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The Journal of Clinical Endocrinology & Metabolism 88(12):5887–5892
Copyright © 2003 by The Endocrine Society
doi: 10.1210/jc.2002-021816
Exon 6 and 2 Peroxisome Proliferator-Activated
Receptor-␥ Polymorphisms in Polycystic
Ovary Syndrome
FRANCESCO ORIO, JR., GIUSEPPE MATARESE, SEBASTIANO DI BIASE, STEFANO PALOMBA,
DONATO LABELLA, VERONICA SANNA, SILVIA SAVASTANO, FULVIO ZULLO,
ANNAMARIA COLAO, AND GAETANO LOMBARDI
Department of Molecular & Clinical Endocrinology and Oncology (F.O., S.S., A.C., G.L.), University “Federico II”, 80131
Naples, Italy; Immunoendocrinology Group (G.M., V.S.), Institute of Endocrinology and Experimental Oncology, National
Research Council, 80131 Naples, Italy; MeriGen Molecular Biology Laboratory (S.D.B., D.L.), 80131 Naples, Italy; and
Chair of Obstetrics and Gynecology (S.P., F.Z.), University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
Obesity affects about 44% of women with polycystic ovary
syndrome (PCOS). Peroxisome proliferator-activated receptor-␥ (PPAR-␥) is one of the genes involved in the differentiation of adipose tissue. In an attempt to shed light on the high
percentage of obesity in PCOS, we examined polymorphisms
at exons 6 and 2 of the PPAR-␥ gene in 100 PCOS patients and
in 100 healthy controls matched for age and body mass index
(BMI). The T allele frequency of exon 6 was significantly
higher (P < 0.05) in PCOS patients compared with control
women. In addition, the BMI and leptin levels were signifi-
cantly higher (P < 0.05) in PCOS patients carrying the C3 T
substitution than in controls. There was no significant difference in leptin levels after normalization for BMI. The Pro12Ala
polymorphism at exon 2 was unrelated to BMI and/or leptin
levels in PCOS women. In conclusion, the higher frequency of
the C3 T substitution in exon 6 of the PPAR-␥ gene in PCOS
women suggests that it plays a role in the complex pathogenetic mechanism of obesity in PCOS, whereas the Pro12Ala
polymorphism does not seem to affect BMI in PCOS women.
(J Clin Endocrinol Metab 88: 5887–5892, 2003)
P
obese humans (15). PPAR-␥ is a susceptibility gene for both
diabetes and obesity (16). Moreover, the Pro12Ala variant in
PPAR-␥ exon 2 is associated with an increased body mass
index (BMI) (17) and attenuated insulin resistance (18).
Numerous genes have been tested in relation to the etiopathogenesis of PCOS (19). Moreover, the mechanism and
cause of obesity in this syndrome are unknown. In an attempt
to understand the high percentage of obesity in PCOS, we
examined polymorphisms in exons 6 and 2 of the PPAR-␥
gene in patients affected by PCOS and in healthy women.
OLYCYSTIC OVARY SYNDROME (PCOS) is a widespread endocrine-metabolic disorder characterized by
obesity, hyperandrogenism, and insulin resistance (1). Although it primarily affects fertility, PCOS is a plurimetabolic
syndrome (2). Obesity, which is a complex metabolic disorder with a strong genetic component (3), affects about 44%
of PCOS women (4). Many determinants and/or genetic
factors are involved in adipocyte differentiation (5), among
which is peroxisome proliferator-activated receptor-␥
(PPAR-␥) (6 –9).
PPAR-␥ is expressed mainly in adipose tissue and is also
involved in lipid and glucose metabolism (9). It is a candidate
gene for the development of obesity and regulation of adipose tissue metabolism in humans (10, 11). Because single
gene defects are very rarely associated with obesity (12), it is
likely that a combination of polymorphisms in one or more
candidate genes may contribute to the development of obesity (13). In fact, enhanced PPAR-␥ signaling, owing to a
mutation that increases its intrinsic activity, is associated
with human obesity (14).
