Digital Twin Metabolic Phenotype Mapping for Diabetes Simulation
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Solution Overview
Problem
Current diabetes treatment methods lack a comprehensive, personalized approach for simulating the effects of treatment changes before clinical implementation, particularly for glucose dysregulation conditions like Type 1 and Type 2 diabetes, obesity, and pre-diabetes, which hinders optimal management and forecasting of treatment outcomes.
Innovation Solution
A method and system that create a 'digital twin' or Virtual Image of a Person (VIP) by mapping a subject's metabolic and demographic data onto an in silico entity, allowing for iterative simulation of treatment scenarios, medication optimization, and real-time monitoring, using algorithms and databases to simulate glucose metabolism traits and behavioral characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current diabetes treatment methods are used, then treatment can be implemented clinically, but the ability to simulate and forecast treatment outcomes is limited
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the patient's metabolic system that replicates their unique physiological characteristics. This virtual model allows clinicians to simulate and forecast treatment outcomes by testing different therapeutic scenarios in the digital replica before applying them to the actual patient, thereby improving reliability of outcome prediction while maintaining adaptability to individual patient needs.
Solution Approach 2:
The digital twin model enables preliminary simulation of treatment effects before clinical implementation. By performing virtual experiments and forecasting treatment outcomes in advance using the patient-specific metabolic model, clinicians can optimize treatment plans and avoid adverse outcomes, thus improving reliability without compromising personalized adaptability.
2Adaptability or versatility
If a digital twin model is created for personalized simulation, then treatment optimization is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal metabolic modeling framework that can be applied across different patient populations and treatment scenarios. The in silico population comprises diverse virtual subjects representing various metabolic phenotypes, allowing the same modeling system to handle multiple diabetes types, treatment regimens, and clinical questions, thereby achieving high adaptability without proportionally increasing system complexity.
Solution Approach 2:
The digital twin model utilizes parameter optimization techniques to match virtual subjects with actual patients based on key metabolic parameters (e.g., HbA1c, fasting glucose, insulin sensitivity). By adjusting and optimizing these parameters systematically, the model achieves personalized accuracy while maintaining computational efficiency and manageable system complexity through standardized parameter sets and matching algorithms.
3Measurement precision
If comprehensive metabolic data is collected for accurate modeling, then measurement precision is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on the most critical metabolic parameters needed for accurate digital twin creation, such as HbA1c, fasting glucose, insulin levels, and key demographic factors. By selecting only the essential parameters rather than processing all available metabolic data, the system achieves high measurement precision for phenotype characterization while minimizing data processing requirements and computational burden.
Solution Approach 2:
The model applies different levels of data collection and processing intensity to different aspects of metabolic characterization based on their relative importance. Critical parameters like HbA1c and fasting glucose receive higher measurement precision and more rigorous processing, while less critical parameters use standard processing approaches, thereby optimizing the balance between accuracy and computational efficiency.
Data Source
AI summary
Provided are a method, system and computer-readable storage medium for mapping a metabolic phenotype of a subject to a database entity for creating a digital twin of the subject, and enabling as to such subject, one or more of in silico titration of treatment as to such subject prior to initiating clinical intervention, in silico adjustment and optimization of medication dosing and timing, personalized training through in silico replay of particularized treatment scenario, and tracking over time of any divergence in one or more metabolic traits as between the subject and the digital twin to thereby detect deterioration or improvement in the subject's condition.