Single-Cell HbA1c Analysis for Long-Term Glucose Trend Reconstruction
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Solution Overview
Problem
Existing methods for estimating blood glucose levels, such as continuous glucose monitoring, are limited by high costs and lack of long-term, continuous glucose fluctuation data, failing to provide comprehensive glycemic information.
Innovation Solution
A method and system that analyze single-cell glycated hemoglobin (HbA1c) distributions to reconstruct historical glucose concentration trajectories and assess glycemic variability, using biophysical models to process and iteratively refine glucose data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous blood glucose monitoring sensors are used to determine blood glucose levels continuously, then blood glucose fluctuations can be estimated, but the sensors must be changed every 2 weeks and the cost limits widespread use
Solution Approach 1:
The patent creates a computational model that copies the information provided by continuous glucose monitoring sensors but derives it from a different source - single RBC HbA1c measurements. Instead of physically implanting and replacing sensors, the system uses an in vitro assay combined with mathematical modeling to reconstruct glucose trajectories, thereby eliminating the need for frequent sensor replacements while maintaining the ability to estimate glucose fluctuations
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with a biochemical assay and computational analysis system. Instead of using physical sensors that require implantation and replacement, the invention uses an in vitro HbA1c measurement on single RBCs combined with a glycation model to mathematically reconstruct historical glucose levels, substituting mechanical measurement with biochemical analysis and mathematical modeling
2Measurement precision
If glycated hemoglobin measurements are used to assess average blood glucose level, then 3-month average can be determined, but no information regarding blood glucose fluctuations is provided
Solution Approach 1:
The patent segments the HbA1c measurement into single-RBC-level data points, allowing each RBC's glycation level to be measured individually. This segmentation enables the reconstruction of temporal information because RBCs have different ages and circulation times, so their varying HbA1c levels reflect glucose levels at different times in the past, thereby recovering fluctuation information that would be lost in bulk measurements
Solution Approach 2:
The patent adds a temporal dimension to the HbA1c measurement by considering the age distribution of RBCs. Instead of treating HbA1c as a single aggregate value, the invention measures HbA1c across multiple RBCs with different ages, effectively adding a time dimension that allows reconstruction of historical glucose trajectories and fluctuation patterns from what would otherwise be a static average measurement
3Loss of time
If single-cell glycated hemoglobin distributions are analyzed to reconstruct historical glucose trajectories, then long-term glucose trends can be obtained instantly, but complex computational processing is required
Solution Approach 1:
The patent performs preliminary action by establishing a glycation model A(t) and RBC age distribution PDFAge(t) before analyzing patient data. These pre-established models allow the system to quickly reconstruct glucose trajectories from single measurements without requiring complex real-time computations during patient testing, as the mathematical framework is prepared in advance
Solution Approach 2:
The patent introduces an intermediary computational model (the glycation model A(t)) that bridges the gap between the simple in vitro HbA1c measurement and the desired historical glucose trajectory. This intermediary model performs the complex mathematical transformation, allowing the system to obtain long-term glucose information instantly from a single measurement while containing the computational complexity within the model itself rather than requiring complex processing during actual use
Data Source
AI summary
A method of data analysis is provided. The method is used for finding a long-term trend of blood glucose concentration. The method builds a model for estimating long-term glycemic variability and long-term blood glucose trajectory. Based on single-erythrocyte-level glycated hemoglobin distribution, the glycemic variability is analyzed. A first analysis method is to give a number. The number shows the level of the historical glycemic variabilities. A second analysis method is to restore the blood glucose trajectory over the past 20 weeks. Based on the single-erythrocyte-level glycated hemoglobin distribution, the present invention easily assesses blood-glucose-related clinical information for about 150 days. Hence, an important complement is obtained for diabetes-related or glucose-monitoring-related clinical applications.