J Bone Miner Metab DOI 10.1007/s00774-013-0560-8
ORIGINAL ARTICLE
Association of plasma GDF-9 or GDF-15 levels with bone parameters in polycystic ovary syndrome Zehra Berberoglu • Aynur Aktas • Yasemin Fidan Ayse Canan Yazici • Yalcin Aral
•
Received: 18 March 2013 / Accepted: 8 December 2013 Ó The Japanese Society for Bone and Mineral Research and Springer Japan 2014
Abstract We aimed to determine plasma levels of growth and differentiation factor (GDF)-9 and GDF-15, and their possible association with bone turnover parameters and bone mineral density (BMD), in patients with polycystic ovary syndrome (PCOS). Forty-two obese PCOS women aged 25–35 years, 23 women with idiopathic hirsutism (IH) and 20 healthy controls matched for age and body mass index were enrolled. Anthropometric, metabolic and hormonal patterns, plasma GDF-9 and GDF15 concentrations, bone turnover markers and BMD were measured. No significant differences were observed in bone turnover markers, BMD measurements, plasma GDF9 and GDF-15 levels in subjects with PCOS compared with the other two groups. In the combined population of all three groups, GDF-15 concentrations were negatively correlated with osteocalcin (r = -0.317, p \ 0.01). Analysis of PCOS patients showed a significant correlation of GDF-15 concentrations with age and homeostasis model assessment index (r = 0.319, p \ 0.05, and r = 0.312, p \ 0.05, respectively). In addition, GDF-15 con-
The study has been submitted to www.clinicaltrials.gov (ID:NCT01644305). Z. Berberoglu (&) A. Aktas Y. Aral Department of Endocrinology and Metabolism, Ankara Education and Research Hospital, S¸ u¨kriye Mh, 06340 Sıhhıye, Ankara, Turkey e-mail: [email protected] Y. Fidan Department of Biochemistry, Ankara Education and Research Hospital, Ankara, Turkey A. C. Yazici Department of Biostatistics, Baskent University Faculty of Medicine, Ankara, Turkey
centrations were negatively correlated with osteocalcin (r = -0.395, p \ 0.01) and positively correlated with urine deoxypyridinoline (r = 0.353, p \ 0.05). GDF-9 did not correlate with bone markers and BMD measurements. In conclusion, plasma GDF-9 and GDF-15 levels as well as bone turnover markers and BMD measurements in subjects with PCOS (25–35 years of age) were comparable with those either in subjects with IH or in healthy controls with similar anthropometric and metabolic profiles. GDF-15 might be a marker of a crossregulation between bone and energy metabolism. Keywords Polycystic ovary syndrome GDF-9 GDF-15 Bone parameters
Introduction Polycystic ovary syndrome (PCOS), a frequently encountered endocrinopathy in women of reproductive age, is characterized by anovulation, hyperandrogenism, obesity, insulin resistance (IR) and/or polycystic ovaries (PCOs). Many PCOS symptoms may have long-term effects on bone mineral density (BMD). Previous studies reported normal [1] or increased BMD [2] in patients with PCOS compared with controls. Obesity may contribute to the conserved BMD in PCOS due to the beneficial mechanical loading impact of body weight on bone formation. Adiposity has been reported to be positively associated with BMD [3]. Furthermore, hyperinsulinemia and IR, independent of body mass index (BMI), may protect against the development of osteoporosis [4]. Androgen treatment may not only inhibit osteoclast formation directly [5] but also modulate osteoblast/osteoclast interaction via osteoprotegerin [6].
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Growth and differentiation factors (GDF) belong to the transforming growth factor-b superfamily which is composed of a number of structurally related polypeptides, including activins/inhibins, decapentaplegic and bone morphogenetic proteins [7–9]. They have diverse biological functions. GDF-9, known to be secreted by oocytes in primary human follicles, is obligatory for normal folliculogenesis. It is involved in granulosa cell proliferation and in differentiation of theca cells during follicular development from preantral to early antral stage [10]. GDF-9 enhances preantral follicle growth by upregulating theca cell androgen production [11] and promotes follicular survival during this early stage by suppressing granulosa cell apoptosis and follicular atresia [10]. Delayed and reduced expression of GDF-9 messenger RNA (mRNA) has been found in oocytes of women with PCOS and in PCOs during their growth and differentiation phase [12]. The question whether the changes in GDF-9 mRNA in developing oocytes in women with PCOS and PCOs are coupled with changes in GDF-9 protein synthesis or activity has remained unanswered. On the other hand, although GDF-9 mRNA has been reported to be exclusively expressed in the normal ovary, a study has addressed the possibility of GDF-9 expression in non-ovarian tissues [13]. GDF-9 mRNA was observed in diverse nongonadal tissues, including pituitary, uterus and bone marrow. Non-ovarian expression of GDF-9 mRNA in various tissues is likely due to its presence in white blood cells [14]. GDF-15, also known as macrophage inhibitory cytokine-1 (MIC-1), is a stress-induced cytokine. It carries prognostic information about cardiovascular events and mortality either in patients with coronary artery disease [15] and heart failure [16] or in elderly women with no previous evidence of cardiovascular disease [17]. Additionally, serum levels of MIC-1 were demonstrated to be significantly increased in obese individuals [18]. This cytokine was positively correlated with glucose, insulin, HbA1c and homeostasis model assessment (HOMA) index [19]. Furthermore, it has been reported that androgen positively regulates the expression of GDF15/ MIC-1 in vivo [20, 21]. These correlations highlight the importance of characterizing GDF-15 in women with PCOS, many of whom also have features of metabolic syndrome. On the other hand, GDF-15 was shown to suppress the formation of mature osteoclasts in a dosedependent manner, suggesting a crosstalk between bone and energy metabolism [22]. In view of this complex context, we sought to determine plasma levels of GDF-9 and GDF-15, and their possible association with bone turnover parameters and BMD, in patients with PCOS.
