AMH Human

What is Anti-Müllerian Hormone and what are its molecular characteristics in humans?

Anti-Müllerian Hormone (AMH) is a disulfide-linked 140 kDa glycoprotein belonging to the transforming growth factor-β (TGF-β) superfamily. It consists of two distinct regions: a pro region (58 kDa) and a mature region (12 kDa). Research indicates that AMH requires the N-terminal domain to potentiate activity of the C-terminal domain for full bioactivity .

The molecular structure of AMH includes:

• Disulfide bonds critical for proper folding and stability

• Glycosylation sites that affect protein half-life and bioactivity

• Cleavage sites for proteolytic processing

• Highly conserved mature region responsible for homology between species

This glycoprotein is primarily produced by granulosa cells of small, growing ovarian follicles in females, serving as a biomarker for ovarian reserve assessment . Understanding AMH's molecular characteristics is essential for developing reliable assays and interpreting test results in research settings.

What methodological approaches exist for quantifying AMH in human samples?

The evolution of AMH measurement technology spans multiple generations of assays with varying analytical characteristics:

First-generation assays:

• Initial ELISA developed by Hudson et al. (1990) using monoclonal antibody 6E11 (recognizing the pro region) and polyclonal antibodies

• Sensitivity limited to approximately 0.5 ng/mL

• Never commercially developed

Second-generation assays:

• Immunotech-Beckman Coulter (IOT) assay: Utilized monoclonal antibodies 11F8 and 22A2 recognizing different epitopes

• DSL assay: Employed antibodies targeting the mature region

• Sensitivity improved to 0.1 ng/mL

Current automated platforms:

• Elecsys® AMH assay (Roche)

• Access AMH assay (Beckman Coulter)

• Offer improved precision, throughput, and standardization

When designing research protocols, investigators must consider that different assays can yield substantially different results for the same sample, with coefficients of variation reported as high as 42% in some studies .

How should researchers design studies incorporating AMH measurements to ensure reliability?

To maximize reliability when incorporating AMH measurements into research protocols, investigators should implement these methodological approaches:

Assay selection and reporting:

• Clearly document the specific AMH assay used, including manufacturer, generation, and detection limits

• Consider using the most recent automated platforms for improved precision

• When comparing with historical data, acknowledge cross-assay comparison limitations

• Report both the method and the reference ranges specific to that method

Sample handling protocols:

• Standardize collection timing (ideally morning samples and consistent cycle day for premenopausal women)

• Define consistent processing parameters (centrifugation speed, time to processing)

• Document storage conditions and duration (-80°C for long-term storage)

• Minimize freeze-thaw cycles, as AMH stability can be affected

Study population considerations:

• Stratify by age given the strong age-dependence of AMH

• Document relevant characteristics that may affect AMH (hormonal contraceptive use, BMI, smoking status)

• Use adequately sized cohorts (>100 participants recommended for reference range studies)

• Report inclusion/exclusion criteria comprehensively

Statistical approaches:

• Use appropriate methods for reference range establishment (e.g., quantile regression for age effects)

• Consider log-transformation of AMH values given its typically skewed distribution

• Account for relevant covariates in multivariate analyses

• Document analytical and biological variation when interpreting serial measurements

Researchers should note that small sample sizes (23-142 women) and variable age ranges (23-56 years) have limited many previous studies .

What are the current challenges in standardizing AMH measurement assays and how does this impact research?

Despite two decades of clinical application, AMH measurement standardization remains problematic, with several critical challenges:

Lack of international standardization:
Even 20 years after the first AMH ELISA development, no international standard existed until recent WHO efforts. The absence of uniformly calibrated assays has limited the development of standardized AMH cutoff values, potentially compromising patient safety and leading to misinterpretation by clinicians .

Inter-laboratory and inter-assay variability:
The World Health Organization's collaborative study evaluating a recombinant AMH candidate standard (coded 16/190) found a geometric mean of 511 ng/amp with a 95% confidence interval of 426-612 ng/amp and a geometric coefficient of variation of 42% . This substantial variability complicates cross-study comparisons.

Molecular heterogeneity challenges:
AMH exists in multiple forms in circulation:

• Full-length AMH (inactive precursor)

• Cleaved AMH (bioactive form)

• Various degradation products

Different assays may recognize these forms with varying efficiency, contributing to discrepant results .

