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Introduction

Polycystic ovary syndrome (PCOS) is characterized by three major clinical features: Oligo-ovulation/anovulation, clinical and/or biochemical hyperandrogenism and polycystic ovaries (1). Skeletal muscle and adipose tissue exert an important role in the regulation of metabolism in PCOS (2–4), and are responsible for the production of numerous peptides that are classified as myokines (5–7). Myostatin, also known as growth differentiation factor 8, is a member of the transforming growth factor-β superfamily (8). It is predominantly expressed in the skeleton, although it is also synthesized by adipose tissue and cardiomyocytes (8,9). Myostatin is the major negative regulator of post-natal skeletal muscle growth via paracrine and endocrine functions (10,11). It acts by binding to myostatin activin type II receptor (12). The essential function of myostatin is to regulate the general metabolic balance between fat and skeletal muscle in the body (13). In this context, it co-ordinates the associations between lipids, proteins, obesity and insulin resistance (IR) (14–17).

Over the last few years, there has been an increasing interest in investigating the functional roles of myostatin outside the musculoskeletal system. In this regard, it has been revealed that myostatin serves important roles in the pathogenesis of various gynecological diseases, including PCOS (18–20). However, to the best of our knowledge, only one study has previously been published that investigated the association between myostatin and PCOS. In that study, Chen et al (20) demonstrated that the level of myostatin was positively associated with abdominal obesity, although it was negatively associated with the circulating levels of dehydroepiandrosterone sulfate (DHEA-S) in women with PCOS.

The essential regulators of the serum levels of myostatin have not yet be to fully identified. Abdominal obesity (20), androgens (21) and skeletal muscle mass (10,11) are the major components involved in myostatin synthesis and release. However, the mechanisms of the association between circulating insulin, glucose levels and serum myostatin have remained to be fully elucidated. Numerous studies have failed to explain the importance of the autocrine effects of myostatin on insulin sensitivity. Certain studies have revealed an association between serum myostatin levels and IR (17,22,23), whereas others reported no significant association (24–26). Similarly, the association between the levels of blood glucose and muscle myostatin has not previously been clearly demonstrated. Therefore, the aim of the present study was to investigate the effects of hormonal levels and demographic features of PCOS patients on their serum myostatin levels.

Patients and methods

Subject enrollment and data collection

The study protocol was in accordance with the Declaration of Helsinki, and was approved by the Local Ethics Committee of Kafkas University School of Medicine. Written informed consent was obtained from all subjects included. The circulating myostatin levels were evaluated in the serum obtained during the early follicular phase from 46 normal-weight patients with PCOS, and from 42 control subjects without PCOS. Patients in the PCOS and control groups were matched in body mass index (BMI) value. The detailed history (including medical and family histories) of all of the patients was recorded and anthropometric data [age, body weight (in kg), height (in cm) and waist circumference (in cm)] were determined. The body height and weight of the patients were measured with the patient standing barefoot in light clothes. The BMI (in kg/m2) was calculated according to the following formula: Body weight/body height2. The waist circumference was measured at the level halfway between the lower rib and the iliac crest. Participants in the PCOS group were diagnosed based on the revised Rotterdam criteria (1), which require the presence of two of the following three clinical manifestations: i) Oligo-ovulation and/or anovulation; ii) clinical and/or biochemical hyperandrogenism; and iii) polycystic ovaries determined by ultrasonography. The ultrasound criteria used for diagnosis of polycystic ovary were the presence of 12 or more follicles in each ovary measuring 2–9 mm in diameter, and/or an increased ovarian volume (>10 ml). Biochemical hyperandrogenism was defined as the total testosterone level being above the normal range of 0.34–2.60 nmol/l. Clinical hyperandrogenism was determined as patients scoring at least 8 points according to the Ferriman-Gallwey hyperandrogenism scoring system (27). Women in the control group had regular menses and normal biochemical and hormonal profiles, thereby excluding PCOS. The women in the control group exhibited no evidence of hyperandrogenic manifestations or polycystic appearance upon ultrasonography of the ovaries. Menstrual periods were characterized by oligomenorrhea (absence of menstruation for ≥45 days) or by amenorrhea (no menstrual period for ≥3 months) in the PCOS group. Menstrual periods in the control group were regular, with a cycle of 25–32 days. None of the patients included in the present study received any therapeutic treatment for PCOS. The patients were also evaluated for physical activity. It was observed that the majority of patients did not engage in any regular sporting activity beyond daily routine. Smokers and subjects taking anti-androgens, anti-diabetics, insulin sensitizers, lipid-lowering medications, glucocorticoids or any type of hormonal drug were excluded from the study. All participants had normal renal and hepatic function test results. Patients were also excluded from the study if they were pregnant or lactating, or had hyperprolactinemia, Cushing's syndrome, congenital adrenal hyperplasia, thyroid disorders, impaired glucose tolerance, type 1 or type 2 diabetes mellitus or a history of ovarian surgery.

