CHAPTER
4
*Associations of abdominal and thigh adiposity with metabolic syndrome
features in overweight and obese women
Paulo M. Rocha1,José T. Barata1, Claudia S. Minderico1, Analiza M. Silva1, Pedro J.
Teixeira1 and Luís B. Sardinha1
1
Exercise and Health Laboratory, Faculty of Human Movement, Technical University of
Lisbon, Portugal
Running Head: Body fat distribution and metabolic syndrome
Correspondence author:
Luis Bettencourt Sardinha, PhD
Exercise and Health Laboratory, Faculty of Human Movement,
Technical University of Lisbon, Estrada da Costa,
1495-688 Cruz-Quebrada, Portugal, Tel: (351) 21 414 91 60
Fax: (351) 21 414 91 93, lsardinha@fmh.utl.pt
*Rocha, P.M., Barata, J.T., Minderico, C.S., Silva, A.M., Teixeira, P.J. & Sardinha, L.B.
(2007). Associations of abdominal and thigh adiposity with metabolic syndrome features in
ABSTRACT
There remains debate as to which of abdominal and thigh adipose tissue (AT) depots is of
greater contributor to metabolic syndrome outcomes. Our purpose was to examine the
independent associations between abdominal and thigh AT with major metabolic syndrome
features, proinflammatory and atherothrombotic risk factors in overweight and obese
women. Abdominal and thigh adipose and muscle tissue were assessed by computed
tomography (CT) in 140 premenopausal women (age: 38.2±5.8 yr; BMI: 30.3±3.7 kg/m2).
Serum lipid and glucose metabolism markers, inflammatory and atherothrombotic
disturbances were also measured. After adjusting for age, BMI and VO2max, both total thigh
AT (TTAT) and total thigh subcutaneous AT (TTSAT) were inversely associated with
plasminogen activator inhibitor-1 and hemoglobin A1c concentrations, and with
LDL-cholesterol/cholesterol ratio (p<0.05). TTSAT was additionally related with
HDL-cholesterol (p<0.05). On the contrary, total thigh subfascial AT (TTSFAT) was related with
dyslipidemia and both inflammatory and atherogenic disturbances (p<0.05). Similarly to
TTSFAT, VAT reflected a higher metabolic risk profile (p<0.05). Abdominal subcutaneous
AT (Ab SAT) was also associated with inflammatory and atherothrombotic features but not
with glucose and lipid metabolism disturbances (p>0.05). Larger TTAT and TTSAT areas
were related with a lower thrombotic and atherogenic risk, while TTSFAT reflected an
unfavourable metabolic profile independently of age, BMI and VO2max. Both VAT and Ab
SAT were significant predictors of atherothrombotic and inflammatory disturbances.
However, Ab SAT was not a significant correlate for dyslipidemia and insulin resistance
markers in this sample of overweight and obese women.
Keywords: Abdominal and thigh adipose tissue, metabolic syndrome, inflammatory and
INTRODUCTION
Evidence have been suggesting that body fat distribution can predict cardiovascular
disease (CVD) risk, being abdominal adiposity more closely related to an unfavourable
metabolic syndrome profile than peripheral adiposity (42). Insulin resistance (IR) seems to
be the underlying link between abdominal obesity and several metabolic syndrome clinical
features such as hypertension (5, 36), dyslipidemia (6, 26) and type 2 diabetes mellitus (DM)
(3, 22). Computed tomography (CT) has made possible to discriminate different
morphologic and functionally adipose tissue (AT) compartments (4), raising new questions
regarding the independent relevance of visceral adipose tissue (VAT) and abdominal
subcutaneous adipose tissue (Ab SAT) to IR and metabolic syndrome aetiology.
Despite the underlying mechanisms between IR and both VAT and Ab SAT are still
unclear, it has been suggested that free fatty acid flux from visceral adipocytes to liver might
be the primary cause of hepatic IR, increased gluconeogenesis and dyslipidemia (2). Indeed,
VAT is independently associated with an increased disease risk (25, 34). Other studies have
been reporting that abdominal subcutaneous adipocytes of central obese women, exhibiting
an increased lipolysis rate, can also contribute to increase peripheral IR (20). However, it is
not totally clear if Ab SAT contributes to an increased metabolic syndrome and CVD risk,
independently of VAT (10).
