Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis

Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis View ORCID Profile Patrícia Jorge Schwenck de Carvalho , View ORCID Profile Giovana De Nardo Maffazioli , View ORCID Profile Ricardo Santos Simões , View ORCID Profile Renata Ferri Macchione , View ORCID Profile Sebastião Freitas de Medeiros , View ORCID Profile José Maria Soares Jr , View ORCID Profile Edmund Chada Baracat , View ORCID Profile Gustavo Arantes Rosa Maciel doi: https://doi.org/10.1101/2025.01.25.25321110 Patrícia Jorge Schwenck de Carvalho ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrícia Jorge Schwenck de Carvalho For correspondence: patriciajschwenck{at}gmail.com Giovana De Nardo Maffazioli ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Giovana De Nardo Maffazioli Ricardo Santos Simões ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ricardo Santos Simões Renata Ferri Macchione ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Renata Ferri Macchione Sebastião Freitas de Medeiros ² Department of Gynecology and Obstetrics, Medical School, Federal University of Mato Grosso , 78055-728 Cuiaba, MT, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sebastião Freitas de Medeiros José Maria Soares Jr ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for José Maria Soares Jr Edmund Chada Baracat ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edmund Chada Baracat Gustavo Arantes Rosa Maciel ¹ Disciplina de Ginecologia, Departamento de Obstetrícia E Ginecologia, Faculdade de Medicina de São Paulo, Hospital das Clínicas , São Paulo, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gustavo Arantes Rosa Maciel Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Structured abstract Objective This study aims to evaluate antimüllerian hormone (AMH) levels across Polycystic Ovary Syndrome (PCOS) phenotypes (A, B, C, D) based on the Rotterdam diagnostic criteria. Data Sources A systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed, Embase, ScienceDirect, and Web of Science were searched for studies published between January 2009 and June 2024. Study Eligibility Criteria Studies reporting AMH levels stratified by PCOS phenotypes based on the Rotterdam criteria, along with population characteristics and AMH measurement methods, were included. Animal studies, reviews, case reports, opinion articles, letters to the editor, other non-original research. and those lacking complete phenotype stratification were excluded. Study Appraisal and Synthesis Methods Study quality was assessed using the ROBINS-E tool. Primary outcomes included mean AMH levels across phenotypes, analyzed as tAMH (without assay differentiation) and bcAMH (Beckman Coulter Gen II assay). Secondary outcomes included age and BMI by phenotype. Random-effects meta-analysis was used to calculate mean levels and standardized mean differences (SMD) with 95% confidence intervals (CI). Heterogeneity was assessed using the I² statistic. Results Forty-nine studies involving 15,535 subjects revealed that AMH levels vary significantly by phenotype, being highest in A, followed by D, C, and B. For phenotype A: tAMH=9.87 ng/mL (SMD 2.97, CI 9.29–10.48); bcAMH=11.49 ng/mL (SMD 3.06, CI 10.47–12.61). For phenotype B: tAMH=5.71 ng/mL (SMD 0.98, CI 4.96–6.57); bcAMH=6.25 ng/mL (SMD 1.36, CI 5.20–7.51). There were no significant differences in age or BMI among phenotypes. High heterogeneity suggests additional factors influence AMH levels beyond phenotypic categories. Conclusions AMH levels are elevated in PCOS and vary across phenotypes, underscoring their potential to improve PCOS subtype characterization and guide management strategies. The findings highlight the need for standardizing AMH measurement techniques for consistent clinical application. Introduction Polycystic ovary syndrome (PCOS) is a complex pathological condition characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries that affects women of reproductive age, with life-long impacts from adolescence to post-menopause [ 1 ]. It is a disorder, with an incidence varying from 4% to 10% of women in their reproductive years [ 2 ]. Different consensus of diagnostic criteria for PCOS were designed but the most widely accepted is the Rotterdam criteria (2003), which was updated by the latest international PCOS guidelines in 2023 [ 1 ]. According to the combination of Rotterdam criteria used for the diagnosis of PCOS, it can be classified into phenotypic presentations: phenotype A (PCOS-A) - hyperandrogenism (HA), oligo-amenorrhea, and polycystic ovarian morphology (PCOM) on ultrasound (US); phenotype B (PCOS-B) - HA and oligo-amenorrhea; phenotype C (PCOS-C) - HA and PCOM; phenotype D (PCOS-D) - oligo-amenorrhea and PCOM [ 3 ]. Although the Rotterdam diagnostic criteria are widely accepted, some points are still matters of controversy: the assessment of clinical and laboratory parameters of hyperandrogenism and hyperandrogenemia, the definition of oligo-amenorrhea throughout the reproductive age, the criteria to define the polycystic appearance of the ovaries on the ultrasound, the role of anti-müllerian hormone (AMH) in the diagnosis and phenotypes, among others, make its management, a clinical challenge [ 4 ]. There is a trend in the search for objective diagnostic criteria that encourages research into biomarkers in PCOS. A promising diagnostic parameter is the anti-Müllerian hormone (AMH), a dimeric glycoprotein that plays an important role in sexual differentiation and regulation of folliculogenesis. It is named for its ability to inhibit the development of the Müllerian ducts in male fetuses[ 5 ]. AMH regulates ovarian folliculogenesis by inhibiting the recruitment of primordial follicles [ 6 ]. There are several possible