Coronary Artery Disease Prediction via OxPL and IL-1 Genotyping
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Solution Overview
Problem
Current methods lack effective and efficient means to predict and diagnose coronary artery disease (CAD) due to the complex interplay of genetic, inflammatory, and lipid-related factors, often resulting in late or missed diagnoses.
Innovation Solution
A method involving the measurement of oxidized phospholipids (OxPL) associated with apolipoprotein B-100 (apoB) particles and lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, combined with genetic analysis of the IL-1 gene cluster, to assess the predisposition to CAD, providing a high-throughput assay for predicting cardiovascular events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for coronary artery disease, then the diagnostic process is simple, but the predictive power and accuracy are insufficient
Solution Approach 1:
The patent combines multiple diagnostic markers (OxPL/apoB ratio, Lp-PLA2 activity, and IL-1 gene cluster genotyping) into a comprehensive diagnostic approach. This merging of multiple measurement systems provides synergistic information that significantly increases the hazard ratio for predicting new cardiovascular events, achieving superior predictive power compared to single-marker methods.
Solution Approach 2:
The diagnostic method serves multiple functions simultaneously: it detects oxidative stress markers (OxPL), measures enzymatic activity (Lp-PLA2), and identifies genetic predispositions (IL-1 genotypes). This multi-functional approach allows a single diagnostic protocol to assess multiple aspects of cardiovascular risk, improving both accuracy and comprehensive risk evaluation.
2Measurement precision
If multiple biomarkers are measured simultaneously to improve prediction accuracy, then the predictive power increases, but the measurement complexity and time increase
Solution Approach 1:
The diagnostic protocol segments the measurement process into three distinct components: (1) OxPL/apoB ratio determination, (2) Lp-PLA2 activity measurement, and (3) IL-1 gene cluster genotyping. This segmentation allows each marker to be measured using optimized specialized techniques while maintaining overall coordination, enabling comprehensive assessment without excessive time loss.
Data Source
AI summary
Provided herein are compositions and methods for identifying individuals at risk for developing coronary artery disease (CAD).