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ORIGINAL ARTICLE
Metabolic normality in overweight and obese subjects.
Which parameters? Which risks?
Z Pataky
1
, V Makoundou
1
, P Nilsson
2
, RS Gabriel
3
, K Lalic
4
, E Muscelli
5
, A Casolaro
5
, A Golay
1
,
E Bobbioni-Harsch
1
and the RISC Investigators
6
1
Service of Therapeutic Education for Chronic Diseases, WHO Collaborating Centre, Geneva University Hospitals and
University of Geneva, Geneva, Switzerland;
2
Department of Medicine, Malmo
¨University Hospital, Malmo
¨, Sweden;
3
Hospital Universitario La Paz, Madrid, Spain;
4
Institute for Endocrinology, Diabetes and Metabolic Diseases, Clinical
Center of Serbia, Belgrade, Serbia and
5
Department of Internal Medicine, University of Pisa, Pisa, Italy
Objectives: The objective of this study was to define metabolic normality and to investigate the cardiometabolic profile of
metabolically normal obese.
Design: Cross-sectional study conducted at 21 research centers in Europe.
Subjects: Normal body weight (nbw, n¼382) and overweight or obese (ow/ob, n¼185) subjects free from metabolic
syndrome and with normal glucose tolerance, were selected among the Relationship between Insulin Sensitivity and
Cardiovascular Disease study participants.
Main outcome measures: Insulin sensitivity was assessed by the clamp technique. On the basis of quartiles in nbw subjects, the
limits of normal insulin sensitivity and of normal fasting insulinemia were established. Subjects with normal insulin sensitivity and
fasting insulin were defined as metabolically normal.
Results: Among ow/ob subjects, 11% were metabolically normal vs 37% among nbw, Po0.0001. Ow/ob subjects showed
increased fasting insulin (P¼0.0009), low-density lipoprotein cholesterol (LDL-cholesterol) (P¼0.004), systolic (P¼0.0007)
and diastolic (P¼0.001) blood pressure, as compared with nbw. When evaluating the contribution of body mass index (BMI),
hyperinsulinemia and insulin resistance, BMI showed an isolated effect on high-density lipoprotein (P¼0.007), high-sensitivity
C-reactive protein (Po0.0001), systolic (P¼0.002) and diastolic (P¼0.008) blood pressures. BMI shared its influence with
insulinemia on total cholesterol (P¼0.04 and 0.003, respectively), LDL-cholesterol (P¼0.003 and 0.006, respectively) and
triglycerides (P¼0.02 and 0.001, respectively).
Conclusion: In obese subjects, fasting insulin should be taken into account in the definition of metabolic normality. Even when
metabolically normal, obese subjects could be at increased risk for cardiometabolic diseases. Increased BMI, alone or with fasting
insulin, is the major responsible for the less favorable cardio-metabolic profile.
International Journal of Obesity (2011) 35, 1208–1215; doi:10.1038/ijo.2010.264; published online 4 January 2011
Keywords: metabolic normality; insulin sensitivity; cardiometabolic risk factors
Introduction
Obesity (ob) and overweight (ow), as defined by the body
mass index (BMI) value, are associated to a higher incidence
of diabetes and cardiovascular diseases (CVD).
1,2
More
recently, however, the concept that an increased fat mass is
‘per se’ a risk factor for metabolic and CVD has been brought
into question. For instance, it has been demonstrated that,
within a USA population, 32% of the phenotypically obese
subjects are metabolically normal, whereas 23.5% of the
subjects with a BMI in the normal range (that is, o25kg m
2
)
exhibit clustering of cardiometabolic abnormalities.
3
Several
other studies report the presence, among adult
4,5
and young
6
obese population, of metabolically normal subjects.
A difficulty, when examining these studies, is represented
by the definition of metabolic normality. In fact, some
authors have selected, as criterion of normality, the presence
of 0 or 1 of the ATP III parameters for the metabolic
syndrome,
7
whereas other have based their definition on
insulin sensitivity. In turn, insulin sensitivity has been
Received 25 August 2010; revised 20 October 2010; accepted 9 November
2010; published online 4 January 2011
Correspondence: Dr E Bobbioni-Harsch, Service of Therapeutic Education for
Chronic Diseases, WHO Collaborating Centre, Geneva University Hospital,
4 Gabrielle-Perret-Gentil, Geneva 14 CH 1211, Switzerland.
E-mail: elisabetta.harsch@hcuge.ch
6
For the list of RISC investigators, see Acknowledgments.
International Journal of Obesity (2011) 35,1208– 1215
&
2011 Macmillan Publishers Limited All rights reserved 0307-0565/11
www.nature.com/ijo
estimated either on the base of an oral glucose tolerance
test and homeostasis model assessment index,
8
or using a
literature-reported value of glucose uptake, measured by an
euglycemic clamp.
9
Insulin resistance, which is strongly associated to obesity,
is a well-defined risk factor for diabetes and has also been
proposed to underlie the metabolic disorders leading to an
increased risk for CVD.
