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Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

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Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

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dc.contributor.author Colás, Ana es_ES
dc.contributor.author Vigil, Luis es_ES
dc.contributor.author Vargas, Borja es_ES
dc.contributor.author Cuesta Frau, David es_ES
dc.contributor.author Varela, Manuel es_ES
dc.date.accessioned 2021-01-28T04:32:01Z
dc.date.available 2021-01-28T04:32:01Z
dc.date.issued 2019-12-18 es_ES
dc.identifier.issn 1932-6203 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160085
dc.description.abstract [EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm. es_ES
dc.description.sponsorship This study has been funded by Instituto de Salud Carlos III through the project PI17/00856 (Co-funded by the European Regional Development Fund, A way to make Europe). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. es_ES
dc.language Inglés es_ES
dc.publisher Public Library of Science es_ES
dc.relation.ispartof PLoS ONE es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1371/journal.pone.0225817 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ISCIII//PI17%2F00856/ES/Predicción de la fiebre y optimización del rendimiento diagnóstico de los hemocultivos/ 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 Colás, A.; Vigil, L.; Vargas, B.; Cuesta Frau, D.; Varela, M. (2019). Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics. PLoS ONE. 14(12):1-15. https://doi.org/10.1371/journal.pone.0225817 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1371/journal.pone.0225817 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 12 es_ES
dc.identifier.pmid 31851681 es_ES
dc.identifier.pmcid PMC6919578 es_ES
dc.relation.pasarela S\401356 es_ES
dc.contributor.funder Instituto de Salud Carlos III es_ES
dc.contributor.funder European Regional Development Fund es_ES
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