The combination of these observations highlighting the prominent AT role in health
risk with the knowledge that adipocytes can act as endocrine secretory cells (30) have raised
the scientific interest to the understanding of the relative contributions of each AT
compartment to adipokines expression, as well as their interactions with metabolic syndrome
clinical features. In fact, a wide range of signals emanated from white adipocytes, such as
interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), plasminogen activator inhibitor-1
Several functions including insulin sensitivity, lipid and carbohydrate metabolism and satiety
regulation, as well as inflammatory and atherothrombotic disturbances have been associated
with some of these adipokines, which seem to be differentially expressed in AT (12).
Metabolic syndrome is characterized by a cluster of several metabolic abnormalities
associated with increased type 2 DM and CVD risk (17). Together with obesity, IR seems to
be a major underlying risk factor linking these metabolic disorders with energy metabolism
disturbances (31). As some adipokines may influence insulin signalling (12), their regulation
and expression assume a key role in obesity and IR aetiology, and thus, in metabolic
syndrome disturbances. On the other hand, obesity is associated with ectopic fat storage
within the muscle, which can be deposited between muscle fibbers or infiltrated
intramuscularly (46).
Furthermore, it was recently reported that intermuscular AT could be similar in size
to VAT at low levels of adiposity (13). Despite the compelling evidence linking both VAT
and Ab SAT adipocytes with cytokines expression (11, 19) and to health risk (15, 25, 34),
little is known about the thigh AT compartments relevance to adipokines secretion.
Therefore, we sought to examine the independent relationships between both abdominal and
thigh AT compartments with major metabolic syndrome features, as well as with
proinflammatory and atherothrombotic risk factors in overweight and obese women.
Additionally, we investigated the associations between thigh muscle tissue and the referred
metabolic syndrome features.
MATERIALS AND METHODS
Subjects
One hundred forty sedentary premenopausal Caucasian women were recruited by
elsewhere in detail (45). Subjects were included in this investigation if they were no
currently pregnant, had a body mass index (BMI)>24.9 kg/m2, were older than 24 years and
not under any medication that could affect weight, body composition or other metabolic
variables.
Subjects with previous history of cancer, type 2 DM, CVD and stroke were excluded.
It were also excluded all women with resting or exercise abnormal electrocardiograms
(ECG), Cushing syndrome, hypercholesterolemia, hypertriglyceridemia, and hormonal
dysfunctions and medication. All subjects were informed about the study design before they
gave their fully informed and written consent. The institutional Human Subjects Review
Board of the Faculty of Human Movement, Technical University of Lisbon, approved the
study protocol which was performed in accordance to the principles of the Helsinki
Declaration.
Body composition assessments
Anthropometric variables.
Body mass was measured to the nearest 0.01 kg in a calibrated digital scale and
height was measured to the nearest 0.1 cm using a calibrated stadiometer (Seca, Hamburg,
Germany). Waist-to-hip ratio (WHR) was defined as waist circumference (WC) divided by
hip circumference (HC) and body mass index (BMI) was calculated as weight divided by
heightsquared.
Dual energy X-ray absorptiometry (DXA).
Dual energy X-ray absorptiometry (QDR-1500 Hologic, Inc. Waltham, MA) was
used to measure trunk fat mass (TFM), total body fat mass (TBFM) and total body lean mass
(TBLM). Scans were performed by the same technician using standard procedures. The
Computed tomography (CT). Measurement of abdominal adipose tissue distribution.
Abdominal and thigh compartments cross-sectional images were acquired with an
electron beam computed tomography scanner (Siemens, Somaton Plus) using established
protocols (15, 23). While subjects were in a supine position, with arms extended above their
head, contiguous 7-mm-thickaxial images from caudal heart region to iliac crest were
obtained. All images were obtained using 120kVp for 2-s and 480 mA with a 512×512
matrix and a 48-cm field of view. In each subject, a single slice image at L4-L5 vertebral
disc space was selected to measure abdominal AT compartments.
In this image, total abdominal adipose tissue (TAAT), VAT, abdominal subcutaneous
adipose tissue, and superficial and deep Ab SAT areas and attenuations were measured.