10
Therefore, it seems sound to take
into account insulin sensitivity together with lipid profile for
the evaluation of metabolic normality in obese subjects. On
the other hand, consistent reports indicate that fasting
hyperinsulinemia and insulin resistance are not necessarily
associated in the same individual and may independently
contribute to different disorders leading to an increased risk
for metabolic and CVD.
11,12
Insulin resistance and fasting
hyperinsulinemia should therefore be separately evaluated.
Up today, a normal range of insulin-mediated glucose uptake
and of fasting insulinemia are not defined. For this reason,
the classification according to quartiles within a given study
group is currently adopted.
12
In our study, we have investigated the roles of BMI, insulin
sensitivity and fasting insulin on several cardiometabolic
parameters in normal body weight (nbw) and in overweight
or obese (ow/ob) subjects free from metabolic syndrome,
according to the International Diabetes Federation (IDF)
criteria,
13
and with a normal glucose tolerance.
In our study population, glucose uptake was evaluated
during a euglycemic–hyperinsulinemic clamp test. Common
and internal carotid intima–media thickness were used as an
indicator of early-stage atherosclerosis.
14
Subjects and methods
Study group
The study population was constituted by the Relationship
between Insulin Sensitivity and Cardiovascular Disease (RISC)
study cohort. The rationale and design of the RISC study has
been previously published.
15
In brief, participants of an
European ancestry were recruited from the local population at
21 centers in 14 European countries, according to the following
inclusion criteria: either of the sex, age between 30 and 60 years
and clinically healthy. Exclusion criteria were the presence
of chronic diseases, overt CVD, carotid stenosis 440%, and
treatment for hypertension, diabetes and dyslipidemia.
Moreover, after screening, subjects only (n¼1227, 619 nbw
and 608 ow/ob) with blood pressure (o140/o90 mm Hg),
plasma cholesterol (o7.8 mmol l
1
), triglycerides (TG) (o4.6
mmol l
1
), and fasting and 2-h glucose (o7.0 and
11.1 mmol l
1
, respectively) were enrolled.
Among this cohort, we selected subjects free from meta-
bolic syndrome, according to the IDF definition,
13
as well as
with a normal glucose tolerance. This selection procedure
provided 567 subjects (389 women and 178 men) with fasting
glucose o5.6 mmol l
1
,fastingTGo1.7 mmol l
1
, high-density
lipoprotein (HDL) cholesterol o1.03 and o1.29 mmol l
1
,
respectively, in men and women, and blood pressure o130/
85 mm Hg. As the large majority (67.4% in our ow/ob
population) of ow and/or ob subjects have a waist girt larger
than 94/80 cm, waist circumference was not taken into account.
Among the nbw subjects, 15.5% showed a waist circumference
494/80 cm. This selection, therefore, provided subjects with 0
or 1 risk factors of metabolic syndrome, according to the IDF
definition. Finally, subjects only with 2-h plasma glucose
o7.8 mmol l
1
, after an oral glucose load, were included.
Subjects were classified according to their BMI (that is,
body weight/height
2
) as nbw (that is, BMIo25 kg m
2
,
n¼382) or ow/ob (that is, BMIX25 kg m
2
,n¼185).
The study protocol was approved by the local Ethical
Committee. Participants were informed about the aims of
the study, and gave their written consent.
Insulin sensitivity
Insulin sensitivity was evaluated by an euglycemic–
hyperinsulinemic clamp.
9
Insulin was continuously infused
at a rate of 240 pmol min
1
m
2
, whereas a glucose solution
(20%) was infused at variable rates to maintain a constant
plasma glucose concentration between 4.5 and 5.5mmol l
1
.
Plasma glucose was measured at 5–10 min intervals to ensure it
remained within the target glucose concentration. The steady-
state period (for calculation of insulin sensitivity) was between
80 and 120 min. Glucose uptake, measured during the steady-
state period, was expressed in mmol per kg fat-free mass per
min per pmol insulin (mmol per kg FFM per min per pmol ins).
Fat-free mass was measured by TANITA bioimpedance balance
(Tanita International Division, Yiewsley, Middlessex, UK).
Definition of limits for insulin resistance and hyperinsulinemia
As demonstrated in a previous
16
analysis and confirmed by
our results (Table 1), women and men displayed a signifi-
cantly different insulin sensitivity. Quartiles of glucose
uptake were therefore established separately in nbw women
and men: subjects in the first quartile of glucose uptake were
considered as insulin resistant. The cutoff values so estab-
lished were o130 mmol per kg FFM per min per pmol ins for
women and o107 mmol per kg FFM per min per pmol ins for
men. The ow/ob subjects were than classified as insulin
sensitive or resistant based on these criteria.
Quartiles of fasting insulinemia were established in nbw
women and men taken together, as this parameter was not
influenced by sex (Table 1). Subjects in the fourth quartile of
fasting insulin (427 pmol l
1
) were considered as hyper-
insulinemic and ow/ob subjects were classified accordingly.
Carotid artery ultrasound imaging and blood pressure
measurements
Carotid artery intima–media imaging followed a validated
protocol.
