Microbiome Signature Analysis for Non-Caloric Substance Tolerance
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Solution Overview
Problem
Current methods fail to accurately predict individual responses to non-caloric substances like artificial sweeteners due to variations in gut microbiome composition, leading to differing tolerance levels among subjects, and are not considerate of the impact of circadian rhythm disruptions on microbiome function.
Innovation Solution
A method involving the analysis of gut microbiome signatures by comparing them to reference signatures from tolerant and intolerant subjects, using statistical similarity to determine tolerance and considering circadian rhythm alignment to assess microbiome disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gut microbiome analysis is used to predict individual response to non-caloric substances, then prediction accuracy is improved, but measurement complexity increases
Solution Approach 1:
The microbiome analysis is segmented into specific operational taxonomic units (OTUs) at different taxonomic levels (phylum, class, order, family, genus, species). This segmentation allows the system to focus on specific bacterial groups known to be relevant for predicting response to non-caloric substances, reducing the complexity of analyzing the entire microbiome while maintaining prediction accuracy.
Solution Approach 2:
The patent applies local quality by identifying and analyzing specific OTUs with distinct functional characteristics rather than treating the microbiome as a homogeneous entity. Different OTUs are weighted and analyzed based on their specific roles in metabolism and response to non-caloric substances, enabling precise predictions through targeted analysis of functionally relevant microbial communities.
2Measurement precision
If comprehensive microbiome composition analysis is performed, then individual variation detection is improved, but analysis time increases
Solution Approach 1:
The patent performs preliminary action by pre-defining reference microbiome compositions from subjects with known responses (responders and non-responders) to non-caloric substances. These reference profiles are established beforehand, allowing rapid comparison with test subjects' microbiomes using statistical methods, thereby reducing analysis time while maintaining detection of individual variations.
Solution Approach 2:
The system creates simplified copies of the complex microbiome data by representing it as relative abundances of specific OTUs that can be compared against reference profiles. This copying approach uses statistical similarity metrics to capture essential individual variations without requiring complete comprehensive analysis, thus reducing analysis time while preserving detection accuracy.
3Reliability
If microbiome disruption from circadian misalignment is identified, then health risk assessment is improved, but diagnostic complexity increases
Solution Approach 1:
The patent extracts specific OTUs and their relative abundances from the complex microbiome composition that are particularly sensitive to circadian misalignment. By isolating and analyzing these specific microbial indicators, the system can assess health risks related to circadian disruption without needing to analyze the entire microbiome, thereby reducing diagnostic complexity while maintaining assessment reliability.
Solution Approach 2:
The system merges microbiome analysis with circadian rhythm assessment by integrating data on microbial composition changes with information about circadian misalignment. This combination allows simultaneous evaluation of both microbiome health and circadian disruption effects, improving comprehensive health risk assessment while using a unified diagnostic approach that reduces overall complexity.
Data Source
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AI summary
A method of determining tolerance to an agent in a healthy subject is disclosed. The method comprises: (a) determining a signature of a microbiome in a sample of the healthy subject who has been subjected to the agent or condition; and (b) comparing the signature of the microbiome of the healthy subject to at least one reference signature of a pathological microbiome, wherein when the signature of the microbiome of the healthy subject is statistically significantly similar to the reference signature of the pathological microbiome, it is indicative that the healthy subject is intolerant to the agent.