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Insulin: Dosing Regimen and Adverse Effects01:16

Insulin: Dosing Regimen and Adverse Effects

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Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
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Insulin Formulations: Types and Delivery01:27

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Insulin preparations are categorized by their duration of action into short-acting and long-acting types. Two strategies are used to modify insulin's absorption and pharmacokinetic profile: slowing the absorption post-subcutaneous injection, or altering human insulin's amino acid sequence or protein structure. These changes retain the insulin's ability to bind to the insulin receptor, but alter its behavior in solution or after injection.
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Insulin: Biosynthesis, Chemistry, and Preparation01:25

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The endoplasmic reticulum (ER) of pancreatic β-cells synthesizes preproinsulin, which consists of a signal peptide, A and B chains, and a C-peptide. Preproinsulin is then cleaved and folded into proinsulin, which translocates to the Golgi apparatus for sorting and packaging into secretory granules. In these granules, enzymatic clipping generates insulin and C-peptide.
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Incretins include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which stimulate insulin secretion post-meals. In type 2 diabetes, GIP's efficacy is reduced, making GLP-1 a viable drug target. GIP originates from preproGIP.
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Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.

Clara Mosquera-Lopez1, Leah M Wilson2, Joseph El Youssef2

  • 1Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA. mosquera@ohsu.edu.

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Summary

The robust artificial pancreas (RAP) system uses machine learning for automated meal detection and insulin dosing. RAP significantly reduces time above target glucose levels compared to traditional methods.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Diabetes Technology

Background:

  • Automated insulin delivery systems aim to improve glucose control in diabetes management.
  • Accurate meal detection and carbohydrate estimation are crucial for effective mealtime insulin dosing.
  • Current systems often require manual input, posing a challenge for real-world application.

Purpose of the Study:

  • To evaluate the performance of a novel robust artificial pancreas (RAP) system.
  • To compare postprandial glucose control between the RAP system and a hybrid model predictive control (MPC) algorithm.
  • To assess the efficacy of RAP's automated meal detection and insulin dosing capabilities.

Main Methods:

  • A randomized, single-center crossover trial was conducted.
  • Participants used both the RAP system and a hybrid MPC algorithm for postprandial glucose control.
  • The RAP system incorporates a neural network for automated meal detection and carbohydrate estimation.
  • Performance was evaluated over four hours following unannounced meals.

Main Results:

  • The RAP system's meal detection algorithm achieved 83.3% sensitivity and a 16.6% false discovery rate with a mean detection time of 25.9 minutes.
  • No significant difference in incremental area under the glucose curve was observed between RAP and MPC.
  • RAP significantly reduced time above range (glucose >180 mg/dL) by 10.8% (P=0.04).
  • RAP showed a trend towards increasing time in range (70-180 mg/dL) by 9.1%.

Conclusions:

  • The robust artificial pancreas (RAP) system demonstrates improved glucose control by reducing hyperglycemia.
  • Automated meal detection and dosing in RAP enhance usability and potentially improve diabetes management.
  • Further research is warranted to optimize RAP performance and explore its long-term benefits.