Abstract

BACKGROUND

Women with polycystic ovary syndrome (PCOS) have been reported to have subclinical cardiovascular disease (CVD) and increased abdominal fat. The aim of this study was to evaluate the relationship between visceral fat (VF) and early markers of CVD in PCOS women.

METHODS

Two hundred overweight PCOS women [(mean ± SD) age 24.6 ± 3.2 years, body mass index (BMI) 28.5 ± 2.8 kg/m2] and 100 healthy age- and BMI-matched volunteer controls entered this cross-sectional study. In all subjects, the amount of VF was measured by ultrasonography. Anthropometric measurements [BMI and waist circumference (WC)], complete hormonal and metabolic pattern, carotid intima-media thickness (IMT), brachial arterial flow-mediated dilation (FMD) and inflammatory biomarkers [C-reactive protein (CRP), fibrinogen, white blood cells count and plasminogen activated inhibitor-1] were also obtained from all subjects. A stepwise linear regression model was used in PCOS patients to verify if IMT or FMD as dependent variables are affected by other independent variables.

RESULTS

VF amount was significantly (P < 0.001) higher in PCOS subjects than in healthy controls [31.4 ± 7.3 versus 28.0 ± 6.1 (mean±SD) mm, respectively] and directly related to insulin resistance: HOMA (r = 0.918, P < 0.001) and AUCINS (r = 0.879, P < 0.001), and to WC (r = 0.658; P < 0.001). In PCOS, the two linear regression analyses showed that IMT is positively affected by VF and CRP, whereas FMD is positively affected by IMT and negatively by VF and CRP.

CONCLUSIONS

VF amount is associated with subclinical CVD in PCOS patients.

Introduction

The polycystic ovary syndrome (PCOS) is a common endocrine-metabolic disease, affecting ∼5–10% of women of reproductive age (Ehrmann, 2005). PCOS is associated with an adverse metabolic and cardiovascular risk (CVR) profile, including obesity, insulin resistance (IR), dyslipidemia and low-grade chronic inflammation (Ovalle and Azziz, 2002; Orio et al., 2006).

Obesity, and specifically central obesity, is a common PCOS feature that worsens its phenotype (Gambineri et al., 2002). Compared with weight-matched healthy women, those with PCOS have a similar amount of total and trunk fat, but a higher quantity of central abdominal fat (Carmina et al., 2007). Additionally, central fat excess is associated with an increase in low-grade chronic inflammation and IR (Puder et al., 2005; Carmina et al., 2007) and with metabolic dysfunction in women with PCOS (Lord et al., 2006).

Increased visceral fat (VF) may also be present in non-obese PCOS women, likely contributing to development of glucose and lipid metabolism disorders (Yildrim et al., 2003).

Excess visceral or periomental fat seems to be predictive not only of the metabolic syndrome but also of cardiovascular disease (CVD) (Sowers, 1998; Grundy, 2002). VF is indeed a source of several hormones and cytokines inducing a proinflammatory state and oxidative damage leading to initiation and progression of atherosclerosis (Panagiotakos et al., 2005). In fact, subclinical CVD and early impairment of endothelial structure and function have been previously reported in PCOS women (Tiras et al., 1999; Paradisi et al., 2001; Orio et al., 2004a,b; Lakhani et al., 2005; Vural et al., 2005; Cussons et al., 2006).

Although magnetic resonance imaging (MRI) and computerized tomography (CT) represent the gold standard procedures for assessing the amount of VF, the use of these techniques is limited by exposition to ionizing radiation, high costs and limited availability. Ultrasonography (US) is considered a cost-effective, reliable and precise imaging tool for assessing the amount of visceral adipose tissue (Iacobellis, 2005, Palomba et al., 2007) and even for identifying patients at high CVD risk (Ribeiro-Filho et al., 2001; Kim et al., 2004).

On the basis of these considerations, the present study was carried out to evaluate whether the amount of VF may be considered as predictor for early CVD in PCOS, and to investigate the potential relationship between VF and some CVR factors and early atherosclerotic markers in PCOS.

Materials and Methods

The study was approved by the Institutional Review Board of the University ‘Federico II' of Naples, Italy. Written informed consent was obtained at the study entry from all subjects.

Subjects

Two hundred patients with PCOS were consecutively enrolled in this study at the Department of Molecular and Clinical Endocrinology and Oncology in Naples, Italy. The diagnosis of PCOS was made according to the ESHRE/ASRM criteria for the PCOS diagnosis (Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, 2004) based on the presence of two of the three following criteria: oligo- and/or anovulation, clinical and/or biochemical signs of hyperandrogenism and polycystic ovaries (PCO) at US.

Exclusion criteria included: age <18 years or >28 years, body mass index (BMI) <18.5 kg/m2 or ≥35 kg/m2, pregnancy, hypothyroidism, hyperprolactinemia, Cushing's syndrome, congenital adrenal hyperplasia, androgen-secreting tumors, hypertension, current or previous (within the last 6 months) use of oral contraceptives, glucocorticoids, antiandrogens, ovulation induction or dopaminergic agents, antidiabetes, antiobesity, use of antihyperlipidemic or antihypertensive drugs, or use of anti-inflammatory drugs or other hormonal drugs during the last month. None of the patients was affected by neoplastic, metabolic or cardiovascular disorders or any other concurrent medical illness (including diabetes or kidney, liver, thyroid, autoimmune, cerebrovascular and ischemic heart disease).

One hundred healthy women age- (±2 years) and BMI-matched (±1 kg/m2) to the patients were enrolled as controls. The healthy state of the women in the control group was determined by medical history, physical and pelvic examination and complete blood chemistry. Their normal ovulatory state was confirmed by transvaginal US and plasma progesterone (P) assay. Both procedures were performed during the luteal phase of the menstrual cycle (7 days before the expected menses). The presence of fluid in the cul-de-sac at transvaginal US and a plasma P assay>31.8 nmol/l (>10 ng/ml) were considered criteria for ovulation having occurred (Barbieri, 1999). Exclusion criteria for healthy controls were PCO at transvaginal US and/or clinical or biochemical hyperandrogenism.

All patients and controls received a long, careful and simple explanation of the purposes of this study, of its preliminary remarks and pathophysiological basis. All subjects were non-smokers, had normal glucose tolerance tests (Gabir et al., 1999), performed normal physical activity and none was heavy alcoholic beverages drinkers.

Study protocol

At study entry, all subjects underwent the following.

  1. Assessment of anthropometric measurements as: weight, height, BMI (kg/m2), waist circumference (WC) at the midpoint between the lateral iliac crest and the lowest rib margin at the end of normal expiration, waist to hip ratio (WHR), with the hip measured at the widest level of the greater trochanteres.

  2. Clinical assessment as Ferriman–Gallwey (FG) score, heart rate (HR), systolic (SBP) and diastolic (DBP) blood pressure (with a mercury sphygmomanometer in a relaxed sitting position).

  3. Endocrine assessment by measurement of levels of serum luteinizing hormone (LH), follicle-stimulating hormone (FSH), 17ß-estradiol (E2), P, 17-hydroxyprogesterone (17-OHP), testosterone (T), androstenedione (A), dehydroepiandrosterone sulfate (DHEAS), prolactin (PRL) by specific radioimmunoassays (RIA), sex-hormone-binding globulin (SHBG) by IRMA and free androgen index (FAI) calculation [T(nmol/l)/SHBG(nmol/l)×100].

  4. Glucose metabolism assessment by measuring fasting levels of insulin (by a solid-phase chemioluminescent enzyme immunoassay) and glucose (by oxidase method) and estimate of IR by homeostasis model assessment [HOMA score; fasting serum insulin (μU/ml) × fasting plasma glucose (mmol/l)/22.5]. Glucose and insulin concentrations were also measured after an oral glucose tolerance test (OGTT: 75 g glucose load orally) at 30 min intervals for 3 h (times 30, 60, 90, 120, 150 and 180 min). The response to the OGTT was analyzed by calculating the area under curve (AUC) for glucose (AUCGLU) and insulin (AUCINS) (Tai, 1994). The AUCGLU/AUCINS ratio was also calculated (Legro et al., 1998).

  5. Lipid profile assessment by measurement of fasting serum total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C) and triglycerides (TG).

  6. Assessment of inflammation by measurement of fasting C-reactive protein (CRP), plasminogen-activated inhibitor-1 (PAI-1) and white blood cell (WBC) count.

