Glucose Homeostasis Phenotype Classification via Control Model
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Solution Overview
Problem
Current methods for assessing glucose homeostasis are limited by their reliance on simple heuristics, failing to provide insights into the body's ability to maintain physiological balance under external stimuli, and are inadequate for identifying prediabetic and diabetic states effectively, leading to a need for a more nuanced evaluation of glycemic control.
Innovation Solution
A system and method that classify subjects into glucose homeostasis phenotypes using a control model describing glucose homeostasis as a control system, incorporating proportional-integral controller equations and differential equations, with coefficients representing response to glucose deviations, metabolic rates, and feedback mechanisms, derived from continuous glucose monitoring data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple heuristics are used to measure and assess health (single time point values), then the assessment is efficient and unambiguous, but it fails to explain the fundamental control mechanisms and physiological systems that lead to healthy values
Solution Approach 1:
The patent transitions from single time point measurements (1D) to dynamic trajectories over time (adding temporal dimension). By monitoring physiological biometrics continuously and analyzing the paths taken to reach target values, the system captures the dynamic control processes that single snapshots miss, thereby resolving the contradiction between efficiency and information completeness
Solution Approach 2:
The patent applies dynamics by shifting from static health assessment to dynamic monitoring. Instead of taking discrete measurements at single time points, the system continuously tracks physiological biometrics and analyzes how values change over time, revealing the body's real-time control mechanisms and responses to stressors
2Ease of operation
If discrete single time point values of physiological biometrics are used, then the measurement is simple and clear, but it provides no indication of how effective the body is at controlling the biometric under stress
Solution Approach 1:
The patent implements feedback by analyzing the trajectory of physiological biometrics toward target values. The system monitors how the body responds to deviations from homeostasis and adjusts control mechanisms in real-time, providing reliable assessment of homeostatic effectiveness rather than relying on static snapshots that cannot capture dynamic control responses
3Device complexity
If traditional risk factors (BMI, activity levels, family history) are used for classification, then the classification is easy to obtain, but it does not provide insights into disease progression or guide interventions
Solution Approach 1:
The patent applies preliminary action by using dynamic trajectory analysis to identify prediabetic states before they progress to full diabetes. By continuously monitoring glucose levels and analyzing temporal patterns, the system detects early physiological changes and enables preventive interventions before irreversible damage occurs, rather than waiting for traditional risk factors to manifest
Data Source
AI summary
Described are methods and systems for classifying a subject into a glucose homeostasis phenotype such as a prediabetic subphenotype based on modelling glycemic control and glucose homeostasis in the subject. Also described is a model of glucose homeostasis based on proportional and integral terms in a control system. A representative curve is generated based on glucose time series data and fit to the model in order to determine coefficients for each subject. The coefficients provide a digital biomarker of glycemic control for the subject and may be used to classify subjects into different glucose homeostasis phenotypes.