17
Longitudinal B-mode image was taken of the
right and left common carotid arteries (CCAs) and internal
carotid artery (ICA) from the anterior, lateral and posterior
Obesity and cardiometabolic risk
Z Pataky et al
1209
International Journal of Obesity
angles. The images from all the participating centers were
evaluated by a single reader. Diastolic frames of CCA and ICA
were selected to provide images of the near- and far-wall
intima–media complex. Frames were digitized and analyzed
by an image analysis system
18
(MIP: Institute of Clinical
Physiology, CNR, Pisa, Italy). Lines were drawn along the
lumen–intimal and medial–adventitial interfaces, and the
intima–media thickness (IMT) was computed as an average
of several measurements. A 30-s longitudinal B-mode image
was made of the right CCA, and concomitant blood pressure
was measured using a sphygmomanometric cuff. An auto-
matic contour detection algorithm determined the average
minimum and maximum CCA diameter.
Sitting blood pressure and heart rate are the mean values of
three measurements (OMRON 705 cp, OMRON Healthcare
Europe, Hoofddorp, The Netherlands).
Assessment of physical activity
Physical activity was measured objectively by a small single-
axis accelerometer (Actigraph, AM7164-2.2; Computer
Science and Applications, Pensacola, FL, USA).
19,20
The
acceleration signal was digitized with 10 samples per second,
registered as counts counted over 1-min intervals. The
accelerometer was worn for up to 8 days on a belt in the
small of the back, from waking to bedtime, except during
water-based activities. We analyzed participants with at least
3 days of data, including days when the device was worn for
more than 10 h; we assumed it was not worn if there were
60 consecutive minutes with no counts. Data were checked
for spurious recording: high counts 20 000 counts per min or
repeated counts.
21
Daily physical activity was calculated as an average
number of counts per minute when accelerometer was worn.
Statistical analysis
The differences in percent prevalence of metabolic normality
were evaluated by w
2
test (Figure 1). The values presented in
tables are expressed as means±s.d. Because of their non-
normal distributions, values were statistically evaluated after
log transformation. Factorial analysis of variance was used to
evaluate the effects of sex and BMI (Table 1). The influences
Table 1 Anthropometric and cardio-metabolic characteristics of IDF-based metabolic syndrome-free and glucose-tolerant female and male subjects classified
according to their BMI
Women (n¼389) Men (n¼178) Association
with BMI
Association
with sex
Nbw (n¼281) Ow/ob (n¼108) Nbw (n¼101) Ow/ob (n¼77) P-value P-value
Age (years) 42±845
±941
±943
±8 0.003 ns
BMI (kg m
2
) 21.7±1.9 28.2±3.2 22.7±1.5 27.4±2.0 Fns
Glucose (mmol l
1
)4.7
±0.4 4.8±0.4 4.9±0.4 5.0±0.4 0.006 o0.0001
Insulin (pmol l
1
) 21.9±10.3 30.8±15.1 21.4±10.4 31.4±24.7 o0.0001 ns
Glucose uptake (mmol per kg FFM
per min per pmol insulin)
176±69 145±53 156±67 123±57 o0.0001 0.0005
Total chol (mmol l
1
) 4.62±0.80 4.85±0.81 4.64±0.82 4.85±0.81 0.002 ns
HDL chol (mmol l
1
) 1.74±0.32 1.61±0.26 1.42±0.28 1.31±0.21 o0.0001 o0.0001
LDL chol (mmol l
1
) 2.53±0.71 2.84±0.76 2.82±0.71 3.09±0.76 o0.0001 o0.0001
Triglycerides (mmol l
1
) 0.76±0.29 0.86±0.31 0.86±0.30 0.95±0.27 0.0003 0.0003
Hs-CRP (mg l
1
) 0.58±0.87 1.22±1.88 0.72±1.28 1.14±1.72 o0.0001 ns
Systolic BP (mm Hg) 108±10 114±9116
±8 117±8o0.0001 o0.0001
Diastolic BP (mm Hg) 69±773
±773
±773
±6 0.0001 0.0007
Heart rate (b.p.m.) 69±11 70±10 63±10 65±9nso0.0001
CCA IMT (mm) 0.56±0.07 (n¼261) 0.58±0.08 (n¼100) 0.60±0.08 (n¼95) 0.62±0.07 (n¼65) 0.002 o0.0001
ICA IMT (mm) 0.58±0.10 (n¼237) 0.60±0.13 (n¼89) 0.63±0.14 (n¼90) 0.63±0.11 (n¼59) ns 0.0002
Physical activity (c.p.m.) 380±156 (n¼181) 346±143 (n¼61) 453±216 (n¼65) 390±198 (n¼48) 0.03 0.002
Abbreviations: BMI, body mass index; BP, blood pressure; chol, cholesterol; c.p.m., counts per minute; CCA IMT, common carotid artery intima–media thickness;
FFM, fat-free mass; Hs-CRP, high-sensitivity C-reactive protein; HDL-chol, high-density lipoprotein cholesterol; ICA IMT, internal carotid artery intima–media
thickness; LDL-chol, low-density lipoprotein cholesterol; Ow/Ob, overweight/obesity; Nbw, normal body weight; ns, not significant. Means±s.d.