TAAT reflected whole AT cross-sectional area. The abdominal and oblique muscles in
continuity with the deep fascia of the paraspinal muscles and the anterior aspect of the
vertebral body were used to discriminate VAT from Ab SAT (7). Subcutaneous fascia
identification was used to clearly identify both superficial and deep Ab SAT areas (23).
Measurement of thigh adipose tissue and muscle distribution.
Contiguous 7-mm-thick axial images of both thighs were obtained between the
inferior ischial tuberosity and the superior border of the patella to measure the total thigh
adipose tissue (TTAT), total thigh subcutaneous AT (TTSAT), total thigh subfascial AT
(TTSFAT) and muscle tissue areas and attenuations.
The thigh AT compartments volumes (litters) were converted to mass units
(kilograms) by multiplying the volume by the constant fat density (0.92 kg/L) (43). Total
thigh muscle tissue (TTMT) mass was also determined by multiplying volume by the
constant density assumed for adipose tissue-free skeletal muscle (1.04 kg/L) (43). A single
7-mm-thick axial image of both mid-thighs was selected at the mid-point distance between
Measurements reliability.
The same technician examined in 30 women the reliability for abdominal and thigh
adipose and muscle tissue compartments. Repeated analyses on same images were
performed separated by 3 months. The intra-observer coefficients of variation (CV) for
VAT, TAAT and Ab SAT were 0.9%, 0.7%, 0.8%, respectively. The intra-observer CV for
TTMT, TTAT and TSFAT were, respectively, 0.1%, 0.4%, 2.5%.
Image analysis.
Briefly, once obtained, CT data was analysed using a specific computer software
(Slice-O-matic, Version 4.2, Tomovision Inc, Montreal, Canada) based on established
procedures described elsewhere (29). The AT segmentation was computed using an
attenuation range of –190 to –30 Hounsfield units (HU) (15, 29). For muscle tissue
segmentation were used the standard HU range (-29 to 150) (15). The high-density (31 to
150 HU) and low-density muscle areas (-29 to 30 HU) (15) were also measured in both
mid-thighs. Thigh fascia was used to discriminate thigh subcutaneous AT from SFAT (15).
Maximal oxygen uptake
Cardiorespiratory fitness (CRF) was determined using modified Balke maximal
exercise test protocol, symptom-limited, performed on a variable speed and incline treadmill
(Quinton Treadmill, Model 640, 90TM Series). After a warm up, the subject started the
protocol with the treadmill speed set at 3.4 miles per hour and 0% grade during the first
minute of exercise, maintaining a constant speed during the entire exercise test. In each
subsequent minute, the grade was further increased by 1%, until subjects were exhausted.
Simultaneously, it were continuously measured the ventilated gas volumes, flow rates
and respiratory gas exchange by a breath-by-breath air flow measurement system
attained when at least two of the fowling three criteria were obtained: a heart rate at or above
the age-predicted maximum (208-0.7*age) (44), and/or a respiratory exchange ratio greater
than 1, and/or no increase in VO2max despite further grade increases.
Blood analysis
A blood sample of ~15 mL was drawn from antecubital vein in each subject, after a 12-hour
overnight fast. Serum triglycerides (TG), total cholesterol (TC), low-density lipoprotein
cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and uric acid were
determined by enzymatic colorimetric methods. Fasting plasma glycemia was assessed by a
hexoquinase method, insulin was determined by electrochemiluminescence immunoassay
(ECLIA), and IL-6 was measured by a chemiluminescence immunoassay. Serum leptin and
adiponectin concentrations, as well as urine cortisol were measured by radioimmunoassay
(RIA).
Tumor necrosis factor-alpha was measured using a high-sensitivity enzyme-linked
immunosorbent assay (ELISA) principle. Fibrinogen concentrations were measured by
clotting time, hemoglobin A1c (Hb A1c) was obtained by high-pressure liquid
chromatography (HPLC), and plasminogen activator inhibitor-1was measured in iced
citrated plasma using the Coatest PAI method (enzyme immunoassay - EIA). Plasma
concentrations of C-reactive protein (CRP), apolipoprotein A1 (apo A1), apolipoprotein
B100 (apo B100), and microalbuminuria were assessed by a high-sensitivity
particle-enhanced turbidimetric assay.
