ORIGINAL ARTICLE

Biochemical hyperandrogenism is associated with metabolicsyndrome independently of adiposity and insulin resistancein Romanian polycystic ovary syndrome patients

Alice Albu • Serban Radian • Simona Fica •

Carmen Gabriela Barbu

Received: 23 December 2013 / Accepted: 12 June 2014

� Springer Science+Business Media New York 2014

Abstract The aim of the study was to determine whether

Romanian polycystic ovary syndrome (PCOS) patients

have an increased prevalence of metabolic syndrome

(MetS) and to study the involvement of adiposity, insulin

resistance and hyperandrogenism in the pathogenesis of

MetS in PCOS. A total of 398 PCOS patients and 126

controls were evaluated between January 2006 and

December 2012. MetS was defined by National Cholesterol

Education Program, Adult Treatment Panel III criteria.

Principal component analysis (PCA) was used to analyze

the correlations among variables of interest by grouping

them in few components, and principal component (PCs)

scores were saved and used as independent variables in

logistic regression. The prevalence of MetS was higher

among patients with PCOS (20.4 %) than in controls

(11.1 %, p \ 0.05). In PCOS patients, PCA extracted three

PCs from the analyzed variables. First PC aggregated

variables related to adiposity and insulin resistance, with

factor loadings showing strong relationship between these

parameters. The second PC included markers of hyperan-

drogenemia and was best represented by free androgen

index (FAI) which correlated strongly and exclusively with

this PC. The third component was best represented by

hirsutism. Logistic regression analysis revealed that in

PCOS patients, the first and the second PCs were inde-

pendently associated with MetS, whereas the third com-

ponent was not. Romanian PCOS patients have an

increased risk for MetS; adiposity, insulin resistance and

hyperandrogenemia, but not hirsutism, are independent

predictors of MetS presence. Our data also suggest that

insulin resistance is only secondary to increased adiposity

and FAI is a good marker of biochemical hyperandroge-

nism with little influences from the metabolic component.

Keywords Polycystic ovary syndrome � Insulin

resistance � Hyperinsulinemia � Hyperandrogenemia �Metabolic syndrome

Introduction

More than 75 years have passed from the first description

by Stein and Leventhal of the disease known today as

polycystic ovary syndrome (PCOS) [1]. Although at the

beginning PCOS was considered mainly a reproductive

disorder, studies of the last two decades showed that PCOS

also implies a predisposition to metabolic complications

and an association with cardiovascular and metabolic risk

factors [2, 3]. Metabolic syndrome (MetS) is among the

most studied cardio-metabolic risk factors in PCOS

patients, and a recent meta-analysis reported an increased

risk for MetS of PCOS patients [3]. Nevertheless, not all

studies of PCOS patients found an association with MetS

[4]. This could be due to heterogeneity of the study pop-

ulations with regard to various confounding factors such as

age, adiposity and ethnicity. For example, the prevalence

of obesity ranges from 25.9 % in Italian PCOS patients [5]

to 61 and 76 %, respectively, in the USA [6] and Australia

A. Albu � S. Radian � S. Fica � C. G. Barbu

‘‘Carol Davila’’ University of Medicine and Pharmacy, Dionisie

Lupu Street 37, Bucharest, Romania

A. Albu � S. Fica (&) � C. G. Barbu

Endocrinology and Diabetes Department, Elias University

Hospital, Marasti Street 17, Bucharest, Romania

e-mail: simonafica@yahoo.com

S. Radian

‘‘CI Parhon’’ National Institute of Endocrinology, Aviatorilor

Blvd 34-36, Bucharest, Romania

123

Endocrine

DOI 10.1007/s12020-014-0340-9

[7]. Ethnicity seems to influence the metabolic profile and

the predisposition to metabolic complications as suggested

by the high prevalence of hypertension and cardiovascular

complications among women of African descent, whereas

Hispanic women are more likely to present comorbidities

such as MetS and type 2 diabetes mellitus [8]. Few pre-

vious studies [9–11] highlighted the contribution of genetic

influences, lifestyle factors and cultural practices to the

variability of the metabolic and cardiovascular risk profiles

in PCOS. Therefore, characterizing the metabolic pheno-

type of a specific population is of great importance for

clinical practice.

