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February 8, 2026Journal of the Turkish-German Gynecological Association0 citationsOpen Access

Comparative analysis of hormonal and metabolic indices in phenotypic subgroups of polycystic ovary syndrome

BÜBelgin Savran ÜçokTATürkan Dikici AktaşEOEmel Ozalp

Key Points

  • To investigate the hormonal and metabolic differences among phenotypic subgroups of polycystic ovary syndrome (PCOS).
  • Comparison of hormonal and metabolic patterns across different PCOS phenotypes.
  • Identification of phenotype A as the most severe and phenotype D as milder.
  • Assessment of the role of FAI as a biomarker for hirsutism.
  • Phenotype A shows more severe metabolic and androgenic symptoms than phenotype D.
  • FAI is identified as the best marker for assessing hirsutism in PCOS.
  • Distinct patterns exist among phenotypes, indicating the need for a phenotype-oriented clinical approach.

Abstract

PCOS phenotypes demonstrate distinct hormonal and metabolic patterns. Phenotype A represents the most metabolically and androgenically severe subgroup, whereas phenotype D is comparatively mild. FAI emerges as the most informative marker for hirsutism, supporting a phenotype-oriented approach to clinical assessment and follow-up in PCOS.

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Cite This Study

Üçok et al. (2026) studied this question.

synapsesocial.com/papers/6988270a0fc35cd7a8845ee0https://doi.org/10.4274/jtgga.galenos.2026.2025-9-22
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Phenotypic Variations in Polycystic Ovary Syndrome: Metabolic Risks and Emerging Biomarkers2025
  2. 2Metabolic characteristics of different phenotypes in reproductive-aged women with polycystic ovary syndrome2024 · 21 citations
  3. 3Cardiovascular and Metabolic Risk Profiles Across Different Phenotypes of Polycystic Ovary Syndrome2025
  4. 4Polycystic ovary syndrome phenotypes in Iranian infertile women: a cross-sectional study2026
  5. 5Characterization of distinct polycystic ovary syndrome subtypes by cluster and principal component analyses2025