HDL Protein Biomarker Panels for Accurate CVD Risk Stratification
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Solution Overview
Problem
Current clinical risk assessment tools for cardiovascular disease (CVD) are limited in their accuracy and applicability, particularly in identifying high-risk patients who do not undergo aggressive risk factor modification and in refocusing health care resources to those who stand to benefit most, and there is a need for more robust and efficient platforms for high-throughput cardiac risk stratification using genomic, proteomic, and metabolomic technologies.
Innovation Solution
Methods and systems for detecting HDL-associated proteins, such as ApoC3, ApoA1, SAA1/2, and other biomarkers, in samples from subjects with or suspected of having CVD, using mass spectrometry and immunoassays, to determine CVD risk and cholesterol efflux capacity, with normalization to total HDL particles or ApoA1 levels, and employing algorithms to generate risk scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current clinical risk assessment tools are used, then healthcare resources can be allocated, but the accuracy in identifying high-risk patients is insufficient
Solution Approach 1:
The patent segments the CVD risk assessment into multiple independent protein biomarker measurements (ApoC3, ApoA1, SAA1/2, and other HDL-associated proteins) rather than using a single composite score. This segmentation allows each biomarker to be measured and evaluated independently, improving measurement precision while maintaining high-throughput capability through automated analysis of individual components.
Solution Approach 2:
The patent employs a multi-functional detection platform that simultaneously measures multiple protein biomarkers, HDL particle levels, and cholesterol efflux capacity using a single integrated system. This universal approach improves risk identification accuracy by capturing multiple aspects of cardiovascular risk in one high-throughput assay, eliminating the need for separate testing procedures.
2Measurement precision
If holistic array-based phenotyping technologies are incorporated, then cardiac risk stratification is improved, but the complexity and difficulty of implementation increases
Solution Approach 1:
The patent merges multiple phenotyping technologies (proteomic analysis of HDL-associated proteins, metabolomic assessment of cholesterol efflux capacity, and genomic risk scoring) into a single integrated platform. By combining these previously separate technologies into one unified system, the patent improves cardiac risk stratification accuracy while reducing the operational complexity that would arise from managing multiple separate technologies.
Solution Approach 2:
The detection platform is designed with universal multi-functionality to perform proteomic measurements, metabolomic assessments, and genomic risk calculations through a single integrated system. This multi-functional design improves risk stratification precision by capturing multiple biological dimensions simultaneously while avoiding the device complexity of maintaining separate specialized instruments for each type of analysis.
3Measurement precision
If multiple HDL-associated proteins are detected, then CVD risk determination is more accurate, but the detection process becomes more complex
Solution Approach 1:
The patent segments the detection of multiple HDL-associated proteins into distinct, independently measurable components (ApoC3, ApoA1, SAA1/2, and other specific proteins). Each protein is targeted with specific antibodies or detection reagents, allowing precise measurement of individual biomarkers. This segmentation improves CVD risk determination accuracy by capturing nuanced information from each protein while managing detection complexity through modular, standardized assays for each component.
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
Provides more accurate identification of CVD risk and cholesterol efflux capacity, enabling targeted healthcare resource allocation and therapeutic interventions based on precise risk stratification.
Implementation Method 1
detecting the level of at least one HDL-associated protein in a sample from a subject, wherein the at least one HDL-associated protein comprises ApoC3
Implementation Method 2
detecting the level of at least one HDL-associated protein in a sample from a subject
Data Source
AI summary
Provided herein are methods, systems, and compositions for detecting one or more HDL-associated proteins (e.g., ApoC3; ApoC3 and ApoA1; ApoC3 and SAA1/2; or proteins in Biomarker Panels 1-30) in a sample from a subject with, or suspected of having, cardiovascular disease (CVD) or other HDL related disease. In certain embodiments, such methods, systems, and compositions are used to determine the approximate risk of CVD (or other disease) for a subject, and/or the approximate cholesterol efflux capacity (CEC) of a sample.