The unifying factor between PCOS and MetS is

assumed to be insulin resistance (IR), a central patho-

genic mechanism in both conditions. Another possible

contributor is hyperandrogenism, although not all studies

agree on this aspect [12, 13]. Although IR is considered

an intrinsic characteristic of PCOS, with a different

pathogenic basis than the IR of obesity, the high preva-

lence of obesity and central adiposity among PCOS

patients could exacerbate the already existing IR. The

hyperinsulinism (HI) contributes to hyperandrogenism in

PCOS by stimulating ovarian androgen production and by

increasing androgen bioavailability through decreased

hepatic sex-hormone-binding globulin (SHBG) produc-

tion. Increased adiposity could contribute directly to hy-

perandrogenism, independently of IR, through

overactivation of 17beta-hydroxysteroid dehydrogenase

and 5alfa-reductase in adipose tissue, which results in

production of the potent androgens testosterone and

dihydrotestosterone [14]. The increased androgens may

amplify both IR and visceral adiposity, as suggested by

clinical and in vitro studies [15–18]. Since all these

factors presumably contributing to the high prevalence of

MetS in PCOS are subject to this complex interplay, it is

difficult to interpret their individual contributions to MetS

pathogenesis in PCOS.

The aims of our study were as follows: (1) to establish

whether Romanian PCOS patients have an increased

prevalence of MetS (previously not published) and (2) to

study the involvement of adiposity, IR/HI and hyperan-

drogenism in the pathogenesis of MetS in PCOS.

Due to assumed close relationship between adiposity,

IR/HI and hyperandrogenism, we decided to submit the

variables related to these parameters to principal compo-

nent analysis (PCA): a statistical method able to analyze

the relation between variables and to reduce the number of

correlated variables which capture the same information by

grouping them in a smaller number of uncorrelated vari-

ables called principal components (PCs). One of the major

advantages of PCA is that all the correlated variables can

be included in the analysis without the limitations due to

collinearity.

Subjects and methods

The study was performed in the academic setting of the

Endocrinology Department of Elias University Emergency

Hospital and C. I. Parhon National Institute of Endocri-

nology, both in Bucharest Romania. The study protocol

was approved by the local ethics committee. All subjects

gave written informed consent before evaluation. PCOS

subjects included in the study were recruited from PCOS

patients consecutively admitted in our departments

between January 2006 and December 2012. PCOS patients

were women referred by a wide range of specialist: der-

matologists, gynecologists, nutritionists, primary care

physicians. The presenting complaints were menstrual

irregularities, hirsutism, acne and infertility. Controls were

women with regular menstrual cycles and no clinical or

biochemical hyperandrogenism recruited during routine

evaluation for thyroid dysfunction (proven euthyroid),

obesity, fertile women who presented in the Gynecology

Department of Elias Hospital for routine gynecological

exam and hospital employees at periodical checkup. PCOS

was diagnosed according to the Rotterdam Consensus cri-

teria [19], i.e., two out of the following three criteria: (1)

chronic oligo/anovulation, (2) clinical and/or biochemical

hyperandrogenism and (3) polycystic appearance of the

ovaries on ultrasound and exclusion of hyperprolactinemia,

hypercortisolism, thyroid dysfunction, hypogonadism and

hyperandrogenism of other causes. PCOS and control

subjects had to be off oral contraceptives, insulin sensi-

tizers or antiandrogens for at least 3 months before eval-

uation, without any treatment that could significantly affect

hirsutism (cosmetic treatment or oral contraceptives/anti-

androgens for more than three months) and without any

significant associated disease. Only postmenarcheal and

premenopausal non-pregnant women were included in the

study.

Clinical evaluation

For all subjects, we recorded age, weight, height, waist

(WC) and hip circumference (HC) and menstrual history.

Weight was measured in light clothing without shoes and

height was measured using a stadiometer and the body

mass index (BMI) was calculated. Waist and hip circum-

ferences were measured with the patient standing, at the

level of umbilicus and pubic symphysis, respectively.

Blood pressure was measured with the patient seated after

at least 10 min of rest.

Clinical hyperandrogenism was considered in the pre-

sence of hirsutism, defined as a modified Ferriman–Gall-

wey (FG) score of at least 8 [20], evaluated by the

investigators through systematic use of the FG maps.