Blood pressure
Systolic and diastolic blood pressures were measured in seated position, after at least
FL). Cuff sizes were selected based on upper-arm girth and length. It was calculated the
mean of three measurements made in each arm.
Statistical analysis
Data are presented as means ± SD. It was studied the normality and homocedasticity
of all variables. When necessary, log transformation was used to normalize distributions.
Partial correlation coefficients were calculated in order to study the relationships between
metabolic syndrome features and both abdominal AT depots and thigh adipose and muscle
tissue compartments.
Type I error was set at p<0.05. Data were analyzed using the Statistical Package for
the Social Sciences, Version 12.0 for Windows (SPSS, Chicago, IL, USA).
RESULTS
Subjects presented a wide variation range in BMI and in abdominal and thigh
adiposity (Table 1). About 43.9% of the subjects had a WC higher than 88 cm, comprising
VAT only 23.6% of overall abdominal AT area. On the contrary, abdominal subcutaneous
AT represented 76.4% of all AT cross-sectional area at L4-L5 level. Thigh subcutaneous AT
represented 94.0% of total thigh AT mass, while TTMT mass represented 42.1% of total
thigh mass.
Wide variation ranges were also observed in several metabolic syndrome features
(Table 2). In fact, hypercholesterolemia and hypertriglyceridemia were found, respectively,
in 39.7% and 22.3% of the subjects. However, impaired fasting glucose, defined as glucose
Table 1. Subject characteristics (n=140)
Mean±SD Range
Anthropometric data
Age, y 38.3±0.5 25.0-49.0
BMI, kg/m2 30.4±0.3 25.1-45.2
WC, cm 87.2±0.8 71.1-123.4
DXA data 111.4±0.7 94.7-134.6
TFM, kg 18.0±0.4 9.4-32.3
TBFM, kg 36.1±0.7 23.5-60.3
TBLM, kg 41.2±4.6 29.7-55.6
Abdominal AT area (L4-L5)
TAAT, cm2 470.9±12.1 211.9-910.8
VAT, cm2 111.3±4.3 24.9-266.8
Ab SAT, cm2 353.6±9.1 145.0-633.4
Superficial, cm2 192.2±5.0 90.6-384.2
Deep, cm2 161.8±5.4 54.7-344.9
Thigh muscle tissue area
Muscle area, cm2 234.3±2.6 176.3-324.7
Muscle Attenuation, HU 44.0±0.3 33.4-51.7
HDM, cm2 189.3±2.3 145.7-264.4
LDM, cm2 32.8±0.9 15.7-80.0
Total thigh compartments mass
TTAT, kg 8.4±2.1 4.0-14.8
TTSAT, kg 7.9±2.1 3.8-14.0
TTSFAT, kg 0.6±0.2 0.3-1.5
TTMT, kg 6.1±0.9 4.4-10.3
Estimated VO2max, mL.kg-1.min-1 24.2±0.4 9.5-36.6
Table 2. Subject metabolic characteristics (n=140)
Mean±SD Range
Fasting insulin, µIU/mL 8.2±0.3 2.4-17.9
Fasting glycemia, mg/dL 89.5±0.7 73.0-113.0
Triglycerides, mg/dL 101.5±4.9 32.0-329.0
TC, mg/dL 194.7±3.9 101.0-307.0
HDL-C, mg/dL 54.1±1.1 29.0-91.0
LDL-C, mg/dL 123.5±3.5 45.0-255.0
TC/HDL-C ratio 3.7±1.1 2.0-9.6
LDL-C/HDL-C ratio 2.4±0.9 0.9-6.1
Apo A1, mg/dL 139.1±2.3 77.0-195.0
Apo B100, mg/dL 86.7±2.4 38.0-156.0
Apo A1/Apo B100 ratio 1.7±0.1 0.8-3.3
Systolic BP, mm Hg 120.7±1.4 90.0-175.0
Diastolic BP, mm Hg 75.8±0.9 50.0-101.0
CRP, mg/dL 0.45±0.03 0.03-1.14
IL-6, pg/mL 10.3±0.6 0.8-31.5
TNF-α, pg/mL 3.8±0.2 0.9-14.1
PAI-1, ng/mL 21.2±2.0 1.0-100.0
Fibrinogen, mg/dL 369.4±6.5 201.0-552.0
Hb A1c, % 4.9±0.0 4.0-7.0
Uric acid, mg/dL 4.4±0.1 2.4-8.5
Microalbuminuria, µg/min 2.7±0.7 0.5-89.8
Cortisol, ug/day 41.0±1.7 6.0-105.0
Leptin, ng/mL 32.9±43.3 0.9-167.4
Adiponectin, ng/mL 9.2±6.4 2.9-41.0
Values are presented as means ± SD. TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BP, blood pressure; CRP, C-reactive protein; IL-6, interleukin-6; TNF-α, tumor necrosis factor-alpha; PAI-1, plasminogen activator inhibitor-1; Hb A1c, haemoglobin A(1c); Apo A1, apolipoprotein A1; Apo B100, apolipoprotein B100.
Further analyses were developed to study the associations between both abdominal
and thigh adipose and muscle tissue compartments and metabolic syndrome disturbances. In
Table 3 are presented the partial correlation coefficients between abdominal AT depots and
was related with higher TG and PAI-1 concentrations, as well as with higher TC/HDL-C and
LDL-C/HDL-C ratios. Additionally, TAAT revealed inverse associations with HDL-C
concentrations. When controlling for same confounders, VAT revealed similar associations
as those observed for TAAT, being additionally related with LDL-C and fasting glycemia.
When adjusted for Ab SAT, VAT remained significantly correlated with the previous
referred variables and revealed additional associations with IR markers. Larger VAT areas
were associated with both higher CRP and lower adiponectin concentrations, after control
for deep Ab SAT.
Contrarily to VAT, which reflected a dyslipidemic and an atherothrombotic
metabolic profile, Ab SAT was uniquely associated with TNF-α, after adjustment for age,
BMI and VO2max. However, when controlling for VAT, a larger Ab SAT area was
additionally associated with leptin, as well as with inflammatory and prothrombotic
disturbances. Similarly to Ab SAT, larger superficial Ab SAT area was also associated with
an inflammatory atherothrombotic state, and revealed an additional association with fasting
insulin (not shown). On the other hand, after similar adjustment for VAT, a larger deep Ab
SAT area was related with higher fibrinogen and uric acid concentrations.
The associations between thigh AT compartments and metabolic risk factors are
presented in Table 4. Higher TTAT and TTSAT areas were associated with lower PAI-1 and
Hb A1c concentrations, as well as a lower LDL-C/HDL-C ratio, independently of age, BMI
and VO2max. The increase of TTSAT was also correlated with higher HDL-C and IL-6
concentrations and a lower TC/HDL-C ratio. The increase of both TTAT and TTSAT areas
were similarly related with higher concentrations of inflammatory and atherothrombotic state
markers, after adjustment for TTMT. However, excepting HDL-C which remained
significantly related with TTSAT, no further associations were verified between both thigh
Table 3. Partial correlation coefficients between abdominal AT depots and metabolic syndrome features.
Abdominal adipose tissue compartments (cm2)**
TAAT VAT Ab SAT
Adj1 Adj1 Adj2 Adj3 Adj1 Adj4
Fasting insulin, µIU/mL 0.22* 0.27†
Fasting glycemia, mg/dL 0.29‡ 0.33‡ 0.33‡
Triglycerides, mg/dL 0.26† 0.30† 0.29‡ 0.32‡
Total cholesterol, mg/dL
HDL cholesterol, mg/dL -0.26† -0.34‡ -0.30‡ -0.29‡
LDL cholesterol, mg/dL 0.19* 0.21* 0.23*
TC/HDL-C ratio 0.28† 0.42‡ 0.38‡ 0.41‡
LDL-C/HDL-C ratio 0.21* 0.39‡ 0.37‡ 0.39‡
Apo A1/Apo B100 ratio -0.27† -0.23* -0.25†
Systolic BP, mmHg
Diastolic BP, mmHg
CRP, mg/dL 0.19* 0.29‡
IL-6, pg/mL 0.18*
TNF-α, pg/mL 0.19* 0.25†
PAI-1, ng/mL 0.33‡ 0.41‡ 0.45‡ 0.44‡
Fibrinogen, mg/dL 0.24†
Hb A1c; % 0.21* 0.28†
Uric acid, mg/dL 0.22*
Microalbuminuria, µg/min
Cortisol, ug/day
Leptin, ng/mL 0.35‡
Adiponectin, ng/mL -0.20*
TAAT, total abdominal adipose tissue; VAT, visceral adipose tissue; Ab SAT, abdominal subcutaneous adipose tissue; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; Apo A1, apolipoprotein A1; Apo B100, apolipoprotein B100; BP, blood pressure; CRP, C-reactive protein; IL-6, interleukin-6; TNF-α, tumor necrosis factor-alpha; PAI-1, plasminogen activator inhibitor-1; Hb A1c, hemoglobin A1c.
