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 editorsHyvärinen, Matti; Karvanen, Juha; Aukee, Pauliina; Tammelin, Tuija H.; Sipilä, Sarianna; Kujala, Urho M.; Kovanen, Vuokko; Rantalainen, Timo; Laakkonen, Eija K.

Journal or seriesMenopause: the Journal of the North American Menopause Society

ISSN1530-0374

eISSN1530-0374

Publication year2021

Publication date12/04/2021

Volume28

Issue number7

Pages range792-799

PublisherLippincott Williams & Wilkins

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1097/GME.0000000000001774

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/75212

Additional informationVideo Summary:http://links.lww.com/MENO/A743.


Abstract

Objective: To predict the age at natural menopause (ANM).
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.


Keywordsmiddle agemenopausemenstruationforecastslifestyle habitsstatistical models

Free keywordsfinal menstrual period; menopausal transition; menopause prediction; perimenopause; premenopause.


Contributing organizations


Related projects


Related research datasets


Ministry reportingYes

Reporting Year2021

JUFO rating1


Last updated on 2024-26-03 at 09:19