Chronic ovulatory dysfunction was diagnosed in the

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presence of menstrual irregularities: oligomenorrhea

(menstrual cycles of 35–90 days) or amenorrhea (absence

of menses for more than 90 days). In patients with regular

menstrual cycles, chronic anovulation was considered if

absence of ovulation was documented by ultrasound and/or

mid-luteal phase serum progesterone in two consecutive

cycles.

Laboratory evaluation

Blood samples were collected after an overnight fast,

between days 3 and 5 of a spontaneous or progesterone-

induced menstrual cycle. Total testosterone (TT), SHBG,

thyroid stimulating hormone (TSH), glucose, insulin, total

cholesterol, high-density lipoprotein (HDL) cholesterol,

LDL cholesterol and triglycerides were measured in all

subjects (PCOS and controls). Luteinizing hormone (LH),

follicle-stimulating hormone (FSH), estradiol, prolactin

and oral glucose tolerance test with 75 g of glucose and

measurements of glucose and insulin fasting and 2 h after

glucose ingestion were performed only in PCOS patients.

Morning serum 17-hydroxyprogesterone and cortisol after

an overnight suppression test were evaluated only in

selected patients with a high clinical suspicion index.

Biochemical hyperandrogenism was defined in the pre-

sence of TT C 0.73 ng/mL and/or a free androgen index

(FAI) C 4.5. These cutoff values were determined as the

95th percentile of a group of 150 reproductive age women

without hirsutism/acne and with regular menstrual cycles,

from the same population (unpublished data).

HOMA-IR was calculated as [fasting glycemia (mg/

dL) 9 fasting insulinemia (lUI/mL)]/405. FAI was

obtained by dividing total testosterone (nmol/L) 9 100 to

SHBG (nmol/L).

The hormonal blood tests were performed using a

chemiluminescent immunometric assay (Immulite 2000,

Siemens Healthcare Diagnostics Products Ltd.).

Metabolic syndrome evaluation

Metabolic syndrome diagnosis was based on the National

Cholesterol Education Program, Adult Treatment Panel III

(NCEP ATP III) criteria (at least 3 of the following):

WC [ 88 cm, serum triglycerides C150 mg/dL, HDL

cholesterol \50 mg/dL, systolic blood pressure C130 and/

or diastolic blood pressure C85 mm Hg, serum glucose

C110 mg/dL [21].

Statistical analysis

Statistical analysis was performed using SPSS version 20.0

(SPSS Inc., Chicago, IL, USA). Results are reported as

medians and interquartile range or percentages as

appropriate. In order to test the normality of the distribu-

tion of variables Kolmogorov–Smirnov test was used. For

comparisons between groups, we used the Mann–Whitney

U test for numerical variables and chi-square test for cat-

egorical variables. Binary logistic regression was per-

formed in order to adjust for age or adiposity markers when

analyzing parameters associated with MetS.

Eleven parameters related to adiposity, HI/IR and hy-

perandrogenism were submitted to PCA.

PCA is a statistical method useful for both data reduc-

tion and structure detection. The purpose of data reduction

is to identify from a panel of data the variables which are

highly correlated and to reduce the entire data set to a

smaller number of uncorrelated variables PCs. PCA is also

able to perform structure detection, meaning to examine

the underlying relationship between variables. There are

many advantages when using PCA. For example, it could

be difficult to decide which marker of adiposity to choose

to investigate the link between MetS and adiposity,

knowing the differences in metabolic activity of specific

type of adiposity distribution. Using PCA, all the markers

of adiposity can be submitted to analysis, grouped in PCs

and then the PCs can be introduced in a model of multi-

variate regression to study their association with MetS. In

this way, we are able to take into consideration all the

variables representing adiposity, avoiding in the same time

collinearity. Moreover, PCA can identify the marker which

best represent the group by having the highest correlation

coefficient in this particular PC and weak correlation

coefficients with the other PCs, but also the markers which

represent a ‘‘bridge’’ between PCs by having high corre-

lation coefficients with many PCs.

Parameters with non-Gaussian distribution were log-

transformed before PCA. All factors with eigenvalues

exceeding one were retained for analysis. Varimax rotation

was applied in order to facilitate the PCs interpretation.

PCs loadings were interpreted as follow: between 0.3 and

0.5 demonstrated small correlations, and loadings above

0.5 identified strong correlations. The PCs scores were

saved and used to analyze their relationship with MetS in

binary logistic regression. p values \0.05 were considered

indicative of statistical significance.

