Polymorphisms in Polycystic Ovary Syndrome

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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)

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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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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, Lau¨ felfingen, 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-


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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

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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-


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’Universita` e della Ricerca Scientifica e Tecnologica, Nota prot. n. 400/14.3.2001).

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