Abstract
Purpose
To update the evidence of anti–müllerian hormone (AMH) as predictive factors for live birth outcome in women undergoing assisted conception and discover the modulating effect of age.
Methods
PubMed, Embase, Medline, and Web of Science were searched for studies published until June 2021. We included studies that measured serum AMH levels and reported the subsequent live birth outcomes. Random effects models and hierarchical summary receiver operating characteristics (HSROC) models were used. The QUADAS–2 checklist was employed to assess the quality of the included studies.
Results
We included 27 studies (27,029 women) investigating the relationship between AMH and live birth outcome after assisted conception. The diagnostic odds ratios (DOR) from random effects models were ruled out due to high heterogeneity. Our findings suggested that AMH was associated with live birth. The DOR was 2.21 (95% CI 1.89–2.59), and 2.49 (95% CI 1.26–4.91) for studies on women with unspecified ovarian reserve and women with low ovarian reserve, respectively. The DOR of those with advanced ages was 2.50 (95% CI 1.87–2.60). For younger women, the DOR was 1.41 (95% CI 0.99–2.02). HSROCs showed that AMH had no predictive ability towards live birth in women with diminished ovarian reserve or younger age. Exclusion of Chinese cohorts lowered the heterogeneity.
Conclusions
This study revealed that AMH had better prediction for live birth in advanced–age women. AMH may have implicative predictive value for assisted conception counseling of couples of advanced ages.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Our findings will guide further studies on predictive ability of AMH and facilitate clinicians note in mind that higher AMH levels in advanced-age women links to live birth more compared to younger women. |
Introduction
Age is one of the most vital predictors of outcome in assisted conception. However, age alone is incapable of predicting assisted reproduction technology (ART) outcome precisely. As a competitive candidate for ART outcome prediction, (AMH) has been widely used as a promising marker of ovarian reserve and ovarian response. It is also strongly correlated with the number of retrieved oocytes of women undergoing ovarian stimulation [1,2,3,4,5]. Expressed by granulosa cells from pre–antral and antral follicles [6], AMH acts to reduce both primordial follicle initiation and follicle sensitivity to follicle–stimulating hormone (FSH) by inhibition of aromatase [7]. Previous meta-analyses from Tal et al. and Iliodromiti et al. with 5373 women and 6356 women, respectively, presented that AMH had weak predictive ability in predicting implantation and clinical pregnancy and poor accuracy in predicting live birth [8, 9]. Till today, plethoric researches focusing on the association between AMH and live birth after assisted conception have been conducted, and the evidence should be updated. Moreover, Wang and colleagues found that AMH had limited predictive value for IVF outcomes regarding extremes of female reproductive age [10]. However, previous meta-analyses had not investigated the impact of age on AMH predicting live birth among women undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). Meanwhile, results from women with low ovarian reserve were not conclusive due to small sample size (n = 542 women) and needed to be substantiated in more extensive studies [8].
To further assess the predictive capacity of AMH for live birth in women undergoing IVF/ICSI and to provide insights on the modulating effect of age, we performed an updated systematic review and meta-analysis of all eligible studies. To explore the predictive ability of AMH for live birth among different subpopulations of infertile patients, we separately analyzed studies including only women with diminished ovarian reserve and those including women with unspecified ovarian reserve. In addition, we divided original data of enrolled studies according to age and evaluated the effect of age on AMH predictive capacity for live birth.
Methods
This systematic review and meta-analysis was conducted according to the PRISMA guidelines [11]. A well-established structured procedure was followed from the start of this study.
Eligibility criteria
Studies were included if they met the following criteria: (i) women of reproductive age undergoing IVF/ICSI cycles with any stimulation protocols; (ii) serum AMH was measured before ovarian stimulation; (iii) live birth outcome was recorded for all participants; (iv) any study design other than case reports. Additionally, studies referring to oocyte donation programs were excluded.
Literature search and selection strategy
The following databases were searched: PubMed, Embase, Medline, and Web of Science. The systematic search was performed using combinations of the following keywords: “live birth” (MeSH: live birth, pregnancy, ongoing pregnancy) and key words “anti–müllerian hormone”, “AMH”, “müllerian–inhibiting substance”, or “müllerian–inhibiting factor”. Studies published up to June 2021 were included, and there was no language restriction. Two researchers (N.J.L. and Q.Y.Y.) screened the abstract of all identified studies independently. Any disagreement between the two researchers was resolved with discussion. If a study met the eligibility criteria, it was included in the systematic review. If a study displayed data to construct a 2 × 2 table, in which a specific cut-off value of AMH level was related to live birth outcome, the study was selected for final inclusion in the meta-analysis. If a study was chosen for the systematic review but had no extractable data, an email requesting for data would be sent to the author. If the author did not reply, the study was not included in the meta-analysis. The article and data would be included in the meta-analysis when a study did not provide wanted data but could be extracted using a plot digitizer.
For each study, the first author, year of publication, number of cycles, number of patients, stimulation protocol, mean/median age of the patients, suggested cut-off point of AMH (converted to ng/ml using the conversion formula 1 ng/ml = 7.14 pmol/l), AMH assay used, number of live births below or above the cut-off point, study design and patient selection were extracted.
Quality assessment
Each study was assessed based on the QUADAS–2 checklist to judge the risk of bias and applicability of primary diagnostic accuracy studies [12]. QUADAS–2 checklist consists of four main domains: patient selection, index test, reference test, and flow of each study. A funnel plot, which plots estimates of diagnostic accuracy against sample size, was constructed to visually assess the risk of publication bias. A linear regression of log diagnostic ratios on the inverse root of effective sample sizes was performed to quantitatively assess publication bias, where a non-zero slope coefficient (p < 0.10) suggests significant asymmetry and slight study bias. Because this meta-analysis used only published data obtained from online resources, no approval from the institutional review board was required.
