Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods
David Cuesta-Frau,
Daniel Novák,
Vacláv Burda,
Daniel Abasolo,
Tricia Adjei,
Manuel Varela,
Borja Vargas,
Milos Mraz,
Petra Kavalkova,
Marek Benes,
Martin Haluzik
Affiliations
David Cuesta-Frau
Technological Institute of Informatics, Universitat Politecnica de Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell, 2, 03801 Alcoi, Spain
Daniel Novák
Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
Vacláv Burda
Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
Daniel Abasolo
Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
Tricia Adjei
Department of Electrical and Electronic Engineering, Imperial College, London, UK
Manuel Varela
Department of Internal Medicine, Teaching Hospital of Mostoles, Madrid, Spain
Borja Vargas
Department of Internal Medicine, Teaching Hospital of Mostoles, Madrid, Spain
Milos Mraz
Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Petra Kavalkova
Department of Medical Biochemistry and Laboratory Diagnostics, General University Hospital, Charles University in Prague 1st Faculty of Medicine, Prague, Czech Republic
Marek Benes
Hepatogastroenterology Department, Transplant Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Martin Haluzik
Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
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.