New funding from the federal government and partners will bolster research focused on identifying new biomarkers for neurodegenerative diseases, such as Alzheimer’s disease.
“So far, no disease-altering interventions for Alzheimer’s disease have been successful,” said Roger Dixon, professor in the University of Alberta’s Department of Psychology in the Faculty of Science. “For this reason, we aim to discover the earliest signals of the disease, known as biomarkers, so that prevention protocols can be implemented and we can better understand how neurodegenerative diseases work.”
Age-related cognitive decline and dementia, including Alzheimer’s diseases, currently affects more than 400,000 Canadians, and will impact as many as 1.5 million Canadians by 2031.
The project, spearheaded by Dixon, is part of the Canadian Consortium on Neurodegeneration in Aging (CCNA). CCNA is a national research program on dementia research now in its second phase with $46 million in funding from the federal government and other partners, and includes 310 researchers from 39 universities across Canada.
“Projected deliverables include developing the best—most accurate, least expensive, most replicable, most generalizable across populations—combinations of biomarkers for neurodegenerative diseases,” said Dixon, who is a member of UAlberta’s Neuroscience and Mental Health Institute.
Identifying biomarkers allows research to precisely detect neurodegenerative diseases and better understand how they progress, allowing for precise and personalized approaches to interventions and therapy. Dixon’s national team will build on a strong foundation of research including non-invasive testing techniques such as saliva tests, and understanding factors for healthy and impaired memory at any age. The team includes more than 30 members from 12 institutes, including Liang Li (chemistry), Peggy McFall (psychology), and David Wishart (biological sciences and computing science) from the Faculty of Science.
“Contemporary biomarker research benefits greatly from new artificial intelligence technologies,” said Dixon. “Thus, we use machine learning, data mining, interactive or network modeling, and precision analyses and applications. For example, we employ data-driven OMICs platforms such as genomics, metabolomics, and connectomics.”