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

doi:10.21203/rs.3.rs-4945396/v1

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

https://doi.org/10.21203/rs.3.rs-4945396/v1

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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

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.

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.

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)
Excessive menstrual bleeding	Present	67 (%22)
BMI SDS, mean ± SS, (n)		0,6 ± 1,6 (277)
Hirsutism	Present	56 (%18.4)
Hirsutism	Normal	249 (%81.6)
Suprapubic Ultrasonography	Performed	277 (%90,8)
Suprapubic Ultrasonography	Not performed	28 (%9,2)
OCS treatment	Received	83 (%27,2)
OCS treatment	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^**
Menstrual pattern	Secondary amenorrhea (over 6 months)	5 (%13,9)	25(%10,9)	1 (%7,1)	0,793^**
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^**
OCS treatment	Not-received	23(%63,9)	168(%73,4)	9(%64,3)	0,412^**
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^**
Menstrual pattern	Amenorrhea (over 6 months)	4(%33,3)	25(%10,9)	0,042^**
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^**
OCS treatment	Not-received	10(%83,3)	168(%73,4)	0,737^**
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
	Area	p	Lower limit	Upper limit	Sensitivity	Specificity	PPV	NPD
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.

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.

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.

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.

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

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Statistical Methods:

Results

Characteristics of the patients:

Evaluation of the patient groups with PCOS, Anovulatuary cycles, and Hyperinsulinism:

Free Androgen Index analysis:

Evaluation of the patients with Anovulatuary cycles and Malnutrition:

Receiver operating characteristic (ROC) analysis:

Discussion

PCOS, FAI and Hyperinsulinism

Ovarian cysts

Conclusion

Declarations

References

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