Results

Clinical and paraclinical parameters of the study

subjects

We observed that the PCOS patients (n = 398) were sig-

nificantly younger than controls (n = 126) (p \ 0.001).

Age ranged between 17 and 40 years in PCOS subjects and

between 17 and 42 years in controls. BMI ranged between

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18.5 and 45 kg/m2 in PCOS study group and between 19

and 49 kg/m2 in controls. The two groups were similar in

terms of BMI, waist and hip circumferences, but PCOS

subjects had significantly higher waist–hip ratio (WHR)

(Table 1).

Prevalence of the metabolic syndrome and individual

components

The prevalence of MetS was significantly higher among

patients with PCOS (20.4 %, n = 81/398) than in controls

(11.1 %, n = 14/126, p = 0.019). In PCOS patients,

among the individual criteria used to define MetS, low

HDL cholesterol was the most prevalent (50.33 %), fol-

lowed by increased waist circumference (43.5 %), high

blood pressure (19.25 %), hypertriglyceridemia (18.3 %)

and high blood glucose (4.53 %). In controls, low-HDL

cholesterol and WC had very close prevalences (40.27 %

and 41.46 %, respectively), followed in the same order by

high blood pressure (17.2 %), high triglycerides levels

(6.45 %) and high blood glucose (3.36 %).

After adjustment for age using binary logistic regression

with MetS as dependent variable and age and diagnosis

(PCOS/controls) as independent variables, we found that

PCOS patients still had an increased risk of MetS (OR

3.36, CI 1.71–6.5, p \ 0.0001). However, after including

WHR in the logistic regression model, PCOS was no

longer significantly associated with MetS, although age and

WHR continued to be independent predictors of MetS.

Comparison between PCOS patients

with and without MetS

In PCOS patients, the presence of MetS was associated

with increased BMI, WC, HC, WHR and higher insuline-

mia (fasting and 2 h) and insulin resistance (HOMA-IR)

(Table 2). Patients with both PCOS and MetS also had

more severe hyperandrogenism, both clinically (signifi-

cantly higher FG score of hirsutism) and biochemically, as

reflected by the FAI levels, but not by TT levels (Table 2).

Principal component analysis

Eleven variables representing adiposity (BMI, WHR, WC,

HC), insulinemia (fasting and 2 h insulin), insulin resis-

tance (HOMA-IR) and hyperandrogenism (TT, SHBG,

FAI, hirsutism) were used for PCA and three PCs were

extracted, accounting for 74.6 % of the total variance

(Table 3). First component (‘‘adiposity and insulin resis-

tance’’) aggregated variables related to adiposity, insuli-

nemia and insulin resistance and explained 41.6 % of the

Table 1 Clinical and biochemical data in PCOS and controls

PCOS

(n = 398)

Controls

(n = 126)

p

Age (years) 24 (7) 28 (10) \0.0001

BMI (kg/m2) 26.1 (10.9) 25.61 (12) NS

WC (cm) 83 (27.6) 80 (27.26) NS

HC (cm) 103(20) 107 (23) NS

WHR 0.81 (0.13) 0.77 (0.12) \0.0001

Fasting glycemia

(mg/dL)

84 (12) 84 (15) NS

2 h glycemia (mg/dL) 99 (32.7) NA

Fasting insulin

(lUI/mL)

11.53 (12.25) 7.42 (6.32) \0.0001

2 h insulin (lUI/mL) 54.9 (64.6) NA

HOMA-IR 2.41 (2.83) 1.64 (1.52) \0.0001

Total testosterone

(ng/mL)

0.72 (0.4) 0.45 (0.3) \0.0001

FAI 6.32 (6.81) 1.69 (2.64) \0.0001

SHBG (nmol/L) 39.69 (33.3) 79.69 (76.22) \0.0001

Data are expressed as median and interquartile range. For compari-

sons between groups, Mann–Whitney U est was used

BMI body mass index; WC waist circumference; HC hip circumfer-

ence; WHR waist–hip ratio; HOMA-IR homeostasis model of

assessment of insulin resistance; FAI free androgen index; SHBG sex-

hormone-binding globulin; NS not significant. NA not applicable

Table 2 Clinical and biochemical parameters in PCOS patients with

and without MetS

PCOS with

MetS (n = 81)

PCOS without

MetS (n = 317)

p

Age (yrs) 27 (11) 23 (7) \0.0001

Hirsutism

(FG score)