RISC Population
N=1227
Normal Body Weight
N=619
Overweight/Obese
N=608
Metabolic Syndrome-Free
and
Glucose Tolerant
N=382 (62%)
Metabolic Syndrome-Free
and
Glucose Tolerant
N=185 (37%) ***
Insulin Sensitive
and
Normoinsulinaemic
N=227 (37%)
Insulin Sensitive
and
Normoinsulinaemic
N=65 (11%) ***
Figure 1 Percent incidence of normal glucose tolerance and absence of any
metabolic syndrome-related alteration (middle panel) and of metabolic
normality (lower panel) in normal body weight (nbw) and overweight/obese
subjects (ow/ob) among the RISC study population. ***Po0.0001.
Obesity and cardiometabolic risk
Z Pataky et al
1210
International Journal of Obesity
of BMI, fasting insulin and insulin sensitivity on various
measured parameters, adjusted for age, sex and physical
activity were first investigated separately (Model 1, Table 2)
and then all together (Model 2, Table 2) by multiple
regression analysis. For these analyses, both BMI and fasting
insulin were used as categorical variables.
Results
Prevalence of metabolic normality
As illustrated by Figure 1, among the clinically healthy RISC
population (n¼1227, 619 nbw and 608 ow/ob), 62% of nbw
(that is, 382 subjects) and 37% of ow/ob (that is, 185
subjects) had a normal glucose tolerance and did not show
any criterion of metabolic syndrome, according to the
IDF criteria,
13
with the exception of waist circumference.
The percent prevalence was significantly different between
the two groups (w
2
P-value o0.0001).
The limits for fasting hyperinsulinemia and for insulin
resistance were determined as described in the Subjects and
methods section. The absence of any metabolic syndrome
criteria, a normal glucose tolerance together with normal
insulin sensitivity and normal fasting insulin were observed
in 37% of the overall nbw population (that is, 227 out of 619
nbw subjects) and in 11% of ow/ob overall group (that is, 65
out of 608 ow/ob subjects, w
2
P-value o0.0001).
Role of BMI and sex in metabolic syndrome-free subjects
with normal glucose tolerance
Table 1 illustrates the anthropometric and cardiometabolic
characteristics of the population free from metabolic
syndrome and with normal glucose tolerance, classified
according to sex and BMI.
Overweight/obese groups were slightly older than nbw
controls (P¼0.003). Higher levels of fasting glucose were
significantly associated to ow/ob BMI (P¼0.006) and to male
sex (Po0.0001). On the contrary, glucose uptake was lower
in ow/ob (Po0.0001) and in men (P¼0.0005). Fasting
insulin, (Po0.0001), total cholesterol (P¼0.002) and high-
sensitivity C-reactive protein levels (Po0.0001) were higher
in ow/ob of both the sexes. In addition to the well-
established sex-linked difference, HDL was decreased in
ow/ob categories (Po0.0001).
The Ow/ob BMI and male sex showed significant associa-
tions with increased LDL-cholesterol (Po0.0001 for both),
TG (P¼0.0003 for both), systolic blood pressure (Po0.0001
for both), diastolic blood pressure (P¼0.0001 for BMI and
P¼0.0007 for sex), common carotid artery intima–media
thickness (P¼0.002 for BMI and Po0.0001 for sex).
Heart rate was higher in women (Po0.0001). Internal
carotid artery intima–media thickness was influenced only
by sex (P¼0.0002). Ow/ob as well as women were less active
when compared with their counterparts, respectively,
(P¼0.03 for BMI and P¼0.002 for sex).
Role of BMI and sex in metabolically normal subjects
Table 2 reports the anthropometric and cardiometabolic
characteristics of the subjects who, among the population
free from metabolic syndrome and with normal glucose
tolerance, also showed normal values of fasting insulin and
of glucose uptake.