uses for the AMH assessment: evaluation of the ovarian reserve, prediction of controlled ovarian stimulation, prediction of the natural age of menopause, assessment of the ovarian function, differentiation of some disorders of sex development, and tumor marker [ 7 ]. It is known that women with PCOS present elevated levels of AMH, correlating with testosterone levels, free androgen index, LH, average ovarian volume, the number of follicles on transvaginal ultrasound, and also with ovulatory dysfunction, including oligomenorrhea and amenorrhea [ 5 ]. As the levels of anti-Müllerian hormone can be influenced by each of the Rotterdam PCOS consensus diagnostic criteria, the various phenotypes present distinct AMH values, which may impact the use of this hormone as a diagnostic indicator for PCOS [ 8 ]. Objectives This systematic review aims to comprehensively evaluate and compare the mean levels of antimüllerian hormone (AMH) among the different phenotypes (A, B, C, and D) of Polycystic Ovary Syndrome (PCOS). Additionally, we aim to explore potential factors that may influence AMH levels within each phenotype, assess heterogeneity across studies, and conduct a meta-analysis to provide summary estimates of AMH levels in each PCOS phenotype. Methods a. Eligibility criteria, information sources, search strategy The present research has been registered on the PROSPERO platform under the number CRD42023444193 and follows the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement [ 9 ]. This systematic review includes studies that meet the following criteria: women diagnosed with Polycystic Ovary Syndrome (PCOS) based on the Rotterdam criteria; studies that specifically report Anti-Mullerian Hormone (AMH) levels categorized into PCOS phenotypes A, B, C, and D; studies published in English, Spanish, Portuguese or French; both prospective and retrospective studies; and all types of study designs (randomized controlled trials, cohort studies, case-control studies, cross-sectional studies) as long as they provide the necessary data. The exclusion criteria involve studies that do not include populations diagnosed with PCOS according to the Rotterdam criteria. These studies do not provide specific data on AMH levels, age, and body mass index (BMI), studies that do not distinguish between different phenotypes of PCOS according to the Rotterdam criteria, animal studies, reviews, case reports, opinion articles, letters to the editor, and other non-original research. Two independent reviewers performed a comprehensive search of PubMed, Embase, Science Direct, and Web of Science databases for studies published up to July 2024. The following search terms were applied: “PCOS” or “Polycystic Ovary Syndrome,” “AMH” or “Anti-Mullerian Hormone” and “Phenotype” or “Phenotypic”. In addition, the reference lists of relevant publications were manually searched to include further relevant studies. b. Study selection The reviewers screened independently the titles and abstracts of the retrieved articles based on the predefined inclusion and exclusion criteria. Full-text articles were obtained for potentially relevant studies. Any disagreements between the reviewers were resolved through discussion and consensus. c. Data extraction Data extraction was performed independently by two reviewers using a standardized data extraction spreadsheet. Any discrepancies were resolved through discussion and consensus. Data collected included study characteristics (authors, publication year, study design, country of the study), participant characteristics - mean age and standard deviation (SD), mean BMI and SD by phenotype; mean AMH levels and SD by phenotype, and AMH assay used. In cases where specific age and BMI data for distinct phenotypes were not available, general age and BMI data from the PCOS control population were utilized. When articles reported only the median and interquartile range (iQR) without providing the standard deviation (SD), a formula was applied to estimate the mean and SD [ 11 ]. This approach allowed the inclusion of these studies in the meta-analysis, ensuring consistency in combining results across trials. In the studies where AMH levels were measured in pmol/L, we converted these values to ng/mL using the conversion factor, where 1 ng/mL is equivalent to 7.14 pmol/L [ 11 ]. To achieve greater reliability in the results, AMH values were converted to Beckman Coulter Generation II (Gen II) assay standards using validated formulas: Access AMH = 0.128 + 0.781 x Gen II for Access assay, Elecsys AMH = 0.253 + 0.688 x Ansh Labs and Elecsys AMH = 0.087 + 0.729 x Gen II for Ansh and Elecsys assays[ 11 ], Gen II = 1.40 x DSL - 0.62 for DSL [ 12 ], and Gen II = 1.353 × AMH (IOT) + 0.051 for Immunotech (IOT) [ 13 ]. The Elecsys formula was also applied to VIDAS AMH values due to their high correlation [ 14 ]. Studies that did not specify the AMH assay used or those without a conversion method were excluded from the final Gen II results. These conversion formulas were utilized for calculating both the mean AMH and standard deviation, ensuring consistency in measuring proportional variations across different assays. d. Assessment of risk of bias The assessment of the risk of bias for the included studies was conducted using The Risk Of Bias In Non-randomized Studies - of Exposure (ROBINS-E) tool [ 15 ], a comprehensive instrument specifically tailored for evaluating bias in observational epidemiological studies, which evaluates seven domains including confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported result. Two independent reviewers applied the ROBINS-E tool to each study, with any discrepancies resolved through discussion or consultation with a third reviewer. e. Data synthesis The statistical analysis