Insulin Dosing via Macronutrient Profile Analysis
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Solution Overview
Problem
Current insulin delivery systems for diabetes management primarily rely on carbohydrate counting, neglecting the impact of macronutrient profiles, leading to variability in blood glucose responses and suboptimal insulin dosing, particularly for meals high in fat and protein, which can cause delayed and sustained hyperglycemia.
Innovation Solution
A machine-learning model is trained for individual patients to predict post-prandial blood glucose levels based on the macronutrient profile of meals, allowing for optimized bolus dosing and split timing, incorporating continuous glucose monitoring data and user input to adjust insulin delivery dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If insulin dosing is based solely on carbohydrate grams, then the dosing calculation is simple, but blood glucose control becomes suboptimal for meals high in fat and protein
Solution Approach 1:
The system changes the input parameters from simple carbohydrate grams to a comprehensive macronutrient profile including fat and protein content. This allows the insulin dosing algorithm to account for the delayed and sustained glucose absorption effects of fat and protein, improving blood glucose control accuracy without significantly increasing complexity through the use of standardized nutritional data.
Solution Approach 2:
The insulin bolus dose is segmented into multiple components: immediate bolus for carbohydrates, extended bolus for fat-induced delayed absorption, and additional adjustments for protein-induced gluconeogenesis. This segmentation allows each macronutrient's unique effect on blood glucose to be addressed with appropriate timing and dosage, resolving the contradiction between simple calculation and accurate control.
2Reliability
If macronutrient information is incorporated into insulin dosing, then blood glucose control improves, but the system complexity increases
Solution Approach 1:
The system uses a multi-functional approach where a single insulin pump device performs both traditional carbohydrate-based dosing and the new macronutrient-based dosing. The pump integrates multiple dosing algorithms that can be selected or combined based on the meal composition, allowing one device to handle diverse nutritional scenarios without requiring separate specialized equipment for each macronutrient type.
Solution Approach 2:
The system introduces an intermediary layer between meal intake and insulin delivery in the form of a macronutrient analysis module. This intermediary processes the composition of the meal and translates it into dosing recommendations, acting as a mediator that converts complex nutritional information into actionable insulin dosing parameters without requiring direct complex interactions between all system components.
3Object-affected harmful factors
If bolus insulin is delivered to match glucose absorption timing, then hypoglycemia risk decreases, but the dosing strategy becomes more complex
Solution Approach 1:
The system performs preliminary action by calculating and delivering an extended bolus component before the peak glucose absorption occurs. Based on the detected fat and protein content, the system proactively delivers additional insulin over an extended period (e.g., 3-5 hours) to preemptively counteract the delayed glucose entry into the bloodstream, preventing hypoglycemia before it occurs rather than reacting after the fact.
Solution Approach 2:
The dosing strategy uses periodic action by dividing the total insulin bolus into multiple delivery phases: an immediate bolus for rapid-acting insulin, followed by an extended periodic delivery of additional insulin over several hours. This periodic delivery pattern matches the multi-phase glucose absorption profile from macronutrients, delivering insulin in rhythm with glucose entry to maintain stable blood glucose levels and prevent hypoglycemia.
Data Source
AI summary
The disclosed embodiments are directed to systems and methods for providing optimized, individualized bolus dosing of insulin based on a macronutrient profile of meals ingested by the patient. Optimized bolus dosing may be provided by varying the overall quantity of insulin delivered in the post-prandial window, as well as the split between a portion of the insulin delivered immediately after the meal and a portion of the insulin delivered later in the post-prandial window, based on an analysis of the macronutrient profile of the meal and the behavior of the blood glucose trace of the patient from past meals.