GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography

Diabetes Mellitus Clinical Trial

— GLEAM  

Official title:

GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography - a Pilot Project

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06223204
• Other study ID #: GLEAM
• Status: Completed
• Phase: N/A
• Start date: January 31, 2024
• Est. completion date: April 10, 2024

Study information

• Verified date: April 2024
• Source: Insel Gruppe AG, University Hospital Bern
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The GLEAM study aims at assessing the potential of electrical impedance tomography (EIT) for noninvasive glucose measurement.

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 information / eligibility

• Status: Completed
• Enrollment: 16
• Est. completion date: April 10, 2024
• Est. primary completion date: April 10, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 60 Years

Eligibility

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.)

Related Conditions & MeSH terms

• Diabetes Mellitus

Intervention

Other:
• Controlled 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.

Locations

Country	Name	City	State
Switzerland	Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism	Bern

Sponsors (3)

Lead Sponsor	Collaborator
Insel Gruppe AG, University Hospital Bern	CSEM Centre Suisse d'Electronique et de Microtechnique SA, Idiap Research Institute

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.	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.	5 hours
Secondary	Change of hypoglycemia symptoms across the glycemic trajectory.	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).	5 hours
Secondary	Voice parameters indicative of dysglycemia	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.	5 hours
Secondary	Change in cognitive performance across the glycemic trajectory.	Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance).	5 hours
Secondary	Change in cognitive performance across the glycemic trajectory.	Cognitive performance will be assessed using the Digit Symbol Substitution Test (higher score means better cognitive performance).	5 hours
Secondary	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).	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	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).	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	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).	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours
Secondary	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.	Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.	5 hours