was executed using the R software environment, incorporating the ’meta’, ’metaphor’, and ’ggplot2’ libraries for meta-analysis and data visualization. We opted for a random-effects model due to the expected study-to-study variability. The extent of heterogeneity was quantified using the I² statistic, which measures the percentage of variation across studies attributable to heterogeneity rather than chance. Additionally, we computed a 95% prediction interval to illustrate where the true effects of similar future studies are predicted to reside. Results a. Study selection The literature search across all four databases yielded a total of 684 citations; of these, 201 were duplicates and were removed. The titles and abstracts of the remaining 483 citations were reviewed for relevance and screened against the inclusion and exclusion criteria. Ultimately, 82 full-text articles were assessed for eligibility. Of these, 35 were excluded, with the most common reason being the lack of specific data on AMH levels by the phenotypes of PCOS. In the end, 49 articles were included in this review, with 4 of them identified through citation searches. Further details on the search strategy and study selection process are presented in the PRISMA flow diagram ( Figure 1 ). Download figure Open in new tab Figure 1. PRISMA flow diagram Overview of study identification, screening, eligibility, and inclusion for the systematic review and meta-analysis. b. Study characteristics The systematic review included a total of 49 studies, reflecting a variety of populations. The studies included were conducted between 2009 and 2024, with the number of PCOS women ranging from 31 to 3,775, amounting to a total of 15,388 subjects. The geographical distribution of these studies covered a comprehensive range of countries across all continents except Central and South America. The inclusion criteria for age varied among studies, with some specifying a range of 18-40 years, while others accepted a broader age group. Each study’s approach to the inclusion of infertile women also varied, with some studies explicitly including only non-infertile women, while others were inclusive or did not specify the fertility status of participants. In terms of assay methods used to measure AMH levels, a variety of assays were employed. Several studies utilized the Beckman Coulter Generation II (GenII) assay, while others utilized the Roche Elecsys, ACCESS, Immunotech (IOT), DSL, and VIDAS. In some studies, the AMH assay was not determined. This variety in AMH assay methodologies show the importance of considering assay-specific differences when interpreting AMH levels across different populations. Study characteristics are shown in Table 1 . View this table: View inline View popup Table 1. Characteristics of included studies Summary of study year, country, sample sizes for total and PCOS phenotypes (A, B, C, D), AMH levels (ng/mL), and assay methods (BC: Beckman Coulter; ACCESS: Access Immunoassay; BC GEN II: Beckman Coulter Generation II; ELISA: Enzyme-Linked Immunosorbent Assay; ELECSYS: Roche Elecsys; IMMUNOTECH: Immunotech; DSL: Diagnostic Systems Laboratories; VIDAS: bioMérieux VIDAS; UNION: Union Immunoassay). [ 16 ], [ 17 ], [ 18 ], [ 19 ], [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 24 ], [ 25 ], [ 26 ], [ 27 ], [ 28 ], [ 29 ], [ 30 ], [ 31 ], [ 32 ], [ 33 ], [ 34 ], [ 35 ], [ 36 ], [ 37 ], [ 38 ], [ 39 ], [ 40 ], [ 41 ], [ 42 ], [ 43 ], [ 44 ], [ 45 ], [ 46 ], [ 47 ], [ 48 ], [ 49 ], [ 50 ], [ 51 ], [ 52 ], [ 53 ], [ 54 ], [ 55 ], [ 56 ], [ 57 ], [ 58 ], [ 59 ], [ 60 ], [ 61 ], [ 62 ], [ 63 ], [ 64 ]. c. Risk of bias of included studies The risk of bias within the included studies was assessed using the ROBINS-E tool. The evaluation revealed that most studies maintained a low risk of bias across several domains, particularly in the measurement of exposure and outcome and reporting of results, reflecting methodological robustness. However, concerns were noted in certain areas. Bias due to participant selection and due to confounding were evident, with several studies showing some concerns and high risk, pointing to potential limitations in study design or execution. The overall risk of bias was moderated in the majority of studies, although some exhibited a high risk. This evaluation underscores the need for a cautious interpretation of the results. The detailed findings of this risk assessment are presented in Figure 2 . Download figure Open in new tab Download figure Open in new tab Figure 2. Risk of bias assessment using ROBINS-E Combined graphical and tabular representation of the risk of bias evaluation for included studies. Figure 2A shows the proportion of studies categorized as low risk, some concerns, high risk, and no information across the specified domains. Figure 2B provides a detailed tabular overview of the risk of bias assessment for each study across all domains. d. Synthesis of results The results from our systematic review highlight significant variability in AMH levels when stratified by PCOS phenotypes ( figure 3 ). The PCOS phenotype A had the highest mean AMH level measured without assay differentiation at 9.87 ng/mL (SMD 2.97, CI 95% 9.29-10.48) , and with the Beckman Coulter Gen II assay conversion at 11.49 ng/mL (SMD 3.06, CI 95% 10.47-12.61). In contrast, phenotype B showed the lowest median AMH levels in both measurements, 5.71 ng/mL (SMD 0.98, CI 95% 4.96-6.57) without assay differentiation and 6.25 ng/mL (SMD 1.36, CI 95% 5.20-7.51) with Beckman Coulter Gen II. PCOS C mean AMH without assay differentiation 6.91 ng/mL (SMD 1.42, CI 95% 6.30-7.57) and converted to BC GENII 7.98 ng/mL (SMD 1.52, CI 95% 7.16-8.90) , PCOS D without assay differentiation 7.80 ng/mL (SMD 2.27, CI 95% 7.20-8.45) and