Clinical trial NCT06223204: GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography
Official title: GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography - a Pilot Project
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Diabetes Mellitus
Lead sponsor: Insel Gruppe AG, University Hospital Bern
Sponsor class: OTHER
Collaborators: name: CSEM Centre Suisse d'Electronique et de Microtechnique SA
name: Idiap Research Institute
First posted: 2024-01-25
Enrollment: 16
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

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.

Eligibility criteria: 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.)

Primary outcomes:
measure: Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.
timeFrame: 5 hours
Secondary outcomes:
measure: Change of hypoglycemia symptoms across the glycemic trajectory.
timeFrame: 5 hours
measure: Voice parameters indicative of dysglycemia
timeFrame: 5 hours
measure: Change in cognitive performance across the glycemic trajectory.
timeFrame: 5 hours
measure: Change in cognitive performance across the glycemic trajectory.
timeFrame: 5 hours
measure: 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).
timeFrame: 5 hours
measure: Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.
timeFrame: 5 hours
measure: Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.
timeFrame: 5 hours
measure: 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).
timeFrame: 5 hours
measure: 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).
timeFrame: 5 hours
measure: Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.
timeFrame: 5 hours
measure: 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.
timeFrame: 5 hours
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Study officials:
affiliation: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism; Bern, Switzerland
name: Christoph Stettler, Prof. MD
role: PRINCIPAL_INVESTIGATOR
References: