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Completed

NCT Number: NCT06223204

GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography

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

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Key information

Conditions

Diabetes Mellitus Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year–60 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism

Bern, Switzerland

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.)

Treatment and study plan

Controlled euglycemia, hypoglycemia and hyperglycemia

Other

EIT measurements are collected in different glycemic states (euglycemia, hypoglycemia and hyperglycemia). Venous blood glucose is measured using a gold-standard glucose analyzer.

Primary outcomes

  1. Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.

    Time frame: 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 outcomes

  1. Change of hypoglycemia symptoms across the glycemic trajectory.

    Time frame: 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).

  2. Voice parameters indicative of dysglycemia

    Time frame: 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.

  3. Change in cognitive performance across the glycemic trajectory.

    Time frame: 5 hours

    Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance).

  4. Change in cognitive performance across the glycemic trajectory.

    Time frame: 5 hours

    Cognitive performance will be assessed using the Digit Symbol Substitution Test (higher score means better cognitive performance).

  5. 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).

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  6. Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  7. Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  8. 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).

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  9. 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).

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  10. Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

  11. 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.

    Time frame: 5 hours

    Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.

Sponsors and collaborators

Lead sponsor

Insel Gruppe AG, University Hospital Bern

Other

Collaborators

  • CSEM Centre Suisse d'Electronique et de Microtechnique SA
  • Idiap Research Institute

Registry information

Official study title

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

Acronym: GLEAM

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Jan 25, 2024
Registry last updated
May 2, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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