Multi-Biomarker Assessment for Musculoskeletal Disease Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective biomarkers for early identification of individuals at risk of developing musculoskeletal and connective tissue diseases, limiting targeted prevention and treatment strategies.
Innovation Solution
A method involving the measurement of specific biomarkers such as albumin, glycoprotein acetyls, fatty acid ratios, and amino acids in biological samples, followed by comparison to control values, to determine the risk of developing these diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for disease identification, then the process is simple, but the accuracy of predicting disease risk is insufficient
Solution Approach 1:
The patent combines multiple biomarker measurements (albumin, glycoprotein acetyls, fatty acid ratios, amino acids) into a comprehensive risk assessment system. By merging these different biomarker types and their respective measurements, the system achieves higher predictive accuracy for musculoskeletal and connective tissue diseases while managing the complexity through systematic integration.
2Reliability
If multiple biomarkers are measured, then the predictive accuracy improves, but the measurement complexity and cost increase
Solution Approach 1:
The patent segments the disease risk prediction into multiple independent biomarker measurements (albumin, glycoprotein acetyls, fatty acid ratios, amino acids). Each biomarker is measured and evaluated separately, then combined to provide comprehensive risk assessment. This segmentation allows for systematic measurement of reliable predictors while managing complexity through modular assessment of individual biomarkers.
Data Source
AI summary
A method for determining whether a subject is at risk of developing a musculoskeletal and/or connective tissue disease.