CpG Methylation and SNP Model for Cardiovascular Risk Prediction
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Solution Overview
Problem
Current risk estimators for cardiovascular disease, particularly coronary heart disease, lack sensitivity and specificity, leading to inadequate identification of individuals at risk.
Innovation Solution
An integrated genetic-epigenetic model that combines the methylation status of specific CpG loci and genotype of single nucleotide polymorphisms (SNPs) to predict cardiovascular disease incidence, using kits, methods, and systems that include nucleic acid primers, bisulfite conversion, and machine learning algorithms to analyze biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk estimators (Framingham Risk Score, ASCVD Pooled Cohort Equation) are used to predict cardiovascular disease, then the assessment is simple and widely applicable, but the sensitivity and specificity are insufficient leading to inadequate identification of at-risk individuals
Solution Approach 1:
The patent combines multiple biomarkers including DNA methylation status at specific CpG loci, single nucleotide polymorphism (SNP) genotypes, and traditional clinical risk factors into an integrated risk prediction model. This merging of diverse data types enhances the sensitivity and specificity of cardiovascular disease risk prediction beyond what traditional estimators alone can achieve
Solution Approach 2:
The patent introduces novel parameters including methylation status at specific CpG loci (e.g., cg00300879, cg09552548, cg14789911) and specific SNP genotypes (e.g., rs11716050, rs6560711, rs3735222) as predictive parameters. These new parameters provide more granular and accurate risk stratification compared to traditional clinical parameters alone
2Measurement precision
If traditional risk estimators are used, then the assessment method is straightforward, but the gender gap in risk assessment accuracy remains significant
Solution Approach 1:
The patent applies local quality by incorporating biomarkers and parameters that specifically address gender-differential cardiovascular risk patterns. The integrated model includes interactions between biomarkers that capture gender-specific physiological differences, thereby improving accuracy for both men and women rather than applying a uniform assessment approach
3Measurement precision
If integrated genetic-epigenetic models with multiple biomarkers are implemented, then the prediction accuracy improves, but the cost and complexity of testing increases
Solution Approach 1:
The patent segments the risk assessment into modular components: DNA methylation analysis at specific CpG loci, SNP genotyping at specific positions, and integration with traditional clinical factors. This segmentation allows for staged implementation where laboratories can adopt components progressively, and enables the development of targeted assays that reduce overall testing complexity and cost
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model significantly improves the accuracy of identifying individuals at risk, achieving a higher sensitivity in predicting cardiovascular events compared to traditional risk calculators, particularly by reducing the gender gap in risk assessment.
Implementation Method 1
contacting the first portion of the biological sample with a first oligonucleotide primer at least 8 nucleotides in length that is complementary to a sequence that comprises a first CpG dinucleotide at a GC locus selected from the group consisting of cg00300879, cg09552548, and cg14789911
Data Source
AI summary
This document describes methods and compositions for predicting cardiovascular disease (CVD). Specifically, this document describes methods and compositions for determining the methylation status of at least one CpG locus and the sequence of at least one single nucleotide polymorphism (SNP) that are predictive for the incidence (e.g., one-year, three-year, five-year incidence) of CVD.