A1 Journal article (refereed)
Predicting the age at natural menopause in middle-aged women (2021)
Hyvärinen, M., Karvanen, J., Aukee, P., Tammelin, T. H., Sipilä, S., Kujala, U. M., Kovanen, V., Rantalainen, T., & Laakkonen, E. K. (2021). Predicting the age at natural menopause in middle-aged women. Menopause: the Journal of the North American Menopause Society, 28(7), 792-799. https://doi.org/10.1097/GME.0000000000001774
JYU authors or editors
Publication details
All authors or editors: Hyvärinen, Matti; Karvanen, Juha; Aukee, Pauliina; Tammelin, Tuija H.; Sipilä, Sarianna; Kujala, Urho M.; Kovanen, Vuokko; Rantalainen, Timo; Laakkonen, Eija K.
Journal or series: Menopause: the Journal of the North American Menopause Society
ISSN: 1530-0374
eISSN: 1530-0374
Publication year: 2021
Publication date: 12/04/2021
Volume: 28
Issue number: 7
Pages range: 792-799
Publisher: Lippincott Williams & Wilkins
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1097/GME.0000000000001774
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75212
Additional information: Video Summary:http://links.lww.com/MENO/A743.
Abstract
Methods: Cox models with time-dependent covariates were utilized for ANM prediction using longitudinal data from 47 to 55-year-old women (n ¼279) participating in the Estrogenic Regulation of Muscle Apoptosis study. The ANM was assessed retrospectively for 105 women using bleeding diaries. The predictors were chosen from the set of 32 covariates by using the lasso regression (model 1). Another easy-to-access model (model 2) was created by using a subset of 16 self-reported covariates. The predictive performance was quantified with c-indices and by studying the means and standard deviations of absolute errors (MAE ±SD) between the predicted and observed ANM.
Results: Both models included alcohol consumption, vasomotor symptoms, self-reported physical activity, and relationship status as predictors. Model 1 also included estradiol and follicle-stimulating hormone levels as well as SD of menstrual cycle length, while model 2 included smoking, education, and the use of hormonal contraception as additional predictors. The mean c-indices of 0.76 (95% CI 0.71-0.81) for model 1 and 0.70 (95% CI 0.65-0.75) for model 2 indicated good concordance between the predicted and observed values. MAEs of 0.56 ± 0.49 and 0.62 ± 0.54 years respectively for model 1 and 2 were clearly smaller than the MAE for predicted sample mean (1.58 ±1.02).
Conclusions: In addition to sex hormone levels, irregularity of menstrual cycle, and menopausal symptoms, also life habits and socioeconomic factors may provide useful information for ANM prediction. The suggested approach could add value for clinicians’ decision making related to the use of contraception and treatments for menopausal symptoms in perimenopausal women.
Keywords: middle age; menopause; menstruation; forecasts; lifestyle habits; statistical models
Free keywords: final menstrual period; menopausal transition; menopause prediction; perimenopause; premenopause.
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Related research datasets
Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 1