Data analysis
The statistical analysis was performed using the Stata/SE (version 12.0, Stata Corp, USA) software. We built a 2 × 2 contingency table for each study consisting of true positive, false positive, false negative, and true negative based on accordance between live birth and AMH levels. Using the random effects model or fixed effects model with metan command, the pooled estimate for live birth among the participants with AMH below and above a cut-off point was calculated. A summary estimate of diagnostic odds ratio (DOR) and 95% confidence intervals (CI) were generated. The DOR compiles the diagnostic accuracy of the AMH tests. It elucidates the odds of AMH above a particular cut-off value among women with live births regarding the odds of AMH below the cut-off value among women without live births. Heterogeneity between studies was measured by I-squared. High heterogeneity was delineated when I-squared was greater than 50% and should be appropriately treated by finding its source. Given that ethnicities may result in variations between studies [13], we further excluded studies referring to the Chinese population to minimize heterogeneity. Given that PCOS related to AMH levels [14], we perform analyses according to whether studies had excluded women with PCOS.
To further explore the predictive capability of AMH on live birth in different populations, studies were categorized into those with unspecified ovarian reserve and those with diminished ovarian reserve. “Unspecified ovarian reserve” stands that the ovarian reserve of subjects is not stated clearly. In addition, using the midas command, a summary receiver operating characteristic curve, sensitivity, specificity, positive and negative likelihood ratio were generated by fitting a two-level mixed logistic regression model restricted to sensitivity and specificity of each study and a bivariate normal model for the logit transforms of sensitivity and specificity between studies. A hierarchical model was used to estimate the characteristics of the receiver operating characteristics (ROC) curve and DOR. The hierarchical summary receiver operating characteristics (HSROC) and study-specific estimates, with a no-discrimination line, were plotted. If the 95% prediction region reached the line of no discrimination, the predictive accuracy of AMH on live birth was considered none. For stratified analysis, we built a new 2 × 2 contingency table in which live birth results were categorized by AMH levels and age. Thus, studies with strict participants’ age categorization or limited to advanced age were included in the stratified analysis. Similarly, random effects model, hierarchical model and HSROC were used in the stratified analysis. No specific cut-off value of age was used. Using the macro “METADAS” in SAS 9.4, we further used the hierarchical model to estimate the statistical independence of AMH by observe the relative diagnostic odds ratio (RDOR) after adjusting for age and AMH assay. Median or mean age of each study was used and treated as continuous variable. DSL and GEN II assay was set as dummy variables. If 95% CI of RDOR included 1, it indicated that DOR of AMH was statistically independent of the adjusted covariate.
Results
Search results
The systematic search retrieved a total of 880 articles through PubMed, Embase, and Web of Science. After title and abstract screening, 104 articles were selected and 39 were included in the systematic review (Fig. 1). Five of these were excluded from the meta-analysis as extraction of relevant data was not possible even after contacting the authors [10, 15,16,17,18]. One study [19] was excluded because the data were included in another study [20], which contributed to the meta-analysis. Two articles were excluded as the participants had not undergone IVF/ICSI cycles [21, 22]. Two studies were excluded from the meta-analysis as their original data had been obtained from SART CORS database and may had repetition with other included studies [23, 24]. Two studies were excluded due to lack of cut-off value of AMH levels [25, 26]. One of the studies was in French [27]. Two studies had enrolled women with advanced age only [28, 29]. Four studies excluded women with polycystic ovary syndrome (PCOS) [30,31,32,33]. Five studies had participants categorized not only by their serum AMH level but also by age groups [33,34,35,36,37], which allowed us to extract original data stratified by age for further analysis. Finally, 27 studies were included in the quantitative meta-analysis. The characteristics of the studies included in the meta-analysis are listed in Table 1.
Accuracy of AMH in prediction of live birth
We presented data on 27,911 cycles (27,029 women) undergoing IVF or ICSI. First of all, the univariate pooled DOR of all 27 studies for AMH predicting a live birth was 2.14 (95% CI: 1.85–2.48) (Fig. 2). The estimated I-squared was 73.0%, suggesting high heterogeneity between the studies. To reduce the heterogeneity, we first categorize studies by ovarian reserve. The studies were categorized into women with diminished ovarian reserve (n = 2981) and those with women with unspecified ovarian reserve (n = 24,048). The pooled DOR among women with unspecified ovarian reserve was 2.15 (95% CI 1.88–2.45) (Fig. 3a). The estimated I-squared was 62.3%. The pooled DOR for women with expected low ovarian reserve was 2.45 (95% CI 1.19–5.02). The estimated I-squared was 84.2% (Fig. 3b). The subgroup analysis by ovarian reserve did not lower the heterogeneity between studies. Pooled DOR of studies which had not excluded women with PCOS was 2.29 (95% CI 2.05, 2.58) (I-squared 37.5%) (Supplementary data, Fig. S1), while pooled DOR of studies which had excluded women with PCOS was 1.52 (95% CI: 0.91, 2.52) (I-squared 90.0%) (Supplementary data, Fig. S2).
Conducted through a hierarchical logistic regression model, the overall DOR was 2.19 (95% CI 1.85–2.58). For women with unspecified ovarian reserve, the DOR was 2.21 (95% CI 1.90–2.56). For women with low ovarian reserve, the DOR was 2.49 (95% CI 1.26–4.90). The DOR of studies which had not excluded women with PCOS was 2.28 (95% CI 1.97, 2.65), while pooled DOR of studies which had excluded women with PCOS was 1.49 (95% CI 0.93, 2.39).
The hierarchical summary receiver operating characteristics (HSROC) were plotted to predict live birth with respect to ovarian reserve (Fig. 4a, b). For all studies, the summary receiver operating characteristics did not cross the no-discrimination line while the 95% CIs were on the margin. The summary estimates of overall 27 studies for AMH and live birth were sensitivity of 78.1% (95% CI 70.4–84.3%) and specificity of 38.0% (95% CI 30.3–46.3%). The AUC was 0.61 (95% CI 0.56–0.65). The summary estimates of 21 studies of those with unspecified ovarian reserve were sensitivity of 82.6% (95% CI 76.5–87.4%) and specificity of 31.7% (95% CI 25.7–38.4%). The AUC was 0.59 (95% CI 0.54–0.63). The summary estimates of 6 studies of those with low ovarian reserve were sensitivity of 60.5% (95% CI 35.0–81.3%) and specificity of 61.9% (95% CI 43.9–77.1%). The AUC was 0.65 (95% CI 0.61–0.69). The summary estimates of studies which had not excluded women with PCOS were sensitivity of 80.1% (95% CI 73.0–85.8%) and specificity of 36.1% (95% CI 28.0–45.1%). The AUC was 0.63 (95% CI 0.59–0.67) and the confidence region did not cross the no-discrimination line. The summary estimates of studies which had excluded women with PCOS were sensitivity of 60.5% (95% CI 37.1–79.9%) and specificity of 49.4% (95% CI 32.8–66.1%). The AUC was 0.55 (95% CI 0.51–0.60), but the confidence region crossed the no-discrimination line (Supplementary data, Fig. S3). After adjusting for age, DSL assay and GEN II assay, the RDOR was 0.97 (95% CI 0.67, 1.40), 0.76 (95% CI 0.52, 1.10) and 1.26 (95% CI: 0.91, 1.75), respectively, indicating statistical independence of AMH from age and AMH assay used.