Laboratory analyses

Venous blood samples were taken from all participants for analysis of the hormonal status, lipid profile, fasting blood glucose (FBG) and insulin levels. The blood samples were taken in the morning following overnight fasting, at the second day of early follicular phase of spontaneous bleeding. The blood samples were centrifuged at 1,000 × g for 15 min at 2–8°C, and aliquots of the serum obtained were subsequently stored at −80°C until analysis. Serum myostatin concentrations were assessed using an Elabscience® ELISA (cat. no. E-EL-H1437; Elabscience Biotechnology Co., Ltd., Wuhan, China). The sensitivity of this test to serum myostatin was 0.47 ng/ml and the detection range was 0.78 to 50 ng/ml. Furthermore, FBG, serum insulin, high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), triglyceride, total cholesterol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), prolactin (PRL), thyroid-stimulating hormone (TSH), total testosterone, DHEA-S, sex hormone-binding globulin (SHBG), high-sensitivity C-reactive protein (hs-CRP), albumin and the complete blood count (comprising neutrophils, lymphocytes, monocytes and platelets) were measured. FBG (cat. no. 3L82-21), hs-CRP (cat. no. 6K26-30), total cholesterol (cat. no. 7D62-21), HDL-C (cat. no. 3K33-20), LDL-C (cat. no. 1E31-20), and triglyceride (cat. no. 7D74-21) levels were determined using an Abbott Architect™ C 16000 automated analyzer (Abbott Pharmaceutical Co. Ltd., Lake Bluff, IL, USA) with kits provided by the manufacturer (Abbott Diagnostics GmbH, Wiesbaden, Germany). Serum FSH (cat. no. 33520), LH (cat. no. 33510), E2 (cat. no. 33540), insulin (cat. no. 33410), total testosterone (cat. no. 33560), PRL (cat. no. 33530), TSH (cat. no. 33820), SHBG (cat. no. A48617) and DHEA-S (cat. no. A10826) levels were measured via CMIA (Beckman Coulter Inc., Brea, CA, USA).

Fasting insulin levels were measured in the subjects of the PCOS and control groups in order to determine their insulin sensitivity. IR was calculated using the homeostasis model assessment IR index (HOMA-IR) according to the following formula: HOMA-IR=fasting serum insulin (µIU/ml)xFBG (mg/dl)/405 (28). The free androgen index (FAI) was calculated as 100× the total testosterone count (nmol/l)/SHBG (nmol/l).

Statistical analysis

Data analyses were performed using SPSS version 24.0 (IBM Corp., Armonk, NY, USA). For assessing the data for being normally distributed, the Kolmogorov-Smirnov test was performed instead of the Shapiro-Wilk test, since the study sample was >30. With the exception of those variables that were not distributed normally, including PRL, LDL-C, total cholesterol, albumin, neutrophil, lymphocyte and platelets, the non-parametric Mann-Whitney U test was applied. As no significant differences were identified using analysis of variance with the Kruskal-Wallis test, no post-hoc test was performed. A student's t-test was used for parameters with normal distribution. Pearson correlation and the non-parametric Spearman correlation test were applied to examine the correlations between myostatin levels and clinical data of subjects with or without PCOS. Multiple regression analysis was applied to investigate the associations between myostatin levels and other parameters. Values are expressed as the mean ± standard deviation. P<0.05 was considered to indicate a statistically significant difference.

