The difference between how old someone’s brain actually is and how it’s functioning, is extremely useful information, both scientifically and medically. But being able to estimate that difference, known as the brain age gap (BAG), is not yet standard. That may change thanks to a new systematic study across large gene sets.
A Chinese team has used magnetic resonance imaging (MRI) and deep learning models trained on the UK Biobank and validated their findings with three other datasets to identify two unreported loci and seven previously reported loci associated with this feature.
Their report appeared in Science Advances and the lead author is Fan Yi, of the Department of Neurology, Peking Union Medical College Hospital.
The authors wrote, “Our study provides insights into the genetic basis of brain aging, potentially facilitating drug development for brain aging to extend the health span.”
For their analysis, the team integrated Mendelian Randomization (MR) and colocalization analysis on eQTL and pQTL data, and prioritized seven genetically supported druggable genes, including MAPT, TNFSF12, GZMB, SIRPB1, GNLY, NMB, and C1RL, as promising targets for brain aging. They rediscovered 13 potential drugs with evidence from clinical trials of aging and prioritized several drugs with strong genetic support.
Brain aging trajectories vary due to differences in genetic factors, lifestyles, environmental factors, and chronic diseases of the patient. Pinpointing genetic factors explaining the BAG may thus identify genetically supported targets and facilitate potential therapeutic opportunities to prevent, slow down, or even reverse brain aging.
Most studies on this topic emphasize the polygenic architecture of brain aging, genetically supported drug target studies of brain aging to pinpoint promising BAG-associated loci, the authors said could provide a resource for reasoning targets of abnormal brain aging.
As reported in the ChEMBL database, there are 45 drugs in clinical trials related to aging. Other researchers have systematically reviewed the most promising agents to maintain health for longer periods and to slow down aging.
These include drugs for managing diabetes [Sodium/Glucose cotransporter 2 (SGLT)-2 inhibitors, metformin, and acarbose], mammalian target of rapamycin (mTOR) inhibitors (rapamycin and its analogs), drugs for management of hypertension (angiotensin-converting enzyme inhibitors and angiotensin receptor blockers), and nonsteroidal anti-inflammatory drugs.
The team began by estimating brain age using seven state-of-the-art deep learning models, with MRI data from 38,961 UK Biobank (UKB) participants and validating these models on three external datasets. The three-dimensional vision transformer (3D-ViT) model outperformed others in brain age estimation and was used to measure BAG in subsequent analyses.
Next, they conducted GWAS on genetic data from 31,520 UKB individuals to identify genomic regions associated with BAG. Through MR and genetic association analyses, the team explored the relationships between BAG and 18 brain disorders, as well as eight phenotypic traits.
Although most causal relationships were nonsignificant, they identified a significant causal effect of BAG on intelligence. They then identified 64 druggable genes using “drug target MR” and colocalization analysis with eQTL and pQTL data. Seven druggable genes—MAPT, TNFSF12, GZMB, SIRPB1, GNLY, NMB, and C1RL—emerged as strong causal candidates for brain aging. A phenome-wide scan further explored their associations with 44 additional traits associated with these targets.
