Abstract
Objectives
Determine variability of serum anti-Müllerian hormone (AMH) levels during ovulatory menstrual cycles between different women (inter-participant), between non-consecutive cycles (inter-cycle) and within a single cycle (intra-cycle) in healthy women.
Methods
Eligible participants were women aged 18–40 years with regular ovulatory menstrual cycles. Serum samples were collected every second day during two non-consecutive menstrual cycles. AMH levels were measured in triplicate using the Elecsys® AMH Plus immunoassay (Roche Diagnostics). AMH level variability was evaluated using mixed-effects periodic regression models based on Fourier series. The mesor was calculated to evaluate inter-participant and inter-cycle variability. Inter- and intra-cycle variability was evaluated using peak-to-peak amplitudes. Separation of biological and analytical coefficients of variation (CVs) was determined by analysing two remeasured AMH levels (with and without original AMH levels).
Results
A total of 47 women were included in the analysis (42 assessed over two cycles; five one cycle only). CV of unexplained biological variability was 9.61%; analytical variability was 3.46%. Inter-participant variability, given by time-series plots of AMH levels, was greater than inter-cycle variability. Between individual participants, both mesor and peak-to-peak amplitudes proved variable. In addition, for each participant, intra-cycle variability was higher than inter-cycle variability.
Conclusions
Inter-participant and intra-cycle variability of AMH levels were greater than inter-cycle variability. Unexplained biological variability was higher than analytical variability using the Elecsys AMH Plus immunoassay. Understanding variability in AMH levels may aid in understanding differences in availability of antral ovarian follicles during the menstrual cycle, which may be beneficial in designing gonadotropin dosage for assisted reproductive technology.
Introduction
Anti-Müllerian hormone (AMH) is a dimeric glycoprotein belonging to the transforming growth factor beta superfamily [1]. AMH is named for its key role in male genital organ differentiation by inducing and promoting the regression of the Müllerian ducts [2]. In the absence of AMH, the Müllerian ducts evolve into female reproductive organs, including the uterus, the fallopian tubes and the upper part of the vagina [1, 3, 4].
In women, ovarian expression of AMH can be detected as early as 36 weeks’ gestation [5], though circulating levels remain low until between the ages of 2 and 4 years. After this time, AMH levels vary with reproductive age [6], rising until their peak at approximately 24.5 years of age, followed by a gradual decline until menopause [7]. However, not all AMH level variability is age-dependent [7]. Variability in AMH levels has been reported both during the menstrual cycle and across menstrual cycles within individual women [8], [9], [10]. Production of AMH by the ovarian granulosa cells of pre- and small-antral follicles is a negative regulator of the early stages of follicular development as it restricts the recruitment and follicle-stimulating hormone (FSH)-stimulated growth of follicles [11]. Serum AMH levels correlate with the number of primordial follicles in the ovaries. Thus, AMH can serve as a direct serum marker of ovarian reserve [12], [13], [14]. At any age, ovarian reserve varies among individuals.
The worldwide prevalence of infertility is estimated at 8–12% for women aged 20–44 years, with 1 in 6 couples experiencing some form of infertility problem [15]. Many couples experiencing infertility are treated with some form of assisted reproductive technology (ART), which usually requires ovarian hyperstimulation. The responsiveness to ovarian hyperstimulation with exogenous gonadotropins depends on the degree of ovarian reserve. In addition to age, ovarian reserve is a major determinant of the cumulative outcome of ART [16], [17], [18].
AMH levels and antral follicle count (AFC) are the preferred methods for ovarian reserve assessment [2, 19]. However, AFC does not provide the same level of sensitivity as AMH owing to inter-observer variability, technical limitations, and differences in methodology [20, 21]. AMH levels may vary between women due to genetic and environmental factors [14, 22]. Whereas some fluctuations of the AMH levels in blood circulation have been demonstrated, these are not considered clinically relevant for the estimation of ovarian reserve within individual women [11]. Currently, the extent of variability in serum AMH levels between non-consecutive menstrual cycles is unknown. Understanding variability in AMH levels may aid with ovarian reserve assessment and provide a reliable prediction of response to ovarian stimulation in women undergoing assisted reproduction. The Elecsys® AMH Plus immunoassay (Roche Diagnostics International Ltd, Rotkreuz, Switzerland) provides a fully automated and robust method for the rapid measurement of serum AMH levels [23], [24], [25].
Our study aimed to model variability components of serum AMH levels during the natural, ovulatory menstrual cycle of healthy women, including variability between different women (inter-participant), between non-consecutive menstrual cycles (inter-cycle) and within a single menstrual cycle (intra-cycle) in individual women. Unexplained biological variability not captured by the aforementioned components and analytical variability due to replicated measurements of AMH Plus were also investigated.
Materials and methods
Study design
This prospective, non-interventional, single-centre (University Hospital Basel, Basel, Switzerland) study aimed to quantify serum AMH levels in healthy women using the Elecsys AMH Plus immunoassay and was conducted between January 2017 and September 2018. Eligible women were aged 18–40 years and were stratified into two age groups (18–25 years and 26–40 years). All participants had regular otherwise untreated menstrual cycles between 24 and 32 days in length (self-reported), had a body mass index (BMI) of 19–26 kg/m2, were non-smokers and were readily available for blood draws every two days during two non-consecutive menstrual cycles. Additional inclusion criteria required participating women to test negative for hepatitis B virus, hepatitis C virus, and human immunodeficiency virus (HIV). Women were excluded from the study if they were currently taking any hormonal drugs (in particular hormonal contraceptives); were pregnant or lactating; had known infertility, polycystic ovary syndrome or other hormonal diseases; or if they had participated in other clinical studies within the last 3 months.
The study was presented to and approved by the local Ethics Review Board (EKNZ ID 2016-01824). All activities were conducted in accordance with the Declaration of Helsinki of 1975 (revised 2013), the International Council on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use Good Clinical Practice guidelines and current national guidelines. All participants provided written informed consent prior to the first serum sampling.
Sample collection and measurement
From the second or third day of the menstrual cycle, serum samples were collected every two days during two non-consecutive menstrual cycles, separated by at least one menstrual cycle. Serum samples were separated into two aliquots, one for the primary analysis conducted during the ongoing menstrual cycle and the second stored at −80 °C until remeasurement. Serum AMH levels were quantified using the Elecsys AMH Plus immunoassay on the cobas e 411 analyser at the collection site and re-quantified in duplicate on a cobas e 601 analyser (both Roche Diagnostics International Ltd, Rotkreuz, Switzerland) at a secondary laboratory (TRIGA-S Scientific Solutions, Habach, Germany). To ascertain fulfilment of the inclusion and exclusion criteria, the primary samples were also tested at the primary site for hepatitis B/C virus, HIV, and beta human chorionic gonadotropin to exclude pregnancy.
Assessments
The primary determinations of AMH levels and the remeasured values were used to assess the variability of AMH levels between different women (inter-participant), between non-consecutive menstrual cycles (inter-cycle), and within a single menstrual cycle (intra-cycle) in individual women. In order to make the menstrual cycles comparable between women, cycle lengths were standardized to 28 days using the formula
where
Statistical analyses
Mixed-effects periodic regression models based on Fourier series were performed to evaluate the inter-participant, inter-cycle, and intra-cycle variability (please see Supplementary Materials for further details). This regression modelling allowed for the analysis of cyclical phenomena by finite Fourier series. The AMH values were transformed logarithmically to achieve a normal distribution for the mixed-effects modelling. Based on three AMH measurement results, it is possible to extract different analytical variability estimates (in the form of CVs) including repeatability (within-site), reproducibility (covering within-site and inter-site), and biological variability (estimation based on replicate AMH measurements over two menstrual cycles). Therefore, the following models were used to separate the observed biological variability from the analytical variability; residuals reflected the unexplained biological variability.
The first model used the original AMH values, allowing only the estimation of the biological (i.e. cyclical) variability. The unexplained (residual) variability is a mixture of the unexplained biological variability and the analytical variability.
The second model used the two remeasured AMH values, allowing the separation of the expected biological variability from both the unexplained biological variability and the analytical variability (repeatability).
The third model was the same as the second model. However, in addition to the remeasured AMH values, the primary AMH serum levels were considered, allowing the separation of the expected biological variability from both the unexplained biological variability and the analytical variability (reproducibility).
The mean AMH value (mesor) based on distribution of values across the cycle(s) allowed for evaluation of inter-participant and inter-cycle variability, and peak-to-peak amplitudes allowed for evaluation of inter-cycle and intra-cycle variability. The estimation of the mixed-effects models was performed by SAS software (version 9.4) and R (version 3.4.0). A method comparison was performed where the original AMH values were compared with the average of the two remeasured AMH values using a Passing-Bablok regression using R (version 3.4.0).