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Materials and methods The study was approved by the local institutional ethics committee of the hospital. All participants provided their written informed consent before study enrollment. Subjects Obese female subjects aged 25–35 years were included in the present study. We studied 42 women with newly diagnosed or untreated PCOS, 23 women with idiopathic hirsutism (IH) and 20 healthy controls matched for age and BMI. Using the revised Rotterdam criteria (Rotterdam ESHRE/ASRM-sponsored PCOS Consensus Workshop Group 2004), PCOS was defined as the existence of at least two of the following three features: oligomenorrhea involving less than nine cycles a year or amenorrhea lasting 3 months or more, clinical or biochemical signs of hyperandrogenism, and PCOs [23]. Table 1 shows the clinical and biochemical diagnostic features of the PCOS patients. The diagnosis of IH was based on the presence of hirsutism (Ferriman–Gallwey score [8), regular ovulatory menstrual cycles and a normal serum androgen profile. The presence of PCOS was excluded. Inclusion criteria for the control group were: (1) regular ovulatory menstrual cycles (28 ± 2 days, blood progesterone levels[10 ng/mL in two consecutive cycles); (2) absence of hyperandrogenism; and (3) normal ovarian morphology on ultrasound imaging. Exclusion criteria were: (1) pregnancy; (2) androgensecreting tumors, Cushing’s syndrome, congenital adrenal hyperplasia, hyperprolactinemia; (3) acute or chronic infection; (4) a thyroid, parathyroid, pituitary, metabolic, nutritional, autoimmune, hepatic, renal, cardiovascular or neoplastic disorder; (5) current or previous treatment with Table 1 Clinical and biochemical diagnostic features of the PCOS women studied Features
n (%)
Anovulatory infertility
33 (79)
Oligo/amenorrhea
33 (79)
Clinical hyperandrogenism Hirsutisma Acne Biochemical hyperandrogenism
42 (100) 42 (100) 9 (21) 28 (67)
Testosterone
28 (67)
DHEA-S
16 (38)
LH/FSH ratio [2
15 (36)
Polycystic ovary at ultrasonography
23 (55)
DHEA-S dehydroepiandrosterone sulfate, LH luteinizing hormone, FSH follicle-stimulating hormone a
As evaluated by Ferriman–Gallwey score
J Bone Miner Metab
drugs that might affect calcium (Ca) or bone metabolism; (6) drugs known to interfere with cytokine release; (7) current or previous use of medications known to affect gonadotropin secretion, serum sex steroid levels, metabolic parameters and insulin sensitivity; (8) smoking and excessive alcohol/caffeine consumption; (9) excessive physical training; and (10) any skeletal deformity that may affect measurement of BMD. The dietary Ca intake was determined via a food-frequency questionnaire. Methods At study entry, all subjects underwent a complete clinical examination, anthropometric measurements and laboratory tests. Body fat measurements that were used to determine the body fat percentage and total body fat mass were obtained in fasting subjects by a leg-to-leg bioelectric impedance device (TBF-300 M; Tanita Corp., Tokyo, Japan). All measurements at a single frequency of 50 kHz were obtained by the same investigator adhering to the manufacturer’s guidelines. Analysis of the impedance values from this device revealed a within-day coefficient of variation (CV) of 0.7 % [standard deviation (SD) 0.6] and a between-day CV of 2.6 % (SD 1.5). Laboratory parameters included hormonal analyses, fasting glucose and insulin levels, HOMA index [24], serum GDF-9 and GDF-15 concentrations, levels of serum bone-specific alkaline phosphatase (bsALP) and human osteocalcin (OCL) concentrations as markers of bone formation, and urine deoxypyridinoline (DPD) and pyridinoline (PYD) levels as markers of bone resorption. Other nonspecific bone markers, including serum total ALP activity and urinary Ca and phosphate (PO4) concentrations, were also measured. Urine concentrations of Ca (mmol/L) and PO4 (mmol/L) were corrected for their respective urine creatinine (Cr) concentrations in mmol/L (urine Ca/Cr and urine PO4/Cr, respectively). BMD of lumbar spine and left proximal femur was measured by dual-energy X-ray absorptiometry using a LUNAR DPX instrument (GE Lunar, Fitchburg, WI, USA). Quality control scans for the densitometer were performed daily using a manufacturer-supplied anthropomorphic spine phantom. The CV for BMD measurements in our laboratory is 1 % for both lumbar spine and left proximal femur. Biochemical assays All blood samples were obtained in the morning between 08.00 and 09.00 a.m. after overnight fasting, during the early follicular phase (cycle days 2–5) of a spontaneous or progestin-induced menstrual cycle. The first morning
urine sample was collected without preservative before 10.00 a.m. to avoid any possible effect from diurnal variation on the DPD and PYD. Blood samples for the measurement of GDF-9 and GDF-15 were centrifuged with sera aliquoted immediately after collection. They were deep-frozen and stored at -20 °C. Plasma glucose levels were assayed by a glucose hexokinase method on an Olympus AU2700 analyzer (Beckman Coulter Clinical Diagnostics, Nyon, Switzerland). Serum fasting insulin levels were measured by enzyme-linked immunosorbent assay (ELISA) with an DSX 5.19 analyzer (DYNEX Technologies Inc., Chantilly, VA, USA). HbA1c was assayed with a high-performance liquid chromatography (HPLC) method (Agilent Technologies GmbH & Co. KG, Waldbronn, Germany). Serum gonadotropins [luteinizing hormone, folliclestimulating hormone (FSH)], dehydroepiandrosterone sulfate (DHEA-S), estradiol, total testosterone and prolactin were analyzed by a direct chemiluminescence immunoassay (Siemens, ADVIA Centaur XP Immunoassay System, Tarrytown, NY, USA). Free testosterone and 17-hydroxyprogesterone (17-OHP) were determined using a radioimmunoassay (Gamma-C 12 DPC, Berthold, Germany). Serum bsALP and total OCL assays were performed with an ELISA method (DYNEX Technologies Inc.). Levels of urine DPD and PYD were measured via HPLC (Agilent Technologies). GDF-9 and GDF-15 levels were measured by ELISA on an DSX 5.19 analyzer (DYNEX Technologies Inc.). Statistical analysis The normal distribution of continuous variables was checked using the Shapiro–Wilk test. Homogeneity of groups’ variances was controlled by Levene’s test. Group means were compared by using one-way analysis of variance and then multiple comparisons between pairs of groups were carried out according to Tukey’s HSD test. Parametric test assumptions were not available for some variables, so the comparisons were performed with the Kruskal–Wallis test and then Dunn’s test was used for multiple comparisons. The results of statistical analyses were expressed as mean ± SD ðX Sx Þ and/or median and minimum–maximum values [M (min–max)]. The Pearson product-moment correlation coefficient was employed to evaluate the correlations between normally distributed variables. The Spearman rho correlation coefficient was used to evaluate the relationships between not normally distributed variables. Data analyses were performed using SPSS 17.0 statistical software (SPSS Inc., Chicago IL, USA). A p value \0.05 was considered statistically significant.
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Results
GDF-9
Demographic and hormonal data
GDF-9 levels did not differ significantly between the groups (Table 3).
As shown in Table 2, groups were matched for age, BMI and waist-to-hip ratio. As expected, women with PCOS had significantly higher luteinizing hormone (p \ 0.01), total testosterone (p \ 0.001) and free testosterone (p \ 0.001) levels than controls.
GDF-15 As shown in Table 3, there were no significant differences in GDF-15 levels between the groups.
Metabolic profile
Metabolic bone markers and BMD
Subjects in all three groups had similar fasting glucose, insulin and HbA1c levels (Table 3). HOMA-IR was similar across all three groups.
