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Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface

Periodic Reporting for period 3 - MODELAGE (Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface)

Reporting period: 2018-10-01 to 2020-03-31

MODELAGE aims at making a significant step towards the big challenge of characterizing human heart aging. Aging is associated with a decline in the physiological functions of the body. Specifically regarding the heart, aging has been associated with changes in both cardiac structure and function, which have been mostly described in animal species. Structural remodeling includes, among others, left ventricular hypertrophy and alterations in the extracellular matrix composition, while functional remodeling involves variations in the electrical action potential and compromised ability to increase contractility, particularly in response to activities with high demand. Although these structural and functional changes represent overall manifestations of cardiac aging, not all individuals age at the same pace and widely varied levels of age-associated myocardial remodeling, spanning from mild to severe, have been reported.

The first overall objective of MODELAGE is to provide a comprehensive description of how aging manifests in the human heart at a range of scales, from the cell to the body surface, which is currently not well characterized in humans due to lack of data availability and other technical reasons. Importantly, MODELAGE seeks to shed light on how diverse remodeling manifestations in individuals of the same or similar chronological age can be indicative of different biological aging rates. As a final contribution, MODELAGE will move forward to link the diverse patterns of cardiac aging, and their associated biological aging rates, to the predisposition of suffering from cardiac arrhythmias, which are highly prevalent in the elderly.

The relevance of investigating the aging of the human heart is well substantiated considering the fact that aging is a major independent risk factor for cardiovascular diseases. These diseases constitute the leading cause of mortality and morbidity in the world, accounting today for more than 17 million deaths every year. With the aging of the population, this figure is expected to rise and reach 23 million deaths per year by 2030. Improvements in the characterization of age-induced cardiac remodeling would facilitate our understanding of why aging predisposes to cardiovascular diseases in general, and cardiac arrhythmias in particular, and might open a door to therapies aimed at controlling the effects of aging when designing strategies to treat these diseases.
To accomplish MODELAGE objectives, myocardial tissue samples, electrocardiograms and blood samples were collected from individuals of various ages as well as from large mammals, like pig and sheep, for prior optimization of the protocols required to process the collected data. In vitro investigations were undertaken to characterize tissue, cellular and subcellular properties as a function of age.

Results from the in vitro studies were integrated into computational models. A Bayesian methodology was developed in MODELAGE to identify individual cellular characteristics from experimental measurements. Baseline conditions as well as the response to sympathetic provocations were modeled and simulations were run to investigate cardiac temporal and spatial properties. Tissue characterizations were additionally incorporated to build multi-scale computational models, which will be used to relate spatio-temporal dynamics with chronological / biological age and ascertain underlying mechanisms. In this regard, initial investigations were performed to identify genes involved in cardiac electro-mechanical activity that are differentially expressed in young vs old individuals, considering either chronological or biological age.

At the level of the body-surface electrocardiogram, novel non-invasive markers providing a more comprehensive characterization of cardiac variability were proposed as part of MODELAGE investigations. Robustness against noise and artifacts was first tested in synthetically generated data and, next, electrophysiological modeling and simulation was used to show the markers’ capacity to reflect heterogeneities in temporal and/or spatial cardiac activity. In electrocardiographic recordings of healthy subjects performing autonomic manoeuvers, the proposed indices were shown to present changes related to autonomic variations. The association with arrhythmic risk of some of the proposed markers was subsequently demonstrated in a large followed-up population of ambulatory chronic heart failure patients. Also, a novel strategy for arrhythmic risk stratification was proposed that involved combining those markers with other clinical and electrocardiographic variables into a risk score.
MODELAGE has made an important progress beyond the state of the art in developing experimental methods to characterize tissue, cellular and subcellular properties related to the electro-mechanical activity of the heart in a population of individuals spanning a wide range of ages. In vitro studies investigating cardiac structural and functional properties, and the response to autonomic interventions, as a function of age are limited, being most of them focused on rodents, which notably hampers the extrapolation to humans. In MODELAGE, all experimental protocols were first developed for research in large mammals, which is also scarcely addressed in the literature, and subsequently applied to humans. The number of so far analyzed samples from young, middle-age and senescent individuals is reduced and is expected to be completed during the last period of the project.

From the computational point of view, MODELAGE has notably gone beyond the state of the art in developing novel stochastic approaches to integrate in vitro experimental data into computational simulations in such a way that characteristics of individual cells can be reproduced on a one-to-one basis. This allows a more accurate description of cardiac temporal and spatial variability than with state of the art methods, which at most allow recapitulating statistical distributions of experimental variability measurements when a relatively large number of such measurements is available. Of note, computational methods have been proposed for research on human cardiac variability not only under basal conditions but also in response to sympathetic provocations, which involve adrenergic-induced changes concomitant with changes in mechanical stretch. There are no state of the art stochastic models providing coupled descriptions of human ventricular electrophysiology, calcium dynamics, mechanics and beta-adrenergic signaling, as in MODELAGE investigations. Expected results until the end of the project involve further work on the construction of tissue models able to incorporate additional characterizations from the analysis of a more extensive dataset of collected tissue samples.

As regards the study of the relationship between cardiac electro-mechanical activity in different individuals and their corresponding chronological / biological age, MODELAGE has made a step forward with respect to the state of the art in identifying microRNAs and associated predictive targets orchestrating aging of the human heart. Future work until the end of the project includes full in vitro validation and analysis of companion blood samples.

Finally, MODELAGE has significantly advanced in the proposal of more robust spatio-temporal variability markers that can be non-invasively quantified from the electrocardiographic signal. This represents a step forward with respect to the state of the art, as the proposed markers show stronger association with arrhythmic risk and, importantly, the provided information is independent of that from other common electrocardiogram and clinical variables. Future MODELAGE work involves the study of such markers in relation to chronological / biological age and the use of computational modeling and simulation to investigate mechanisms underlying their capability for arrhythmia prediction in the context of aging.