Hemostatic Abnormalities and Relationships to Metabolic and Hormonal Status in Polycystic Ovarian Syndrome

Coagulation parameters predictive of polycystic ovary syndrome

OBJECTIVE

To explore coagulation parameters in association with polycystic ovarian syndrome (PCOS) and establish a model for predicting the risk of PCOS.

STUDY DESIGN

This study included 181 outpatients with PCOS. A total of 301 women who attempted to seek pre-pregnancy consultation at the Department of Gynecology of our hospital were included in the control group, and six coagulation parameters were measured for all included subjects. A logistic regression model was built based on the training dataset using the purposeful selection method to select important predictors. The performance of the established model was validated on the test dataset.

RESULTS

There were statistically significant differences found among all coagulation parameters except D-Dimer (DD, P = 0.080). The purposeful selection method selected age (odds ratio [OR] = 0.89; p = 0.008), prothrombin time (PT, OR = 0.68, p < 0.0001), thrombin time (TT, OR = 3.30; p = 0.0005), and fibrin degradation products (FDP, OR = 0.24; p = 0.0002) as important predictors of PCOS risk. The receiver operating characteristic (ROC) curve analysis indicated that the area under the ROC curve (AUC) of the model was 0.81 for the training dataset with an optimal cut-off point of the predicted probability of 0.45, leading to a sensitivity of 0.71 and a specificity of 0.82. The AUC was 0.79 for the test data.

CONCLUSIONS

It was found that the coagulation parameters, including PT, TT, and FDP, are predictive of PCOS. These results highlight the potential of anti-coagulation therapies to lower the risk of adverse outcomes in women with PCOS.

Expression of the plasminogen system in the physiological mouse ovary and in the pathological polycystic ovary syndrome (PCOS) state

BACKGROUND

The fibrinolytic system and its inhibitors play a number of roles, apart from their function in blood haemostasis and thrombosis, namely in ovarian folliculogenesis and in ovulation. Plasminogen is converted to active plasmin at the time of follicular rupture through a decrease in plasminogen activator inhibitor-1 (PAI-1) and an increase in plasminogen activators. Oligo-/anovulation and follicle arrest are key characteristics of PCOS, but studies evaluating fibrinolytic/proteolytic markers within human or animal PCOS ovaries are lacking. We aimed to investigate and compare the expression and distribution of the plasminogen system markers in PCOS and control ovaries.

METHODS

A hyperandrogenised PCOS mouse model was used that mimics the ovarian, endocrine and metabolic features of the human condition. Immunohistochemistry and digital image analysis were used to investigate and compare fibrinolytic/proteolytic markers plasminogen, plasminogen/plasmin, tissue plasminogen activator, urokinase plasminogen activator and inhibitor PAI-1 in PCOS and control ovaries. Student's t-test was used to compare data sets for normally distributed data and Wilcoxon-Mann Whitney test for non-normally distributed data.

RESULTS

We noted differences in the ovarian distribution of PAI-1 that was expressed throughout the PCOS ovary, unlike the peripheral distribution observed in control ovaries. Plasminogen was present in small follicles only in PCOS ovaries but not in small follicles of control ovaries. When we assessed and compared PAI-1 expression within follicles of different developmental stages we also noted significant differences for both the PCOS and control ovaries. While we noted differences in distribution and expression within specific ovarian structures, no differences were noted in the overall ovarian expression of markers assessed between acyclical PCOS mice and control mice at the diestrus stage of the estrous cycle.

CONCLUSIONS

Our novel study, that comprehensively assessed the fibrinolytic/proteolytic system in the mouse ovary, showed the expression, differential localisation and a potential role for the plasminogen system in the physiological mouse ovary and in PCOS. Androgens may be involved in regulating expression of the ovarian plasminogen system. Further studies evaluating these markers at different time-points of ovulation may help to further clarify both physiological and potential pathological actions these markers play in ovulatory processes distorted in PCOS.

Anthropometric and body composition analysis of infertile women with polycystic ovary syndrome

Objectives

To evaluate the body composition and anthropometric profile of infertile women who have been diagnosed with polycystic ovary syndrome (PCOS) and to investigate the incidence of PCOS and to examine body fat composition as a risk factor for this disease.

Methods

This hospital-based case controlled study was conducted on a cohort of 132 patients with and without PCOS. Bioelectrical impedance analysis was used to record body composition parameters, such as total body fat, visceral fat, subcutaneous fat, skeletal muscle composition and their distribution in the trunk, legs and arms, as well as blood pressure. Anthropometric profile parameters, including body mass index (BMI), ideal body weight (IBW), waist circumference, hip circumference and waist-to-hip ratio, were also recorded.

Results

The mean age of incidence of PCOS was 29.74 ± 3.32 years (OR 1.417), and most of the cohort exhibited high to very high visceral fat with significant correlation (p < 0.001). Total body fat distribution and whole, trunk, arm and leg subcutaneous fat were significantly higher in patients with PCOS (p < 0.001). The mean BMI, waist and hip circumference of the PCOS group were 28.2 ± 6.08, 97.44 ± 15.11 cm and 109.22 ± 17.39 cm, respectively. The results also indicated significant increases in DP and MAP (OR 1.528) in patients with PCOS compared to the control group (p < 0.001).

Conclusion

This study exhibits higher levels of BMI, body fat distribution, waist and hip circumference, diastolic and mean blood pressure, visceral fat, and a disproportionate increase in the level of global fat and its distribution.

Polycystic ovary syndrome: cardiovascular risk factors according to specific phenotypes

INTRODUCTION

Polycystic ovary syndrome (PCOS) is associated with obesity and insulin resistance. The objective of this cross-sectional study was to investigate the impact of insulin resistance and body mass index (BMI) on inflammatory and hemostatic variables associated with long-term risk of cardiovascular disease in women with PCOS.

MATERIAL AND METHODS

149 premenopausal women with PCOS were recruited consecutively from April 2010 to February 2012 at three Danish University Hospitals. The study was conducted at the Department of Gynecology and Obstetrics, Herlev University Hospital, Denmark. PCOS was diagnosed in accordance with the Rotterdam criteria and the women were classified into four phenotypes according to BMI and insulin resistance measured by the homeostasis model assessment of insulin resistance index. Body composition was determined by dual-energy X-ray absorptiometry. Main outcome measures were the biomarkers C-reactive protein (CRP), plasminogen activator inhibitor-1 (PAI-1), and von Willebrand factor antigen.

RESULTS

Normal weight insulin-resistant PCOS women were characterized by abdominal obesity and elevated levels of plasma PAI-1. Overweight/obese insulin-resistant PCOS women had increased levels of both PAI-1 and CRP. Of the three Rotterdam criteria, only hyperandrogenemia was significantly associated with the hemostatic risk marker of long-term cardiovascular disease risk.

CONCLUSIONS

Surrogate risk markers for cardiovascular disease are elevated in women with PCOS, especially insulin-resistant and overweight/obese women.