A body of evidence implicates PPAR-␥ gene variants in
metabolic disorders (15–18). A silent C3 T substitution
in exon 6 of the PPAR-␥ gene affects plasma leptin levels in
Abbreviations: A, Androstenedione; AUC, area under curve; BMI,
body mass index; CV, coefficient(s) of variation; DHEA-S, dehydroepiandrosterone sulfate; E2, 17␤-estradiol; OGTT, oral glucose tolerance
test; 17 OH-P, 17-hydroxyprogesterone; P, progesterone; PCOS, polycystic ovary syndrome; PPAR-␥, peroxisome proliferator-activated receptor-␥; PRL, prolactin; T, testosterone; TV-USG, transvaginal ultrasonography; WHR, waist-to-hip ratio.
Subjects and Methods
Subjects
One hundred women with PCOS and 100 healthy young volunteer
females, matched for age and BMI, were enrolled in this case-control
study protocol.
PCOS was diagnosed from anovulatory infertility (confirmed by luteal progesterone assay), normal serum FSH levels (normal range, 1.0 –
10.0 IU/liter), and at least two of the following: hirsutism (Ferriman and
Gallwey score ⬎ 8) (20); elevated serum androgen levels [total testosterone (T) ⬎ 2 nmol/liter); and/or androstenedione (A) above 15 nmol/
liter; and/or dehydroepiandrosterone sulfate (DHEA-S) above 10
␮mol/liter; a LH/FSH ratio above 2; and polycystic ovaries identified
with transvaginal ultrasonography (TV-USG) (21). All patients fulfilled
the National Institute of Child Health and Human Development criteria
for PCOS (22).
The healthy state of the controls was determined by medical history,
physical and pelvic examination, and blood chemistry tests. Their normal ovulatory state was confirmed by TV-USG and plasma progesterone
(P) levels during the luteal phase of the cycle. Women with clinical
and/or biochemical hyperandrogenism were excluded from the control
group. The controls were not genetically related to the PCOS group.
Exclusion criteria for both groups were pregnancy, hypothyroidism,
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Orio et al. • PPAR-␥ Polymorphisms in PCOS Women
J Clin Endocrinol Metab, December 2003, 88(12):5887–5892
hyperprolactinemia, Cushing’s syndrome, nonclassical congenital adrenal hyperplasia, and current or previous (within the last 6 months) use
of oral contraceptives, glucocorticoids, antiandrogens, ovulation induction agents, antidiabetic and antiobesity drugs, and other hormonal
drugs. Subjects with glucose intolerance, as evaluated according to
World Health Organization criteria (23) with the oral glucose tolerance
test (OGTT), were excluded from the study. No patient had diabetes, or
renal, neoplastic, metabolic, hepatic, cardiovascular, or malabsorptive
disorders. All subjects were nonsmokers and had a normal physical
activity level, and none drank alcoholic beverages.
Hyperprolactinemia was diagnosed when a single assay showed a
serum prolactin (PRL) concentration below 25 ng/ml (24). It was excluded when the average of three serum PRL measurements, taken at
15-min intervals starting at 0800 h, was above 25 ng/ml. Nonclassical
congenital adrenal hyperplasia was excluded with a single assay of
serum 17-hydroxyprogesterone (17 OH-P) levels (normal value less than
6.0 nmol/liter) (25).
Study protocol
The procedures used in this study were in accordance with the guidelines of the Helsinki Declaration on human experimentation. The Institutional Review Board of the University of Naples “Federico II” approved the study. The purpose of the protocol was explained to patients
and control women, and written consent was obtained from them before
beginning the study.
At study entry, venous blood was withdrawn from both groups for
the genetic study and for hormonal (including leptin), lipid profile,
glucose, insulin, and homocysteine assays. Glucose and insulin values
were measured also after the OGTT. Blood samples were obtained
between 0800 and 0900 h after an overnight fast with the individual
resting in bed, during the early follicular phase (second to fifth days) of
the spontaneous or progesterone-induced menstrual cycle. During the
same visit, subjects underwent TV-USG, anthropometric measurements,
including BMI (kilograms per square meter), and waist-to-hip ratio
(WHR), systolic and diastolic blood pressure, adiponectin measurements (26), echocardiographic assessment, and echocolor-Doppler with
evaluation of intima media thickness.
