Prediction of age at menopause in women with polycystic ovary syndrome
Minooee, S., Ramezani Tehrani, F., Rahmati, M., Mansournia, M. Ali, and Azizi, F. (2018) Prediction of age at menopause in women with polycystic ovary syndrome. Climacteric, 21 (1). pp. 29-34.
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Abstract
Objective: Considering the role of anti-Müllerian hormone (AMH) in female fertility and its high levels in women with polycystic ovary syndrome (PCOS), the longer reproductive span of these women is in doubt. In the present study, we aimed to improve earlier predictions using a non-linear model to substantiate the question as to whether PCOS women reach menopause later.
Methods: In total, 1162 women aged 20–50 years, comprising 378 PCOS cases and 784 eumenorrheic non-hirsute women, met the eligibility criteria. A scatterplot matrix was drawn to detect the association between age and AMH; this association was explored using a fractional polynomial regression model. Model assumptions were checked by examining the distribution of the residuals and plotting the standardized residuals against the functional form of AMH.
Results: The serum concentration of AMH among PCOS participants was significantly higher than in the controls (5.4 ng/ml (IQR 2.8–9.1 ng/ml) vs. 1.4 ng/ml (IQR 0.6–2.7 ng/ml), p < 0.001). The estimated mean age at menopause was 51.4 (95% CI 45–59) years and 49.7 (95% CI 45–55) years in PCOS cases and healthy controls, respectively.
Conclusions: These findings provide the insight that, as reflected through significantly higher average levels of AMH in PCOS women, their predicted reproductive lifespan could be 2 years longer than their normo-ovulatory counterparts.
Item ID: | 77745 |
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Item Type: | Article (Research - C1) |
ISSN: | 1473-0804 |
Copyright Information: | © 2017 International Menopause Society |
Date Deposited: | 28 Nov 2023 00:32 |
FoR Codes: | 32 BIOMEDICAL AND CLINICAL SCIENCES > 3215 Reproductive medicine > 321502 Obstetrics and gynaecology @ 100% |
SEO Codes: | 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100% |
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