Differentiating PCOS from Anovulatory Cycles in Adolescents: A Comprehensive Evaluation of FAI, SHBG, and LH/FSH Ratio

Differentiating PCOS from Anovulatory Cycles in Adolescents: A Comprehensive Evaluation of FAI, SHBG, and LH/FSH Ratio | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differentiating PCOS from Anovulatory Cycles in Adolescents: A Comprehensive Evaluation of FAI, SHBG, and LH/FSH Ratio Emre OZER, Demet TAŞ, Seçil ÇAKIR GÜNDOĞAN, Mehmet BOYRAZ, Fatih GÜRBÜZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Menstrual irregularities are common among adolescents, often linked to anovulatory cycles. This study aims to establish diagnostic cut-off values for Polycystic Ovary Syndrome (PCOS) and differentiate these from anovulatory dysfunction in adolescents. Additionally, we assessed the sensitivity of using the Free Androgen Index (FAI) and Sex Hormone Binding Globulin (SHBG) in diagnosing PCOS. Methods This study included 305 adolescents presenting with oligomenorrhea at a tertiary center. Data were analyzed statistically and Receiver operating characteristic (ROC) curves were plotted to evaluate diagnostic performance. Results Of the 305 patients, 229 (75%) had anovulatory cycles and 36 (11.8%) had PCOS. The mean FAI values for anovulatory cycles, PCOS, and hyperinsulinism were 3.5 ± 2, 8.0 ± 5, and 8.3 ± 4, respectively (p < 0.001). A significant positive correlation was found between FAI and both HOMA-IR (r = 0.389; p < 0.001) and BMI (r = 0.499; p < 0.001). ROC analysis determined the LH threshold of 9.7 U/L and LH/FSH ratio threshold of 2.62 as predictive markers for PCOS. Conclusions Anovulatory cycles are the most frequent cause of menstrual irregularities in adolescents, with hyperandrogenism being crucial for diagnosing PCOS. The FAI may be unreliable for PCOS diagnosis due to similar values in adolescents with hyperinsulinemia and obesity. Lower SHBG levels in hyperinsulinemic obese adolescents further complicate the use of FAI, indicating that glucose/insulin metabolism significantly influences FAI/SHBG levels. Comprehensive diagnostic criteria, including androgen levels, LH/FSH ratio, SHBG, FAI levels, and ovarian ultrasound, are essential for accurate PCOS diagnosis. Free androgen index Menstrual irregularity LH-FSH Oligomenorrhea Polycystic ovary syndrome Sex hormone binding globuline Figures Figure 1 Introduction Abnormal uterine bleeding (AUB) is a frequent health concern among adolescents in whom oligomenorrhea often presents with anovulatory cycles due to the immaturity of the Hypothalamic Pituitary Ovarian (HPO) axis ( 1 – 4 ) The prevalence of Polycystic Ovary syndrome (PCOS) in adolescents ranges from 3.4–19.6%. Hyperandrogenism and oligomenorrhea are key features of PCOS, with this differential diagnosis between PCOS and anovulatory dysfunction can be challenging in clinical practice. The pathogenesis of PCOS, which remains incompletely understood, involves dysregulation of ovarian steroidogenesis, the hypothalamic-pituitary-adrenal (HPA) axis, and alterations in insulin metabolism ( 5 – 10 ). Genes such as DENND1A (DENN Domain-Containing Protein 1A) and LHCGR (Luteinizing Hormone/Chorio Gonadotropin Receptor), INSR (Insulin Receptor are some of the candidates frequently proposed in Genome-wide Association Studies (GWAS) ( 11 , 12 ). PCOS is associated with an increased risk of metabolic syndrome, infertility, endometrial cancer, and psychological disorders such as anxiety and depression. In this study, we aimed to determine diagnostic cut-off values for PCOS and differentiate these values from those of patients with anovulatory dysfunction. Additionally, we aimed to assess the sensitivity of using the Free Androgen Index (FAI) and Sex Hormone Binding Globuline (SHBG) in the diagnosis of PCOS. Methods Patients : A total of 305 adolescents who apllied with complaints of oligomenorrhea to a tertirary center between August 2019 and March 2023 were included into the study. Data of the patients were examined retrospectively by 4 Pediatric Endocrinologists and 1 Pediatrician specialized in Adolescent Health. Oligomenorrhea is defined by infrequent or irregular menstrual periods, with the definition based on the postmenarcheal year: in the first year, an average cycle length greater than 90 days; in the second year, greater than 60 days; in the third year, greater than 45 days; and in the fourth year, a cycle length greater than 38 days ( 13 ). The International Federation of Gynecology and Obstetrics (FIGO) categorizes AUB etiologies using the acronym PALM-COEIN (polyp, adenomyosis, leiomyoma, malignancy, coagulopathy, ovulatory dysfunction, endometrial disorders, iatrogenic, and not yet classified) ( 7 ). In adolescents, anovulatory cycles are the most common cause of oligomenorrhea, which is a diagnosis made after excluding other PALM-COEIN etiologies( 14 ). As anovulatory menstrual cycles, are frequent in the first years after menarche and clinical features of PCOS such as menstrual irregularities, hirsutism, and acne are common during adolescence, the diagnosis of PCOS is controversial when adult criteria are used. The diagnosis of PCOS in adolescents considered when the presence of oligomenorrhea presents 2 years after menarche for at least 6 months and clinical and/or biochemical hyperandrogenism. Clinical hyperandrogenism was defined as moderate-severe hirsutism (a modified Ferriman-Gallwey score of 16 or higher), and biochemical hyperandrogenism as a total testosterone level of 50 ng/dl or higher ( 13 , 15 ). Obesity and related insülin resistance which are growing concerns among pediatric populations worldwide, are known to be related to dysregulation in ovulatory functions in females, leading to ovulatory dysfunction ( 16 , 17 ). Patients with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) levels greater than 4 and fasting insulin levels greater than 25 mU/L were grouped under the Hyperinsulinemia subgroup. HOMA-IR is calculated with the following formula: Fasting plasma insulin concentration (mU/ L) X fasting plasma glucose (mg/dl) / 405 ( 18 , 19 ). The authors evaluated and categorized patients into groups: Anovulatory, PCOS, Hyperinsulinemic, Malnutrition, and those with chronic diseases. A flow-chart of patient selection is presented in Fig. 1. Free Androgen Index (FAI): The FAI was calculated using the formula: (Total Testosterone (nmol/L)/SHBG (nmol/L)) × 100 ( 20 ). Statistical Methods: The data were analyzed using SPSS 22 software (Statistical Package for Social Sciences; SPSS Inc., Chicago, IL). Descriptive statistics were used to report categorical data as “n”, and percentages, while continuous data were presented as means ± standard deviation (± SD). Pearson's chi-square test was used to compare categorical variables between groups. The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. The Mann-Whitney U-test was used to compare two groups, and the Kruskal-Wallis test was used for comparisons between more than two groups. Spearman correlation test was used to examine the relationship between continuous variables. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of various parameters. The level of statistical significance was set at p < 0.05 for all analyses. Results Characteristics of the patients: The average age at diagnosis was 15.3 ± 1.5 years, and the average age at onset of complaints was 13.6 ± 1.6 years. The mean age of menarche was 12.5 ± 1.2 years, and on average, patients reported the start of their complaints was 1.0 ± 1.3 years after menarche. The average time elapsed between the start of complaints and patients' application to our center was 1.7 ± 1.5 years. Of the 305 patients in study group; 229(%75) diagnosed with anovulatory cycles, 36(%11,8) with PCOS, n:14 (%4,6) with obesity and hyperinsulinism, n:12 (%3,9) with severe malnutrition and others (5 patients with chronic diseaes, 4 patients with hypogonadism, 2 patients with congenital adrenal hyperplasia and 1 patient with estradiol secreting ovarian cyst). Additional patient characteristics are presented in Table 1 . Table 1 Characteristics of the patients Menstrual pattern n; (%) Oligomenorrhea 264 (%86.6) Secondary amenorrhea (over 6 months) 41 (%13.4) Excessive menstrual bleeding Normal 238 (%78) Present 67 (%22) BMI SDS, mean ± SS, (n) 0,6 ± 1,6 (277) Hirsutism Present 56 (%18.4) Normal 249 (%81.6) Suprapubic Ultrasonography Performed 277 (%90,8) Not performed 28 (%9,2) OCS treatment Received 83 (%27,2) Not-received 222 (%72,8) BMI: Body mass index; m-FG: modified Feriman-Gallwey; OCS: Oral contraceptive. Evaluation of the patient groups with PCOS, Anovulatuary cycles, and Hyperinsulinism: We aimed to determine diagnostic cut-off values for PCOS and differentiate these values from those of patients with anovulatory dysfunction. The data of PCOS adolescents were evaluated along with those of other groups. We wanted to highlight this topic, which has not been sufficiently determined in adolescents recently. Hirsutism showed a significant difference between the PCOS and Anovulatory group (p = 0.007). In terms of total Testosterone and DHEAS, significant differences were found (p < 0.001) between the PCOS group and the other groups. Additional analyses are presented in Table 2 . Table 2 Analysis of the patients with PCOS, Anovulatuary cycles, and Hyperinsulinism PCOS (n = 36) Anovulatuary (n = 229) Hyperinsulinism (n = 14) p * Age at the onset of complaints 13,7 ± 1,5 (35) 13,5 ± 1,6 (220) 13,1 ± 1,7 (12) 0,374 Age at menarche 12,5 ± 1,2 (35) 12,5 ± 1,2 (221) 12,2 ± 1,4 (13) 0,835 Time between the menarche and the onset of complaints (years) 1,3 ± 1,4 (35) 0,9 ± 1,2 (214) 0,9 ± 1,0 (12) 0,645 The time between the onset of complaints and the patient's application,(years) 2,1 ± 1,6 (35) 1,7 ± 1,5 (220) 1,7 ± 1,3 (12) 0,352 Menstrual pattern Oligomenorrhea 31(%86,1) 204(%89,1) 13(%92,9) 0,793 ** Secondary amenorrhea (over 6 months) 5 (%13,9) 25(%10,9) 1 (%7,1) BMI, SDS 1,2 ± 1,5 b (36) 0,6 ± 1,3 c (203) 2,9±,6 a (13) < 0,001 Hirsutism 14(%38,9 b ) 38(%16,6 a ) 3 (%21,4 a,b ) 0,007 ** m-FG score 17,1 ± 4,5 a (14) 9,8 ± 1,6 b (37) 10,0 ± 1,0 a,b (3) < 0,001 Insulin, mU/L 18,6 ± 12,1 b (22) 14,3 ± 6,2 b (89) 47,5 ± 48,1 a (12) < 0,001 HOMA-IR 4,1 ± 2,7 b (21) 3,0 ± 1,4 b (89) 11,0 ± 12,4 a (12) < 0,001 FSH, U/L 6,4 ± 1,5 (36) 6,1 ± 2,3 (187) 6,8 ± 2,3 (13) 0,332 LH, U/L 13,5 ± 8,5 (36) 10,5 ± 8,2 (187) 9,8 ± 6,0 (13) 0,103 LH/FSH 2,1 ± 