  7. Cardiovascular evaluation by echocolor-Doppler examination of the brachial and carotid artery according with Orio et al. (2004b, 2005). Briefly, longitudinal ultrasonographic scans of the carotid artery were obtained by echocolor-Doppler (G.E. Vingmed Ultrasound, Horten, Norway) with a high-resolution 10 MHz linear probe. A blinded experienced operator (B.D.S.) scanned the right and left common carotid arteries and the carotid bifurcation bulb area from multiple planes. The intima-media thickness (IMT) of the posterior (far) wall of both common carotid arteries was measured at the end diastole from the B-mode screen as the distance between the junction of the lumen and intima and that of the media and adventitia. The mean IMT for each side was calculated as the average of 10 measurements made in the right and left carotid arteries using electronic calipers. According to our previous experience, the intra-observer coefficient of variation (CV) for the repeated measurements of IMT was 7.0% (Orio et al., 2004b). During the same visit, the vascular reactivity was studied in the dominant brachial artery with the use of a 7.5 MHz linear phased array ultrasound transducer (Corretti et al., 2002). Subjects were asked to fast for at least 8–12 h and to refrain from physical activity for at least 4–6 h before the examination. Blood pressure and the electrocardiogram were recorded and monitored during the exams. After baseline images of brachial arterial diameter were obtained, on the upper arm a standard sphygmomanometry cuff was inflated to 40 mmHg above SBP for 4 min, inducing ischemia. The brachial artery diameter was measured at 30 s and 1, 2, 3 and 4 min after ischemia (Coretti et al., 2002). The flow-mediated dilation (FMD) of the brachial artery was expressed as the percentage change in the arterial diameter from baseline to 4 min after deflation cuff. According to our previous experience, the intra-observer CV was 2.3% (Orio et al., 2004b). The inter-observer CV was avoided since there was only one operator.

  8. Measurement of abdominal fat distribution by US performed using a convex multifrequency probe 2.5–5 MHz (Esaote MPX 2004, Genoa, Italy) by a skillful operator (I.D.S.). All subjects were examined in the supine position and asked to hold their breath during the examination while the frozen images were taken, to avoid the influence of the respiratory status or abdominal wall tension. Special care was taken to keep the probe just touching the skin to prevent compression of the fat layers. VF layer thickness was measured between the internal face of the recto-abdominal muscle and the anterior wall of the aorta, measured 1 cm above the umbilicus on the xipho-umbilical line (Armellini et al., 1991; Suzuki et al., 1993; Palomba et al., 2007). The intra-operator CV was 1.85%, whereas the inter-operator CV was avoided since there was only one operator.

Statistical analysis

For continuous variables, the Kolmogorov–Smirnov statistic with a Lilliefors significance correction was used for testing normality, and data were analyzed with unpaired t-test. Bivariate correlations computing Pearson's coefficient with their significance levels were calculated between VF and other variables in PCOS and control patients.

In PCOS, we performed two multiple linear regression analysis (stepwise method) with IMT or FMD as the dependent variable and VF mass, BMI, age, SBP, DBP, TC, LDL-C, HDL-C, TG, CRP, WBC, PAI-1, WC, FAI, HDL-C, AUCINS, AUCGLU fasting insulin and glucose, and HOMA as independent variables. IMT and FMD were used as dependent variables alternately. For assessing the suitability of the data for a linear regression model, the collinearity diagnostics were evaluated.

Data are presented as mean and standard deviation, and a P-value of <0.05 was considered statistically significant. All analyses were run using SPSS 15.0.0 (SPSS Inc., Chicago, IL, USA).

Results

In PCOS and controls, the demographic, clinical and anthropometrical profiles are shown in Table I, hormonal and metabolic data are shown in Tables II and III, respectively, and the cardiovascular profile is shown in Table IV.

Table I.

Clinical profile in PCOS and controls.

 PCOS (n = 200) Controls (n = 100) P-value* 
Age (years) 24.6 ± 3.2 24.0 ± 2.8 0.11 
Body mass index (kg/m228.5 ± 2.8 28.8 ± 2.7 0.37 
Waist:Hip ratio 0.86 ± 0.1 0.84 ± 0.1 0.10 
Waist circumference (cm) 95.2 ± 7.3 92.5 ± 7.0 0.002 
Ferriman–Gallwey score 12.5 ± 3 4.3 ± 1.5 <0.001 
 PCOS (n = 200) Controls (n = 100) P-value* 
Age (years) 24.6 ± 3.2 24.0 ± 2.8 0.11 
Body mass index (kg/m228.5 ± 2.8 28.8 ± 2.7 0.37 
Waist:Hip ratio 0.86 ± 0.1 0.84 ± 0.1 0.10 
Waist circumference (cm) 95.2 ± 7.3 92.5 ± 7.0 0.002 
Ferriman–Gallwey score 12.5 ± 3 4.3 ± 1.5 <0.001 

*Student's t-test. Data are expressed as mean ± SD.

