Menopause Symptoms and Cogniton: Real-world evidence from reproductive health tracking applications

This project at the Centre for Addiction and Mental Health addresses a defined evidence gap in women’s brain health: despite women comprising roughly two-thirds of Alzheimer’s disease cases, there is limited scalable, person-centred data on how specific menopause symptom patterns and menopausal hormone therapy (MHT) exposures relate to cognitive outcomes. In partnership with two major reproductive health tracker apps, Clue and Natural Cycles, the project will use large-scale app-based data to identify data-driven menopause subtypes based on symptom co-occurrence using clustering methods, and model how these symptom clusters, symptom frequency/severity, and MHT type and route (e.g., estradiol vs. progesterone; pill vs. patch) predict cognitive complaints such as brain fog, forgetfulness, and difficulty concentrating. Findings will directly inform feature-engineering and risk-modeling strategies for a larger lab initiative to develop a female-specific Alzheimer’s disease risk calculator drawing on 350,000+ participants across multiple aging databases. The expected benefit to CAMH is the development of reproducible analytic pipelines, empirically derived menopause subtypes, and predictive models that strengthen its capacity to build targeted prevention tools, improve early risk identification in midlife women, and enhance Canada’s leadership in data-driven women’s health research at the intersection of science and industry.

Faculty Supervisor:

Jennifer Brooks

Student:

Partner:

Centre for Addiction and Mental Health

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

University of Toronto

Program: