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Related Concept Videos

Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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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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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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Glucagon-like Receptor Agonists01:24

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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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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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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Hypoglycemia event prediction from CGM using ensemble learning.

Jesper Fleischer1,2, Troels Krarup Hansen1, Simon Lebech Cichosz3

  • 1Steno Diabetes Center Aarhus, Aarhus, Denmark.

Frontiers in Clinical Diabetes and Healthcare
|March 30, 2023
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Summary

This study developed an algorithm using continuous glucose monitor (CGM) data to predict hypoglycemia in type 1 diabetes patients. The model shows high accuracy, offering potential for early warnings to prevent low blood sugar events.

Keywords:
Dexcom G4 platinumblood glucose (BG)continuous glucose monitoring (CGM)diabetesevent predictionhypoglycaemiamachine learningtype 1 diabetes

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Area of Science:

  • Biomedical Engineering
  • Endocrinology
  • Data Science

Background:

  • Hypoglycemia is a common complication in type 1 diabetes management.
  • Predicting hypoglycemia is crucial for patient safety and quality of life.

Purpose of the Study:

  • To evaluate the efficacy of standalone continuous glucose monitor (CGM) data for hypoglycemia prediction.
  • To develop and validate an ensemble learning algorithm for early hypoglycemia detection.

Main Methods:

  • Utilized 3.7 million CGM measurements from 225 type 1 diabetes patients.
  • Trained and tested an ensemble learning algorithm for hypoglycemia prediction within 40 minutes.
  • Validated the algorithm on 11.5 million synthetic CGM data points.

Main Results:

  • Achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.988.
  • Demonstrated a precision-recall area under the curve (PR AUC) of 0.767.
  • In event-based analysis, achieved 90% sensitivity with a 17.5-minute lead time and 38% false-positive rate.

Conclusions:

  • Standalone CGM data, combined with ensemble learning, shows significant potential for hypoglycemia prediction.
  • The developed algorithm can provide timely alerts for impending hypoglycemic events.
  • This technology could empower patients to initiate countermeasures, improving diabetes self-management.