An academic research group in Finland has built a predictive model that takes into account 32 variables to predict the age of natural menopause of women in their 40s and 50s. The model was based on longitudinal data taken over four years from around 1400 Finnish women in pre-, peri-, or post- menopause. The data included both laboratory data (blood-based biomarkers, in this case) and survey data.

The variable data taken into account that together predicted age at natural menopause include objective metrics, such as education and hormone levels in the bloodstream (including estradiol and follicle stimulating hormone, or FSH); as well as more subjective self-reported metrics such as smoking, alcohol consumption, level of physical activity, relationship status, the use of hormonal contraception, vasomotor symptoms, and menstruation details such as cycle regularity and heaviness of blood flow.

The predictive model has multiple clinical implications. It could aid women with their decision-making around family planning and contraception. It could also help them better understand whether symptoms they are experiencing are related to menopause, enabling them to receive more tailored healthcare treatments and decide whether and when they want to begin hormone therapy (HT). And finally, it could help them pinpoint with more precision at what point their risk increases for heart disease, osteoporosis, and other conditions that are caused by low estrogen, allowing for more effective early intervention, including behavior modification.