Modulating effect of age
Eleven studies (Table 2) were selected to perform stratified analysis and were categorized into those with advanced age (n = 4479 women) and those with younger age (n = 11,087 women). The univariate pooled DOR of studies with advanced ages for AMH predicting a live birth was 2.15 (95% CI 1.47–3.15) (Fig. 3c). The estimated I-squared was 58.1%, suggesting a moderate heterogeneity. The pooled DOR of studies with younger ages was 1.97 (95% CI 1.51–2.58) (Fig. 3d). The estimated I-squared was 47.3%, suggesting a moderate heterogeneity.
Conducted through a hierarchical logistic regression model, the DOR of those with advanced ages was 2.25 (95% CI 1.62–3.12). For those with younger ages, the DOR was 1.41 (95% CI 0.99–2.02).
The hierarchical summary receiver operating characteristics (HSROC) were also plotted to predict live birth with respect to age groups (Fig. 4c, d). The summary estimates of studies with advanced ages for AMH and live birth were sensitivity of 77.1% (95% CI 62.0–87.4%) and specificity of 40.0% (95% CI 25.4–56.6%). The AUC was 0.63 (95% CI 0.59–0.67). The summary estimates of studies with younger ages for AMH and live birth were sensitivity of 89.7% (95% CI: 83.5–93.6%) and specificity of 14.0% (95% CI 6.7–26.6%). The AUC was 0.63 (95% CI 0.58–0.67).
Heterogeneity resulting from human races
Differences in serum AMH level may be present between Chinese women and Caucasian women [13]. Therefore, to furthermore lower the heterogeneity, we removed the possible source of heterogeneity resulting from human races. Therefore, five cohorts based on Chinese participants were excluded, which is a subset of studies regarding women with unspecified ovarian reserve. The pooled DOR among women with unspecified ovarian reserve was 2.15 (95% CI 1.94–2.38) (I-squared 27.8%). However, the pooled DOR for selected five cohorts was 1.90 (95% CI 1.35–2.68) (I-squared 87.1%). The pooled DOR among women with advanced ages was 2.08 (95% CI 1.22–3.53) (I-squared 42.0%). The pooled DOR among women with younger ages was 1.69 (95% CI 1.02–2.81) (I-squared 51.0%) (Supplementary data, Fig. S4).
Study quality assessment and publication bias
The quality assessment of selected 27 studies is represented as percentage of high, low or unclear bias in each domain assessed by the QUADAS–2 tool (Supplementary data, Fig. S5). Most studies reported live birth per transfer cycle start or per patient [27, 29,30,31,32,33,34,35,36, 38,39,40,41,42,43,44,45,46,47,48,49,50], and one study reported live birth per ovum retrieval [28] and three reported the cumulative live birth rate[20, 37, 51]. The majority of the studies measured AMH using the Beckman Coulter Generation II assay (GenII) assay [20, 29,30,31,32,33, 36, 45, 50, 51], nine studies used the Diagnostic System Laboratories (DSL) assay [28, 34, 38,39,40,41, 43, 44, 52], three studies used the Immunotech–Beckman Coulter (IBC) assay [27, 42, 46], two studies used the Roche kit [47, 48] and one study used the Ansh Lab ELISA kit [37]. Selection bias was present in the majority of the studies. Six studies included only women with low ovarian reserve recognized by advanced age or high FSH or low AMH [28,29,30, 33, 40, 44]. Seven studies excluded women with polycystic ovary syndrome [27, 30,31,32,33, 39, 42], one study excluded couples with severe male factor infertility [42]. Publication bias was assessed using the funnel plot (Supplementary data, Fig. S6). The funnel plot for live birth suggests asymmetry, revealing that studies with smaller sample size or results lacking statistical significance are required. However, the statistical test for publication bias did not reach statistical significance (p = 0.118).
Discussion
This systematic review and meta-analysis of 27 studies (27,029 women) summarized current evidence regarding the predictive ability of AMH for live birth among women undergoing IVF or ICSI. It suggested that AMH had some association with live birth but the predictive ability is weak. High heterogeneity ruled out the reliability of results from random effect model. From hierarchical model, the pooled DOR among 24,048 women with unspecified ovarian reserve was 2.21, whereas the AUC was 0.59. Among 2981 women with diminished ovarian reserve, AMH had the better but still small predictive ability with the DOR of 2.49, whereas the AUC was 0.65. The HSROC model and 95% CIs of the pooled data concerning those with unspecified ovarian reserve did not cross the line of no discrimination, indicating that AMH has some value in predicting live birth among women with unspecified ovarian reserve. In addition, the 95% prediction region, which suggests the confidence region for a forecast of the true specificity and sensitivity in a future study, did not cross the line of no-discrimination either. It indicates that a future predictive value of AMH will be located restricted to the prediction region. Nonetheless, among women with diminished ovarian reserve, the HSROC model and 95% CIs of the pooled data, along with the prediction region, crossed the no-discrimination line, suggesting that AMH was not a suitable predictor for live birth in women with diminished ovarian reserve. From the analysis where age was categorized, the pooled DOR among 5082 women with advanced age was 2.24, whereas the AUC was 0.62. The pooled DOR among 11,087 women with younger age was 1.40, whereas the AUC was 0.53. The HSROC model and 95% CIs of the pooled data of studies with advanced age did not cross a no-discrimination line, suggesting that AMH has some value in predicting live birth among advanced–age women. For women with younger age, the HSROC model and 95% CIs of the pooled data had an intersection with the no-discrimination line, indicating that AMH has no role in predicting live birth among younger–age women. To lower the heterogeneity between studies, five Chinese cohort studies were selected out of pooling, which did not cause dramatic changes in DOR but in the I-squared of random effects model. It suggests that race could be the possible source of heterogeneity between studies. Women with PCOS were excluded in four studies with different study designs. Pooled estimates from excluding studies without PCOS patients was not materially changed compared to the DOR obtained from overall 27 studies. The heterogeneity lowered from 73.0% to 37.5%, indicating studies ruling out PCOS may be a source of heterogeneity. In our results, AMH predicts better in women with advanced age than those with younger age, while prevalence of PCOS is higher in younger women [14], indicating not ruling out PCOS may not confound our results concerning women with advanced age. The DOR from four studies having had excluded women with PCOS was not statistically significant, with high heterogeneity, suggesting the estimate was not reliable and should not be interpreted. More studies concerning women without PCOS are warranted to elucidate the predictive ability of AMH on live birth.
