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ORIGINAL ARTICLE
Year : 2021  |  Volume : 24  |  Issue : 1  |  Page : 15-21

Correlation of phenotypes of polycystic ovarian syndrome with anti-Müllerian hormone levels


Department of Obstetrics and Gynaecology, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidyapeeth, Puducherry, India

Date of Submission16-May-2020
Date of Decision03-Jul-2020
Date of Acceptance21-Jul-2020
Date of Web Publication31-Mar-2021

Correspondence Address:
Dr. R Santhiya
Department of Obstetrics and Gynaecology, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidiyapeeth, Puducherry - 607 402
India
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DOI: 10.4103/smj.smj_50_20

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  Abstract 


Background: Polycystic ovarian syndrome (PCOS) is a frequently encountered endocrine disorders in women of the reproductive age. Various studies conclude there is no uniform correlation between the phenotypes of the PCOS and serum anti-Mullerian Hormone (AMH) levels. Aim and Objective: The objective of the study to estimate the association between different phenotypes of PCOS and the serum AMH level. Materials and Methods: This was a cross-sectional analytical study which included sixty subjects with PCOS according to Rotterdam's criteria. After procuring the detailed history, clinically examination and ultrasound scan subjects were classified into one of the phenotypes of PCOS. Auto-analyzer was used to measure serum AMH levels and was correlated with the various phenotypes of PCOS. Results: The study group categorized 28 patients under phenotype D, which was a predominant form. Serum AMH mean was 6.1 (±3.2) ng/ml. The mean serum AMH levels for phenotype A was 7.5 ± 3.0 ng/ml which was higher than the other phenotypes. Phenotype A had high mean body mass index which was significant ( 29.1 ±6.6) kg/m2 (P = 0.046). Phenotype B had significantly higher Hirsutism score 19.8 (±1.7). Phenotype A had significantly higher mean follicular count (19.7 ± 5.1). The difference of mean or median among the phenotypes was compared using Kruskal–Wallis test or ANOVA. P < 0.05 was considered as statistically significant. Conclusion: A positive correlation was seen between the serum AMH levels and the phenotypes of the PCOS. Thus, AMH levels can be used as an adjunct tool in the diagnosis and monitoring of PCOS.

Keywords: Anti-Mullerian hormone, Hirsuitism, polycystic ovary syndrome


How to cite this article:
Santhiya R, Habeebullah S, Ghose S. Correlation of phenotypes of polycystic ovarian syndrome with anti-Müllerian hormone levels. Sahel Med J 2021;24:15-21

How to cite this URL:
Santhiya R, Habeebullah S, Ghose S. Correlation of phenotypes of polycystic ovarian syndrome with anti-Müllerian hormone levels. Sahel Med J [serial online] 2021 [cited 2021 Apr 15];24:15-21. Available from: https://www.smjonline.org/text.asp?2021/24/1/15/312739




  Introduction Top


Polycystic ovarian syndrome (PCOS) is a globally prevalent endocrine disorder in women affecting 5%–10% of the reproductive age.[1] In India, PCOS prevalence is estimated to be 11.96.[2] Heterogeneous by nature, PCOS is defined by an androgen excess and ovarian dysfunction in the absence of other specific diagnoses. Although the etiology remains unknown, PCOS is often considered as a complex multigenic disorder with strong environmental and epigenetic influences, including diet and lifestyle factors.[3]

