CKD Prognosis Biomarker Panel C3a-desArg sTNFR1 NGAL
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Solution Overview
Problem
Current methods for predicting chronic kidney disease (CKD) progression and mortality are inadequate, particularly at the individual patient level, as conventional tools like eGFR and uACR show weaker prognostic value, and existing biomarker studies have limitations in predicting adverse outcomes across various CKD severities and etiologies.
Innovation Solution
A method involving the determination of serum biomarkers such as C3a-desArg, sTNFR1, and NGAL levels, combined with statistical methodologies, to predict CKD prognosis, with a panel of probes binding specifically to these biomarkers for screening and monitoring CKD progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tools (eGFR and uACR) are used for CKD prognosis, then the method is simple and widely available, but the prognostic value is weaker at the individual patient level with significant variability in risks
Solution Approach 1:
The patent combines multiple circulating biomarkers (C3a-desArg, sTNFR1, NGAL, sTNFR2) with conventional clinical tools (eGFR, uACR) to create a composite prognostic model. This merging approach integrates the simplicity and availability of conventional tools with the enhanced individual-level predictive capability of biomarkers, resolving the contradiction between measurement precision and device complexity by layering biomarker assessment onto existing clinical workflows
Solution Approach 2:
The patent develops a multi-functional prognostic system that can be applied across the full spectrum of CKD severity (stages 1-5) and various CKD aetiologies (diabetic, hypertensive, glomerulonephritis, etc.). The biomarker panel serves multiple functions: risk stratification, treatment guidance, and monitoring progression, thereby achieving universal applicability that maintains prognostic value across diverse patient populations without requiring disease-specific customization
2Measurement precision
If multiple circulating biomarkers are measured simultaneously, then the ability to uncover patient subgroups with differing risks is improved, but the challenge of assimilating predictive value to meaningfully communicate risks increases
Solution Approach 1:
The patent implements a feedback mechanism where biomarker levels are interpreted in the context of established risk thresholds and clinical outcomes. The system provides feedback to clinicians about individual patient risk levels, enabling meaningful communication of complex multi-biomarker data through simplified risk categories and actionable recommendations that reflect the integrated predictive value of all measured markers
Solution Approach 2:
The patent transforms multiple biomarker parameters into a unified risk assessment framework by establishing reference ranges and risk thresholds for each marker (C3a-desArg, sTNFR1, NGAL, sTNFR2). This parameter transformation approach converts complex multi-dimensional biomarker data into interpretable risk stratification categories, resolving the contradiction between precise risk identification and data integration complexity
3Adaptability or versatility
If existing biomarker studies are used, then specific subgroups (e.g., diabetic kidney disease) are well-characterized, but predictive performance across the spectrum of CKD severity and aetiology in real-world practice is underexplored
Solution Approach 1:
The patent performs preliminary validation of the biomarker panel across diverse CKD populations before clinical implementation. By pre-establishing the biomarker panel's performance characteristics across multiple CKD stages and aetiologies in a comprehensive cohort study, the system ensures reliable predictive performance is confirmed in advance for real-world applicability, resolving the contradiction between adaptability and reliability through proactive multi-population validation
Data Source
AI summary
The current invention provides methods which can be implemented alongside clinical variables as risk prediction tools for improved prediction of CKD progression and mortality. Surprisingly it was found that decreased C3a-desArg was associated with adverse outcome in CKD, and when combined with increased STNFR1 and NGAL it gave an excellent predictor of progression to a composite endpoint in CKD patients.