Table II.

Hormonal profile in PCOS patients and controls.

 PCOS (n = 200) Controls (n = 100) P-value* 
FSH (IU/l) 10.2 ± 2.3 9.8 ± 1.9 0.13 
LH (IU/l) 26.5 ± 5.5 8.6 ± 3.4 <0.001 
PRL (ng/ml) 10.1 ± 1.6 9.9 ± 1.5 0.29 
E2 (pmol/l) 119 ± 34 118 ± 31 0.80 
P (nmol/l) 1.2 ± 0.8 1.9 ± 0.7 <0.001 
17-OHP (nmol/l) 1.6 ± 0.4 0.8 ± 0.5 <0.001 
T (nmol/l) 2.8 ± 0.5 0.9 ± 0.4 <0.001 
A (nmol/l) 5.3 ± 0.8 1.2 ± 0.7 <0.001 
DHEAS (μmol/l) 4287 ± 545 2790 ± 372 <0.001 
SHBG (nmol/l) 26.5 ± 6.3 43.2 ± 5.9 <0.001 
FAI 10.5 ± 1.9 2.1 ± 0.7 <0.001 
 PCOS (n = 200) Controls (n = 100) P-value* 
FSH (IU/l) 10.2 ± 2.3 9.8 ± 1.9 0.13 
LH (IU/l) 26.5 ± 5.5 8.6 ± 3.4 <0.001 
PRL (ng/ml) 10.1 ± 1.6 9.9 ± 1.5 0.29 
E2 (pmol/l) 119 ± 34 118 ± 31 0.80 
P (nmol/l) 1.2 ± 0.8 1.9 ± 0.7 <0.001 
17-OHP (nmol/l) 1.6 ± 0.4 0.8 ± 0.5 <0.001 
T (nmol/l) 2.8 ± 0.5 0.9 ± 0.4 <0.001 
A (nmol/l) 5.3 ± 0.8 1.2 ± 0.7 <0.001 
DHEAS (μmol/l) 4287 ± 545 2790 ± 372 <0.001 
SHBG (nmol/l) 26.5 ± 6.3 43.2 ± 5.9 <0.001 
FAI 10.5 ± 1.9 2.1 ± 0.7 <0.001 

Data are expressed as mean ± SD. *Student's t test. PRL, prolactin; E2, estradiol; P, progesterone; 17-OHP, 17β-hydroxyprogesterone; T, testosterone; A, androstenedione; DHEA-S, dehydroepiandrosterone sulfate; SHBG, sex-hormone-binding globulin; FAI, free androgen index.

Table III.

Metabolic and biochemical profile in PCOS patients and controls.

 PCOS (n = 200) Controls (n = 100) P-value* 
Fasting glucose (mg/dl) 95.3 ± 8.8 93.8. ± 8.6 0.16 
Fasting insulin (μU/ml) 20.7 ± 4 12.6 ± 2.1 <0.001 
HOMA 4.8 ± 1.1 2.91 ± 0.6 <0.001 
AUCINS 16 420 ± 950 4850 ± 1210 <0.001 
AUCGLU 12 310 ± 3580 11 720 ± 2950 0.15 
AUCGLU/AUCINS ratio 0.7 ± 0.4 2.4±0.7 <0.001 
TC (mg/dl) 191.1 ± 17.4 170 ± 22.5 <0.001 
LDL-C (md/dl) 117.2 ± 18.4 94.0 ± 17.9 <0.001 
HDL-C (mg/dl) 42.2 ± 7.1 47.1 ± 8.6 <0.001 
TG (mg/dl) 158.6 ± 33.9 144.3 ± 21.3 <0.001 
 PCOS (n = 200) Controls (n = 100) P-value* 
Fasting glucose (mg/dl) 95.3 ± 8.8 93.8. ± 8.6 0.16 
Fasting insulin (μU/ml) 20.7 ± 4 12.6 ± 2.1 <0.001 
HOMA 4.8 ± 1.1 2.91 ± 0.6 <0.001 
AUCINS 16 420 ± 950 4850 ± 1210 <0.001 
AUCGLU 12 310 ± 3580 11 720 ± 2950 0.15 
AUCGLU/AUCINS ratio 0.7 ± 0.4 2.4±0.7 <0.001 
TC (mg/dl) 191.1 ± 17.4 170 ± 22.5 <0.001 
LDL-C (md/dl) 117.2 ± 18.4 94.0 ± 17.9 <0.001 
HDL-C (mg/dl) 42.2 ± 7.1 47.1 ± 8.6 <0.001 
TG (mg/dl) 158.6 ± 33.9 144.3 ± 21.3 <0.001 

