with comorbid conditions.
Gastric Bypass Surgery Complications: 14-Year Follow-Up
Complicatons Number Percent
Vitamin B12 deficiency 239 39.9
Readmit for various reasons 229 38.2
Incisional hernia 143 23.9
Depression 142 23.7
Staple line failure 90 15.0
Gastritis 79 13.2
Cholecystitis 68 11.4
Anastomotic problems 59 9.8
Dehydration malnutrition 35 5.8
Dilated pouch 19 3.2


Step 7:
Review the Weekly Food and Activity Diary with the patient. 1) Remind the patient that record-keeping has been shown to be one of the most successful behavioral techniques for weight loss and maintenance. 2) Write down the diet, physical activity, and behavioral goals you have agreed on at the bottom.
Step 8:
Give the patient copies of the 1) dietary information 2) the Guide to Physical Activity (appendix 1) 3) the Guide to Behavior Change (appendix 2) 4) the Weekly Food and Activity Diary
Step 9:
Enter the patient's information and the goals you have agreed on in the Weight and Goal Record 1) It is important to keep track of the goals you have set and to ask the patient about them at the next visit to maximize compliance. 2) Have the patient schedule an appointment to see you or your staff for follow up in 2 to 4 weeks.



--
Shigenoi Haruki

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