GI Tract Dysbiosis Detection via Multivariate Statistical Analysis
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Solution Overview
Problem
Current methods for assessing gastrointestinal (GI) tract microbiota deviations, such as dysbiosis, are not straightforward, reliable, or flexible enough to be used with various techniques for measuring microorganism levels in GI tract samples, limiting their effectiveness in diagnosing and monitoring associated diseases.
Innovation Solution
A computer-implemented method using orthogonal latent variables and statistical analysis, including Q-residual and Hotelling's T² calculations, to determine the likelihood of GI tract dysbiosis by comparing microbiota profiles to normobiotic and dysbiotic thresholds, allowing for flexible application across different measurement techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current methods for assessing GI tract microbiota deviations are used, then assessment can be performed, but the methods are not straightforward, reliable, or flexible enough
Solution Approach 1:
The patent transforms the complex microbiota assessment problem into a statistical parameter analysis problem by applying multivariate statistical methods (PCA, PLS, LDA) to microbiota composition data. This converts biological complexity into mathematical parameters that can be systematically analyzed, improving reliability while maintaining manageable complexity through standardized statistical workflows.
Solution Approach 2:
The patent introduces statistical models and computational algorithms as intermediary layers between raw microbiota measurements and clinical interpretation. These intermediaries process the complex microbial data through standardized statistical transformations, making the assessment both more reliable and more straightforward by abstracting away the biological complexity.
2Adaptability or versatility
If current assessment methods are used, then some diagnosis capability is provided, but they lack flexibility to be used with various measurement techniques
Solution Approach 1:
The patent creates a universal statistical framework that can process microbiota data from multiple different measurement techniques (sequencing, microarrays, PCR) through the same analytical pipeline. This multi-functional approach allows the same methodology to be applied across diverse measurement platforms, enhancing flexibility without sacrificing precision through standardized statistical processing.
Solution Approach 2:
The patent transforms diverse measurement outputs into standardized statistical parameters that can be uniformly analyzed. By converting different measurement techniques' outputs into comparable statistical forms (composition matrices, abundance profiles), the system achieves both versatility across techniques and precision in dysbiosis determination through consistent statistical evaluation.
3Measurement precision
If detailed microbiota profiling is performed to accurately detect dysbiosis, then diagnostic accuracy improves, but the complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex microbiota analysis problem into distinct statistical processing stages: data normalization, principal component analysis for dimensionality reduction, discriminant analysis for classification, and validation procedures. This segmentation breaks down the computationally intensive task into manageable modules, maintaining high detection accuracy while reducing overall computational complexity through systematic decomposition.
Solution Approach 2:
The patent extracts the essential dysbiosis detection capability from the complex microbiota data by applying dimensionality reduction techniques (PCA) that separate the critical diagnostic information from the overwhelming detail. This extraction process isolates the key discriminative features needed for accurate detection while removing redundant information, thereby reducing computational complexity without sacrificing precision.
Data Source
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AI summary
The invention provides a method for determining the likelihood of GI tract dysbiosis in a subject, said method comprising providing a test data set, wherein said test data set comprises at least one microbiota profile, said microbiota profile being a profile of the relative levels of a plurality of microorganisms or groups of microorganisms in a sample from the GI tract of the subject and wherein each level of each microorganism or group of microorganisms is a profile element of said test data set, applying to said test data set at least one loading vector determined from latent variables within the profiles of the levels of said plurality of microorganisms or groups of microorganisms in corresponding GI tract samples from a plurality of normal subjects, thereby producing a first projected data set, applying to said first projected data set a transposed version of said at least one loading vector, thereby producing a second projected data set, comparing said test data set with said second projected data set and combining the differences between the corresponding profile elements of the second projected data set and the test data set and comparing the combined differences with a normobiotic to dysbiotic threshold value determined from the corresponding analysis of said plurality of microorganisms or groups of microorganisms in corresponding GI tract samples from a plurality of normal subjects and/or subjects with dysbiosis, applying at least one eigenvalue to said first projected data set, said eigenvalue determined from said at least one loading vector, and combining the resulting values for each profile element and comparing the combined values with a normobiotic to dysbiotic threshold value determined from the corresponding analysis of said plurality of microorganisms or groups of microorganisms in corresponding GI tract samples from a plurality of normal subjects and/or subjects with dysbiosis, wherein a microbiota profile with said combined differences or said combined resulting values in excess of said respective normobiotic to dysbiotic thresholds is indicative of a likelihood of dysbiosis.