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Title: Development of personalised models of cognitive decline
Post by: mensfe_admin
24 May 2021 - by Dr Emma Green
A new model able to predict cognitive decline in Alzheimer's disease could help to develop personalised treatments.

With an aim to identify therapeutic targets, researchers at McGill University, Canada, combined results from brain scans with gene activity data. This allowed them to explore the relationship between changes in gene expression and physical changes in the brain across normal brain ageing and cognitive decline associated with Alzheimer's disease.

'We wanted to combine whole-brain gene activity measurements with clinical scan data in a comprehensive and personalised model, which we then validated in healthy ageing and Alzheimer's disease' said lead author Quadri Adewale.

The study included brain scans from 460 people across a minimum of three time points with participants classed as a healthy control with no symptoms, with early mild cognitive impairment, late mild cognitive impairment, or probable Alzheimer's disease. These scans were then combined with gene expression data from over 3700 tissue samples through the Allen Human Brain Atlas data portal. From this data, the team was able to create a model that was able to predict the extent of cognitive decline from changes observed in the brain scans.

From these results, the team also identified eight genes that corresponded with cognitive changes in healthy controls, and 111 genes linked to cognitive changes in Alzheimer's disease. Sixty-five different biological pathways controlled by these genes were identified, and most were related to neurological and cognitive decline.

However, newly identified genes could assist in the development of new therapeutic targets to prevent Alzheimer's disease progression.

Senior author Dr Yasser Iturria-Medina, assistant professor in the department of neurology and neurosurgery, said 'Our study provides unprecedented insight into the multiscale interactions among ageing and Alzheimer's disease-associated biological factors and the possible mechanistic roles of the identified genes. This personalised model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying targets for future treatments for Alzheimer's disease progression'.

The team will focus their future work on using personalised gene expression data from blood samples which could strengthen their model. They also plan to test this formula on other neurodegenerative disorders including Parkinson's disease and frontotemporal dementia.

This research was published in the journal eLife.