MASH Biomarker Composition for Non-Invasive Diagnostic Accuracy
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Solution Overview
Problem
Current diagnostic methods for metabolic dysfunction-associated steatohepatitis (MASH) are invasive, costly, and have low patient acceptance, with existing non-invasive markers showing limited accuracy for differentiation between MASH and simple steatosis.
Innovation Solution
A biomarker composition comprising specific protein biomarkers such as CXCL10, CK-18, P62/SQSTM1, SQLE, CA3, and fibroblast growth factor (FGF21) combined with clinical biochemical markers like BMI, HbA1c, ALT, and LDL-C, is used to develop diagnostic models for MASH using machine learning algorithms like support vector machines and logistic regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If liver biopsy is used for MASH diagnosis, then diagnostic accuracy is improved, but patient acceptance and ease of operation deteriorate due to invasiveness and cost
Solution Approach 1:
The patent creates a non-invasive diagnostic model that copies the diagnostic functionality of liver biopsy by using a combination of blood-based biomarkers (CK-18, FGF21, adiponectin, leptin) analyzed through machine learning algorithms. This virtual copy achieves comparable diagnostic accuracy without the invasiveness of actual biopsy procedures
Solution Approach 2:
The patent introduces blood-based biomarkers as intermediary substances that mediate between the patient's liver condition and the diagnostic process. These biomarkers serve as proxies that convey liver health information through blood tests, eliminating the need for direct tissue sampling while maintaining diagnostic capability
2Ease of operation
If single biomarkers are used for MASH diagnosis, then ease of operation is improved, but diagnostic accuracy deteriorates due to limited sensitivity and specificity
Solution Approach 1:
The patent merges multiple biomarkers (CK-18, FGF21, adiponectin, leptin) into a unified diagnostic model that combines their individual diagnostic signals. This combination approach synergistically improves sensitivity and specificity beyond what any single biomarker could achieve alone, while still maintaining operational simplicity through automated analysis
Solution Approach 2:
The patent creates a composite diagnostic approach by integrating multiple biomarker types (apoptosis markers, metabolic factors, adipocyte factors) into a single diagnostic framework. This composite strategy leverages the complementary strengths of different biomarker classes to achieve superior diagnostic performance
3Ease of operation
If existing non-invasive markers are used, then patient acceptance is improved, but diagnostic accuracy deteriorates due to low sensitivity and specificity
Solution Approach 1:
The patent changes the parameters of non-invasive diagnosis by selecting and optimizing specific biomarker combinations (CK-18, FGF21, adiponectin, leptin) and their analytical parameters. This parameter optimization transforms previously inaccurate non-invasive markers into a high-accuracy diagnostic system that maintains patient acceptance
Solution Approach 2:
The patent implements a feedback mechanism through machine learning algorithms that analyze biomarker levels and provide diagnostic feedback. The system learns from training data and continuously improves its diagnostic accuracy, adapting to different patient populations and clinical scenarios while maintaining non-invasive operation
Data Source
AI summary
A MASH biomarker composition and an application thereof is provided, the biomarker composition including a protein marker and/or clinical biochemical marker for the diagnosis of MASLD and/or MASH, the protein marker being selected from CXCL10, CK-18, P62/SQSTM1, CA3, SQLE, Pro-C3 or one or more of FGF21; the clinical biochemical marker is selected from one or more of BMI, HDL-C, HbA1c, ALT, AST, LDL-C, TG, TC, ALP, or PLT. Biomarker compositions were established by logistic regression and artificial intelligence methods as diagnostic markers for the diagnosis of MASLD and MASH with high sensitivity, specificity, positive predictive value, and negative predictive value in the diagnosis of MASH, independent of age, gender, or metabolic status.