The association between the follicular distribution pattern of polycystic ovaries and metabolic syndrome development in patients with polycystic ovary syndrome a prospective cohort study.

During the study period, a total of 160 patients were evaluated, of which 108 patients met the criteria for inclusion in the study; 51 patients were classified in the PCP ovaries group and 57 patients in the GCP ovaries group. The baseline characteristics of patients were compared between two groups in Table  1 . There was no statistically significant difference in terms of women’ age at the ultrasound evaluation ( P  = 0.328)., Menarche age ( P  = 0.855), body mass index (BMI) ( P  = 0.575), waist circumstance ( P  = 0.657) as well as systolic ( P  = 0.713) and diastolic ( P  = 0.605) blood pressure levels, the Ferriman-Gallwey score ( P  = 0.129) between the two groups. A statistically remarkable difference was observed in terms of oligomenorrhea diagnosis ( P  = 0.006) between the two groups. So that the frequency of cases with oligomenorrhea in the PCP group was significantly higher than the GCP group (Table  1 ). Table 1 Baseline characteristics of PCOS patients stratified by the ovarian morphology. Variable PCP ovaries n  = 51 GCP ovaries n  = 57 p -value* Age at the ultrasound evaluation (year) 26.78 ± 6.149 27.89 ± 5.59 0.328 Menarche age (year) 12.17 ± 1.55 12.12 ± 1.48 0.855 Body mass index (kg/m 2 ) 25.22 ± 4.92 25.73 ± 4.39 0.575 Waist Circumstance (cm) 95.85 ± 3.71 96.94 ± 11.53 0.657 Systolic blood pressure (mmHg), (rang) 108.03 ± 10.39, (90–130) 107.03 ± 17.34, (110–135) 0.713 Diastolic blood pressure (mmHg), (rang) 69.80 ± 10.09, (50–100) 68.77 ± 10.49, (50–90) 0.605 The Ferriman-Gallwey score 15.00 ± 5.51 13.33 ± 5.72 0.129 No. of cases with oligomenorrhea, n (%) 44 (86.27%) 36 (63.15%) 0.006 Significant values are in [bold]. Values are presented as the mean ± Standard deviation (SD) and number (percent). * Obtained by independent t test and Chi square test. Significant level was considered at P  < 0.05. GCP, general cystic pattern; PCOS, polycystic ovary syndrome; PCP, peripheral cystic pattern. Baseline characteristics of PCOS patients stratified by the ovarian morphology. Significant values are in [bold]. Values are presented as the mean ± Standard deviation (SD) and number (percent). * Obtained by independent t test and Chi square test. Significant level was considered at P  < 0.05. GCP, general cystic pattern; PCOS, polycystic ovary syndrome; PCP, peripheral cystic pattern. No significant relationship was found between the follicular distribution pattern (FDP) and ovarian reserve tests (the baseline serum levels of FSH ( P  = 0.991), LH ( P  = 0.539), AMH ( P  = 0.559). Moreover, there were no significant differences in terms of hyperandrogenism representing tests (serum levels of total testosterone ( P  = 0.490), SHBG ( P  = 0.516), FAI ( P  = 0.411)) between two groups. In similar way, no remarkable difference regarding insulin resistance index (HOMA-IR) ( P  = 0.217), any components of MetS (serum levels of FBS ( P  = 0.428), Chol ( P  = 0.578), TG ( P  = 0.762), and HDL ( P  = 0.145) levels) as well as MetS rate ( P  = 0.570) was observed between two groups (Table  2 ). Table 2 The comparison of hormonal and biochemical tests and metabolic features between two ovarian morphology groups. Variable PCP ovaries n  = 51 GCP ovaries n  = 57 p -value* Baseline serum FSH (IU/L) 5.93 ± 2.00 6.00 ± 1.98 0.991 Baseline serum LH (IU/L) 7.49 ± 5.00 6.48 ± 3.79 0.539 Serum AMH (ng/mL) 6.62 ± 4.78 6.10 ± 4.21 0.554 Total serum testosterone (ng/dL) 0.39 ± 0.29 0.39 ± 0.22 0.490 Serum SHBG level (nmol/L) 41.18 ± 35.30 53.88 ± 48.05 0.516 FAI value 1.36 ± 1.28 1.16 ± 1.09 0.411 Fasting blood sugar (mg/dL), 92.72 ± 8.89 91.63 ± 7.42 0.428 Serum fasting insulin (mIU/L) 11.12 ± 6.28 13.57 ± 9.56 0.374 HOMA-IR value 2.58 ± 1.57 3.03 ± 1.99 0.217 Serum triglycerides (mg/dL), 114.10 ± 63.24 110.71 ± 51.43 0.762 Serum cholesterol level (mg/dL) 164.00 ± 34.76 167.67 ± 33.02 0.578 Serum HDL, (mg/dL) 47.80 ± 9.96 45.02 ± 9.62 0.145 Metabolic syndrome rate, n (%) 17 (33.30%) 22 (38.60%) 0.570 Values are presented as the mean ± Standard deviation (SD) and number (percent). * Obtained by independent t test and chi square