According to Rotterdam, the American Society for Reproductive Medicine (ASRM) and European Society for Human Reproduction and Embryology (ESHRE), the diagnostic criteria for PCOS with the addition of the ultrasound (US) assessment of ovarian morphology can be defined when at least two of the following features are present: oligo-and/or anovulation (O), clinical (Hirsutism) and/or biochemical signs of hyperandrogenism (H), and polycystic ovaries on pelvic US (P) and exclusion of other identifiable endocrine disorders such as late-onset congenital adrenal hyperplasia, hyperprolactinemia, thyroid dysfunction, neoplastic androgen secretion, or drug-induced androgen excess. Based on a combination of these features, a novel phenotypic approach to diagnose PCOS was introduced, ESHRE/ASRM/Rotterdam's criteria in which women can be classified into various phenotypes i.e., Phenotype A, Phenotype B, Phenotype C, and Phenotype D. [4],[5] Although PCOS pathogenesis remains ambiguous, the failure of follicular maturation resulting in anovulation and accumulation of preantral and small antral follicles mark the characteristic features, which contribute significantly to the production of anti-Mullerian Hormone (AMH). AMH also inhibits aromatase activity and contributes to the severity of PCOS.[6]

Extensive studies in this regard have failed to delineate a consistent correlation between the various phenotypes and AMH. This disparity may affirm the severity of the syndrome. Significant ethnic and racial variations are evident in the clinical presentation of PCOS.[2] It is also uncertain whether the serum levels of AMH can predict the phenotypes of PCOS. Thus, to fill this paucity in literature, the following research was conducted.


  Materials and Methods Top


This cross-sectional study was conducted at, a tertiary care center from January 2018 to June 2019 for 18 months. Considering the PCOS prevalence in the general population as 9.13% and confidence interval of 95% and allowable error of 10%, the sample size was calculated using the formula n = Z1−α/2 2pq/d2, where Z1−α/22 = 1.96 @ 95% confidence α = 0.01 P = 9.13% = 0.0913 q = 1 − p d = 10% = 0.1 allowable error.[2] The sample size was calculated as 56, which was rounded off to 60. Continuous sampling technique was used in the study. The Institutional Medical Ethics Committee approved this study.

The Institution's Ethical Committee approval (P. G DISSERTATION/12/2017/88) by Institutional Human Ethics Committee, Mahatma Gandhi Medical College and research Institute, Pondicherry on December 21, 2017. All the procedures have been carried out as per the guidelines given in Declaration of Helsinki 2013. The informed consent from all the study participants, for participating in study and sharing the data for research/publication purposes was procured. The women aged 20–40 years, diagnosed with PCOS according to Rotterdam's criteria with infertility were included in the study.[4] PCOS women with infertility due to tubal/uterine/cervical factors and diagnosed or suspected case of thyroid disorder, hyperprolactinemia, and history of prior ovarian surgery, were excluded from the study. Sixty subjects were included for the assessment.

A comprehensive history was obtained to identify the ovulatory dysfunction. Clinical examination was conducted applying the Ferriman–Gallwey (FG) coring for Hirsutism and transvaginal US examination was carried out to identify the morphology of both the ovaries.[7] PCOS was diagnosed based on Rotterdam's revised 2003 criteria, considering 2 out of 3 features, i.e., Oligo ovulation and/or anovulation (Oligo menorrhea is defined as the length of the menstrual cycle >35 days or <10 periods per year), clinical signs of hyperandrogenism, i.e., FG score >8 3. Polycystic ovaries on ultrasonography (the sonographic measures for PCOS involves 12 or more follicles in either ovary measuring 2–9 mm in diameter and/or increased ovarian volume >10 mm3). Two milliliter of blood was collected from the women who fulfilled the inclusion criteria for serum AMH estimation using Vidas® AMH kit. The quantitative measurement of serum AMH was done using Vidas® AMH kit. The assay principle combines a one-step enzyme immunoassay sandwich method with a final fluorescent detection (enzyme-linked fluorescent assay). The Solid Phase Receptacle (SPR®) serves as the solid phase as well as the pipetting device. All of the assay steps are performed automatically by the instrument. The values 1.0–4.0 ng/ml were taken normal and values above 4.0 ng/ml were considered abnormal.

Quantitative data were entered into Microsoft Excel and analyzed using Stata version 14. (StataCorp, Texas, USA). Continuous variables such as age, height, weight, body mass index (BMI), Hirsutism score, follicular number, and serum AMH were expressed as mean (standard deviation) based on the distribution. The distribution of categorical variables such as age categories, BMI categories, ovaries affected, and type of phenotypes was summarized as proportions. The comparisons of different categorical parameters with type of phenotypes were assessed using a Chi-square test. The mean or median difference between the phenotypes was compared using ANOVA or Kruskal–Wallis test. The correlation between BMI, Hirsutism score, and follicular number with serum AMH was assessed using Spearman's correlation based on distribution. P < 0.05 was considered as statistically significant.


  Results Top


A total of 60 subjects were included in the study. The mean age was 24.3 (±3.4) years and mean BMI was 25.2 (±4.5) kg/m2. The mean Hirsutism score was 13.6 (±4.6) and follicular number was 18 (±4.8). The mean serum AMH was 6.1 ng/ml (3.4–7.9). The results are represented in [Table 1].
Table 1: Mean (standard deviation) distribution of various parameters (n=60)

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Among the 60 samples, 50% were aged between 22 and 25 years and 28.3% were 26–29 years. 6.7% of the patients were aged 30 years and above, are represented in [Figure 1].
Figure 1: Age distribution of the study population

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BMI status was assessed and a little more than half (55%) of the patients were graded as normal. One-third (33.3%) were overweight and 11.7% were obese. The details are shown in [Table 2].
Table 2: Body mass index distribution

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Among the 60 samples, the most common presentation was seen bilaterally (52, 86.7%). While four subjects (6.7%) featured unilaterally, Phenotype B (i.e., hyperandrogenism and oligomenorrhea [H + O]), no polycystic ovaries were observed in four patients.

Phenotype D was the most prevalent form in the study (46.7%) phenotype C, A, and B were distributed as 31.7%, 15%, and 6.7%, respectively. The details are represented in [Figure 2].
Figure 2: Distribution of various PCOD related phenotypes amongst the study population

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On gathering the baseline data, the variables were correlated with the phenotypes to assess for the statistical significance. Correlation of the age with the phenotypes was found to be insignificant (P = 0.854) and is represented in [Table 3].
Table 3: Correlation of age with phenotype

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Correlation of BMI with phenotype shows that the mean BMI for phenotype A was 29.1 (±6.6) kg/m2 and for phenotype B was 28.5 (±3.4) kg/m2. Mean BMI of phenotype C was 24.6 (3) kg/m2 and phenotype D 23.8 (±3.8) kg/m2. The difference in mean BMI between the phenotypes was assessed using ANOVA test and was found to be statistically significant. P = 0.004 was noticed in the BMI, which is depicted in [Table 4].
Table 4: Correlation of body mass index with phenotypes

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Correlation of Hirsutism score with the phenotype yielded the following results: the mean (SD) Hirsutism score for phenotype 1 was 14.6 (5.3) and phenotype 2 was 19.8 (1.7). While the mean (SD) Hirsutism score of phenotype 3 was 13.4 (5.2), the phenotype 4 was 12.6 (3.7). The difference in the mean (SD) Hirsutism score in the phenotypes was found to be significant (P = 0.027) and are represented in [Table 5].
Table 5: Correlation of Hirsutism score with phenotypes

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The Hirsutism score between various phenotypes was analyzed using ANOVA test and a significant correlation was evident. Hence, post hoc test was done and Hirsutism Score was found to be significantly different between the Phenotypes B and D through TUKEY Multiple Comparisons of Means are tabulated in [Table 6].
Table 6: Post hoc test

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Correlation of follicular number with phenotype shows that the mean (SD) follicular number for phenotype A was 19.7 (±5.1) and for phenotype B was 15.8 (±6.55). Mean (SD) follicular number of phenotype C was 18.1 (±3.96) and phenotype D was 17.7 (±5.02).The difference in mean (SD) follicular number between the phenotypes was found to be in-significant (P = 0.538) which implies that the follicular number is not different across phenotypes as shown in [Table 7].
Table 7: Correlation of follicular number with phenotypes