Pre-analytical variables:
Sample stability studies indicate that AMH levels can be affected by:

• Storage temperature (room temperature vs. refrigerated vs. frozen)

• Duration of storage before processing

• Freeze-thaw cycles

• Sample type (serum vs. plasma)

These standardization challenges impact research by:

• Compromising the validity of meta-analyses combining data from different assay platforms

• Requiring assay-specific reference ranges and clinical decision thresholds

• Potentially limiting reproducibility of findings across different laboratories

• Creating challenges for longitudinal studies spanning assay transitions

How do demographic and biological factors affect AMH levels and what are the implications for research design?

Multiple demographic and biological factors influence AMH levels, necessitating careful research design:

Age-related effects:

• AMH shows a consistent age-related decline pattern

• The rate of decline accelerates after age 30

• Age-specific reference ranges are essential for accurate interpretation

• Age stratification should be a standard feature of AMH research protocols

Ethnicity and geographic considerations:

• Studies suggest potential ethnic differences in AMH profiles

• Asian populations may show different age-related decline patterns than Caucasian populations

• Genome-wide association studies have identified polymorphisms associated with AMH levels

• Research should include diverse populations and report detailed demographic data

Hormonal and reproductive factors:

• Hormonal contraceptive use may suppress AMH by 15-20%

• Pregnancy transiently decreases AMH levels

• PCOS is associated with 2-3 fold higher AMH levels

• Smoking accelerates follicular depletion, potentially affecting AMH

Other biological variables:

• BMI may have an inverse relationship with AMH levels

• Vitamin D status has been correlated with AMH in some studies

• Autoimmune conditions may affect ovarian reserve and AMH

• Certain medications (e.g., chemotherapeutic agents) can dramatically alter AMH

Research design implications:

• Studies should collect comprehensive data on potential confounding variables

• Exclusion criteria should be carefully considered and documented

• Stratified analysis or statistical adjustment for these factors is often necessary

• Longitudinal designs are preferable for studying factors affecting AMH over time

• Population-specific reference ranges may be needed

These population differences explain some of the variability in research findings, with studies including women of different fertility statuses, age ranges, and clinical presentations yielding divergent results .

What are the key contradictions and uncertainties in current AMH research literature?

Several important contradictions and uncertainties exist in the current AMH research literature that require methodological attention:

Predictive value for natural fertility outcomes:

• Only 5% of clinicians in a survey believed AMH testing was moderately/very useful in predicting natural conception

• Research shows conflicting results regarding AMH's ability to predict time-to-pregnancy in naturally conceiving couples

• The relationship between AMH and miscarriage risk remains controversial

• Methodological differences in study design contribute to these contradictions

AMH fluctuations during menstrual cycle:

• Early research suggested AMH was stable throughout the menstrual cycle

• Recent studies with more sensitive assays have detected subtle fluctuations

• Contradictory findings may relate to assay sensitivity, sample timing, and statistical approaches

• Research protocols should standardize collection timing or document cycle day

Relationship between AMH and oocyte quality:

• AMH effectively predicts oocyte quantity but not necessarily quality

• Studies examining AMH correlation with embryo euploidy show inconsistent results

• The relationship between AMH and live birth outcomes in ART remains controversial

• These contradictions highlight the complex relationship between quantitative and qualitative aspects of ovarian reserve

Menopause prediction accuracy:

• Only 22% of clinicians believed AMH was useful for predicting premature menopause

• Studies show AMH can predict final menstrual period within ±3-5 years

• Individual prediction precision remains limited

• Methodological variations in prediction models contribute to these uncertainties

Interventions to modify AMH levels:

• Research on lifestyle interventions (weight loss, exercise) shows conflicting effects on AMH

• Vitamin D supplementation studies report contradictory findings

• DHEA effects on AMH in diminished ovarian reserve remain controversial

• These contradictions may relate to heterogeneous study populations and intervention designs

Researchers addressing these contradictions should employ robust methodologies including:

• Larger sample sizes with adequate statistical power

• Clear definition of outcome measures

• Comprehensive assessment of confounding variables

• Longitudinal designs where appropriate

• Transparent reporting of methods and limitations

How can researchers effectively analyze AMH data given its non-normal distribution and age-dependency?

