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The pancreatic islets comprising only 1%-2% of the volume are highly vascularized and innervated mini-organs. They contain five endocrine cell types, including β cells that secrete insulin, which is synthesized as a single polypeptide chain, preproinsulin, processed to proinsulin, and finally to insulin and C-peptide. This process is complex and regulated, involving the Golgi complex, the endoplasmic reticulum, and the secretory granules of the β cell.
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Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery

Drew Cooper1, Bernd Reinhold2, Arsalan Shahid3

  • 1Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Journal of Diabetes Science and Technology
|September 26, 2023
PubMed
Summary

Open-source automated insulin delivery (OS-AID) systems demonstrate real-world efficacy in managing diabetes. Analysis of user data shows these systems help achieve recommended glycemic targets, improving time in range.

Keywords:
CGMautomated insulin deliveryglucoseglycemic variabilitymachine learningtype 1 diabetes

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Area of Science:

  • Endocrinology and Diabetes Technology
  • Biomedical Data Science
  • Artificial Intelligence in Healthcare

Background:

  • Open-source automated insulin delivery (OS-AID) systems integrate commercial insulin pumps and continuous glucose monitors with open-source algorithms.
  • These systems aim to automate insulin dosing for individuals with insulin-requiring diabetes.
  • Anonymized user data from two datasets, OPEN and OpenAPS Data Commons, are utilized for analysis.

Purpose of the Study:

  • To assess glycemic variability (GV) outcomes within the OPEN dataset.
  • To compare GV metrics between the OPEN dataset and a subset of the OpenAPS Data Commons.
  • To quantitatively analyze demographic data and GV metrics.

Main Methods:

  • Glycemic variability (GV) outcomes were assessed using the OPEN dataset.
  • Unsupervised machine learning algorithms were employed for glucose data clustering.
  • Statistical tests were used to quantify GV metrics and compare distributions between datasets.

Main Results:

  • The OPEN dataset (n=75) comprised 36,827 days of data, with a mean Time in Range (TIR) of 82.08%.
  • Low Blood Glucose Index (LBGI) differed significantly by gender (P < .05), while High Blood Glucose Index (HBGI) distributions were similar.
  • Most GV metrics showed statistically significant differences between the OPEN and OpenAPS Data Commons datasets (P < .05).

Conclusions:

  • Both OPEN and OpenAPS Data Commons datasets indicate that OS-AID systems achieve recommended targets for Time Below 70 (TOR < 70), TIR, and Time Above 180 (TOR > 180).
  • This provides further evidence for the real-world effectiveness of OS-AID.
  • Future research should explore dataset variations and the link between user behavior patterns and GV outcomes.