Artificial intelligence (AI) is positioned to make a major impact on almost every industry, including healthcare. A new study suggests that machine learning models can more quickly and affordably identify women with severe subjective cognitive decline during the menopause transition, effectively opening the door to better management of cognitive health. Results of the study are published online today in Menopause, the journal of The Menopause Society.
Subjective cognitive decline is characterized by an individual's awareness of a decrease in their memory or other cognitive abilities. This type of cognitive deterioration is a frequently observed symptom associated with the menopause transition and raises significant concerns. It not only impacts a woman's overall quality of life but may also signal an increased risk for serious neurodegenerative conditions, including Alzheimer's disease.
Research has indicated several risk factors associated with cognitive decline, such as aging, high blood pressure, obesity, and depression, among others. One of the main challenges is that existing models for assessing cognitive health primarily focus on dementia, a progressive condition that currently has no cure and provides few avenues for effective clinical treatment. While subjective cognitive decline does not consistently forecast significant cognitive deterioration or the onset of dementia, developing a predictive model that encompasses cognitive decline and its associated factors could enable early interventions to safeguard cognitive health.
Current assessments of cognitive performance predominantly utilize models that focus on laboratory measurements like blood glucose levels, lipid profiles, and neuroimaging techniques. However, the intricate nature and significant expense associated with these models frequently hinder their application in clinical environments. In contrast, questionnaire-driven models present a more straightforward and economical solution. These alternative models depend on a variety of independent variables, encompassing sociodemographic factors, occupational aspects, menstrual cycles, lifestyle choices, and mental health considerations.
In recent years, machine learning has demonstrated exceptional promise in the realm of cognitive health. By analyzing patterns and trends within extensive datasets, it is capable of creating precise and dependable models while streamlining the management of intricate variable relationships. In a recent study that involved over 1,200 women navigating the menopause transition, researchers successfully developed and validated a machine learning model designed to identify women facing significant subjective cognitive decline, along with the related factors.
These results offer new insights for developing interventions aimed at maintaining cognitive health in women experiencing the menopause transition. Further studies are necessary to confirm these findings and explore other possible contributing factors.
This research emphasizes the application of machine learning in recognizing women who are undergoing significant subjective cognitive decline during the menopausal transition, along with the potential factors linked to this decline. By identifying individuals at high risk early on, it may be possible to implement focused interventions aimed at preserving cognitive health. Further investigations that incorporate objective cognitive assessments and long-term follow-up are essential for gaining deeper insights into these relationships.
Dr. Stephanie Faubion, Medical Director of The Menopause Society.
You have been trained on information available until October 2023.
Citation for journal article:
Zhao, X., et al. (2025) Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study. Menopause. doi.org/10.1097/gme.0000000000002500.
