Ovarian morphology is associated with insulin resistance in women with polycystic ovary syndrome: a cross sectional study

Activity of isoflavone in managing polycystic ovary syndrome symptoms (Review)

Polycystic ovary syndrome (PCOS), a hormonal and metabolic disorder manifested in women of reproductive age, is still being treated using drugs with side effects. As an alternative to these drugs, isoflavone, also identified as phytoestrogen, has anti-PCOS activity. Isoflavone can help relieve PCOS symptoms by lowering the level of testosterone, which causes hyperandrogenism, thereby normalizing the menstrual cycle and restoring normal ovarian morphology. Furthermore, isoflavone influences the improvement of the metabolic profile, which changes because of PCOS, as well as the reduction of inflammatory markers and oxidative stress. However, both significant and non-significant results have been generated on the activity of isoflavones in PCOS. The present review aims to discuss the existing literature on the effect of isoflavone on PCOS symptoms based on in vivo and clinical trial studies.

Ovarian volume is more closely related to the different manifestations of polycystic ovary syndrome than follicle number per ovary

OBJECTIVE

Polycystic ovary (PCO), a diagnostic component of polycystic ovary syndrome (PCOS), requires either an ovarian volume (OV) criterion or a follicle number per ovary (FNPO) criterion. This study investigated the association of OV and FNPO criteria with various manifestations of PCOS.

METHODS

This cross-sectional study was conducted at a university hospital among 100 patients newly diagnosed with PCOS (according to the revised Rotterdam criteria). Fasting blood samples were collected to measure glucose, total testosterone (TT), luteinizing hormone (LH), follicle-stimulating hormone (FSH), lipid, insulin, and hemoglobin A1c levels. An oral glucose tolerance test was performed. Transabdominal or transvaginal ultrasound of the ovaries was done, depending on patients' marital status. All investigations were conducted in the follicular phase of the menstrual cycle. OV >10 mL and/or FNPO ≥12 indicated PCO. A homeostasis model assessment of insulin resistance (IR) value ≥2.6 indicated IR, and metabolic syndrome (MS) was defined according to the international harmonization criteria.

RESULTS

Seventy-six participants fulfilled the OV criterion, 70 fulfilled the FNPO criterion, and 89 overall had PCO. Both maximum OV and mean OV had a significant correlation with TT levels (r=0.239, p=0.017 and r=0.280, p=0.005, respectively) and the LH/FSH ratio (r=0.212, p=0.034 and r=0.200, p=0.047, respectively). Mean OV also had a significant correlation with fasting insulin levels (r=0.210, p=0.036). Multivariate binary logistic regression analysis showed that IR (odds ratio [OR], 9.429; 95% confidence interval [CI], 1.701 to 52.271; p=0.010) and MS (OR, 7.952; 95% CI, 1.821 to 34.731; p=0.006) had significant predictive associations with OV alone, even after adjustment for age and body mass index.

CONCLUSION

OV may be more closely related to the androgenic and metabolic characteristics of PCOS than FNPO.

Diagnostic Performance of Ovarian Morphology on Ultrasonography across Anovulatory Conditions-Impact of Body Mass Index

The study objectives were to determine whether ovarian morphology can distinguish between women with regular menstrual cycles, normo-androgenic anovulation (NA-Anov), and PCOS and whether body mass index (BMI)-specific thresholds improved diagnostic potential. Women with PCOS (biochemical and/or clinical hyperandrogenism and irregular cycles; N = 66), NA-Anov (irregular cycles without clinical and/or biochemical hyperandrogenism; N = 64), or regular cycles (controls; cycles every 21-35 days in the absence of clinical or biochemical hyperandrogenism; N = 51) were evaluated. Participants underwent a reproductive history, physical exam, transvaginal ultrasound, and a fasting blood sample. Linear regression analyses were used to assess the impact of BMI on ovarian morphology across groups. The diagnostic performance of ovarian morphology for anovulatory conditions, and by BMI (lean: <25 kg/m²; overweight: ≥25 kg/m²), was tested using Receiver Operating Characteristic (ROC) curves. Follicle number per ovary (FNPO) and ovarian volume (OV), but not follicle number per cross-section (FNPS), increased across controls, NA-Anov, and PCOS. Overall, FNPO had the best diagnostic performance for PCOS versus controls (AUC_ROC = 0.815) and NA-Anov and controls (AUC_ROC = 0.704), and OV to differentiate between PCOS and NA-Anov (AUC_ROC = 0.698). In lean women, FNPO best differentiated between PCOS and controls (AUC_ROC = 0.843) and PCOS versus NA-Anov (AUC_ROC = 0.710). FNPS better distinguished between NA-Anov and controls (AUC_ROC = 0.687), although diagnostic performance was lower than when thresholds were generated using all participants. In women with overweight and obesity, OV persisted as the best diagnostic feature across all analyses (PCOS versus control, AUC_ROC = 0.885; PCOS versus NA-Anov, AUC_ROC = 0.673; NA-Anov versus controls, AUC_ROC = 0.754). Ovarian morphology holds diagnostic potential to distinguish between NA-Anov and PCOS, with marginal differences in diagnostic potential when participants were stratified by BMI suggesting that follicle number may provide better diagnostic performance in lean women and ovarian size in those with overweight.

The Implication of Mechanistic Approaches and the Role of the Microbiome in Polycystic Ovary Syndrome (PCOS): A Review

As a complex endocrine and metabolic condition, polycystic ovarian syndrome (PCOS) affects women's reproductive health. These common symptoms include hirsutism, hyperandrogenism, ovulatory dysfunction, irregular menstruation, and infertility. No one knows what causes it or how to stop it yet. Alterations in gut microbiota composition and disruptions in secondary bile acid production appear to play a causative role in developing PCOS. PCOS pathophysiology and phenotypes are tightly related to both enteric and vaginal bacteria. Patients with PCOS exhibit changed microbiome compositions and decreased microbial diversity. Intestinal microorganisms also alter PCOS patient phenotypes by upregulating or downregulating hormone release, gut-brain mediators, and metabolite synthesis. The human body's gut microbiota, also known as the "second genome," can interact with the environment to improve metabolic and immunological function. Inflammation is connected to PCOS and may be caused by dysbiosis in the gut microbiome. This review sheds light on the recently discovered connections between gut microbiota and insulin resistance (IR) and the potential mechanisms of PCOS. This study also describes metabolomic studies to obtain a clear view of PCOS and ways to tackle it.

Ovarian volume, not follicle count, is independently associated with androgens in patients with polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is diagnosed based on chronic anovulation, androgen excess (clinical and/or biochemical), and polycystic ovaries in ultrasound. The aim of the present study was to evaluate which parameters in the transvaginal ultrasound (TVUS) of ovaries could be better associated with concurrent hormonal imbalance in the women with PCOS.

METHODS

Using a cross-sectional design, this study focused on 61 subjects (18-40 years) with PCOS. Patients were recruited at three academic hospitals during the 2017-2019 period. PCOS was defined according to the Rotterdam criteria. The association of ovarian morphology with hormonal and metabolic feature was investigated using linear regression models, adjusted for a set of possible confounding variables including age, mensuration status and body mass index (BMI).

RESULTS

The mean volume of both ovaries was positively associated with the total testosterone level (β = 0.025, P value < 0.001), free androgen index (β = 0.041, P value < 0.001) and luteinizing hormone/follicle stimulating hormone (LH/FSH) ratio (β = 0.032, P value = 0.004), even after adjustments made for age, mensuration status and BMI (fully-adjusted model). In contrast, in the fully-adjusted model, antral follicle count (AFC), follicle number per ovary (FNPO), ovarian area, stromal area, and ratio of stromal area to ovarian area (S/A) were not associated with androgen levels and LH/FSH ratio. In addition, after full adjustments, ovarian volume, AFC, FNPO, ovarian area, stromal area and S/A were not associated with insulin resistance, which was estimated by the homeostasis model assessment of insulin resistance (HOMA-IR).

CONCLUSION

Increased ovarian volume is, thus, highly predictive of hyperandrogenemia and high LH/FSH ratio in PCOS patients.

Ovarian volume as an independent marker for metabolic dysfunction in women with suspected androgen excess

Objective

To determine whether ovarian volume (OV) alone is an independent marker for metabolic dysfunction in women with suspected androgen excess.

Design

Retrospective cohort study.

Setting

Tertiary academic reproductive endocrinology clinic.

Patients

Women aged ≥21 years recruited/referred for symptoms related to androgen excess.

Interventions

Transvaginal ovarian ultrasound, physical and medical evaluation, 2-hour 75-g oral glucose tolerance test (oGTT), and blood sampling.

Main Outcome Measures

Prevalence of hyperandrogenism and metabolic dysfunction.

