Glucose Metabolism Evaluation Using Multi-Analyte Vector Space Analysis
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Solution Overview
Problem
Current oral glucose tolerance tests primarily focus on glucose concentration profiles, lacking comprehensive evaluation of glucose metabolism states, particularly in early diabetic stages and pre-diabetic conditions, due to limited analysis of additional analyte concentrations and disease progression trajectories.
Innovation Solution
A method that calculates similarity measures between measured glucose and analyte concentration profiles and predefined reference profiles, projecting data points into a vector space to evaluate glucose metabolism states, utilizing norm trajectories to determine disease progression and metabolic impairment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only glucose concentration profiles are measured and analyzed, then the test procedure remains simple and quick, but the diagnostic accuracy for glucose metabolism states is insufficient
Solution Approach 1:
The patent combines multiple analyte measurements (glucose, insulin, C-peptide) with multidimensional data analysis to create a comprehensive diagnostic system. This merging of multiple data sources enables accurate detection of glucose metabolism states including early diabetic stages and pre-diabetic conditions that cannot be identified by glucose measurement alone.
Solution Approach 2:
The patent introduces additional analytical dimensions by measuring multiple analytes simultaneously and analyzing their temporal profiles. This transforms the diagnosis from a single-dimension glucose-based assessment to a multidimensional evaluation that captures complex metabolic relationships and progression patterns.
2Measurement precision
If multiple analyte concentrations are measured and analyzed, then comprehensive evaluation of glucose metabolism states is achieved, but the calculation and evaluation process becomes complex
Solution Approach 1:
The patent pre-calculates reference profiles for various glucose metabolism states (healthy, pre-diabetic, diabetic) and stores them in a database. During actual testing, the system compares measured analyte profiles against these pre-established references, significantly reducing the computational complexity of real-time diagnosis while maintaining high evaluation accuracy.
Solution Approach 2:
The patent introduces similarity measures as intermediary metrics that quantify the match between measured analyte profiles and reference profiles. These similarity measures serve as intermediaries that translate complex multidimensional data comparisons into interpretable diagnostic indicators, simplifying the evaluation process.
3Reliability
If traditional glucose-only analysis is used, then the test interpretation is straightforward, but early diabetic stages and pre-diabetic conditions cannot be detected
Solution Approach 1:
The patent pre-establishes norm trajectories that represent disease progression patterns from healthy states through pre-diabetic conditions to diabetic states. By projecting measured data points onto these pre-defined trajectories, the system can reliably detect early metabolic changes and stage disease progression without requiring complex real-time analysis.
Solution Approach 2:
The patent implements a feedback mechanism where the position of data points relative to norm trajectories provides continuous information about disease progression. This feedback enables the system to reliably identify metabolic impairment stages and track disease evolution over time, enhancing detection reliability.
Data Source
AI summary
A method is provided for evaluating a set of measurement data from an oral glucose tolerance test. The method may include calculating a similarity measure that quantifies the similarity between a time profile of the series of measured data of the glucose concentration and a corresponding glucose reference profile. The method may include calculating a further similarity measure that quantifies the similarity between the profile of the series of measured values of the further analyte concentration and the corresponding analyte sample profile, wherein the data set is represented by a point in a vector space that comprises coordinate axes that are formed by the similarity measures, whereby the coordinates of said point contain the calculated values of the similarity measures. The method also may include evaluating the position of the point with respect to reference points, which each represent a defined state of health, in order to calculate a parameter that specifies the state of the glucose metabolism of the patient.