Adj1, after control for age, BMI, and VO2max; Adj2, after control for Ab SAT; Adj3, after control for deep Ab SAT; Adj4,
after control for VAT.
*P < 0.05. †P < 0.01. ‡P < 0.001.
Table 4. Partial correlation coefficients between thigh adipose and muscle tissue and metabolic syndrome features.
Thigh adipose and muscle tissue (kg)**
TTAT TTSAT TTSFAT TTMT
Adj1 Adj2 Adj1 Adj2 Adj1 Adj2 Adj1 Adj3
Fasting insulin, µIU/mL
Fasting glycemia, mg/dL 0.20* 0.29* 0.26† 0.23*
Triglycerides, mg/dL
Total cholesterol, mg/dL
HDL cholesterol, mg/dL 0.19* -0.28†
LDL cholesterol, mg/dL
TC/HDL-C ratio -0.20* 0.26†
LDL-C/HDL-C ratio -0.21* -0.23* 0.24†
Apo A1/Apo B100 ratio
Systolic BP, mmHg
Diastolic BP, mmHg
CRP, mg/dL 0.37‡ 0.33‡
IL-6, pg/mL 0.20* 0.24† 0.21* 0.23†
TNF-α, pg/mL 0.27† 0.23*
PAI-1, ng/mL -0.26† -0.31‡ 0.33‡ 0.51‡
Fibrinogen, mg/dL 0.24† 0.22* 0.26†
Hb A1c; % -0.21* -0.20* 0.24† 0.18*
Uric acid, mg/dL 0.21* 0.20*
Microalbuminuria, µg/min 0.24* 0.19*
Cortisol, ug/day
Leptin, ng/mL 0.36‡ 0.35‡ -0.23* -0.21*
Adiponectin, ng/mL
TTAT, total thigh adipose tissue; TTSAT, total thigh subcutaneous adipose tissue; TTSFAT, total thigh subfascial adipose tissue; TTMT, total thigh muscle tissue; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; Apo A1, apolipoprotein A1; Apo B100, apolipoprotein B100; BP, blood pressure; CRP, C-reactive protein; IL-6, interleukin-6; TNF-α, tumor necrosis factor-alpha; PAI-1, plasminogen activator inhibitor-1; Hb A1c, hemoglobin A1c.
Adj1, after control for age; BMI, and VO2max; Adj2, after control for TTMT; Adj3, after control for TTAT. *P < 0.05.
†P < 0.01. ‡P < 0.001.
Independently of age, BMI and VO2max,a higher TTSFAT was a predictor of higher
fasting glycemia, PAI-1 and microalbuminuria concentrations, as well as with lower leptin
values. TTSFAT remained significantly correlated with the same variables after adjustment
for TTMT, showing further associations with other IR and lipid metabolism markers, as well
as with prothrombotic risk factors. Both HDL-C and leptin were inversely related with
TTSFAT. Regarding to muscle tissue, we found that a higher TTMT area was associated
with an increase in fasting glycemia and Hb A1c concentrations, independently of TTAT.
DISCUSSION
While visceral adiposity has been recognized as a health risk predictor in obese
subjects, there is less evidence linking thigh adiposity and metabolic syndrome disturbances.
In our study with overweight and obese women, we found that larger TTAT and TTSAT
areas were associated with lower Hb A1c and PAI-1 concentrations, as well as with a lower
LDL-C/HDL-C ratio, independently of age, BMI and VO2max. It was recently reported that
low subcutaneous thigh fat accumulation was an independent predictor of glucose and lipid
metabolism disturbances (41).