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Correlation of AMH with phenotype shows that the mean (SD) AMH for phenotype A was 7.5 (3.0) and for phenotype B was 7.4 (1.2). The mean (SD) of phenotype C was 5.8 (2.3) and phenotype D was 5.6 (3.9). The difference in the mean (SD) AMH between the phenotypes was assessed using ANOVA test and was found to be insignificant (P = 0.373). Thus, serum AMH levels are not different across the phenotypes [Table 8].
Table 8: Correlation of serum anti-Mullerian hormone level with the phenotypes

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Overall, the difference in mean BMI and Hirsutism score between the phenotypes was found to be statistically significant. Correlation between age, Hirsutism score, and BMI with AMH were analyzed using Spearman's Correlation test. Age and AMH were found to have a weak negative correlation. Hirsutism score and AMH were found to have a weak positive correlation. BMI and AMH were not found to be associated (P value was not found to be significant). Overall, the correlations were not found to be strong and are summarized in [Table 9].
Table 9: Correlation between age, Hirsutism score, and body mass index with anti-Mullerian hormone

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  Discussion Top


PCOS is often characterized by androgen excess, menstrual irregularity, disturbances in glucose metabolism and polycystic ovaries in women of the reproductive age. In normal females, ovulatory cycle AMH levels tends to decrease with age. [2],[8] This study was conducted to gauge the proportion of different phenotypes in women with PCOS and also with high serum levels of AMH and to correlate the phenotype of PCOS with the serum AMH levels. [Table 10] depicts the relationship of these four PCOS phenotypes. Women with “classic” PCOS (phenotypes A and B) are associated with increased insulin levels, higher rates of insulin resistance, pronounced menstrual dysfunction and risk for metabolic syndrome; BMI and prevalence of obesity; and various severe forms of atherogenic dyslipidemia.[9] AMH serum levels reflect the severity of symptoms where anovulation and hyperandrogenic are seen.
Table 10: Classification of polycystic ovarian syndrome phenotypes

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The most common phenotype was D (O + P), followed by phenotype C (H + P) and A (H + O + P), whereas phenotype B (H + O) was least common. The mean AMH serum levels in Phenotype A was observed to be higher, i.e., patient with hyperandrogenism (H), oligomenorrhea (O), and polycystic ovaries (P) followed by phenotype B with hyperandrogenism (H) and oligomenorrhea (O), Phenotype C with hyperandrogenism (H) and polycystic ovaries (P) and phenotype D with oligomenorrhea (O) and polycystic ovaries (P).

Few studies correlated the relationship between AMH level and PCOS phenotypes. Wiweko et al. (2014), studied the serum levels of AMH in various phenotypes of PCOS and normoovulatory patients and found higher level of AMH in phenotype A.[9] In contrast, Parahuleva et al. found the high level of AMH in all four phenotypes.[10] Mahajan and Kaur suggested a AMH concentration of >5.03 ng/ml can be used in the diagnosis of PCOS in women of Indian origin. Phenotype A is the most prevalent PCOS and the mean AMH levels were higher in phenotype A.[6]

In the studies conducted by Parahuleva et al., Romualdi et al., Guastella et al., the mean age was 22 ± 4.3 years, 25.5 ± 5.7 years, 25 ± 4 years, which are comparable to our study.[2],[10],[11],[12] In contrast, Jamil et al., Wiweko et al., and Zhang et al., observed that the mean age was 26.78 ± 4.95 years, 29.55 ± 3.94 years, and 26 ± 4.9 years, respectively, which was higher than our study. [13],[14] The mean age of this study was 24.3 ± 3.4 years. This shows that PCOS is not specific to any age group interval in the reproductive age.

In a study conducted by Jamil et al., all PCOS patients belonged to the obese group with a mean BMI of 31.08 ± 5.82 kg/m2 whereas Romualdi et al., Parahuleva et al., Wiweko et al. PCOS patients belonged to the overweight group (25.99 ± 1.2, 27.21 ± 0.79, 27.72 ± 1.6 kg/m2).[9],[10],[11] In contrast, majority (55%) of our study population belonged to normal BMI. This difference of BMI between other studies and ours may be due to the difference of dietary, genetic, environmental, and ethnic factors.

