ORIGINAL
ARTICLE
E n d o c r i n e
C a r e
Body Composition and Common Carotid Artery
Remodeling in a Healthy Population
Michaela Kozakova, Carlo Palombo, Marco Paterni, Christian-Heinz Anderwald, Thomas Konrad,
Mary-Paula Colgan, Allan Flyvbjerg, and Jacqueline Dekker, on behalf of the Relationship between
Insulin Sensitivity and Cardiovascular risk Investigators*
Department of Internal Medicine (M.K., C.P.), University of Pisa, 56126 Pisa, Italy; Department of Technosciences for Medical Application
and Research (M.P.), Consiglio Nazionale delle Ricerche Institute of Clinical Physiology, 56124 Pisa, Italy; Division of Endocrinology and
Metabolism (C.-H.A.), Department of Internal Medicine III, Medical University of Vienna, A-1090 Vienna, Austria; Institute fur
Stoffwechselforschung (T.K.), D-60327 Frankfurt, Germany; St. James’s Hospital (M.-P.C.), Dublin, 8 Ireland; The Medical Research
Laboratories (A.F.), Clinical Institute and Medical Department M (Diabetes and Endocrinology), Aarhus University Hospital, DK-8000
Aarhus, Denmark; and Institute for Research in Extramural Medicine Institute (J.D.), Vrije University, 1081 BT Amsterdam, The
Netherlands
Context: An independent association between obesity and preclinical carotid atherosclerosis has been
demonstrated, however, the pathophysiological links were not clearly established. Body composition
(BC) influences systemic hemodynamics and may participate in the remodeling of common carotid artery
(CCA), independently of risk factors.
Objective: This study evaluated the association between CCA structure and BC in a large population of
healthy subjects.
Design: This was a cross-sectional study.
Settings: The study was conducted at 19 European centers.
Subjects: The study included 627 healthy subjects (252 men, age 30–60 yr, body mass index 17– 40 kg/m2).
Main Outcome Measures: CCA luminal diameter and intima-media thickness were measured on digitized
ultrasound images. Acoustic properties of CCA wall were evaluated by digital densitometric analysis and
described in terms of mean gray level. BC was assessed by electrical bioimpedance. Insulin sensitivity (euglycemic hyperinsulinemic clamp) and plasma adiponectin levels were measured. Associations between CCA
structure, age, BC, and metabolic and atherosclerotic risk factors were analyzed by multivariate regression
models.
Results: Independent factors affecting CCA diameter were fat-free mass and waist girth (standardized r ⫽ 0.44
and 0.12; P ⬍ 0.01 and ⬍ 0.0001; R2 ⫽ 0.35); independent correlates of intima-media thickness were age, CCA
diameter, systolic blood pressure, and low-density lipoprotein-cholesterol (standardized r ⫽ 0.39, 0.25, 0.10, and
0.14; P ⬍ 0.005–0.0001; R2 ⫽ 0.40). The mean gray level of carotid wall was independently associated with age
and waist girth (standardized r ⫽ 0.23 and 0.12; P ⬍ 0.0001 and ⫽ 0.001; R2 ⫽ 0.30).
Conclusions: Findings of this cross-sectional study suggest that BC modulates CCA diameter, and may
induce adaptive changes in carotid wall thickness, independently of metabolic and atherosclerotic factors. Central adiposity modifies the acoustic properties of carotid wall. (J Clin Endocrinol Metab 93:
3325–3332, 2008)
n increase in intima-media thickness (IMT) of the common
carotid artery (CCA) is considered a surrogate marker of
subclinical atherosclerosis because relationships between IMT,
A
traditional cardiovascular risk factors, and clinically manifest
cardiovascular disease (CVD) have been clearly demonstrated
(1, 2). However, a growing body of evidence indicates that CCA
0021-972X/08/$15.00/0
Abbreviations: BMI, Body mass index; BP, blood pressure; CCA, common carotid artery;
CHD, coronary heart disease; CVD, cardiovascular disease; DBP, diastolic blood pressure;
FFM, fat-free mass; HDL, high-density lipoprotein; IMT, intima-media thickness; LDL, lowdensity lipoprotein; MGL, mean gray level; M/I, index of insulin sensitivity; OGTT, oral
glucose tolerance test; RISC, Relationship between Insulin Sensitivity and Cardiovascular
risk; ROI, region of interest.
Printed in U.S.A.
Copyright © 2008 by The Endocrine Society
doi: 10.1210/jc.2007-2484 Received November 8, 2007. Accepted June 25, 2008.
First Published Online July 1, 2008
* For a list of members of the Relationship between Insulin Sensitivity and Cardiovascular
risk Investigators, see Acknowledgments.
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
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Carotid Artery and Body Composition
IMT below certain levels (3) may not reflect atherosclerotic disease, but vascular aging (4, 5) and adaptive response to hemodynamic changes leading to alterations in shear and/or tensile
stress (3, 6, 7). Shear and tensile stresses are interrelated, and the
geometrical characteristics of arterial remodeling depend on the
type of hemodynamic stimuli applied on the vessel (6, 8).
Previous clinical observations have shown independent associations of obesity and abdominal adiposity with carotid IMT
(9 –11). The mechanisms by which the obesity is linked to early
carotid atherosclerosis are not clearly established, even though
the role of metabolic factors, like insulin resistance (12, 13), and
altered plasma adiponectin levels (10, 13) have been proposed.
Yet, body composition and fat distribution may influence systemic hemodynamics, both systemic blood pressure (BP) (11)
and total blood volume. Several studies have demonstrated that
total blood volume, stroke volume, and cardiac output increase
with body size (14 –16), central fat distribution (16, 17), and,
above all, with metabolically active fat-free mass (FFM) (14 –16)
that may account for as much as 50% of stroke volume variance
(15). Chronic increase in stroke volume results in a proportional
increase in systolic flow and flow velocity in large arteries (such
as CCA), and can be expected to induce a relative increase in their
luminal diameter (6, 7) aimed to restore the shear stress to normal levels (6). At a given BP, an increase in luminal diameter may
be followed by an increase in wall thickness, i.e. IMT, meant to
normalize tensile stress that is directly proportional to intraarterial pressure and arterial diameter, and inversely to arterial
wall thickness (3, 7, 8).
Based on these observations, a hypothesis can be raised that
carotid remodeling and a certain degree of intima-media thickening can represent a physiological adaptation to hemodynamic
pattern related to body composition. To test this hypothesis, we
examined the associations between CCA structure (luminal diameter and IMT), body composition, and metabolic factors
(including insulin sensitivity measured by a euglycemic hyperinsulinemic clamp and plasma adiponectin levels) in a large population of healthy subjects with low to average cardiovascular
risk, without plaques in extracranial carotid arteries and with
CCA IMT less than or equal to 0.9 mm, i.e. below the value that
is considered indicative for atherosclerotic disease (3). Such a
selected study group, though not representative of the general
population, allows evaluating the effect of body composition on
carotid remodeling without influences of confounding variables.
In addition, acoustic properties of the carotid wall, which are
supposed to reflect the structural composition of the vessel (18, 19),
were evaluated by digital densitometric analysis (18, 20).
Subjects and Methods
The study population is a part of the Relationship between Insulin Sensitivity and Cardiovascular risk (RISC) study cohort (www.egir.org).
The design of the RISC study has been reported elsewhere (21). Briefly,
between June 2002 and July 2004, more than 1400 apparently healthy
Caucasian subjects were recruited in 19 centers in 14 European countries. All recruited subjects were between 30 and 60 yr, and their BP
(⬍140/⬍90 mm Hg), plasma cholesterol (⬍7.8 mmol/liter), triglycerides
(⬍4.6 mmol/liter), and fasting and 2-h glucose (⬍7.0 and 11.1 mmol/
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
liter, respectively) were within normal limits. Exclusion criteria were the
presence of chronic diseases, overt CVD, weight change of 5 kg or more
in the last 6 months, carotid stenosis more than 40%, and treatment for
hypertension, obesity, diabetes, or dyslipidemia. Local ethics committee
approval was obtained by each center, and written consent was obtained
from all participants.
Protocol
All participants underwent a standardized examination that included
interviews, BP measurements, resting electrocardiogram, anthropometry, a fasting blood draw, an oral glucose tolerance test (OGTT), a euglycemic hyperinsulinemic clamp and high-resolution ultrasound of extracranial carotid arteries. Information regarding medical history, drug
use, and alcohol and cigarette consumption was collected using a standardized questionnaire. Menopause was defined as menses cessation for
at least 12 consecutive months. Clinical CVD was excluded on the basis
of medical history and resting electrocardiogram. Brachial BP and resting
heart rate were measured three times with a digital electronic tensiometer
(model 705CP; Omron, Kyoto, Japan; regular or large adult cuffs were
used according to arm circumference) and with the subject seated for at
least 10 min. The mean value was used in statistical analysis.
Body composition assessment
Height was measured on a clinic stadiometer. Body weight and FFM
were measured by electrical bioimpedance using a Body Composition
Analyzer Model TB-300 (Tanita Corp., Tokyo, Japan) (22); fat mass was
then obtained as the difference between body weight and FFM. Waist
circumference was measured as the narrowest circumference between the
lower rib margin and anterior superior iliac crest.
OGTT
After 3 d of a 250-g carbohydrate diet and after an overnight fast,
glucose tolerance was assessed by a 2-h, 75-g OGTT. At baseline and at
30-min intervals thereafter, blood samples were obtained for glucose and
insulin determination. Areas under OGTT time-concentration curves
were calculated by the trapezoidal rule.
Insulin sensitivity
On a separate day (within 1 wk of the OGTT), a euglycemic hyperinsulinemic clamp was performed in all subjects following a previously
described procedure standardized across centers (21). Insulin sensitivity
was expressed as the ratio of the M value (averaged over the final 40 min
of the 2-h clamp and normalized by FFM) to the mean plasma insulin
concentration measured during the same interval [index of insulin sensitivity (M/I), ␮mol/min/kgffm/nM] (23).
Analytical procedures
Plasma glucose was measured by the glucose oxidase technique
(Glucose Analyzer; Beckman Coulter, Inc., Fullerton, CA). Serum concentrations of insulin were measured by RIA using a kit specific for
human insulin (LINCO Research, Inc., St. Louis, MO). Serum total and
high-density lipoprotein (HDL)-cholesterol and triglycerides were assayed by standard methods. Total adiponectin was measured in plasma
using a validated in-house time-resolved immunofluorimetric assay, as
previously described (24).
