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Hormones Regulating Blood Glucose01:16

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Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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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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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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Glucose Homeostasis: Regulation of Blood Glucose01:02

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Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
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Updated: Sep 17, 2025

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An AI-based module for interstitial glucose forecasting enabling a "Do-It-Yourself" application for people with type

Antonio J Rodriguez-Almeida1, Guillermo V Socorro-Marrero1, Carmelo Betancort2

  • 1Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, ULPGC, Las Palmas de Gran Canaria, Spain.

Frontiers in Digital Health
|June 30, 2025
PubMed
Summary

This study introduces a do-it-yourself (DIY) deep learning (DL) framework for personalized glucose prediction in type 1 diabetes (T1D) management. The system achieves state-of-the-art accuracy using continuous glucose monitoring (CGM) data for short-term predictions.

Keywords:
continuous glucose monitoringdeep learningmHealthpersonalized medicinetype 1 diabetes

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Endocrinology

Background:

  • Diabetes mellitus (DM) affects over 500 million adults, with Type 1 diabetes (T1D) requiring insulin therapy and challenging glucose control.
  • Current mHealth tools and deep learning (DL) models for glucose prediction often lack long-term user engagement, limiting their benefit in daily T1D self-management.
  • Accurate short-term glucose level prediction is crucial for effective T1D management.

Purpose of the Study:

  • To develop a do-it-yourself (DIY) deep learning (DL) framework for personalized interstitial glucose prediction using continuous glucose monitoring (CGM) data.
  • To enable the generation of a unique DL model for each user, without relying on data from other individuals.
  • To provide accurate glucose level predictions up to one hour ahead for improved T1D self-management.

Main Methods:

  • A DIY module was created to process raw CGM data, preparing it for training and validation of a DL model.
  • A personalized DL model was generated for each user, utilizing their own CGM data.
  • One year of CGM data from 29 T1D subjects was used for model training and validation.

Main Results:

  • The DL-based DIY framework demonstrated prediction performance comparable to state-of-the-art methods, using only CGM data.
  • This work represents the first DL-based DIY approach for fully personalized glucose prediction.
  • The open-source framework is deployable via Docker, allowing standalone use, smartphone integration, or further DL architecture experimentation.

Conclusions:

  • The developed DIY framework offers a novel, personalized approach to glucose prediction for T1D management.
  • The system's accuracy and personalization capabilities have the potential to enhance user engagement and improve T1D self-care.
  • The open-source and adaptable nature of the framework facilitates broader adoption and future research in AI-driven diabetes management.