1,3 (36) 1,7 ± 1,1 (187) 1,3 ± 0,6 (13) 0,134 Total Testosteron, ug/L 54,8 ± 20,7 b (36) 30,0 ± 9,5 a (177) 29,3 ± 8,6 a (12) < 0,001 DHEAS, ug/dl 291,9 ± 118,2 b (34) 199,3 ± 95,6 a (179) 163,6 ± 63,7 a (12) < 0,001 SHBG,nmol/L 29,2 ± 14,5 b (28) 42,1 ± 24,3 c (147) 12,2 ± 4,4 a (13) < 0,001 FAI 8,3 ± 4,1a (8) 8,0 ± 5,1a (6,7) 3,5 ± 2,7b (2,7) < 0,001 OCS treatment Received 13(%36,1) 61(%26,6) 5(%35,7) 0,412 ** Not-received 23(%63,9) 168(%73,4) 9(%64,3) *Kruskal Wallis analysis, ** Chi-square analysis performed. a,b,c = The group from which the difference originates. PCOS: Polcyctic ovarian syndrome; BMI: body mass index; m-FG: modified Feriman-Gallwey; HOMA-IR: Homeostasis Model Assessment – Insulin Resistance; FSH: Follicular stimulating hormone; LH: Luteinizing hormone; SHBG: Sex hormone binding globulin; DHEA-S: Dehydroepiandrosterone-sulfate; OCS: Oral contraceptive. Free Androgen Index analysis: FAI is proposed as a diagnostic criterion in some studies recently. To evaluate the availability of this index, the data of PCOS adolescents were evaluated along with those of other groups. The mean FAI values for 145 patients with anovulatory cycles, 28 patients with PCOS, and 12 patients with hyperinsulinism were 3.5 ± 2, 8.0 ± 5, and 8.3 ± 4, respectively. A significant difference was observed between the anovulatory group and the other 2 groups (PCOS and hyperinsulinism group) (p < 0.001). A significant positive correlation was found between FAI and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (r = 0.389; p < 0.001). Additionally, a significant positive correlation was identified between FAI and Body Mass Index (BMI) (r = 0.499; p < 0.001). Evaluation of the patients with Anovulatuary cycles and Malnutrition: Malnutrition is a well known cause of oligomenorrhea. We analyzed the data between patients with malnutrition and anovulatuary cycles. Blood glucose (p = 0.015), serum insulin (p = 0.023), HOMA-IR (p = 0.026), values were significantly lower in the malnutrition group as expected. Also, LH (p = 0.006), and LH/FSH (p = 0.003) were lower in this group. Additional analyses are presented in Table 3 . Table 3 Comparison of the Malnutrition group and Anovulatory group Malnutrition (n = 12) Anovulatory (n = 229) p * Age at the onset of complaints 14,4 ± 1,4 (12) 13,5 ± 1,6 (220) 0,049 Age at first menstruation 12,6±,9 (10) 12,5 ± 1,2 (221) 0,527 Time between the menarche and the onset of complaints,(years) 1,7 ± 1,4 (10) 0,9 ± 1,2 (214) 0,033 Time between th onset of complaints and the patients' application,(years) 1,0 ± 1,4 (12) 1,7 ± 1,5 (220) 0,035 BMI, SDS -2,9 ± 1,2 (12) 0,6 ± 1,3 (203) < 0,001 Menstrual pattern Oligomenorrhea 8(%66,7) 204(%89,1) 0,042 ** Amenorrhea (over 6 months) 4(%33,3) 25(%10,9) Insulin, mU/L 8,0 ± 6,7 (5) 14,3 ± 6,2 (89) 0,023 HOMA-IR 1,6 ± 1,4 (5) 3,0 ± 1,4 (89) 0,026 FSH, U/L 6,2 ± 2,3 (8) 6,1 ± 2,3 (187) 0,735 LH, U/L 5,1 ± 6,7 (9) 10,5 ± 8,2 (187) 0,006 LH/FSH 0,7 ± 0,7 (8) 1,7 ± 1,1 (187) 0,003 Total Testosteron, ug/L 22,7 ± 12,5 (6) 30,0 ± 9,5 (177) 0,121 DHEAS, ug/dl 159,1 ± 100,3 (7) 199,3 ± 95,6 (179) 0,242 SHBG,nmol/L 61,0 ± 33,2 (4) 42,1 ± 24,3 (147) 0,213 OCS treatment Received 2(%16,7) 61(%26,6) 0,737 ** Not-received 10(%83,3) 168(%73,4) *Mann Whitney U analysis, **KChi-square analysis performed. BMI: body mass index; m-FG: modified Feriman-Gallwey; HOMA-IR: Homeostasis Model Assessment – Insulin Resistance; FSH: Follicular Stimulating hormone; LH: Luteinizing hormone; SHBG: Sex hormone binding globulin; DHEA-S: Dehydroepiandrosterone-sulfate; OCS: Oral contraceptive. Receiver operating characteristic (ROC) analysis: The predictive ability of various values for PCOS was investigated through ROC analysis, and cut-off values were determined. The LH decision threshold of 9.7 U/L exhibited a sensitivity of 63.9%, specificity of 59.9%, and statistically significant predictive capability. The LH/FSH ratio decision threshold of 2.62 had a sensitivity of %38,9, a specificity of 86.8%, and was found to be statistically a good predictor. (Table 4 ) Table 4 ROC analysis of the patient's data to predict PCOS. Area p %95safetyzone Sensitivity Specificity PPV NPD Lower limit Upper limit SHGB ≤ 23 nmol/L 0,624 0,019 0,553 0,692 53,6 70,0 22,7 90,2 FSH > 5,3 U/L 0,561 0,164 0,498 0,623 83,3 36,2 17,5 93,0 LH > 9,7 U/L 0,626 0,014 0,563 0,685 63,9 59,9 20,5 91,1 LH/FSH > 2,62 0,608 0,049 0,545 0,668 38,9 86,8 32,6 89,7 E2 > 54 pg/ml 0,560 0,224 0,494 0,625 62,5 54,3 18,0 90,0 DHEA-S > 226 ug/dl 0,740 < 0,001 0,680 0,794 79,4 68,1 28,7 95,3 SHBG: Sex hormone binding globulin; FSH: Follicular Stimulating hormone; LH: Luteinizing hormone; E2: Estradiol; DHEA-S: Dehydroepiandrosterone-sulfate; PPV: Positive predictive value; NPV: Negative predictive value. Discussion The main finding in our study is that FAI values were similar between adolescents with PCOS and non-PCOS cases with hyperinsulinemia and obesity, indicating using the FAI (Free Androgen Index) for diagnosing PCOS may be unreliable. Additionally, SHBG levels were found to be lower than adolescents with PCOS than hyperinsulinemic obese adolescents with oligomenorrhea, suggesting that FAI and SHBG should not be used as a diagnostic criterion, particularly in the presence of hyperinsulinism. In healthy adolescents, the average age of menarche is around 12–13 years. In Zuchelo et al.'s meta-analysis, it is stated that the age of menarche in adolescents with PCOS is similar to that of non-PCOS adolescents ( 21 ). In our study, the similar average age of menarche in adolescents with oligomenorrhea suggests that oligomenorrhea is unrelated to the age of menarche. Adolescents' awareness of menstrual cycles and menstrual disorders can vary depending on social and educational factors which can delay adolescents from seeking medical help ( 22 ). The persistence of complaints for a considerable amount of time as in our patients may be related to factors like lack of knowledge, feelings of shame, and etc. Particularly menarche can be the first indication of an underlying bleeding disorder. As coagulopathy is reported to occur in up to 20% of patients presenting with heavy uterine bleeding, it is crucial to rule out underlying bleeding disorders. The frequency of coagulopathy among our patients with menorrhagia was consistent with the literature ( 23 ). While anovulatory cycles are the most common cause of oligomenorrhea, Polycystic Ovary Syndrome (PCOS) has been reported to have a prevalence ranging from 4–19.6% among adolescents in numerous studies, which is consistent with our findings ( 5 , 7 , 8 ). Increased androgen levels due to Functional Ovarian Hyperandrogenism lead to increased activity of the GnRH pulse generator and result in elevated levels of LH in PCOS. This rise in LH level and LH/FSH ratio have been investigated as a diagnostic criterion for PCOS in various studies( 8 ). Le et al.'s research, which included 441 PCOS patients and 442 non-PCOS patients, reported a mean LH/FSH ratio of 2.08. Their study also identified an LH/FSH cutoff greater than 1.33, which exhibited a sensitivity of 65.76% and specificity of 95.24% for diagnosing PCOS in ROC analysis ( 24 ). In a study conducted in China involving 111 PCOS patients, a mean LH/FSH ratio of 1.87 ± 0.76 was observed ( 25 ). Additionally, Khashchenko et al. investigated 130 adolescents with PCOS and 30 healthy controls, proposing an LH/FSH ratio cutoff greater than 1.23, which has a sensitivity of over 75.0% and specificity of over 83.0% ( 26 ). Although the LH/FSH means were higher in our patients with PCOS than in these studies, there was no statistical difference in terms of the LH/FSH ratio between the anovulatory patients. Unlike the control groups of Khashchenko et al. and Le et al., our study was conducted on adolescents presenting with complaints of oligomenorrhea. While oligomenorrhea is the common complaint for both PCOS and anovulation, attempting to make a diagnostic aproach between these groups as in our study may be more specific for adolescent PCOS. Additionally, there are varying reports concerning the upper limit of total testosterone used in the diagnosis of PCOS. And hirsutizm can vary among populations, for instance in Middle Eastern populations, a modified Ferriman-Gallwey (m-FG) score up to 9–10 is considered within the normal range( 27 ). In a recent review/meta-analysis, Zuchelo et al. accepted patients who have serum testosterone levels between 48 to 82 ng/dl as having biochemical hyperandrogenism ( 21 ). In previosuly mentioned Le et al.'s study, the average total testosterone in PCOS patients was 36.9 ng/dL ± 25. Khashchenko et al. applied the Rotterdam criteria to adolescent patients and found that the mean total testosterone in PCOS patients was 54 ng/dL (34.6–72 ng/dL; as median 25–75 percentiles), indicating that up to one-third of the patients had testosterone levels below 50 ng/dL, which was the cutoff we used as a biochemical criterion. Particularly, in our anovulatory group, 59 oligomenorrheic patients were in the “grey zone” who had a total testosterone level between 40–50 ng/dL and/or a m-FG score between 8–16. The value obtained in the ROC analysis for LH/FSH exhibited good specificity but had low sensitivity for our PCOS group. The reduced sensitivity of the LH/FSH ratio may be tied to the parameters set during patient selection, specifically the acceptance of a lower limit of 50 ng/dL for hyperandrogenism. Given the wide spectrum of severity from atypical to typical PCOS and the challenges of diagnosing adolescent patients, the long-term follow-up of this high-risk group patients may provide valuable data on determining a testosterone cut-off level in the diagnosis of PCOS. PCOS, FAI and Hyperinsulinism Obesity and insulin resistance are health problems frequently associated with menstrual irregularities during adolescence, and PCOS is uniquely accompanied by disruptions in insulin and lipid metabolism. In a study by Green et al., involving 18 non-obese adolescents with PCOS and 20 healthy controls, it was demonstrated that PCOS patients exhibited greater insulin resistance and liver fat accumulation despite they were having lower mean daily caloric intake (1379 kcal/day vs. 1577 kcal/day). They associated the metabolic alterations observed in PCOS with muscle mitochondrial deficiency ( 28 ). PCOS also carries an 18-fold increased risk for diabetes, with a prevalence of approximately 16% in non-obese and 50–70% in obese PCOS patients. Similiar to previous reports approximately one-quarter of the patients with PCOS had insuline resistance in our study. We aimed to compare the pivotal clinical and hormonal features of patients with PCOS to those of hyperinsulinemic/obese patients with oligomenorrhea. In a meta-analysis by Li et al., involving 13 studies and 756 patients, it was noted that obese adolescents with PCOS had higher levels of total testosterone, free testosterone, fasting insulin, HOMA-IR, fasting glucose, lipid profile abnormalities, and leptin, as well as lower SHBG levels compared to normal-weight adolescents with PCOS. Also similiar findings were mentioned between obese adolescents with PCOS and obese adolescents without PCOS in their study ( 29 ). These findings indicate that obesity exacerbates the clinical presentation of PCOS. Similarly in our study, patients with PCOS had significantly higher total testosterone and DHEA-S levels, and lower BMI and HOMA-IR levels than patients with hyperinsulinism. Although hyperinsulinemia contributes to existing hyperandrogenemia by increasing LH sensitivity in theca cells, the higher LH and LH/FSH levels in our PCOS patients compared to hyperinsulinemic adolescents may suggest that functional ovarian hyperandrogenism (FOH) plays a more pivotal role in the development of PCOS and hyperandrogenism ( 28 , 30 , 31 ). The Free Androgen Index (FAI), a ratio of total testosterone to SHBG, has been widely discussed as a potential diagnostic index for PCOS in adolescents. Sağsak et al. proposed a FAI level above 6.15 in adolescents, demonstrating a sensitivity of 89% and a specificity of 77% ( 32 ). In Khashchenko et al.’s study on 130 adolescents with PCOS and 30 healthy controls, significantly higher mean FAI levels were found in PCOS patients (FAI: 5.5 vs. 1.6). For a FAI > 2.75 cut-off, a sensitivity of 75% and a specificity of 93% were found in ROC analyses( 26 ). In a study by Yetim et al. conducted on 53 adolescents with PCOS and 26 healthy controls, the FAI value was significantly higher in PCOS patients (6.7 vs. 3.0) ( 33 ). However, the interpretation of FAI values is complex due to the interactions of SHBG with hyperinsulinemia, diabetes, and hypothyroidism. Obesity, characterized by disrupted glucose-insulin metabolism, is associated with decreased SHBG levels ( 34 ). Bideci et al. previously reported lower SHBG levels in patients with PCOS and obesity compared to non-obese PCOS patients ( 30 ). In Khashchenko et al.’s study the adolescents with PCOS had a significantly higher BMI than healthy controls (p = 0.0002) and despite lack of postprandial glucose and insulin data higher leptin levels. In Yetim et al.'s study, the mean BMI of adolescents with PCOS was higher than that of healthy controls (1.4 vs. 0.8) ( 33 ). In our study, to demonstrate the impact of hyperinsulinemia/obesity on FAI and SHBG, we compared patients with PCOS to hyperinsulinemic/obese adolescents. The slightly higher FAI values in hyperinsulinemic/obese adolescents (8.30 vs. 8.0) and the lower SHBG levels (12.2 vs. 29.2 mol/L) suggest that glucose/insulin metabolism has a greater influence on FAI/SHBG levels than androgens. This suggests the need for selective consideration in the use of FAI for diagnosing PCOS. Ovarian cysts The use of the Rotterdam criteria for assessing ovarian cyst counts in adolescents is debated due to frequent anovulatory cycles and the physiological presence of up to 24 ovarian cysts in this age group. Additionally this debate is further complicated by the challenges of performing transvaginal ultrasonography (USG) and pelvic Magnetic Resonance Imaging in adolescents. However, ovarian volume, rather than the number of cysts, is considered more diagnostic in this population and previous studies have proposed a significant ovarian volume for PCOS diagnosis in adolescents as 15 cc in one ovary or 12 cc in both ( 7 , 10 , 35 , 36 ). Similarly, our study using suprapubic ultrasonography revealed a mean ovarian volume of 11.8 cc in patients with PCOS, indicating use of overian volumes in diagnosis should be reliable. Eating disorders typically occur during mid-to-late puberty, especially in the 15–19 age group. Oligomenorrhea onset was longer in malnourished patients than those with anovulatory cycles, and they were more likely to experience secondary amenorrhea ( 37 ). Malnutrition may have a greater impact on LH pulsatility by affecting the hypothalamic-pituitary-gonadal axis and leading to decreased LH secretion and consequently decreased ovarian function and anovulation. Notably LH secretion is more affected than FSH and E2 secretion in our patients affected by malnutrition. Retrospective planing of the study limited our ability to control for potential confounding factors and to collect detailed information. Prospective studies with larger population sizes may provide more statistical power and allow for more detailed subgroup analyses. Besides, having a relatively large sample size of 305 patients with various etiologies presenting with oligomenorrhea at a tertiary center and using utilized diagnostic criteria to identify/analyze patients with PCOS were the strengths of the study. Conclusion Anovulatory cycles are the most frequent cause of menstrual irregularities in adolescents, and hyperandrogenism is key for diagnosing PCOS. The Free Androgen Index (FAI) may be unreliable for PCOS diagnosis, as FAI values were similar between adolescents with PCOS and those with hyperinsulinemia and obesity. Lower SHBG levels in hyperinsulinemic obese adolescents further complicate FAI use, suggesting that glucose/insulin metabolism has a greater influence on FAI/SHBG levels than androgens. Diagnosing PCOS should involve multiple criteria, including LH/FSH ratio, SHBG, FAI levels, and ovarian USG. Further research is necessary to refine diagnostic criteria and confirm associations. Declarations Authors’ contributions: E.Ö. took part in concept, data collection, literature search, analysis and writing .D.T. took part in concept, data collection, literature search and writing.S.Ç.G. took part in concept, data collection and writing .M.B. took part in concept, literature search, and writing. F.G. took part in concept, literature search, analysis and writing. All authors reviewed the manuscript. Funding: There is no funding provided for this study Availability of data and materials: Available upon request to the corresponding author. Ethics approval: This study was undertaken at …….. The protocols used in this study followed the Declaration of Helsinki and were approved by the Ethics Committee of ………. (February 2023 / Approval number: E2-23-3382). Conflict of interest: No financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article. The authors have no conflict of interest to declare. References De Sanctis V, Soliman AT, Tzoulis P, Daar S, Di Maio S, Millimaggi G, et al. Hypomenorrhea in Adolescents and Youths: Normal Variant or Menstrual Disorder? Revision of Literature and Personal Experience. Acta Biomed. 2022;93(1):e2022157. Elmaoğulları S, Aycan Z. Abnormal Uterine Bleeding in Adolescents. J Clin Res Pediatr Endocrinol. 2018;10(3):191–7. Kabra R, Fisher M. Abnormal uterine bleeding in adolescents. Curr Probl Pediatr Adolesc Health Care. 2022;52(5):101185. Yaşa C, Güngör Uğurlucan F. Approach to Abnormal Uterine Bleeding in Adolescents. 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Endocr Rev. 2016;37(5):467–520. Witchel SF, Oberfield S, Rosenfield RL, Codner E, Bonny A, Ibáñez L et al. The Diagnosis of Polycystic Ovary Syndrome during Adolescence. Horm Res Paediatr. 2015. Ibáñez L, Oberfield SE, Witchel S, Auchus RJ, Chang RJ, Codner E, et al. An International Consortium Update: Pathophysiology, Diagnosis, and Treatment of Polycystic Ovarian Syndrome in Adolescence. Horm Res Paediatr. 2017;88(6):371–95. Witchel SF, Oberfield SE, Peña AS. Polycystic Ovary Syndrome: Pathophysiology, Presentation, and Treatment With Emphasis on Adolescent Girls. J Endocr Soc. 2019;3(8):1545–73. Rosenfield RL. Perspectives on the International Recommendations for the Diagnosis and Treatment of Polycystic Ovary Syndrome in Adolescence. J Pediatr Adolesc Gynecol. 2020;33(5):445–7. Deligeoroglou E, Karountzos V. Abnormal Uterine Bleeding including coagulopathies and other menstrual disorders. Best Pract Res Clin Obstet Gynaecol. 2018;48:51–61. Rosenfield RL. The Diagnosis of Polycystic Ovary Syndrome in Adolescents. Pediatrics. 2015;136(6):1154–65. Athar F, Karmani M, Templeman NM. Metabolic hormones are integral regulators of female reproductive health and function. Biosci Rep. 2024;44(1). Pasquali R, Patton L, Gambineri A. Obesity and infertility. Curr Opin Endocrinol Diabetes Obes. 2007;14(6):482–7. Fasipe OJ, Ayoade OG, Enikuomehin AC. Severity Grade Assessment Classifications for Both Insulin Resistance Syndrome and Status of Pancreatic Beta Cell Function in Clinical Practice Using Homeostasis Model Assessment Method Indices. Can J Diabetes. 2020;44(7):663–9. Tang Q, Li X, Song P, Xu L. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future. Drug Discov Ther. 2015;9(6):380–5. Morris PD, Malkin CJ, Channer KS, Jones TH. A mathematical comparison of techniques to predict biologically available testosterone in a cohort of 1072 men. Eur J Endocrinol. 2004;151(2):241–9. Zuchelo LTS, Alves MS, Baracat EC, Sorpreso ICE, Soares JM. Jr. Menstrual pattern in polycystic ovary syndrome and hypothalamic-pituitary-ovarian axis immaturity in adolescents: a systematic review and meta-analysis. Gynecol Endocrinol. 2024;40(1):2360077. Marques P, Madeira T, Gama A. Menstrual cycle among adolescents: girls' awareness and influence of age at menarche and overweight. Rev Paul Pediatr. 2022;40:e2020494. Cheong Y, Cameron IT, Critchley HOD. Abnormal uterine bleeding. Br Med Bull. 2017;123(1):103–14. Le MT, Le VNS, Le DD, Nguyen VQH, Chen C, Cao NT. Exploration of the role of anti-Mullerian hormone and LH/FSH ratio in diagnosis of polycystic ovary syndrome. Clin Endocrinol (Oxf). 