AMH data presents specific statistical and analytical challenges requiring specialized methodological approaches:

Distribution characteristics:

• AMH typically shows a right-skewed (non-normal) distribution in population studies

• Log-transformation is often necessary to achieve approximate normality

• Non-parametric statistical methods may be appropriate for untransformed data

• Outlier detection and management protocols should be established a priori

Age-dependency modeling:

• The relationship between age and AMH is non-linear

• Polynomial regression models (typically cubic) better capture this relationship than linear models

• Quantile regression can establish age-specific percentiles more effectively than simple reference ranges

• Smoothing functions (e.g., LOESS) may provide more accurate representation of age-related changes

Longitudinal data analysis:

• Mixed-effects models account for within-subject correlation in repeated measures

• Rate-of-change calculations provide insights beyond absolute values

• Bayesian approaches can incorporate prior knowledge about age-related patterns

• Missing data should be handled using appropriate methods (e.g., multiple imputation)

Recommended analytical framework:

• Data visualization first (scatterplots with age, histograms, Q-Q plots)

• Normality assessment (Shapiro-Wilk test, distribution plots)

• Transformation if needed (typically log or square root)

• Age-adjustment using appropriate modeling approach

• Analysis of residuals to identify other potential predictors

• Consideration of interaction effects (e.g., age×BMI, age×smoking status)

• Sensitivity analyses with different statistical approaches

Reporting standards:

This methodological framework addresses the complex statistical properties of AMH data while maximizing the validity of research findings and facilitating cross-study comparisons.

What experimental evidence supports the use of AMH as a biomarker in polycystic ovary syndrome (PCOS) research?

PCOS research has established a distinct AMH profile with diagnostic and pathophysiological implications:

Elevated AMH levels in PCOS:

• Women with PCOS typically show 2-3 fold higher serum AMH levels compared to age-matched controls

• These elevations correlate with the severity of the PCOS phenotype

• Longitudinal studies indicate that the age-related AMH decline occurs in PCOS but from a higher baseline

• This consistent elevation provides a potential objective biomarker

Experimental evidence for pathophysiological role:
In vitro and animal studies demonstrate that AMH contributes to PCOS pathogenesis by:

• Inhibiting follicle sensitivity to FSH, contributing to follicular arrest

• Decreasing aromatase activity, affecting estrogen production

• Enhancing androgen production through direct effects on theca cells

• Creating a self-reinforcing cycle with androgens, as androgens stimulate AMH production

Diagnostic application evidence:

• ROC curve analyses suggest AMH cutoff values of 4-5 ng/ml (28-35 pmol/L) can distinguish PCOS from controls with sensitivity and specificity of 80-85%

• AMH may serve as a surrogate for polycystic ovarian morphology in cases where ultrasound assessment is challenging

• Combining AMH with other biochemical markers (androgens, LH/FSH ratio) improves diagnostic accuracy

• Longitudinal studies show relatively stable AMH levels in PCOS, supporting its utility as a diagnostic marker

Research limitations and considerations:

• Heterogeneity of PCOS phenotypes requires careful subgroup analysis

• Diagnostic thresholds must be assay-specific due to standardization issues

• BMI and ethnicity may influence the AMH-PCOS relationship

• AMH performance varies depending on which PCOS diagnostic criteria are applied (Rotterdam vs. NIH vs. AE-PCOS)

The consistent relationship between AMH and PCOS makes it a valuable biomarker for both diagnostic research and investigations into pathophysiological mechanisms .

What is the experimental evidence for AMH as a cancer biomarker and potential therapeutic target?

Emerging research has identified intriguing applications for AMH in cancer research:

Granulosa Cell Tumors (GCTs):

• Adult-type granulosa cell tumors consistently express AMH, providing a highly specific biomarker

• Longitudinal studies with 10.5-year follow-up have validated serum AMH for monitoring recurrence

• AMH demonstrates superior sensitivity compared to inhibin B and estradiol for GCT detection

• AMH levels correlate with tumor burden, enabling monitoring of treatment response

Epithelial Ovarian Cancer:
Experimental evidence from preclinical models indicates:

• AMH/MIS inhibits growth of human ovarian cancer cell lines both in vitro and in vivo

• In mouse xenograft models, parenteral AMH administration reduced OVCAR 8 and IGROV 1 tumor implant size compared to controls

• The mechanism involves MIS receptor-mediated signaling pathways

• Gene expression analysis has identified downstream targets in cancer pathways

Cervical Cancer:
Molecular research has revealed:

• HPV-related cervical cancer cells express AMH receptors

• AMH treatment elevates p16 and p107 expression, proteins related to cell cycle arrest