The BMI subgroups did not differ in terms of age. As
compared with nbw, ow/ob subjects showed increased levels
Table 2 Anthropometric and cardio-metabolic characteristics of metabolically normal (that is, insulin sensitive and normo-insulinaemic) female and male subjects
classified according to their BMI
Females (n¼199) Males (n¼93) Association
with BMI
Association
with sex
Nbw (n¼161) Ow/ob (n¼38) Nbw (n¼66) Ow/ob (n¼27) P-value P-value
Age (years) 42±843
±10 41±844
±9nsns
BMI (kg m
2
) 21.4±1.8 26.9±1.7 22.5±1.7 26.4±1.2 F0.003
Glucose (mmol l
1
)4.7
±0.4 4.7±0.4 4.9±0.3 5.1±0.4 ns o0.0001
Insulin (pmol l
1
) 17.0±4.9 18.6±4.6 17.0±5.2 20.8±5.0 0.0009 ns
Glucose uptake (mmol per kg FFM
per min per pmol insulin)
208±66 197±40 181±65 176±50 ns 0.001
Total chol (mmol l
1
) 4.55±0.78 4.73±0.79 4.59±0.84 4.99±1.00 0.02 ns
HDL-chol (mmol l
1
) 1.77±0.30 1.69±0.30 1.42±0.29 1.36±0.23 ns o0.0001
LDL-chol (mmol l
1
) 2.45±0.69 2.70±0.77 2.78±0.74 3.19±0.94 0.004 0.0001
Triglycerides (mmol l
1
) 0.72±0.25 0.76±0.27 0.85±0.26 0.97±0.26 0.04 o0.0001
Hs-CRP (mg l
1
) 0.54±0.71 0.99±1.92 0.84±1.54 0.89±1.02 ns ns
Systolic BP (mm Hg) 108±10 114±9 115±9 117±8 0.0007 o0.0001
Diastolic BP (mm Hg) 69±773
±772
±773
±7 0.001 0.008
Heart rate (b.p.m.) 67±10 67±10 62±11 64±9 ns 0.0001
CCA IMT (mm) 0.56±0.08 (n¼154) 0.57±0.07 (n¼37) 0.60±0.09 (n¼63) 0.63±0.07 (n¼26) ns o0.0001
ICA IMT (mm) 0.57±0.10 (n¼142) 0.59±0.14 (n¼35) 0.65±0.15 (n¼60) 0.64±0.08 (n¼25) ns o0.0001
Physical activity (c.p.m.) 402±166 (n¼116) 376±128 (n¼28) 472±222 (n¼44) 373±182 (n¼17) ns ns
Abbreviations: BMI, body mass index; BP, blood pressure; chol, cholesterol; c.p.m., counts per minute; CCA IMT, common carotid artery intima–media thickness;
FFM, fat-free mass; Hs-CRP, high-sensitivity C-reactive protein; HDL-chol, high-density lipoprotein cholesterol; ICA IMT, internal carotid artery intima–media
thickness; LDL-chol, low-density lipoprotein cholesterol; Ow/Ob, overweight/obesity; Nbw, normal body weight; ns, not significant. Means±s.d.
Obesity and cardiometabolic risk
Z Pataky et al
1211
International Journal of Obesity
of fasting insulin (P¼0.0009) and a comparable glucose
uptake that was only influenced by sex (P¼0.001).
Total cholesterol (P¼0.02) was higher in ow/ob of both
sexes. The Ow/ob BMI and male sex were linked to an
increased LDL-cholesterol (P¼0.004 for BMI and P¼0.0001
for sex), TG (Po0.04 for BMI and Po0.0001 for sex), systolic
blood pressure (P¼0.0007 for BMI and Po0.0001 for sex),
diastolic blood pressure (P¼0.001 for BMI and P¼0.008
forsex).HDL-cholesterol,heartrate,commoncarotidartery
and internal carotid artery intima–media thickness were
influenced only by sex (Po0.0001 for all).
Role of BMI, insulin sensitivity and fasting insulinemia
The effect of BMI, insulin sensitivity and fasting insulin were
analyzed by multiple regression analysis, after adjustment
for age, sex and physical activity. This analysis was, there-
fore, performed only in subjects in whom physical activity
was assessed (n¼355).
As illustrated in Table 3 (Model 1), when analyzed
alone, BMI was significantly linked to the majority of the
cardiometabolic parameters, except heart rate and common
carotid artery intima–media thickness. Insulin sensitivity
was associated only with waist circumference, whereas
fasting insulin was linked to waist circumference, fasting
glucose, total and LDL-cholesterol, TG and heart rate.
When analyzed together with insulin sensitivity and
fasting insulin (Table 3, Model 2 and Figure 2), BMI kept
a significant, isolated effect on HDL-cholesterol (P¼0.007),
systolic (P¼0.002) and diastolic (P¼0.008) blood pressure
and high-sensitivity C-reactive protein (Po0.0001). BMI
shared an influence on total and LDL-cholesterol and TG
with fasting insulin and on waist circumference with both
fasting insulin and insulin sensitivity.
Plasma glucose level was influenced by fasting insulin and
insulin sensitivity. Fasting insulin showed an isolated effect
on heart rate (Po0.0001).
Discussion
As a first result (Table 1), our data show that, even when free
from metabolic syndrome and with normal glucose toler-
ance, ow/ob subjects have a less favorable cardiometabolic
profile, characterized by a higher total and LDL-cholesterol
value, lower HDL, increased values of high-sensitivity
C-reactive protein and blood pressure, as well as thicker
intima media of common carotid segment. This suggests a
potentially higher susceptibility to CVD.
In addition, these subjects show a reduced glucose uptake
and elevated levels of fasting insulin (Table 1), demonstrating
that metabolic syndrome-free obese yet are at higher risk for
diabetes, even when presenting a normal glucose tolerance.
Finally, this last result underlines the fact that the
definition of metabolic normality should take into
Table 3 Relationships between BMI, insulin sensitivity and fasting insulin and various cardiometabolic parameters measured in metabolic syndrome-free and glucose-tolerant normal body weight and
overweight/obese subjects
Model 1 Model 2
BMI Insulin sensitivity Fasting insulin BMI Insulin sensitivity Fasting insulinemia
Coef.
(95% CI)
P-value Coef.
(95% CI)
P-value Coef.
(95% CI)
P-value Coef.
(95% CI)
P-value Coef.
(95% CI)
P-value Coef.
