Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Background: Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of...

Full description

Saved in:
Bibliographic Details
Published in:Journal of medical Internet research 2021-10, Vol.23 (10), p.e26957-e26957
Main Authors: Worth, Chris, Harper, Simon, Salomon-Estebanez, Maria, O'Shea, Elaine, Nutter, Paul W, Dunne, Mark J, Banerjee, Indraneel
Format: Article
Language:English
Subjects:
Behavior
Behavior change
Brain damage
Brain injured people
Brain research
Cerebral glucose
Childhood
Clinical assessment
Clustering
Diabetes
Diabetics
Disease prevention
Genotype & phenotype
Glucose
Glucose monitoring
High risk
Homeostasis
Human-computer interaction
Hypoglycemia
Idiopathic
Injuries
Insulin
Males
Original Paper
Patients
Physiology
Recurrent
Trends
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background: Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal. Objective: This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events. Methods: Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/26957