Diabetes Mellitus
Conditions
Brief summary
The GLEAM study aims at assessing the potential of electrical impedance tomography (EIT) for noninvasive glucose measurement.
Detailed description
Within the GLEAM study, paired samples of EIT and blood glucose measurements will be collected in individuals with type 1 diabetes during standardized euglycemia, hypoglycemia and hyperglycemia. These samples will be used to assess the potential of EIT for noninvasive glucose measurement and/or dysglycemia detection.
Interventions
OTHERControlled euglycemia, hypoglycemia and hyperglycemia
EIT measurements are collected in different glycemic states (euglycemia, hypoglycemia and hyperglycemia). Venous blood glucose is measured using a gold-standard glucose analyzer.
Sponsors
CSEM Centre Suisse d'Electronique et de Microtechnique SA
Idiap Research Institute
Insel Gruppe AG, University Hospital Bern
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 60 Years
Healthy volunteers
No
Inclusion criteria
* Written, informed consent * Type 1 Diabetes mellitus as defined by WHO for at least 6 months * Aged 18 - 60 years * HbA1c ≤ 9.0 % * Insulin treatment with good knowledge of insulin self-management * Use of a continuous (CGM) or flash glucose monitoring system (FGM) * Native language German or Swiss German
Exclusion criteria
* Incapacity to give informed consent * Contraindications to insulin aspart (NovoRapid®) * Known allergies to adhesives of the EIT device (e.g., gel electrodes) * Pregnancy, breast-feeding or lack of safe contraception * Active heart, lung, liver, gastrointestinal, renal or psychiatric disease * Patients with implantable electronic devices (e.g., pacemaker or implantable cardioverter defibrillator (ICD)) or thoracic metal implants * Epilepsy or history of seizure * Active drug or alcohol abuse * Chronic neurological or ear-nose-and-throat (ENT) disease influencing voice or history of voice disorder * Thoracic or back deformities * Body mass index (BMI) \>35.0 kg/m2 * Open wounds, burns, or rashes on the upper thorax * Active smoking * Medication known to interfere with voice or to induce listlessness (e.g., opioids, benzodiazepines, etc.)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory. | 5 hours | EIT signals will be collected at multiple frequencies between 50 kHz and 1 MHz from the thoracic region in euglycemia, hypoglycemia and hyperglycemia using a multi-channel EIT measurement device. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Voice parameters indicative of dysglycemia | 5 hours | Voice data will be collected using a microphone in euglycemia, hypoglycemia and hyperglycemia. After sampling, an interpretable machine learning (ML) method will be used to identify voice parameters indicative of dysglycemia. |
| Change in cognitive performance across the glycemic trajectory. | 5 hours | Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance). |
| Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as area under the receiver operating characteristics curve (AUROC). | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity. | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Change of hypoglycemia symptoms across the glycemic trajectory. | 5 hours | Hypoglycemia symptoms will be collected in euglycemia, hypoglycemia and hyperglycemia using a standardized questionnaire (Edinburgh Hypoglycemia Scale, a higher score means more symptoms, minimum score 7 points, maximum score 77 points). |
| Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as root mean squared error (RMSE). | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as mean absolute relative difference (MARD). | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots. | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using the Clarke Error Grid. | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
| Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity. | 5 hours | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. |
Countries
Switzerland
Outcome results
None listed