Carotid artery ultrasound imaging
High-resolution B-mode ultrasound of extracranial carotid arteries
was performed bilaterally, according to a previously described protocol
(1). In the RISC study, the carotid images were obtained in each recruiting center by trained and certified technicians following a standardized
protocol (21) and using high-resolution ultrasound scanners, all with a
7.5 or 10.0 MHz linear-array transducer.
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
Carotid artery ultrasound analysis
Carotid images were analyzed in a centralized reading center (Pisa),
by a single reader (M.K.) blinded to clinical data, using a high-resolution
video recorder (Panasonic AG-MD830; Matsushita Electric Industrial
Co., Ltd., Osaka, Japan) coupled with the computer-driven image analysis system Medical Image Processing (Institute of Clinical Physiology,
Consiglio Nazionale delle Ricerche, Pisa, Italy) (20). For the purpose of
this study, end-diastolic unzoomed frames of the left CCA in longitudinal
projection with well-defined intima-media complex of the near and far
wall were selected. Selected images were digitized with a resolution of
576 ⫻ 768 pixels and 256-degree gray scale per pixel. In the digitized
image, a segment of adequate length (⬃20 mm) was selected before the
flow divider. In this CCA segment, the following measurements and
analyses were performed: 1) the far-wall IMT, 2) the luminal diameter,
and 3) a digital densitometry of the far-wall intima-media complex. The
far-wall IMT was measured manually at five measurement points; the
value used for statistical analysis represents an average of five measurements. The minimum luminal diameter was measured manually as the
distance between the lumen-intima interfaces of the near and far wall (3)
at five measurement points; the diameter used for statistical analysis
represents an average of five measurements. The wall tensile stress in
CCA at end diastole was estimated by the product between diastolic BP
(DBP) and the ratio of lumen radius (r ⫽ diameter/2) and wall thickness
(wall tensile stress ⫽ DBP r/IMT, kPa) (25).
Digital densitometric analysis was performed by specifically developed and validated software (Institute of Clinical Physiology, Consiglio
Nazionale delle Ricerche, Pisa, Italy) (20). A region of interest (ROI),
including the intima-media complex of the far wall, was selected. Within
the ROI, digitized images were analyzed by first-order statistical analysis
that generates a histogram representing the frequency distribution of
gray levels of pixels by plotting the gray values on the abscissa and the
frequency of the occurrence on the ordinate. The histogram was described in terms of average pixel intensity, i.e. mean gray level (MGL). To
adjust for different ultrasound attenuation and different gain settings in
different study subjects, two calibration steps were introduced into the
analysis of each subject. The effect of gain setting was restrained by
calibrating the gray level amplitude of the ROI against vessel lumen
(blood) taken as the blank (mean gray value ⫽ 0), whereas the effects of
imaging depth and attenuation were minimized by calibration against an
internal reference represented by the adventitia (MGL ⫽ 160) (20, 26).
Densitometric analysis used in this study was previously validated
against histological analysis of intimal lesions (20) and against integrated
backscatter analysis of the carotid wall (27).
Intraobserver variability of all measurements was tested in 60 randomly chosen scans. The mean differences between two readings were
4.4 ⫾ 2.6%, 3.7 ⫾ 2.1%, and 6.8 ⫾ 4.3% for CCA IMT, luminal
diameter, and MGL, respectively.
Stroke volume assessment
To address the contribution of stroke volume to vascular remodeling,
a complete transthoracic Doppler echocardiography was performed for
the participants of the recruitment center in Pisa (n ⫽ 75), and stroke
volume was calculated as the product of aortic valve cross-sectional area
and trans-aortic flow velocity-time integral (16). Trans-aortic flow was
obtained in the apical projection, aortic valve opening was measured in
the long-axis view, and aortic valve area was calculated by circular
geometry. A single reader (M.K.) performed all measurements, and values used for calculation represent an average of five consecutive cardiac
beats. Intraobserver variability of stroke volume measurement (tested in
30 randomly selected recordings) was 4.7 ⫾ 3.1%.
Statistical analysis
Quantitative data are expressed as mean ⫾ SD and categorical data as
percentages. Skewed variables are given as median and (interquartile
range), and were log transformed for statistical analyses. Relations
among the outcome variables (CCA diameter, IMT, and MGL) and continuous variables were evaluated by univariate Pearson correlation co-
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efficients. Multiple regression (or multiple logistical regression) analysis
with center adjustment was then used to study the independence of the
association of continuous variables (or nominal variables) with variables
that did not exhibit excessive colinearity with each other. Statistical significance was set at a value of P ⬍ 0.05. Statistical analysis was performed
by JMP software, version 3.1 (SAS Institute Inc., Cary, NC).
Results
Characteristics of study population
In the present study, we selected 627 subjects who met the
following criteria: 1) an individual 10-yr coronary heart disease
(CHD) risk, estimated from the Framingham Heart Study prediction score sheet, not higher than average as indicated for the
corresponding sex and age (28); 2) IMT of the left CCA 0.90 mm
or less and IMT in any carotid segment less than 2.0 mm; and 3)
left CCA near- and far-wall intima-media complex clearly visualized over the segment of adequate length (⬃20 mm). Clinical
characteristics and CCA parameters for the whole study population and for men and women are shown in Table 1. The 10-yr
CHD risk as estimated from the Framingham prediction score
sheet was low, below average, and average in 77, 18, and 5% of
the study population, respectively.