test. Significant level was considered at P  < 0.05. AMH: anti Müllerian hormone, FAI: free androgen index was calculated by total testosterone/SHBG*100; FSH: follicle stimulation hormone; GCP: general cystic pattern.; HDL: high-density lipoprotein; HOMA-IR: Insulin resistance index was calculated using the formula (FBS*Insulin) /405; LH: luteinizing hormone; SHBG: sex hormone binding globulin; PCP: peripheral cystic pattern. The comparison of hormonal and biochemical tests and metabolic features between two ovarian morphology groups. Values are presented as the mean ± Standard deviation (SD) and number (percent). * Obtained by independent t test and chi square test. Significant level was considered at P  < 0.05. AMH: anti Müllerian hormone, FAI: free androgen index was calculated by total testosterone/SHBG*100; FSH: follicle stimulation hormone; GCP: general cystic pattern.; HDL: high-density lipoprotein; HOMA-IR: Insulin resistance index was calculated using the formula (FBS*Insulin) /405; LH: luteinizing hormone; SHBG: sex hormone binding globulin; PCP: peripheral cystic pattern. For a more thorough investigation, a multivariable logistic regression analysis was applied to explore the factors influencing MetS within the studied population ( n  = 109). Potential influential variables such as women’s age, history of infertility and oligomenorrhea, history of smoking, family history of PCOS, serum level of AMH and vitamin D 3 , insulin resistance index (IR), and FAI were included in the regression model. The findings revealed that only the history of oligomenorrhea (odds ratio (OR); 2.92, 95% confidence interval (CI): 1.09–7.78, P  = 0.032).and IR (OR:1.69, 95% CI:1.27–2.26, P  < 0.001) exhibited a significant relationship with MetS.

This prospective study was conducted Arash Women’s Hospital, a university-affiliated facility located in Tehran, Iran, over the period from September 2023 to June 2024. The study protocol has received approval from the scientific board and the ethics committees of Tarbiat Modares University (Ethics approval number: IR.MODARES.REC.1402.079). A total of 160 Iranian non-pregnant women between the ages of 20 and 40, who were not pregnant and had been diagnosed with PCOS, and were referred for any gynecologic examination over the study period were prospectively screened. Eventually 108 eligible for the study were enrolled in it after providing comprehensive information regarding the study and obtaining written informed consent. Individuals who had utilized hormonal contraceptives, fertility therapies, or insulin sensitizers, and any medications that reduce blood lipids and lower blood pressure, as well as anticoagulants like warfarin and aspirin preceding three months were not included in the study. Additional exclusion criteria encompassed hyperprolactinemia, with or without galactorrhea, thyroid disorders, a history of ovarian surgery, Diabetes mellitus as well as Cushing’s syndrome diagnosis, late-onset adrenal hyperplasia, the existence of a functional ovarian cyst larger than 3 cm observed in the initial ultrasound, and a diagnosis of moderate to severe endometriosis. During the initial consultation, all qualifying patients underwent evaluation by an endocrinologist and metabolism specialist, who assessed the symptoms associated PCOS and the diagnostic criteria for MetS. The PCOS diagnosis was defined according to the Rotterdam criteria 21 . The presence of at least two of the following criteria: menstrual irregularity (cycle length  35 days or variation between consecutive cycles of > 10 days); clinical (presence of hirsutism evaluated by a Ferriman- Gallwey score > 8, severe acne and alopecia) or biochemical (total testosterone concentration > 0.5 ng/ml and/or free testosterone > 3.5 pg/ml) hyperandrogenism; or ultrasound evidence of polycystic ovaries. Hirsutism score was determined according to the Ferriman-Gallwey scoring scale and examination of nine body areas for coarse terminal hair, including upper lip, chin and chest, upper and lower areas of the abdomen, thighs and upper arms. In each part, the severity of hirsutism was graded from 1 to 4 and the participants with the total score of 8 and above considered as having hirsutism 22 , 23 . The presence of more than 12 