Serum total ALP, bsALP and OCL levels did not differ significantly between the groups (Table 3). Urine PYD and DPD were similar in all three groups. As shown in Table 4,
Table 2 Demographic and hormonal profiles in women with and without PCOS
Age (years) 2
PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
29.6 ± 3.7
30.4 ± 3.7
30.9 ± 4.1
NS
BMI (kg/m )
36.5 ± 3.7
36.4 ± 4.2
37.6 ± 4.4
NS
Body fat (%)
43.5 ± 2.3
41.3 ± 4.6
41.7 ± 4.5
NS
43.0 (39.5–49.0)
42.5 (29.3–48.0)
41.2 (31.9–49.0)
Body fat (kg) WHR FSH (IU/mL) LH (IU/mL)
36.8 ± 5.1
36.0 ± 6.3
36.4 ± 7.9
35.1 (29.9–50.5)
37.4 (26.0–49.1)
34.4 (20.8–51.3)
0.88 ± 0.08
0.9 ± 0.06
0.89 ± 0.05
0.89 (0.81–1.0)
0.91 (0.68–1.17)
0.89 (0.79–0.97)
5.9 ± 1.8
5.8 ± 2.2
5.9 ± 2.1
5.8 (2.4–9.7)
5.7 (2.4–10.2)
5.3 (2.8–9.8)
9.1 ± 5.7**
5.7 ± 3.5
5.1 ± 2.4
NS NS NS \0.01
7.8 (2.0–25.8)
4.6 (2.6–17.3)
4.4 (1.6–12.1)
Prolactin (ng/mL)
9.6 ± 4.9
8.9 ± 4.3
10.6 ± 5.6
NS
Estradiol (pg/mL)
8.3 (2.7–24.1) 47.7 ± 14.3
7.8 (3.6–19.7) 44.7 ± 13.8
9.2 (4.4–26.8) 46.9 ± 7.8
NS
50.4 (22.4–72.8)
44.0 (7.3–59.6)
45.4 (34.3–59.3)
1.2 ± 0.4
1.2 ± 0.5
1.3 ± 1.0
1.2 (0.5–1.9)
1.2 (0.4–1.9)
0.9(0.5–4.5)
67.2 ± 24.7***
49.8 ± 16.9
46.7 ± 15.6
75.9 (18.3–103.8)
47.3 (18.8–79.9)
49.9 (21.0–75.7)
17-OHP (ng/mL) Testosterone (ng/dL) Free testosterone (pg/mL) DHEA-S (lg/dL)
2.2 ± 1.0***
1.5 ± 0.8
1.6 ± 0.9
2.0 (0.7–5.6)
1.4 (0.2–3.3)
1.4 (0.6–4.5)
231.4 ± 117.7
225.1 ± 97.5
219.7 ± 73.4
211.5 (69.9–498.0)
231.0 (15.0–378.0)
224.5 (71.4–344.0)
NS \0.001 \0.001 NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, BMI body mass index, WHR waist-to-hip ratio, LH luteinizing hormone, FSH folliclestimulating hormone, 17-OHP 17-hydroxyprogesterone, DHEA-S dehydroepiandrosterone sulfate, NS not significant ** p \ 0.01 *** p \ 0.001
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J Bone Miner Metab Table 3 Metabolic profile, GDF 9, GDF 15 and bone markers in women with and without PCOS PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
BMD (g/ cm2)
NS
Lumbar spine
Fasting glucose (mg/dL)
90.9 ± 6.2
89.2 ± 6.4
90.7 ± 5.9
92 (76–99)
90 (74–99)
91 (79–99)
Fasting insulin (lIU/L)
14.4 ± 6.3
14.5 ± 5.4
14.0 ± 5.8
14.7 (2.0–31.8)
14.2 (3.4–29.0)
12.8 (4.1–26.0)
HOMA-IR
3.2 ± 1.4
3.2 ± 1.1
3.1 ± 1.4
3.0 (0.5–7.5)
3.3 (0.7–5.9)
3.0 (0.9–6.2)
HbA1c (%) GDF-9 (pg/ mL) GDF-15 (pg/ mL)
5.6 ± 0.3
5.6 ± 0.3
5.5 ± 0.3
5.6 (4.9–6.0)
5.6 (5.2–6.1)
5.6 (5.6–6.0)
83.8 ± 46.6
79.7 ± 34.6
81.3 ? ±48.2
77.5 (17–247)
74.0 (17–151)
83.0 (17–219)
171.1 ? ±49.1
183.9 ± 57.1
160.1 ± 42.2
168 (80–310)
170 (123–387)
155 (99–289)
Urine DPD
11.9 ± 7.0
12.2 ± 3.5
12.5 ± 6.7
(pmol/lmol Cr)
9.9 (3.0–34.1)
7.4 (3.8–31.6)
12.7 (3.7–30.2)
BsALP (IU/ L)
Table 4 Bone mineral density measurements in women with and without PCOS
NS
NS
Femoral neck Trochanter
NS Total hip NS NS NS
PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
NS
1.05 ± 0.10
1.07 ± 0.09
1.04 ± 0.10
1.04 (0.77–1.38)
1.08 (0.83–1.29)
1.05 (0.89–1.28)
0.91 ± 0.14
0.93 ± 0.10
0.97 ± 0.21
0.92 (0.64–1.32)
0.92 (0.72–1.19)
0.95 (0.69–1.67)
0.74 ± 0.09
0.75 ± 0.08
0.72 ± 0.08
0.74 (0.55–0.98)
0.76 (0.60–0.91)
0.73 (0.59–0.86)
1.04 ± 0.13
1.03 ± 0.09
1.03 ± 0.11
1.03 (0.74–1.32)
1.00 (0.88–1.29)
1.06 (0.83–1.19)
NS
NS
NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, BMD bone mineral density, NS not significant
29.8 ± 12.5
27.1 ± 9.3
30.3 ± 9.5
28.0 (13.2–68.7)
26.6 (13.0–55.2)
31.5 (8.5–42.8)
NS
OCL (ng/dL)
6.0 ± 3.7
6.3 ± 2.9
7.5 ± 3.8
5.6 (0.2–23.0)
6.6 (2.0–11.4)
6.7 (3.0–19.8)
Total ALP (IU/L)
75.7 ± 19.5
66.5 ± 16.3
76.9 ± 20.8
70.0 (40–114)
63.0 (43–111)
80.5 (29–104)
Urine PYD
38.6 ± 14.2
38.2 ± 12.6
39.1 ± 15.2
(pmol/lmol Cr)
38.4 (7.4–77.4)
30.1 (9.3–74.1)
36.4 (19.9–79.3)
Urine Ca/Cr
0.3 ± 0.3
0.3 ± 0.2
0.3 ± 0.4
NS
Urine PO4/Cr
1.9 ± 0.8
1.8 ± 0.9
1.8 ± 0.8
NS
NS NS NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, HOMA-IR homoeostasis model assessment of insulin resistance, GDF-9 growth differentiation factor-9, GDF-15 growth differentiation factor-15, bsALP bone-specific alkaline phosphatase, OCL osteocalcin, ALP alkaline phosphatase, DPD deoxypyridinoline, PYD pyridinoline, PO4 phosphate, Cr creatinine, NS not significant
there were no significant differences in femoral neck, trochanter and total hip BMD and in BMD of lumbar spine between the groups. Correlations in the combined population of all three groups GDF-9 concentrations were positively correlated with FSH levels (r = 0.295, p \ 0.01 and r = 0.240, p \ 0.05,
respectively). In addition, there was a negative correlation between GDF-9 and 17-OHP concentations (r = -0.240, p \ 0.05). OCL levels were negatively correlated with fasting glucose levels (r = -0.291, p \ 0.01), insulin (r = 0.290, p \ 0.01), HOMA index (r = -0.335, p \ 0.01) and HbA1c levels (r = -0.282, p \ 0.01). GDF-15 concentrations were negatively correlated with OCL (r = -0.317, p \ 0.01). Correlations in women with PCOS GDF-15 concentrations were positively correlated with age and HOMA index (r = 0.319, p \ 0.05, and r = 0.312, p \ 0.05, respectively). In addition, GDF-15 concentrations were negatively correlated with OCL (r = -0.395, p \ 0.01) and positively correlated with DPD (r = 0.353, p \ 0.05). GDF-9 concentrations were positively correlated with BMI (r = 0.350, p \ 0.05). There was a negative correlation between GDF-9 and fasting glucose levels (r = 0.403, p \ 0.01). OCL concentrations were negatively correlated with insulin levels (r = -0.379, p \ 0.05) and HOMA index (r = -0.422, p \ 0.01). DHEA-S levels were positively correlated with trochanter BMD (r = 0.315, p \ 0.05) and total hip BMD (r = 0.358, p \ 0.05).
