TBI Detection via DNA Methylation and Metabolomics
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Solution Overview
Problem
Current methods lack reliable and sensitive biomarkers for accurately diagnosing concussions, particularly in pediatric populations, leading to underdiagnosis and potential long-term neurological consequences.
Innovation Solution
Measurement of methylation levels at specific CpG loci in leucocyte DNA, combined with metabolomic analysis and AI/machine learning techniques, to identify biomarkers for concussion detection, providing high diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for concussion detection, then the diagnostic process is simple and quick, but the diagnostic accuracy is low leading to underdiagnosis
Solution Approach 1:
The patent segments the diagnostic approach by dividing it into multiple independent components: epigenetic markers (DNA methylation patterns), metabolomic markers (metabolite levels), and neurocognitive test results. Each component can be measured separately and then integrated through machine learning algorithms to achieve high diagnostic accuracy without requiring a single complex test
Solution Approach 2:
The patent measures changes in multiple parameters simultaneously - DNA methylation levels at specific CpG sites, metabolite concentrations in biofluids, and neurocognitive performance metrics. By monitoring changes across these different parameters rather than relying on a single parameter, the diagnostic accuracy is significantly improved
2Reliability
If no specific biomarkers are available for concussion, then the diagnostic method remains simple, but the sensitivity and specificity of diagnosis are insufficient
Solution Approach 1:
The patent merges multiple types of biomarkers into a single integrated diagnostic system. It combines epigenetic markers (DNA methylation patterns at specific CpG sites), metabolomic markers (levels of various metabolites in blood or urine), and clinical data. This combination through machine learning integration provides high reliability for concussion diagnosis while accounting for the quantity of biomarkers needed
Solution Approach 2:
The patent creates a composite diagnostic approach by combining different classes of biomarkers that would individually provide limited information. The composite system integrates epigenetic, metabolomic, and neurocognitive data, where the combined information provides diagnostic reliability that exceeds the sum of individual markers, similar to how composite materials exhibit properties superior to their individual components
Data Source
AI summary
The present disclosure describes significant methylation changes in multiple genes and multiple metabolites in body fluid in response to TBI. Gene pathways affected included several known to be involved in neurological and brain function. A large number of good to excellent biomarkers for the detection of TBI was identified. The combination of epigenomic, clinical and metabolomic markers in different combinations overall were highly accurate for the detection of pediatric concussion using Artificial Intelligence-based techniques.