• Pathway analysis identified 52 genes regulated by AMH mapped to cancer pathways and 13 genes mapped to apoptosis

• These findings suggest potential therapeutic applications for HPV16-related cervical cancer

Other Hormone-Responsive Cancers:

• Human endometrial cancer, breast cancer, and prostate cancer tissues express MIS/AMH type II receptors

• This receptor expression pattern suggests broader potential applications

• The anti-proliferative mechanisms observed in ovarian and cervical models may extend to these cancers

• Preclinical studies are ongoing to evaluate efficacy

Methodological considerations for cancer research:

• AMH receptor expression should be characterized in target tissues

• Recombinant AMH stability and delivery systems require optimization

• Combination approaches with established cancer therapies should be evaluated

• Biomarker validation studies need standardized protocols and prospective designs

These experimental findings highlight AMH's dual potential as both a biomarker and therapeutic agent in ovarian and other cancers .

How does AMH perform as a predictor of ovarian response in controlled ovarian stimulation protocols?

Extensive research has established AMH as a valuable predictor of ovarian response in assisted reproductive technology:

Prediction of poor ovarian response:

• AMH outperforms age, FSH, and inhibin B in identifying women likely to yield few oocytes

• Meta-analyses report AUC values of 0.78-0.81 for AMH prediction of poor response

• AMH thresholds of <0.5-1.1 ng/mL (depending on assay) identify high-risk patients

• The negative predictive value of normal AMH levels exceeds 85% for poor response prediction

Prediction of excessive response and OHSS risk:

• AMH shows superior performance to other single markers for identifying hyper-responders

• AMH values >3.5-4.0 ng/mL (assay-dependent) predict increased OHSS risk

• Combining AMH with antral follicle count further improves prediction accuracy

• Individualized stimulation protocols based on AMH have reduced OHSS incidence in clinical trials

Dosing algorithm development:

• Nomograms incorporating AMH for gonadotropin dosing show improved outcomes:

  • Fewer cycle cancellations for poor response

  • Reduced incidence of excessive response

  • More patients reaching embryo transfer

  • Potentially improved cost-effectiveness

Methodological considerations for research:

• AMH threshold values must be assay-specific

• Patient age significantly modifies the AMH-response relationship

• Other factors (BMI, ethnicity, PCOS) affect response prediction

• Validation in diverse populations is essential for algorithm development

This robust predictive relationship has led to widespread implementation of AMH-based protocols in clinical practice, though further refinement through ongoing research continues to improve individualization strategies.

What methodological approaches best evaluate AMH's ability to predict age at menopause?

Research into AMH as a predictor of menopause timing requires specialized methodological approaches:

Optimal study designs:

• Prospective longitudinal cohorts with decades-long follow-up provide highest quality evidence

• Accelerated longitudinal designs can reduce follow-up time

• Cross-sectional studies with women at different stages offer preliminary insights

• Family-based designs may help assess heritability of AMH profiles and menopausal timing

Statistical modeling approaches:

• Time-to-event analysis (survival analysis) with menopause as the endpoint

• Mixed effects models to account for repeated AMH measurements

• Machine learning algorithms incorporating multiple predictors

• Bayesian approaches that can incorporate prior knowledge about menopause distributions

Critical measurement considerations:

• Serial AMH measurements improve prediction compared to single timepoint

• Rate-of-change calculations provide additional predictive value

• Measurements at younger ages (20s-30s) may offer earlier predictive capability

• Ultra-sensitive assays are required to detect low AMH levels in women approaching menopause

Model validation requirements:

• Internal validation using bootstrapping or split-sample approaches

• External validation in independent cohorts

• Calibration assessment (agreement between predicted and observed outcomes)

• Discrimination assessment (ability to distinguish earlier vs. later menopause)

Findings and limitations from current research:

• AMH can predict final menstrual period within a broad range (±3-5 years)

• Prediction performs better for early menopause (<45 years) than for normal menopause timing

• Models incorporating multiple AMH measurements show improved accuracy

• The precise relationship between very low AMH values and time to menopause remains uncertain

Only 22% of clinicians believed AMH was moderately/very useful for predicting premature menopause, reflecting the methodological challenges and current limitations of this application .

What are the patterns of AMH test utilization among healthcare providers and what does this reveal about knowledge translation?

