Our large-scale clustering analysis identifies four reproducible subtypes of polycystic ovary syndrome, which we validated across diverse populations. These subtypes have distinct reproductive and metabolic trajectories that could guide precision management strategies.
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References
Stener-Victorin, E. et al. Polycystic ovary syndrome. Nat. Rev. Dis. Primers 10, 27 (2024). A review that presents the aspects of PCOS diagnosis, prevalence, associated comorbidities and current management.
Teede, H. J. et al. Recommendations from the 2023 international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Fertil. Steril. 120, 767–793 (2023). The latest international guideline, which highlights PCOS diagnostic and therapeutic challenges.
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018). A landmark study that successfully used a similar data-driven clustering approach to reclassify type 2 diabetes, demonstrating the power of this methodology for heterogeneous diseases.
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This is a summary of: Gao, X. et al. Data-driven subtypes of polycystic ovary syndrome and their association with clinical outcomes. Nat. Med. https://doi.org/10.1038/s41591-025-03984-1 (2025).
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Clustering analysis uncovers four reproducible PCOS subtypes with distinct clinical outcomes. Nat Med 31, 4002–4003 (2025). https://doi.org/10.1038/s41591-025-04040-8
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DOI: https://doi.org/10.1038/s41591-025-04040-8