2019;90(4):579–85. Ma CS, Lin Y, Zhang CH, Xu H, Li YF, Zhang SC, et al. [Preliminary study on the value of ratio of serum luteinizing hormone/follicle-stimulating hormone in diagnosis of polycystic ovarian syndrome among women with polycystic ovary]. Zhonghua fu chan ke za zhi. 2011;46(3):177–80. Khashchenko E, Uvarova E, Vysokikh M, Ivanets T, Krechetova L, Tarasova N et al. The Relevant Hormonal Levels and Diagnostic Features of Polycystic Ovary Syndrome in Adolescents. J Clin Med. 2020;9(6). Escobar-Morreale HF, Carmina E, Dewailly D, Gambineri A, Kelestimur F, Moghetti P, et al. Epidemiology, diagnosis and management of hirsutism: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2012;18(2):146–70. Cree-Green M, Rahat H, Newcomer BR, Bergman BC, Brown MS, Coe GV, et al. Insulin Resistance, Hyperinsulinemia, and Mitochondria Dysfunction in Nonobese Girls With Polycystic Ovarian Syndrome. J Endocr Soc. 2017;1(7):931–44. Li L, Feng Q, Ye M, He Y, Yao A, Shi K. Metabolic effect of obesity on polycystic ovary syndrome in adolescents: a meta-analysis. J Obstet Gynaecol. 2017;37(8):1036–47. Bideci A, Camurdan MO, Yeşilkaya E, Demirel F, Cinaz P. Serum ghrelin, leptin and resistin levels in adolescent girls with polycystic ovary syndrome. J Obstet Gynaecol Res. 2008;34(4):578–84. Leibel NI, Baumann EE, Kocherginsky M, Rosenfield RL. Relationship of adolescent polycystic ovary syndrome to parental metabolic syndrome. J Clin Endocrinol Metab. 2006;91(4):1275–83. Sagsak E, keskin m, Çetinkaya S, Savas Erdeve S, aycan z. The Diagnostic Value of Free Androgen Index in Obese Adolescent Females with Idiopathic Hirsutism and Polycystic Ovary Syndrome. JAREM. 2021;11(1):81–5. Yetim A, Yetim Ç, Baş F, Erol OB, Çığ G, Uçar A, et al. Anti-Müllerian Hormone and Inhibin-A, but not Inhibin-B or Insulin-Like Peptide-3, may be Used as Surrogates in the Diagnosis of Polycystic Ovary Syndrome in Adolescents: Preliminary Results. J Clin Res Pediatr Endocrinol. 2016;8(3):288–97. Xing C, Zhang J, Zhao H, He B. Effect of Sex Hormone-Binding Globulin on Polycystic Ovary Syndrome: Mechanisms, Manifestations, Genetics, and Treatment. Int J Womens Health. 2022;14:91–105. Dewailly D, Lujan ME, Carmina E, Cedars MI, Laven J, Norman RJ, et al. Definition and significance of polycystic ovarian morphology: a task force report from the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2014;20(3):334–52. Kenigsberg LE, Agarwal C, Sin S, Shifteh K, Isasi CR, Crespi R et al. Clinical utility of magnetic resonance imaging and ultrasonography for diagnosis of polycystic ovary syndrome in adolescent girls. Fertil Steril. 2015;104(5):1302-9.e1-4. Golden NH, Carlson JL. The pathophysiology of amenorrhea in the adolescent. Ann N Y Acad Sci. 2008;1135:163–78. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4945396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345785163,"identity":"3aad0c1e-0b71-479f-8032-19ee91cc0e43","order_by":0,"name":"Emre OZER","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDCCA3AWM4gpIUOKFrYEkBYeUrTwGIBJgjr4bh8+urmiwsaeX/rM51c3aix4GNgPH92AT4vkubS0m2fOpDFL9uVus845BnQYT1raDXxaDM7wmN1sbDvMZnCGd5txDhtQiwSPGQEt/N9uNv47zGN/hueZcc4/orTwsN1sbDgsYcDDw/w4t40ILZJn2MxuNhxLM5AAMphz+yR42Aj5he8M87ObDTXAEOthfvw551udHD/74WN4tSADNgkwSaxyEGD+QIrqUTAKRsEoGDkAADZTRa+ZLgAHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8475-9070","institution":"Ankara Bilkent City Hospital: Ankara Sehir Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Emre","middleName":"","lastName":"OZER","suffix":""},{"id":345785164,"identity":"6307918b-d96d-4aba-805a-8b68cd6a7097","order_by":1,"name":"Demet TAŞ","email":"","orcid":"","institution":"Ankara Bilkent City Hospital: Ankara Sehir Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Demet","middleName":"","lastName":"TAŞ","suffix":""},{"id":345785165,"identity":"9f9c6028-5dff-4f07-af84-f21e77eb4db0","order_by":2,"name":"Seçil ÇAKIR GÜNDOĞAN","email":"","orcid":"","institution":"Ankara Bilkent City Hospital: Ankara Sehir Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Seçil","middleName":"ÇAKIR","lastName":"GÜNDOĞAN","suffix":""},{"id":345785166,"identity":"22ac58c4-b64f-4fc2-8cba-ba2eef476932","order_by":3,"name":"Mehmet BOYRAZ","email":"","orcid":"","institution":"Ankara Bilkent City Hospital: Ankara Sehir Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"BOYRAZ","suffix":""},{"id":345785167,"identity":"383f7b86-42a7-4300-8484-89edc63c668f","order_by":4,"name":"Fatih GÜRBÜZ","email":"","orcid":"","institution":"Ankara Bilkent City Hospital: Ankara Sehir Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Fatih","middleName":"","lastName":"GÜRBÜZ","suffix":""}],"badges":[],"createdAt":"2024-08-20 13:34:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4945396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4945396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66865064,"identity":"1b97b11b-4dd2-44f0-92ca-174f6c9f2fd2","added_by":"auto","created_at":"2024-10-17 08:59:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59096,"visible":true,"origin":"","legend":"\u003cp\u003eCAH, Congenital adrenal hyperplasia; E2, Estradiol.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4945396/v1/31e4ce2736c872595158855f.png"},{"id":67570175,"identity":"d3790223-ade1-4367-8208-e8b716a2184b","added_by":"auto","created_at":"2024-10-27 08:51:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":722150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945396/v1/2e26b4f6-fe1d-4a83-9bdf-f6c3ba923635.pdf"}],"financialInterests":"","formattedTitle":"Differentiating PCOS from Anovulatory Cycles in Adolescents: A Comprehensive Evaluation of FAI, SHBG, and LH/FSH Ratio","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbnormal uterine bleeding (AUB) is a frequent health concern among adolescents in whom oligomenorrhea often presents with anovulatory cycles due to the immaturity of the Hypothalamic Pituitary Ovarian (HPO) axis (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The prevalence of Polycystic Ovary syndrome (PCOS) in adolescents ranges from 3.4\u0026ndash;19.6%. Hyperandrogenism and oligomenorrhea are key features of PCOS, with this differential diagnosis between PCOS and anovulatory dysfunction can be challenging in clinical practice. The pathogenesis of PCOS, which remains incompletely understood, involves dysregulation of ovarian steroidogenesis, the hypothalamic-pituitary-adrenal (HPA) axis, and alterations in insulin metabolism (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Genes such as DENND1A (DENN Domain-Containing Protein 1A) and LHCGR (Luteinizing Hormone/Chorio Gonadotropin Receptor), INSR (Insulin Receptor are some of the candidates frequently proposed in Genome-wide Association Studies (GWAS) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). PCOS is associated with an increased risk of metabolic syndrome, infertility, endometrial cancer, and psychological disorders such as anxiety and depression. In this study, we aimed to determine diagnostic cut-off values for PCOS and differentiate these values from those of patients with anovulatory dysfunction. Additionally, we aimed to assess the sensitivity of using the Free Androgen Index (FAI) and Sex Hormone Binding Globuline (SHBG) in the diagnosis of PCOS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003ePatients\u003c/b\u003e: A total of 305 adolescents who apllied with complaints of oligomenorrhea to a tertirary center between August 2019 and March 2023 were included into the study. Data of the patients were examined retrospectively by 4 Pediatric Endocrinologists and 1 Pediatrician specialized in Adolescent Health. Oligomenorrhea is defined by infrequent or irregular menstrual periods, with the definition based on the postmenarcheal year: in the first year, an average cycle length greater than 90 days; in the second year, greater than 60 days; in the third year, greater than 45 days; and in the fourth year, a cycle length greater than 38 days (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The International Federation of Gynecology and Obstetrics (FIGO) categorizes AUB etiologies using the acronym PALM-COEIN (polyp, adenomyosis, leiomyoma, malignancy, coagulopathy, ovulatory dysfunction, endometrial disorders, iatrogenic, and not yet classified) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In adolescents, anovulatory cycles are the most common cause of oligomenorrhea, which is a diagnosis made after excluding other PALM-COEIN etiologies(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). As anovulatory menstrual cycles, are frequent in the first years after menarche and clinical features of PCOS such as menstrual irregularities, hirsutism, and acne are common during adolescence, the diagnosis of PCOS is controversial when adult criteria are used. The diagnosis of PCOS in adolescents considered when the presence of oligomenorrhea presents 2 years after menarche for at least 6 months and clinical and/or biochemical hyperandrogenism. Clinical hyperandrogenism was defined as moderate-severe hirsutism (a modified Ferriman-Gallwey score of 16 or higher), and biochemical hyperandrogenism as a total testosterone level of 50 ng/dl or higher (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Obesity and related ins\u0026uuml;lin resistance which are growing concerns among pediatric populations worldwide, are known to be related to dysregulation in ovulatory functions in females, leading to ovulatory dysfunction (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Patients with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) levels greater than 4 and fasting insulin levels greater than 25 mU/L were grouped under the Hyperinsulinemia subgroup. HOMA-IR is calculated with the following formula: Fasting plasma insulin concentration (mU/ L) X fasting plasma glucose (mg/dl) / 405 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The authors evaluated and categorized patients into groups: Anovulatory, PCOS, Hyperinsulinemic, Malnutrition, and those with chronic diseases. A flow-chart of patient selection is presented in Fig.\u0026nbsp;1. Free Androgen Index (FAI): The FAI was calculated using the formula: (Total Testosterone (nmol/L)/SHBG (nmol/L)) \u0026times; 100 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods:\u003c/h2\u003e \u003cp\u003eThe data were analyzed using SPSS 22 software (Statistical Package for Social Sciences; SPSS Inc., Chicago, IL). Descriptive statistics were used to report categorical data as \u0026ldquo;n\u0026rdquo;, and percentages, while continuous data were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;SD). Pearson's chi-square test was used to compare categorical variables between groups. The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. The Mann-Whitney U-test was used to compare two groups, and the Kruskal-Wallis test was used for comparisons between more than two groups. Spearman correlation test was used to examine the relationship between continuous variables. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of various parameters. The level of statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the patients:\u003c/h2\u003e \u003cp\u003eThe average age at diagnosis was 15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 years, and the average age at onset of complaints was 13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 years. The mean age of menarche was 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 years, and on average, patients reported the start of their complaints was 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 years after menarche. The average time elapsed between the start of complaints and patients' application to our center was 1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 years. Of the 305 patients in study group; 229(%75) diagnosed with anovulatory cycles, 36(%11,8) with PCOS, n:14 (%4,6) with obesity and hyperinsulinism, n:12 (%3,9) with severe malnutrition and others (5 patients with chronic diseaes, 4 patients with hypogonadism, 2 patients with congenital adrenal hyperplasia and 1 patient with estradiol secreting ovarian cyst). Additional patient characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMenstrual pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en; (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligomenorrhea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (%86.6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary amenorrhea (over 6 months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (%13.4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExcessive menstrual bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (%78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (%22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI SDS, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SS, (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,6 (277)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHirsutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (%18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (%81.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSuprapubic Ultrasonography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerformed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277 (%90,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (%9,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOCS treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceived\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (%27,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot-received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (%72,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI: Body mass index; m-FG: modified Feriman-Gallwey; OCS: Oral contraceptive.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the patient groups with PCOS, Anovulatuary cycles, and Hyperinsulinism:\u003c/h2\u003e \u003cp\u003eWe aimed to determine diagnostic cut-off values for PCOS and differentiate these values from those of patients with anovulatory dysfunction. The data of PCOS adolescents were evaluated along with those of other groups. We wanted to highlight this topic, which has not been sufficiently determined in adolescents recently. Hirsutism showed a significant difference between the PCOS and Anovulatory group (p\u0026thinsp;=\u0026thinsp;0.007). In terms of total Testosterone and DHEAS, significant differences were found (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the PCOS group and the other groups. Additional analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the patients with PCOS, Anovulatuary cycles, and Hyperinsulinism\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCOS (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnovulatuary (n\u0026thinsp;=\u0026thinsp;229)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperinsulinism (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at the onset of complaints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,6 (220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,1\u0026thinsp;\u0026plusmn;\u0026thinsp;1,7 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,2\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTime between the menarche and the onset of complaints (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,3\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,0 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThe time between the onset of complaints and the patient's application,(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,1\u0026thinsp;\u0026plusmn;\u0026thinsp;1,6 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5 (220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,3 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenstrual pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligomenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(%86,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204(%89,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(%92,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,793\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary amenorrhea (over 6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (%13,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(%10,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (%7,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,2\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5\u003csup\u003eb\u003c/sup\u003e (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,3\u003csup\u003ec\u003c/sup\u003e (203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,9\u0026plusmn;,6\u003csup\u003ea\u003c/sup\u003e (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHirsutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(%38,9\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(%16,6\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (%21,4\u003csup\u003ea,b\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,007\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003em-FG score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,1\u0026thinsp;\u0026plusmn;\u0026thinsp;4,5\u003csup\u003ea\u003c/sup\u003e (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,8\u0026thinsp;\u0026plusmn;\u0026thinsp;1,6\u003csup\u003eb\u003c/sup\u003e (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,0\u0026thinsp;\u0026plusmn;\u0026thinsp;1,0\u003csup\u003ea,b\u003c/sup\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInsulin, mU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,6\u0026thinsp;\u0026plusmn;\u0026thinsp;12,1\u003csup\u003eb\u003c/sup\u003e (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,3\u0026thinsp;\u0026plusmn;\u0026thinsp;6,2\u003csup\u003eb\u003c/sup\u003e (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47,5\u0026thinsp;\u0026plusmn;\u0026thinsp;48,1\u003csup\u003ea\u003c/sup\u003e (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,1\u0026thinsp;\u0026plusmn;\u0026thinsp;2,7\u003csup\u003eb\u003c/sup\u003e (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,0\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4\u003csup\u003eb\u003c/sup\u003e (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,0\u0026thinsp;\u0026plusmn;\u0026thinsp;12,4\u003csup\u003ea\u003c/sup\u003e (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFSH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,4\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,1\u0026thinsp;\u0026plusmn;\u0026thinsp;2,3 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,8\u0026thinsp;\u0026plusmn;\u0026thinsp;2,3 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,5\u0026thinsp;\u0026plusmn;\u0026thinsp;8,5 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,5\u0026thinsp;\u0026plusmn;\u0026thinsp;8,2 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,8\u0026thinsp;\u0026plusmn;\u0026thinsp;6,0 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLH/FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,1\u0026thinsp;\u0026plusmn;\u0026thinsp;1,3 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,1 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3\u0026thinsp;\u0026plusmn;\u0026thinsp;0,6 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal Testosteron, ug/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,8\u0026thinsp;\u0026plusmn;\u0026thinsp;20,7\u003csup\u003eb\u003c/sup\u003e (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,0\u0026thinsp;\u0026plusmn;\u0026thinsp;9,5\u003csup\u003ea\u003c/sup\u003e(177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,3\u0026thinsp;\u0026plusmn;\u0026thinsp;8,6\u003csup\u003ea\u003c/sup\u003e (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDHEAS, ug/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291,9\u0026thinsp;\u0026plusmn;\u0026thinsp;118,2\u003csup\u003eb\u003c/sup\u003e (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199,3\u0026thinsp;\u0026plusmn;\u0026thinsp;95,6\u003csup\u003ea\u003c/sup\u003e(179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163,6\u0026thinsp;\u0026plusmn;\u0026thinsp;63,7\u003csup\u003ea\u003c/sup\u003e (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHBG,nmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,2\u0026thinsp;\u0026plusmn;\u0026thinsp;14,5\u003csup\u003eb\u003c/sup\u003e (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,1\u0026thinsp;\u0026plusmn;\u0026thinsp;24,3\u003csup\u003ec\u003c/sup\u003e (147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,2\u0026thinsp;\u0026plusmn;\u0026thinsp;4,4\u003csup\u003ea\u003c/sup\u003e (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,3\u0026thinsp;\u0026plusmn;\u0026thinsp;4,1a (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,0\u0026thinsp;\u0026plusmn;\u0026thinsp;5,1a (6,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,5\u0026thinsp;\u0026plusmn;\u0026thinsp;2,7b (2,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOCS treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceived\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(%36,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(%26,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(%35,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,412\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot-received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(%63,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168(%73,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(%64,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Kruskal Wallis analysis, ** Chi-square analysis performed. \u003csup\u003e\u003cb\u003ea,b,c\u003c/b\u003e\u003c/sup\u003e = The group from which the difference originates. PCOS: Polcyctic ovarian syndrome; BMI: body mass index; m-FG: modified Feriman-Gallwey; HOMA-IR: Homeostasis Model Assessment \u0026ndash; Insulin Resistance; FSH: Follicular stimulating hormone; LH: Luteinizing hormone; SHBG: Sex hormone binding globulin; DHEA-S: Dehydroepiandrosterone-sulfate; OCS: Oral contraceptive.