CCA ultrasound measures, age, body composition, BP,
and metabolic parameters
Univariate correlates between CCA ultrasound measures
and age, body composition, BP, and metabolic parameters are
shown in Table 2. No relationships were observed between
CCA diameter and post-load plasma glucose or insulin concentrations (at any time point during the OGTT or as areas
under the respective OGTT curves). CCA IMT increased only
with glucose OGTT area (r ⫽ 0.15; P ⬍ 0.001). Table 2 also
shows the relationships among body composition, BP, and metabolic parameters.
To assess whether any of the variables that showed a significant association with CCA measures in univariate analysis contributed independently to the variability of these measures, multiple regression analyses were performed, entering standardized
diameter, IMT, and MGL as dependent variables and their significant correlates (Table 2) as independent variables. All analyses were adjusted for center, sex, and smoking habit, and in
women for menopausal status. Independent correlates of CCA
diameter were FFM and waist girth, together explaining 35% of
diameter variability (Table 3). The inclusion of sex, which correlated highly with FFM (r ⫽ 0.87; P ⬍ 0.0001), did not cancel anthropometric measures in the regression model (␤ ⫾ SE for sex, FFM and
waist ⫽ 29 ⫾ 7, 17 ⫾ 7 and 15 ⫾ 5; P ⬍ 0.05–0.0001; R2⫽0.36).
Neither body mass index (BMI) nor any metabolic parameter improved the regression model or replaced/canceled FFM in the model.
When divided according to sex, independent predictors of CCA diameterwereage,FFM,andwaistgirthinmen,andmenopausalstatusand
FFM in women.
Independent factors affecting far-wall IMT were age, luminal
diameter, low-density lipoprotein (LDL)-cholesterol, and systolic BP accounting for 40% of IMT variance (Table 3, model 1).
When diameter was not included in the model, independent cor-
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Carotid Artery and Body Composition
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
TABLE 1. Characteristics of the study population
No.
Age (yr)
Weight (kg)
FFM (kg)
Fat mass (kg)
BMI (kg/m2)
Waist girth (cm)
Systolic BP (mm Hg)
DBP (mm Hg)
Mean BP (mm Hg)
HDL-cholesterol (mmol/liter)
LDL-cholesterol (mmol/liter)
Triglycerides (mmol/liter)b
Fasting glucose (mmol/liter)
Fasting insulin (pmol/liter)b
Glucose area (mol/liter per 2 h)b
Insulin area (nmol/liter per 2 h)b
M/I value (␮mol/min/kgFFM/nM)b
Adiponectin (mg/liter)b
Alcohol consumption(g/wk)
Smoking (never:current:ex) (%)
Menopause no:yes:yes plus HRT (%)
10-yr CHD risk (%)
CCA luminal diameter (mm)
CCA far-wall IMT (␮m)
CCA wall tensile stress (kPa)
All subjects
Men
Women
627
44 ⫾ 8
72.2 ⫾ 13.4
52.4 ⫾ 11.1
19.9 ⫾ 8.2
24.8 ⫾ 3.6
84.2 ⫾ 11.8
116 ⫾ 12
74 ⫾ 8
88 ⫾ 9
1.50 ⫾ 0.38
2.83 ⫾ 0.75
0.88 (0.50)
5.1 ⫾ 0.6
28 (21)
0.80 (0.23)
22.9 (16.3)
145 (89)
8.2 (4.8)
75 ⫾ 87
50:23:27
252
44 ⫾ 8
81.2 ⫾ 10.9
64.1 ⫾ 7.6
17.1 ⫾ 7.0
25.6 ⫾ 2.9
91.2 ⫾ 9.4
122 ⫾ 10
76 ⫾ 8
91 ⫾ 8
1.30 ⫾ 0.29
3.02 ⫾ 0.72
1.01 (0.63)
5.3 ⫾ 0.5
29 (21)
0.85 (0.21)
23.8 (16.5)
124 (93)
6.6 (3.5)
108 ⫾ 107
54:19:27
3.2 ⫾ 2.5
5.65 ⫾ 0.68
593 ⫾ 84
47.5 ⫾ 8.8
4.8 ⫾ 2.8
6.07 ⫾ 0.65
612 ⫾ 86
50.8 ⫾ 9.2
375
44 ⫾ 8
66.2 ⫾ 11.3a
44.5 ⫾ 4.2a
21.6 ⫾ 8.4a
24.2 ⫾ 3.9a
79.5 ⫾ 10.9a
113 ⫾ 12a
72 ⫾ 8a
86 ⫾ 9a
1.63 ⫾ 0.37a
2.71 ⫾ 0.75a
0.81 (0.42)a
5.0 ⫾ 0.6a
27 (19)
0.76 (0.22)a
22.7 (16.4)
157 (87)a
9.5 (4.9)a
52 ⫾ 62a
47:25:28
82:12:6
2.1 ⫾ 2.6a
5.38 ⫾ 0.52a
579 ⫾ 80a
45.0 ⫾ 7.6a
HRT, Hormone replacement therapy.
a
Men vs. women P ⬍ 0.0001.
b
Median and (interquartile range).
relates of IMT were age, FFM, LDL-cholesterol, and systolic BP
(Table 3, model 2). BMI, fat mass, and waist girth did not replace/
cancel FFM in the model. When multivariate analyses were performed separately for men and women, luminal diameter, but
not FFM, was independently related to CCA IMT.
In the whole study population and in women, MGL of carotid
wall was independently related to age and waist girth, whereas
in men MGL was related only to age (Table 3). IMT was not an
independent predictor of carotid wall MGL.