(95% CI)
P-value
Glucose (mmol l
1
) 0.08 (0.001 to 0.159) ns 0.05 (0.13 to 0.03) ns 0.11 (0.03 to 0.19) 0.01 0.07 (0.01 to 0.15) ns 0.10 (0.18 to 0.01) 0.02 0.12 (0.04 to 0.20) 0.007
Total cholesterol (mmol l
1
) 0.22 (0.04 to 0.40) 0.009 0.05 (0.13 to 0.24) ns 0.30 (0.12 to 0.48) 0.001 0.17 (0.01 to 0.35) 0.04 0.06 (0.25 to 0.13) ns 0.28 (0.09 to 0.47) 0.003
HDL-chol. (mmol l
1
)0.11 (0.17 to 0.04) 0.003 0.04 (0.11 to 0.03) ns 0.04 (0.11 to 0.03) ns 0.10 (0.17 to 0.03) 0.007 0.01 (0.09 to 0.06) ns 0.02 (0.09 to 0.06) ns
LDL-chol. (mmol l
1
) 0.29 (0.13 to 0.45) o0.0001 0.07 (0.09 to 0.24) ns 0.28 (0.12 to 0.44) 0.001 0.25 (0.08 to 0.41) 0.003 0.04 (0.21 to 0.12) ns 0.24 (0.07 to 0.41) 0.006
Triglycerides (mmol l
1
) 0.10 (0.03 to 0.16) 0.003 0.04 (0.03 to 0.11) ns 0.14 (0.07 to 0.21) o0.0001 0.078 (0.01 to 0.14) 0.02 0.01 (0.08 to 0.06) ns 0.13 (0.06 to 0.20) 0.001
Systolic BP (mm Hg) 3.60 (1.42 to 5.78) 0.001 0.59 (1.67 to 2.85) ns 0.71 (1.52 to 2.94) ns 3.36 (1.37 to 5.88) 0.002 0.11 (2.46 to 2.25) ns 0.02 (2.35 to 2.31) ns
Diastolic BP (mm Hg) 2.23 (0.66 to 3.79) 0.004 0.42 (1.19 to 2.04) ns 1.07 (0.52 to 2.66) ns 2.12 (0.50 to 3.74) 0.008 0.188 (–1.87 to 1.50) ns 0.68 (0.99 to 2.34) ns
Heart rate (b.p.m.) 0.78 (1.53 to 3.09) ns 0.66 (1.71 to 3.02) ns 5.50 (3.28 to 7.80) o0.00010.25 (2.56 to 2.07) ns 0.97 (3.39 to 1.44) ns 5.86 (3.47 to 8.25) o0.0001
hs-CRP (mg l
1
) 0.64 (0.35 to 0.93) o0.0001 0.34 (0.04 to 0.64) ns 0.27 (0.02 to 0.57) ns 0.58 (0.29 to 0.88) o0.0001 0.20 (0.11 to 0.51) ns 0.108 (0.21 to 0.40) ns
ICA IMT (mm) 0.001 (0.026 to 0.029) ns 0.016 (0.044 to 0.012) ns 0.019 (0.047 to 0.009) ns 0.007 (0.022 to 0.035) ns 0.012 (0.0042 to 0.018) ns 0.016 (0.047 to 0.014) ns
CCA IMT (mm) 0.016 (0.001 to 0.032) 0.04 0.005 (0.012 to 0.021) ns 0.014 (0.003 to 0.030) ns 0.013 (0.003 to 0.030) ns 0.014 (0.019 to 0.016) ns 0.011 (0.006 to 0.029) ns
Abbreviations: BMI, body mass index; BP, blood pressure; CCA IMT, common carotid artery intima–media thickness; CI, confidence interval; Coef., coefficient; hs-CRP, high-sensitivity C-reactive protein;
FFM, fat-free mass; HDL-chol, high-density lipoprotein cholesterol; ICA IMT, internal carotid artery intima–media thickness; LDL-chol, low-density lipoprotein cholesterol; ns, not significant. BMI, fasting
insulin sensitivity and insulin were analyzed, as independent variables, for their relationships with various cardio-metabolic parameters adjusted for age, sex and physical activity. The independent variables
were tested separately in Model 1 and taken together in Model 2.
Obesity and cardiometabolic risk
Z Pataky et al
1212
International Journal of Obesity
account measurements of insulin sensitivity and fasting
insulin levels.
Among the overall RISC ow/ob population, only 65 (that
is, 11%) had a normal glucose tolerance, no metabolic
syndrome, as well as normal insulin sensitivity and fasting
insulinemia, thus fitting the definition of metabolic normal-
ity. Of note, the fact that among nbw subjects also, the
prevalence of metabolic normality is quite low (that is, 37%).
This could be because of several reasons. First, among the
various definitions of the metabolic syndrome, the one
proposed by the IDF has rather restrictive threshold values;
22
second, subjects with impaired glucose tolerance were not
included. Finally, we took into account both fasting
insulinemia and insulin sensitivity that increases the degree
of severity for the selection. The relatively small number of
selected subjects could also have reduced the power of the
statistical analysis and, therefore, explain the less pro-
nounced association between BMI and the various other
cardiometabolic parameters (Table 2).
The prevalence of metabolic normality among obese
subjects is widely variable throughout the studies. For
instance, Kuk et al.