11 (10) 9 (7) 0.035

BMI (kg/m2) 34 (8.38) 23 (9.1) \0.0001

HC (cm) 116 (16.7) 100 (16) \0.0001

WHR 0.9 (0.09) 0.78 (0.11) \0.0001

Fasting insulin

(lUI/mL)

22.5 (18) 10.27 (9.52) \0.0001

2 h insulin

(lUI/mL)

113.4 (126) 49 (49.1) \0.0001

HOMA-IR 5.38 (4.8) 2.15 (2.03) \0.0001

Total

testosterone

(ng/mL)

0.76 (0.42) 0.72 (0.4) NS

FAI 10.26 (11.8) 5.64 (6.1) \0.0001

SHBG (nmol/L) 27.2 (21.5) 44.11 (40) \0.0001

Data are expressed as medians and interquartile range. For compari-

sons between groups, Mann–Whitney U test was used

BMI body mass index; WC waist circumference; HC hip circumfer-

ence; WHR waist–hip ratio; HOMA-IR homeostasis model of

assessment of insulin resistance; FAI free androgen index; SHBG sex-

hormone-binding globulin; HDL high-density lipoprotein; FG Ferri-

man–Gallway; NS not significant; NA not applicable

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123

total variance. Factor loadings showed significant rela-

tionship between variables representing adiposity and IR/

HI. First PC was best represented by WC which had the

highest correlation coefficient (0.912) and did not correlate

with the other PCs. The second PC (‘‘biochemical hyper-

androgenism’’) explained 19.8 % of total variance and

included markers of hyperandrogenemia (TT, FAI) and

SHBG which correlated strongly with this PC. None of the

other parameters were correlated with the second PC,

except for the 2 h insulinemia. FAI was the main repre-

sentative of this PC due to highest correlation coefficients

with the second PC and weak correlation coefficients with

the other PCs. TT behaved like a bridge between compo-

nents having small loading with the first and the third PCs.

The third component (‘‘clinical hyperandrogenism,’’

explaining 13.2 % of the total variance) was best repre-

sented by hirsutism which correlated exclusively with this

PC, but also gathered adiposity markers (BMI and HC),

TT, SHBG which all had weak correlation coefficients and

explained 13.2 % of the total variance.

Logistic regression analysis

In order to avoid influences of latent factors and collin-

earity when studying the association between clinical and

biochemical parameters and MetS in PCOS patients, we

used logistic regression to construct a model with MetS as

dependent variable and age and the three PCs identified by

PCA as independent predictors. After adjustment for age,

the first PC, representing adiposity and insulin resistance,

and the second PC, representing hyperandrogenemia, were

independently associated with MetS (p \ 0.0001 and

p = 0.003, respectively), whereas the third PC represent-

ing clinical hyperandrogenism was not significantly related

to MetS (Table 4).

Discussion

The present study is to our knowledge the first specifically

assessing the association of MetS and PCOS in Romanian

patients and detailing the data structure by means of PCA.

The prevalence of MetS was similar to that reported by

previous studies of European PCOS populations [22, 23],

but visibly lower than in the US and Australian studies [24,

25]. The increased WHR seems to be responsible for the

high prevalence of MetS in our population of PCOS

patients, as the significant association between MetS and

PCOS was lost after adjustment for WHR in binary logistic

regression analysis. WHR was demonstrated to have a

good correlation with visceral adiposity measured by

Table 3 Loading matrix of the three PCs extracted by PCA in PCOS patients

First PC (41.6 %*) Second PC (19.8 %*) Third PC (13.2 %*)

‘‘Adiposity and

insulin resistance’’

‘‘Biochemical

hyperandrogenism’’

‘‘Clinical

hyperandrogenism’’

BMI 0.821 0.192 0.387

WHR 0.738 0.055 -0.047

WC 0.912 0.121 0.267

HC 0.778 0.125 0.418

Fasting insulin 0.832 0.222 -0.081

2 h insulin 0.604 0.303 -0.133

HOMA-IR 0.837 0.204 -0.072

TT 0.311 0.650 20.379

SHBG -0.071 20.768 20.468

FAI 0.240 0.950 0.163

Hirsutism (FG score) 0.069 0.100 0.801

The eleven variables showed in the table were submitted to PCA and the three PCs were extracted. Significant correlation coefficients ([0.3

showing small and [0.5 showing strong correlations) are in bold. * Percentage of the total variance explained by the corresponding PC