Results

Demographic and laboratory characteristics of the study population

The clinicopathological parameters of the subjects are presented in Table I. The age, the levels of FSH and LH, and the FAI were identified to be significantly different between the patient and control groups. Furthermore, myostatin levels were detected within the normal range in the PCOS and control groups and no significant differences were identified between the PCOS (17.52±11.2) and the control (28.27±35.48) group (P>0.05).

Table I. Comparison of the demographic and laboratory characteristics of the patients.

Table I.

Comparison of the demographic and laboratory characteristics of the patients.

Parameter	Controls (n=42)	PCOS (n=46)	Whole population (n=88)	Reference rangesa	P-valueb
Age (years)	29.02±6.85	24.89±6.11	26.86±25.00		0.004b
BMI (kg/m2)	25.00±4.79	25.34±5.54	25.18±23.90	18.50–24.90	0.943
Waist circumference (cm)	77.29±10.87	78.22±11.68	77.77±77.00	<80.00	0.676
FSH (mIU/ml)	8.71±4.03	6.93±1.58	7.78±7.37	3.85–8.78	0.008b
LH (mIU/ml)	5.13±2.16	7.20±3.92	6.21±5.48	2.12–10.89	0.010b
Estradiol (pg/ml)	81.63±56.94	68.85±44.95	74.95±60.00	27.00–122.00	0.356
Prolactin (ng/ml)	12.15±6.87	13.20±5.52	12.70±11.73	3.34–26.72	0.429
TSH (µIU/ml)	1.80±0.92	2.34±1.60	2.08±1.79	0.34–5.60	0.172
HDL-C (mg/dl)	57.20±11.96	57.59±12.74	57.40±56.0	45.00–65.00	0.904
LDL-C (mg/dl)	93.31±25.70	88.56±27.49	90.83±92.10	<130.00	0.406
Triglyceride (mg/dl)	123.38±61.83	138.83±82.40	131.45±108.00	<200.00	0.613
Total cholesterol (mg/dl)	172.38±36.50	173.91±30.45	173.18±172.00	<200.00	0.831
DHEA-S (µg/dl)	180.08±97.00	218.26±109.00	200.04±177.15	23.00–266.00	0.087
Total testosterone (nmol/l)	1.53±0.69	1.73±1.07	1.64±1.51	0.34–2.60	0.722
SHBG (nmol/l)	63.58±24.39	58.21±35.63	60.77±56.70	19.80–155.20	0.070
FAI (%)	2.75±1.86	4.24±3.70	3.53±2.69	0.50–6.50	0.038b
FBG (mg/dl)	91.48±9.55	93.96±14.86	92.77±92.00	70.00–100.00	0.403
Insulin (µIU/ml)	11.76±10.65	14.64±21.05	13.27±8.54	1.90–23.00	0.056
HOMA-IR	2.77±2.74	4.00±8.25	3.42±1.91	<3.00	0.072
Myostatin (ng/ml)	28.27±35.48	17.52±11.20	22.65±15.93	7.00–32.00	0.475
hs-CRP (mg/l)	0.20±0.20	0.33±0.71	0.27±0.14	<0.50	0.884
Albumin (g/dl)	4.57±0.34	4.48±0.30	4.52±4.51	3.50–5.20	0.176
Neutrophils (×103/µl)	3.91±1.02	4.21±1.24	4.07±3.99	1.80–7.80	0.219
Lymphocytes (×103/µl)	1.89±0.50	1.91±0.50	1.90±1.90	0.90–3.70	0.808
Monocytes (×103/µl)	0.40±0.15	0.43±0.15	0.42±0.38	0.30–0.90	0.318
Platelets (×103/µl)	277.24±70.47	277.07±61.44	277.15±276.50	149.00–371.00	0.990

{ label (or @symbol) needed for fn[@id='tfn1-etm-0-0-7080'] } Values are expressed as the mean ± standard deviation.

a Reference ranges were obtained from the laboratory of Kafkas University School of Medicine. Reference hormonal values in the early follicular phase of menstruation is presented.

b P<0.05, control vs. PCOS, as determined by a non-parametric Mann-Whitney U-test or t-test. PCOS, polycystic ovary syndrome; BMI, body mass index; FSH, follicle-stimulating hormone; LH, luteinizing hormone; TSH, thyroid-stimulating hormone; HDL-C, high-density-lipoprotein cholesterol; LDL-C, low-density-lipoprotein cholesterol; DHEA-S, dehydroepiandrosterone sulfate; SHBG, sex hormone-binding globulin; FAI, free androgen index; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment insulin resistance index; hs-CRP, high-sensitivity C-reactive protein.