BC GEN II 8.97 ng/mL (SMD 2.44, CI 95% 8.04-10.00). Download figure Open in new tab Figure 3. AMH levels across PCOS phenotypes Violin plots displaying the distribution of AMH levels (ng/mL) across PCOS phenotypes A, B, C, and D, without assay differentiation (left) and after Beckman Coulter Gen II assay normalization (right). Mean values and standardized mean differences (SMD) are highlighted for each phenotype. The age distribution across phenotypes was relatively similar, with means ranging from 26.17 to 27.57 years, indicating that age may not be a distinguishing factor in AMH levels among the included studies. The body mass index (BMI) also presented low differences, with the mean BMI being the lowest in phenotype D at 24.78 kg/m² and high in PCOS A (mean 26.11 kg/m²) ( figure 4 ). Download figure Open in new tab Figure 4. Age and BMI distribution across PCOS phenotypes Violin plots illustrate the distribution of age (years) and body mass index (BMI, kg/m²) among PCOS phenotypes A, B, C, and D. Mean values with confidence intervals (CI) are displayed for each phenotype. Our results showed notable disparities in sample sizes across the four PCOS phenotypes, reflecting the prevalence of each phenotype in the studied populations. Phenotype A displayed the largest sample sizes, suggesting that this phenotype is more commonly studied and also more prevalent. Larger sample sizes generally provide more precise estimates, as indicated by the narrower confidence intervals in our forest plots. This implies that our findings for Phenotype A might be the most reliable indicator of AMH levels among the PCOS phenotypes we studied. In contrast, Phenotype B often had smaller sample sizes, indicating that this phenotype might be less common, less frequently studied, or more challenging to diagnose. As a result, while the estimated AMH levels for Phenotype B are valuable, they are subject to more variability and might require cautious interpretation and verification in larger cohorts. Phenotypes C and D had moderate sample sizes, providing a balance between the extremes of A and B. Our meta-analysis has revealed a pronounced degree of heterogeneity in AMH levels, as evidenced by I² values close to or at 98% across all phenotypes, which is indicative of significant variability among the included studies. This heterogeneity is substantial and merits careful consideration. Small-study effects, methodological differences, or even publication bias could be influencing the results. Variations in study populations, ethnicity, AMH assays, and differences in PCOS symptoms might lead to variability in AMH levels. The significant heterogeneity underscores the complexity of PCOS as a syndrome with a spectrum of manifestations and the challenges in using AMH as a uniform biomarker. Additional forest plots displaying subgroup analyses and details of the meta-analysis are provided in the supplementary material (Supplementary File 1). Download figure Open in new tab Figure 5. Forest plot of AMH levels by PCOS phenotypes without assay differentiation Forest plot illustrating mean AMH levels (ng/mL) and 95% confidence intervals (CI) for PCOS phenotypes A, B, C, and D. Results were calculated using random-effects models. Download figure Open in new tab Figure 6. Forest plot of AMH levels by PCOS phenotypes converted to Beckman Coulter Gen II assay Forest plot showing mean AMH levels (ng/mL) and 95% confidence intervals (CI) for PCOS phenotypes A, B, C, and D, standardized to Beckman Coulter Gen II assay values. Comment a. Principal Findings Our meta-analysis found significant variability in anti-Müllerian hormone (AMH) levels across different phenotypes of polycystic ovary syndrome (PCOS). Phenotype A consistently exhibited the highest AMH levels, followed by phenotypes D, C, and B. These results align with previous research indicating that women with more severe ovulatory dysfunction or hyperandrogenism, particularly in phenotype A, tend to have elevated AMH levels [ 47 ];[ 16 ]. Interestingly, we observed that phenotype D had higher AMH levels than phenotype C, suggesting that oligoanovulation may exert a greater influence on AMH levels than hyperandrogenism alone. This is supported by studies showing that ovulatory dysfunction is closely linked to follicular excess, which in turn elevates AMH levels [ 24 ]. Conversely, phenotype B showed the lowest AMH levels, this supports the hypothesis that polycystic ovarian morphology is the primary factor driving elevated AMH levels in PCOS, as previously suggested by Carmina et. al. [ 26 ], who found that PCOM correlates more strongly with AMH than hyperandrogenism or oligoanovulation. This gradient in AMH levels suggests that AMH may serve as a useful clinical indicator for distinguishing between PCOS phenotypes, which could have implications for individualized treatment approaches. b. Comparison with Existing Literature Previous meta-analyses, such as those by Anand et al. [ 65 ] and Tsukui et al. [ 66 ], have focused on the diagnostic accuracy of anti-Müllerian hormone (AMH) in distinguishing PCOS patients from controls, consistently showing elevated AMH levels in women with PCOS. A recent study showed the potential AMH as a diagnostic biomarker for polycystic ovary syndrome (PCOS). A serum AMH concentration of at least 5.39 ng/mL was associated with an increased risk of PCOS, with a sensitivity of 88.6%, specificity of 92.75%, an area under the curve (AUC) of 0.95. [ 67 ] However, these analyses did not differentiate between the various PCOS phenotypes, which we believe is crucial given the metabolic heterogeneity within the syndrome. As highlighted by Soares-Jr et al. (2023) [ 68 ], different PCOS phenotypes exhibit distinct metabolic profiles, with phenotype A, in