9/ Drug repurposing is an attractive strategy to identify promising tools against aging and age related disease, at a fraction of the cost and effort required to discover and develop entirely new therapeutic agents.

4/ The analysis of BAG scores against gene expression patterns of participants blood and brain tissues identified genes that are likely to drive brain aging.

5/ 64 genes were identified with causal relationships to brain aging, of which 37 and 7 genes were validated as having two and three pieces of genetic evidence supporting their effect, respectively.

6/ The identified key genes also have associations with phenotypes and conditions that are linked to accelerated brain aging including: increased BMI, high blood pressure, smoking, asthma, Alzheimer and Parkinson disease.

Some specific examples
+ MAPT correlated with increased…

0/ Published last week in @ScienceAdvances a study reported the use of brain scans with multiple deep learning models combined with gene expression analysis in blood samples and brain tissues from to identify genes that drive brain aging.

Background and details

1/ The study used brain scans and gene expression analysis data from 38,961 subjects from the UK Biobank, 6637 of which with a diagnosed brain disorders with “healthy” brains.

2/ The study aimed to match structural (MRI) and genetic insights into brain aging to guide drug repurposing for delaying and slowing down brain aging and related disorders.

3/ The analysis of brain MRI scans using 7 deep learning models yields an estimation of the biological age of the brain, Brain Age Gap BAG, the estimated gap between chronological and brain biological age.

Study uses MRI ad machine learning to estimate brain age, and identifies 64 “druggable” genes that drive brain aging

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