Herein we report the results concerning PPAR-␥ exons 6 and 2 and
the hormonal assessment.
Biochemical assay
The following hormone levels were measured in basal blood samples:
LH, FSH, 17␤-estradiol (E2), P, T, A, DHEA-S, PRL, TSH, and SHBG.
Blood samples for each woman were assayed in duplicate and immediately centrifuged, and the serum was stored at ⫺80 C until analysis.
The mean of two hormonal results was calculated.
Plasma PRL, LH, TSH, FSH, E2, P, T, A, and DHEA-S were measured
by specific RIA, as previously described (24, 27). Serum 17 OH-P levels
were determined with a RIA (Diagnostic Systems Laboratories 5000,
Webster, TX) that has a sensitivity of 0.5 nmol/liter and intraassay and
interassay coefficients of variation (CV) of 8.9 and 9.0%, respectively (25).
Levels of SHBG were measured with an immunoradiometric assay
(Radim S.p.A, Pomezia, Rome, Italy) that has a sensitivity of 2.5 nmol/
liter and intraassay and interassay CV of 5.1 and 5.2%, respectively (28).
Leptin concentrations were determined with human leptin ELISA kits
(Alexis Corporation, Laüfelfingen, Switzerland) and calculated from
standard curves generated for each assay using recombinant human
leptin, according to the manufacturer’s instructions, with the fourparameter function (29). The minimum detection limit of the assay was
0.2 ng/ml. The intra- and interassay CV were below 5%. Samples were
measured in duplicate at 450 nm, using an ELISA plate reader (Bio-Rad
Laboratories, Inc., Hercules, CA).
Glucose and insulin concentrations were measured 30 min after insertion of the iv catheter to evaluate the fasting levels (time 0) before
OGTT. Successively, each subject received a 75-g glucose oral load. Other
blood samples (10 ml each) were obtained at 30-min intervals for the next
3 h during infusion (at 30, 60, 90, and 120 min), and glucose and insulin
concentrations were determined. Plasma glucose levels were determined by the glucose oxidase method on a Beckman Glucose Analyzer
(Beckman Coulter, Inc., Fullerton, CA) that has a sensitivity of 0.3 mmol/
liter, and intraassay and interassay CV of 1.0 and 1.2%, respectively.
Serum insulin was measured by a solid-phase chemiluminescent enzyme immunoassay using commercially available kits (Immunolite Diagnostic Products Co, Los Angeles, CA) that have a sensitivity of 2.0
␮U/ml and intraassay and interassay CV of 5.5 and 5.8%, respectively.
The glucose and insulin response to the OGTT was also analyzed
by calculating the area under curve (AUC). The AUCs for glucose
(AUCglucose) and insulin (AUCinsulin) were determined according to the
Tai procedure (30) for the metabolic curves. The AUCglucose/AUCinsulin
ratio was also calculated (31).
DNA analysis
Blood samples were collected in tubes containing disodium-EDA as
anticoagulant and stored at 4 C until DNA extraction. DNA was extracted by the salt phenol chloroform method from the buffy coat cells
(32). The extracted DNA was stored at ⫺20 C until analysis. We used the
restriction fragment length polymorphism technique and the PCR to
examine the C to T substitution in exon 6 of the PPAR-␥ gene. The
primers used for exon 6 (5⬘CCAGAAAATGACAGACCTCAGACA3⬘
forward and 5⬘CAGAATAGTGCAACTGGAAGAAGG3⬘ reverse) generated a 181-bp DNA fragment. The C sequence is recognized by the PmlI
restriction enodonulease, which digested the 181-bp fragment into 142and 39-bp fragments. The most common allele has a C residue at 142 bp,
whereas the variant allele has a T at this position. Exon 2 of the PPAR-␥
gene was amplified by PCR using the primers G2F (5⬘CTGATGTCTTGACTCATGGG3⬘) and G2R (GGAAGACAAACTACAAGAGC3⬘). The
295-bp PCR product was digested overnight with HgaI, which cleaves
the G allele to generate two DNA fragments of 178 and 117 bp, respectively (18). The DNA fragments and the PCR products were separated
on 3% agarose gel electrophoresis and visualized under UV light after
ethidium bromide staining. Genotypes were expressed in exon 6 as CC,
CT, and TT for homozygous normal, heterozygous, and homozygous
mutant, respectively, and in exon 2 as CC and CG for homozygous
normal and heterozygous, respectively.