particular, being associated with a higher risk of metabolic syndrome and type 2 diabetes. This reinforces the importance of stratifying PCOS by phenotypes to better understand the variability in clinical and metabolic outcomes. Our study adds to the literature by providing insights into the variability of AMH levels across these phenotypes, suggesting that polycystic ovarian morphology (PCOM) plays a more significant role in elevating AMH levels than other features, such as hyperandrogenism or ovulatory dysfunction. c. Strengths and Limitations A major strength of this meta-analysis is the inclusion of a large sample size (15,535 women) across diverse populations, which enhances the generalizability of the findings. Additionally, the use of conversion formulas for standardizing AMH measurements across different assays adds reliability to the results. However, significant heterogeneity across studies, as indicated by high I² values, suggests that factors beyond phenotypic classification, such as ethnicity, assay methods, and inclusion criteria, may influence AMH levels. The variability in sample sizes across phenotypes, particularly the smaller cohort for phenotype B, represents another limitation that could affect the precision of our estimates for this group. d. Conclusions and Implications Our findings suggest that AMH levels could serve as an important diagnostic indicator for differentiating between PCOS phenotypes, particularly when integrated with other clinical parameters [ 27 ]. The distinct AMH profiles across phenotypes underscore the need for phenotype-specific approaches in PCOS management. However, the variability in AMH assays across studies highlights the importance of standardized measurement techniques to ensure consistent clinical application [ 22 ]. Moving forward, future research should focus on reducing heterogeneity by standardizing AMH measurements and exploring the role of AMH in guiding personalized treatments for PCOS. Data Availability All data produced in the present study are available upon reasonable request to the authors Footnotes This systematic review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (under identifier CRD42023444193) on July 23, 2023. PROSPERO Registration ID: CRD42023444193 All authors have reviewed and approved the final manuscript, declare no conflicts of interest, and confirm that no funding was received for this study. References 1. ↵ Teede HJ , Tay CT , Laven JJE , et al. Recommendations From the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome . The Journal of Clinical Endocrinology and Metabolism . 2023 ; 108 ( 10 ): 2447 – 2469 . doi: 10.1210/clinem/dgad463 OpenUrl CrossRef 2. ↵ Chang RJ , Dumesic DA. Polycystic Ovary Syndrome and Hyperandrogenic States . Yen and Jaffe’s Reproductive Endocrinology . Published online 2019 : 520 – 555 .e13. doi: 10.1016/b978-0-323-47912-7.00021-4 OpenUrl CrossRef 3. ↵ Lizneva D , Suturina L , Walker W , Brakta S , Gavrilova-Jordan L , Azziz R . Criteria, prevalence, and phenotypes of polycystic ovary syndrome . Fertility and Sterility . 2016 ; 106 ( 1 ): 6 – 15 . doi: 10.1016/j.fertnstert.2016.05.003 OpenUrl CrossRef PubMed 4. ↵ Sivanandy MS , Ha SK . The Role of Serum Anti-Mullerian Hormone Measurement in the Diagnosis of Polycystic Ovary Syndrome . Diagnostics . 2023 ; 13 ( 5 ): 907 . doi: 10.3390/diagnostics13050907 OpenUrl CrossRef PubMed 5. ↵ Rudnicka E , Kunicki M , Calik-Ksepka A , et al. Anti-Müllerian Hormone in Pathogenesis, Diagnostic and Treatment of PCOS . International Journal of Molecular Sciences . 2021 ; 22 ( 22 ). doi: 10.3390/ijms222212507 OpenUrl CrossRef 6. ↵ Dewailly D , Andersen CY , Balen A , et al. The physiology and clinical utility of anti-Mullerian hormone in women . Human Reproduction Update . 2014 ; 20 ( 3 ): 370 – 385 . doi: 10.1093/humupd/dmt062 OpenUrl CrossRef PubMed Web of Science 7. ↵ Arantes G , Baracat EC , Felipe M . About the Anti-Müllerian Hormone (AMH) Uses in the Clinical Practice . Revista Brasileira Ginecologia e Obstetrícia . 2018 ; 40 ( 11 ): 661 – 663 . doi: 10.1055/s-0038-1676059 OpenUrl CrossRef 8. ↵ Jacob SL , Field HP , Calder N , Picton HM , Balen AH , Barth JH . Anti-Müllerian hormone reflects the severity of polycystic ovary syndrome . Clinical Endocrinology . 2016 ; 86 ( 3 ): 395 – 400 . doi: 10.1111/cen.13269 OpenUrl CrossRef PubMed 9. ↵ Moher D , Liberati A , Tetzlaff J , Altman DG . Preferred Reporting Items for Systematic Reviews and meta-analyses: the PRISMA Statement . BMJ . 2009 ; 339 ( 339 ): b2535 – b2535 . doi: 10.1136/bmj.b2535 OpenUrl FREE Full Text 10. Wan X , Wang W , Liu J , Tong T . Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range . BMC Medical Research Methodology . 2014 ; 14 ( 1 ). doi: 10.1186/1471-2288-14-135 OpenUrl CrossRef PubMed 11. ↵ Nelson SM , Pastuszek E , Kloss G , et al. Two new automated, compared with two enzyme-linked immunosorbent, antimüllerian hormone assays . Fertility and Sterility . 2015 ; 104 ( 4 ): 1016 – 1021 .e6. doi: 10.1016/j.fertnstert.2015.06.024 OpenUrl CrossRef PubMed 12. ↵ Wallace AM , Faye S , Fleming R , Nelson SM . A multicentre evaluation of the new Beckman Coulter anti-Müllerian hormone immunoassay (AMH Gen II) . Annals of Clinical Biochemistry . 2011 ; 48 ( 4 ): 370 – 373 . doi: 10.1258/acb.2011.010172 OpenUrl CrossRef PubMed 13. ↵ Li H , Hung E , Benancy P.C. Wong , Anderson RA , Pak Chung Ho , William S.B. Yeung . Correlation between three assay systems for anti-Müllerian hormone (AMH) determination . Journal of Assisted Reproduction and Genetics . 2012 ; 29 ( 12 ): 1443 – 1446 . doi: 10.1007/s10815-012-9880-1 OpenUrl CrossRef PubMed 14. ↵ Pastuszek E , Lukaszuk A , Kunicki M , et al. New AMH assay allows rapid point of care measurements of ovarian reserve . Gynecological Endocrinology . 