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

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

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dc.contributor.author Cuesta Frau, David es_ES
dc.contributor.author Novák, Daniel es_ES
dc.contributor.author Burda, Vaclav es_ES
dc.contributor.author Abasolo, Daniel es_ES
dc.contributor.author Adjei, Tricia es_ES
dc.contributor.author Varela, Manuel es_ES
dc.contributor.author Vargas, Borja es_ES
dc.contributor.author Mraz, Milos es_ES
dc.contributor.author Kavalkova, Petra es_ES
dc.contributor.author Benes, Marek es_ES
dc.contributor.author Haluzik, Martin es_ES
dc.date.accessioned 2020-11-05T04:32:38Z
dc.date.available 2020-11-05T04:32:38Z
dc.date.issued 2019-02-14 es_ES
dc.identifier.issn 1076-2787 es_ES
dc.identifier.uri http://hdl.handle.net/10251/154097
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. No funding was received to support this research work by the Spanish and British partners es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Complexity es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2019/6070518 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CVUT//CZ.02.1.01%2F0.0%2F0.0%2F16-019%2F0000765/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CVUT//SGS16%2F231%2FOHK3%2F3T%2F13/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MZCR//DRO IKEM 000023001/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MZCR//RVO VFN 64165/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Cuesta Frau, D.; Novák, D.; Burda, V.; Abasolo, D.; Adjei, T.; Varela, M.; Vargas, B.... (2019). Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods. Complexity. 2019. https://doi.org/10.1155/2019/6070518 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1155/2019/6070518 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2019 es_ES
dc.relation.pasarela S\401347 es_ES
dc.contributor.funder Czech Technical University in Prague es_ES
dc.contributor.funder Ministry of Health, República Checa es_ES
dc.description.references Kassirer, J. P., & Angell, M. (1998). Losing Weight — An Ill-Fated New Year’s Resolution. New England Journal of Medicine, 338(1), 52-54. doi:10.1056/nejm199801013380109 es_ES
dc.description.references Van Gaal, L., & Dirinck, E. (2016). Pharmacological Approaches in the Treatment and Maintenance of Weight Loss. Diabetes Care, 39(Supplement 2), S260-S267. doi:10.2337/dcs15-3016 es_ES
dc.description.references De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2015). Impact of Duodenal-Jejunal Exclusion on Satiety Hormones. Obesity Surgery, 26(3), 672-678. doi:10.1007/s11695-015-1889-y es_ES
dc.description.references Muñoz, R., Dominguez, A., Muñoz, F., Muñoz, C., Slako, M., Turiel, D., … Escalona, A. (2013). Baseline glycated hemoglobin levels are associated with duodenal-jejunal bypass liner-induced weight loss in obese patients. Surgical Endoscopy, 28(4), 1056-1062. doi:10.1007/s00464-013-3283-y es_ES
dc.description.references Ogata, H., Tokuyama, K., Nagasaka, S., Ando, A., Kusaka, I., Sato, N., … Yamamoto, Y. (2007). Long-range Correlated Glucose Fluctuations in Diabetes. Methods of Information in Medicine, 46(02), 222-226. doi:10.1055/s-0038-1625411 es_ES
dc.description.references Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831 es_ES
dc.description.references DeFronzo, R. A. (2004). Pathogenesis of type 2 diabetes mellitus. Medical Clinics of North America, 88(4), 787-835. doi:10.1016/j.mcna.2004.04.013 es_ES
dc.description.references Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601 es_ES
dc.description.references Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102 es_ES
dc.description.references Bian, C., Qin, C., Ma, Q. D. Y., & Shen, Q. (2012). Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85(2). doi:10.1103/physreve.85.021906 es_ES
dc.description.references Zhao, L., Wei, S., Zhang, C., Zhang, Y., Jiang, X., Liu, F., & Liu, C. (2015). Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. Entropy, 17(12), 6270-6288. doi:10.3390/e17096270 es_ES
dc.description.references Weinstein, R. L., Schwartz, S. L., Brazg, R. L., Bugler, J. R., Peyser, T. A., & McGarraugh, G. V. (2007). Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System: Comparison with frequent laboratory reference measurements. Diabetes Care, 30(5), 1125-1130. doi:10.2337/dc06-1602 es_ES
dc.description.references Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043 es_ES
dc.description.references Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853 es_ES
dc.description.references Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018 es_ES
dc.description.references Cuesta–Frau, D., Varela–Entrecanales, M., Molina–Picó, A., & Vargas, B. (2018). Patterns with Equal Values in Permutation Entropy: Do They Really Matter for Biosignal Classification? Complexity, 2018, 1-15. doi:10.1155/2018/1324696 es_ES
dc.description.references Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(S6). doi:10.1186/1471-2105-15-s6-s2 es_ES
dc.description.references Sheng Lu, Xinnian Chen, Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008). Automatic Selection of the Threshold Value $r$ for Approximate Entropy. IEEE Transactions on Biomedical Engineering, 55(8), 1966-1972. doi:10.1109/tbme.2008.919870 es_ES
dc.description.references Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035 es_ES
dc.description.references Cuesta, D., Varela, M., Miró, P., Galdós, P., Abásolo, D., Hornero, R., & Aboy, M. (2007). Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Medical & Biological Engineering & Computing, 45(7), 671-678. doi:10.1007/s11517-007-0200-3 es_ES
dc.description.references Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005 es_ES
dc.description.references Xiao-Feng, L., & Yue, W. (2009). Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), 2690-2695. doi:10.1088/1674-1056/18/7/011 es_ES
dc.description.references Fadlallah, B., Chen, B., Keil, A., & Príncipe, J. (2013). Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87(2). doi:10.1103/physreve.87.022911 es_ES


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