Statistical analysis
The data were analyzed with the SPSS 11.0 (SPSS Inc., Chicago, IL)
package. Continuous data were expressed as mean ⫾ sd. A P ⬍ 0.05
value was considered statistically significant. The demographic characteristics and the hormone concentrations in the two groups were
compared by the Student t test for unpaired data. The data between and
within the PPAR-␥ genotype groups were compared by ANOVA. The
Student t test for unpaired data was also used to evaluate the differences
in mean serum leptin levels between the PCOS and control groups.
Allelic and genotypic frequencies were determined from observed genotype counts. Differences in the allelic and genotypic frequencies of
exons 6 and 2 PPAR-␥ polymorphisms were assessed by the onesized Fisher’s exact test when appropriate. The differences in mean
AUCglucose, AUCinsulin, and the AUCglucose/AUCinsulin ratio after OGTT
between and within the different groups of PPAR-␥ genotypes were
studied with ANOVA. A multivariate two-way ANOVA was also used
to evaluate the possible interactions between variables.
Results
Table 1 shows the clinical and biochemical diagnostic features of the PCOS group. Table 2 shows the demographic,
hormonal, and metabolic characteristics of the PCOS patients
and controls. Both groups were Caucasians of European
ancestry (Campania region, southern Italy). The two groups
were closely matched for BMI and age. The PCOS group had
higher (P ⬍ 0.05) circulating levels of LH, E2, 17 OH-P, T, A,
DHEA-S, and IGF-I, and significantly lower (P ⬍ 0.05) circulating concentrations of P and SHBG. Ferriman-Gallwey
scores were significantly (P ⬍ 0.05) increased in PCOS vs. the
control population. Serum leptin levels were similar in the
two groups. Fasting glucose levels and AUCglucose were also
similar in the two groups, whereas fasting insulin levels,
AUCinsulin, and the AUCglucose/AUCinsulin ratio were signif-
Orio et al. • PPAR-␥ Polymorphisms in PCOS Women
J Clin Endocrinol Metab, December 2003, 88(12):5887–5892 5889
TABLE 1. Clinical and biochemical diagnostic features of the 100
PCOS women studied
a
b
Features
%
Anovulatory infertility
Normal FSH levels
Oligo/amenorrheaa
Clinical hyperandrogenisma
Hirsutismb
Acne
Biochemical hyperandrogenisma
T ⬎ 2 nmol/liter
A ⬎ 15 nmol/liter
DHEA-S ⬎ 10 ␮mol/liter
LH/FSH ratio ⬎2
Polycystic ovary at TV-USG
100
100
100
100
100
43
50
33
30
27
100
33
NIH PCOS criteria.
As evaluated by Ferriman-Gallwey score.