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFree Androgen Index analysis:\u003c/h2\u003e \u003cp\u003eFAI is proposed as a diagnostic criterion in some studies recently. To evaluate the availability of this index, the data of PCOS adolescents were evaluated along with those of other groups.\u003c/p\u003e \u003cp\u003eThe mean FAI values for 145 patients with anovulatory cycles, 28 patients with PCOS, and 12 patients with hyperinsulinism were 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2, 8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5, and 8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4, respectively. A significant difference was observed between the anovulatory group and the other 2 groups (PCOS and hyperinsulinism group) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant positive correlation was found between FAI and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (r\u0026thinsp;=\u0026thinsp;0.389; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, a significant positive correlation was identified between FAI and Body Mass Index (BMI) (r\u0026thinsp;=\u0026thinsp;0.499; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the patients with Anovulatuary cycles and Malnutrition:\u003c/h2\u003e \u003cp\u003eMalnutrition is a well known cause of oligomenorrhea. We analyzed the data between patients with malnutrition and anovulatuary cycles. Blood glucose (p\u0026thinsp;=\u0026thinsp;0.015), serum insulin (p\u0026thinsp;=\u0026thinsp;0.023), HOMA-IR (p\u0026thinsp;=\u0026thinsp;0.026), values were significantly lower in the malnutrition group as expected. Also, LH (p\u0026thinsp;=\u0026thinsp;0.006), and LH/FSH (p\u0026thinsp;=\u0026thinsp;0.003) were lower in this group. Additional analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the Malnutrition group and Anovulatory group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalnutrition (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnovulatory (n\u0026thinsp;=\u0026thinsp;229)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at the onset of complaints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,4\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,6 (220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge at first menstruation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,6\u0026plusmn;,9 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,5\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTime between the menarche and the onset of complaints,(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTime between th onset of complaints and the patients' application,(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,0\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5 (220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,2 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,3 (203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenstrual pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligomenorrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(%66,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204(%89,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0,042\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmenorrhea (over 6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(%33,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(%10,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInsulin, mU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,0\u0026thinsp;\u0026plusmn;\u0026thinsp;6,7 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,3\u0026thinsp;\u0026plusmn;\u0026thinsp;6,2 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,0\u0026thinsp;\u0026plusmn;\u0026thinsp;1,4 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFSH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,2\u0026thinsp;\u0026plusmn;\u0026thinsp;2,3 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,1\u0026thinsp;\u0026plusmn;\u0026thinsp;2,3 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,1\u0026thinsp;\u0026plusmn;\u0026thinsp;6,7 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,5\u0026thinsp;\u0026plusmn;\u0026thinsp;8,2 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLH/FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,7\u0026thinsp;\u0026plusmn;\u0026thinsp;0,7 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u0026thinsp;\u0026plusmn;\u0026thinsp;1,1 (187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal Testosteron, ug/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,7\u0026thinsp;\u0026plusmn;\u0026thinsp;12,5 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,0\u0026thinsp;\u0026plusmn;\u0026thinsp;9,5 (177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDHEAS, ug/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159,1\u0026thinsp;\u0026plusmn;\u0026thinsp;100,3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199,3\u0026thinsp;\u0026plusmn;\u0026thinsp;95,6 (179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHBG,nmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,0\u0026thinsp;\u0026plusmn;\u0026thinsp;33,2 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,1\u0026thinsp;\u0026plusmn;\u0026thinsp;24,3 (147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOCS treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceived\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(%16,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(%26,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,737\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot-received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(%83,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168(%73,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Mann Whitney U analysis, **KChi-square analysis performed. BMI: body mass index; m-FG: modified Feriman-Gallwey; HOMA-IR: Homeostasis Model Assessment \u0026ndash; Insulin Resistance; FSH: Follicular Stimulating hormone; LH: Luteinizing hormone; SHBG: Sex hormone binding globulin; DHEA-S: Dehydroepiandrosterone-sulfate; OCS: Oral contraceptive.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eReceiver operating characteristic (ROC) analysis:\u003c/h2\u003e \u003cp\u003eThe predictive ability of various values for PCOS was investigated through ROC analysis, and cut-off values were determined. The LH decision threshold of 9.7 U/L exhibited a sensitivity of 63.9%, specificity of 59.9%, and statistically significant predictive capability. The LH/FSH ratio decision threshold of 2.62 had a sensitivity of %38,9, a specificity of 86.8%, and was found to be statistically a good predictor. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC analysis of the patient's data to predict PCOS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e%95safetyzone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNPD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHGB\u0026thinsp;\u0026le;\u0026thinsp;23 nmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e90,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH\u0026thinsp;\u0026gt;\u0026thinsp;5,3 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH\u0026thinsp;\u0026gt;\u0026thinsp;9,7 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH/FSH\u0026thinsp;\u0026gt;\u0026thinsp;2,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u0026thinsp;\u0026gt;\u0026thinsp;54 pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e90,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHEA-S\u0026thinsp;\u0026gt;\u0026thinsp;226 ug/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSHBG: Sex hormone binding globulin; FSH: Follicular Stimulating hormone; LH: Luteinizing hormone; E2: Estradiol; DHEA-S: Dehydroepiandrosterone-sulfate; PPV: Positive predictive value; NPV: Negative predictive value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main finding in our study is that FAI values were similar between adolescents with PCOS and non-PCOS cases with hyperinsulinemia and obesity, indicating using the FAI (Free Androgen Index) for diagnosing PCOS may be unreliable. Additionally, SHBG levels were found to be lower than adolescents with PCOS than hyperinsulinemic obese adolescents with oligomenorrhea, suggesting that FAI and SHBG should not be used as a diagnostic criterion, particularly in the presence of hyperinsulinism.\u003c/p\u003e \u003cp\u003eIn healthy adolescents, the average age of menarche is around 12\u0026ndash;13 years. In Zuchelo et al.'s meta-analysis, it is stated that the age of menarche in adolescents with PCOS is similar to that of non-PCOS adolescents (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In our study, the similar average age of menarche in adolescents with oligomenorrhea suggests that oligomenorrhea is unrelated to the age of menarche. Adolescents' awareness of menstrual cycles and menstrual disorders can vary depending on social and educational factors which can delay adolescents from seeking medical help (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The persistence of complaints for a considerable amount of time as in our patients may be related to factors like lack of knowledge, feelings of shame, and etc. Particularly menarche can be the first indication of an underlying bleeding disorder. As coagulopathy is reported to occur in up to 20% of patients presenting with heavy uterine bleeding, it is crucial to rule out underlying bleeding disorders. The frequency of coagulopathy among our patients with menorrhagia was consistent with the literature (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While anovulatory cycles are the most common cause of oligomenorrhea, Polycystic Ovary Syndrome (PCOS) has been reported to have a prevalence ranging from 4\u0026ndash;19.6% among adolescents in numerous studies, which is consistent with our findings (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Increased androgen levels due to Functional Ovarian Hyperandrogenism lead to increased activity of the GnRH pulse generator and result in elevated levels of LH in PCOS. This rise in LH level and LH/FSH ratio have been investigated as a diagnostic criterion for PCOS in various studies(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Le et al.'s research, which included 441 PCOS patients and 442 non-PCOS patients, reported a mean LH/FSH ratio of 2.08. Their study also identified an LH/FSH cutoff greater than 1.33, which exhibited a sensitivity of 65.76% and specificity of 95.24% for diagnosing PCOS in ROC analysis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In a study conducted in China involving 111 PCOS patients, a mean LH/FSH ratio of 1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 was observed (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, Khashchenko et al. investigated 130 adolescents with PCOS and 30 healthy controls, proposing an LH/FSH ratio cutoff greater than 1.23, which has a sensitivity of over 75.0% and specificity of over 83.0% (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Although the LH/FSH means were higher in our patients with PCOS than in these studies, there was no statistical difference in terms of the LH/FSH ratio between the anovulatory patients. Unlike the control groups of Khashchenko et al. and Le et al., our study was conducted on adolescents presenting with complaints of oligomenorrhea. While oligomenorrhea is the common complaint for both PCOS and anovulation, attempting to make a diagnostic aproach between these groups as in our study may be more specific for adolescent PCOS. Additionally, there are varying reports concerning the upper limit of total testosterone used in the diagnosis of PCOS. And hirsutizm can vary among populations, for instance in Middle Eastern populations, a modified Ferriman-Gallwey (m-FG) score up to 9\u0026ndash;10 is considered within the normal range(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In a recent review/meta-analysis, Zuchelo et al. accepted patients who have serum testosterone levels between 48 to 82 ng/dl as having biochemical hyperandrogenism (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In previosuly mentioned Le et al.'s study, the average total testosterone in PCOS patients was 36.9 ng/dL\u0026thinsp;\u0026plusmn;\u0026thinsp;25. Khashchenko et al. applied the Rotterdam criteria to adolescent patients and found that the mean total testosterone in PCOS patients was 54 ng/dL (34.6\u0026ndash;72 ng/dL; as median 25\u0026ndash;75 percentiles), indicating that up to one-third of the patients had testosterone levels below 50 ng/dL, which was the cutoff we used as a biochemical criterion. Particularly, in our anovulatory group, 59 oligomenorrheic patients were in the \u0026ldquo;grey zone\u0026rdquo; who had a total testosterone level between 40\u0026ndash;50 ng/dL and/or a m-FG score between 8\u0026ndash;16. The value obtained in the ROC analysis for LH/FSH exhibited good specificity but had low sensitivity for our PCOS group. The reduced sensitivity of the LH/FSH ratio may be tied to the parameters set during patient selection, specifically the acceptance of a lower limit of 50 ng/dL for hyperandrogenism. Given the wide spectrum of severity from atypical to typical PCOS and the challenges of diagnosing adolescent patients, the long-term follow-up of this high-risk group patients may provide valuable data on determining a testosterone cut-off level in the diagnosis of PCOS.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePCOS, FAI and Hyperinsulinism\u003c/h2\u003e \u003cp\u003eObesity and insulin resistance are health problems frequently associated with menstrual irregularities during adolescence, and PCOS is uniquely accompanied by disruptions in insulin and lipid metabolism. In a study by Green et al., involving 18 non-obese adolescents with PCOS and 20 healthy controls, it was demonstrated that PCOS patients exhibited greater insulin resistance and liver fat accumulation despite they were having lower mean daily caloric intake (1379 kcal/day vs. 1577 kcal/day). They associated the metabolic alterations observed in PCOS with muscle mitochondrial deficiency (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). PCOS also carries an 18-fold increased risk for diabetes, with a prevalence of approximately 16% in non-obese and 50\u0026ndash;70% in obese PCOS patients. Similiar to previous reports approximately one-quarter of the patients with PCOS had insuline resistance in our study. We aimed to compare the pivotal clinical and hormonal features of patients with PCOS to those of hyperinsulinemic/obese patients with oligomenorrhea. In a meta-analysis by Li et al., involving 13 studies and 756 patients, it was noted that obese adolescents with PCOS had higher levels of total testosterone, free testosterone, fasting insulin, HOMA-IR, fasting glucose, lipid profile abnormalities, and leptin, as well as lower SHBG levels compared to normal-weight adolescents with PCOS. Also similiar findings were mentioned between obese adolescents with PCOS and obese adolescents without PCOS in their study (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). These findings indicate that obesity exacerbates the clinical presentation of PCOS. Similarly in our study, patients with PCOS had significantly higher total testosterone and DHEA-S levels, and lower BMI and HOMA-IR levels than patients with hyperinsulinism. Although hyperinsulinemia contributes to existing hyperandrogenemia by increasing LH sensitivity in theca cells, the higher LH and LH/FSH levels in our PCOS patients compared to hyperinsulinemic adolescents may suggest that functional ovarian hyperandrogenism (FOH) plays a more pivotal role in the development of PCOS and hyperandrogenism (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The Free Androgen Index (FAI), a ratio of total testosterone to SHBG, has been widely discussed as a potential diagnostic index for PCOS in adolescents. Sağsak et al. proposed a FAI level above 6.15 in adolescents, demonstrating a sensitivity of 89% and a specificity of 77% (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In Khashchenko et al.\u0026rsquo;s study on 130 adolescents with PCOS and 30 healthy controls, significantly higher mean FAI levels were found in PCOS patients (FAI: 5.5 vs. 1.6). For a FAI\u0026thinsp;\u0026gt;\u0026thinsp;2.75 cut-off, a sensitivity of 75% and a specificity of 93% were found in ROC analyses(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In a study by Yetim et al. conducted on 53 adolescents with PCOS and 26 healthy controls, the FAI value was significantly higher in PCOS patients (6.7 vs. 3.0) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, the interpretation of FAI values is complex due to the interactions of SHBG with hyperinsulinemia, diabetes, and hypothyroidism. Obesity, characterized by disrupted glucose-insulin metabolism, is associated with decreased SHBG levels (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Bideci et al. previously reported lower SHBG levels in patients with PCOS and obesity compared to non-obese PCOS patients (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In Khashchenko et al.\u0026rsquo;s study the adolescents with PCOS had a significantly higher BMI than healthy controls (p\u0026thinsp;=\u0026thinsp;0.0002) and despite lack of postprandial glucose and insulin data higher leptin levels. In Yetim et al.'s study, the mean BMI of adolescents with PCOS was higher than that of healthy controls (1.4 vs. 0.8) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In our study, to demonstrate the impact of hyperinsulinemia/obesity on FAI and SHBG, we compared patients with PCOS to hyperinsulinemic/obese adolescents. The slightly higher FAI values in hyperinsulinemic/obese adolescents (8.30 vs. 8.0) and the lower SHBG levels (12.2 vs. 29.2 mol/L) suggest that glucose/insulin metabolism has a greater influence on FAI/SHBG levels than androgens. This suggests the need for selective consideration in the use of FAI for diagnosing PCOS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOvarian cysts\u003c/h2\u003e \u003cp\u003eThe use of the Rotterdam criteria for assessing ovarian cyst counts in adolescents is debated due to frequent anovulatory cycles and the physiological presence of up to 24 ovarian cysts in this age group. Additionally this debate is further complicated by the challenges of performing transvaginal ultrasonography (USG) and pelvic Magnetic Resonance Imaging in adolescents. However, ovarian volume, rather than the number of cysts, is considered more diagnostic in this population and previous studies have proposed a significant ovarian volume for PCOS diagnosis in adolescents as 15 cc in one ovary or 12 cc in both (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Similarly, our study using suprapubic ultrasonography revealed a mean ovarian volume of 11.8 cc in patients with PCOS, indicating use of overian volumes in diagnosis should be reliable.\u003c/p\u003e \u003cp\u003eEating disorders typically occur during mid-to-late puberty, especially in the 15\u0026ndash;19 age group. Oligomenorrhea onset was longer in malnourished patients than those with anovulatory cycles, and they were more likely to experience secondary amenorrhea (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Malnutrition may have a greater impact on LH pulsatility by affecting the hypothalamic-pituitary-gonadal axis and leading to decreased LH secretion and consequently decreased ovarian function and anovulation. Notably LH secretion is more affected than FSH and E2 secretion in our patients affected by malnutrition.