Unveiled Components Restoring Youth to Brain Cells Inside the Aging Clock
A groundbreaking study, published in the journal Advanced Science (2025), has identified 453 compounds that could potentially reverse age-related cell decline in the human brain. This research, led by an international team of researchers from Spain and Luxembourg, has developed a machine learning aging clock and used it to screen over 43,000 gene expression perturbations.
The Machine Learning Aging Clock
The aging clock, based on brain gene expression data from 2,456 postmortem samples of 778 healthy individuals aged 20 to 97, estimates biological age in brain cells with an accuracy of 4 to 6 years. It uses transcriptional data from 365 genes linked to aging and has been applied to neurodegeneration-positive samples, revealing that the presence and severity of neurodegenerative diseases significantly increase predicted age.
Validation and Experimental Results
Three compounds (5-azacytidine, tranylcypromine, and JNK-IN-8) were tested in aged mice for four weeks. The results showed improved behaviors related to reduced anxiety-like symptoms and some improvement in spatial memory. Moreover, gene expression profiles shifted toward more youthful patterns, and molecular markers indicated rejuvenation of brain cells.
Next Steps
To access more detailed information about these 453 promising compounds, readers are encouraged to:
- Read the full paper in Advanced Science, which includes detailed methods, compound lists, and molecular pathways involved.
- Check in-depth analysis or supplementary data that often accompanies such publications for exact compound names, mechanisms, and predicted targets.
- Review related citations and follow-up research on neuroprotective agents and aging clocks.
- Review the computational model’s repository or database if publicly released; some aging clock projects share their compound libraries or code openly.
Additional Resources
- Neuroscience News (July 22, 2025) provides a concise overview of the study.
- StudyFinds offers a summary covering the AI approach, compound numbers, and experimental validation.
- Multimedia presentations, such as the video explaining the "Unlocking the Aging Clock" project, are also available.
This combined approach will provide comprehensive scientific details and updates on these 453 promising compounds, their discovery process, and their potential application in neurodegenerative disease therapies aimed at reversing brain aging.
Some of the identified interventions are known to extend lifespan in animal models, and they include drugs already used to treat neurological disorders, including Alzheimer's disease. The study highlights the computational aging clock developed by the researchers as a valuable resource for identifying brain-rejuvenating interventions with therapeutic potential in neurodegenerative diseases.
- The machine learning aging clock, based on brain gene expression data, estimates the biological age in brain cells with an accuracy of 4 to 6 years.
- The aging clock uses transcriptional data from 365 genes linked to aging and has been applied to neurodegeneration-positive samples.
- Three compounds (5-azacytidine, tranylcypromine, and JNK-IN-8) were tested in aged mice for four weeks, showing improved behaviors related to reduced anxiety-like symptoms and some improvement in spatial memory.
- The study, published in the journal Advanced Science (2025), has identified 453 compounds that could potentially reverse age-related cell decline in the human brain.
- Reading the full paper in Advanced Science provides detailed methods, compound lists, and molecular pathways involved in the research.
- The study highlights the computational aging clock developed by the researchers as a valuable resource for identifying brain-rejuvenating interventions with therapeutic potential in neurodegenerative diseases.
- Some of the identified interventions are known to extend lifespan in animal models and include drugs already used to treat neurological disorders, such as Alzheimer's disease.