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Discussion GDF9 is essential for follicular development and differentiation. It promotes a molecular dialogue between the oocyte and the surrounding somatic cells, stimulating granulosa cell mitosis and cumulus expansion by a paracrine effect [25]. Experiments in animals have shown that FSH could promote the production of GDF9 in normal oocytes [26]. Additionally, it was demonstrated that GDF-9 treatment of human theca cells inhibited progesterone biosynthesis and reduced the production of 17a-OHP, implicating GDF-9 in the modulation of follicular steroidogenesis [27]. In accordance, GDF-9 concentrations were positively correlated with FSH and negatively correlated with 17-OHP in all subjects in the present study. There is controversy regarding GDF-9 expression levels in patients with PCOS. Some authors reported no significant difference in GDF-9 expression levels in oocytes between patients with PCOS and control subjects [28]. These results were inconsistent with those of another study showing delayed and decreased expression of GDF-9 mRNA in oocytes from ovarian biopsy of seven patients with PCOs and five patients with PCOS who received laparoscopic treatment but no ovulation stimulation [12]. A mutational analysis of the coding region of GDF9 has discovered variants in GDF9 in association with PCOS [29]. In the present study, plasma GDF-9 levels (during the early follicular phase) did not differ significantly between the groups. The expression of GDF9 was demonstrated to decrease greatly in animal models with hyperinsulinism, also a crucial factor for PCOS [30]. However, our PCOS patients had similar anthropometric and metabolic profiles to controls, which could be a possible explanation for the above-mentioned finding. Additionally, it is not known whether plasma GDF-9 concentrations correlate with GDF-9 expression level in the ovary. Although GDF-9 mRNA had been reported to be exclusively expressed in the ovary [31], it was detected in a wide variety of non-reproductive tissues [13]. Non-ovarian expression of GDF-9 mRNA in various tissues has been reported to be likely due to the presence of white blood cells [14]. On the other hand, we found no correlation between GDF-9 and bone markers. It is clear that further research is required to clarify the expression level of GDF-9 mRNA in ovaries of women with PCOS and its correlation with plasma GDF-9 concentrations and to establish whether GDF9 can serve as a genetic marker for PCOS. GDF-15 has been reported by some authors as a osteoblastic factor that negatively regulates osteoclast differentiation and function [22]. On the other hand, a study found that GDF15 significantly promoted osteoclastic differentiation in a concentration-dependent manner after secretion
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from adjacent osteocytes during disuse and/or ischemia in bone [32]. Additionally, MIC-1 over-expressing prostate cancer cells that grow in bone induce osteoclast formation [33]. In accordance, GDF-15 concentrations in women with PCOS were negatively correlated with OCL and positively correlated with DPD levels in the present study. Plasma GDF-15 levels, however, were similar in all three groups. Additionally, neither bone formation markers or markers of bone resorption differed significantly between the groups. On the other hand, GDF-15 concentrations were positively correlated with age and HOMA index in women with PCOS in the present study. Furthermore, a study reported that GDF-15 levels at baseline were associated with the risk of having abnormal glucose control at 4 years in obese nondiabetic individuals [34]. Relevantly, OCL concentrations were negatively correlated with fasting glucose, insulin and HbA1c levels, and HOMA index in all subjects in our study. Lee et al. [35] showed that mice lacking OCL displayed decreased beta-cell proliferation, glucose intolerance and insulin resistance and that OCL could improve glucose tolerance in vivo. These findings suggest that osteoblast-derived OCL and GDF-15 have endocrine functions affecting glucose homeostasis. Furthermore, our above-mentioned results showed reciprocal regulatory effects of GDF-15 and OCL on either bone or energy metabolisms. Additionally, women with PCOS have increased rates of type 2 diabetes mellitus compared with BMI-matched controls [36]. Therefore, further studies are needed to determine changes over time in GDF-15 levels in patients with PCOS and their relationship with bone markers. With regard to BMD in PCOS, there are conflicting results in the current literature. Some authors reported higher BMD in amenorrheic PCOS cases [37], while others found lower BMD in amenorrheic patients with PCOS compared with healthy controls [38]. McCleary [39] measured lumbar BMD by quantitated computed tomography and observed that it was not significantly different between PCOS cases older than 30 years (mean age was 47.6 years) and controls. Furthermore, when stratified to adjust for factors known to affect lumbar BMD, there were still no significant differences in BMD between cases and controls. Adami et al. [40] demonstrated that spine and femoral neck BMD in PCOS subjects (17–33 years of age) were similar to those in healthy women. Thirty-eight of the 51 PCOS patients were amenorrheic (defined as 4 menstrual cycles/year). In this subgroup of PCOS patients with associated amenorrhea, spine and femoral neck BMD were comparable to control values, but were significantly lower than those in subjects with IH and nonamenorrheic PCOS despite comparable estradiol levels. However, the percent of young subjects who did not reach peak bone
J Bone Miner Metab
mass (a confounding factor) was not clear. Furthermore, no differences were observed in biochemical bone turnover parameters in subjects with either PCOS (amenorrheic or eumenorrheic) or IH compared with control women. Similarly, we found no significant differences in the femoral neck, trochanter, total hip and lumbar BMD between the groups. Thirty-three of the 42 PCOS patients were amenorrheic. In contrast to the results of Adami et al. [40], spine and femoral neck BMD in our subgroup of PCOS patients with associated amenorrhea were comparable to those in subjects with IH, nonamenorrheic PCOS and healthy controls (data not shown). Additionally, a long-term follow-up study has demonstrated that older women with PCOS, who have most likely been exposed to high androgen levels for several decades and who have reached the postmenopausal period, have similar BMD and incidence of fractures as age- and BMI-matched controls [41]. DHEA-S was positively correlated with trochanter and total hip BMD in our PCOS group. Johnston [42] observed similar associations in a group of healthy premenopausal women. No correlation between BMD and other androgens was found. Another study of women with PCOS around 28 years of age also demonstrated no correlation between BMD and androgens or free androgen index [43]. Specific androgens may possibly exert differential influences on the various bone sites. DHEA-S has little androgenic activity. Its direct stimulatory effect on bone, although possible, is unlikely. Furthermore, no differences were observed in biochemical bone turnover parameters in subjects with either PCOS (amenorrheic or eumenorrheic) or the control groups. Therefore, any direct or persistent effects of higher androgen levels on bone formation may be limited. In conclusion, bone turnover markers, BMD measurements, GDF-9 and GDF-15 levels in subjects with PCOS (25–35 years of age) were comparable with either subjects with IH or healthy control women who had similar anthropometric and metabolic profiles. GDF-9 did not correlate with bone markers and BMD measurements. GDF-15 might be another marker of a crossregulation between bone and energy metabolism. Conflict of interest
All authors have no conflicts of interest.