Data are expressed as mean ± SD. *Student's t-test. HOMA, homeostatic model assessment; AUC, area under the curve; AUCGLU, AUC for glucose; AUCINS, AUC for insulin; TC, total cholesterol; LDL-C, low-density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; TG, triglyceride.

Table IV.

Cardiovascular profile in PCOS and controls.

 PCOS (n = 200) Controls (n = 100) P-value* 
Heart rate (beats/min) 77.8 ± 4.8 76.9 ± 4.5 0.11 
SBP (mmHg) 118 ± 9 117 ± 8 0.35 
DBP (mmHg) 80 ± 4.8 79 ± 4.6 0.08 
IMT (mm) 0.46 ± 0.16 0.38 ± 0.09 <0.001 
FMD (%) 13.7 ± 2.3 17.8 ± 2.2 <0.001 
CRP (mg/l) 1.9 ± 0.8 0.8 ± 0.4 <0.001 
WBC count (cell/mm37350 ± 380 5260 ± 230 <0.001 
PAI-1 (IU/ml) 2.6 ± 0.7 1.7 ± 0.6 <0.001 
Visceral fat (mm) 31.4 ± 7.3 28.0 ± 6.1 <0.001 
 PCOS (n = 200) Controls (n = 100) P-value* 
Heart rate (beats/min) 77.8 ± 4.8 76.9 ± 4.5 0.11 
SBP (mmHg) 118 ± 9 117 ± 8 0.35 
DBP (mmHg) 80 ± 4.8 79 ± 4.6 0.08 
IMT (mm) 0.46 ± 0.16 0.38 ± 0.09 <0.001 
FMD (%) 13.7 ± 2.3 17.8 ± 2.2 <0.001 
CRP (mg/l) 1.9 ± 0.8 0.8 ± 0.4 <0.001 
WBC count (cell/mm37350 ± 380 5260 ± 230 <0.001 
PAI-1 (IU/ml) 2.6 ± 0.7 1.7 ± 0.6 <0.001 
Visceral fat (mm) 31.4 ± 7.3 28.0 ± 6.1 <0.001 

Data are expressed as mean ± SD. *Student's t test. SBP, systolic blood pressure; DBP, diastolic blood pressure; IMT, intima-media thickness; FMD, flow-mediated dilation; CRP, C-reactive protein; WBC, white blood cells count; PAI-1, plasminogen-activated inhibitor.

Compared with controls, PCOS patients had increased WC (P = 0.002; Table I), fasting insulin, HOMA and AUCINS (P < 0.001; Table III), TC, LDL-C, and TG levels (P < 0.001; Table III), VF (P < 0.001; Table IV), PAI-1, CRP and WBC levels (P < 0.001, Table IV), and IMT (P < 0.001, Table IV), decreased HDL-C levels (P < 0.001; Table III) and FMD (P < 0.001; Table IV) and similar SBP and HR (Table IV).

A significant direct correlation (P < 0.001) between VF and HOMA, AUCINS and WC was observed in both groups (Table V), as also shown in Fig. 1. BMI was also shown to correlate with VF and WC (Fig. 2).

Figure 1:

Scatter plots of the relationships between visceral fat and HOMA, AUCINS and WC, in each group separately, and in the entire population combined

Figure 1:

Scatter plots of the relationships between visceral fat and HOMA, AUCINS and WC, in each group separately, and in the entire population combined

Figure 2:

Scatter plots of the relationships between BMI and WC and VF in each group separately, and in the entire population combined

Figure 2:

Scatter plots of the relationships between BMI and WC and VF in each group separately, and in the entire population combined

Table V.

