Theoretical analysis of autonomic nervous system effects on cardiac electrophysiology and its relationship with arrhythmic risk
- Autores: David Adolfo Sampedro Puente
- Directores de la Tesis: Jesús Fernández Bes (dir. tes.), Esther Pueyo Paules (dir. tes.), P. Laguna (dir. tes.)
- Lectura: En la Universidad de Zaragoza ( España ) en 2020
- Idioma: español
- Tribunal Calificador de la Tesis: Violeta Monasterio Bazán (presid.), Carlos Sánchez Tapia (secret.), Alfonso Bueno Orovio (voc.), Jean Rene Bragard (voc.), Beatriz Ana Trenor Gomis (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Biomédica por la Universidad Politécnica de Catalunya y la Universidad de Zaragoza
- Materias:
- Enlaces
- Tesis en acceso abierto en: Zaguán
- Resumen
Cardiovascular diseases represent the main cause of mortality and morbidity in industrialized societies. A significant percentage of deaths associated with these diseases is related to the generation of cardiac arrhythmias, defined as abnormalities in the electrical functioning of the heart. Three major elements are involved in the development of arrhythmias, which include an arrhythmogenic substrate, a trigger and modulating factors. The Autonomic Nervous System (ANS) is the most relevant of these modulators. The ANS is composed of two branches, sympathetic and parasympathetic, which to a certain extent act antagonistically to each other. The possibility of revealing how the sympathetic nervous system modulates the activity of the ventricles (lower heart chambers) and participates in the development of arrhythmias, as reported experimentally, could be crucial to advance in the design of new clinical therapies aimed at preventing or treating these rhythm abnormalities. This thesis investigates spatio-temporal variability of human ventricular repolarization, its modulation by the sympathetic nervous system, the mechanisms behind highly elevated variability and the relationship to the generation of ventricular arrhythmias. To that end, methodologies combining signal processing of ventricular signals and in silico modeling of human ventricular myocytes are proposed. The developed in silico models include coupled theoretical descriptions of electrophysiology, calcium dynamics, mechanical stretch and -adrenergic signaling. To account for temporal (beat-to-beat) repolarization variability, stochasticity is added into the equations defining the gating of the ion channels of the main currents active during action potential (AP) repolarization, i.e. during the return of the cell to the resting state after an excitation. To account for spatial (cell-to-cell) repolarization variability, a population of models representative of different cellular characteristics are constructed and calibrated based on available experimental data. The theoretical computational research of this study, combined with the processing of clinical and experimental ventricular signals, lays the ground for future studies aiming at improving arrhythmic risk stratification methods and at guiding the search for more efficient anti-arrhythmic therapies.
In Chapter 2, a population of experimentally-calibrated stochastic human ventricular computational cell models coupling electrophysiology, mechanics and -adrenergic signaling are built to investigate spatio-temporal variability. Model calibration is based on experimental ranges of a number of AP-derived markers describing AP duration, amplitude and shape. By using the proposed population of stochastic AP models, the experimentally reported interactions between a particular type of temporal variability associated with low-frequency (LF) oscillations of AP duration (APD) and overall beat-to-beat variability of repolarization (BVR) in response to enhanced sympathetic activity are reproduced. Ionic mechanisms behind correlated increments in both phenomena are investigated and found to be related to downregulation of the inward and rapid delayed rectifier K+ currents and the L-type Ca2+ current. Concomitantly elevated levels of LF oscillations of APD and BVR in diseased ventricles are shown to lead to electrical instabilities and arrhythmogenic events.
In Chapter 3, the time delay for manifestation of LF oscillations of APD, as a particular form of repolarization variability, is investigated in ventricular myocytes in response to sympathetic provocation. By using an experimentally-calibrated population of human ventricular AP models, as in Chapter 2, this oscillatory latency is demonstrated to be associated with the slow phosphorylation kinetics of the slow delayed rectifier K+ current IKs in response to -adrenergic stimulation. Prior stimulation of -adrenoceptors substantially reduces the time required for the development of LF oscillations. In addition, short time lapses are shown to be related to large APD oscillatory magnitudes, as measured in Chapter 2, particularly in cells susceptible to develop arrhythmogenic events in response to sympathetic stimulation.
The experimental calibration of the population of models used in Chapter 2 and Chapter 3, despite ensuring that simulated population measurements lie within experimental limits, does not guarantee that each model in the constructed population represents the experimental measurements of an individual human ventricular cardiomyocyte. It is for that reason that in Chapter 4 a novel methodology is developed to construct computational populations of human ventricular cell models that more faithfully recapitulate individual available experimental evidences. The proposed methodology is based on the formulation of nonlinear state-space representations and the use of the Unscented Kalman Filter (UKF) to estimate parameters and state variables of an underlying stochastic AP model given any input voltage trace. Tests performed over synthetic and experimental voltage traces demonstrate that this methodology successfully renders a one-to-one match between input AP traces and sets of model parameters (ionic current conductances) and state variables (ionic gating variables and intracellular concentrations). The proposed methodology is shown to be robust for investigation of spatio-temporal variability in human ventricular repolarization.
Chapter 5 improves the methodology developed in Chapter 4 to more accurately estimate parameters and state variables of stochastic human ventricular cell models from individual input voltage traces and to reduce the converge time so as to provide faster estimation. The improvements are based on the combined use of the UKF method of Chapter 4 together with Double Greedy Dimension Reduction (DGDR) method with automatic generation of biomarkers. Additionally, on top of estimating ionic current conductances at baseline conditions, the approach presented in this chapter also provides a set of -adrenergic-induced phosphorylation levels, thus contributing to the analysis of spatio-temporal repolarization patterns with and without autonomic modulation.
In conclusion, this thesis presents novel methodologies for characterization of spatio-temporal variability of human ventricular repolarization, for dissection of its underlying mechanisms and for ascertainment of the relationship between elevated variability and increased risk for ventricular arrhythmias and sudden cardiac death.
Sets of stochastic human computational cell models with representation of ventricular electrophysiology, mechanics and -adrenergic signaling are developed and used to analyze overall beat-to-beat and cell-to-cell repolarization variability as well as a particular type of variability in the form of LF oscillations. To faithfully reproduce experimentally measured variability patterns in a one-to-one manner, methodologies are proposed to construct populations of human ventricular AP models where the parameters and state variables of a model are estimated from a given input voltage trace. These personalized models open the door to more robust investigation of the causes and consequences of spatio-temporal variability of human ventricular repolarization.
Bibliography:
D. A. Sampedro Puente, J. Fernandez Bes, L. Virág, A. Varró, E. Pueyo. “Data-driven Identification of Stochastic Model Parameters and State Variables: Application to the Study of Cardiac Beat-to-beat Variability”. IEEE Journal of Biomedical and Health Informatics, 2019. DOI: 10.1109/JBHI.2019.2921881.
D. A. Sampedro Puente, J. Fernandez-Bes, B. Porter, S. Duijvenboden, P. Taggart E. Pueyo. “Mechanisms Underlying Interactions Between Low-Frequency Oscillations and Beat-to-Beat Variability of Celullar Ventricular Repolarization in Response to Sympathetic Stimulation: Implications for Arrhythmogenesis.” Frontiers in Physiology, 2019. DOI: 10.3389/fphys.2019.00916.
D. A. Sampedro Puente, J. Fernandez-Bes, N. Szentandrássy, P. P. Nánasi, P. Taggart and E. Pueyo. “Time Course of Low-Frequency Oscillatory Behavior in Human Ventricular Repolarization Following Enhanced Sympathetic Activity and Relation to Arrhythmogenesis.” Frontiers in Physiology, 2019. DOI: 10.3389/fphys.2019.01548.
D. A. Sampedro Puente, F. Raphel, J. Fernandez-Bes, P: Laguna, D. Lombardi, E. Pueyo. “Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and under -Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction.” IEEE Journal of Biomedical and Health Informatics, 2019.
review process.
S. van Duijvenboden, B. Porter, E. Pueyo, D. A. Sampedro Puente, J. Fernandez-Bes, B. Sidhu, J. Gould, M. Orini, Martin Bishop, B. Hanson, P.
Lambiase, R. Razavi, C. A. Rinaldi, J. S. Gill and P. Taggart. “Complex Interaction between Low-Frequency APD Oscillations and Beat-to-Beat APD Variability in Humans is Governed by the Sympathetic Nervous System.” Frontiers in Physiology, 2019. DOI: 10.3389/fphys.2019.01582.