Correlation analysis

In the control group, no significant correlations were identified between the myostatin levels and any of the other parameters. Only the LDL-C levels in the PCOS group were identified to have a significant, but negative correlation with the myostatin values (correlation coefficient, r=−0.362; P<0.05; Table II). These results suggested that in PCOS patients, a higher serum level of LDL-C was associated with a lower serum level of myostatin.

Table II. Correlation between myostatin levels and laboratory parameters.

Table II.

Correlation between myostatin levels and laboratory parameters.

	PCOS		Controls		Whole population
Parameter	r	P-value	r	P-value	r	P-value
Age (years)	−0.083	0.582	−0.028	0.861	−0.044	0.681
BMI (kg/m2)	0.019	0.899	−0.138	0.384	−0.063	0.557
Waist circumference (cm)	−0.234	0.118	0.067	0.673	−0.108	0.318
FSH (mIU/ml)	0.037	0.809	0.099	0.532	0.083	0.444
LH (mIU/ml)	−0.147	0.329	0.117	0.461	−0.045	0.676
Estradiol (pg/ml)	−0.048	0.752	−0.034	0.831	−0.024	0.822
Prolactin (ng/ml)	−0.198	0.188	−0.014	0.932	−0.066	0.540
TSH (µIU/ml)	0.113	0.453	0.141	0.372	0.112	0.297
HDL-C (mg/dl)	0.094	0.533	−0.007	0.965	0.051	0.638
LDL-C (mg/dl)	−0.362	0.018a	−0.217	0.148	−0.254	0.016a
Triglyceride (mg/dl)	0.067	0.659	0.071	0.657	0.066	0.542
Total cholesterol (nmol/l)	−0.028	0.855	−0.248	0.114	−0.184	0.087
DHEA-S (µg/dl)	0.060	0.693	0.064	0.689	0.045	0.680
Total testosterone (nmol/l)	−0.016	0.916	0.041	0.798	0.015	0.891
SHBG (nmol/l)	−0.204	0.175	−0.175	0.268	−0.193	0.071
FAI (%)	0.050	0.743	0.196	0.214	0.104	0.335
FBG (mg/dl)	−0.413	0.004	0.067	0.673	−0.179	0.094
Insulin (µIU/ml)	−0.053	0.728	0.270	0.084	0.065	0.544
HOMA-IR	−0.130	0.390	0.216	0.169	0.030	0.783
hs-CRP (mg/l)	0.051	0.736	0.053	0.738	0.058	0.589
Albumin (g/dl)	−0.273	0.067	0.050	0.751	0.008	0.940
Neutrophils (×103/µl)	−0.122	0.419	−0.018	0.909	−0.067	0.536
Lymphocytes (×103/µl)	0.007	0.964	−0.150	0.343	−0.100	0.353
Monocytes (×103/µl)	−0.081	0.591	−0.168	0.288	−0.138	0.199
Platelets (×103/µl)	−0.153	0.311	−0.082	0.604	−0.088	0.416

{ label (or @symbol) needed for fn[@id='tfn4-etm-0-0-7080'] } Pearson's and non-parametric Spearman correlation analysis was performed.

a P<0.05. r, correlation coefficient; PCOS, polycystic ovary syndrome; BMI, body mass index; FSH, follicle-stimulating hormone; LH, luteinizing hormone; TSH, thyroid-stimulating hormone; HDL-C, high-density-lipoprotein cholesterol; LDL-C, low-density-lipoprotein cholesterol; DHEA-S, dehydroepiandrosterone sulfate; SHBG, sex hormone-binding globulin; FAI, free androgen index; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment insulin resistance index; hs-CRP, high-sensitivity C-reactive protein.