Correlation of visceral fat amount with HOMA, AUCINS and WC, and of BMI with WC

 r P-value* 
PCOS   
HOMA 0.918 <0.001 
AUCINS 0.879 <0.001 
WC 0.658 <0.001 
BMIa 0.776 <0.001 
Controls   
HOMA 0.653 <0.001 
AUCINS 0.681 <0.001 
WC 0.613 <0.001 
BMIa 0.626 <0.001 
All patients   
HOMA 0.941 <0.001 
AUCINS 0.913 <0.001 
WC 0.507 <0.001 
BMIa 0.746 <0.001 
 r P-value* 
PCOS   
HOMA 0.918 <0.001 
AUCINS 0.879 <0.001 
WC 0.658 <0.001 
BMIa 0.776 <0.001 
Controls   
HOMA 0.653 <0.001 
AUCINS 0.681 <0.001 
WC 0.613 <0.001 
BMIa 0.626 <0.001 
All patients   
HOMA 0.941 <0.001 
AUCINS 0.913 <0.001 
WC 0.507 <0.001 
BMIa 0.746 <0.001 

*Pearson correlation.

aBMI correlation with WC. HOMA, homeostatic model assessment; AUCINS, area under the curve for insulin; WC, waist circumference (cm); BMI, body mass index (kg/m2).

In PCOS, the two linear regression analyses showed that IMT is positively affected by VF and CRP (Table VI), whereas FMD is positively affected by IMT and negatively by VF and CRP (Table VII). No influences were found in controls (data not shown).

Table VI.

Final model of multiple linear regression analysis of IMT as dependent variable in PCOS patients.

 Unstandardized coefficient (SE) Standardized coefficient P-value 
VF 0.003 (0.001) 0.424 <0.001 
FMD 0.009 (0.003) 0.238 0.002 
CRP 0.062 (0.008) 0.663 <0.001 
Constant 0.167   
 Unstandardized coefficient (SE) Standardized coefficient P-value 
VF 0.003 (0.001) 0.424 <0.001 
FMD 0.009 (0.003) 0.238 0.002 
CRP 0.062 (0.008) 0.663 <0.001 
Constant 0.167   

Multiple linear regression analysis (stepwise method).

VF, visceral fat; FMD, flow-mediated dilation; CRP, C-reactive protein.

Table VII.

Final model of multiple linear regression analysis of FMD as dependent variable in PCOS patients.

 Unstandardized coefficient (SE) Standardized coefficient P-value 
IMT 5.401 (1.703) 0.205 0.002 
VF −0.100 (0.015) −0.498 <0.001 
CRP −1.421 (0.194) −0.580 <0.001 
Constant 15.468   
 Unstandardized coefficient (SE) Standardized coefficient P-value 
IMT 5.401 (1.703) 0.205 0.002 
VF −0.100 (0.015) −0.498 <0.001 
CRP −1.421 (0.194) −0.580 <0.001 
Constant 15.468   

Multiple linear regression analysis (stepwise method).

IMT, intima-media thickness; VF, visceral fat; CRP, C-reactive protein.

Discussion

The present study was aimed at evaluating the role of VF amount in predicting early CVD in PCOS. For this purpose, we investigated VF by the ultrasonographic method and vascular damage by measuring both carotid IMT and brachial FMD, and inflammation by evaluating CRP, fibrinogen, WBC count and PAI-1.

PCOS is known to be characterized by several biochemical and metabolic alterations potentially increasing the CVD risk. Early signs of vascular damage and increased CVR have been previously described (Carmina et al., 2006; Cussons et al., 2006; Orio et al., 2006). Increased weight and obesity (mostly abdominal) are common in PCOS women (Gambineri et al., 2002; Apridonidze et al., 2005). Abdominal fat excess is known to be associated with increased risk of atherosclerosis (Gasteyger and Tremblay, 2002) and CVD mortality (Dagenais et al., 2005).

Our results confirm that the amount of VF is significantly higher in PCOS women than in controls. Furthermore, in PCOS, IMT was positively affected by FMD, VF, and CRP, whereas FMD was positively affected by IMT and negatively by VF and CRP.

Visceral obesity has been demonstrated to be related to endothelial dysfunction and premature atherosclerosis (Kawamoto et al., 2002; Liu et al., 2005). Kawamoto et al. (2002) showed that the abdominal wall fat index (AFI) can be considered an independent contributing factor to IMT in women. Pre-peritoneal and mesenteric fat thickness were also found to be correlated with IMT (Liu et al., 2005). Furthermore, Hashimoto et al. (1998) demonstrated that subjects with visceral type obesity, rather than those with the subcutaneous type, showed impaired FMD of the brachial artery.