Results
Participant disposition and demographics
In total, 47 women were included in the analysis; of these, 42 were assessed across two non-consecutive menstrual cycles and five were assessed for one menstrual cycle only (Figure 1). Three cycles were found to be anovulatory and were excluded from the analysis. Therefore, the final analysis included data from 39 women assessed over two cycles and eight women assessed for one cycle only. Prior to standardization of menstrual cycle lengths, the cycle lengths varied between 19 and 35 days. The mean age of women in the 18–25 years age group was 22.5 years (standard deviation [SD]=1.6) and in the 26–40 years age group was 30.5 years (SD=3.6). Mean BMI was higher in the 26–40 years age group, 23.0 kg/m2 (SD=1.8), compared with 21.4 kg/m2 (SD=1.7) in the 18–25 years age group. Two women with BMI <19 kg/m2 were included in the analysis: one due to having a BMI very close to 19 kg/m2 (BMI=18.82), the other exhibited regular menstrual cycles (BMI=18.21 kg/m2).
Participant disposition.
Two women with BMI <19 kg/m2 were included in the analysis; one due to having a BMI very close to 19 kg/m2 (BMI=18.82 kg/m2), the other exhibited regular menstrual cycles (BMI=18.21 kg/m2).
Primary measurements totalled 1,137 measurements, of which 1,124 were performed in triplicate, one was performed in duplicate, and 12 had the primary measurement only. In addition, five samples had missing primary measurements but two replicate measurements. This provided 2,259 replicate measurements to assess repeatability and an overall total of 3,396 (1,137 + 2,259) measurements for the analysis of reproducibility.
AMH level variability
Compared with inter-cycle variability, time series plots of serum AMH levels indicated substantial variability between women (inter-participant variability; Figure 2). Statistical modelling showed large inter-participant variability in both the mesor and peak-to-peak amplitude (Figure 3). For each woman, intra-cycle variability was higher than inter-cycle variability. The inter-cycle mesors showed a strong correlation between cycles (r=0.95), while the peak-to-peak amplitude was weakly correlated (r=0.23). The smallest mesors and smallest peak-to-peak amplitudes were seen in the 26–40 years age group. Moderately similar correlation between the mesor and peak-to-peak amplitude were seen across both cycles (cycle 1: r=0.63; cycle 2: r=0.65).
Plot of AMH Plus results on standardized time stratified by cycles and participants with fitted menstrual cycles (n=47). AMH, anti-Müllerian hormone.
Correlations of (A) mesors of menstrual cycle 1 and cycle 2 (r=0.95), (B) peak-to-peak amplitude of menstrual cycle 1 and cycle 2 (r=0.23), and correlations of the mesor and peak-to-peak amplitude over both cycles (C with r=0.63, D with r=0.65) stratified by the two age cohorts. AMH, anti-Müllerian hormone.
Precision of the AMH Plus immunoassay
A method comparison showed a strong correlation between the original and the remeasured AMH levels (Kendall’s tau=0.94; Figure 4). Further, precision estimates were derived using three models as described in the ‘Statistical analyses’ section of the methods:
Based on the first mixed-effects model (1), the original AMH measurement results showed a CV of 10.79% indicative of unexplained biological variability and analytical variability.
The duplicate re-measurement results were used to separate the expected biological variability from the unexplained biological variability and the repeatability, providing CVs of 10.73 and 1.54%, respectively, based on the second mixed-effects model (2).
Triplicate AMH measurement results (from two sites) provided a CV of unexplained biological variability of 9.61% and reproducibility of 3.46% based on the third mixed-effects model (3).
Scatter plot with method comparison of AMH Plus results measured in a day-to-day stream mode at University of Basel and AMH Plus results after being remeasured in batch mode (averaged over two replicates). AMH, anti-Müllerian hormone.
Discussion
The results of this study showed that inter-participant and intra-cycle variability of serum AMH levels were larger than inter-cycle variability. The high variability between individual women is consistent with another similar study [27]. Large variability in inter-participant AMH levels is not surprising, given the variability in ovarian reserve between women. AMH levels in the blood are largely affected by age, with a yearly reduction of approximately 0.384 μg/L (2.74 pmol/L) [22]. In addition to age, several other factors are reported to have an impact on circulating AMH levels, such as vitamin D deficiency, obesity, and smoking [22]. The latter two environmental factors were accounted for by the inclusion and exclusion criteria of our study. A patient’s endocrine and metabolic condition may also impact circulating AMH levels. Low AMH levels were observed in patients diagnosed with hypogonadotropic hypogonadism [28] and during intake of oral contraceptives [29, 30]. Exceedingly high AMH levels are observed in women diagnosed with polycystic ovary syndrome [31], but not in overweight women with regular, ovulatory menstrual cycles [32].
