# Modeling and modulating insulin delivery in automated insulin delivery systems to accommodate for meal compositions

> **NIH NIH K23** · STANFORD UNIVERSITY · 2020 · $193,743

## Abstract

PROJECT SUMMARY
The candidate, Laya Ekhlaspour, MD, is dedicated to advancing diabetes management by decreasing the
burden of diabetes care. This proposal will provide a structured clinical research training experience with
formal mentorship that will enable Dr. Ekhlaspour to become an independent clinical researcher with expertise
in closed loop systems. Currently, carbohydrates are considered the primary and major determinant affecting
postprandial glucose control and the insulin bolus is based solely on the carbohydrate content of the meal
along with the blood glucose at the time of the bolus. Given the known benefits of normalizing postprandial
glucose excursions, the goal of this research is to specifically address problems related to providing adequate
insulin coverage for meals that have variable fat and protein content that might result in both early
hypoglycemia and prolonged hyperglycemia, when automated insulin systems are used.
This proposal will take advantage of existing data collected during an observational study at the Barbara Davis
Center, and the feasibility closed-loop trials at Stanford. In order to characterize the effect of macronutrient
food content on postprandial glucose levels, a retrospective review of meals with known glucose, protein and
fat content combined with CGM and insulin values in both open loop and closed loop situations will be
conducted. This analysis will provide data to allow modeling for insulin requirements with meals of variable
patterns of postprandial glycemic levels. It will also determine whether this modeling will allow for setting the
percent of insulin required upfront and how long the insulin delivery needs to be extended in pumps which
allow for extended meal boluses. The result of this analysis can be used in closed-loop control real-time
modeling of meal boluses when there is a real-time adaptation to food absorption patterns, which will
contribute to eventual fully closed-loop glucose control. The developed model will be based on assessing the
hourly glucose and insulin requirements which could be integrated into prandial dosing algorithms in closed
loop systems in order to optimize postprandial glycemic control. The long-term goal is to have a fully-closed
loop algorithm, which will recognize the need for additional insulin with a high fat or protein meal based on the
CGM postprandial tracing and insulin requirements during closed-loop control. This will reduce the burden of
diabetes management significantly because the patient does not have to announce a meal.
The proposed studies will provide the preliminary meal data for modeling of meal-responses that will account
for both the carbohydrate, protein and fat content of the meal without user input. The following step will be to
validate this model through in silico experiments and then conduct a randomized, controlled trial of the
implementation of a potential refined meal algorithm that could handle postprandial glucose levels without a...

## Key facts

- **NIH application ID:** 9976303
- **Project number:** 1K23DK121942-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Laya Ekhlaspour
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $193,743
- **Award type:** 1
- **Project period:** 2020-07-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976303

## Citation

> US National Institutes of Health, RePORTER application 9976303, Modeling and modulating insulin delivery in automated insulin delivery systems to accommodate for meal compositions (1K23DK121942-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9976303. Licensed CC0.

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