AI Model Successfully Predicts Cognitive Decline in Menopausal Women
Key Insights
New research demonstrates that artificial intelligence can effectively identify menopausal women experiencing severe subjective cognitive decline (search), potentially enabling earlier interventions for brain health.
The study found that menopausal symptoms, transition stage, socioeconomic status, sleep quality, and emotional state were the most significant factors affecting cognitive function during menopause (search).
Researchers noted that women with more positive emotions showed less cognitive decline, while those with intense symptoms like hot flashes and sleep disturbances faced higher risks of memory and thinking problems.
A new artificial intelligence model has successfully identified menopausal women at risk for cognitive decline, according to research published January 14 in the journal Menopause (search). This breakthrough could provide a faster, more cost-effective method for detecting early signs of memory and thinking problems during the menopausal transition.
The study, led by Professor Ping Li from Shandong University (search) School of Nursing and Rehabilitation in China, examined nearly 1,300 menopausal nurses from 16 hospitals in China's Shandong Province. Researchers measured subjective cognitive decline (search) (SCD) using a 9-item questionnaire and trained an AI algorithm to identify patterns associated with cognitive issues.
"The menopause (search) transition stage is a significant factor of severe SCD," wrote Li and colleagues. "Women at different stages of this transition face varying risks of cognitive decline, likely due to hormonal fluctuations, emotional changes, and physiological alterations."
SCD, characterized by self-perceived confusion or memory problems, can be one of the earliest detectable symptoms of Alzheimer's disease (search) or dementia (search). According to the Centers for Disease Control and Prevention, approximately one in nine U.S. adults experiences SCD, primarily middle-aged individuals and seniors.
Key Risk Factors Identified
The AI model revealed several critical factors influencing cognitive decline during menopause (search). Menopausal symptoms showed the strongest correlation with cognitive issues, followed by menopause transition stage, socioeconomic status, sleep satisfaction, and emotional state.
"In the early stages, mild menopausal symptoms may not be significantly associated with cognition, but as symptoms become more intense—particularly with frequent or severe hot flashes, mood swings, and sleep disturbances—the risk of cognitive decline rises markedly," the researchers explained.
Interestingly, the study found that women with more positive emotions experienced less cognitive decline, possibly because positive emotional states help alleviate stress and promote neural flexibility.
These findings align with a separate March 2025 study published in PLOS One, which suggested that difficult menopause (search) experiences might serve as early warning signs for future dementia (search). That research found women reporting more severe menopausal symptoms tended to develop greater cognitive and behavioral impairments later in life.
Clinical Implications and Future Directions
Dr. Stephanie Faubion, medical director for The Menopause Society (search), highlighted the significance of the AI approach: "This study demonstrates how machine learning can be employed to identify women experiencing severe subjective cognitive decline (search) during the menopause (search) transition and potential associated factors."
She added, "Early identification of high-risk persons may allow for targeted interventions to protect cognitive health."
The connection between menopause (search) and cognitive health may be related to declining estrogen levels. Estradiol, a form of estrogen, contributes to the development of synapses and neurons, which decline in neurodegenerative diseases. The PLOS One study suggested that "the experience of menopausal symptoms may act as an indicator of how well females tolerate estradiol changes."
While the AI model showed promising results in identifying women at risk, researchers acknowledged that additional validation is needed. Future studies should incorporate objective cognitive measures and longitudinal follow-up to better understand these associations.
Potential for Personalized Interventions
The ability to predict cognitive decline during menopause (search) could open new avenues for personalized interventions. Early identification of at-risk women might allow healthcare providers to implement targeted strategies to protect brain health before significant decline occurs.
The PLOS One study found that hormone replacement therapy (search) helped improve behavioral impairment scores in menopausal women, though it did not significantly affect cognitive scores. This suggests that different interventions might be needed to address various aspects of brain health during and after menopause (search).
As women are known to have three times the risk of developing Alzheimer's disease (search) compared to men, these findings represent an important step toward understanding and potentially mitigating this disparity.
With further refinement, AI-based screening tools could become valuable resources for clinicians to identify and support women experiencing cognitive challenges during the menopausal transition, potentially reducing their long-term risk of dementia (search) and other neurodegenerative conditions.