Reported rates of inter-cycle variability of AMH levels vary greatly. Some studies report little inter-cycle variability [9, 33, 34], whereas others report high levels of variability [35, 36]. This is the first study to prospectively examine fluctuations in serum AMH levels in two non-consecutive menstrual cycles. All other studies have reported data from consecutive menstrual cycles. Other predictors of ovarian reserve, such as FSH, inhibin B, and AFC have all been demonstrated to vary more between cycles than AMH [34, 37, 38]. Low inter-cycle variability, depicted in Figure 3A, indicates the utility of AMH as a biomarker of ovarian reserve, as a random AMH measurement is likely to be representative for an individual woman during that same period of the menstrual cycle [33].
There are conflicting reports in the literature regarding the degree of intra-cycle variability of AMH serum levels. Several studies have reported either no variability in AMH levels during the menstrual cycle, or that variability is minimal or not clinically relevant [27, 39], [40], [41]. However, there are also a number of studies reporting significant variability of AMH levels throughout the menstrual cycle, in particular in young women of 25 years or younger [8, 36, 42], [43], [44], [45]. In addition, there are conflicting reports detailing intra-cycle variability of AMH levels with increasing age [44, 46]. The smaller mesors and peak-to-peak amplitudes observed in our study for the 26–40 years age group compared with the 18–25 years age group suggests there is less variability in AMH levels with increasing age. We observed higher circulating AMH levels during the follicular phase of the menstrual cycles, as compared to the luteal phase. Although the reasons for these observed differences are unclear, it has been postulated that measured serum AMH levels reflect the recruited follicle pool rather than the overall number of primordial follicles resident in both ovaries [7]. This biological variability of the serum levels of AMH during the first and second phase of the menstrual cycle remains to be explored.
A key strength of our study is the frequency and number of serum samples collected. Serum samples were collected every two days over two menstrual cycles at a single study site. In addition, serum AMH levels were first quantified on site at the time of sample collection during routine clinical practice and then remeasured twice under ideal laboratory conditions. This extensive work provides a large source of data that enables the separation of biological and analytical variability. The correspondence between the results achieved under both conditions is high (Figure 4). To the best of our knowledge, this is the first study to differentiate biological variability from analytical variability by combining a periodic model with a mixed-effects model. The low analytical variability demonstrated by our results confirms the robust assay method to determine serum AMH levels in women.
A limitation of our study is that the results were all generated using the Elecsys® AMH Plus immunoassay. The observed variability in AMH levels may differ from that of other manufacturers [47].
In conclusion, our study demonstrated greater inter-participant and intra-cycle variability than inter-cycle variability when measured on the fully automated Elecsys AMH Plus immunoassay. Understanding variability in AMH levels may aid in understanding differences in availability of antral ovarian follicles during the menstrual cycle, which may be beneficial in designing gonadotropin dosage for assisted reproductive technology.
Funding source: Roche Diagnostics International Ltd
Acknowledgments
The authors would like to thank Laura Schlieker for statistical analysis support, and Veit Peter Grunert and Antje Ziegler for data management support. Third-party medical writing assistance, under the direction of the authors, was provided by Ashlie Butler, PhD and Rebecca Benatan, BSc of Ashfield MedComms, Macclesfield, UK, an Ashfield Health company, and was funded by Roche Diagnostics International Ltd, Rotkreuz, Switzerland. COBAS, COBAS E, and ELECSYS are trademarks of Roche.
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Research funding : This study was funded by University Hospital Basel, Basel, Switzerland and Roche Diagnostics International Ltd, Rotkreuz, Switzerland.
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Author contributions: RPL, MH and CDG designed the study; MB, KB and CDG acquired the data; all authors analysed and interpreted the data, provided critical review of the manuscript and provided final approval of the manuscript for submission.
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Competing interests: RP Laubender is an employee of Roche Diagnostics GmbH and is visiting scientist at the Institute for Medical Information Processing, Biometry, and Epidemiology of the LMU Munich, Germany. K Buck is an employee of Roche Diagnostics GmbH. M Hund is an employee of Roche Diagnostics International Ltd and is an owner of shares in F. Hoffmann-La Roche Ltd. The remaining authors declare no competing interests.
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Informed consent: All participants provided written informed consent prior to the first serum sampling.
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Ethical approval: The study was presented to and approved by the local Ethics Review Board (EKNZ ID 2016-01824). All activities were conducted in accordance with the Declaration of Helsinki of 1975 (revised 2013), the International Council on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use Good Clinical Practice guidelines and current national guidelines.
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Study registration: NCT03398603 at Clinicaltrials.gov.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2021-0698).
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