The predictive value of AMH for ART outcomes has been studied in recent years. AMH tends to have a weak predictive ability towards implantation and clinical pregnancy [9]. Consistent with our results restricting to women with unspecified ovarian reserve, a meta-analysis demonstrated that AMH had small predictive effect on live birth [8]. On the contrary, the predictive effect of AMH on women with low ovarian reserve was invalid based on existing researches, hence the results of the meta-analysis in 2014 cannot be substantiated. The discordance between categorization of the ovarian reserve may result from oocyte quality. While studies showed the independent value of AMH for live birth in patients with low ovarian reserve [15, 40, 42, 45, 52, 53], Pereira et al. showed that AMH was not associated with live birth rates in patients aged under 35 years but with diminished ovarian reserve [30]. These findings along with our results would suggest that the predictive effect of AMH for live birth not only focuses on ovarian reserve, but also on oocyte quality, which both decline with age [54]. However, several studies did not find an association between serum AMH and oocyte or embryo quality [49, 51, 55,56,57,58,59,60,61,62,63,64,65,66,67], while others found a positive association [16, 41, 43, 68,69,70,71,72,73,74,75]. An animal study demonstrated that AMHR II is expressed in both oocytes and cumulus cells and supplementation of 100 ng/ml of rh–AMH into IVM medium together with FSH and EGF improves oocyte quality [76]. Two studies reported a positive association between follicular fluid of AMH and oocyte or embryo quality [58, 77]. Taken together, serum AMH may not strongly associate with oocyte quality, but oocyte quality was positively associated with AMH in culture environing oocytes.
The predictive ability of AMH for live birth was found to be modified by age in the current analysis. AMH had better predictive ability for live birth in women with advanced age. However, the effect of age on the association between AMH and live birth remained contradictory. Several studies demonstrated that age and AMH are independently associated with live birth [4, 38, 40,41,42, 78]. Goswami et al. found that AMH level better predicts live birth following IVF in older women and has limited predictive value in women aged below 35 years, which was consistent with our results [79]. Wang et al. found a positive relationship between serum AMH levels and IVF pregnancy outcomes and the association was modulated by age [10]. Animal studies showed that AMH remained constant in young mice despite growing age as well as declining primordial follicles, while AMH reflects the reserve of primordial follicles in elder mice. Meanwhile, AMH is always associated with growing follicles at all ages [80]. Looking at AMH at all ages in humans, AMH rose to maximum by 15.8 years of age and then remained stable until 25 years of age where it started to decline [81]. Taken together, AMH and its association with live birth are not stable among all ages. The age modulating effect on the association between AMH and live birth may suggest that the extent of oocyte quality decline can be partially compensated by utilizing the excessive ovarian reserve.
In our analysis, the human race was found to be a source of heterogeneity in the pooled univariate analysis, which had been underestimated in previous meta-analyses [8, 9]. Heterogeneity substantially decreased after the removal of Chinese cohorts in univariate analyses, especially in women with unspecified ovarian reserve, and in women with advanced ages but not in women with younger ages. It indicated that the association between AMH and live birth in Chinese women may differ from those in western countries, or say, Caucasian women. Chinese healthy women initially showed higher AMH levels than those in European women but tended to have significantly lower AMH concentrations than those in European women after age 25 [82]. Caucasian women had consistently higher AMH levels than all other ethnic groups until age 35 [83]. These findings may suggest that Chinese women have a higher decreasing rate of AMH levels compared to Caucasian women, which substantiated our results. These findings suggest that racial differences can contribute to heterogeneity among studies on AMH. The predictive power of AMH for live births may also vary by race and should be noted in future studies.
Implications for clinical practice
The clinical application of the present findings is that AMH among different groups of women provides additional information for advanced–age couples considering assisted reproduction. However, the diagnostic accuracy towards live birth remains poor and cannot be treated as a diagnostic test for live birth. In addition, clinicians should note in mind that higher AMH levels in advanced-age women links to live birth more compared to younger women. Although age is a strong denominator in assisted reproduction, the effect of race should not be neglected when judging patient’s possible outcomes with their AMH levels. Sensitivity, specificity, AUC and DOR and cut-off value range from Table 3 could be references for clinical practice according to the subgroup population. However, an optimal cut-off value of AMH level is impossible to calculate in our meta-analysis due to lack of individual patient data.
Implications for future researches
Previous meta-analysis found AMH predicting live birth is independent of age [8], while we found its predictive ability appeared better in women with advanced ages. This suggests that future prediction models should carefully include AMH as an alternative covariate, considering the effect of age. Among advanced-age women, we observed higher prediction ability of AMH on live birth. We speculated that AMH may also limit higher miscarriage rate in elder patients. To obtain the predictive ability of AMH on miscarriage, literature on AMH and miscarriage must be reviewed. In addition, human race already showed significant heterogeneity between studies in the current analysis, which should be treated with caution as a confounder in future studies on AMH. Inclusion of AMH may improve the predictive effects of existing models. However, critical calibration and adjustment of age and race, will increase the reliability and validity in prediction of live birth.
