Food Frequency Questionnaire Models for Fprau Level Estimation
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Solution Overview
Problem
Current methods for assessing Faecalibacterium prausnitzii (Fprau) levels in the gut microbiome require invasive sample collection, laboratory processing, and specialized expertise, which are costly and inaccessible to many, and many individuals are reluctant to provide fecal samples.
Innovation Solution
Estimate Fprau levels using machine-learning models based on nutrient intake data from Food Frequency Questionnaires (FFQs), eliminating the need for biological samples and providing personalized dietary recommendations to maintain or improve Fprau status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fecal sample collection and laboratory processing methods are used to assess Fprau levels, then measurement precision is improved, but ease of operation and accessibility deteriorate due to invasive procedures and specialized requirements
Solution Approach 1:
The patent creates a computational model that copies the relationship between dietary intake and Fprau levels, allowing indirect assessment without physical samples. The machine learning model replicates the biological relationship between nutrients and bacteria abundance, enabling prediction from questionnaire data alone.
Solution Approach 2:
The patent introduces dietary intake data as an intermediary variable to assess Fprau levels. Instead of directly measuring bacteria, the system measures dietary patterns (which are easy to obtain via questionnaire) and uses the established relationship between diet and microbiome to infer Fprau abundance indirectly.
2Measurement precision
If fecal sample collection and laboratory processing methods are used to assess Fprau levels, then measurement precision is improved, but device complexity and cost deteriorate due to specialized equipment and expertise requirements
Solution Approach 1:
The patent replaces the mechanical and chemical laboratory processing system with a computational system. Instead of physical DNA extraction, sequencing, and bioinformatics analysis, the system uses machine learning algorithms to process questionnaire data and predict Fprau levels, eliminating complex laboratory infrastructure requirements.
Solution Approach 2:
The computational model copies the biological relationship between diet and Fprau, creating a virtual assessment system that replicates the information obtained from laboratory methods without requiring physical samples or specialized equipment.
3Measurement precision
If fecal sample collection is required, then measurement precision is improved, but ease of operation deteriorates due to user reluctance and invasive procedures
Solution Approach 1:
The patent uses dietary intake information as an intermediary that users are willing to provide instead of fecal samples. The system measures what users are comfortable reporting (dietary habits) and uses this proxy data to assess Fprau levels, bypassing the need for invasive sample collection while maintaining assessment capability.
Solution Approach 2:
The system creates a non-invasive copy of the assessment process by using questionnaire responses to replicate the information gathering function of sample collection, allowing users to participate without providing biological specimens.
Data Source
AI summary
The present invention relates to systems and methods for estimating an individual's Faecalibacterium prausnitzii (Fprau) amounts and for providing personalized recommendations to maintain or improve the Fprau. In several embodiments of the invention, the individual's Fprau amounts are estimated based on their Food FrequencyQuestionnaire (FFQ) records. In several embodiments, the methods are implemented by a computer system. In several embodiments of the invention, personalized recommendations and dietary advice are given to the individual to maintain or improve said individual's Fprau.