2017 ; 33 ( 8 ): 638 – 643 . doi: 10.1080/09513590.2017.1306735 OpenUrl CrossRef PubMed 15. ↵ Julian P.T. Higgins , Morgan RL , Rooney AA , et al. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E) . Environment International . 2024 ; 186 ( 186 ): 108602 – 108602 . doi: 10.1016/j.envint.2024.108602 OpenUrl CrossRef PubMed 16. ↵ Adamska A , Łebkowska A , Krentowska A , et al. Ovarian Reserve and Serum Concentration of Thyroid Peroxidase Antibodies in Euthyroid Women With Different Polycystic Ovary Syndrome Phenotypes . Frontiers in Endocrinology . 2020 ; 11 . doi: 10.3389/fendo.2020.00440 OpenUrl CrossRef 17. ↵ Albahlol IA , Mustafa Neamatallah , Mohamed Saad Serria , et al. Vitamin D receptor gene polymorphism and polycystic ovary syndrome susceptibility . BMC Medical Genomics . 2023 ; 16 ( 1 ). doi: 10.1186/s12920-023-01541-8 OpenUrl CrossRef PubMed 18. ↵ Alebić MŠ , Stojanović N , Duhamel A , Dewailly D . The phenotypic diversity in per-follicle anti-Müllerian hormone production in polycystic ovary syndrome . Human Reproduction . 2015 ; 30 ( 8 ): 1927 – 1933 . doi: 10.1093/humrep/dev131 OpenUrl CrossRef PubMed 19. ↵ Amini P , Omani-Samani R , Hosseini R , Ahmadi J , Maroufizadeh S . A cross-sectional comparison of clinical and endocrine parameters among phenotypes of polycystic ovarian syndrome in iranian population . Middle East Fertility Society Journal . 2018 ; 23 ( 4 ): 425 – 430 . doi: 10.1016/j.mefs.2018.07.005 OpenUrl CrossRef 20. ↵ Armstrong J , Cortes C , Hawkins K , Younis A . Serum Anti-Mullerian Hormone (AMH) Levels are Effective in Predicting the Diagnosis of Four Polycystic Ovarian Syndrome (PCOS) Phenotypes . Gynecology & Reproductive Health . 2022 ; 6 ( 5 ). doi: 10.33425/2639-9342.1203 OpenUrl CrossRef 21. ↵ Bahadur A , Verma N , Mundhra R , et al. Correlation of Homeostatic Model Assessment-Insulin Resistance, Anti-Mullerian Hormone, and BMI in the Characterization of Polycystic Ovary Syndrome . Cureus. Published online June 30 , 2021 . doi: 10.7759/cureus.16047 OpenUrl CrossRef 22. ↵ Bell RJ , Islam RM , Skiba MA , Herbert D , Martinez Garcia A , Davis SR . Substituting serum anti-Müllerian hormone for polycystic ovary morphology increases the number of women diagnosed with polycystic ovary syndrome: a community-based cross-sectional study . Human Reproduction . 2021 ; 37 ( 1 ): 109 – 118 . doi: 10.1093/humrep/deab232 OpenUrl CrossRef PubMed 23. ↵ Bhide P , Kulkarni A , Dilgil M , et al. Phenotypic variation in anti-Mullerian hormone (AMH) production per follicle in women with polycystic ovary syndrome (PCOS) and isolated polycystic ovarian morphology (PCOM): an observational cross-sectional study . Gynecological Endocrinology . 2017 ; 33 ( 10 ): 801 – 806 . doi: 10.1080/09513590.2017.1320377 OpenUrl CrossRef PubMed 24. ↵ Bozdag G , Mumusoglu S , Coskun ZY , Yarali H , Yildiz BO . Anti-Müllerian hormone as a diagnostic tool for PCOS under different diagnostic criteria in an unselected population . Reproductive BioMedicine Online . 2019 ; 39 ( 3 ): 522 – 529 . doi: 10.1016/j.rbmo.2019.04.002 OpenUrl CrossRef PubMed 25. ↵ Ngọc Thành Cao , Minh Tâm Lê , Vu , et al. Defining polycystic ovary syndrome phenotype in Vietnamese women . Journal of Obstetrics and Gynaecology Research . 2019 ; 45 ( 11 ): 2209 – 2219 . doi: 10.1111/jog.14097 OpenUrl CrossRef 26. ↵ Carmina E , Campagna AM , Fruzzetti F , Lobo RA . Amh Measurement Versus Ovarian Ultrasound In The Diagnosis Of Polycystic Ovary Syndrome In Different Phenotypes . Endocrine Practice . 2016 ; 22 ( 3 ): 287 – 293 . doi: 10.4158/ep15903.or OpenUrl CrossRef PubMed 27. ↵ Carmina E , Lobo RA . Comparing Lean and Obese PCOS in Different PCOS Phenotypes: Evidence That the Body Weight Is More Important Than the Rotterdam Phenotype in Influencing the Metabolic Status . Diagnostics . 2022 ; 12 ( 10 ): 2313 . doi: 10.3390/diagnostics12102313 OpenUrl CrossRef PubMed 28. ↵ Cela V , Obino MER , Alberga Y , et al. Ovarian response to controlled ovarian stimulation in women with different polycystic ovary syndrome phenotypes . Gynecological Endocrinology . 2017 ; 34 ( 6 ): 518 – 523 . doi: 10.1080/09513590.2017.1412429 OpenUrl CrossRef PubMed 29. ↵ Mackens S , Drakopoulos P , Moeykens MF , et al. Cumulative live birth rate after ovarian stimulation with freeze-all in women with polycystic ovaries: does the polycystic ovary syndrome phenotype have an impact? Reproductive BioMedicine Online . 2022 ; 44 ( 3 ): 565 – 571 . doi: 10.1016/j.rbmo.2021.11.009 OpenUrl CrossRef PubMed 30. ↵ Eftekhar M , Mirhashemi ES , Tabibnejad N . Assisted reproductive outcomes in women with different polycystic ovary syndrome phenotypes . International Journal of Gynecology & Obstetrics . 2018 ; 144 ( 2 ): 147 – 152 . doi: 10.1002/ijgo.12707 OpenUrl CrossRef PubMed 31. ↵ Fraissinet A , Robin G , Pigny P , Lefebvre T , Catteau-Jonard S , Dewailly D . Use of the serum anti-Müllerian hormone assay as a surrogate for polycystic ovarian morphology: impact on diagnosis and phenotypic classification of polycystic ovary syndrome . Human Reproduction . 2017 ; 32 ( 8 ): 1716 – 1722 . doi: 10.1093/humrep/dex239 OpenUrl CrossRef PubMed 32. ↵ Gupta M , Yadav R , Reeta Mahey , et al. Correlation of body mass index (BMI), anti-mullerian hormone (AMH), and insulin resistance among different polycystic ovary syndrome (PCOS) phenotypes – a cross-sectional study . Gynecological Endocrinology . 2019 ; 35 ( 11 ): 970 – 973 . doi: 10.1080/09513590.2019.1613640 OpenUrl CrossRef PubMed 33. ↵ Halder A , Kumar H , Sharma M , Jain M , Kalsi AK , Pandey S . Serum anti-Müllerian hormone: A potential biomarker for polycystic ovary syndrome . The Indian journal of medical research . 