Strengths and limitations
This is an updating meta-analysis exhibiting pooled data of numerous cycles to assess the predictive ability of serum AMH in live birth after IVF/ICSI, meanwhile focusing on the possible modification effect caused by age categorization and race. The strengths of this review lie in sufficient literature searching, in compliance with recent guidelines [84], using robust statistical analysis without language restriction. Although the process of systematic literature review and meta-analysis is a robust way of generating a more powerful estimate of true effect size with less random error than individual studies, it does come with limitations, bias, and heterogeneity resulting from original studies. First, the heterogeneity between studies needs to be addressed as it may affect the justification for pooling the data into one analysis. In the case of the present meta-analysis, heterogeneity may have been from stimulation protocols, AMH threshold, AMH assay, and other baseline characteristics. For the stratified analysis concerning age categorization, heterogeneity may have been from age threshold. The statistical estimation of heterogeneity was high in the random effects model, which could not be interpreted directly. Removal of Chinese cohorts, and studies ruling out PCOS patients lowered the heterogeneity to an acceptable level. In addition, HSROC analysis includes a thorough range of variation in the data, diversity within study from between–study variability and systematic variation from random variability [85]. Secondly, introduced bias may generate from literatures having not been included. We excluded studies due to not extractable data even after contacting the authors [10, 15,16,17,18], and due to data from subgroups from other studies [20, 39]. The largest three of these studies showed positive association between AMH and live birth (n = 1558 [10]; n = 1152 [20]; n = 609 [18]), while the others with small sample size showed null association (n = 213 [39]; n = 128 [15]; n = 83 [16]; n = 192 [17]). In addition, the asymmetry in the funnel plot for this meta-analysis showed small studies with negative or not statistically significant results may be missing. Studies with smaller sample size are generally conducted with less methodological rigor. Statistical error from sample size is estimated by re-parameterization of the asymptotic estimator. Egger’s test was used to assess the statistical significance of sample size effect. As a result, the test was not statistically significant. Another limitation was the use of different cut-off values for AMH among studies. However, a single threshold should not be used due to different clinical characteristics such as AMH assay and race. The studies included in the meta-analysis reported AMH according to the DSL, GEN II, IBC, Roche, or Ansh Lab kit assays. As the DSL and IBC assays do not give comparable values due to different pairs of monoclonal antibodies, the conversion of formula of the DSL assay data into IBC values of 2.02*DSL = IBC has been used for data aggregation studies [86]. The AMH value measured with GEN II assay has been shown to be significantly lower compared with the DSL assay [87]. A good correlation was found between the Roche AMH and Gen II ELISA methods for the entire measuring range [88]. The Ansh Lab kit assay was found to have similar performance characteristics to the GEN II assay [89, 90]. However, we adjusted the AMH assays in the hierarchical model and the result was not materially changed. Consistent with our result, previous meta-analysis showed slight change in DOR for both women of unspecified ovarian reserve and all women after adjustment for AMH assay [8]. This may suggest the prediction value of AMH of live birth is irrespective of the assays used. The included studies measured serum AMH at different time points, which may confound the results. However, AMH has been proven to be stable throughout menstrual cycles [86, 91, 92]. We cannot obtain an optimal AMH cut-off value due to lack of individual patient data. Pooled analysis with individual data of studies are needed to obtain an optimal cut-off value.
Conclusion
Based on the current evidence, we found that AMH had limited value in predicting live birth, however, with the modification effect of age, and under the influence of race. Despite the 95% CIs and prediction 95% CIs not crossing the no-discrimination line, the predictive ability was still limited and should not be overestimated. This study did not aim to seek an applicable threshold to determine the possible live birth outcome based on AMH among women undergoing assisted conception but to provide evidence for future researches. Thus, AMH may have some clinical value in counseling women undergoing fertility treatment regarding their live birth outcome, particularly for those with advanced age.
References
Seifer DB, Maclaughlin DT (2007) Mullerian Inhibiting Substance Is An Ovarian Growth Factor Of Emerging Clinical Significance. Fertil Steril 88(3):539–546
Nelson SM, Anderson RA, Broekmans FJ et al (2012) Anti-Müllerian hormone: clairvoyance or crystal clear? Hum Reprod (Oxf, Engl) 27(3):631–636
Muttukrishna S, Suharjono H, McGarrigle H et al (2004) Inhibin B and anti-Mullerian hormone: markers of ovarian response in IVF/ICSI patients? BJOG 111(11):1248–1253
La Marca A, Sighinolfi G, Radi D et al (2010) Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART). Hum Reprod Update 16(2):113–130
Muttukrishna S, McGarrigle H, Wakim R et al (2005) Antral follicle count, anti-mullerian hormone and inhibin B: predictors of ovarian response in assisted reproductive technology? BJOG 112(10):1384–1390
Rajpert-De Meyts E, Jørgensen N, Graem N et al (1999) Expression of anti-Müllerian hormone during normal and pathological gonadal development: association with differentiation of Sertoli and granulosa cells. J Clin Endocrinol Metab 84(10):3836–3844