To minimize the effect of the statistical distribution of variables, the associations between CCA diameter or wall thickness
and age, body composition, BP, and metabolic variables were
TABLE 2. Matrix of univariate correlations between CCA structural characteristics and age, office BP, body composition,
and metabolic parameters
IMT Diameter MGL Age
IMT
Diameter
MGL
Age
SBP
FFM
FM
BMI
Waist
LDL
HDL
TGa
FPG
FPIa
M/I valuea
Adiponectina
1.0
0.30
1.0
SBP
FFM
FM
0.24 0.47 0.27
0.19 0.14
0.11 NS 0.24
0.51 NS
1.0
0.33 0.14
NS
NS
1.0 0.16 ⫺0.14 0.15
1.0
0.39 0.13
1.0
NS
1.0
BMI Waist LDL
0.17
0.21
NS
NS
0.32
0.40
0.79
1.0
0.27
0.37
0.23
0.12
0.39
0.62
0.53
0.76
1.0
0.31
NS
0.17
0.31
0.13
0.15
NS
0.16
0.27
1.0
HDL
TGa
FPG
FPIa
NS
0.17
0.17
0.11
⫺0.20
0.15
0.23
NS
NS
0.12
0.12
NS
0.11
NS
0.20
NS
⫺0.15
0.23
0.22
0.15
⫺0.47
0.29
0.25
0.16
⫺0.15
0.18
0.16
0.48
⫺0.36
0.29
0.27
0.53
⫺0.48
0.39
0.35
0.49
⫺0.22
0.33
0.13
0.17
1.0 ⫺0.42 ⫺0.13 ⫺0.33
1.0
0.21
0.35
1.0
0.33
1.0
FM, Fat mass; FPG, fasting plasma glucose; FPI, fasting plasma insulin; NS, not significant; SBP, systolic BP; TG, plasma triglycerides.
a
Log-transformed variable.
M/I
valuea
⫺0.12
NS
NS
NS
⫺0.14
⫺0.27
⫺0.29
⫺0.40
⫺0.40
⫺0.18
0.36
⫺0.31
NS
⫺0.45
1.0
Adiponectina
NS
⫺0.15
NS
0.12
⫺0.19
⫺0.48
NS
⫺0.30
⫺0.43
⫺0.16
0.53
⫺0.37
⫺0.23
⫺0.36
0.38
1.0
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TABLE 3. Independent correlates of CCA diameter, wall thickness, and acoustic characteristics in the whole study population
(n ⫽ 627): multivariate analyses
All subjects
␤ⴞ
Luminal diameter (mm)
Menopause (yes)
Age (yr)
FFM (kg)
Waist girth (cm)
Cumulative R2
SE
Men
a
(P values)
0.44 ⫾ 0.04 (⬍0.0001)
0.12 ⫾ 0.05 (⬍0.01)
0.35 (⬍0.0001)
Far-wall IMT (␮m)
Model 1
Age (yr)
Luminal diameter (mm)
SBP (mm Hg)
LDL (mmol/liter)
Cumulative R2
Model 2
Age (yr)
FFM (kg)
SBP (mm Hg)
LDL (mmol/liter)
Cumulative R2
0.41 ⫾ 0.04 (⬍0.0001)
0.15 ⫾ 0.04 (⬍0.0005)
0.11 ⫾ 0.04 (⬍0.005)
0.13 ⫾ 0.04 (⬍0.0005)
0.36 (⬍0.0001)
MGL of IMT
Age (yr)
Waist girth (cm)
Cumulative R2
0.23 ⫾ 0.04 (⬍0.0001)
0.12 ⫾ 0.04 (0.001)
0.30 (⬍0.0001)
0.39 ⫾ 0.03 (⬍0.0001)
0.25 ⫾ 0.03 (⬍0.0001)
0.10 ⫾ 0.03 (⬍0.005)
0.14 ⫾ 0.03 (⬍0.0001)
0.40 (⬍0.0001)
␤ⴞ
SE
Women
a
(P values)
0.12 ⫾ 0.06 (0.05)
0.15 ⫾ 0.07 (⬍0.05)
0.15 ⫾ 0.07 (⬍0.05)
0.24 (⬍0.0001)
0.35 ⫾ 0.06 (⬍0.0001)
0.27 ⫾ 0.06 (⬍0.0001)
␤ⴞ
SE
(P values)a
0.25 ⫾ 0.12 (⬍0.05)
0.15 ⫾ 0.05 (⬍0.005)
0.12 ( 0.0004)
0.20 ⫾ 0.06 (⬍0.001)
0.37 (⬍0.0001)
0.42 ⫾ 0.05 (⬍0.0001)
0.19 ⫾ 0.04 (⬍0.0001)
0.12 ⫾ 0.04 (0.005)
0.11 ⫾ 0.04 (0.01)
0.41 (⬍0.0001)
0.38 ⫾ 0.06 (⬍0.0001)
0.42 ⫾ 0.05 (⬍0.0001)
0.18 ⫾ 0.05 (⬍0.005)
0.32 (⬍0.0001)
0.14 ⫾ 0.04 (⬍0.005)
0.09 ⫾ 0.04 (⬍0.05)
0.38 (⬍0.0001)
0.26 ⫾ 0.06 (⬍0.0001)
0.31 (⬍0.0001)
0.22 ⫾ 0.05 (⬍0.0001)
0.11 ⫾ 0.05 (⬍0.05)
0.31(⬍0.0001)
Menopause, Menopause without hormone replacement therapy; SBP, systolic BP.
a
␤ ⴝ standardized regression coefficient.
cantly associated with a CCA diameter value in the upper quartile of the population (R2 ⫽ 0.27) (Fig. 1), and with age, luminal
diameter, systolic BP, and LDL-cholesterol significantly associated
with an IMT value in the upper quartile of distribution (R2 ⫽ 0.29).
