In a significant advancement in the field of neuroscience and gerontology, a study published in Science Advances explored the intersection of artificial intelligence and genetic research to uncover gene targets and potential drugs that may slow brain aging. The research was led by Sanjukta Mondal and employed deep learning models trained on vast datasets from the UK Biobank.

The Implications of Brain Aging

Understanding brain aging is critical as it is closely associated with an increased risk of neurodegenerative diseases like Alzheimer’s disease and cognitive decline. The study defined the concept of the brain age gap (BAG), which is the difference between an individual’s chronological age and the estimated biological age of their brain, as determined through imaging techniques such as MRI.

A larger BAG not only indicates accelerated aging but also correlates with poor cognitive performance and health outcomes. Identifying the biological underpinnings of this phenomenon could hold the key to developing preventative strategies that enhance longevity and quality of life.

Methodology: Deep Learning and Gene Identification

The researchers utilized a robust dataset comprising MRI scans, lifestyle characteristics, health records, and genetic data from approximately 39,000 participants of the UK Biobank, whose average age was 64 years. The study focused on:

  • Utilizing deep learning techniques to analyze the MRI data.
  • Employing a saliency map analysis to identify critical brain regions linked to brain age estimation.
  • Determining gene targets associated with biological brain aging.

Key Findings: Genes and Drug Targets

The study successfully identified seven genes that contribute to a widening BAG:

  • MAPT
  • TNFSF12
  • GZMB
  • SIRPB1
  • GNLY
  • NMB
  • C1RL

In addition to the genetic findings, the researchers identified 13 existing drugs and supplements that could be repurposed to target these genes, potentially mitigating brain aging. Some notable examples include:

Drug/Supplement Potential Target Gene
Hydrocortisone TNFSF12
Testosterone MAPT
Diclofenac GZMB
Metformin SIRPB1

Regional Limitations and Future Directions

“The genetic basis of aging uncovered in this study can facilitate the development of new drugs to slow brain aging and improve overall health.” – Sanjukta Mondal, Lead Researcher

Although these findings point toward promising avenues for therapeutic development, the researchers caution that the data was derived from a specific demographic, necessitating further studies across diverse populations to fully evaluate the impact of their results and ensure broad applicability.

In summary, the intersection of deep learning and genetic research opens new frontiers in understanding brain aging, emphasizing the potential for drug repurposing as a viable strategy to combat age-related cognitive decline.

Additional Resources

For further reading and comprehensive understanding, the study referenced can be accessed via the following link: Deep learning uncovers gene targets and potential drugs to slow brain aging.

Literature Cited

[1] Yi, F. et al. (2025). Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank. Science Advances. DOI: 10.1126/sciadv.adr3757.