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ORIGINAL ARTICLE
Association of plasma GDF-9 or GDF-15 levels with bone parameters in polycystic ovary syndrome Zehra Berberoglu • Aynur Aktas • Yasemin Fidan Ayse Canan Yazici • Yalcin Aral
•
Received: 18 March 2013 / Accepted: 8 December 2013 Ó The Japanese Society for Bone and Mineral Research and Springer Japan 2014
Abstract We aimed to determine plasma levels of growth and differentiation factor (GDF)-9 and GDF-15, and their possible association with bone turnover parameters and bone mineral density (BMD), in patients with polycystic ovary syndrome (PCOS). Forty-two obese PCOS women aged 25–35 years, 23 women with idiopathic hirsutism (IH) and 20 healthy controls matched for age and body mass index were enrolled. Anthropometric, metabolic and hormonal patterns, plasma GDF-9 and GDF15 concentrations, bone turnover markers and BMD were measured. No significant differences were observed in bone turnover markers, BMD measurements, plasma GDF9 and GDF-15 levels in subjects with PCOS compared with the other two groups. In the combined population of all three groups, GDF-15 concentrations were negatively correlated with osteocalcin (r = -0.317, p \ 0.01). Analysis of PCOS patients showed a significant correlation of GDF-15 concentrations with age and homeostasis model assessment index (r = 0.319, p \ 0.05, and r = 0.312, p \ 0.05, respectively). In addition, GDF-15 con-
The study has been submitted to www.clinicaltrials.gov (ID:NCT01644305). Z. Berberoglu (&) A. Aktas Y. Aral Department of Endocrinology and Metabolism, Ankara Education and Research Hospital, S¸ u¨kriye Mh, 06340 Sıhhıye, Ankara, Turkey e-mail: [email protected] Y. Fidan Department of Biochemistry, Ankara Education and Research Hospital, Ankara, Turkey A. C. Yazici Department of Biostatistics, Baskent University Faculty of Medicine, Ankara, Turkey
centrations were negatively correlated with osteocalcin (r = -0.395, p \ 0.01) and positively correlated with urine deoxypyridinoline (r = 0.353, p \ 0.05). GDF-9 did not correlate with bone markers and BMD measurements. In conclusion, plasma GDF-9 and GDF-15 levels as well as bone turnover markers and BMD measurements in subjects with PCOS (25–35 years of age) were comparable with those either in subjects with IH or in healthy controls with similar anthropometric and metabolic profiles. GDF-15 might be a marker of a crossregulation between bone and energy metabolism. Keywords Polycystic ovary syndrome GDF-9 GDF-15 Bone parameters
Introduction Polycystic ovary syndrome (PCOS), a frequently encountered endocrinopathy in women of reproductive age, is characterized by anovulation, hyperandrogenism, obesity, insulin resistance (IR) and/or polycystic ovaries (PCOs). Many PCOS symptoms may have long-term effects on bone mineral density (BMD). Previous studies reported normal [1] or increased BMD [2] in patients with PCOS compared with controls. Obesity may contribute to the conserved BMD in PCOS due to the beneficial mechanical loading impact of body weight on bone formation. Adiposity has been reported to be positively associated with BMD [3]. Furthermore, hyperinsulinemia and IR, independent of body mass index (BMI), may protect against the development of osteoporosis [4]. Androgen treatment may not only inhibit osteoclast formation directly [5] but also modulate osteoblast/osteoclast interaction via osteoprotegerin [6].
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Growth and differentiation factors (GDF) belong to the transforming growth factor-b superfamily which is composed of a number of structurally related polypeptides, including activins/inhibins, decapentaplegic and bone morphogenetic proteins [7–9]. They have diverse biological functions. GDF-9, known to be secreted by oocytes in primary human follicles, is obligatory for normal folliculogenesis. It is involved in granulosa cell proliferation and in differentiation of theca cells during follicular development from preantral to early antral stage [10]. GDF-9 enhances preantral follicle growth by upregulating theca cell androgen production [11] and promotes follicular survival during this early stage by suppressing granulosa cell apoptosis and follicular atresia [10]. Delayed and reduced expression of GDF-9 messenger RNA (mRNA) has been found in oocytes of women with PCOS and in PCOs during their growth and differentiation phase [12]. The question whether the changes in GDF-9 mRNA in developing oocytes in women with PCOS and PCOs are coupled with changes in GDF-9 protein synthesis or activity has remained unanswered. On the other hand, although GDF-9 mRNA has been reported to be exclusively expressed in the normal ovary, a study has addressed the possibility of GDF-9 expression in non-ovarian tissues [13]. GDF-9 mRNA was observed in diverse nongonadal tissues, including pituitary, uterus and bone marrow. Non-ovarian expression of GDF-9 mRNA in various tissues is likely due to its presence in white blood cells [14]. GDF-15, also known as macrophage inhibitory cytokine-1 (MIC-1), is a stress-induced cytokine. It carries prognostic information about cardiovascular events and mortality either in patients with coronary artery disease [15] and heart failure [16] or in elderly women with no previous evidence of cardiovascular disease [17]. Additionally, serum levels of MIC-1 were demonstrated to be significantly increased in obese individuals [18]. This cytokine was positively correlated with glucose, insulin, HbA1c and homeostasis model assessment (HOMA) index [19]. Furthermore, it has been reported that androgen positively regulates the expression of GDF15/ MIC-1 in vivo [20, 21]. These correlations highlight the importance of characterizing GDF-15 in women with PCOS, many of whom also have features of metabolic syndrome. On the other hand, GDF-15 was shown to suppress the formation of mature osteoclasts in a dosedependent manner, suggesting a crosstalk between bone and energy metabolism [22]. In view of this complex context, we sought to determine plasma levels of GDF-9 and GDF-15, and their possible association with bone turnover parameters and BMD, in patients with PCOS.