23
report that only 6% of obese population
was insulin sensitive and metabolic syndrome free. In this
study, a sample of 6011 obese from a general population was
investigated, without any exclusion for subjects showing an
overt pathology: this could explain the lower incidence
found by these authors. On the other hand, Wildman et al.
3
and Iacobellis et al.
24
found an prevalence of 31 and 27.5%,
respectively, of metabolically healthy obese subjects, among
general populations. The slight differences in the para-
meters of normality selected (ATP III vs IDF criteria) together
with insulin-sensitivity assessment (homeostasis model
assessment index vs euglycemic–hyperinsulinemic clamp)
could, at least in part, explain the notable difference in
the prevalence of metabolic normality found in our and
theses studies.
Among our study group, 67.4% of ow/ob showed a waist
494/80 cm. For this reason, waist was not taken into
account as an exclusion criteria. A similar approach has
been adopted by several other authors who investigated
metabolic normality in obese subjects.
3,24
Furthermore, a
recent study
25
has clearly shown that an enlarged waist, in
absence of additional risk factors, is not associated to an
increased risk CVD mortality. Nevertheless, it is possible that
a larger depot of visceral fat contributes to the observed
differences between the two BMI classes. To discriminate the
contribution of the fat mass in general vs one of the visceral
fat, subgroups of nbw subjects, with altered waist circumfer-
ence, should be used as a comparison group. The cohort of
our nbw subjects is not large enough to make this analysis, as
only 15.5% of the nbw showed a waist 494/80 cm.
Even when taking into account insulin sensitivity and
fasting insulinemia as criteria of metabolic normality, ow/ob
keep their less favorable cardiometabolic profile in compar-
ison with nbw, as indicated by plasma lipids and blood
pressure values (Table 2).
In addition, multiple regression analysis (Table 3 and
Figure 2), indicates that BMI, alone or in combination with
hyperinsulinemia, exerts a significant influence on the
majority of the measured cardiometabolic parameters. Thus,
the presence of ow and/or ob not only is per se a risk factor
for reduced insulin sensitivity and hyperinsulinemia (Tables
1 and 2) but also for impaired cardiovascular parameters.
Of note, we observed that fasting insulin is significantly
associated with both total and LDL-cholesterol. In a previous
study, Bonora et al.
11
demonstrated an independent con-
tribution of insulin sensitivity and fasting insulin levels to
TG and HDL-cholesterol levels; here, we show that insuline-
mia also affects other lipid parameters. On the other hand,
we could not detect any significant association between
insulin sensitivity and plasma lipids. In the majority of
the cases, hyperinsulinemia compensates insulin resistance,
in the early phase of impaired insulin action. It is possible
that our study population was in an early phase of insulin
resistance, that is, when the effects of hyperinsulinemia are
already present, whereas those of insulin resistance are not
yet detectable.
Working on the overall RISC population, Ferrannini et al.,
16
demonstrated that BMI, waist circumference, insulin sensi-
tivity and insulinemia are the main factors influencing the
cardiovascular risk parameters, but no one seemed to be the
driving force of the cluster. On the contrary, our data indicate
the BMI as the major factor in influencing the cardiometa-
bolic risk parameters. This apparent contradiction could be
explained by the differences in the selected study population.
In fact, the analysis carried out by Ferrannini was about the
overall RISC population, whereas we investigated only glucose
tolerant, metabolic syndrome-free subjects (that is, 62% of the
BMI
HDL-chol.
Systolic BP
Diastolic BP
hs-CRP
Insulin
sensitivity
Total chol.
LDL-chol.
Triglycerides
Heart rate
Fasting
glucose
Fasting insulin
Figure 2 Role of BMI, insulin sensitivity and fasting insulinemia in nbw and
ow/ob subjects free from metabolic syndrome and with a normal glucose
tolerance.
Obesity and cardiometabolic risk
Z Pataky et al
1213
International Journal of Obesity
nbw and 37% of the ow/ob individuals participating in the
RISC study, Figure 1). Our results, therefore, only apply on the
cohort of subjects who, by definition, do not show any
metabolic feature commonly associated to obesity.
It could be possible that, when hyperinsulinemia and,
particularly, insulin resistance are of moderate degree (as it is
the case in our subgroups), they do not have a major role in
impairing the cardiometabolic profile of ow/ob subjects.
However, the duration and/or degradation of insulin resis-
tance and/or fasting insulin levels could progressively
enhance their impact on the cardiometabolic parameters.
Although not confirmed by longitudinal observations, our
results suggest that increased BMI initiates the sequence of
events that link obesity to enhanced cardiometabolic risk.
Several questions remain to be answered to support this
hypothesis: for instance, whether a progressive degradation
does necessarily occur in any ow/ob individuals. Second,
which is the critical degree that insulin resistance has to
reach, to relevantly influence the cardiometabolic risk
factors? Finally, is increased BMI alone enough to induce a
progressive degradation of insulin resistance and/or hyper-
insulinemia or the presence of other factors such as age,
lifestyle and genetic background are needed?
Our results show differences in physical activity between
nbw and ow/ob subjects, thus confirming the well-known
link between excess body weight and sedentarity.