The mean Hirsutism score by Yilmaz et al., Romualdi et al., Guastella et al., was 19.06 ± 5.16,11.46 ± 7.01, and 11.4 ± 3.1, which is comparable to our study.[11],[12],[15] However, Bhide et al. in a study conducted in the UK, found Hirsutism score highest was 9.[16] This may be due to the difference in ethnicity. In the studies conducted by Romualdi et al., Zhang et al., Yilmaz et al., Guastella et al., the mean Hirsutism score were higher in Phenotype B, i.e., 14.3 ± 8.77), (11.2 ± 6.7), (19.06 ± 5.16), and (11.5 ± 5), respectively and low Hirsutism score in phenotype D (4.4 ± 3.0, 3.2 ± 1.8, 4.1 ± 1.3, 3 ± 1).[11],[14],[15] This is similar to our study, i.e., phenotype B (19.8 ± 1.7) and phenotype D (12.6 ± 3.7). It can be presumed that significant variations in the Hirsutism score among the populations exist.

In the studies conducted by Zhang et al., Wiweko et al., the most common form was Phenotype D, i.e., 52.2,63.4%, respectively, and least common was phenotype B which is similar to our study. [9],[14] In contrast, the study conducted by Guastella et al., Romualdi et al. showed the most prevalent was phenotype A (53.9%). This variation also may be due to genetic and ethnic variation.[12] Lizneva et al. found the mean BMI was significantly higher in phenotype A which is similar to our study (29.1 ± 6.6 kg/m2).[17]

Zhang et al., Romualdi et al. found mean follicular count was higher in phenotype B which was 11.2 ± 6.7,14.3 ± 8.77. [11],[14] However, the mean follicular count was significantly higher in this study with Phenotype A (19.7 ± 5.1) and lower in phenotype D (15.7 ± 2.03) (P = 0.01). This may be due to inter observer variation in counting follicles.

In studies conducted by Wiweko et al., Sahmay et al., Mahajan et al., Piouka et al., it was that the found phenotype A had high serum levels of AMH, i.e., 11.7, 10.9, 9.05, and 5.09 ng/ml.[18],[19],[20] When comparing AMH levels, all studies had high AMH levels than us except Piouka et al. Sahmay et al., Wiweko et al. found phenotype D had low serum AMH levels (3.06 ± 2.8 ng/ml). [19],[20] In contrast, Mahajan et al., Bhide et al. found phenotype B had low serum AMH levels (4.3 ± 2.3, 3.32 ± 2.03 ng/mL).[16] Piouka et al. documented a progressive decrease in AMH levels ranging from phenotype A to D which is similar to our study.[18] This variation in AMH levels in different phenotypes may be due to variation in antral follicular count. Alebić et al. analyzed the phenotypic diversity in follicular AMH production and found serum AMH levels remains unaltered in all four phenotypes.[21]

Limitations of this study were the small number of subjects in each phenotype group and inter-observer variation for counting the follicular numbers using US. A multi-centric study with large sample size, across various age groups is required to find the existence of significant correlation between different phenotypes of PCOS and serum AMH levels. Assessing the correlation between PCOS and other hormones requires more studies. A long-term follow-up of different phenotypes and its correlation with metabolic syndrome may also be suggested for future research. As variations are observed in different ethnic groups, a population specific study is mandated. This was the first study to assess the AMH levels and phenotypes correlation in the South Indian population between the age group of 20–40 years.


  Conclusion Top


Based on the observation of this study, it is concluded that the nonclassic phenotypes were more common than the classic phenotypes and most prevalent phenotype was phenotype D. Although phenotype A had raised serum AMH levels, it was not statistically significant. Larger study population, with a long-term follow-up to note the correlation could be delved in the future researches. Identifying the phenotypes aids in the comprehensive management of the patients, thus enhancing the prospects of fertility.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10]



 

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