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Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

Cuesta-Frau, David, Novák, Daniel, Burda, Vacláv, Abasolo, Daniel, Adjei, Tricia, Varela, Manuel, Vargas, Borja, Mraz, Milos, Kavalkova, Petra, Benes, Marek and Haluzik, Martin (2019) Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods Complexity, 2019, 6070518. pp. 1-10.

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Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel–Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave–One–Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
Cuesta-Frau, David
Novák, Daniel
Burda, Vacláv
Adjei, Tricia
Varela, Manuel
Vargas, Borja
Mraz, Milos
Kavalkova, Petra
Benes, Marek
Haluzik, Martin
Date : 14 February 2019
DOI : 10.1155/2019/6070518
Copyright Disclaimer : Copyright © 2019 David Cuesta-Frau et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User : Clive Harris
Date Deposited : 15 Feb 2019 12:54
Last Modified : 15 Feb 2019 12:54

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