follicles measuring between 2 and 9 mm in each ovary and/or an increased ovarian volume exceeding 10 mL is used to define a PCOM as observed via ultrasound. Menstrual regularity was assessed by inquiring about the length of the majority of menstrual cycles. Oligomenorrhea was defined as infrequent menstrual cycles occurring more than 35 days apart or < 8 cycles per year 24 . The identification of MetS was based on the criteria established by the National Cholesterol Education Program 2005 25 : a blood glucose level exceeding 100 mg/dL or the use of medication to manage blood sugar levels; high-density lipoprotein (HDL) levels below 40 mg/dL in men and below 50 mg/dL in women, or the administration of medical treatment to address this issue; triglyceride levels surpassing 150 mg/dL or the use of medication to control elevated triglycerides; a waist circumference (WC) of 95 cm or more for Iranian women; and blood pressure readings exceeding 130/85 mmHg or the use of medication to regulate blood pressure 26 . Measurements of height, weight, waist and hip circumferences, as well as blood pressure, were conducted following a standardized protocol by an experienced midwife. A transvaginal Doppler ultrasound was performed on days 2 to 5 of either a spontaneous or progestin-induced menstrual cycle. The assessment of follicle count and distribution was conducted by one experienced gynecologist utilizing a Philips Affiniti 70w equipped with a trans-vaginal 3.5–10 MHz probe (USA). The images obtained were categorized into two distinct groups based on ovarian morphology: (1) PCP: This group included 12 or more follicles arranged around a dense stromal core, occupying at least 50% of the ovarian diameter peripherally. (2) GCP: This group comprised 12 or more follicles dispersed throughout the ovary, with no more than 49% exhibiting a peripheral distribution 7 . The gynecologist was unaware of the other’s findings, and a skilled radiologist was consulted to establish the final diagnosis. In this study, the patients presenting with identical morphology in both ovaries, indicating that both ovaries displayed either PCP or GCP morphology were included. Hormonal and biochemical assessments were conducted for all participants in the study at the Arash Women’s Hospital laboratory. Baseline serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), anti-Mullerian hormone (AMH), total testosterone, and sex hormone binding globulin (SHBG), were assessed through a venous blood sample collected from each participant on the second or third day of their menstrual cycle. Free androgen index (FAI) was calculated by total testosterone/SHBG*100. The concentrations of FSH, LH, total testosterone, SHBG were determined using Electrochemiluminescence immunoassay (ECLIA) with a fully automated Immulite 20,000 analyzer and a commercial kit from SIEMENS, Germany. The measurement of AMH concentration was performed using a commercial enzyme-linked immunosorbent assay (ELISA) kit from DiaZist, Iran, which demonstrated inter- and intra-assay coefficient of variations (CVs) of less than 5%. Furthermore, the indicators of insulin metabolism, specifically fasting blood sugar (FBS) and fasting insulin levels, along with lipid profiles including, serum levels of cholesterol, triglycerides and high-density lipoprotein (HDL) were assessed. HOMA-IR index (Homeostatic Model Assessment for Insulin Resistance index was measured using the formula (FBS*Insulin) /405). Biochemical tests were performed utilizing the fully automated COBAS C-501 device in conjunction with a commercial kit from Roche. The coefficients of variation for both inter-assay and intra-assay measurements were all below 5%. The statistical analysis was performed using SPSS software (version 22; Inc, Chicago, IL, USA). Data were expressed as mean ± standard deviation (SD) and count (percentage). The analysis between groups was carried out using the two-tailed Student’s t-test and the Chi-square test, as applicable. A p-value of less than 0.05 was considered statistically significant. The multivariable logistic regression was performed to detect the most significant variables related to the MetS in the study population. A p-value of less than 0.05 was considered statistically significant.