We previously showed that IMT and FMD, two early atherosclerotic indexes, are increased and reduced, respectively, in young PCOS women (Orio et al., 2004b). Consistent with previous studies (Hashimoto et al., 1998; Kawamoto et al., 2002; Liu et al., 2005), here we also found a correlation between VF and early signs of vascular damage, increased IMT and reduced FMD, but for the first time we report this association in PCOS patients.

CRP has also been demonstrated to be closely related to the degree of VF accumulation (Cigolini et al., 1996). Recently, Puder et al. (2005) evaluated the impact of body fat distribution by dual X-ray absorptiometry (DXA) on the serum inflammatory markers in PCOS patients and demonstrated a significant association between some markers of low-grade chronic inflammation (CRP, IL-1Ra and procalcitonin) and central fat excess. In agreement with Puder et al. (2005), in the present study we also found increased levels of inflammatory markers (fibrinogen, WBC, CRP and PAI-1) in PCOS patients.

A common feature to both obesity and PCOS is IR. Visceral adipose tissue has been demonstrated as a strong correlate of IR independent of non-abdominal and abdominal subcutaneous adipose tissue (Ross et al., 2002). Even in PCOS women, central fat excess has been associated with IR increase (Puder et al., 2005; Carmina et al., 2007). Lord et al. (2006) found a strong linear correlation between CT-measured VF and IR in PCOS women and reported that VF was the variable correlating at the highest significance with metabolic dysfunction (Lord et al., 2006). Recently, Carmina et al. (2007) also found an increased fat accumulation in the abdominal region (evaluated by DXA), which significantly correlated with higher insulin levels and reduced insulin sensitivity and demonstrated that this finding was only observed in normoweight or overweight PCOS women, but not in obesity. In the present study, we confirmed the correlation between VF and IR markers and also demonstrated an adverse lipid profile (higher triglycerides and lower HDL concentrations) in PCOS women. This further supports the hypothesis of Lord et al. (2006) and Carmina et al. (2007) that VF might be one of the determinants of IR in PCOS. In this study, we enrolled overweight PCOS women to compare with controls of similar body weight in order to avoid the confounding factor of obesity. Although IR is usually associated with obesity, lean subjects can also have IR because of accumulation of VF. In fact, even non-obese women with PCOS were found to have increased intra-abdominal fat accumulation that may contribute to the development of glucose and lipid metabolism disorders (Yildrim et al., 2003). In the present study, we selected patients and controls with similar BMI, but as expected, PCOS women showed greater WC than controls, consistent with previous studies (Liu et al., 2003; Bosy-Westphal et al., 2006). Janssen et al. (2004) suggested that WC is more informative than BMI in indicating obesity-related health risk. Although BMI and WC may independently contribute in predicting obesity (Janssen et al., 2002) and obesity-related metabolic risk (Bosy-Westphal et al., 2006), it should be considered that for a given BMI value, subjects with higher WC have a greater health risk than those with lower WC (Janssen et al., 2004). For all of the above, WC measurement as a clinical marker of variations in VF should be encouraged in the daily practice, even considering that it cannot distinguish between subcutaneous and VF.

One potential limitation of the current study is the evaluation of VF by US. Currently, the best estimation methods of VF accumulation are CT at L4–5 level or MRI. The wide use of these methods is prevented by exposition to ionizing radiation for the former and high costs and limited availability for the latter. Another method to assess fat quantity and distribution is DXA. The main advantage of DXA method is that it gives numerical data that are independent of the operator. Unfortunately, its disadvantage is the impossibility of differentiating subcutaneous from visceral abdominal fat (Carmina et al., 2007). However, despite the fact that US is largely operator-dependent, it probably represents at the moment the best cost-effective method (Iacobellis, 2005) for assessing VF amount. Additionally, in the current study, US measurements were performed by using a latest generation high-resolution sonography. The diagnostic accuracy of these instruments for the evaluation of VF (Ribeiro-Filho et al., 2001; Kim et al., 2004) has been shown to be comparable with those obtained with CT (Hirooka et al., 2005) and MRI (Abe et al., 1995). VF thickness measured by US has been considered also as a reliable index for identifying diabetic patients, who are at high risk of CVD (Kim et al., 2004), and patients with adverse metabolic profile (Ribeiro-Filho et al., 2001).

In conclusion, in the current study, we demonstrate that US-measured VF amount is increased in PCOS patients and that it is associated with subclinical CVD.

Funding

FIRB (RBAU01FMEY).

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