However, the inverse associations verified in our study between both thigh AT
compartments and PAI-1 are novel observations that were not been previously reported in
overweight and obese women. In this sense, these observations suggest an independent
relative protective role of femoral-gluteal AT against atherogenic, prothrombotic and IR risk
factors. Indeed, it has been suggested that these thigh AT depots may provide a buffer for
circulating FFA (9), preventing muscle ectopic fat storage (35), which can be located not
only adjacently or between muscle fibbers, but also intramyocytes (13). As muscle ectopic
15), it has been suggested that this possible mechanism may attenuate the metabolic
disturbances commonly present in overweight and obese subjects (41).
On the contrary, after statistical control for age, BMI and VO2max, a larger TTSFAT
area was positively related with higher fasting glycemia, PAI-1, and microalbuminuria
concentrations. This thigh AT depot remained significantly associated with same metabolic
syndrome features and revealed additional associations with inflammatory and atherogenic
risk factors, after adjustment for TTMT. Similarly, previous evidence has also linked
TTSFAT with metabolic disturbances mainly related with IR (15). These observations seem
also to be reinforced since we found that, after adjustment for TTMT, both TTAT and
TTSAT were significant predictors of leptin, while TTSFAT was inversely related with this
adipokine. In fact, leptin, directly or indirectly through hypothalamic-mediated responses,
may promote an insulin sensitizing effect in muscle tissue, increasing muscle glucose uptake
and fatty acid oxidation (28).
In this sense, the results obtained in our study suggest that these thigh lipid pools are
associated with adipokines expression and could act as important local modulators of
skeletal muscle metabolism. However, more research is needed to clarify the relevance of
each thigh AT compartments to health risk, since a larger thigh fat accumulation has been
associated with metabolic disturbances.
Regarding muscle tissue, we found that a larger TTMT area was independently
associated with increased fasting glycemia and Hb A1c concentrations. However, these
associations did not remain significant when controlling for weight or total body fat mass
(data not shown), suggesting that these associations may be mediated by total body mass or
total body fat mass which is increased in obese subjects.
On the other hand, we found that VAT was an independent predictor of an
sample of overweight and obese women. The VAT mean area observed was higher than 110
cm2, a suggested cut-point to observe severe dyslipidemia and glucose metabolism
disturbances (32), which might justify the relationships found between VAT and both lipid
and IR markers. The fact that VAT remained a significant predictor of metabolic syndrome
disturbances independently of both Ab SAT and deep Ab SAT reinforce the independent
VAT relevance to a higher disease risk in overweight and obese women.
Not only visceral but also subcutaneous abdominal AT have been related with an
unfavourable metabolic profile, independently of VAT (16), liver fat storage and
cardiorespiratory fitness (37). However, there remains a debate regarding the independent
relevance of each fat depot, VAT or Ab SAT to metabolic syndrome components and to a
new variety of more specific inflammatory and atherothrombotic risk factors. Although both
VAT and Ab SAT have been demonstrating cross-sectional associations with insulin
sensitivity (1, 16) and IR (47), VAT seems to present greater clinical importance. However,
the underlying mechanisms are still unclear. In has been suggested that the influx of free
fatty acids (FFA), drained from visceral adipocytes into portal circulation might be
responsible for primary hepatic IR, consubstantiating the so-called “portal hypothesis” (2).
On the other hand, in abdominal obese women, subcutaneous adipocytes, existing in higher
amounts when compared to visceral adipocytes, may exhibit an increased lipolysis rate. The
resulting FFA release into systemic circulation may contribute to increase peripheral IR (20).
Looked until recently as a passive storage tissue, AT is now dynamically faced has
an endocrine organ (12). Several hormones, growth factors and cytokines are expressed in
white AT. Both VAT and Ab SAT can secrete leptin, TNF-α, IL-6, PAI-1 and many other
cytokines that could induce or worsen IR, and thus, increase health risk. Despite the absence
of associations between VAT and both TNF-α and IL-6 concentrations verified in out study,
receptor signalling and is a possible cause of IR development in obesity (18), while IL-6 can
increase hepatic TG synthesis, contributing to the hypertriglyceridemia commonly present in
these patients (30). However, we found a close association between visceral adiposity and
PAI-1 concentrations. By inhibiting the tissue plasminogen activator, PAI-1 is the main
regulator of the endogenous fibrinolytic system, favouring the development of
thromboembolic disturbances, which seem to underlie the relationship between CVD and
both obesity (21, 40) and IR (21). Despite the pathophysiological mechanisms responsible
for PAI-1 increase are not completely elucidated, it is known that at adipocyte level, insulin
and transforming growth factor-beta, an adipokine that increases preadipocytes cell
proliferation, appear to be the main PAI-1 synthesis inducers (39).