In the current investigation, no association was identified between the pattern of follicular distribution and metabolic syndrome, as well as the profiles of sex hormones, glucose, and lipids in patients with PCOS; however, the peripheral cystic pattern was linked to menstrual irregularities. Conducting comprehensive studies that meticulously examine the distinguishing sonographic markers (FDP, ovarian volume, and stromal blood flow) in individuals diagnosed with PCOS may significantly assist in predicting the complications and severity of the condition, as well as in selecting suitable strategies to mitigate its endocrine disorders.

The main objective of the present study was to assess whether the follicular distribution pattern in PCOM ovaries can be useful in predicting the MetS as well as menstruation status, clinical and biochemical manifestations of hyperandrogenism in women with PCOS diagnosis. From a clinical point of view, it is important to understand how different PCOM morphologies are related to the severity of the disease itself. The results of the present study indicated that mensuration status was significantly related with PCP follicular distribution pattern. No remarkable associations were found between the pattern of follicular distribution and the serum concentrations of sex hormones, the clinical and biochemical indicators of hyperandrogenism, the glucose profile, insulin resistance, lipid profile, and the prevalence of MetS in patients diagnosed with PCOS. In this domain, Mills and colleagues conducted a retrospective study revealing that the PCP follicular distribution pattern exhibited a stronger correlation with menstrual irregularities and elevated serum levels of LH as well as both total and free testosterone 7 . These results corroborate earlier studies that established a link between follicular distribution patterns and hyperandrogenism, thereby reinforcing the hypothesis that the ovarian morphologies of PCP and GCP may differ in their endocrine and pathophysiological mechanisms 11 , 12 . It is postulated that the presence of PCP ovarian morphology arises from a stromal core characterized by increased density and enhanced vascular blood flow, which may lead to the peripheral development of ovarian follicles 7 . Prior research has indicated that stromal density and vascular flow are predictive of the severity of PCOS, as they correlate with levels of ovarian hyperandrogenism 12 . In contrast, GCP ovaries do not exhibit increased stromal density and are likely to induce a lesser degree of androgenic disturbance in affected individuals 7 . In the current research, consistent with the findings of Mills et al., a notable correlation between menstrual disorders and follicular patterns was identified. However, in contrast to their study, no significant association was detected between follicular patterns and the clinical or biochemical manifestations of hyperandrogenism. The possible explanations for this discrepancy can be due to the difference in the studied population and the number of sample sizes. Elsewhere, in agreement to our findings, Christ and co-workers in a cross-sectional observational study of 49 women with PCOS concluded that there was no significant relationship between FDP and any reproductive marker or metabolic parameter associated with PCOS 13 . Recently, Alviggi and co-workers conducted a retrospective study that examined the ovarian ultrasonographic parameters in women with PCOS, specifically comparing those with insulin resistance to those exhibiting a hyperandrogenic profile. They categorized 78 patients diagnosed with PCOS, according to the Rotterdam criteria, into two distinct groups based on specific transvaginal ultrasound findings. Group A consisted of subjects whose follicles predominantly measured between 5 and 9 mm in diameter, with an ultrasonographically determined stroma/total area (S/A) exceeding 0.34, and a prominent “necklace” sign of antral follicles. Conversely, Group B included subjects with more than 50% of their antral follicles measuring between 2 and 4 mm in diameter, an S/A of 0.34 or less, and an absence of the “necklace” sign, characterized instead by a uniform distribution of follicles as observed via ultrasound 12 . The study revealed that patients with a type A ovary, particularly those displaying the classic “necklace” sign, had significantly lower BMI, waist-to-hip ratio, and homeostasis model assessment (HOMA) values compared to those with a type B ovary 12 . These findings imply a potential association between insulin resistance and a specific ultrasound pattern in women with PCOS 12 . Notably, the morphological classification of Group A, characterized by the necklace sign, aligns with the distribution of PCP follicles, while the morphology of Group B corresponds to