Timing of Hypoglycaemia in Patients with Hyperinsulinism (HI): Extension of the Digital Phenotype

BACKGROUND Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycaemia in childhood. High cerebral glucose utilisation in the early hours results in high risk of hypoglycaemia for people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycaemia is the cornerstone of management for HI but the risk of hypoglycaemia at night or indeed the timing of hypoglycaemia in children with HI have not been studied, and thus the digital phenotype remains incomplete and management suboptimal. OBJECTIVE We aimed to quantify the timing of hypoglycaemia in patients with HI, to describe glycaemic variability and to extend the digital phenotype. This will facilitate future work using computational modelling to enable behaviour change and reduce exposure of HI patients to injurious hypoglycaemia 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 Hypoglycaemia (IKH) (n = 24). CGM data was analysed for temporal trends. Hypoglycaemia was defined as glucose 30 minutes. CONCLUSIONS In this study, we have taken the first step in extending the digital phenotype of HI by describing the glycaemic trends and identifying the timings of hypoglycaemia measured by CGM. We have identified the early hours as a time of high hypoglycaemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycaemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycaemia and must target personalised hypoglycaemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modelling to produce small improvements in hypoglycaemia prediction accuracy. CLINICALTRIAL