Polygenic Risk Score Model for Chronic Kidney Disease Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current risk prediction models for chronic kidney disease primarily rely on clinical factors and lack consideration for genetic factors, which vary across different populations, leading to inadequate early detection and increased prevalence of the disease.
Innovation Solution
A method and system that utilize a reference database with polygenic risk score data, clinical data, and genetic marker data, combined with genetic testing and machine learning algorithms to calculate a polygenic risk score, enabling accurate assessment of chronic kidney disease risk by analyzing nucleic acid samples and clinical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If risk prediction models rely only on clinical factors, then the assessment method is simple, but the prediction accuracy is insufficient and cannot account for genetic background variations
Solution Approach 1:
The patent combines clinical factor assessment with genetic factor assessment into a unified risk prediction model. The system integrates multiple data sources including clinical data (age, sex, eGFR, albumin, calcium, phosphate, bicarbonate) and genetic data (polygenic risk scores, genetic marker data) to comprehensively assess CKD risk, thereby improving prediction accuracy while accounting for population-specific genetic backgrounds
Solution Approach 2:
The assessment system is designed to be universally applicable across different populations by incorporating polygenic risk scores that capture genetic background variations. The model can adapt to different ethnic groups (Asian and European populations) while maintaining a consistent framework, making the complex assessment method broadly useful
2Measurement precision
If genetic factors are incorporated into risk prediction, then the prediction accuracy improves, but the assessment complexity increases
Solution Approach 1:
The patent performs preliminary processing of genetic data by calculating polygenic risk scores before integrating them with clinical factors. This pre-computation step organizes complex genetic information into a standardized format that can be easily combined with clinical data, reducing the complexity of the overall assessment process while maintaining high prediction accuracy
Solution Approach 2:
The patent uses polygenic risk scores as an intermediary that bridges genetic data and clinical factors. Instead of directly processing raw genetic data, the system converts it into polygenic risk scores that serve as a mediator, making it easier to integrate genetic information with clinical parameters in a unified assessment model
Data Source
AI summary
A method for assessing risk of chronic kidney disease includes following steps. A reference database is provided. A nucleic acid sample and a biological dataset of a subject are provided. A genetic testing step is performed. A risk score calculating step is performed. A model establishing step is performed, wherein a plurality of reference polygenic risk score data, a plurality of reference clinical data and a plurality of reference genetic marker data of the reference database are trained to achieve a convergence by a machine learning algorithm so as to obtain an analysis model. A data analysis step is performed, wherein a polygenic risk score, a clinical data and a genetic marker data of a subject are analyzed by the analysis model so as to obtain a risk analysis result.