Pellatt L, Rice S, Mason HD (2010) Anti-Müllerian hormone and polycystic ovary syndrome: a mountain too high? Reproduction 139(5):825–833
Iliodromiti S, Kelsey TW, Wu O et al (2014) The predictive accuracy of anti-Müllerian hormone for live birth after assisted conception: a systematic review and meta-analysis of the literature. Hum Reprod Update 20(4):560–570
Tal R, Tal O, Seifer BJ et al (2015) Antimüllerian hormone as predictor of implantation and clinical pregnancy after assisted conception: a systematic review and meta-analysis. Fertil Steril 103(1):119–30.e3
Wang JG, Douglas NC, Nakhuda GS et al (2010) The association between anti-Müllerian hormone and IVF pregnancy outcomes is influenced by age. Reprod Biomed Online 21(6):757–761
Liberati A, Altman DG, Tetzlaff J et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339:b2700
Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536
Kotlyar AM, Seifer DB (2020) Ethnicity/race and age-specific variations of serum AMH in women-a review. Front Endocrinol 11:593216
Liang SJ, Hsu CS, Tzeng CR et al (2011) Clinical and biochemical presentation of polycystic ovary syndrome in women between the ages of 20 and 40. Hum Reprod (Oxf, Engl) 26(12):3443–3449
Weghofer A, Dietrich W, Barad DH et al (2011) Live birth chances in women with extremely low-serum anti-Mullerian hormone levels. Hum Reprod (Oxf, Engl) 26(7):1905–1909
Lin WQ, Yao LN, Zhang DX et al (2013) The predictive value of anti-Mullerian hormone on embryo quality, blastocyst development, and pregnancy rate following in vitro fertilization-embryo transfer (IVF-ET). J Assist Reprod Genet 30(5):649–655
Mutlu MF, Erdem M, Erdem A et al (2013) Antral follicle count determines poor ovarian response better than anti-Müllerian hormone but age is the only predictor for live birth in in vitro fertilization cycles. J Assist Reprod Genet 30(5):657–665
Lukaszuk K, Liss J, Kunicki M et al (2014) Anti-Müllerian hormone (AMH) is a strong predictor of live birth in women undergoing assisted reproductive technology. Reprod Biol 14(3):176–181
Li L, Chenette P (2013) Predictors of mature oocytes in non-infertile women undergoing fertility preservation. Fertil Steril 99(3 SUPPL. 1):S29
Li HW, Lee VC, Lau EY et al (2014) Ovarian response and cumulative live birth rate of women undergoing in-vitro fertilisation who had discordant anti-Mullerian hormone and antral follicle count measurements: a retrospective study. PLoS ONE 9(10):e108493
Stochino-Loi E, Darwish B, Mircea O et al (2017) Does preoperative antimüllerian hormone level influence postoperative pregnancy rate in women undergoing surgery for severe endometriosis? Fertil Steril 107(3):707–13.e3
McCormack CD, Leemaqz SY, Furness DL et al (2019) Anti-Müllerian hormone levels in recurrent embryonic miscarriage patients are frequently abnormal, and may affect pregnancy outcomes. J Obstet Gynaecol 39(5):623–627
Tal R, Seifer DB, Wantman E et al (2018) Antimüllerian hormone as a predictor of live birth following assisted reproduction: an analysis of 85,062 fresh and thawed cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System database for 2012–2013. Fertil Steril 109(2):258–265
Ligon S, Lustik M, Levy G et al (2019) Low Antimüllerian hormone (AMH) is associated with decreased live birth after in vitro fertilization when follicle-stimulating hormone and AMH are discordant. Fertil Steril 112(1):73-81.e1
Hamdine O, Eijkemans MJC, Lentjes EGW et al (2015) Antimüllerian hormone: prediction of cumulative live birth in gonadotropin-releasing hormone antagonist treatment for in vitro fertilization. Fertil Steril 104(4):891–8.e2
Morin SJ, Patounakis G, Juneau CR et al (2018) Diminished ovarian reserve and poor response to stimulation in patients <38 years old: a quantitative but not qualitative reduction in performance. Hum Reprod (Oxf, Engl) 33(8):1489–1498
Grzegorczyk-Martin V, Khrouf M, Bringer-Deutsch S et al (2012) Low circulating anti-Müllerian hormone and normal follicle stimulating hormone levels: which prognosis in an IVF program? Gynecol Obstet Fertil 40(7–8):411–418
Fridén B, Sjöblom P, Menezes J (2011) Using anti-Müllerian hormone to identify a good prognosis group in women of advanced reproductive age. Aust N Z J Obstet Gynaecol 51(5):411–415
Lee Y, Kim TH, Park JK et al (2018) Predictive value of antral follicle count and serum anti-Müllerian hormone: Which is better for live birth prediction in patients aged over 40 with their first IVF treatment? Eur J Obstet Gynecol Reprod Biol 221:151–155
Pereira N, Setton R, Petrini AC et al (2016) Is anti-Müllerian hormone associated with IVF outcomes in young patients with diminished ovarian reserve? Womens Health (Lond) 12(2):185–192
Zheng H, Chen S, Du H et al (2017) Ovarian response prediction in controlled ovarian stimulation for IVF using anti-Müllerian hormone in Chinese women: a retrospective cohort study. Medicine (Baltimore) 96(13):e6495
Li XL, Huang R, Fang C et al (2018) Basal serum Anti-Müllerian Hormone level as a predictor of clinical outcomes in freezing-all embryo transfer program. Curr Med Sci 38(5):861–867
Preaubert L, Shaulov T, Phillips S et al (2019) Live birth rates remain stable in modified natural IVF despite low anti-Müllerian hormone: analysis of 638 cycles. Reprod Biomed Online 39(3):461–466
Khader A, Lloyd SM, McConnachie A et al (2013) External validation of anti-Müllerian hormone based prediction of live birth in assisted conception. J Ovarian Res 6(1):3
Amsiejiene A, Drasutiene G, Usoniene A et al (2017) The influence of age, body mass index, waist-to-hip ratio and anti-Mullerian hormone level on clinical pregnancy rates in ART. Gynecol Endocrinol 33(sup1):41–43
Tarasconi B, Tadros T, Ayoubi JM et al (2017) Serum Antimüllerian hormone levels are independently related to miscarriage rates after in vitro fertilization-embryo transfer. Fertil Steril 108(3):518–524
Zhang B, Meng Y, Jiang X et al (2019) IVF outcomes of women with discrepancies between age and serum anti-Müllerian hormone levels. Reprod Biol Endocrinol 17(1):58