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Materials and methods The study was approved by the local institutional ethics committee of the hospital. All participants provided their written informed consent before study enrollment. Subjects Obese female subjects aged 25–35 years were included in the present study. We studied 42 women with newly diagnosed or untreated PCOS, 23 women with idiopathic hirsutism (IH) and 20 healthy controls matched for age and BMI. Using the revised Rotterdam criteria (Rotterdam ESHRE/ASRM-sponsored PCOS Consensus Workshop Group 2004), PCOS was defined as the existence of at least two of the following three features: oligomenorrhea involving less than nine cycles a year or amenorrhea lasting 3 months or more, clinical or biochemical signs of hyperandrogenism, and PCOs [23]. Table 1 shows the clinical and biochemical diagnostic features of the PCOS patients. The diagnosis of IH was based on the presence of hirsutism (Ferriman–Gallwey score [8), regular ovulatory menstrual cycles and a normal serum androgen profile. The presence of PCOS was excluded. Inclusion criteria for the control group were: (1) regular ovulatory menstrual cycles (28 ± 2 days, blood progesterone levels[10 ng/mL in two consecutive cycles); (2) absence of hyperandrogenism; and (3) normal ovarian morphology on ultrasound imaging. Exclusion criteria were: (1) pregnancy; (2) androgensecreting tumors, Cushing’s syndrome, congenital adrenal hyperplasia, hyperprolactinemia; (3) acute or chronic infection; (4) a thyroid, parathyroid, pituitary, metabolic, nutritional, autoimmune, hepatic, renal, cardiovascular or neoplastic disorder; (5) current or previous treatment with Table 1 Clinical and biochemical diagnostic features of the PCOS women studied Features
n (%)
Anovulatory infertility
33 (79)
Oligo/amenorrhea
33 (79)
Clinical hyperandrogenism Hirsutisma Acne Biochemical hyperandrogenism
42 (100) 42 (100) 9 (21) 28 (67)
Testosterone
28 (67)
DHEA-S
16 (38)
LH/FSH ratio [2
15 (36)
Polycystic ovary at ultrasonography
23 (55)
DHEA-S dehydroepiandrosterone sulfate, LH luteinizing hormone, FSH follicle-stimulating hormone a
As evaluated by Ferriman–Gallwey score
J Bone Miner Metab
drugs that might affect calcium (Ca) or bone metabolism; (6) drugs known to interfere with cytokine release; (7) current or previous use of medications known to affect gonadotropin secretion, serum sex steroid levels, metabolic parameters and insulin sensitivity; (8) smoking and excessive alcohol/caffeine consumption; (9) excessive physical training; and (10) any skeletal deformity that may affect measurement of BMD. The dietary Ca intake was determined via a food-frequency questionnaire. Methods At study entry, all subjects underwent a complete clinical examination, anthropometric measurements and laboratory tests. Body fat measurements that were used to determine the body fat percentage and total body fat mass were obtained in fasting subjects by a leg-to-leg bioelectric impedance device (TBF-300 M; Tanita Corp., Tokyo, Japan). All measurements at a single frequency of 50 kHz were obtained by the same investigator adhering to the manufacturer’s guidelines. Analysis of the impedance values from this device revealed a within-day coefficient of variation (CV) of 0.7 % [standard deviation (SD) 0.6] and a between-day CV of 2.6 % (SD 1.5). Laboratory parameters included hormonal analyses, fasting glucose and insulin levels, HOMA index [24], serum GDF-9 and GDF-15 concentrations, levels of serum bone-specific alkaline phosphatase (bsALP) and human osteocalcin (OCL) concentrations as markers of bone formation, and urine deoxypyridinoline (DPD) and pyridinoline (PYD) levels as markers of bone resorption. Other nonspecific bone markers, including serum total ALP activity and urinary Ca and phosphate (PO4) concentrations, were also measured. Urine concentrations of Ca (mmol/L) and PO4 (mmol/L) were corrected for their respective urine creatinine (Cr) concentrations in mmol/L (urine Ca/Cr and urine PO4/Cr, respectively). BMD of lumbar spine and left proximal femur was measured by dual-energy X-ray absorptiometry using a LUNAR DPX instrument (GE Lunar, Fitchburg, WI, USA). Quality control scans for the densitometer were performed daily using a manufacturer-supplied anthropomorphic spine phantom. The CV for BMD measurements in our laboratory is 1 % for both lumbar spine and left proximal femur. Biochemical assays All blood samples were obtained in the morning between 08.00 and 09.00 a.m. after overnight fasting, during the early follicular phase (cycle days 2–5) of a spontaneous or progestin-induced menstrual cycle. The first morning
urine sample was collected without preservative before 10.00 a.m. to avoid any possible effect from diurnal variation on the DPD and PYD. Blood samples for the measurement of GDF-9 and GDF-15 were centrifuged with sera aliquoted immediately after collection. They were deep-frozen and stored at -20 °C. Plasma glucose levels were assayed by a glucose hexokinase method on an Olympus AU2700 analyzer (Beckman Coulter Clinical Diagnostics, Nyon, Switzerland). Serum fasting insulin levels were measured by enzyme-linked immunosorbent assay (ELISA) with an DSX 5.19 analyzer (DYNEX Technologies Inc., Chantilly, VA, USA). HbA1c was assayed with a high-performance liquid chromatography (HPLC) method (Agilent Technologies GmbH & Co. KG, Waldbronn, Germany). Serum gonadotropins [luteinizing hormone, folliclestimulating hormone (FSH)], dehydroepiandrosterone sulfate (DHEA-S), estradiol, total testosterone and prolactin were analyzed by a direct chemiluminescence immunoassay (Siemens, ADVIA Centaur XP Immunoassay System, Tarrytown, NY, USA). Free testosterone and 17-hydroxyprogesterone (17-OHP) were determined using a radioimmunoassay (Gamma-C 12 DPC, Berthold, Germany). Serum bsALP and total OCL assays were performed with an ELISA method (DYNEX Technologies Inc.). Levels of urine DPD and PYD were measured via HPLC (Agilent Technologies). GDF-9 and GDF-15 levels were measured by ELISA on an DSX 5.19 analyzer (DYNEX Technologies Inc.). Statistical analysis The normal distribution of continuous variables was checked using the Shapiro–Wilk test. Homogeneity of groups’ variances was controlled by Levene’s test. Group means were compared by using one-way analysis of variance and then multiple comparisons between pairs of groups were carried out according to Tukey’s HSD test. Parametric test assumptions were not available for some variables, so the comparisons were performed with the Kruskal–Wallis test and then Dunn’s test was used for multiple comparisons. The results of statistical analyses were expressed as mean ± SD ðX Sx Þ and/or median and minimum–maximum values [M (min–max)]. The Pearson product-moment correlation coefficient was employed to evaluate the correlations between normally distributed variables. The Spearman rho correlation coefficient was used to evaluate the relationships between not normally distributed variables. Data analyses were performed using SPSS 17.0 statistical software (SPSS Inc., Chicago IL, USA). A p value \0.05 was considered statistically significant.
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Results
GDF-9
Demographic and hormonal data
GDF-9 levels did not differ significantly between the groups (Table 3).
As shown in Table 2, groups were matched for age, BMI and waist-to-hip ratio. As expected, women with PCOS had significantly higher luteinizing hormone (p \ 0.01), total testosterone (p \ 0.001) and free testosterone (p \ 0.001) levels than controls.
GDF-15 As shown in Table 3, there were no significant differences in GDF-15 levels between the groups.
Metabolic profile
Metabolic bone markers and BMD
Subjects in all three groups had similar fasting glucose, insulin and HbA1c levels (Table 3). HOMA-IR was similar across all three groups.
