Gut Microbiota Risk Modeling for Non-Invasive MCI Screening
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Solution Overview
Problem
Existing methods for assessing the risk of mild cognitive impairment require invasive blood sampling to measure L-Ergothioneine levels, lacking a non-invasive technique using intestinal microbiota data.
Innovation Solution
A system and method for calculating the risk of mild cognitive impairment by extracting intestinal microbiota data, creating an association model, and inputting it into a score estimation model to predict the probability of developing or having mild cognitive impairment using stool samples and questionnaire data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blood sampling is used to measure L-Ergothioneine levels for assessing cognitive function, then measurement precision is improved, but ease of operation deteriorates due to invasive procedure requirements
Solution Approach 1:
The patent introduces intestinal microbiota as an intermediary biomarker that indirectly reflects cognitive function status. Instead of directly measuring L-Ergothioneine in blood, the system analyzes microbial composition in stool samples, which serve as a mediator between the target cognitive function assessment and the actual measurement, thereby eliminating the need for invasive blood sampling while maintaining assessment accuracy
Solution Approach 2:
The patent replaces the mechanical/invasive blood collection procedure with a non-invasive stool sample analysis system. This substitution eliminates the need for needles, blood draw equipment, and trained medical personnel for sample collection, while using standard laboratory techniques to analyze microbial DNA from stool samples
2Ease of operation
If intestinal microbiota data is used to calculate risk indicator, then ease of operation is improved through non-invasive sampling, but measurement precision may deteriorate due to indirect measurement method
Solution Approach 1:
The system incorporates feedback mechanisms by using machine learning models that are trained on large datasets of microbiota profiles correlated with cognitive function outcomes. The model continuously refines its predictions by comparing calculated risk indicators with actual cognitive status data, thereby improving measurement precision over time while maintaining the non-invasive nature of the assay
Solution Approach 2:
The patent analyzes the composite composition of intestinal microbiota, examining the relative abundances and interactions of multiple bacterial species rather than relying on a single marker. This composite analysis approach, which considers the microbial ecosystem as a whole, enhances measurement precision by capturing the complexity of the gut-brain axis relationship
3Reliability
If a comprehensive model with multiple variables is used to calculate risk probability, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex risk assessment model into distinct functional modules: a data extraction module that processes microbiota composition data, a feature selection module that identifies relevant microbial markers, a risk calculation module that applies statistical models, and a result interpretation module that translates probabilities into clinical insights. This segmentation reduces device complexity by making each module independent and manageable while maintaining the reliability of the comprehensive multi-variable analysis
Data Source
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AI summary
A technique for calculating a risk of mild cognitive impairment for a subject who submitted a stool sample and a questionnaire , and a system for calculating a risk indicator for mild cognitive impairment is presented. The system includes an extraction unit configured to extract, from a first data related to intestinal microbiota data of a user, a second data related to a factor or intestinal microorganisms that is indicated to relate to the mild cognitive impairment; and a calculation unit configured to input the second data into a model for calculating a probability of the user having the mild cognitive impairment, the model having a third data as a variable, the third data being related to one or more intestinal microorganisms that represents a difference between intestinal microbiota of healthy participants and intestinal microbiota of patients with the mild cognitive impairment, and to calculate the risk of the user for the mild cognitive impairment, wherein the risk for the mild cognitive impairment is a probability of developing or having already developed the mild cognitive impairment.