Sidra Medicine advances genetic approach to predict cognitive decline
The researchers focused on polygenic risk scores, which combine the effects of multiple genetic variants to estimate a person’s inherited susceptibility to a disease. However, existing methods may overlook genetic information associated with disease risk.
QATAR—Sidra Medicine has reported new research that could improve early prediction of cognitive decline and support more personalized approaches to brain health.
The findings, published in Genome Medicine, were highlighted as the hospital marked World Alzheimer’s Day.
The study introduces a method for incorporating genetic information into models that predict cognitive decline.
By combining information from thousands of genetic variants across the genome, researchers aim to better identify individuals who may face a higher risk of future cognitive impairment.
Combining Genetic Data With Brain Imaging
The study, titled “Multi-threshold polygenic risk improves hippocampal-based cognitive decline prediction,” was led by Dr. Mohamed Janahi, a postdoctoral researcher in the laboratory of Prof. Younes Mokrab, principal investigator and director of the Neuroscience Research Program at Sidra Medicine.
The team collaborated with Prof. Andre Altmann and colleagues at University College London.
Alzheimer’s disease and other forms of dementia can develop over many years before symptoms become noticeable.
During these early stages, changes can occur in the hippocampus, a brain region essential for memory.
The researchers focused on polygenic risk scores, which combine the effects of multiple genetic variants to estimate a person’s inherited susceptibility to a disease. However, existing methods may overlook genetic information associated with disease risk.
To address this limitation, the team developed a multi-threshold approach that captures a broader range of genetic signals. The method improved the use of genetic data and brain imaging to identify patterns associated with cognitive impairment and predict future decline.
Testing the Prediction Models
The researchers developed their models using data from nearly 24,000 UK Biobank participants.
They then evaluated the models using data from approximately 3,000 participants in two independent studies: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimer’s Disease (EPAD) cohort.
Dr. Janahi said Alzheimer’s disease can affect the brain years before symptoms become noticeable.
Identifying people at higher risk during these early stages could create opportunities for earlier monitoring and prevention.
He added that the research improves how genetic information is used to predict cognitive decline and supports the development of more personalized approaches to brain health.
By combining genetic risk information with brain imaging, the researchers can examine changes associated with cognitive decline in greater detail.
With further validation, these methods could help identify people who may benefit from closer monitoring or earlier intervention.
The approach could also help select participants for clinical trials investigating Alzheimer’s disease and other neurodegenerative conditions.
Expanding the Use of Genetic Information
Prof. Mokrab said common diseases are influenced by thousands of genetic variants, each contributing a small amount to overall risk.
He explained that the team’s approach brings more of these genetic signals together, improving the use of brain changes to predict cognitive decline.
He also noted that the method could have applications beyond neurodegenerative diseases.
The findings do not provide a diagnostic test for Alzheimer’s disease.
Instead, the researchers developed a framework that could support future tools for assessing the risk of cognitive decline.
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