AI Model Identifies Women at Risk of Severe Cognitive Decline During Menopause
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Machine learning identifies women at risk of severe cognitive decline during menopause
The Menopause SocietyJan 15 2025 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 refers to a person's perceived decline in memory or other cognitive functions. Cognitive decline, one of the more common symptoms related to the menopause transition, is especially concerning, because it not only affects a woman's quality of life but can also indicate a higher risk of severe neurodegenerative diseases, such as Alzheimer disease. Previous evidence suggests a number of risk factors for cognitive decline, including aging, hypertension, obesity, and depression, among others. A challenge is that most current models for cognitive health are centered around dementia, an incurable disease that offers limited opportunities for clinical intervention. Although subjective cognitive decline does not always predict long-term cognitive changes or dementia, a predictive model for cognitive decline and related factors could allow for early intervention to protect cognitive health. Existing testing for cognitive performance is largely based on models typically incorporating various laboratory indicators such as blood glucose, blood lipids, and brain imaging. The complexity and high cost of these models often make them impractical to implement in a clinical setting. In comparison, questionnaire-based models offer a simpler and more cost-effective alternative. These models rely on a number of independent variables, including sociodemographic, work-related, menstrual-related, lifestyle-related, and mental health-related factors. Machine learning has shown tremendous potential in the field of cognitive health in recent years. By mining patterns and trends from large datasets, it can construct accurate, reliable models and automate the handling of complex variable relationships. In this latest study involving more than 1,200 women undergoing the menopause transition, researchers were able to develop and validate a machine learning model for identifying women experiencing severe subjective cognitive decline, along with associated factors. These findings provide a novel guidance for interventions designed to preserve cognitive health in women undergoing the menopause transition. Additional research is needed to validate these results and identify additional potential influencing factors. This study highlights how the use of machine learning can be employed to identify women experiencing severe subjective cognitive decline during the menopause transition and potential associated factors. Early identification of high-risk persons may allow for targeted interventions to protect cognitive health. Future studies involving objective measures of cognition and longitudinal follow-up are crucial to better understanding these associations." Dr. Stephanie Faubion, Medical Director, The Menopause Society The Menopause Society Journal reference: 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.
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AI helps to identify subjective cognitive decline during the menopause transition
Artificial intelligence (AI) is positioned to make a major impact on almost every industry, including health care. 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. Study results are published in the article "Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study" online in Menopause. Subjective cognitive decline refers to a person's perceived decline in memory or other cognitive functions. Cognitive decline, one of the more common symptoms related to the menopause transition, is especially concerning, because it not only affects a woman's quality of life but can also indicate a higher risk of severe neurodegenerative diseases, such as Alzheimer's disease. Previous evidence suggests a number of risk factors for cognitive decline, including aging, hypertension, obesity, and depression, among others. A challenge is that most current models for cognitive health are centered around dementia, an incurable disease that offers limited opportunities for clinical intervention. Although subjective cognitive decline does not always predict long-term cognitive changes or dementia, a predictive model for cognitive decline and related factors could allow for early intervention to protect cognitive health. Existing testing for cognitive performance is largely based on models typically incorporating various laboratory indicators such as blood glucose, blood lipids, and brain imaging. The complexity and high cost of these models often make them impractical to implement in a clinical setting. In comparison, questionnaire-based models offer a simpler and more cost-effective alternative. These models rely on a number of independent variables, including sociodemographic, work-related, menstrual-related, lifestyle-related, and mental health-related factors. Machine learning has shown tremendous potential in the field of cognitive health in recent years. By mining patterns and trends from large datasets, it can construct accurate, reliable models and automate the handling of complex variable relationships. In this latest study involving more than 1,200 women undergoing the menopause transition, researchers were able to develop and validate a machine learning model for identifying women experiencing severe subjective cognitive decline, along with associated factors. These findings provide a novel guidance for interventions designed to preserve cognitive health in women undergoing the menopause transition. Additional research is needed to validate these results and identify additional potential influencing factors. "This study highlights how the use of machine learning can be employed to identify women experiencing severe subjective cognitive decline during the menopause transition and potential associated factors. Early identification of high-risk persons may allow for targeted interventions to protect cognitive health," says Dr. Stephanie Faubion, medical director for The Menopause Society. "Future studies involving objective measures of cognition and longitudinal follow-up are crucial to better understanding these associations."
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A new study demonstrates how machine learning can quickly and affordably identify women experiencing severe subjective cognitive decline during menopause, potentially leading to better cognitive health management.
AI Model Predicts Cognitive Decline in Menopausal Women
A groundbreaking study has revealed the potential of artificial intelligence (AI) in identifying women at risk of severe cognitive decline during menopause. Published in the journal Menopause, the research demonstrates how machine learning models can efficiently and cost-effectively detect subjective cognitive decline, paving the way for improved cognitive health management
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.Understanding Subjective Cognitive Decline
Subjective cognitive decline refers to an individual's perceived deterioration in memory or other cognitive functions. This symptom is particularly common during the menopause transition and raises concerns due to its impact on quality of life and potential association with higher risks of severe neurodegenerative diseases, including Alzheimer's
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.Advantages of Machine Learning Approach
The study, involving over 1,200 women undergoing menopause, developed and validated a machine learning model to identify those experiencing severe subjective cognitive decline. This approach offers several advantages over traditional methods:
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Cost-effectiveness: Unlike existing models that rely on expensive laboratory tests and brain imaging, the AI model uses questionnaire-based data, making it more accessible and affordable
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Comprehensive analysis: The model considers various factors, including sociodemographic, work-related, menstrual-related, lifestyle-related, and mental health-related variables
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Early intervention potential: By identifying high-risk individuals early, the model opens up possibilities for targeted interventions to protect cognitive health
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Implications for Cognitive Health Management
Dr. Stephanie Faubion, Medical Director of The Menopause Society, emphasized the significance of this research, stating, "This study highlights how the use of machine learning can be employed to identify women experiencing severe subjective cognitive decline during the menopause transition and potential associated factors"
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.The findings provide novel guidance for developing interventions aimed at preserving cognitive health in menopausal women. By leveraging AI's ability to mine patterns and trends from large datasets, healthcare providers can potentially offer more personalized and timely care
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.Future Research Directions
While the study presents promising results, researchers acknowledge the need for further investigation:
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Validation studies: Additional research is required to confirm these findings and identify other potential influencing factors
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Objective measures: Future studies should incorporate objective measures of cognition to complement the subjective assessments
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Longitudinal follow-up: Long-term studies are crucial to better understand the associations between identified risk factors and cognitive decline
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As AI continues to make significant impacts across industries, this research demonstrates its potential to revolutionize healthcare, particularly in the realm of women's health and cognitive well-being during menopause.
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