2023 ; 158 ( 4 ): 397 – 406 . doi: 10.4103/ijmr.IJMR_4608_20 OpenUrl CrossRef PubMed 34. ↵ Huijgen NA , Laven JSE , Labee CT , Louwers YV , Willemsen SP , Steegers-Theunissen RPM . Are Dieting and Dietary Inadequacy a Second Hit in the Association with Polycystic Ovary Syndrome Severity? Cao C, ed . PLOS ONE . 2015 ; 10 ( 11 ): e0142772 . doi: 10.1371/journal.pone.0142772 OpenUrl CrossRef PubMed 35. ↵ Hwang YI , Sung NY , Koo HS , et al. Can high serum anti-Müllerian hormone levels predict the phenotypes of polycystic ovary syndrome (PCOS) and metabolic disturbances in PCOS patients? Clinical and Experimental Reproductive Medicine . 2013 ; 40 ( 3 ): 135 . doi: 10.5653/cerm.2013.40.3.135 OpenUrl CrossRef 36. ↵ Ibrahim IYM . The Role of Vitamin D in Metabolic Syndrome in Polycystic Ovary Syndrome . Indian Journal of Forensic Medicine & Toxicology . 2022 ; 16 ( 1 ). doi: 10.37506/ijfmt.v16i1.17566 OpenUrl CrossRef 37. ↵ Jamil AS , Alalaf SK , Al-Tawil NG , Al-Shawaf T . Comparison of clinical and hormonal characteristics among four phenotypes of polycystic ovary syndrome based on the Rotterdam criteria . Archives of Gynecology and Obstetrics . 2015 ; 293 ( 2 ): 447 – 456 . doi: 10.1007/s00404-015-3889-5 OpenUrl CrossRef PubMed 38. ↵ Le NSV , Le MT , Nguyen ND , Tran NQT , Nguyen QHV , Cao TN . A Cross-Sectional Study on Potential Ovarian Volume and Related Factors in Women with Polycystic Ovary Syndrome from Infertile Couples . International Journal of Women’s Health . 2021 ;Volume 13 : 793 – 801 . doi: 10.2147/ijwh.s329082 OpenUrl CrossRef 39. ↵ Mackens S , Stéphanie Pareyn , Drakopoulos P , et al. Outcome of in-vitro oocyte maturation in patients with PCOS: does phenotype have an impact? Human Reproduction . 2020 ; 35 ( 10 ): 2272 – 2279 . doi: 10.1093/humrep/deaa190 OpenUrl CrossRef PubMed 40. ↵ Kaur J , Mahajan N . Establishing an Anti-Müllerian hormone cutoff for diagnosis of polycystic ovarian syndrome in women of reproductive age-bearing Indian ethnicity using the automated Anti-Müllerian hormone assay . Journal of Human Reproductive Sciences . 2019 ; 12 ( 2 ): 104 . doi: 10.4103/jhrs.jhrs_149_18 OpenUrl CrossRef PubMed 41. ↵ Malhotra N , Mahey R , Cheluvaraju R , et al. Serum Anti-Mullerian Hormone (AMH) Levels Among Different PCOS Phenotypes and Its Correlation with Clinical, Endocrine, and Metabolic Markers of PCOS . Reproductive Sciences . Published online March 2, 2023 . doi: 10.1007/s43032-023-01195-y OpenUrl CrossRef 42. ↵ Mitra S , Saharia GK , Jena SK . Cardio-metabolic risk in Rotterdam clinical phenotypes of PCOS . Annales d’endocrinologie . 2024 ; 85 ( 1 ): 44 – 47 . doi: 10.1016/j.ando.2023.06.001 OpenUrl CrossRef PubMed 43. ↵ Ozay AC , Emekcı Ozay O , Gulekli B . Comparison of Anti-müllerian Hormone (AMH) and Hormonal Assays for Phenotypic Classification of Polycystic Ovary Syndrome . Ginekologia Polska . 2020 ; 91 ( 11 ): 661 – 667 . doi: 10.5603/gp.a2020.0122 OpenUrl CrossRef PubMed 44. ↵ Piouka A , Farmakiotis D , Katsikis I , Macut D , Gerou S , Panidis D . Anti-Müllerian hormone levels reflect severity of PCOS but are negatively influenced by obesity: relationship with increased luteinizing hormone levels . American Journal of Physiology-Endocrinology and Metabolism . 2009 ; 296 ( 2 ): E238 – E243 . doi: 10.1152/ajpendo.90684.2008 OpenUrl CrossRef PubMed Web of Science 45. ↵ Asih D , None Ashon Sa’adi , None Budi Utomo , None Arif Tunjungseto . Relationship between angiopoietin-like-protein-2 levels and anti-mullerian hormone levels in polycystic ovary syndrome of reproductive age . Bali Medical Journal . 2023 ; 12 ( 1 ): 861 – 865 . doi: 10.15562/bmj.v12i1.4201 OpenUrl CrossRef 46. ↵ Ramezanali F , Ashrafi M , Hemat M , Arabipoor A , Jalali S , Moini A . Assisted reproductive outcomes in women with different polycystic ovary syndrome phenotypes: the predictive value of anti-Müllerian hormone . Reproductive BioMedicine Online . 2016 ; 32 ( 5 ): 503 – 512 . doi: 10.1016/j.rbmo.2016.01.010 OpenUrl CrossRef PubMed 47. ↵ Romualdi D , Di Florio C , Tagliaferri V , et al. The Role of Anti-Müllerian Hormone in the Characterization of the Different Polycystic Ovary Syndrome Phenotypes . Reproductive Sciences . 2016 ; 23 ( 5 ): 655 – 661 . doi: 10.1177/1933719115611751 OpenUrl CrossRef PubMed 48. ↵ Gainder S , Sachdeva G , Suri V , Sachdeva N , Chopra S . Comparison of the different pcos phenotypes based on clinical metabolic, and hormonal profile, and their response to clomiphene . Indian Journal of Endocrinology and Metabolism . 2019 ; 23 ( 3 ): 326 . doi: 10.4103/ijem.ijem_30_19 OpenUrl CrossRef 49. ↵ Sezai Şahmay , Nil Atakul , Mahmut Öncül , Abdullah Tüten , Begüm Aydoğan , Hakan Seyisoğlu . Serum anti-mullerian hormone levels in the main phenotypes of polycystic ovary syndrome . European Journal of Obstetrics & Gynecology and Reproductive Biology . 2013 ; 170 ( 1 ): 157 – 161 . doi: 10.1016/j.ejogrb.2013.05.019 OpenUrl CrossRef PubMed 50. ↵ Sahmay S , Mathyk BA , Sofiyeva N , Atakul N , Azemi A , Erel T . Serum AMH levels and insulin resistance in women with PCOS . European Journal of Obstetrics & Gynecology and Reproductive Biology . 2018 ; 224 : 159 – 164 . doi: 10.1016/j.ejogrb.2018.03.007 OpenUrl CrossRef PubMed 51. ↵ R. Santhiya , Syed Habeebullah , Ghose S . Correlation of phenotypes of polycystic ovarian syndrome with anti-Müllerian hormone levels . Sahel Medical Journal . 2021 ; 24 ( 1 ): 15 – 21 . doi: 10.4103/smj.smj_50_20 OpenUrl CrossRef 52. ↵ Si M , Xu W , Qi X , et al. Metabolic Syndrome Rather Than Other Phenotypes in PCOS as a Predictive Indicator for Clinical Outcomes in IVF: Comprehensive Phenotypic Assessment across All PCOS Classifications . Journal of Clinical Medicine . 2023 ; 12 ( 15 ): 5073 – 5073 . doi: 10.3390/jcm12155073 OpenUrl CrossRef PubMed 53. ↵ Song DK , Oh JY , Lee H , Sung YA . Differentiation between polycystic ovary syndrome and polycystic ovarian morphology by means of an anti-Müllerian hormone cutoff value . The Korean Journal of Internal Medicine . 