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dc.contributor.author | Rodríguez de Castro, Carmen | |
dc.contributor.author | Vigil Medina, Luis | |
dc.contributor.author | Vargas, Borja | |
dc.contributor.author | García Delgado, Emilio | |
dc.contributor.author | García Carretero, Rafael | |
dc.contributor.author | Ruiz Galiana, Julián | |
dc.contributor.author | Varela, Manuel | |
dc.date.accessioned | 2020-03-07T19:13:05Z | |
dc.date.available | 2020-03-07T19:13:05Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2017). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. https://doi.org/10.1002/dmrr.2831 | spa |
dc.identifier.issn | 1520-7560 | |
dc.identifier.uri | http://hdl.handle.net/11268/8716 | |
dc.description.abstract | Background Complexity analysis of glucose profile may provide valuable information about the gluco‐regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk. Methods A total of 206 patients with any of the following risk factors (1) essential hypertension, (2) obesity or (3) a first‐degree relative with a diagnosis of diabetes were included in a survival analysis study for a diagnosis of new onset type 2 diabetes. At inclusion, a glucometry by means of a Continuous Glucose Monitoring System was performed, and DFA was calculated for a 24‐h glucose time series. Patients were then followed up every 6 months, controlling for the development of diabetes. Results In a median follow‐up of 18 months, there were 18 new cases of diabetes (58.5 cases/1000 patient‐years). DFA was a significant predictor for the development of diabetes, with ten events in the highest quartile versus one in the lowest (log‐rank test chi2 = 9, df = 1, p = 0.003), even after adjusting for other relevant clinical and biochemical variables. In a Cox model, the risk of diabetes development increased 2.8 times for every 0.1 DFA units. In a multivariate analysis, only fasting glucose, HbA1c and DFA emerged as significant factors. Conclusions Detrended fluctuation analysis significantly performed as a harbinger of type 2 diabetes development in a high‐risk population. Complexity analysis may help in targeting patients who could be candidates for intensified treatment. | spa |
dc.description.sponsorship | Sin financiación | spa |
dc.language.iso | eng | spa |
dc.title | Glucose time series complexity as a predictor of type 2 diabetes | spa |
dc.type | article | spa |
dc.description.impact | 3.904 JCR (2017) Q2, 39/142 Endocrinology & Metabolism | spa |
dc.identifier.doi | 10.1002/dmrr.2831 | |
dc.rights.accessRights | closedAccess | spa |
dc.subject.uem | Diabetes | spa |
dc.subject.uem | Tecnología médica | spa |
dc.subject.unesco | Sistema cardiovascular | spa |
dc.subject.unesco | Enfermedad | spa |
dc.subject.unesco | Tecnología médica | spa |
dc.description.filiation | UEM | spa |
dc.relation.publisherversion | http://ezproxy.universidadeuropea.es/login?url=http:/ /dx.doi.org/10.1002/dmrr.2831 | spa |
dc.peerreviewed | Si | spa |
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