Fibrosis Risk Prediction Using Genetic and Environmental Markers
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Solution Overview
Problem
Existing methods fail to reliably predict a subject's risk of suffering fibrosis, hindering preventive measures and decision-making, particularly in the context of postoperative fibrosis following surgical interventions.
Innovation Solution
An in vitro method that combines the analysis of genetic polymorphisms (MMP3, NEDD4, SMAD4, IL6R, CTGF, IL-6, BMP4) and environmental variables (degree of obesity, use of platelet-rich plasma) using an algorithm to calculate a fibrosis risk value, providing high sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing diagnostic methods are used for fibrosis prediction, then the diagnostic process is simple, but the prediction reliability is insufficient
Solution Approach 1:
The patent combines multiple genetic polymorphism analyses (MMP3, NEDD4, SMAD4, and their interactions) with environmental factor assessments into a unified prediction model. This integration of multiple diagnostic elements resolves the contradiction by achieving high prediction reliability through comprehensive analysis while maintaining a systematic approach that manages complexity.
Solution Approach 2:
The prediction method uses a composite approach combining genetic data (multiple polymorphisms and their interactions) with environmental factors. This composite methodology achieves reliable fibrosis risk prediction by synthesizing information from multiple sources, resolving the contradiction between reliability and complexity through a structured composite assessment framework.
2Measurement precision
If comprehensive genetic and environmental analysis is performed, then prediction accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent performs preliminary analysis of key genetic polymorphisms (MMP3 rs679620, NEDD4 rs8032158, SMAD4 rs12456284) and their interactions before final risk assessment. This preliminary screening approach allows for efficient identification of high-risk individuals, improving measurement precision while reducing overall diagnostic time by focusing resources on the most predictive markers.
Solution Approach 2:
The comprehensive analysis is segmented into distinct components: individual polymorphism analysis, interaction analysis (epistasis), and environmental factor assessment. This segmentation allows each component to be evaluated independently and efficiently, then integrated to achieve high prediction precision without requiring all analyses to be performed simultaneously, thus reducing time loss.
3Adaptability or versatility
If multiple genetic polymorphisms and environmental variables are analyzed, then predictive capability increases, but the complexity of the method increases
Solution Approach 1:
The prediction model serves multiple functions: it assesses individual polymorphism effects, evaluates gene-gene interactions (epistasis), and incorporates environmental factors. This multi-functional approach increases predictive capability for different fibrosis scenarios while using a universal statistical framework that manages the complexity of analyzing multiple variables simultaneously.
Solution Approach 2:
The method systematically varies parameters including genetic polymorphism genotypes, interaction strengths, and environmental factor weights to optimize predictive capability. By adjusting these parameters within a structured model framework, the method achieves high adaptability and versatility while maintaining manageable complexity through parameterized analysis rather than ad hoc approaches.
Data Source
AI summary
The present invention relates to an in vitro method of obtaining data useful for predicting a subject's risk of suffering fibrosis, being comprised in the field of personalized medicine. Specifically, said method is based on the analysis of a series of genetic polymorphisms of the subject, as well as other environmental data thereof, which together are useful and allow predicting a subject's risk of developing fibrosis.