Nelson SM, Yates RW, Fleming R (2007) Serum anti-Müllerian hormone and FSH: prediction of live birth and extremes of response in stimulated cycles–implications for individualization of therapy. Hum Reprod (Oxf, Engl) 22(9):2414–2421
Lee TH, Liu CH, Huang CC et al (2009) Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles. Reprod Biol Endocrinol 7:100
Gleicher N, Weghofer A, Barad DH (2010) Anti-Müllerian hormone (AMH) defines, independent of age, low versus good live-birth chances in women with severely diminished ovarian reserve. Fertil Steril 94(7):2824–2827
Majumder K, Gelbaya TA, Laing I et al (2010) The use of anti-Müllerian hormone and antral follicle count to predict the potential of oocytes and embryos. Eur J Obstet Gynecol Reprod Biol 150(2):166–170
La Marca A, Nelson SM, Sighinolfi G et al (2011) Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction. Reprod Biomed Online 22(4):341–349
Brodin T, Hadziosmanovic N, Berglund L et al (2013) Antimüllerian hormone levels are strongly associated with live-birth rates after assisted reproduction. J Clin Endocrinol Metab 98(3):1107–1114
Merhi Z, Zapantis A, Berger DS et al (2013) Determining an anti-Mullerian hormone cutoff level to predict clinical pregnancy following in vitro fertilization in women with severely diminished ovarian reserve. J Assist Reprod Genet 30(10):1361–1365
Reijnders IF, Nelen WL, IntHout J et al (2016) The value of Anti-Müllerian hormone in low and extremely low ovarian reserve in relation to live birth after in vitro fertilization. Eur J Obstet Gynecol Reprod Biol 200:45–50
Keane K, Cruzat VF, Wagle S et al (2017) Specific ranges of anti-Mullerian hormone and antral follicle count correlate to provide a prognostic indicator for IVF outcome. Reprod Biol 17(1):51–59
Alson SSE, Bungum LJ, Giwercman A et al (2018) Anti-müllerian hormone levels are associated with live birth rates in ART, but the predictive ability of anti-müllerian hormone is modest. Eur J Obstet Gynecol Reprod Biol 225:199–204
Metello JL, Tomás C, Ferreira P (2019) Can we predict the IVF/ICSI live birth rate? JBRA Assist Reprod 23(4):402–407
Dai X, Wang Y, Yang H et al (2020) AMH has no role in predicting oocyte quality in women with advanced age undergoing IVF/ICSI cycles. Sci Rep 10(1):19750
Peuranpää P, Hautamäki H, Halttunen-Nieminen M et al (2020) Low anti-Müllerian hormone level is not a risk factor for early pregnancy loss in IVF/ICSI treatment. Hum Reprod (Oxf, Engl) 35(3):504–515
Arce JC, La Marca A, Mirner Klein B et al (2013) Antimüllerian hormone in gonadotropin releasing-hormone antagonist cycles: prediction of ovarian response and cumulative treatment outcome in good-prognosis patients. Fertil Steril 99(6):1644–1653
Lukaszuk K, Kunicki M, Liss J et al (2013) Use of ovarian reserve parameters for predicting live births in women undergoing in vitro fertilization. Eur J Obstet Gynecol Reprod Biol 168(2):173–177
Seifer DB, Tal O, Wantman E et al (2016) Prognostic indicators of assisted reproduction technology outcomes of cycles with ultralow serum antimüllerian hormone: a multivariate analysis of over 5,000 autologous cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System database for 2012–2013. Fertil Steril 105(2):385–93.e3
Nybo Andersen AM, Wohlfahrt J, Christens P et al (2000) Maternal age and fetal loss: population based register linkage study. BMJ 320(7251):1708–1712
Smeenk JM, Sweep FC, Zielhuis GA et al (2007) Antimüllerian hormone predicts ovarian responsiveness, but not embryo quality or pregnancy, after in vitro fertilization or intracyoplasmic sperm injection. Fertil Steril 87(1):223–226
Lie Fong S, Baart EB, Martini E et al (2008) Anti-Müllerian hormone: a marker for oocyte quantity, oocyte quality and embryo quality? Reprod Biomed Online 16(5):664–670
Guerif F, Lemseffer M, Couet M et al (2009) Serum antimüllerian hormone is not predictive of oocyte quality in vitro fertilization. Ann Endocrinol (Paris) 70(4):230–234
Mashiach R, Amit A, Hasson J et al (2010) Follicular fluid levels of anti-Mullerian hormone as a predictor of oocyte maturation, fertilization rate, and embryonic development in patients with polycystic ovary syndrome. Fertil Steril 93(7):2299–2302
Riggs R, Kimble T, Oehninger S et al (2011) Anti-Müllerian hormone serum levels predict response to controlled ovarian hyperstimulation but not embryo quality or pregnancy outcome in oocyte donation. Fertil Steril 95(1):410–412
Anckaert E, Smitz J, Schiettecatte J et al (2012) The value of anti-Mullerian hormone measurement in the long GnRH agonist protocol: association with ovarian response and gonadotrophin-dose adjustments. Hum Reprod (Oxf, Engl) 27(6):1829–1839
Kedem-Dickman A, Maman E, Yung Y et al (2012) Anti-Müllerian hormone is highly expressed and secreted from cumulus granulosa cells of stimulated preovulatory immature and atretic oocytes. Reprod Biomed Online 24(5):540–546
Szafarowska M, Molinska-Glura M, Jerzak MM (2014) Anti-Müllerian hormone concentration as a biomarker of pregnancy success or failure. Neuro Endocrinol Lett 35(4):322–326
Aydın GA, Yavuz A, Terzi H et al (2015) Assessment of the relationship of basal serum anti-mullerian hormone levels with oocyte quality and pregnancy outcomes in patients undergoing ICSI. Iran J Reprod Med 13(4):231–236
Scheffer JB, Scheffer BB, de Carvalho RF et al (2017) Age as a predictor of embryo quality regardless of the quantitative ovarian response. Int J Fertil Steril 11(1):40–46
Dević Pavlić S, Tramišak Milaković T, Panić Horvat L et al (2019) Genes for anti-Müllerian hormone and androgen receptor are underexpressed in human cumulus cells surrounding morphologically highly graded oocytes. SAGE Open Med 7:2050312119865137
Pacchiarotti A, Iaconianni P, Caporali S et al (2020) Severe endometriosis: low value of AMH did not affect oocyte quality and pregnancy outcome in IVF patients. Eur Rev Med Pharmacol Sci 24(22):11488–11495
Korkidakis A, Cho KK, Albert A et al (2020) Anti-Müllerian hormone and embryo quality as determined by time-lapse imaging. Minerva Ginecol 72(3):132–137
