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Sara Riyadh Saifallah Aseel Ghazi Rifat

Abstract

Background: The key features of polycystic ovary syndrome (PCOS) are oligo-anovulation, hyperandrogenism, and polycystic ovaries. Elevated plasma prolidase activity is closely related to the pathophysiology of PCOS, in which increased ovarian stromal volume and atretic follicles may be accompanied by changes in extracellular matrix remodeling.


Objectives: Assessment of plasma prolidase levels in PCOS and to compare these levels between its different phenotypes as a marker for diagnosis and prognosis of the syndrome.


Patients and Methods: A case–control study included 44 women who were diagnosed with PCOS, and 48 healthy, ovulatory women. Plasma prolidase levels were measured and compared between the two groups and across different PCOS phenotypes.


Results: The mean prolidase level was significantly higher in PCOS (103.8 ± 82 ng/ml) than in controls (8.4 ± 4.1 ng/ml), the cut-off value (>31 ng/ml) was found to be 81.82% sensitive, 93.75% specific as a predictor of PCOS. The mean prolidase levels were higher in the PCOS group with ≥20 antral follicles; a positive correlation was found between prolidase level and the number of antral follicles in PCOS. Additionally, the mean prolidase levels varied significantly among the different PCOS phenotypes, with the polycystic ovary. hyperandrogenism oligo-anovulation (PHO) phenotype found to have the highest level.


Conclusion: Plasma prolidase has a potential to serve as a useful, inexpensive and easily assessable marker for the diagnosis of PCOS. Its levels were directrly correlated with the number of antral follicles in PCOS women, suggesting that it can be used as a tool to replase ultrasound imaging.  Proidase levels vary significantly among the different PCOS phenotypes, so it could be used for monitoring the disease.


Keywords: Polycystic ovary syndrome, Prolidase, Rotterdam criteria.

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How to Cite
[1]
Sara Riyadh Saifallah and Aseel Ghazi Rifat, “Evaluation of Plasma Prolidase Level as a Diagnostic and Prognostic Biomarker for Polycystic Ovary Syndrome”, djm, vol. 30, no. 1, pp. 57–69, Apr. 2026.
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