TABLE 2. Clinical, hormonal, and metabolic characteristics of
women with PCOS and controls
PCOS
(n ⫽ 100)
Age (yr)
BMI (kg/m2)
WHR
Ferriman-Gallwey score
FSH (IU/liter)
LH (IU/liter)
PRL (ng/ml)
E2 (pmol/liter)
P (nmol/liter)
17 OH-P (nmol/liter)
T (nmol/liter)
A (nmol/liter)
DHEA-S (␮mol/liter)
SHBG (nmol/liter)
IGF-I (nmol/liter)
Leptin (ng/ml)
Fasting glucose (mmol/liter)
Fasting insulin (␮U/ml)
OGTT
AUCglucose
AUCinsulin
AUCglucose/AUCinsulin ratio
Controls
(n ⫽ 100)
23.1 ⫾ 4.3
32.2 ⫾ 6.9
0.88 ⫾ 0.3
12.5 ⫾ 1.3a
9.1 ⫾ 1.2
32.5 ⫾ 3.4a
10.6 ⫾ 0.6a
166.5 ⫾ 17a
1.2 ⫾ 0.4a
4.4 ⫾ 0.3a
2.6 ⫾ 0.3a
7.5 ⫾ 0.5a
6.7 ⫾ 2.8a
27.6 ⫾ 5.1a
46.2 ⫾ 2.3a
15.2 ⫾ 4.4
6.4 ⫾ 3.0
20.6 ⫾ 6.5a
23.0 ⫾ 3.4
30.6 ⫾ 6.4
0.86 ⫾ 0.4
5.1 ⫾ 0.4
9.4 ⫾ 2.1
16.1 ⫾ 1.5
11.4 ⫾ 0.8
144 ⫾ 16
2.3 ⫾ 0.7
4.1 ⫾ 0.5
1.3 ⫾ 0.3
4.4 ⫾ 0.6
3.8 ⫾ 2.1
49.8 ⫾ 8.1
30.6 ⫾ 9.2
14.4 ⫾ 3.8
5.7 ⫾ 2.8
8.0 ⫾ 2.0
1247 ⫾ 485
7818 ⫾ 1431a
0.16 ⫾ 0.05a
1205 ⫾ 268
2541 ⫾ 500
0.47 ⫾ 0.04
Data expressed as mean ⫾ SD.
a
P ⬍ 0.05 vs. control group.
icantly (P ⬍ 0.05) higher in PCOS patients than in controls.
There was a significant (P ⬍ 0.05) relation between BMI and
leptin levels in both PCOS (R⫽ 0.81) and controls (R⫽ 0.84).
Table 3 shows the allelic and genotypic frequencies of
PPAR-␥ exons 6 and 2. Genotype frequencies for both exons
were similar and conformed to the Hardy-Weinberg equilibrium (33). For exon 6, the CC genotype was significantly
(P ⬍ 0.05) more frequent than genotypes CT and TT in both
groups. Furthermore, the T allele was significantly (P ⬍ 0.05)
more frequent in PCOS patients than in control women. For
exon 2, the CC genotype was not significantly more frequent
than the CG genotype in both groups. The frequency of the
G allele of the exon 2 (Pro12Ala polymorphism) was similar
in PCOS and controls.
As regards exon 6, in PCOS patients, BMI and leptin levels
were significantly (P ⬍ 0.05) higher in the CT/TT genotype
vs. the CC genotype (Table 4). They were also higher in the
CT/TT genotype in patients vs. both the CC and CT/TT
TABLE 3. Allelic and genotypic frequencies of exons 6 and 2 of
the PPAR-␥ gene
Alleles n (%)
Exon 6
PCOS
Controls
Exon 2
PCOS
Controls
C
Genotypes n (%)
T
172 (86%) 28 (14%)
187 (93.5%) 13 (6.5%)
CC
CT
TT
79 (79%)
88 (88%)
14 (14%)
11 (11%)
7 (7%)
1 (1%)
CG
C
G
CC
193 (96.5%)
195 (97.5%)
7 (3.5%)
5 (2.5%)
93 (93%)
95 (95%)
7 (7%)
5 (5%)
C, Proline; G, alanine. Exon 6: Alleles, one-sided Fisher’s exact test
P ⫽ 0.0062; genotypes (CC vs. CT/TT) one-sided Fisher’s exact test P ⫽
0.0355. Exon 2: Alleles, one-sided Fisher’s exact test P ⫽ 0.1953;
genotypes (CC vs. CG) one-sided Fisher’s exact test P ⫽ 0.1973.
genotypes in controls (Table 4). Differently, BMI, leptin levels, and the leptin/BMI ratio did not differ between the CC
and CT/TT genotypes in control women (Table 4). On the
contrary, considering exon 2, BMI, leptin levels, and leptin/
BMI ratio did not differ significantly between or within the
PCOS and control groups (Table 4).