the distribution of GCP follicles observed in the current study. The researchers asserted that insulin resistance acts as the main driving force behind the onset of PCOS, resulting in considerable changes in ovarian morphology 12 . In contrary to their findings, we found no relationship between BMI, waist circumstance as well as glucose profile and insulin resistance index (HOMA) and follicular distribution patterns. The variation in the outcomes of the studies may be attributed to differences in their methodologies. The present study is prospective in nature, with all laboratory tests conducted at a single center. Therefore, it is recommended to carry out further prospective studies with a larger sample size to validate these findings. In the current investigation, no relationship was found between the rate of MesS and its diagnostic components, which include waist circumference, blood pressure, lipid profile, and glucose levels in relation to follicular distribution patterns. In alignment with our findings, Sipahi and colleagues did not find a relationship between MetS and follicular distribution pattern observed in Doppler ultrasound in PCOS women; furthermore, their study indicated that ovarian volume serves as a more significant indicator for assessing the risk of MetS in these patients 17 . In addition, Reid and co-workers showed that increased ovarian volume is linked to indicators of insulin resistance in individuals with PCOS. Moreover, the number of antral follicles is a reliable predictor of metabolic risk and ovarian volume could serve as a valuable marker to assist clinicians in their risk stratification and counseling of patients with PCOS 27 . This phenomenon may be attributed to the increased in ovarian volume in patients with MetS, which is not a result of an increased number of follicles, but instead is linked to a proliferation of the stroma caused by hyperplasia of the ovarian theca cells 27 . One of the limitations of our study was the absence of ovarian volume measurement, which restricts our ability to engage in a discussion on this topic. Considering that the studies in this field are limited, therefore prospective studies with a larger sample size are suggested in this field. It is important to highlight that the present study applied a multivariable regression model to analyze the variables influencing the MetS. As it is determined, in our study population, oligomenorrhea and IR were the major factors which significantly related to the MetS; so that history of oligomenorrhea was associated with a 2.9-fold increase in the risk of MetS. In addition, an increase of one unit in the insulin resistance index was associated to a 1.6-fold increase in the risk of developing MetS. Similarly, some previous researches have focused on finding the related factors to MetS in women with PCOS 28 – 32 . Some factors have been identified as being linked to MetS in these population including: IR and elevated free testosterone levels 29 ; age (28. 30), BMI 28 , 31 , and waist-to-hip ratio 30 , increased WC and hyperandrogenism 32 . It is clear that there is a lack of consensus regarding the specific factors that elevate the risk of developing MetS in women with PCOS, although obesity is frequently noted by many researchers. Given that WC and BMI, FBS, and lipid profiles are part of the diagnostic criteria and their association with MetS has been established, we opted not to include these factors into the regression model. Our findings are consistent with those of Dey et al. 29 , which identified IR as a significant predictor of MetS. Furthermore, our research is pioneering in demonstrating the link between the occurrence of oligomenorrhea and the risk of MetS. Understanding the risk factors associated with metabolic syndrome in patients with PCOS is crucial for implementing preventive strategies. Consequently, it is advisable for healthcare professionals to consider the factors identified in research studies and to address the management of these risk factors in individuals with PCOS. The potential strength of the present study was its prospective design and all sonographic evaluations and the laboratory tests were performed by the dedicated specialists’ team. The constraints on the sample size in our research stem from time limitations, resulting in a restricted availability of qualified participants at the time of the study. Additionally, the assessment of ultrasound markers is contingent upon the evaluator’s expertise, and this investigation was conducted at a single center. Consequently, it is recommended for future research to design a multicenter approach, considering the consistency among evaluators, to thoroughly explore the correlation between all significant sonographic markers and severity of PCOS as well as developing MetS.

Researches utilizing ultrasound technology in the medical field has revealed that approximately 25% of women possess polycystic ovaries; however, the majority of these individuals do not exhibit additional symptoms associated with polycystic ovary syndrome (PCOS) 1 . Women diagnosed with PCOS may encounter several challenges, including: menstrual disorders, hirsutism, acne and various skin issues, alopecia, infertility and complications during pregnancy, and insulin resistance and obesity 2 , 3 . Polycystic ovary morphology (PCOM) is frequently observed in women exhibiting the characteristic clinical signs of PCOS 4 . The advancement of ultrasound imaging software has made it feasible to conduct an objective assessment of ovarian stroma today 5 . Although there has been ongoing discussion and updates regarding the number of follicles necessary to characterize PCOM, the examination of follicular distribution patterns has received comparatively less attention. Additionally, various underlying pathophysiological mechanisms affecting folliculogenesis may lead to distinct patterns of follicular development in ovaries associated with PCOM 6 , 7 . The ovarian morphology are divided into two categories based on the distribution of follicles in the ovary: 1- Peripheral cystic pattern (PCP) 2- General cystic pattern (GCP) in which small follicles occupy the entire ovarian parenchyma 8 . The radiographic feature associated with the first type is referred to as the string of pearls sign 9 . Prior research on the characteristics of PCOM has indicated that the presence of 10 or more follicles arranged peripherally around the central area of the ovary serves as a highly sensitive criterion for diagnosing PCOS 10 . In PCP, small antral follicles are situated beneath the capsular area, whereas in GCP, those are distributed throughout the entire ovarian parenchyma 7 . The average thickness of the ovarian capsule in GCP is considerably greater than that observed in PCP 7 . At first, Takahashi and colleges indicated that there are histopathological distinctions between GCP and PCP 11 . Furthermore, the histological examination of GCP and PCP reveals differences in their endocrinological profiles 11 . Thereafter, some previous studies have highlighted the correlation between ovarian ultrasound parameters and the clinical features of patients diagnosed with PCOS 7 , 12 , 13 . Metabolic syndrome (MetS) is a widespread metabolic disorder that has emerged as a consequence of the rising rates of obesity 14 . It appears that the underlying mechanisms of this disease are primarily associated with insulin resistance and the excessive accumulation of fatty acids. In addition to central obesity observed in these individuals, other significant factors for diagnosis include elevated blood triglyceride levels, reduced high-density lipoprotein (HDL) levels, increased blood pressure, elevated fasting blood sugar (FBS), and insulin resistance (IR) 14 . Approximately 34% of women diagnosed with PCOS are classified as obese 15 . The occurrence of MetS in individuals with PCOS is more prevalent than in the general population 16 ; however, a definitive marker for predicting the development of MetS in patients with PCOS has yet to be established 17 . In 2019, Sipahi et al. determined that the likelihood of developing MetS appears to be elevated in patients with PCOS who exhibit increased ovarian volume measurements 17 . Recently, the examination of ovarian ultrasound parameters has emerged as a significant predictor of both the severity of symptoms associated with PCOS and the effectiveness of treatment. So that a positive correlation between the number of follicles, ovarian volume, stromal characteristics and markers of insulin resistance has been reported 18 . Considering the significant occurrence of MetS among various phenotypes of patients with PCOS 19 , 20 , the introduction of a predictive factor to assess the risk of MetS in these individuals could prove invaluable in its prevention 17 . Since a limited number of studies have investigated the different pattern of follicular distribution in the ovaries of PCOS patients and their relationships with menstrual irregularities, sex hormone levels, and metabolic disorders. Consequently, this topic has garnered significant interest. Therefore, the present study was conducted to explore the association between MetS components, insulin resistance, sex steroids, and the distribution pattern of ovarian follicles in individuals diagnosed with PCOS.