Figure 2 shows the distribution of CCA
wall tensile stress by quartiles of luminal diameter. After adjustment for sex and age, significant differences in tensile stress were observed between the quartiles of diameter.
The percent increase in tensile stress between
the first and fourth quartiles was 27%.
In a subgroup of 75 subjects with Doppler
echocardiography (37 men, mean age ⫽ 44 ⫾
8 yr, BMI ⫽ 26.5 ⫾ 4.6 kg/m2, FFM ⫽ 54.8 ⫾
11.7 kg, fat mass ⫽ 22.7 ⫾ 9.8 kg, waist
girth ⫽ 89.3 ⫾ 12.9 cm, and LDL-cholesterol ⫽ 2.93 ⫾ 0.72 mmol/liter), stroke volume (77 ⫾ 12 ml) was directly related to BMI,
FFM, and waist girth (r ⫽ 0.34, 0.57, and
0.45, P at least ⬍0.005), but not to fat mass.
By multivariate analysis, independent predictors of stroke volume were age, FFM, and
heart rate accounting for 44% of stroke volume variability (Table 4). Independent predictors of CCA luminal diameter (5.67 ⫾ 0.53
FIG. 1. A, Multiple logistical model for the dependence of CCA luminal diameter (as upper quartile of its
mm) were stroke volume, waist girth, and
distribution) on age, FFM, and mean BP. Odds ratios are calculated for 1 SD of the independent variables. B,
mean BP (Table 4, model 1), explaining 52%
Multiple logistical model for the dependence of CCA IMT (as upper quartile of its distribution) on age, CCA
of diameter variance. When stroke volume
diameter, systolic BP, and LDL-cholesterol. Odds ratios are calculated for 1 SD of the independent variables.
also assessed by logistical regression using the highest quartile of
CCA luminal diameter (ⱖ6.1 mm, mean 6.58) or IMT (ⱖ660
␮m, mean 726) as dependent variables. The models yielded a
similar set of predictors, with age, FFM and mean BP signifi-
3330
Kozakova et al.
Carotid Artery and Body Composition
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
Discussion
FIG. 2. Distribution of CCA wall tensile stress by increasing quartiles (from left to
right) of CCA diameter. Values are mean (SD). *, P ⬍ 0.001; †, P ⬍ 0.0001.
was not included in the model, independent predictors of diameter
were FFM, waist girth, and mean BP (model 2). Independent correlates of CCA wall thickness (637 ⫾ 88 ␮m) were similar as those
in the whole study population (Tables 3 and 4).
TABLE 4. Independent correlates of stroke volume, CCA
diameter, and wall thickness in the subgroup with Doppler
echocardiography (n ⫽ 75): multivariate analysis
␤ⴞ
Stroke volume
Age (yr)
FFM (kg)
Heart rate (bpm)
Cumulative R2
Luminal diameter
Model 1
Stroke volume (ml)
Waist girth (cm)
Mean BP (mm Hg)
Cumulative R2
Model 2
FFM (kg)
Waist girth (cm)
Mean BP (mm Hg)
Cumulative R2
Far-wall IMT
Model 1
Age (yr)
Luminal diameter (mm)
LDL (mmol/liter)
Cumulative R2
Model 2
Age (yr)
FFM (kg)
LDL (mmol/liter)
Cumulative R2
bpm, Beats per minute.
a
␤ ⴝ standardized regression coefficient.
SE
a
P value
0.17 ⫾ 0.09
0.55 ⫾ 0.09
⫺0.26 ⫾ 0.09
0.44
0.05
⬍0.0001
⬍0.01
⬍0.0001
0.43 ⫾ 0.09
0.31 ⫾ 0.10
0.23 ⫾ 0.09
0.52
⬍0.0001
⬍0.001
0.01
⬍0.0001
0.35 ⫾ 0.12
0.27 ⫾ 0.13
0.21 ⫾ 0.10
0.44
⬍0.01
⬍0.05
⬍0.05
⬍0.0001
0.41 ⫾ 0.09
0.36 ⫾ 0.09
0.20 ⫾ 0.08
0.51
⬍0.0001
⬍0.0005
⬍0.05
⬍0.0001
0.50 ⫾ 0.08
0.43 ⫾ 0.08
0.18 ⫾ 0.08
0.57
⬍0.0001
⬍0.0001
⬍0.05
⬍0.0001
The results of this cross-sectional study suggest that in a healthy
population with low to average cardiovascular risk, body composition, mainly FFM, may influence CCA luminal diameter and
consequently also IMT, independently of age, established atherosclerotic risk factors, and metabolic factors, like insulin resistance and plasma adiponectin levels. Furthermore, central adiposity seems to alter the acoustic properties of carotid wall.