26
Further-
more, a previous RISC study,
27
clearly demonstrated the
influence of physical activity on insulin sensitivity. For this
reason, in our study, the influence of BMI, hyperinsulinemia
and insulin resistance were analyzed after adjustment for
physical activity, in addition to age and sex.
In a recent paper, Messier et al.
28
underline the necessity of
a consensus to standardize the definition of metabolic
normality, in obese subjects. We suggest that such a defi-
nition should be based on an well-established fasting
lipid and glucose values, but should also consider fasting
insulinemia. This is in keeping with the results of a recent
paper from our group demonstrating that fasting insulin has
a stronger association with an adverse cardiometabolic risk
profile than insulin resistance.
12
In conclusion, excess body weight, alone or associated to
fasting hyperinsulinemia, is the major contributor to the
impaired, yet normal, cardiometabolic profile of moderately
obese, metabolically normal subjects, in whom insulin
resistance have only a marginal role.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
The RISC study was supported by the EU grant no. QLG1-CT-
2001-01252 and by an additional grant from AstraZeneca
(Sweden). Locally, the Geneva center received the support
of the Whilsdorf Foundation and of the Swiss Life Insurance
Foundation.
RISC investigators
RISC recruiting centers: Amsterdam, The Netherlands:
RJ Heine, J Dekker, G Nijpels, W Boorsma; Athens, Greece:
A Mitrakou, S Tournis, K Kyriakopoulou, P Thomakos; Belgrade,
Serbia and Montenegro: N Lalic, K Lalic, A Jotic, L Lukic,
M Civcic; Dublin, Ireland: J Nolan, TP Yeow, M Murphy,
C DeLong, G Neary, MP Colgan, M Hatunic; Frankfurt,
Germany: T Konrad, H Bo
¨hles,SFuellert,FBaer,HZuchhold;
Geneva, Switzerland: A Golay, E Harsch Bobbioni,
V Barthassat, V Makoundou, TNO Lehmann, T Merminod;
Glasgow, Scotland: JR Petrie (now Dundee), C Perry, F Neary,
C MacDougall, K Shields, L Malcolm; Kuopio, Finland:
M Laakso, U Salmenniemi, A Aura, R Raisanen, U Ruotsalainen,
T Sistonen, M Laitinen, H Saloranta; London, UK: SW Coppack,
N McIntosh, P Khadobaksh; Lyon, France: M Laville, F Bonnet,
A Brac de la Perriere, C Louche-Pelissier, C Maitrepierre,
J Peyrat, A Serusclat; Madrid, Spain: R Gabriel, EM Sa
´nchez,
R Carraro, A Friera, B Novella; Malmo
¨,Sweden(1):PNilsson,
M Persson, G O
¨stling; (2): O Melander, P Burri; Milan, Italy:
PM Piatti, LD Monti, E Setola, E Galluccio, F Minicucci,
A Colleluori; Newcastle-upon-Tyne, UK: M Walker, IM Ibrahim,
M Jayapaul, D Carman, K Short, Y McGrady, D Richardson;
Odense, Denmark: H Beck-Nielsen, P Staehr, K Hojlund,
V Vestergaard, C Olsen, L Hansen; Perugia, Italy: GB Bolli,
F Porcellati, C Fanelli, P Lucidi, F Calcinaro, A Saturni; Pisa,
Italy: E Ferrannini, A Natali, E Muscelli, S Pinnola, M Kozakova;
Rome, Italy: G Mingrone, C Guidone, A Favuzzi, P Di Rocco;
Vienna,Austria:CAnderwald,MBischof,MPromintzer,
MKrebs,MMandl,AHofer,ALuger,WWaldha
¨usl, M Roden.
Project management board: B Balkau (Villejuif, France),
SW Coppack (London, UK), JM Dekker (Amsterdam, The
Netherlands), E Ferrannini (Pisa, Italy), A Mari (Padova,
Italy), A Natali (Pisa, Italy) and M Walker (Newcastle, UK).
Core laboratories and reading centers: lipids, Dublin, Ireland:
PGaffney,JNolan,GBoran;hormones,Odense,Denmark:
C Olsen, L Hansen, H Beck-Nielsen; albumin/creatinine,
Amsterdam, The Netherlands: A Kok, J Dekker; genetics,
Newcastle-upon-Tyne, UK: S Patel, M Walker; stable isotope
laboratory, Pisa, Italy: A Gastaldelli, D Ciociaro.
Ultrasound reading center: Pisa, Italy: M Kozakova; ECG
reading, Villejuif, France: MT Guillanneuf; data manage-
ment, Villejuif, France: B Balkau, L Mhamdi; mathematical
modeling and website management, Padova, Italy: A Mari,
G Pacini, C Cavaggion; coordinating office, Pisa, Italy: SA
Hills, L Landucci, L Mota.
Further information on the RISC Study and participating
centers can be found at http://www.egir.org.
All the co-authors had full access to all data in the study
and take responsibility for the integrity of the data and the
accuracy of the data analysis.
Obesity and cardiometabolic risk
Z Pataky et al
1214
International Journal of Obesity
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