Serum total ALP, bsALP and OCL levels did not differ significantly between the groups (Table 3). Urine PYD and DPD were similar in all three groups. As shown in Table 4,
Table 2 Demographic and hormonal profiles in women with and without PCOS
Age (years) 2
PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
29.6 ± 3.7
30.4 ± 3.7
30.9 ± 4.1
NS
BMI (kg/m )
36.5 ± 3.7
36.4 ± 4.2
37.6 ± 4.4
NS
Body fat (%)
43.5 ± 2.3
41.3 ± 4.6
41.7 ± 4.5
NS
43.0 (39.5–49.0)
42.5 (29.3–48.0)
41.2 (31.9–49.0)
Body fat (kg) WHR FSH (IU/mL) LH (IU/mL)
36.8 ± 5.1
36.0 ± 6.3
36.4 ± 7.9
35.1 (29.9–50.5)
37.4 (26.0–49.1)
34.4 (20.8–51.3)
0.88 ± 0.08
0.9 ± 0.06
0.89 ± 0.05
0.89 (0.81–1.0)
0.91 (0.68–1.17)
0.89 (0.79–0.97)
5.9 ± 1.8
5.8 ± 2.2
5.9 ± 2.1
5.8 (2.4–9.7)
5.7 (2.4–10.2)
5.3 (2.8–9.8)
9.1 ± 5.7**
5.7 ± 3.5
5.1 ± 2.4
NS NS NS \0.01
7.8 (2.0–25.8)
4.6 (2.6–17.3)
4.4 (1.6–12.1)
Prolactin (ng/mL)
9.6 ± 4.9
8.9 ± 4.3
10.6 ± 5.6
NS
Estradiol (pg/mL)
8.3 (2.7–24.1) 47.7 ± 14.3
7.8 (3.6–19.7) 44.7 ± 13.8
9.2 (4.4–26.8) 46.9 ± 7.8
NS
50.4 (22.4–72.8)
44.0 (7.3–59.6)
45.4 (34.3–59.3)
1.2 ± 0.4
1.2 ± 0.5
1.3 ± 1.0
1.2 (0.5–1.9)
1.2 (0.4–1.9)
0.9(0.5–4.5)
67.2 ± 24.7***
49.8 ± 16.9
46.7 ± 15.6
75.9 (18.3–103.8)
47.3 (18.8–79.9)
49.9 (21.0–75.7)
17-OHP (ng/mL) Testosterone (ng/dL) Free testosterone (pg/mL) DHEA-S (lg/dL)
2.2 ± 1.0***
1.5 ± 0.8
1.6 ± 0.9
2.0 (0.7–5.6)
1.4 (0.2–3.3)
1.4 (0.6–4.5)
231.4 ± 117.7
225.1 ± 97.5
219.7 ± 73.4
211.5 (69.9–498.0)
231.0 (15.0–378.0)
224.5 (71.4–344.0)
NS \0.001 \0.001 NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, BMI body mass index, WHR waist-to-hip ratio, LH luteinizing hormone, FSH folliclestimulating hormone, 17-OHP 17-hydroxyprogesterone, DHEA-S dehydroepiandrosterone sulfate, NS not significant ** p \ 0.01 *** p \ 0.001
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J Bone Miner Metab Table 3 Metabolic profile, GDF 9, GDF 15 and bone markers in women with and without PCOS PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
BMD (g/ cm2)
NS
Lumbar spine
Fasting glucose (mg/dL)
90.9 ± 6.2
89.2 ± 6.4
90.7 ± 5.9
92 (76–99)
90 (74–99)
91 (79–99)
Fasting insulin (lIU/L)
14.4 ± 6.3
14.5 ± 5.4
14.0 ± 5.8
14.7 (2.0–31.8)
14.2 (3.4–29.0)
12.8 (4.1–26.0)
HOMA-IR
3.2 ± 1.4
3.2 ± 1.1
3.1 ± 1.4
3.0 (0.5–7.5)
3.3 (0.7–5.9)
3.0 (0.9–6.2)
HbA1c (%) GDF-9 (pg/ mL) GDF-15 (pg/ mL)
5.6 ± 0.3
5.6 ± 0.3
5.5 ± 0.3
5.6 (4.9–6.0)
5.6 (5.2–6.1)
5.6 (5.6–6.0)
83.8 ± 46.6
79.7 ± 34.6
81.3 ? ±48.2
77.5 (17–247)
74.0 (17–151)
83.0 (17–219)
171.1 ? ±49.1
183.9 ± 57.1
160.1 ± 42.2
168 (80–310)
170 (123–387)
155 (99–289)
Urine DPD
11.9 ± 7.0
12.2 ± 3.5
12.5 ± 6.7
(pmol/lmol Cr)
9.9 (3.0–34.1)
7.4 (3.8–31.6)
12.7 (3.7–30.2)
BsALP (IU/ L)
Table 4 Bone mineral density measurements in women with and without PCOS
NS
NS
Femoral neck Trochanter
NS Total hip NS NS NS
PCOS ðX Sx Þ [M (min–max)] n = 42
IH ðX Sx Þ [M (min–max)] n = 23
Controls ðX Sx Þ [M (min–max)] n = 20
p
NS
1.05 ± 0.10
1.07 ± 0.09
1.04 ± 0.10
1.04 (0.77–1.38)
1.08 (0.83–1.29)
1.05 (0.89–1.28)
0.91 ± 0.14
0.93 ± 0.10
0.97 ± 0.21
0.92 (0.64–1.32)
0.92 (0.72–1.19)
0.95 (0.69–1.67)
0.74 ± 0.09
0.75 ± 0.08
0.72 ± 0.08
0.74 (0.55–0.98)
0.76 (0.60–0.91)
0.73 (0.59–0.86)
1.04 ± 0.13
1.03 ± 0.09
1.03 ± 0.11
1.03 (0.74–1.32)
1.00 (0.88–1.29)
1.06 (0.83–1.19)
NS
NS
NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, BMD bone mineral density, NS not significant
29.8 ± 12.5
27.1 ± 9.3
30.3 ± 9.5
28.0 (13.2–68.7)
26.6 (13.0–55.2)
31.5 (8.5–42.8)
NS
OCL (ng/dL)
6.0 ± 3.7
6.3 ± 2.9
7.5 ± 3.8
5.6 (0.2–23.0)
6.6 (2.0–11.4)
6.7 (3.0–19.8)
Total ALP (IU/L)
75.7 ± 19.5
66.5 ± 16.3
76.9 ± 20.8
70.0 (40–114)
63.0 (43–111)
80.5 (29–104)
Urine PYD
38.6 ± 14.2
38.2 ± 12.6
39.1 ± 15.2
(pmol/lmol Cr)
38.4 (7.4–77.4)
30.1 (9.3–74.1)
36.4 (19.9–79.3)
Urine Ca/Cr
0.3 ± 0.3
0.3 ± 0.2
0.3 ± 0.4
NS
Urine PO4/Cr
1.9 ± 0.8
1.8 ± 0.9
1.8 ± 0.8
NS
NS NS NS
The results of statistical analyses were expressed as mean ± standard deviation ðX Sx Þ and/or median and minimum–maximum values [M (min–max)] PCOS polycystic ovary syndrome, IH idiopathic hirsutism, HOMA-IR homoeostasis model assessment of insulin resistance, GDF-9 growth differentiation factor-9, GDF-15 growth differentiation factor-15, bsALP bone-specific alkaline phosphatase, OCL osteocalcin, ALP alkaline phosphatase, DPD deoxypyridinoline, PYD pyridinoline, PO4 phosphate, Cr creatinine, NS not significant
there were no significant differences in femoral neck, trochanter and total hip BMD and in BMD of lumbar spine between the groups. Correlations in the combined population of all three groups GDF-9 concentrations were positively correlated with FSH levels (r = 0.295, p \ 0.01 and r = 0.240, p \ 0.05,
respectively). In addition, there was a negative correlation between GDF-9 and 17-OHP concentations (r = -0.240, p \ 0.05). OCL levels were negatively correlated with fasting glucose levels (r = -0.291, p \ 0.01), insulin (r = 0.290, p \ 0.01), HOMA index (r = -0.335, p \ 0.01) and HbA1c levels (r = -0.282, p \ 0.01). GDF-15 concentrations were negatively correlated with OCL (r = -0.317, p \ 0.01). Correlations in women with PCOS GDF-15 concentrations were positively correlated with age and HOMA index (r = 0.319, p \ 0.05, and r = 0.312, p \ 0.05, respectively). In addition, GDF-15 concentrations were negatively correlated with OCL (r = -0.395, p \ 0.01) and positively correlated with DPD (r = 0.353, p \ 0.05). GDF-9 concentrations were positively correlated with BMI (r = 0.350, p \ 0.05). There was a negative correlation between GDF-9 and fasting glucose levels (r = 0.403, p \ 0.01). OCL concentrations were negatively correlated with insulin levels (r = -0.379, p \ 0.05) and HOMA index (r = -0.422, p \ 0.01). DHEA-S levels were positively correlated with trochanter BMD (r = 0.315, p \ 0.05) and total hip BMD (r = 0.358, p \ 0.05).