PC principal component; PCA principal component analysis; BMI body mass index; WHR waist–hip ratio; WC waist circumference; HC hip

circumference; HOMA-IR homeostasis assessment of insulin resistance; TT total testosterone; SHBG sex-hormone-binding globulin; FAI free

androgen index; FG Ferriman–Gallwey

Table 4 Logistic regression analysis with MetS as dependent vari-

able and age and the three PCs as independent variables

Coef SE OR 95 %CI of OR p

Age 0.042 0.059 1.04 0.9–1.17 NS

First PC 1.519 0.352 4.56 2.29–9.09 \0.0001

Second PC 0.631 0.225 1.87 1.2–2.92 0.003

Third PC 0.293 0.315 1.34 0.7–1.17 NS

PC principal component; Coef regression coefficient; SE standard

error; OR odds ratio; CI confidence interval; NS not significant

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computed tomography in women [26]; therefore, we could

assume a role for visceral adiposity in increased prevalence

of MetS in Romanian PCOS patients. This finding is in

accord with recent publications highlighting the impor-

tance of central adiposity, rather than global adiposity in

the vicious circle of adiposity–insulin resistance–hyperan-

drogenism, as mediator of the metabolic effects in PCOS

[22]. Although many studies reported an increase in central

adiposity characterizing PCOS patients, this may not be

true for all PCOS populations, as suggested by a well-

designed study showing that PCOS and controls matched

for BMI and fat mass had similar distribution of adiposity

[27]. Indeed, the great metabolic heterogeneity of PCOS

patients is further supported by metabolomics studies

showing that hyperinsulinemia of lean patients with PCOS

is not necessarily secondary to insulin resistance [28].

Interestingly, all the markers of adiposity and HI/IR

aggregated in the same PC suggesting the strong relation-

ship between these two important contributors to PCOS

pathogenesis in our population. This PC explained 41.6 %

of the total variance of the data set, highlighting the

importance of metabolic component in PCOS pathogene-

sis. Similar data were published by Dewailly et al. [29]

who concluded that IR/HI of PCOS is just a state associ-

ated with obesity, but with a higher prevalence compared

with general population. Another recently published report

studying PCOS patients attending an in vitro fertilization

department demonstrated increased prevalence of IR only

in overweight/obese PCOS comparing to controls, but not

in lean PCOS [30]. These results challenge previous studies

reporting that insulin resistance of PCOS is intrinsic to the

syndrome and has a different mechanism of that of obesity.

Moreover, the PCOS patients in our study were not

severely obese (median BMI 26.1 kg/m2); therefore, we

can assume that regional adiposity rather than global adi-

posity is mainly involved in HI/IR in our group. Indeed, the

first PC was best represented by WC, a marker of central

adiposity which correlated strongly and exclusively with

this PC. The positive correlation of 2 h insulinemia with

the second PC challenge a previous report showing that

transient hyperinsulinemia produced by an oral glucose

load produce a decrease in circulating androgen levels [31].

The second PC gathered the androgenic variables: TT,

FAI and SHBG and was best represented by FAI which

correlated strongly and exclusively with this PC. It was

somehow surprising to see FAI and SHBG were not cor-

related with the first PC, knowing the great influence of IR

on SHBG hepatic production. A possible explanation not

tested yet in Romanian patients is the genetic influence on

SHBG levels and consecutively on FAI level reported by

other authors [32]. Therefore, FAI seems to be a specific

marker of hyperandrogenism, without the influence from

the metabolic component, at least in our patients. This

finding is contrasting with a previous report [29] showing

FAI has strong correlation with metabolic status and is not

specific for PCOS. Although TT loaded mainly on the

second PC, it had also weak correlation with the first and

third PC, unraveling the possible bidirectional link between

TT level and metabolic parameters. Interestingly, TT cor-

related weakly and negatively with the third PC mainly

represented by hirsutism. A possible explanation is the

observation that there is an inverse relationship between

clinical and biochemical hyperandrogenism with increas-

ing age, meaning that hirsutism aggravates, whereas hy-

perandrogenemia diminishes [33]. Hirsutism did not

aggregate with the biochemical markers of hyperandroge-

nism, indicating it is not a good surrogate of circulating

androgens. Moreover, it was not linked with the metabolic

component, possibly reflecting other influences. For

example, variants of the 5alpha-reductase type 1 and type 2

genes were shown to be associated with the severity of

hirsutism in PCOS women [34]. Therefore, the presence of

hirsutism and hyperandrogenemia is not equivalent, and

this type of information could be important for clinical

studies of PCOS patients, especially when taking into

consideration the metabolic end points. Indeed, previous

studies suggested that the exclusive presence of hirsutism

in PCOS patients is associated with milder metabolic dis-

turbances compared to those with hyperandrogenemia [35].