2017 ; 32 ( 4 ): 690 – 698 . doi: 10.3904/kjim.2016.038 OpenUrl CrossRef PubMed 54. ↵ Sova H , Unkila-Kallio L , Tiitinen A , et al. Hormone profiling, including anti-Müllerian hormone (AMH), for the diagnosis of polycystic ovary syndrome (PCOS) and characterization of PCOS phenotypes . Gynecological Endocrinology . 2019 ; 35 ( 7 ): 595 – 600 . doi: 10.1080/09513590.2018.1559807 OpenUrl CrossRef PubMed 55. ↵ Sumji S , Bhat A , Rashid A , et al. Efficacy of serum anti-mullerian hormone (AMH) levels for prediction of polycystic ovary syndrome (PCOS) and its association with clinical, biochemical and hormonal parameters . Indian Journal of Clinical Biochemistry . Published online September 6, 2022 . doi: 10.1007/s12291-022-01058-4 OpenUrl CrossRef 56. ↵ Svetlana E , Natalia A , Anzhelika B . Indicators of an ovarian reserve in women of early reproductive age with PCOS depending on the phenotype . Hormone Molecular Biology and Clinical Investigation . 2019 ; 39 ( 3 ). doi: 10.1515/hmbci-2018-0081 OpenUrl CrossRef 57. ↵ Tian X , Ruan X , Mueck AO , et al. Serum anti-Müllerian hormone and insulin resistance in the main phenotypes of non-obese polycystic ovarian syndrome women in China . Gynecological Endocrinology . 2014 ; 30 ( 11 ): 836 – 839 . doi: 10.3109/09513590.2014.943719 OpenUrl CrossRef PubMed 58. ↵ Suslyk H , Urbanovych A , Lishchuk O , Kozlovska K . The Role of Vitamin D in Treatment of Polycystic Ovary Syndrome Depending on Phenotype . Romanian Journal of Diabetes Nutrition and Metabolic Diseases . 2020 ; 27 ( 3 ). doi: 10.46389/rjd-2020-1035 OpenUrl CrossRef 59. ↵ Vural F , Vural B , Kardaş E , Ertürk Coşkun AD , Yildirim İ. The diagnostic performance of antimullerian hormone for polycystic ovarian syndrome and polycystic ovarian morphology . Archives of Gynecology and Obstetrics . 2022 ; 307 ( 4 ): 1083 – 1090 . doi: 10.1007/s00404-022-06874-w OpenUrl CrossRef PubMed 60. ↵ Wiweko B , Maidarti M , Priangga MD , et al. Anti-mullerian hormone as a diagnostic and prognostic tool for PCOS patients . Journal of Assisted Reproduction and Genetics . 2014 ; 31 ( 10 ): 1311 – 1316 . doi: 10.1007/s10815-014-0300-6 OpenUrl CrossRef PubMed 61. ↵ Wiweko B , Indra I , Susanto C , Natadisastra M , Hestiantoro A . The correlation between serum AMH and HOMA-IR among PCOS phenotypes . BMC Research Notes . 2018 ; 11 ( 1 ). doi: 10.1186/s13104-018-3207-y OpenUrl CrossRef PubMed 62. ↵ Yue C , Lu L , Meng L , Zhang QL , Ying C . Threshold value of anti-Mullerian hormone for the diagnosis of polycystic ovary syndrome in Chinese women . PLOS ONE . 2018 ; 13 ( 8 ): e0203129 – e0203129 . doi: 10.1371/journal.pone.0203129 OpenUrl CrossRef PubMed 63. ↵ Zhang M , Liu X , Xu X , et al. The reference value of anti-Müllerian hormone to diagnose polycystic ovary syndrome is inversely associated with BMI: a retrospective study . Reproductive biology and endocrinology . 2023 ; 21 ( 1 ). doi: 10.1186/s12958-023-01064-y OpenUrl CrossRef 64. ↵ Zhao H , Zhou D , Liu C , Zhang L . The Relationship Between Insulin Resistance and Obesity and Serum Anti-Mullerian Hormone Level in Chinese Women with Polycystic Ovary Syndrome: A Retrospective, Single-Center Cohort Study . International Journal of Women’s Health . 2023 ;Volume 15 : 151 – 166 . doi: 10.2147/ijwh.s393594 OpenUrl CrossRef 65. ↵ Anand S , Kumar A , Prasad A , Trivedi K . Updated meta-analysis on the diagnostic accuracy of serum anti-Mullerian hormone in poly cystic ovary syndrome involving 13 509 subjects . Journal of Obstetrics and Gynaecology Research . 2022 ; 48 ( 8 ): 2162 – 2174 . doi: 10.1111/jog.15233 OpenUrl CrossRef 66. ↵ Tsukui Y , Kitahara Y , Hasegawa Y , Kobayashi M , Osuka S , Iwase A . Anti-Müllerian hormone levels in the diagnosis of adolescent polycystic ovarian syndrome: a systematic review and meta-analysis . Endocrine Journal. Published online 2022 . doi: 10.1507/endocrj.ej22-0081 OpenUrl CrossRef 67. ↵ Mostafa Barghi , Heidari Z , Fahimeh Haghighatdoost , Awat Feizi , Mahin Hashemipour . New insights into the relationship between Anti-Mullerian Hormone and Polycystic ovary syndrome and its diagnostic accuracy: An updated and extended meta-analysis using marginal beta-binomial model . American Journal of Obstetrics and Gynecology . 2024 ; 232 ( 2 ). doi: 10.1016/j.ajog.2024.10.004 OpenUrl CrossRef 68. ↵ Soares-Jr JM , Asaka S , Antonio J , et al. Influence of Phenotypes on the Metabolic Syndrome of Women with Polycystic Ovary Syndrome over a Six-Year Follow-Up in Brazil . Biomedicines . 2023 ; 11 ( 12 ): 3262 – 3262 . doi: 10.3390/biomedicines11123262 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 27, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis Patrícia Jorge Schwenck de Carvalho , Giovana De Nardo Maffazioli , Ricardo Santos Simões , Renata Ferri Macchione , Sebastião Freitas de Medeiros , José Maria Soares Jr , Edmund Chada Baracat , Gustavo Arantes Rosa Maciel medRxiv 2025.01.25.25321110; doi: https://doi.org/10.1101/2025.01.25.25321110 Share This Article: Copy Citation Tools Antimullerian Hormone Levels by Phenotypes in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis Patrícia Jorge Schwenck de Carvalho , Giovana De Nardo Maffazioli , Ricardo Santos Simões , Renata Ferri Macchione , Sebastião Freitas de Medeiros , José Maria Soares Jr , Edmund Chada Baracat , Gustavo Arantes Rosa Maciel medRxiv 2025.01.25.25321110; doi: https://doi.org/10.1101/2025.01.25.25321110 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Obstetrics and Gynecology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffa5ba1cbd6f047',t:'MTc3OTQzNzU2OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();