Ebner T, Sommergruber M, Moser M et al (2006) Basal level of anti-Müllerian hormone is associated with oocyte quality in stimulated cycles. Hum Reprod (Oxf, Engl) 21(8):2022–2026
Irez T, Ocal P, Guralp O et al (2011) Different serum anti-Müllerian hormone concentrations are associated with oocyte quality, embryo development parameters and IVF-ICSI outcomes. Arch Gynecol Obstet 284(5):1295–1301
Lehmann P, Vélez MP, Saumet J et al (2014) Anti-Müllerian hormone (AMH): a reliable biomarker of oocyte quality in IVF. J Assist Reprod Genet 31(4):493–498
Kamel HM, Amin AH, Al-Adawy AR (2014) Basal serum anti-Mullerian hormone (AMH) is a promising test in prediction of occurrence of pregnancy rate in infertile women undergoing ICSI cycles. Clin Lab 60(10):1717–1723
Melado Vidales L, Fernández-Nistal A, Martínez Fernández V et al (2017) Anti-Müllerian hormone levels to predict oocyte maturity and embryo quality during controlled ovarian hyperstimulation. Minerva Ginecol 69(3):225–232
Olszak-Wąsik K, Bednarska-Czerwińska A, Olejek A et al (2019) From “every day” hormonal to oxidative stress biomarkers in blood and follicular fluid, to embryo quality and pregnancy success? Oxid Med Cell Longev 2019:1092415
Karakas Alkan K, Alkan H, Kaymaz M (2020) The effect of anti-müllerian hormone and progesterone concentrations on superovulation response and embryo yield in goats. Theriogenology 143:1–9
Sun TC, Zhou SJ, Song LL et al (2021) High anti-Müllerian hormone levels might not reflect the likelihood of clinical pregnancy rate in IVF/ICSI treatment. JBRA Assist Reprod 25(2):266–271
Zhang Y, Shao L, Xu Y et al (2014) Effect of anti-Mullerian hormone in culture medium on quality of mouse oocytes matured in vitro. PLoS ONE 9(6):e99393
Kim JH, Lee JR, Chang HJ et al (2014) Anti-Müllerian hormone levels in the follicular fluid of the preovulatory follicle: a predictor for oocyte fertilization and quality of embryo. J Korean Med Sci 29(9):1266–1270
La Marca A, Sighinolfi G, Giulini S et al (2010) Normal serum concentrations of anti-Müllerian hormone in women with regular menstrual cycles. Reprod Biomed Online 21(4):463–469
Goswami M, Nikolaou D (2017) Is AMH level, independent of age, a predictor of live birth in IVF? J Hum Reprod Sci 10(1):24–30
Kevenaar ME, Meerasahib MF, Kramer P et al (2006) Serum anti-mullerian hormone levels reflect the size of the primordial follicle pool in mice. Endocrinology 147(7):3228–3234
Lie Fong S, Visser JA, Welt CK et al (2012) Serum Anti-Müllerian hormone levels in healthy females: a nomogram ranging from infancy to adulthood. J Clin Endocrinol Metab 97(12):4650–4655
Nelson SM, Aijun S, Ling Q et al (2020) Ethnic discordance in serum anti-Müllerian hormone in healthy women: a population study from China and Europe. Reprod Biomed Online 40(3):461–467
Bleil ME, Gregorich SE, Adler NE et al (2014) Race/ethnic disparities in reproductive age: an examination of ovarian reserve estimates across four race/ethnic groups of healthy, regularly cycling women. Fertil Steril 101(1):199–207
Cochrane (2013) Cochrane Handbook for DTA reviews.
Gatsonis C, Paliwal P (2006) Meta-analysis of diagnostic and screening test accuracy evaluations: methodologic primer. AJR Am J Roentgenol 187(2):271–281
Hehenkamp WJ, Looman CW, Themmen AP et al (2006) Anti-Müllerian hormone levels in the spontaneous menstrual cycle do not show substantial fluctuation. J Clin Endocrinol Metab 91(10):4057–4063
Nelson SM, Iliodromiti S, Fleming R et al (2014) Reference range for the antimüllerian hormone Generation II assay: a population study of 10,984 women, with comparison to the established Diagnostics Systems Laboratory nomogram. Fertil Steril 101(2):523–529
Hyldgaard J, Bor P, Ingerslev HJ et al (2015) Comparison of two different methods for measuring anti-mullerian hormone in a clinical series. Reprod Biol Endocrinol 13:107
Welsh P, Smith K, Nelson SM (2014) A single-centre evaluation of two new anti-Mullerian hormone assays and comparison with the current clinical standard assay. Hum Reprod (Oxf, Engl) 29(5):1035–1041
Gruson D, Homsak E (2015) Measurement of anti-Mullerian hormone: performances of a new ultrasensitive immunoassay. Clin Biochem 48(6):453–455
La Marca A, Stabile G, Artenisio AC et al (2006) Serum anti-Mullerian hormone throughout the human menstrual cycle. Hum Reprod (Oxf, Engl) 21(12):3103–3107
Tsepelidis S, Devreker F, Demeestere I et al (2007) Stable serum levels of anti-Müllerian hormone during the menstrual cycle: a prospective study in normo-ovulatory women. Hum Reprod (Oxf, Engl) 22(7):1837–1840
Funding
No funding was received for this study.
Author information
Authors and Affiliations
Contributions
N.J.L. (M.D., Obstetrics and Gynecology, Reproductive medicine): study design, literature searching, data collection, data analysis, manuscript writing. Q.Y.Y. (M.M., Obstetrics and Gynecology, Reproductive medicine): literature searching, data collection, data analysis, manuscript writing. X.Q.Y. (Ph.D., Obstetrics and Gynecology, Reproductive medicine): methodology reviewing, draft revision. Y.H. (M.D., Obstetrics and Gynecology, Reproductive medicine): data analysis, methodology reviewing. Y.F.L. (M.D., Prof., Obstetrics and Gynecology, Reproductive medicine): study design, draft revision.
Corresponding author
Ethics declarations
Conflict of interest
All the authors declare no conflict of interest.
Ethical approval and informed consent
No patient consent or ethical approval was required because analyses were based on previous published studies.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Li, Nj., Yao, Qy., Yuan, Xq. et al. Anti–müllerian hormone as a predictor for live birth among women undergoing IVF/ICSI in different age groups: an update of systematic review and meta-analysis. Arch Gynecol Obstet 308, 43–61 (2023). https://doi.org/10.1007/s00404-022-06683-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00404-022-06683-1