A battery-less implantable glucose sensor based on electrical impedance spectroscopy.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
23 10 2023
Historique:
received: 19 06 2023
accepted: 17 10 2023
medline: 27 10 2023
pubmed: 24 10 2023
entrez: 23 10 2023
Statut: epublish

Résumé

The ability to perform accurate continuous glucose monitoring without blood sampling has revolutionised the management of diabetes. Newer methods that can allow measurements during longer periods are necessary to substantially improve patients' quality of life. This paper presents an alternative method for glucose monitoring which is based on electrical impedance spectroscopy. A battery-less implantable bioimpedance spectroscope was designed, built, and used in an in vivo study on pigs. After a recovery period of 14 days post surgery, a total of 236 subcutaneous bioimpedance measurements obtained from intravenous glucose tolerance tests, with glucose concentration ranges between 77.4 and 523.8 mg/dL, were analyzed. The results show that glucose concentrations estimated by subcutaneous bioimpedance measurements correlate very well to the blood glucose reference values. The pigs were clinically healthy throughout the study, and the postmortem examinations revealed no signs of adverse effects related to the sensor. The implantation of the sensor requires minor surgery. The implant, being externally powered, could in principle last indefinitely. These encouraging results demonstrate the potential of the bioimpedance method to be used in future continuous glucose monitoring systems.

Identifiants

pubmed: 37872272
doi: 10.1038/s41598-023-45154-8
pii: 10.1038/s41598-023-45154-8
pmc: PMC10593792
doi:

Substances chimiques

Blood Glucose 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

18122

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Stig Ollmar (S)

Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden.

Alejandro Fernandez Schrunder (A)

School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 16440, Kista, Sweden.

Ulrik Birgersson (U)

Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden.

Tomas Kristoffersson (T)

Prevas AB, Stockholm, Sweden.

Ana Rusu (A)

School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 16440, Kista, Sweden.

Elina Thorsson (E)

Pathology Unit, Department of Biomedical Science and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Patricia Hedenqvist (P)

Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Elin Manell (E)

Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Anneli Rydén (A)

Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Marianne Jensen-Waern (M)

Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Saul Rodriguez (S)

School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 16440, Kista, Sweden. saul@kth.se.

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Classifications MeSH