Multivariate regression analysis

Three different regression analyses were applied to the entire study sample (subjects from control and PCOS groups) (Table III). By adding and subtracting models, those models for meaningless variables were eliminated and the most meaningful regression models were identified. The model significance parameter F (F statistic/F-value) was demonstrated to be appropriate, whereas the variance inflation factor (VIF) values were far from the multicollinearity problem, and the coefficient of determination (R2) values were determined to be 11.2% for the whole sample, 23.4% for the control group and 54.2% for the patients with PCOS. In the regression model of the entire study population, an increase in LDL-C and FBG were associated with a decrease in myostatin (P=0.008 and P=0.036 respectively). In the model of the PCOS group, an increase in LDL-C and PRL were associated with a decrease in myostatin (P=0.001 and P=0.013, respectively). Furthermore, a decrease in SHBG, FBG and monocytes were associated with an increase in myostatin (P=0.028, P<0.001 and P=0.026, respectively). An increase in triglycerides was also associated with an increase in myostatin (P=0.001). In the model of the control group, a decrease in LDL-C was associated with an increase in myostatin (P=0.003) and a decrease in thyroid-stimulating hormone was associated with a decrease in myostatin (P=0.028).

Table III. Evaluation of the impact of variables on circulating myostatin using multiple linear regression analysis.

Table III.

Evaluation of the impact of variables on circulating myostatin using multiple linear regression analysis.

		95% CI
Group/parameter	β	Lower	Upper	P-value	Tolerance	VIF	Partial Correlation [Individual R2 (%)]	F (P-value)	R2 (%)
Whole population
(n=88)								5.357 (0.006)
Constant (intercept)	89.836	44.238	135.434	<0.001a					11.20
LDL-C	−0.274	−0.475	−0.073	0.008a	0.988	1.012	−0.281 (6.40)
FBG	−0.456	−0.881	−0.031	0.036a	0.988	1.012	−0.225 (3.60)
Controls (n=42)								5.956 (0.006)
Constant (intercept)	63.684	24.562	102.806	0.002a					23.40
LDL-C	−0.630	−1.038	−0.222	0.003a	0.920	1.087	−0.447 (13.10)
TSH	12.965	1.514	24.416	0.028a	0.920	1.087	0.344 (4.20)
PCOS (n=46)								7.694 (<0.001)
Constant (intercept)	85.397	61.642	109.153	<0.001a					54.20
LDL-C	−0.164	−0.258	−0.070	0.001a	0.898	1.113	−0.491 (4.70)
Prolactin	−0.597	−1.061	−0.133	0.013a	0.919	1.088	−0.385 (3.90)
Triglyceride	0.060	0.027	0.093	0.001a	0.818	1.222	0.507 (6.30)
SHBG	−0.079	−0.149	−0.009	0.028a	0.964	1.037	−0.343 (2.70)
FBG	−0.427	−0.600	−0.253	<0.001a	0.906	1.104	−0.623 (15.70)
Monocytes	−21.123	−39.553	−2.692	0.026a	0.793	1.261	−0.348 (0.50)

a P<0.05. β, unstandardized regression coefficient; CI, confidence interval; VIF, variance inflation factor; F, F statistic/F-value; R2, determination coefficient; LDL-C, low-density-lipoprotein cholesterol; FBG, fasting blood glucose; TSH, thyroid-stimulating hormone; PCOS, polycystic ovary syndrome; SHBG, sex hormone-binding globulin.

Discussion

The size of the circulating myostatin pool regulates the overall metabolic balance between fat and muscle mass (13). Therefore, an association may exist between myostatin levels and IR, and between myostatin levels and obesity. Previous studies reporting conflicting results have indicated that changes in serum myostatin levels may be due to unknown multifactorial effects, which remain elusive (17,22–26). Although a significant age difference was identified between the groups of the present study, it was previously demonstrated that the serum myostatin levels in females did not change with age (29). The results of the present study did not indicate any significant differences in the serum myostatin levels of the patients with PCOS compared with those in the control group. This is inconsistent with the results of a similar previous study by Chen et al (20). They demonstrated that the myostatin levels were higher in patients with PCOS compared with those in female subjects without PCOS. A previous study which assessed a mouse model of obesity and diabetes (30) revealed that inflammatory cytokines, IR and physical inactivity may block myostatin synthesis and release the functions of muscular mass. Myostatin expression is not expected to increase as a result of worse metabolic profiles (31). Theoretically, by blocking the synthesis of myostatin, elevated hs-CRP and HOMA-IR may inhibit any increase in circulating myostatin levels. No significant correlation of HOMA-IR or hs-CRP with myostatin levels was identified in the present study.