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Discussion GDF9 is essential for follicular development and differentiation. It promotes a molecular dialogue between the oocyte and the surrounding somatic cells, stimulating granulosa cell mitosis and cumulus expansion by a paracrine effect [25]. Experiments in animals have shown that FSH could promote the production of GDF9 in normal oocytes [26]. Additionally, it was demonstrated that GDF-9 treatment of human theca cells inhibited progesterone biosynthesis and reduced the production of 17a-OHP, implicating GDF-9 in the modulation of follicular steroidogenesis [27]. In accordance, GDF-9 concentrations were positively correlated with FSH and negatively correlated with 17-OHP in all subjects in the present study. There is controversy regarding GDF-9 expression levels in patients with PCOS. Some authors reported no significant difference in GDF-9 expression levels in oocytes between patients with PCOS and control subjects [28]. These results were inconsistent with those of another study showing delayed and decreased expression of GDF-9 mRNA in oocytes from ovarian biopsy of seven patients with PCOs and five patients with PCOS who received laparoscopic treatment but no ovulation stimulation [12]. A mutational analysis of the coding region of GDF9 has discovered variants in GDF9 in association with PCOS [29]. In the present study, plasma GDF-9 levels (during the early follicular phase) did not differ significantly between the groups. The expression of GDF9 was demonstrated to decrease greatly in animal models with hyperinsulinism, also a crucial factor for PCOS [30]. However, our PCOS patients had similar anthropometric and metabolic profiles to controls, which could be a possible explanation for the above-mentioned finding. Additionally, it is not known whether plasma GDF-9 concentrations correlate with GDF-9 expression level in the ovary. Although GDF-9 mRNA had been reported to be exclusively expressed in the ovary [31], it was detected in a wide variety of non-reproductive tissues [13]. Non-ovarian expression of GDF-9 mRNA in various tissues has been reported to be likely due to the presence of white blood cells [14]. On the other hand, we found no correlation between GDF-9 and bone markers. It is clear that further research is required to clarify the expression level of GDF-9 mRNA in ovaries of women with PCOS and its correlation with plasma GDF-9 concentrations and to establish whether GDF9 can serve as a genetic marker for PCOS. GDF-15 has been reported by some authors as a osteoblastic factor that negatively regulates osteoclast differentiation and function [22]. On the other hand, a study found that GDF15 significantly promoted osteoclastic differentiation in a concentration-dependent manner after secretion
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from adjacent osteocytes during disuse and/or ischemia in bone [32]. Additionally, MIC-1 over-expressing prostate cancer cells that grow in bone induce osteoclast formation [33]. In accordance, GDF-15 concentrations in women with PCOS were negatively correlated with OCL and positively correlated with DPD levels in the present study. Plasma GDF-15 levels, however, were similar in all three groups. Additionally, neither bone formation markers or markers of bone resorption differed significantly between the groups. On the other hand, GDF-15 concentrations were positively correlated with age and HOMA index in women with PCOS in the present study. Furthermore, a study reported that GDF-15 levels at baseline were associated with the risk of having abnormal glucose control at 4 years in obese nondiabetic individuals [34]. Relevantly, OCL concentrations were negatively correlated with fasting glucose, insulin and HbA1c levels, and HOMA index in all subjects in our study. Lee et al. [35] showed that mice lacking OCL displayed decreased beta-cell proliferation, glucose intolerance and insulin resistance and that OCL could improve glucose tolerance in vivo. These findings suggest that osteoblast-derived OCL and GDF-15 have endocrine functions affecting glucose homeostasis. Furthermore, our above-mentioned results showed reciprocal regulatory effects of GDF-15 and OCL on either bone or energy metabolisms. Additionally, women with PCOS have increased rates of type 2 diabetes mellitus compared with BMI-matched controls [36]. Therefore, further studies are needed to determine changes over time in GDF-15 levels in patients with PCOS and their relationship with bone markers. With regard to BMD in PCOS, there are conflicting results in the current literature. Some authors reported higher BMD in amenorrheic PCOS cases [37], while others found lower BMD in amenorrheic patients with PCOS compared with healthy controls [38]. McCleary [39] measured lumbar BMD by quantitated computed tomography and observed that it was not significantly different between PCOS cases older than 30 years (mean age was 47.6 years) and controls. Furthermore, when stratified to adjust for factors known to affect lumbar BMD, there were still no significant differences in BMD between cases and controls. Adami et al. [40] demonstrated that spine and femoral neck BMD in PCOS subjects (17–33 years of age) were similar to those in healthy women. Thirty-eight of the 51 PCOS patients were amenorrheic (defined as 4 menstrual cycles/year). In this subgroup of PCOS patients with associated amenorrhea, spine and femoral neck BMD were comparable to control values, but were significantly lower than those in subjects with IH and nonamenorrheic PCOS despite comparable estradiol levels. However, the percent of young subjects who did not reach peak bone
J Bone Miner Metab
mass (a confounding factor) was not clear. Furthermore, no differences were observed in biochemical bone turnover parameters in subjects with either PCOS (amenorrheic or eumenorrheic) or IH compared with control women. Similarly, we found no significant differences in the femoral neck, trochanter, total hip and lumbar BMD between the groups. Thirty-three of the 42 PCOS patients were amenorrheic. In contrast to the results of Adami et al. [40], spine and femoral neck BMD in our subgroup of PCOS patients with associated amenorrhea were comparable to those in subjects with IH, nonamenorrheic PCOS and healthy controls (data not shown). Additionally, a long-term follow-up study has demonstrated that older women with PCOS, who have most likely been exposed to high androgen levels for several decades and who have reached the postmenopausal period, have similar BMD and incidence of fractures as age- and BMI-matched controls [41]. DHEA-S was positively correlated with trochanter and total hip BMD in our PCOS group. Johnston [42] observed similar associations in a group of healthy premenopausal women. No correlation between BMD and other androgens was found. Another study of women with PCOS around 28 years of age also demonstrated no correlation between BMD and androgens or free androgen index [43]. Specific androgens may possibly exert differential influences on the various bone sites. DHEA-S has little androgenic activity. Its direct stimulatory effect on bone, although possible, is unlikely. Furthermore, no differences were observed in biochemical bone turnover parameters in subjects with either PCOS (amenorrheic or eumenorrheic) or the control groups. Therefore, any direct or persistent effects of higher androgen levels on bone formation may be limited. In conclusion, bone turnover markers, BMD measurements, GDF-9 and GDF-15 levels in subjects with PCOS (25–35 years of age) were comparable with either subjects with IH or healthy control women who had similar anthropometric and metabolic profiles. GDF-9 did not correlate with bone markers and BMD measurements. GDF-15 might be another marker of a crossregulation between bone and energy metabolism. Conflict of interest
All authors have no conflicts of interest.
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