The study drew on an existing single-center database of 495 women evaluated at the hospital’s Reproductive Medicine Center, a cohort defined by its association with infertility care. From that pool, 431 women had complete records for age, body mass index, anti-Müllerian hormone, inhibin B, and a validated eight-steroid panel measured by mass spectrometry. The researchers sorted the women into categories based on historical antral follicle count, the number of small resting follicles visible on ultrasound, combined with menstrual-cycle function. A reference group with low follicle counts and regular cycles comprised 27 women, an isolated high-follicle-count group comprised 69, and a combined group with both high counts and menstrual dysfunction comprised 333, yielding a three-group analysis cohort of 429 women.

Anti-Müllerian hormone, a growth factor secreted by small growing follicles, showed a dramatic and statistically unambiguous gradient across the three categories. Median values climbed from 0.89 nanograms per milliliter in the reference group to 3.85 in the isolated high-count group and 5.76 in the combined group, a difference with a p-value of 2.51 × 10⁻¹⁷. When the researchers adjusted for age, body mass index, and the hormone itself in multinomial regression, each standard deviation increase in anti-Müllerian hormone raised the odds of belonging to the isolated high-count group more than fivefold, and the odds of belonging to the combined group nearly ninefold compared with the reference.

The classical androgens told a different story. Testosterone and androstenedione, the two workhorse androgens traditionally measured in suspected cases of androgen excess, did differ between the groups in raw, unadjusted comparisons. Yet once the models accounted for anti-Müllerian hormone, age, and body mass index, those differences evaporated. The residual distributions of testosterone and androstenedione, conditioned on those covariates, no longer separated the groups, meaning that whatever signal these hormones carried was largely redundant with the information already captured by anti-Müllerian hormone and body characteristics.

Perhaps the most technically ambitious part of the analysis involved the 11-oxygenated androgens, a relatively recently appreciated family of steroid metabolites, including 11β-hydroxytestosterone and 11-ketotestosterone, that are produced through an adrenal-centric pathway and have been proposed as culprits in the androgen excess of polycystic ovary syndrome. Using the mass spectrometry panel, the researchers quantified four of these metabolites and asked whether they distinguished the three ovarian-menstrual categories. The answer was no: none of the four 11-oxygenated androgens differed significantly across the groups, a result that tempers the hypothesis that these novel androgens are central markers of the polycystic ovary phenotype itself.

The story shifted, however, when the team turned to metabolism. In analyses that were not prespecified, an unsupervised k-means clustering of the cohort revealed a subgroup characterized by higher insulin resistance, measured by the homeostatic model assessment of insulin resistance, together with higher body mass index. In that cluster, 11β-hydroxytestosterone and 11-ketotestosterone were significantly elevated, with p-values of 6.78 × 10⁻⁴ and 8.51 × 10⁻⁴ respectively. In an extreme comparison between women with polycystic ovary syndrome and high body mass index versus those without either feature, only 11β-hydroxytestosterone remained significantly different.

To gauge whether a small hormone panel could classify patients, the researchers built a four-marker model and evaluated it with receiver operating characteristic analysis. The apparent area under the curve reached 0.724, but when they applied leave-one-out cross-validation, a stricter test that asks how well the model performs on patients it has never seen, the value dropped to 0.664. That gap between apparent and cross-validated performance is a familiar caution in exploratory biomarker work, a reminder that models fitted to a single dataset tend to overstate their own discriminative power.

The authors are careful about the limitations embedded in their design, and those caveats matter for interpretation. The follicle-count categories came from a historical database in which per-ovary counts were not uniformly available, so the study-specific designation of elevated counts is not equivalent to the current criterion for polycystic ovarian morphology, which requires at least 20 follicles in one ovary. Reliable reclassification to modern definitions was not possible. The isolated menstrual dysfunction group contained only two women and was retained for descriptive purposes alone. Crucially, the metabolic findings were exploratory, were not adjusted for multiple comparisons, and have not been externally validated, so they should be read as hypothesis-generating rather than settled fact.

Those constraints aside, the central message is clinically resonant. In a cohort of women seeking fertility care, anti-Müllerian hormone emerged as the single strongest adjusted correlate of ovarian and menstrual categorization, while the androgens that dominate diagnostic checklists contributed little independent information. If the findings hold up in prospective, appropriately powered studies using current morphology criteria, they could nudge the field toward a reordering of priorities, in which follicular output, read through anti-Müllerian hormone, becomes the anchor biomarker, and androgens, whether classical or 11-oxygenated, are reinterpreted mainly through their relationship with metabolic health rather than ovarian anatomy.

For patients, the practical takeaway is one of measured expectations. This study does not rewrite diagnostic criteria, and its retrospective, single-center design limits generalizability. But it adds weight to a growing view that polycystic ovary syndrome’s heterogeneity is not just clinical noise, it reflects distinct biological axes, with the follicular compartment and the metabolic compartment each carrying their own hormonal signatures. Disentangling those axes, the Beijing team suggests, may ultimately be the key to matching patients with the treatments most likely to help them.

Subject of Research: Hormonal profiling across ovarian and menstrual categories in polycystic ovary syndrome

Article Title: Exploratory analysis of anti-Müllerian hormone and androgen profiles across historical ovarian–menstrual categories defined by antral follicle count in an infertility-associated reproductive endocrine cohort

Article References: Zhang, X., Xu, H., Xiong, X., Zhao, R., Zhao, L., Li, R., & Yang, L. (2026). Exploratory analysis of anti-Müllerian hormone and androgen profiles across historical ovarian–menstrual categories defined by antral follicle count in an infertility-associated reproductive endocrine cohort. Journal of Ovarian Research. https://doi.org/10.1186/s13048-026-02301-9

Image Credits: AI Generated

DOI: 10.1186/s13048-026-02301-9

Keywords: anti-Müllerian hormone, polycystic ovary syndrome, 11-oxygenated androgens, testosterone, androstenedione, antral follicle count, steroid profiling, reproductive endocrinology, insulin resistance, infertility, ovarian morphology, mass spectrometry