No difference in fasting glucose and insulin levels,
AUCglucose, AUCinsulin, and in the AUCglucose/AUCinsulin ratio was detected between the CC and CT/TT genotypes of
exon 6 in either group (Table 5). Similarly, there was no
difference in fasting glucose and insulin levels, AUCglucose,
AUCinsulin, and in the AUCglucose/AUCinsulin ratio between
the CC and CG genotypes of exon 2 in either group (Table
5).
Multivariate two-way ANOVA showed a significant interaction only for the genotypes of exon 6 with regard to BMI
(P ⫽ 0.0415) and leptin (P ⫽ 0.0322). Furthermore, no significant interaction was detected concerning the leptin/BMI
ratio (P ⫽ 0.3956).
Discussion
Our study is the first to evaluate the role of exons 6 and
2 PPAR-␥ polymorphisms in the complex pathogenetic
mechanism of obesity in PCOS. Here, we show the silent
CAC478CAT exon 6 polymorphism (34) and the result of a
CCA-to-CGA missense mutation in codon 12 corresponding
to the Pro12Ala exon 2 polymorphism of PPAR-␥ gene (34).
We confirm that serum leptin levels did not differ between
PCOS women and healthy controls closely matched for age
and BMI (35–38). In accordance with Meirhaeghe et al. (15),
who showed that the C to T substitution is related with BMI
and leptin levels only in obese women, we demonstrate that
in healthy control women, serum leptin levels and BMI do
not differ between CT/TT and CC. On the contrary, in PCOS
women with the CT/TT genotypes, the serum leptin levels
and BMI were significantly higher with respect to the CC
genotype and to the CC and CT/TT genotypes of controls.
After normalization of leptin for BMI, in CT/TT PCOS subjects the serum leptin concentrations were similar to the other
genotype group, which indicates that the enhanced leptin
level is probably due to BMI, although we cannot exclude an
effect of the C to T substitution in this subpopulation.
The T allelic frequency observed in our control group was
similar to that detected in healthy nonobese and obese
women (15). In addition, the CT/TT polymorphism was
5890
Orio et al. • PPAR-␥ Polymorphisms in PCOS Women
J Clin Endocrinol Metab, December 2003, 88(12):5887–5892
TABLE 4. BMI, leptin, and the leptin/BMI ratio according to different genotypes of exons 6 and 2 of PPAR-␥ gene polymorphisms in
PCOS and controls
PCOS
Exon 6
2
BMI (kg/m )
Leptin (ng/ml)
Leptin/BMI ratio
Controls
Interaction
CC
CT/TT
P
CC
CT/TT
P
P
31.1 ⫾ 7.0
14.4 ⫾ 4.3
0.46 ⫾ 0.08
36.3 ⫾ 5.1
18.0 ⫾ 3.7
0.49 ⫾ 0.07
0.0012
0.0003
0.0748
30.6 ⫾ 6.5
14.4 ⫾ 3.9
0.47 ⫾ 0.06
30.5 ⫾ 5.6
14.6 ⫾ 3.6
0.47 ⫾ 0.07
0.9931
0.8780
0.7343
0.0415
0.0322
0.3956
Exon 2
CC
CG
P
CC
CG
P
P
BMI (kg/m2)
Leptin (ng/ml)
Leptin/BMI ratio
32.1 ⫾ 7
15.1 ⫾ 4.5
0.47 ⫾ 0.08
32 ⫾ 5.5
15.1 ⫾ 3.8
0.47 ⫾ 0.05
0.9468
0.9815
0.9880
30.5 ⫾ 6.4
14.4 ⫾ 3.8
0.47 ⫾ 0.06
30.3 ⫾ 4.8
14.3 ⫾ 2.1
0.47 ⫾ 0.03
0.9298
0.9688
0.8507
0.9811
0.9883
0.8939
Interaction between genotypes of exons 2 and 6 is shown for each variable considered.
