No single biological phenotype exists in polycystic ovary syndrome: evidence from cross-space phenotyping
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Context
Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is a biologically heterogeneous disorder, yet previous clustering studies have reported inconsistent phenotype structures. Whether these discrepancies reflect methodological variability or genuine multidimensional disease biology remains unknown.
Objective
To determine whether independently derived endocrine, metabolic, inflammatory, and thyroid phenotypes represent the same underlying biological structure or capture distinct dimensions of PMOS heterogeneity.
Design
Cross-sectional observational study using a cross-space phenotyping framework.
Setting
Tertiary referral outpatient endocrinology and gynecology clinic.
Participants
A total of 1,286 women were diagnosed with PCOS according to the Rotterdam criteria.
Methods
Four predefined biological spaces (endocrine, metabolic, inflammatory, and thyroid) were analyzed independently. Within each space, standardized preprocessing, dimensionality reduction, and unsupervised clustering were performed. Cluster robustness was evaluated using bootstrap resampling, while agreement between independently derived phenotypes was quantified using the adjusted Rand index (ARI). Biological relevance was assessed using independent non-circular validation with variables excluded from phenotype derivation. Sensitivity analyses compared complete-case and imputed datasets.
Results
All four biological spaces produced highly stable clustering solutions (bootstrap ARI: endocrine 0.915, metabolic 0.964, inflammatory 0.930, thyroid 0.990). Despite this robustness, agreement between independently derived phenotypes remained consistently low. The highest concordance was observed between metabolic and inflammatory phenotypes (ARI = 0.208), followed by endocrine and metabolic phenotypes (ARI = 0.159), whereas agreement involving thyroid phenotypes was close to zero. Independent non-circular validation confirmed that all identified phenotypes represented biologically coherent patient subgroups beyond the variables used for clustering. Sensitivity analyses demonstrated high agreement between complete-case and imputed solutions, supporting the robustness of the findings.
Conclusions
Stable biological phenotypes exist within individual physiological domains of PMOS but do not converge into a single overarching biological phenotype. These findings support a multidimensional model of PMOS heterogeneity in which endocrine, metabolic, inflammatory, and thyroid systems describe complementary rather than interchangeable aspects of disease biology. Cross-space phenotyping provides a general framework for investigating biological heterogeneity in complex disorders and may facilitate future precision medicine approaches.