Logistic regression analysis confirmed this information

showing that the third PC represented by hirsutism was not

independently associated with the presence of MetS in

PCOS patients. Instead, the first PC representing adiposity

and IR/HI and the second PC representing hyperandro-

genemia were independently related with MetS.

The relationship between circulating androgens levels

and MetS in women with PCOS continues to be intensely

debated. A recently published meta-analysis [36] showed

that higher total and free testosterone are associated with

increased prevalence of MetS in PCOS patients (although

the link was weaker than in non-PCOS women). Never-

theless, those studies addressing the role of hyperandrog-

enism in the MetS risk in PCOS reported divergent data,

with some studies founding a positive association [12, 24,

25], whereas other authors reported similar androgens

levels in patients with and without MetS [13]. In fact, few

of these studies took into account the potential confounding

effect of adiposity on the relationship between MetS and

androgen levels, making it difficult to interpret the results.

Although not addressing directly the influences of andro-

gens levels on MetS, but rather on MetS components, the

study of Zanolin et al. [37] suggested also an independent

relationship between free testosterone and at least some

metabolic risk factors (arterial blood pressure). In our

study, the link between circulating androgens and MetS

independent of adiposity, insulin resistance and age is

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clearly demonstrated using the exact mathematical tool of

PCA. Moreover, we included in our analysis all anthro-

pometric indices representing both global and abdominal

adiposity, trying to take into consideration the complex

relation between androgens and body fat.

The main reason for using the NCEP ATP III definition

for MetS in our study was to allow comparison with previous

studies on MetS in PCOS patients since most of them used

NCEP ATP III criteria. Moreover, there may be additional

benefits as suggested by existing reports showing that dif-

ferent definitions of MetS identify distinct categories of

patients in terms of cardio-metabolic risk. Therefore, it is

possible that using NCEP ATP III criteria to achieve a better

selection of patients with increased risk of cardiovascular

complication, as compared to the IDF and EGIR criteria [38].

In turn, the predictive value for incident diabetes seems to be

similar for IDF, NCEP ATP III and WHO definitions of MetS

as suggested by recent studies [39, 40].

One of the limitations of our study is the fact that the

PCOS and control groups were not matched for age.

However, we consider this did not have an impact on our

analysis taking into consideration that generally the prev-

alence of MetS is increasing with age, still the PCOS

patients had higher prevalence of MetS despite being

younger. Moreover, the association between MetS and

PCOS was maintained after adjustment for age.

In this study, we did not systematically measure serum

androstenedione and dehydroepiandrosterone sulfate

(DHEAS) levels. Nevertheless, the impact of this is rela-

tively limited given the fact that these two androgens add

little to the PCOS diagnosis, as only 10 % of patients with

biochemical hyperandrogenism have isolated increased

levels of androstenedione or DHEAS [41]. At the same

time, the lack of complete circulating androgen profile did

not allowed us to analyze the metabolic correlations of

DHEAS in our population, and this could be an interesting

aspect since previous studies suggested an association of

DHEAS with a more favorable metabolic profile [42].

In conclusion, our study demonstrated that Romanian

PCOS patients have an increased risk for cardio-metabolic

complications and unraveled some particularities of our

population, namely the increased adiposity as the main

contributor to the association of the two conditions and HI/

IR more likely as a satellite phenomenon of adiposity, than

an intrinsic characteristic of the disease. At the same time,

hyperandrogenemia was found to be an independent con-

tributor to the high prevalence of MetS in PCOS patients,

contrasting with hirsutism which was not involved in this

metabolic condition.

Acknowledgments We are grateful to all of the staff at both centers

and especially to Suzana Florea (Elias Hospital Laboratory) for

technical assistance. The study was partly funded by the CNCSIS

Grant No. 1333/2007 of the Romanian Ministry of Youth, Education

and Research.

Conflict of interest The authors declare that they have no conflict

of interest.

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