It is also noteworthy that after adjustment for deep Ab SAT, beyond the associations
observed with lipid and IR markers, VAT was a significant predictor of CRP, being also
inversely associated with adiponectin. Mainly regulated by circulating IL-6, CRP is an
endothelial dysfunction marker, which independently predicts IR (8) and coronary heart
disease (38). Consistently with our results, it has been reported that abdominal AT is related
with a low chronic inflammatory state (49), linking abdominal obesity with CVD
.
Similarly to leptin, adiponectin may also induce an insulin sensitizing effect on
skeletal muscle through muscle adenosine monophosphate-activated protein kinase (AMPK)
activation (48). However, hypoadiponectinemia is often present in obese and type 2 DM
patients, which might explain the opposite association verified with VAT. On the other hand,
despite hyperleptinemia can be found in IR and type 2 DM subjects and correlates with
obesity measures such as BMI and total body fat mass (27), VAT was not a significant
predictor of leptin in this sample of overweight and obese women.
Contrarily to VAT, a larger Ab SAT area was uniquely related with higher TNF-α
additionally predicted several inflammatory and thromboembolic disturbances. However, Ab
SAT was not a significant correlate of IR and dyslipidemic risk factors, which is consistent
with a recent observation in lean women (24), but contrary to the other observations reported
in previous studies (16, 41). Despite omental adipocytes produces threefold more IL-6 than
subcutaneous adipocytes (11), contributing to hepatic TG synthesis and hypertriglyceridemia
observed in abdominal obese patients (33), we found that IL-6 was related with Ab SAT but
not with VAT.
In further analyzes, while superficial Ab SAT was similarly related with same
inflammatory and prothrombotic disturbances as those observed for Ab SAT, deep Ab SAT
was only a significant correlate for fibrinogen and uric acid, after controlling for VAT.
Contrarily to our observations, evidence have been suggesting that deep Ab SAT, more
metabolically active than superficial Ab SAT, is an important metabolic syndrome predictor
independently of VAT (16, 23). In this sense, more research is needed to clarify these
findings.
The wide range of obesity phenotypes present in this large sample, the abdominal and
thigh adipose and muscle tissue compartments measurements made by CT, and the wide list
of metabolic syndrome features measured might be beneficial for this study. To avoid
metabolic acute exercise interferences, all subjects were counseled to refrain from any type
of exercise at least 48 hours prior to blood sampling. However, despite the rigorous blood
analysis protocol, we were not able to control diet composition prior to blood sampling.
More research is needed to further characterize thigh AT compartments regarding cytokine
secretion relatively to abdominal lipid pools and to understand the cross-talk mechanisms
between both muscle and AT compartments with metabolic syndrome.
In summary, while larger TTAT and TTSAT areas were both associated with a lower
TTSFAT area was a significant correlate of metabolic syndrome risk. Therefore, these thigh
AT compartments are related with adipokines expression, suggesting that they should be also
considered as predictors of more specific metabolic syndrome clinical features. Conversely,
both VAT and Ab SAT were independently related with atherothrombotic and inflammatory
disturbances, reflecting a higher CVD risk. However, only VAT was a significant predictor
of IR and dyslipidemia. Contrarily to evidence highlighting the contribution of deep Ab SAT
to health risk, superficial Ab SAT seemed to better predict metabolic syndrome disturbances
in this sample of overweight and obese women.
ACKNOWLEDGMENTS
The authors are grateful the Exercise and Health Laboratory staff, most particularly
those directly related with this research, for their assistance and expertise in laboratory
assessments. The authors would like also to express a special thank to all volunteers who
participated in this study.
GRANTS
This research was supported by the Portuguese Foundation for Science and
Technology grant (Grant Sapiens 358007/99). The Oeiras City Council, Becel Portugal,
Roche Pharmaceuticals Portugal, and Compal Portugal have also contributed with small
grants.
DISCLOSURES
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