TABLE 5. Glucose metabolism in PCOS women and controls according to different genotypes of exons 6 and 2 of PPAR-␥ gene
polymorphisms
PCOS
Exon 6
Fasting
Glucose (mmol/liter)
Insulin (␮U/ml)
OGTT
AUCglucose
AUCinsulin
AUCglucose/AUCinsulin ratio
Exon 2
Fasting
Glucose (mmol/liter)
Insulin (␮U/ml)
OGTT
AUCglucose
AUCinsulin
AUCglucose/AUCinsulin ratio
CC
CT/TT
Controls
P
CC
CT/TT
Interaction
P
P
6.4 ⫾ 3.0
20.5 ⫾ 6.6
6.6 ⫾ 3.1
20.8 ⫾ 6.2
0.7479
0.8464
5.7 ⫾ 2.8
8.1 ⫾ 2.0
6.1 ⫾ 2.7
8.0 ⫾ 2.5
0.6361
0.9809
0.8654
0.8891
1247 ⫾ 468
7801 ⫾ 1443
0.160 ⫾ 0.05
1246 ⫾ 555
7880 ⫾ 1417
0.159 ⫾ 0.06
0.9909
0.7646
0.8878
1197 ⫾ 267
2535 ⫾ 508
0.472 ⫾ 0.04
1264 ⫾ 276
2641 ⫾ 318
0.487 ⫾ 0.04
0.5812
0.8831
0.3358
0.6610
0.9428
0.4006
CC
CG
P
CC
CG
P
P
6.5 ⫾ 3.0
20.6 ⫾ 6.6
4.6 ⫾ 2.4
19.7 ⫾ 4.3
0.0838
0.6236
5.8 ⫾ 2.8
8.1 ⫾ 2.1
3.9 ⫾ 1.3
8.1 ⫾ 2.6
0.1478
0.9951
0.9809
0.7535
1239 ⫾ 482
7752 ⫾ 1412
0.159 ⫾ 0.05
1352 ⫾ 540
7430 ⫾ 934
0.187 ⫾ 0.079
0.4611
0.4321
0.1466
1199 ⫾ 267
2536 ⫾ 508
0.473 ⫾ 0.04
1322 ⫾ 292
2641 ⫾ 318
0.496 ⫾ 0.06
0.4930
0.8287
0.2988
0.9661
0.4978
0.8772
Interaction between genotypes of exons 2 and 6 is shown for each variable considered.
more frequent in the PCOS group than in healthy BMImatched women. These findings could explain, indeed, the
high frequency of obesity in women with PCOS. In fact, this
polymorphism could affect the ability of PPAR-␥ to induce
differentiation of fibroblasts or other undifferentiated cells
into mature fat cells (39). Furthermore, Meirhaeghe et al. (15)
did not find any difference in C and T allelic frequency
between obese and nonobese women. Thus, it is probable
that PCOS women present a different genetic pattern also
compared with obese subjects. In addition, in PCOS women,
as in obese healthy women (15), the CT/TT genotype is
associated with significantly higher serum leptin concentrations compared with CC women.
Differently, we did not find a significant difference between the CC and CG genotypes of exon 2 as regards BMI,
leptin, and the leptin/BMI ratio between and within the
PCOS and control groups. Consequently, it seems that the
Pro12Ala variant plays a minor role, if any, in the pathogenesis of obesity. It has been suggested that this polymorphism
contributes to the genetic susceptibility for obesity (17, 40 –
43). Beamer et al. (43) found higher BMI in two independent
Caucasian populations with the Pro12Ala polymorphism,
and Valve et al. (17) reported that this polymorphism is
associated with increased BMI, fat mass, and WHR in obese
women. The Ala allele has been variously reported to be
associated with both a higher BMI (15, 44) and a lower BMI
(45– 49), and to be unrelated to BMI (50 –53). By considering
haplotypes, Doney et al. (45) reported opposite associations
of the linked Pro12Ala and C1431T polymorphisms of the
PPAR-␥ gene. In fact, the T1431 and Ala12 alleles were associated with an increased and decreased BMI, respectively
(45). Therefore these two polymorphisms in the PPAR-␥ locus are in close linkage disequilibrium and have an opposite
association with body weight.