Survey research provides insights into current AMH test utilization patterns and knowledge translation challenges:

Utilization patterns by provider type:

• 15% of general practitioners order at least one AMH test monthly

• 40% of gynecologists and reproductive specialists order at least one test monthly

• Specialists typically initiate testing discussions, while GPs more often respond to patient requests

• These patterns suggest a knowledge gradient across provider types

Testing indications and clinical reasoning:
The most common reasons for ordering AMH testing include:

• As part of infertility investigations (51%)

• For patients considering pregnancy and wanting to understand conception chances (19%)

• To assess whether medical conditions had affected fertility (11%)

Knowledge-practice gaps:

• Despite only 5% of clinicians believing AMH predicts natural conception/birth, 40% had ordered it for reproductive planning

• Only 22% believed AMH predicted premature menopause reliably, yet this remains a common testing indication

• These discrepancies suggest factors beyond evidence influence test ordering decisions

Confidence in result interpretation:

• Half of clinicians reported lacking confidence in interpreting (51%) and explaining (48%) AMH results

• This confidence gap likely contributes to potential misapplication of test results

• The rapid evolution of AMH research may challenge providers' ability to stay current

Access patterns among patients:

• Testing rates peak among women aged 35-39 years (14%)

• Testing is associated with higher educational attainment

• Most women access testing through conventional healthcare providers rather than direct-to-consumer routes

These patterns reveal both successes and challenges in knowledge translation, suggesting the need for improved educational resources, clearer guidelines, and decision support tools, particularly as direct-to-consumer testing proliferates .

What factors influence patient and provider decision-making regarding AMH testing?

Multiple interacting factors drive AMH testing decisions among both patients and providers:

Provider-level factors:

• Knowledge and familiarity with current evidence (varies by specialty)

• Interpretation confidence (only 49-52% report confidence in interpreting/explaining results)

• Practice patterns within specialty groups

• Time constraints limiting complex counseling

• Concern about missing significant diagnoses

• Response to perceived patient expectations

Patient-level factors:

• Age-related reproductive concerns (highest testing among women 35-39)

• Educational background (associated with testing uptake)

• Media coverage of "fertility testing" and celebrity disclosures

• Direct-to-consumer marketing

• Prior reproductive experiences

• Family planning timeframes

Healthcare system factors:

• Test availability and accessibility

• Insurance coverage and out-of-pocket costs

• Referral patterns and specialist access

• Clinical guideline recommendations (or lack thereof)

• Integration with electronic health records and decision support

Decision-making models:
A comprehensive research model should examine:

• Initial awareness and knowledge acquisition

• Risk perception formation

• Deliberation process including provider consultation

• Decision outcomes and subsequent behaviors

• Post-test psychological impacts and decision satisfaction

Methodological approaches to study decision-making:

• Mixed-methods designs combining surveys with qualitative interviews

• Decision analysis frameworks to map influential factors

• Discrete choice experiments to quantify preference weights

• Longitudinal studies tracking decision outcomes over time

• Intervention studies testing decision support tools

Understanding these complex decision processes is essential for developing targeted interventions to promote appropriate test utilization while respecting patient autonomy and values .

What implementation science approaches could improve evidence-based use of AMH testing?

Implementation science offers several methodological frameworks to bridge the research-practice gap in AMH testing:

Multilevel intervention targets:

• Provider-level: Education, decision support, audit and feedback

• Patient-level: Shared decision-making tools, educational materials

• System-level: Electronic health record integration, clinical pathways

• Policy-level: Coverage decisions, guideline development

Implementation strategies with supporting evidence:

• Academic detailing:

  • One-on-one educational outreach to clinicians

  • Focus on evidence-based indications and limitations

  • Provision of decision support tools

• Clinical decision support:

  • Electronic health record integration

  • Age-specific reference range automation

  • Indication-based ordering prompts

  • Structured reporting templates

• Audit and feedback:

  • Regular provider-specific utilization reports

  • Comparison to evidence-based benchmarks

  • Peer comparison data

  • Case-based learning from appropriate/inappropriate examples

• Patient decision aids:

  • Balanced information on benefits/limitations

  • Age-specific framing of results interpretation

  • Values clarification exercises

  • Action planning based on results

Evaluation frameworks:

• RE-AIM model (Reach, Effectiveness, Adoption, Implementation, Maintenance)

• CFIR (Consolidated Framework for Implementation Research)

• PRECEDE-PROCEED model for comprehensive program planning

• Implementation fidelity assessment tools

Implementation outcomes to measure:

• Appropriateness of test ordering (alignment with evidence-based indications)

• Provider knowledge and confidence improvement

• Patient decisional quality (knowledge, values concordance)

• Cost-effectiveness of targeted versus untargeted testing

• Sustainability of practice improvements over time

These implementation science approaches acknowledge the complex barriers to evidence-based practice and provide methodological frameworks to develop and evaluate targeted interventions .

