Disclosures: The authors report no relevant financial disclosures.
April 15, 2021
2 min read

Lifestyle, socioeconomic factors help predict age at natural menopause

Disclosures: The authors report no relevant financial disclosures.
You've successfully added to your alerts. You will receive an email when new content is published.

Click Here to Manage Email Alerts

We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.

Menopause indicators, such as follicle-stimulating hormone and estradiol levels, combined with lifestyle habits and socioeconomic factors may help predict when a woman will begin menopause, according to study data.

In a study published in Menopause, researchers created two Cox models using time-dependent covariates associated with age at natural menopause to predict the timing of menopause in women.

Happy Women
Source: Adobe Stock

“The suggested approach for predicting the age at natural menopause could provide a useful and easily accessible tool for assessing the time to approaching natural menopause in middle-aged women,” Matti Hyvärinen, MSc, a doctoral researcher in the Gerontology Research Center and faculty of sport and health sciences at the University of Jyväskylä in Finland, told Healio. “This information can be useful for clinicians when making decisions related to the use of hormonal contraception and treatments for menopausal symptoms in perimenopausal women.”

Matti Hyvärinen

Hyvärinen and colleagues obtained data from women participating in the Estrogenic Regulation of Muscle Apoptosis study. All participants were aged 47 to 55 years and living in Jyväskylä, Finland. Of 279 women in the study, age at natural menopause was determined for 105 participants who were perimenopausal at baseline, remained through the follow-up period until they were postmenopausal, and completed a monthly diary detailing their menstrual bleeding. Age at natural menopause was defined as the age when the last reported bleeding period began.

Researchers identified 32 covariates describing characteristics associated with age at natural menopause or reported to fluctuate during menopause transition. The covariates included blood-based biomarkers, body composition variables, objectively measured physical activity, menstrual cycle characteristics and self-reported data, including gynecologic history, menopause symptoms. Also included were lifestyle and socioeconomic information, such as education level, relationship status and physical activity habits. Two Cox regression models were created, with the first model using all 32 predictors and the second model using only self-reported variables and BMI as candidate covariates. The set of seven predictors with the best predictive performance were chosen for the final models.

Both models demonstrated good predictive performance for age at natural menopause. The first model with all candidate predictors had a median C index of 0.76 (95% CI, 0.71-0.81), whereas the model using only self-reported candidate predictors and BMI had a median C index of 0.7 (95% CI, 0.65-0.75). The mean absolute error was 0.56 years in the first model and 0.62 years in the second model. Both figures were smaller than the mean absolute error of 1.58 years in the predicted sample mean.

Higher estradiol and follicle-stimulating hormone levels, irregular menstrual bleeding and vasomotor symptoms were strong indicators of approaching natural menopause. Although these associations were consistent with earlier findings, the researchers had a few contradictory results.

“One interesting finding was that participants tended to increase their alcohol consumption when approaching the menopause,” Hyvärinen said. “Thus, higher alcohol consumption was a significant predictor of the approaching menopause in our models. Furthermore, smoking has been previously associated with earlier age at the menopause in several previous studies, but we did not observe such association in our study.”

Hyvärinen said he was encouraged by the study’s findings, but he noted the models can be improved with additional predictors.

“Our data lacked some potentially strong predictors of the menopause, such as anti-Müllerian hormone levels, follicle counts and mother’s age at natural menopause,” Hyvärinen said. “Thus, future studies utilizing similar methodological approach with more comprehensive set of predictors and longer follow-up time could lead to better predictive performance and enable the accurate prediction of the age at natural menopause for women in their 30s or early 40s.”

For more information:

Matti Hyvärinen, MSc, can be reached at matti.v.hyvarinen@jyu.fi.