Results

This study included 666 women, of whom 412 (61.9%) and 254 had OVs of >10 and ≤10 mL, respectively. An OV of >10 mL was associated with a higher prevalence of hirsutism (65.1% vs. 51.5%) than an OV of ≤10 mL. Polycystic ovary syndrome by the National Institutes of Health 1990 criteria was found in 67.3% and 51.4% of women with OVs of >10 and ≤10 mL, respectively. Metabolic parameters, including body mass index, waist circumference, and 1-hour insulin levels during the oGTT (odds ratio, 1.98; 95% confidence interval, 1.18-3.31), were significantly higher in women with an OV of >10 mL than in those with an OV of ≤10 mL. An OV of ≤10 mL had a 76.3% negative predictive value for hyperinsulinemia at 1 hour.

Conclusions

In women with suspected androgen excess, an OV of >10 mL in at least 1 ovary is not associated with metabolic syndrome but is associated with younger age; an increased body mass index and waist circumference; a higher prevalence of hirsutism, oligoovulation, and polycystic ovary syndrome; and a higher 60-minute insulin level during the oGTT. Overall, an increased OV appears to be a good marker for hyperinsulinemia and hyperandrogenism in women suspected of having an androgen excess disorder.

Mean platelet volume and polycystic ovary syndrome: a systematic review and meta-analysis

OBJECTIVE

This meta-analysis evaluated the association between the mean platelet volume (MPV) and polycystic ovary syndrome (PCOS).

METHODS

A systematic literature search using PubMed, EMBASE, and Web of Science databases until June 2021 was conducted. Pooled standardized mean differences (SMD) and 95% confidence intervals (CI) were determined using a random effects model.

RESULTS

Ten studies involving 866 women with PCOS and 548 age- and body mass index-matched women without PCOS were included. The MPV was significantly increased in women with PCOS compared with non-PCOS women (SMD = 0.43, 95% CI = 0.13-0.72). Subgroup analyses showed that this trend was consistent in cross-sectional studies (SMD = 0.44, 95% CI = 0.03-0.86) and in Turkish women (SMD = 0.46, 95% CI = 0.13-0.79). Meta-regression analysis revealed a marginally positive correlation between the MPV and the homoeostasis model assessment of insulin resistance in women with PCOS. The sensitivity analysis showed that the effect estimate was robust and stable, and publication bias was not evidenced in the pooled analysis.

CONCLUSIONS

This meta-analysis revealed that women with PCOS have a significantly increased MPV than women without PCOS, which is probably associated with insulin resistance.INPLASY registration number: INPLASY2021100021.

Is there a Relationship between Serum IGF-1 and Thyroid Nodule, Thyroid or Ovarian Volume in Polycystic Ovarian Syndrome?

Context

Studies investigating the association between serum IGF-1, and thyroid nodule, ovarian or thyroid volume in polycystic ovarian syndrome (PCOS) are limited.

Objective

We aimed to analyze the association between serum IGF-1 level, and ovarian or thyroid volume, or thyroid nodule in PCOS.

Design

The study was performed between June 2017 and August 2019 as prospective design.

Subjects and Methods

Adult females with new-onset PCOS were included. The patients having comorbid illness, or using medication were excluded. Basic tests, thyroid and ovarian sonography were performed. The patients were grouped according to thyroid nodule(absent/present) and ovarian volume (<10mL/≥10mL). We planned to find a positive association between IGF-1, and thyroid nodule, thyroid or ovarian volume in PCOS.

Results

Of total 118 patients, 11(9%) had thyroid nodule. The patients with thyroid nodule had a higher ovarian volume (p=0.006). No correlation was found between GH or IGF-1, and thyroid or ovarian volume. IGF-1 was not a predictor for thyroid nodule or higher ovarian volume. Thyroid nodule was a significant predictor for higher ovarian volume.

Conclusion

Our study is the first to analyze the association between IGF-1 and thyroid nodule in PCOS. We found that thyroid nodule was associated with thyroid and ovarian volume, but IGF-1 was not associated with thyroid nodule, thyroid or ovarian volume.

A Cross-Sectional Study on Potential Ovarian Volume and Related Factors in Women with Polycystic Ovary Syndrome from Infertile Couples

Purpose

This study was designed to explore the value of ovarian volume (OV) measured by transvaginal ultrasound and its relationship with anthropometry and serum hormonal levels in a polycystic ovary syndrome (PCOS) population.

Patients and Methods

A total of 119 women with PCOS from infertile couples were recruited in this cross-sectional study. On days 2-4 of the menstrual cycle, transvaginal ultrasound examinations were performed, and hormonal profiles were measured. PCOS diagnosis was based on the Rotterdam 2003 criteria and classified into four phenotype groups. The PCOS group (study group) and the non-PCOS group (control group) were compared.