How might standardization efforts affect future research and clinical applications of AMH testing?

The World Health Organization's efforts to establish an International Standard for AMH have significant implications for future research and clinical practice:

Anticipated impacts of successful standardization:

• Development of universal reference ranges independent of specific assay platforms

• Consistent clinical decision thresholds for fertility treatment protocols

• Improved meta-analysis capabilities across studies using different assays

• Enhanced quality control programs with standardized reference materials

• More reliable longitudinal monitoring across assay transitions

Standards development progress:
WHO collaborative study findings for recombinant AMH candidate standard (16/190):

• Geometric mean: 511 ng/amp (95% CI: 426-612)

• Geometric coefficient of variation: 42%

• Results confirmed low intra-assay variability within methods

• Consistent recognition patterns across different AMH concentrations

Methodological challenges remaining:

• Addressing differences in antibody specificity for various AMH forms

• Standardizing calibrator material preparation and stability

• Establishing commutability with patient samples

• Developing consensus on reporting units and reference ranges

• Implementing standardization across diverse laboratory settings

Future research directions:

• Validation of standardized assays in diverse clinical populations

• Development of assay-independent clinical algorithms

• Improved understanding of AMH molecular heterogeneity

• Point-of-care testing development based on standardized calibration

• Integration with other biomarkers in comprehensive assessment panels

The WHO standardization initiative represents a critical step toward resolving the persistent challenges in AMH measurement, potentially transforming both research capabilities and clinical applications through improved consistency and reliability .

What are the most significant knowledge gaps in AMH research requiring methodological innovation?

Despite substantial progress in AMH research, several critical knowledge gaps remain that require methodological innovation:

• Molecular heterogeneity characterization:
  Developing methods to distinguish between biologically active and inactive AMH forms would enhance our understanding of AMH physiology and improve assay specificity.

• Individual-level prediction models:
  Current models provide population-level estimates but lack precision for individual patients. Advanced statistical approaches combining multiple biomarkers and longitudinal measurements could improve personalized predictions.

• Genetic and epigenetic regulation:
  The genetic architecture underlying AMH production and age-related decline remains poorly understood. Genome-wide association studies and epigenetic profiling offer promising approaches.

• Biological mechanisms of AMH action:
  While AMH correlations with various conditions are established, the causal pathways and molecular mechanisms require further elucidation through mechanistic studies.

• Therapeutic applications development:
  Translating the observed anti-cancer effects of AMH/MIS into viable therapeutic approaches requires innovative drug delivery systems and clinical trial designs.

Addressing these knowledge gaps will require interdisciplinary collaboration between basic scientists, clinicians, statisticians, and implementation researchers to develop integrated research programs that connect molecular mechanisms to clinical applications.

How should researchers approach the challenges of AMH research standardization?

The standardization challenges in AMH research require a comprehensive approach:

The lack of an international AMH standard, even 20 years after the first AMH ELISA development, has been a significant obstacle . The recent WHO initiative to evaluate a candidate standard for AMH (coded 16/190) represents a critical advancement, finding a geometric mean of 511 ng/amp with a wide confidence interval (426-612) .

To address these challenges, researchers should:

• Actively participate in standardization efforts:

  • Contribute to reference material validation studies

  • Implement standardized reporting once established

  • Support external quality assessment programs

• Adopt transparent methodological reporting:

  • Document assay details (manufacturer, detection limits, calibration)

  • Report both absolute values and age-specific percentiles

  • Acknowledge assay limitations in result interpretation

• Employ robust research designs:

  • Include assay comparison sub-studies when methods change

  • Use bridging studies to connect historical and current data

  • Consider parallel testing with multiple assays in critical studies

  • Develop mathematical conversion algorithms between assays

• Incorporate biological variability understanding:

  • Account for factors affecting AMH beyond analytical variation

  • Standardize pre-analytical handling and processing

  • Document relevant biological and demographic variables

These approaches will contribute to more reliable and comparable research results while the field progresses toward full standardization, ultimately enhancing both scientific understanding and clinical applications of AMH measurement.