In our healthy subjects, FFM and waist girth were the only
independent correlates of carotid diameter, and FFM remained
independently related to luminal diameter when multivariate
analysis was performed separately for men and women. Furthermore, in a subgroup of subjects who also underwent Doppler
echocardiography (12% of study population), FFM was the
strongest determinant of stroke volume, which together with
waist girth was independently related to CCA diameter. Altogether these findings support the hypothesis that body composition modulates carotid luminal diameter through changes in
stroke volume, and suggest that differences in body composition
may contribute to well-known differences in CCA diameter between men and women (29). It is also noteworthy that in our
healthy middle-aged population, an independent effect of age on
carotid luminal diameter was observed only in men, but not
women. Age-dependent increase in carotid diameter can reflect
age-associated changes in carotid wall structure (alterations in
the content and integrity of the structural matrix proteins,
namely elastin and collagen) (30) that might be influenced by
endogenous estrogen levels (31). Indeed, a high percentage of our
women (82%) were in the reproductive phase, and menopause
was independently and positively related to CCA diameter.
In our study, as well as in several other studies (3, 25), a
positive independent association was demonstrated between
CCA luminal diameter and IMT. This association indicates the
mutual adaptation between luminal diameter and wall thickness,
and suggests that the FFM-related CCA luminal enlargement
might induce a certain degree of wall thickening. However, it
seems that an increase in wall thickness was not sufficient to
maintain wall tensile stress constant because tensile stress increased with increasing luminal diameter (Fig. 2). Interestingly,
the increase in tensile stress between the first and fourth quartiles
of diameter (27%) was similar to that observed by Chironi et al.
(25) in 394 normotensive subjects (28%).
Besides the luminal diameter, independent predictors of
CCA IMT were age, LDL-cholesterol, and systolic BP, an observation implying that carotid wall thickness represents a
product of vascular aging, physiological adaptation to hemodynamic stimuli, and early atherosclerotic alterations. Therefore, the growing evidence showing the relationship between
IMT and obesity (9 –11, 32) does not merely indicate early
atherosclerosis but may also reflect an adaptive arterial remodeling, as in obese individuals with increasing weight increases BP (11) and FFM, which may account for 20 – 40% of
the weight excess (16, 33).
Remodeling of carotid arteries is accompanied by changes in
the wall composition (7) that modify the interaction between
propagating ultrasound beam and arterial wall, leading to
J Clin Endocrinol Metab, September 2008, 93(9):3325–3332
changes in tissue acoustic reflectivity. Acoustic properties of tissues can be quantitatively evaluated by digital densitometric
analysis, which has identified the predominant cellular composition of atherosclerotic lesions (18, 20). We have previously
demonstrated (using the same densitometric analysis as in this
study) (20) that MGL (or acoustic reflectivity) of initial atherosclerotic lesions is positively related to the smooth muscle cell
content. In the present study, the MGL of the intima-media complex increased with age and waist girth. A positive association
between age and acoustic reflectivity of carotid wall has been
described by other authors (19, 34), and is supposed to mirror the
age-related increase in smooth muscle cells (35) and collagen
fibers within carotid media (19). A positive association between
intima-media MGL and waist girth may be related to the endocrine activity of adipose tissue (36), which expresses (together
with other bioactive peptides) angiotensin II. Angiotensin II
plays an important role in vascular remodeling because it enhances vascular protein synthesis and stimulates smooth muscle
cell growth (37). Furthermore, in elastic-type arteries like the
aorta, angiotensin II induces a shift of vascular smooth muscle
cells to synthetic phenotype, increases the deposition of fibrillar
collagen in tunica media (38), and modulates the expression and
regulation of matrix metalloproteinases (39). These changes can
be expected to modify the acoustic properties of vascular wall,
and indeed, the collagen content in carotid media has influenced
its acoustic reflectivity (19).
Study limitations
The design of the RISC study, a multicenter European study,
allows investigations in a large and well-characterized healthy
population. However, due to the large number of recruiting centers, only standard B-mode ultrasound scanners were used for
carotid imaging, which brought about several limitations. First,
carotid diameter was measured in conventional B-mode images
and not by a radio frequency-based wall-tracking system that has
higher temporal and spatial resolution, and, thus, may determine
the end-diastolic diameter with higher accuracy (6, 32). Second,
carotid flow velocity by Doppler was not recorded, and carotid
shear rate could not be estimated (6). Third, ultrasound tissue
characterization was not performed by backscatter signal analysis but by digital densitometric analysis of standard ultrasound
images that are more influenced by ultrasound attenuation and
gain settings. To restrict this limitation, two calibration steps
were introduced into the analysis of each subject (see Subjects
and Methods). Fourth, BP values used in multivariate models
were obtained at the brachial artery and do not represent an
accurate estimate of local carotid pressure. Due to the pressure
amplification phenomenon, systolic BP and pulse pressure are
higher in brachial artery than in carotid artery, and the difference
between the two measurement sides decreases with age (40).
Finally, the relationships between body composition and
stroke volume and between stroke volume and arterial diameter
were evaluated only in a small subgroup (12% of the whole
population) recruited in a single center where Doppler echocardiography was performed. This subgroup had slightly higher
FFM, waist girth, and CCA IMT, however, the independent correlates of both luminal diameter and wall thickness were similar
jcem.endojournals.org
3331
to those observed in the whole study population, suggesting that
this subgroup can be considered a representative sample.
