Study uses MRI ad machine learning to estimate brain age, and identifies 64 “druggable” genes that drive brain aging
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.
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.
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 blood sugar, ApoA and increased PD risk.
+ C1RL is associated with brain disorder risks including OCD, AD, anorexia, as well as negatively affecting liver and immune functions.
+ SIRPB1 associated with insomnia and increased systemic inflammation.
Some specific examples
+ MAPT correlated with increased blood sugar, ApoA and increased PD risk.
+ C1RL is associated with brain disorder risks including OCD, AD, anorexia, as well as negatively affecting liver and immune functions.
+ SIRPB1 associated with insomnia and increased systemic inflammation.
7/ 466 drugs were identified that can counteract the effects of 29 genes out of the the 64 genes identified to drive brain aging. Of these 29 drugs have shown some effect in clinical trials in counteracting brain aging, and 20 are considered geroprotectors (anti aging drugs).
Among these drugs
+ Non-steroidal Anti-inflammatories: diclofenac, ibuprofen, ketoprofen, naproxen.
+ Metabolism and mitochondrial optimizers: methylene blue, rapamycin,
+ Anti-oxidant/anti-inflammatory: quercitin.
Vitamin D3
Among these drugs
+ Non-steroidal Anti-inflammatories: diclofenac, ibuprofen, ketoprofen, naproxen.
+ Metabolism and mitochondrial optimizers: methylene blue, rapamycin,
+ Anti-oxidant/anti-inflammatory: quercitin.
Vitamin D3
Significance
8/ These results demonstrate the viability of a dual structural and genetic approach to measuring brain aging and identifying targets that can slow it down. The study managed to independently validate various genetic pathways and drugs with known clinical efficacy suggesting this as a viable strategy for drug-reproposing to tackle brain aging.
8/ These results demonstrate the viability of a dual structural and genetic approach to measuring brain aging and identifying targets that can slow it down. The study managed to independently validate various genetic pathways and drugs with known clinical efficacy suggesting this as a viable strategy for drug-reproposing to tackle brain aging.
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.
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