Results

The mean age of the participants was 32.66±4.10 years compared to 33.99±4.78 years in 273 cases (69.6%) without PCOS. The mean OV was statistically larger in the PCOS group than in the non-PCOS group (7.65±3.23 mL vs 6.08±3.67 mL, p < 0.001) and positively correlated with serum anti-Mullerian (AMH) and luteinizing hormone (LH) levels (r=0.30; p < 0.001 and r=0.23; p < 0.001, respectively), and weakly and inversely correlated with age (-0.182, p < 0.001). The area under the receiver operating characteristic (ROC) curve of OV in the diagnosis of PCOS was 0.613 (0.557-0.670, 95% CI).

Conclusion

The enlarged OV is remarkable in women with PCOS and is related to AMH and LH concentrations. Although the diagnostic potential of PCOS is substantially low, OV alone may contribute to predicting the severity of PCOS and better performance for the diagnosis of PCOS phenotypes.

Does body mass index have an effect on the prevalence of various symptoms of polycystic ovary syndrome and their associated risk factors?

The present cross-sectional study is an attempt to understand the effect of body mass index (BMI) on the prevalence of various symptoms of polycystic ovary syndrome (PCOS) and to evaluate its associated risk factors.

A total of 250 PCOS women diagnosed by Rotterdam Criteria (2003), age ranging from 18–45 years, attending OPD of Gynaecology and Obstetrics of PGIMER, Chandigarh, India were enrolled in the study. All the participants were divided in three groups according to their body mass index (BMI).

The polycystic ovaries (83.2%) were the most frequently occurring symptoms of PCOS followed by hirsutism (74.4%), oligomenorrhea (60%), seborrhea (45.2%) and acne (40%). Category wise frequency distribution showed higher prevalence of symptoms among women in overweight/obese category, which were further supported by correspondence analysis. Results of multivariate analysis revealed that marital status, type of diet, socio-economic status and physical activity level were potential risk factors contributing to severe manifestations of PCOS symptoms.

Obesity denoted as an important risk factor can exaggerate many symptoms of PCOS and also be a causative factor for menstrual disturbance.

Differentiation Between Phenotypes of Polycystic Ovarian Syndrome With Sonography

Objectives:

To differentiate between the classic (A/B) from the nonclassic (C/D) phenotypes of the polycystic ovarian syndrome (PCOS) with sonography.

Materials & Methods:

Retrospective analysis was performed upon 72 female patients with PCOS. All patients underwent transvaginal sonography. The patients were classified into classic and nonclassic phenotypes of PCOS. Image analysis was performed for the ovarian volume, follicle counts, follicle size, and endometrial plate thickness.

Results:

There were significant differences in the ovarian volume ( P = .001), follicle counts ( P = .001), follicle size ( P = .001), and endometrial plate thickness ( P = .001) between classic and nonclassic phenotypes of PCOS. The threshold value for ovarian volume, follicle count, follicle size, and endometrial plate thickness used to differentiate classic from nonclassic phenotypes were 12.5, 10.5, 5.25, and 5.75, respectively, with an area under the curve of 0.79, 0.82, 0.83, 0.77 and an accuracy of 75%, 73.6%, 79.2%, and 68.1%, respectively. The serum testosterone level and the body mass index were significantly higher in patients with classic than nonclassic phenotypes of PCOS ( P = .001, .04), respectively.

Conclusion:

Sonography findings can differentiate classic from nonclassic phenotypes of PCOS.

Genetic Susceptibility to Joint Occurrence of Polycystic Ovary Syndrome and Hashimoto's Thyroiditis: How Far Is Our Understanding?

Polycystic ovary syndrome (PCOS) and Hashimoto’s thyroiditis (HT) are endocrine disorders that commonly occur among young women. A higher prevalence of HT in women with PCOS, relative to healthy individuals, is observed consistently. Combined occurrence of both diseases is associated with a higher risk of severe metabolic and reproductive complications. Genetic factors strongly impact the pathogenesis of both PCOS and HT and several susceptibility loci associated with a higher risk of both disorders have been identified. Furthermore, some candidate gene polymorphisms are thought to be functionally relevant; however, few genetic variants are proposed to be causally associated with the incidence of both disorders together.

A comparison of two- and three-dimensional ultrasonographic methods for evaluation of ovarian follicle counts and classification of polycystic ovarian morphology

OBJECTIVES

To determine the level of agreement across assessments of follicle number per ovary (FNPO) and classifying of polycystic ovarian morphology (PCOM; FNPO ≥25) with the use of various real-time (RT) and off-line two-dimensional (2D) and three-dimensional (3D) ultrasonographic methods.

DESIGN

Method comparison study.

SETTING

University-based clinical research unit.

PATIENT(S)

Sixteen women with and without PCOM.

INTERVENTION

Thirty-two ovaries were analyzed with the use of eight ultrasonographic methods: 2D-Grid (reference method), 2D-RT, 2D-RT with Grid, multiplanar view (MPV), MPV-RT, tomographic ultrasound imaging (TUI), TUI-RT, and semiautomated volume calculation (SonoAVC).

MAIN OUTCOME MEASURE(S)

FNPO, PCOM status, and time to obtain FNPO. Clinical feasibility, defined as the time taken to obtain FNPO, also was evaluated.

RESULT(S)

2D-RT overestimated FNPO versus 2D-Grid (3 ± 9 follicles) owing to overcounting in non-PCOM ovaries (6 ± 6 follicles). However, systematic bias was not detected when a grid overlay was incorporated (2D-RT with Grid). SonoAVC underestimated FNPO (-3 ± 5 follicles), particularly in PCOM ovaries (-4.1 ± 5.0 follicles). No bias in FNPO was detected between MPV, TUI, or TUI-RT versus 2D-Grid. 2D-RT significantly misclassified ovaries as PCOM. All methods except MPV took less time to complete FNPO assessments compared with 2D-Grid.

CONCLUSION(S)

Variability in FNPO across ultrasonographic methods limits their interchangeable use, particularly when a precise metric is needed. 2D-RT may be problematic owing to its propensity to misclassify PCOM. 2D-RT with Grid and MPV-RT could represent clinically feasible alternatives to obtain FNPO and classify PCOM. Efforts to reduce variation in FNPO will clarify the relevance of PCOM in women's health.

Ultrasonographic features of ovarian morphology capture nutritional and metabolic influences on the reproductive axis: implications for biomarker development in ovulatory disorders

Ultrasonographic imaging of ovarian morphology is used widely to inform reproductive health status in women. Metabolic disturbances induced by a negative energy balance (e.g. undernutrition) or positive energy balance (e.g. overnutrition, obesity) are known to drive or exacerbate reproductive dysfunction. Whether the utility of ultrasonographic metrics of ovarian morphology could be extended as biomarkers that detect and monitor the integration of metabolic and reproductive dysfunction is an emerging research area, and recent evidence is discussed. We note that unique variations in ovarian morphology emerge across the adiposity spectrum and highlight the potential for reproductive and metabolic 'tipping points' upon which such morphological variations may be detected on ultrasonography.

Ovarian volume is associated with adiposity measures and bone mineral density in postmenopausal women

INTRODUCTION

The present study aimed to assess the association between ovarian volume and demographic and anthropometric parameters, as well as sex hormones and bone mineral density (BMD) in postmenopausal women.

METHODS

161 healthy postmenopausal women participated in this cross-sectional study. Fasting venous blood samples were obtained for biochemical/hormonal assessment. Anthropometric parameters included body mass index (BMI) and waist-to-hip ratio (WHR). Ultrasonography was used to estimate the average ovarian volume for each participant. BMD was measured in the femoral neck (FN) and the lumbar spine (LS) using DXA.

RESULTS

Mean ovarian volume increased linearly with increasing quartiles of BMI (Q1:0.985±0.25, Q2: 1.11±0.29, Q3: 1.07±0.28, Q4: 1.19±0.38, p-value for linear trend 0.013). Ovarian volume correlated positively with BMI (r=0.128, p-value=0.038), FN BMD (r=0.233, p-value=0.003), FN T-score (r=0.223, p-value=0.004) and FN Z-score (r=0.171, p-value=0.027). Multivariate analysis showed that ovarian volume was predicted by WHR (b-coefficient=0.157, p-value=0.047) and SHBG (b-coefficient= -0.160, p-value=0.042), independently of age and BMI. Finally, FN BMD was predicted by ovarian volume, independently of age, menopausal age and BMI.

CONCLUSION

Ovarian volume was positively and independently associated with adiposity indexes and femoral BMD in postmenopausal women. Lower SHBG levels were associated with higher ovarian volume. Insulin resistance may mediate these results. The significance of these findings should be assessed in larger prospective studies.