ArticleCureus2025
Prevalence of Polycystic Ovary Syndrome Phenotypes and Their Association With Vitamin D Levels, Insulin Resistance, and Homeostasis Model Assessment of Insulin Resistance in Women With Polycystic Ovary Syndrome: A Cross-Sectional Analysis.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
10 authors.
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Abstract
objectiveThe objective of this study is to evaluate the association of polycystic ovarian syndrome (PCOS) phenotypes with various anthropometric, hormonal, and metabolic parameters and to explore the correlation between vitamin D deficiency and insulin resistance (IR) in women with PCOS. STUDY
designThis prospective cross-sectional study was conducted over 10 months, including 212 women diagnosed with PCOS based on the Rotterdam criteria. Participants were classified into four phenotypes, and comprehensive anthropometric measurements, hormonal assessments, and metabolic evaluations were performed. Spearman's rank correlation coefficient was used to assess the correlation between homeostasis model assessment of insulin resistance (HOMA-IR) and vitamin D levels (ng/mL). A p-value of less than 0.05 was considered statistically significant.
resultsPhenotype D (n=104, 49.06%) was the most common among participants, followed by Phenotypes A, C, and B. BMI and serum anti-Müllerian hormone (AMH) levels showed significant associations with PCOS phenotypes, with the highest BMI observed in Phenotype A; 53.8% (n=114) of participants had vitamin D deficiency. A significant negative correlation was found between vitamin D levels and IR (correlation coefficient: -0.207), as measured by the HOMA-IR, suggesting that lower vitamin D levels are associated with higher IR.
conclusionThe study demonstrates significant associations between PCOS phenotypes and key anthropometric and hormonal parameters, particularly BMI and serum AMH levels. Additionally, vitamin D deficiency is prevalent in women with PCOS and is correlated with increased IR. These findings may contribute to the development of predictive models for metabolic abnormalities in PCOS and underscore the need for further research with larger, more diverse populations to better understand the complex pathogenesis of PCOS.
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