The association between myostatin and insulin action in humans has yet to be elucidated. Previous studies have reported a positive correlation of myostatin levels with IR and with obesity. Hittel et al (23) reported a positive association between serum myostatin levels and IR, whereas Toloza et al (24) did not identify any association between the serum myostatin levels and IR. Furthermore, Camporez et al (32) observed that insulin sensitivity increased in adult mice treated with anti-myostatin, but did not change in young mice that received the same treatment. In the present study, no correlation was identified between the serum levels of myostatin and hormonal and demographic parameters. It was noteworthy that the regression analysis did not identify any association of IR with myostatin levels. A previous study on skeletal muscle in patients with PCOS revealed that insulin action was reduced, and that the activity of insulin-signaling pathways and glucose uptake were decreased (2). Therefore, reduced insulin activity in the skeleton may exert an inhibitory effect on myostatin synthesis and release.

A regression analysis performed in the present study identified the serum FBG levels as a factor causing a decrease in myostatin levels. The basic mechanisms by which increased glucose levels decrease the myostatin levels remain elusive. Han et al (31) reported that diabetic patients had lower serum myostatin levels compared with control subjects, and that serum myostatin levels were inversely correlated with metabolic syndrome parameters. Furthermore, Palsgaard et al (33) observed elevated muscle myostatin levels in diabetic patients, while Brandt et al (26) were not able to identify any significant differences in serum myostatin levels between diabetic patients and a control group. Due to these conflicting results regarding aberrant myostatin levels in diabetic patients among various studies, including the present one, it is not possible to reach any firm conclusions with this regard. Theoretically, high glucose levels are able to affect the adipose tissue ratios of diabetic patients, leading to a change in circulating myostatin levels.

In the PCOS group, increases in SHBG, LDL-C, PRL, monocytes and FBG levels were determined to be associated with a decrease in serum myostatin levels, whereas an increase in the levels of triglycerides was associated with an increase in myostatin levels. It is known that myostatin is associated with fat metabolism, in which triglycerides have a role (15).

An adequate metabolic profile has been reported to be associated with high myostatin (31). Androgen production is expected to be increased in patients with PCOS (34). Under non-physiological conditions, the association between androgens and muscle mass has remained to be properly defined (35,36). Myostatin levels were reported to increase to stop muscle growth in response to muscle development (30); therefore, myostatin may be increased in PCOS to inhibit muscle development due to the high associated androgen levels. However, the muscle capacity of the patients was not evaluated in the present study and only the BMI was measured. As the BMI in the control group was similar to that in the PCOS group, the effect of muscle mass on myostatin levels may have therefore been negligible.

In conclusion, an improved understanding of the mechanism of action of myostatin will provide novel opportunities for understanding the pathophysiology of PCOS. The small sample size is a limitation of the present study. Detecting potential factors affecting myostatin levels may help to improve obesity, IR and ovulatory dysfunction in patients with PCOS in the future. Myostatin may be used as a biomarker to detect metabolic disorders in PCOS. Elevated or decreased serum myostatin levels in patients with PCOS may be indicative of an impaired metabolic profile. However, future studies which assess the effects of therapeutic interventions for PCOS on serum levels of myostatin are recommended.

Acknowledgements

The author thanks Dr Nilüfer Celik (Clinic of Biochemistry, Behçet Uz Children's Hospital, İzmir, Turkey) for her help with the biochemical analysis.

Funding

No funding was received.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Author's contributions

HA was involved in the acquisition, analysis and interpretation of data for the study, drafting the work and critically revising it for important intellectual content.

Ethics approval and consent to participate

The present study was performed in accordance with the Declaration of Helsinki, and was approved by the Local Ethics Committee of Kafkas University School of Medicine (28 February 2018; no. 80576354-050-99/67). All subjects provided written informed consent to participate in the study.

Patient consent for publication

Not required.

Competing interests

The author declares he has no competing interests.

References