Phenotype-Specific Differences in Insulin Resistance and Androgenic Profiles in Polycystic Ovary Syndrome: A Prospective Observational Study

Background and Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine and metabolic disorder characterized by distinct phenotypic presentations. This study aimed to compare androgenic and metabolic characteristics across Rotterdam-defined PCOS phenotypes, with particular emphasis on insulin resistance. Materials and Methods: This prospective observational study included 261 nulliparous women with PCOS who were prospectively enrolled and classified according to the Rotterdam criteria as phenotype A (n = 101), B (n = 39), C (n = 55), or D (n = 66). Clinical hyperandrogenism, biochemical androgen parameters, hormonal parameters, lipid profiles, and insulin resistance assessed using the homeostasis model assessment of insulin resistance (HOMA-IR) were compared across phenotypes. Insulin resistance was defined as HOMA-IR ≥ 2.5. Multivariable logistic regression was performed to evaluate the association between PCOS phenotype and insulin resistance after adjustment for age and body mass index (BMI). Results: Significant differences were observed among phenotypes in sex hormone-binding globulin (SHBG), free androgen index (FAI), and Ferriman–Gallwey scores (p = 0.005, p < 0.001, and p < 0.001, respectively). Median HOMA-IR differed significantly among phenotypes (p < 0.001) and was lowest in phenotype D. The prevalence of insulin resistance was 58.3%, 56.4%, 49.1%, and 22.6% in phenotypes A, B, C, and D, respectively (p < 0.001). After adjustment for age and BMI, phenotypes A (adjusted odds ratio [aOR] 4.56, 95% CI 2.20–9.42), B (aOR 4.54, 95% CI 1.90–10.87), and C (aOR 3.29, 95% CI 1.47–7.38) were associated with higher odds of insulin resistance compared with phenotype D. HDL-C also differed among phenotypes (p = 0.006), whereas other conventional lipid parameters, the TG/HDL ratio, and AIP were comparable. Conclusions: Rotterdam-defined PCOS phenotypes exhibit distinct androgenic and metabolic profiles. Hyperandrogenic phenotypes, particularly A and B, were associated with greater insulin resistance, whereas phenotype D showed a more favorable metabolic profile. These findings reflect cross-sectional metabolic differences between PCOS phenotypes and should not be interpreted as evidence of future cardiometabolic risk.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2026-09-13
DOI
https://doi.org/10.3390/jcm15187101
Primary Topic
Ovarian function and disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Phenotype-Specific Differences in Insulin Resistance and Androgenic Profiles in Polycystic Ovary Syndrome: A Prospective Observational Study

Nazime Binnur Cömert, Yücel Kaya, Sultan Can, Emrah Dağdeviren et al.
Journal of Clinical Medicine
Ovarian function and disorders
article

Phenotype-Specific Differences in Insulin Resistance and Androgenic Profiles in Polycystic Ovary Syndrome: A Prospective Observational Study

Nazime Binnur Cömert, Yücel Kaya, Sultan Can, Emrah Dağdeviren, Busra Deniz Gelir, Can Tercan, İsmail Alay, Engin Oral, Karolin Ohanoğlu, Kübra Kurt Bilirer, Ahmet Cinar
article en

Abstract

Background and Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine and metabolic disorder characterized by distinct phenotypic presentations. This study aimed to compare androgenic and metabolic characteristics across Rotterdam-defined PCOS phenotypes, with particular emphasis on insulin resistance. Materials and Methods: This prospective observational study included 261 nulliparous women with PCOS who were prospectively enrolled and classified according to the Rotterdam criteria as phenotype A (n = 101), B (n = 39), C (n = 55), or D (n = 66). Clinical hyperandrogenism, biochemical androgen parameters, hormonal parameters, lipid profiles, and insulin resistance assessed using the homeostasis model assessment of insulin resistance (HOMA-IR) were compared across phenotypes. Insulin resistance was defined as HOMA-IR ≥ 2.5. Multivariable logistic regression was performed to evaluate the association between PCOS phenotype and insulin resistance after adjustment for age and body mass index (BMI). Results: Significant differences were observed among phenotypes in sex hormone-binding globulin (SHBG), free androgen index (FAI), and Ferriman–Gallwey scores (p = 0.005, p < 0.001, and p < 0.001, respectively). Median HOMA-IR differed significantly among phenotypes (p < 0.001) and was lowest in phenotype D. The prevalence of insulin resistance was 58.3%, 56.4%, 49.1%, and 22.6% in phenotypes A, B, C, and D, respectively (p < 0.001). After adjustment for age and BMI, phenotypes A (adjusted odds ratio [aOR] 4.56, 95% CI 2.20–9.42), B (aOR 4.54, 95% CI 1.90–10.87), and C (aOR 3.29, 95% CI 1.47–7.38) were associated with higher odds of insulin resistance compared with phenotype D. HDL-C also differed among phenotypes (p = 0.006), whereas other conventional lipid parameters, the TG/HDL ratio, and AIP were comparable. Conclusions: Rotterdam-defined PCOS phenotypes exhibit distinct androgenic and metabolic profiles. Hyperandrogenic phenotypes, particularly A and B, were associated with greater insulin resistance, whereas phenotype D showed a more favorable metabolic profile. These findings reflect cross-sectional metabolic differences between PCOS phenotypes and should not be interpreted as evidence of future cardiometabolic risk.

Journal of Clinical MedicineVol. 15(18)
State Hospital (GB), Turkish Society of Hematology (TR), University Hospital of Umeå (SE), Memorial Sisli Hospital (TR), Erzurum Regional Training and Research Hospital (TR), Turkish Society of Cardiology (TR), University of Health Sciences Antigua (AG), Umeå University (SE)
Good health and well-being
Openalex Percentile: Top 8%
Ovarian function and disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.