IHPME Researchers Receive CIHR Grant to Advance Dementia Prevention

A group of IHME researchers have received funding to explore the risk factors associated with brain aging in the hopes of advancing dementia prevention.

By: Marielle Boutin

The study, led by IHPME researcher Kuan Liu, was awarded funding as part of the CLSA Catalyst Grant program, administered by CIHR.

Liu, an Assistant Professor of Health Systems Research and Biostatistician, is leveraging her work on Bayesian causal inference to explore how lifestyle, medical, and social factors influence memory decline over time. Liu and her research team, which includes IHPME faculty Drs. Husam Abdel-Qadir, and Geoffrey M. Anderson, as well as Post-doctoral Fellow Renzo Jose Carlos Calderon Anyosa, are aiming to apply Bayesian causal methods including Bayesian marginal structural models and Bayesian sensitivity analyses for unmeasured confounding, against data collected from the Canadian Longitudinal Study on Aging (CLSA) to identify which risk factors for dementia can be changed and how different population subgroups are affected.

“This project lets me bring the methods we’ve been building […] into a problem with substantial population health impact,” says Liu. “It fits my broader goal of building advanced statistical methods that make sense of complex health data and turning them into practical, equitable tools that support healthy aging for everyone.”

The CLSA is a national study that follows more than 50,000 Canadians between the ages of 45 and 85 over a 20-year period. Throughout the study, data related to health, lifestyle, and socio-economic factors is collected to help paint a clearer picture of the how and why of aging, with the goal of contributing to better health outcomes for older adults in Canada.

According to Liu, the use of the multi-wave CLSA dataset allows the team to see how risks and cognitive aging change over time, instead of just looking at it as a one-time snapshot. Moreover, the use of Bayesian methods helps make the most of the data.

The research team’s work is grounded in two major goals. Their initial objective is to quantify the time-varying and long-term effects of modifiable risk factors, such as physical activity, on cognitive decline. Second, they will look at these effects among different population subgroups, which will ultimately enable them to tailor dementia prevention strategies, lower dementia risk, and close existing health gaps.

Beyond the support from the Catalyst Grant, the project benefits from a collaborative research environment bolstered by colleagues in health policy, health equity, and clinical epidemiology, allowing the work to move quickly into evidence-based policy and practice.

Ultimately, Liu and her team are aiming to make dementia prevention more precise and effective.

“Our findings will clarify which interventions work best, when in the life course they are most effective, and for which groups,” says Liu. “Better targeting can slow the progression from mild cognitive impairment to dementia, reduce demand for long-term care, and improve quality of life for older adults.”