\u003c/p\u003e \u003cp\u003eRetrospective planing of the study limited our ability to control for potential confounding factors and to collect detailed information. Prospective studies with larger population sizes may provide more statistical power and allow for more detailed subgroup analyses. Besides, having a relatively large sample size of 305 patients with various etiologies presenting with oligomenorrhea at a tertiary center and using utilized diagnostic criteria to identify/analyze patients with PCOS were the strengths of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAnovulatory cycles are the most frequent cause of menstrual irregularities in adolescents, and hyperandrogenism is key for diagnosing PCOS. The Free Androgen Index (FAI) may be unreliable for PCOS diagnosis, as FAI values were similar between adolescents with PCOS and those with hyperinsulinemia and obesity. Lower SHBG levels in hyperinsulinemic obese adolescents further complicate FAI use, suggesting that glucose/insulin metabolism has a greater influence on FAI/SHBG levels than androgens. Diagnosing PCOS should involve multiple criteria, including LH/FSH ratio, SHBG, FAI levels, and ovarian USG. Further research is necessary to refine diagnostic criteria and confirm associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u003c/strong\u003e\u0026nbsp;E.Ö. took part in concept, data collection, literature search, \u0026nbsp;analysis and writing .D.T. took part in concept, data collection, literature search and writing.S.Ç.G. took part in concept, data collection and writing .M.B. \u0026nbsp; took part in concept, \u0026nbsp;literature search, \u0026nbsp;and writing. F.G. took part in concept, literature search, \u0026nbsp;analysis and writing. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;There is no funding provided for this study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: \u0026nbsp;\u003c/strong\u003eAvailable upon request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003e This study was undertaken at …….. The protocols used in this study followed the Declaration of Helsinki and were approved by the Ethics Committee of ………. (February 2023 / Approval number: E2-23-3382).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e No financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article. The authors have no conflict of interest to declare.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDe Sanctis V, Soliman AT, Tzoulis P, Daar S, Di Maio S, Millimaggi G, et al. Hypomenorrhea in Adolescents and Youths: Normal Variant or Menstrual Disorder? Revision of Literature and Personal Experience. Acta Biomed. 2022;93(1):e2022157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElmaoğulları S, Aycan Z. Abnormal Uterine Bleeding in Adolescents. J Clin Res Pediatr Endocrinol. 2018;10(3):191\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKabra R, Fisher M. Abnormal uterine bleeding in adolescents. Curr Probl Pediatr Adolesc Health Care. 2022;52(5):101185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaşa C, G\u0026uuml;ng\u0026ouml;r Uğurlucan F. Approach to Abnormal Uterine Bleeding in Adolescents. J Clin Res Pediatr Endocrinol. 2020;12(Suppl 1):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoham AE, Norman RJ, Stener-Victorin E, Legro RS, Franks S, Moran LJ, et al. Polycystic ovary syndrome. Lancet Diabetes Endocrinol. 2022;10(9):668\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin KA, Anderson RR, Chang RJ, Ehrmann DA, Lobo RA, Murad MH, et al. Evaluation and Treatment of Hirsutism in Premenopausal Women: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2018;103(4):1233\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunro MG, Critchley HOD, Fraser IS. The two FIGO systems for normal and abnormal uterine bleeding symptoms and classification of causes of abnormal uterine bleeding in the reproductive years: 2018 revisions. 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Can J Diabetes. 2020;44(7):663\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Q, Li X, Song P, Xu L. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future. Drug Discov Ther. 2015;9(6):380\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris PD, Malkin CJ, Channer KS, Jones TH. A mathematical comparison of techniques to predict biologically available testosterone in a cohort of 1072 men. Eur J Endocrinol. 2004;151(2):241\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuchelo LTS, Alves MS, Baracat EC, Sorpreso ICE, Soares JM. Jr. Menstrual pattern in polycystic ovary syndrome and hypothalamic-pituitary-ovarian axis immaturity in adolescents: a systematic review and meta-analysis. Gynecol Endocrinol. 2024;40(1):2360077.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques P, Madeira T, Gama A. Menstrual cycle among adolescents: girls' awareness and influence of age at menarche and overweight. Rev Paul Pediatr. 2022;40:e2020494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheong Y, Cameron IT, Critchley HOD. Abnormal uterine bleeding. Br Med Bull. 2017;123(1):103\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe MT, Le VNS, Le DD, Nguyen VQH, Chen C, Cao NT. Exploration of the role of anti-Mullerian hormone and LH/FSH ratio in diagnosis of polycystic ovary syndrome. Clin Endocrinol (Oxf). 2019;90(4):579\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa CS, Lin Y, Zhang CH, Xu H, Li YF, Zhang SC, et al. [Preliminary study on the value of ratio of serum luteinizing hormone/follicle-stimulating hormone in diagnosis of polycystic ovarian syndrome among women with polycystic ovary]. Zhonghua fu chan ke za zhi. 2011;46(3):177\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhashchenko E, Uvarova E, Vysokikh M, Ivanets T, Krechetova L, Tarasova N et al. The Relevant Hormonal Levels and Diagnostic Features of Polycystic Ovary Syndrome in Adolescents. J Clin Med. 2020;9(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscobar-Morreale HF, Carmina E, Dewailly D, Gambineri A, Kelestimur F, Moghetti P, et al. Epidemiology, diagnosis and management of hirsutism: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2012;18(2):146\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCree-Green M, Rahat H, Newcomer BR, Bergman BC, Brown MS, Coe GV, et al. Insulin Resistance, Hyperinsulinemia, and Mitochondria Dysfunction in Nonobese Girls With Polycystic Ovarian Syndrome. J Endocr Soc. 2017;1(7):931\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Feng Q, Ye M, He Y, Yao A, Shi K. Metabolic effect of obesity on polycystic ovary syndrome in adolescents: a meta-analysis. J Obstet Gynaecol. 2017;37(8):1036\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBideci A, Camurdan MO, Yeşilkaya E, Demirel F, Cinaz P. Serum ghrelin, leptin and resistin levels in adolescent girls with polycystic ovary syndrome. J Obstet Gynaecol Res. 2008;34(4):578\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeibel NI, Baumann EE, Kocherginsky M, Rosenfield RL. Relationship of adolescent polycystic ovary syndrome to parental metabolic syndrome. 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Int J Womens Health. 2022;14:91\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewailly D, Lujan ME, Carmina E, Cedars MI, Laven J, Norman RJ, et al. Definition and significance of polycystic ovarian morphology: a task force report from the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2014;20(3):334\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKenigsberg LE, Agarwal C, Sin S, Shifteh K, Isasi CR, Crespi R et al. Clinical utility of magnetic resonance imaging and ultrasonography for diagnosis of polycystic ovary syndrome in adolescent girls. Fertil Steril. 2015;104(5):1302-9.e1-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolden NH, Carlson JL. The pathophysiology of amenorrhea in the adolescent. Ann N Y Acad Sci. 2008;1135:163\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Free androgen index, Menstrual irregularity, LH-FSH, Oligomenorrhea, Polycystic ovary syndrome, Sex hormone binding globuline","lastPublishedDoi":"10.21203/rs.3.rs-4945396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMenstrual irregularities are common among adolescents, often linked to anovulatory cycles. This study aims to establish diagnostic cut-off values for Polycystic Ovary Syndrome (PCOS) and differentiate these from anovulatory dysfunction in adolescents. Additionally, we assessed the sensitivity of using the Free Androgen Index (FAI) and Sex Hormone Binding Globulin (SHBG) in diagnosing PCOS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 305 adolescents presenting with oligomenorrhea at a tertiary center. Data were analyzed statistically and Receiver operating characteristic (ROC) curves were plotted to evaluate diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 305 patients, 229 (75%) had anovulatory cycles and 36 (11.8%) had PCOS. The mean FAI values for anovulatory cycles, PCOS, and hyperinsulinism were 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2, 8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5, and 8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant positive correlation was found between FAI and both HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.389; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI (r\u0026thinsp;=\u0026thinsp;0.499; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis determined the LH threshold of 9.7 U/L and LH/FSH ratio threshold of 2.62 as predictive markers for PCOS.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAnovulatory cycles are the most frequent cause of menstrual irregularities in adolescents, with hyperandrogenism being crucial for diagnosing PCOS. The FAI may be unreliable for PCOS diagnosis due to similar values in adolescents with hyperinsulinemia and obesity. Lower SHBG levels in hyperinsulinemic obese adolescents further complicate the use of FAI, indicating that glucose/insulin metabolism significantly influences FAI/SHBG levels. Comprehensive diagnostic criteria, including androgen levels, LH/FSH ratio, SHBG, FAI levels, and ovarian ultrasound, are essential for accurate PCOS diagnosis.\u003c/p\u003e","manuscriptTitle":"Differentiating PCOS from Anovulatory Cycles in Adolescents: A Comprehensive Evaluation of FAI, SHBG, and LH/FSH Ratio","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 08:59:23","doi":"10.21203/rs.3.rs-4945396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f98bf1be-8254-4e67-8adb-4150678c85c3","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-27T08:43:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 08:59:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4945396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945396","identity":"rs-4945396","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}