Summary:
UCLA researchers in the Department of Neurology have developed a novel method that leverages epigenetic markers to identify recommended and necessary health interventions.
Background:
Disease risk prediction and intervention involves identifying individuals or populations with a higher likelihood of developing a particular condition based on certain factors. This process informs early detection of potential health issues and prevention plans and holds significant potential for improving patient outcomes. Traditional risk assessment methods rely on epidemiological studies that analyze large studies to establish statistically significant associations between risk factors and disease incidence. However, these methods are limited by inaccuracies in epidemiological studies and may not capture the complexity and multifaceted nature of disease development and progression. In addition, confounding factors such as individual epigenetic makeup may limit the accuracy of such traditional methodologies. There remains an unmet need for a computational method that accurately predicts disease outcome within a defined timeframe that can leverage important factors such as epigenetic characteristics.
Innovation:
UCLA researchers in the Department of Neurology developed a two-step approach to harness epigenetic data for disease risk prediction and intervention assessment. The first step involves analysis of patient samples to create prognostic risk scores to allow for prediction of specific disease outcomes within a defined timeframe. The second step applies these computational models to evaluate the effect of intervention on disease risk reduction. Risk scores are assessed before and after intervention, allowing for direct comparison of the impact of the intervention on clinical outcome. Epigenetic data, such as methylation arrays, can be measured before and after disease risk intervention to determine risk prevention efficacy. This invention can potentially accelerate the assessment of various risk reduction and intervention strategies, improving health for individuals and communities.
Potential Applications:
- Personalized treatment plan
- Drug development
- Public health planning
- Remote monitoring
- Genetic counseling
- Preventative medicine
- Family planning
Advantages:
- Data-driven
- Early detection
- Personalized
- Cross-disease application
Development To Date:
First description of technology completed in October 2022.
Reference:
UCLA Case No. 2023-107
Lead Inventor:
Noah Zaitlen & Eran Halperin