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Looking for Biomarkers in Physiological Time Series

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Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues

Abstract

From the point of view of Complexity Sciences, health can be considered as the state of dynamical balance between robustness and adaptability to the changes in the environment. We consider that any human disease can be found in physiological time series by deviations from this point that reflects the loss of this balance. Thus, it is possible to find biomarkers based on non-invasive physiological parameters that characterize the critical healthy state, and could help as early warnings auxiliary for clinical diagnoses of different diseases. In this work, we present a time-domain analysis using the distribution moments, autocorrelation function, Poincaré diagrams, and the spectral analysis of interbeat intervals and blood pressure time series for control subjects of different age and gender, and diabetic patients. As a preliminary result, a statistical significant difference was found between health and disease in the statistical moments of blood pressure and heart rate variability that can be proposed as biomarkers.

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Acknowledgements

Thanks to J. A. López-Rivera for grammatical review. Financial funding for this work was supplied by Dirección General de Asuntos del Personal Académico from Universidad Nacional Autónoma de México (UNAM) grants PAPIIT IN106215, IV100116 and IA105017; grants 2015-02-1093, 2016-01-2277 and CB-2011-01-167441 from Consejo Nacional de Ciencia y Tecnología (CONACyT), and the Newton Advanced Fellowship awarded to RF by the Academy of Medical Sciences through the UK Government’s Newton Fund program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors do not have any conflict of interest.

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Authors and Affiliations

  1. Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Ana Leonor Rivera, Ruben Fossion & Alejandro Frank

  2. Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Mexico City, Mexico

    Ana Leonor Rivera, Bruno Estañol, Juan C. Toledo-Roy & Ruben Fossion

  3. Laboratorio de Neurofisiología Clínica, Departamento de Neurología, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, Mexico City, Mexico

    Bruno Estañol

  4. Posgrado en Investigación Biomédica, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Adriana Robles-Cabrera

  5. Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Alejandro Frank

  6. Colegio Nacional, Mexico City, Mexico

    Alejandro Frank

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  1. Ana Leonor Rivera
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  2. Bruno Estañol
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  3. Adriana Robles-Cabrera
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  4. Juan C. Toledo-Roy
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  5. Ruben Fossion
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Editors and Affiliations

  1. Universidad Autónoma de la Ciudad de México (UACM), Mexico City, Mexico

    Luis Olivares-Quiroz

  2. Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), México City, Mexico

    Osbaldo Resendis-Antonio

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Rivera, A.L., Estañol, B., Robles-Cabrera, A., Toledo-Roy, J.C., Fossion, R., Frank, A. (2018). Looking for Biomarkers in Physiological Time Series. In: Olivares-Quiroz, L., Resendis-Antonio, O. (eds) Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Springer, Cham. https://doi.org/10.1007/978-3-319-73975-5_6

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