GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography
- Conditions
- Diabetes Mellitus
- Interventions
- Other: Controlled euglycemia, hypoglycemia and hyperglycemia
- Registration Number
- NCT06223204
- Lead Sponsor
- Insel Gruppe AG, University Hospital Bern
- 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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 16
- 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
- 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.)
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Controlled euglycemia, hypoglycemia and hyperglycemia Controlled euglycemia, hypoglycemia and hyperglycemia -
- Primary Outcome Measures
Name Time Method 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 Outcome Measures
Name Time Method 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 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.
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.
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 Digit Symbol Substitution Test (higher score means better 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 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.
Trial Locations
- Locations (1)
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
🇨🇭Bern, Switzerland