Glucose Time Series Model for Metabolic Subphenotype Identification
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Solution Overview
Problem
Current methods for diagnosing and treating Type 2 diabetes are inadequate as they fail to account for individual metabolic differences, leading to ineffective treatments due to their blanket approach, which does not consider underlying metabolic dysregulation pathways such as muscular insulin resistance, hepatic insulin resistance, beta-cell dysfunction, and impaired incretin effect.
Innovation Solution
A system and method utilizing a glucose time series curve analyzed via a trained computational model to predict underlying pathologies of metabolic dysregulation, allowing for personalized treatment approaches by identifying specific subphenotypes contributing to hyperglycemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current standard diagnostic methods (OGTT, fasting glucose) are used, then diagnosis can be obtained quickly and easily, but measurement precision is insufficient to capture underlying metabolic differences
Solution Approach 1:
The patent segments the homogeneous diabetes diagnosis into distinct metabolic subphenotypes (muscular insulin resistance, hepatic insulin resistance, beta-cell dysfunction, impaired incretin effect). By dividing the diagnostic approach into specific metabolic pathways, the system achieves higher measurement precision in identifying underlying pathologies while maintaining manageable complexity through modular assessment of each subphenotype.
Solution Approach 2:
The patent transforms the diagnostic approach by changing from single-parameter measurements (fasting glucose, 2-hour glucose) to multi-parameter analysis including glucose time series curves, insulin levels, and derived metabolic indices. This parameter expansion enables precise identification of four distinct metabolic subphenotypes, resolving the contradiction between measurement precision and system complexity.
2Reliability
If blanket treatment approaches are applied to all diabetes patients, then treatment implementation is simple and fast, but treatment efficacy is reduced due to ignoring individual metabolic differences
Solution Approach 1:
The patent applies local quality by matching specific treatments to specific metabolic subphenotypes identified in each patient. Instead of uniform treatment, the system tailors therapeutic interventions to the local metabolic characteristics of each individual (e.g., targeting muscular insulin resistance differently from hepatic insulin resistance), thereby improving treatment efficacy while managing complexity through structured personalization.
Solution Approach 2:
The patent implements preliminary action by conducting comprehensive metabolic subphenotype classification before initiating treatment. By预先 identifying the specific metabolic pathway dysfunction (muscular, hepatic, beta-cell, or incretin-related), the system enables targeted treatment selection in advance, improving reliability of treatment outcomes while organizing the personalization process into manageable sequential steps.
3Reliability
If comprehensive metabolic profiling is performed to identify subphenotypes, then treatment personalization is improved, but loss of time in diagnosis and assessment increases
Solution Approach 1:
The patent applies partial action by implementing a tiered diagnostic approach where the full metabolic profiling is performed only when needed for treatment decision-making. The system can operate with basic glucose measurements for routine cases while offering comprehensive subphenotype classification for patients requiring personalized treatment, thus achieving high accuracy when necessary while minimizing time loss for standard cases.
Data Source
AI summary
Systems and methods to assess metabolic dysregulation are described. Metabolic dysregulation refers to elevated glycemia or insulin resistance. The systems and methods assess metabolic dysregulation by determining which subphenotypes or underlying pathologies are contributing to the metabolic dysregulation. In some instances, a trained computational model utilizes an individual's glucose time series curve to determine the contribution of various metabolic dysregulation subphenotypes to the individual's metabolic dysregulation. Various applications or treatments can be performed based on the determination of metabolic dysregulation subphenotypes.