In the PCOS group, although the TT polymorphism was
associated with a higher BMI, AUCinsulin and the AUCglucose/
AUCinsulin ratio were not significantly higher in the CT/TT
genotype compared with the CC genotype. Consequently,
either the increased TT frequency in PCOS women does not
affect insulin sensitivity or insulin resistance does not affect
this phenotype in women with PCOS. As recently reported
by Azziz (54), insulin resistance in PCOS generally refers to
the impaired action of insulin on glucose transport and lipolysis, principally in adipocytes, in the presence of relatively normal insulin binding (55–58). Nonetheless, the
mechanism underlying the abnormal insulin signaling observed in women with PCOS, both obese and nonobese,
remains unknown.
Although Barroso et al. (59) showed that subjects affected
by loss of function PPAR-␥ mutations share common elements of the insulin-resistance syndrome, improved insulin
sensitivity has been associated with the Pro12Ala polymor-
Orio et al. • PPAR-␥ Polymorphisms in PCOS Women
phism in (Caucasian) healthy populations (17, 41, 46, 60), and
the association of this polymorphism with type 2 diabetes is
controversial (16, 34, 40, 47, 61– 64). Our findings show that
Pro12Ala does not influence glucose metabolism in PCOS and
healthy women. In fact, we found no difference in AUCinsulin
and in the AUCglucose/AUCinsulin ratio in the two groups
regarding the Pro12Ala polymorphism. Most studies of the
association between the PPAR-␥ Pro12Ala polymorphism
and type 2 diabetes were conducted with a small sample.
Altshuler et al. (16) combined samples to achieve adequate
power and found that although PPAR-␥ Pro12Ala was reproducibly associated with type 2 diabetes, this polymorphism could not be the etiologic variant, but rather in linkage
disequilibrium with it (16).
Hara et al. (18) showed an association between the Ala
allele and increased insulin sensitivity only in Caucasian
women with PCOS. Furthermore, their study population had
a significantly higher BMI than our PCOS patients (36.3 ⫾ 0.8
vs. 30.1 ⫾ 9.0 kg/m2). In fact, our PCOS group included
normal, overweight, and obese PCOS women, whereas Hara
et al. (18) enrolled exclusively obese PCOS women. In addition, six diabetic women were included in their analysis of
the Pro/Pro group, whereas metabolic disorders and glucose
intolerance were exclusion criteria in our study protocol.
We found no relationship between exon 2 and exon 6 in
our study population. Therefore, further studies are needed
to clarify better the role, if any, of these two polymorphisms
in PCOS. The exact mechanisms by which PPAR-␥ polymorphisms could affect adipose tissue mass are unknown. Other
epidemiological and genetic studies on the PPAR-␥ gene
locus, and the screening of the whole PPAR-␥ gene to identify
other mutations responsible for the effect of the C/T polymorphism studied and nearby polymorphisms, are needed
to advance our understanding of the complex scenario governing the pathogenesis of PCOS and its relationship with
obesity.
Acknowledgments
We thank Dr. Benito Chinea for valuable assistance in the statistical
analysis and Mr. Christian Siatka (“Ecole de l’ADN”, Nimes, France) for
the analysis and elaboration of the data. We are indebted to Jean Ann
Gilder for editing the text, and to the patients and controls for having
agreed to participate in this study.
Received November 19, 2002. Accepted September 5, 2003.
Address all correspondence and requests for reprints to: Dr.
Francesco Orio, Via Giovanni Santoro 14, 84123 Salerno, Italy. E-mail:
[email protected].
F.O. and S.P. contributed equally to the preparation and final version
of this manuscript.
This work was supported by a grant from the “Progetto Giovani
Ricercatori” of the University of Naples “Federico II” (Ministero
dell’Università e della Ricerca Scientifica e Tecnologica, Nota prot. n.
400/14.3.2001).
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