diabetes mellitus type 1 Type 1 diabetes
Conditions
Interventions
The participants will visit our research unit after an overnight fast, where
they will receive a fixed insulin infusion and a variable glucose infusion at
t=0 to bring the participant in an euglycem
beta cells
Hypoglycemia
Machine learning
Type 1 diabetes
Sponsors
Leids Universitair Medisch Centrum
Eligibility
Age
18 Years to 64 Years
Inclusion criteria
Inclusion criteria: Type 1 diabetes HbA1c
Exclusion criteria
Exclusion criteria: History of epilepsy Use of medicationknow to induce insulin resistance Use of medication for diabetes other than insulin (analogs) History of cardiovascular disease, kidney disease, liver disease or disease of the central nervous system
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| - Change in serum C-peptide concentration - Time delay between plasma glucose and interstitial glucose measured by a FGM and/or implantable CGM during the emergence and the resolution of a hypoglycaemia - Difference in facial expression as determined by a deep learning module of Microsoft before, during and after hypoglycaemia - Difference in heart rate variability before, during and after hypoglycaemia | — |
Secondary
| Measure | Time frame |
|---|---|
| - Association of differences in facial expression and heart rate variability with counterregulatory mechanisms (norepinephrine, epinephrine, heart rate), hypoglycaemic symptoms (semiquantitive symptom questionnaire and simple cognitive testing) - Change in other products of the islets (for instance glucagon and proinsulin) | — |
Countries
The Netherlands
Outcome results
None listed