Conclusions
The results of this cross-sectional study raise the possibility
that in healthy subjects without increased cardiovascular risk,
body composition-related changes in systemic hemodynamics
may modulate the luminal diameter of CCA and induce adaptive
changes in IMT due to a mutual adjustment between carotid
diameter and wall thickness. Central adiposity seems to increase
the acoustic reflectivity of the carotid wall. Further longitudinal
studies are needed to establish clearly the temporal sequences of
changes in the carotid structure.
Acknowledgments
Address all correspondence and requests for reprints to: Michaela
Kozakova, M.D., Ph.D., Department of Internal Medicine, University
of Pisa, Via Roma 67, 56126 Pisa, Italy. E-mail: m.kozakova@int.
med.unipi.it.
This study is supported by European Union Grant QLG1-CT-200101252. Additional support has been provided by AstraZeneca (Sweden).
The European Group for the Study of Insulin Resistance is supported by
Merck Santé, France.
Relationship between Insulin Sensitivity and Cardiovascular risk investigators: Amsterdam, The Netherlands: R. J. Heine, J. Dekker, S. de
Rooij, G. Nijpels, and W. Boorsma; Athens, Greece: A. Mitrakou, S.
Tournis, K. Kyriakopoulou, and P. Thomakos Belgrade; Serbia and
Montenegro: N. Lalic, K. Lalic, A. Jotic, L. Lukic, and M. Civcic; Dublin,
Ireland: J. Nolan, T. P. Yeow, M. Murphy, C. DeLong, G. Neary, M. P.
Colgan, and M. Hatunic; Frankfurt, Germany: T. Konrad, H. Böhles, S.
Fuellert, F. Baer, and H. Zuchhold; Geneva, Switzerland: A. Golay, E.
Harsch Bobbioni,V. Barthassat, V. Makoundou, T. N. O. Lehmann, and
T. Merminod; Glasgow, Scotland, United Kingdom: J. R. Petrie (now
Dundee), C. Perry, F. Neary, C. MacDougall, K. Shields, and L.
Malcolm; Kuopio, Finland: M. Laakso, U. Salmenniemi, A. Aura, R.
Raisanen, U. Ruotsalainen, T. Sistonen, M. Laitinen, and H. Saloranta;
London, United Kingdom: S. W. Coppack, N. McIntosh, J. Ross, L.
Pettersson, and P. Khadobaksh; Lyon, France: M. Laville, F. Bonnet, A.
Brac de la Perriere, C. Louche-Pelissier, C. Maitrepierre, J. Peyrat, S.
Beltran, and A. Serusclat; Madrid, Spain: R. Gabriel, E. M. Sánchez, R.
Carraro, A. Friera, and B. Novella; Malmö, Sweden (1): P. Nilsson, M.
Persson, G. Östling, and (2): O. Melander, and P. Burri; Milan, Italy:
P. M. Piatti, L. D. Monti, E. Setola, E. Galluccio, F. Minicucci, and A.
Colleluori; Newcastle-upon-Tyne, United Kingdom: M. Walker, I. M.
Ibrahim, M. Jayapaul, D. Carman, C. Ryan, K. Short, Y. McGrady, and
D. Richardson; Odense, Denmark: H. Beck-Nielsen, P. Staehr, K.
Hojlund, V. Vestergaard, C. Olsen, and L. Hansen; Perugia, Italy: G. B.
Bolli, F. Porcellati, C. Fanelli, P. Lucidi, F. Calcinaro, and A. Saturni;
Pisa, Italy: E. Ferrannini, A. Natali, E. Muscelli, S. Pinnola, and M.
Kozakova; Rome, Italy: G. Mingrone, C. Guidone, A. Favuzzi, and P. Di
Rocco; Vienna, Austria: C. Anderwald, M. Bischof, M. Promintzer, M.
Krebs, M. Mandl, A. Hofer, A. Luger, W. Waldhäusl, and M. Roden.
Project Management Board: B. Balkau (Villejuif, France); S. W.
Coppack (London, United Kingdom); J. M. Dekker (Amsterdam, The
Netherlands); E. Ferrannini (Pisa, Italy); A. Mari (Padova, Italy); A.
Natali (Pisa, Italy); and M. Walker (Newcastle, United Kingdom).
Core laboratories and reading centers: lipids, Dublin, Ireland: P.
Gaffney, J. Nolan, and G. Boran; hormones, Odense, Denmark: C.
Olsen, L. Hansen, and H. Beck-Nielsen; albumin-creatinine, Amsterdam, The Netherlands: A. Kok and J. Dekker; genetics, Newcastle-uponTyne, United Kingdom: S. Patel and M. Walker; and Stable isotope laboratory, Pisa, Italy: A. Gastaldelli and D. Ciociaro.
Ultrasound reading center: Pisa, Italy: M. Kozakova; electrocardiogram reading, Villejuif, France: M. T. Guillanneuf; data management,
3332
Kozakova et al.
Carotid Artery and Body Composition
Villejuif, France: B. Balkau and L. Mhamdi; mathematical modeling and
web site management, Padova, Italy: A. Mari, G. Pacini, and C.
Cavaggion; coordinating office, Pisa, Italy: S. A. Hills, L. Landucci,
and L. Mota.
Further information on the Relationship between Insulin Sensitivity
and Cardiovascular risk Study and participating centers can be found
at www.egir.org.
Disclosure Summary: M.K., C.P., M.P., C.-H.A., T.K., M.P.C., and
J.D. have nothing to declare. A.F. consulted for Hoffmann-La Roche and
Merck Santé, and lectured for Novo Nordisk and GlaxoSmithKline.
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