| Title: | Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank |
| Journal: | Human Brain Mapping |
| Published: | 20 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40974298/ |
| DOI: | https://doi.org/10.1002/hbm.70331 |
Publication 19452
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Abstract
Neurodegeneration occurs when the body's central nervous system becomes impaired as a person ages, which can happen at an accelerated pace. Neurodegeneration impairs quality of life, affecting essential functions, including memory and the ability to self-care. Genetics play an important role in neurodegeneration and longevity. Brain age gap estimation (BrainAGE) is a biomarker that quantifies the difference between a machine learning model-predicted biological age of the brain and the true chronological age for healthy subjects; however, a large portion of the variance remains unaccounted for in these models, attributed to individual differences. This study focuses on predicting the BrainAGE more accurately, aided by genetic information associated with neurodegeneration. To achieve this, a BrainAGE model was developed based on MRI measures, and then the associated genes were determined with a Genome-Wide Association Study. Subsequently, genetic information was incorporated into the models. The incorporation of genetic information yielded improvements in the model performances by 7% to 12%, showing that the incorporation of genetic information can notably reduce unexplained variance. This work helps to define new ways of determining persons susceptible to neurological aging decline and reveals genes for targeted precision medicine therapies.</p>
15 Keywords
- Adult
- Aged
- Aged, 80 and over
- Aging
- Biological Specimen Banks
- Brain
- Female
- Genome-Wide Association Study
- Humans
- Machine Learning
- Magnetic Resonance Imaging
- Male
- Middle Aged
- UK Biobank
- United Kingdom
8 Authors
- Aashka Mohite
- Karen Ardila
- Pattarawut Charatpangoon
- Emily Munro
- Qingrun Zhang
- Quan Long
- Charlotte Curtis
- M. Ethan MacDonald
1 Application
| Application ID | Title |
|---|---|
| 55274 | Age Prediction from NeuroImaging and Genetic Profiles |
Enabling scientific discoveries that improve human health