Periodic Reporting for period 4 - MODELAGE (Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface)
Periodo di rendicontazione: 2020-04-01 al 2021-09-30
MODELAGE significantly contributed to the big challenge of characterizing human heart aging. Previous studies mostly addressed aging research in animal species. Also, the characterization of structural and functional changes represented overall manifestations of cardiac aging, while not all individuals age at the same pace but widely varied levels of age-associated myocardial remodeling occur in the population.
The first objective of MODELAGE was 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 had been to a large extent hampered by the lack of human data availability and other technical reasons. MODELAGE shed light on how diverse remodeling manifestations in individuals of the same or similar chronological age could be indicative of different biological aging rates. MODELAGE moved 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, as performed in MODELAGE, can facilitate the understanding of why aging predisposes to cardiovascular diseases in general, and cardiac arrhythmias in particular, and can open a door to therapies aimed at controlling the effects of aging when designing strategies to treat these diseases.
The first objective of MODELAGE was 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 had been to a large extent hampered by the lack of human data availability and other technical reasons. MODELAGE shed light on how diverse remodeling manifestations in individuals of the same or similar chronological age could be indicative of different biological aging rates. MODELAGE moved 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, as performed in MODELAGE, can facilitate the understanding of why aging predisposes to cardiovascular diseases in general, and cardiac arrhythmias in particular, and can 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 additionally undertaken to characterize cellular and subcellular properties as a function of age.
Results from the in vivo, ex vivo and in vitro studies were integrated into mathematical models. A Bayesian methodology was developed in MODELAGE to identify the parameters and state variables of those models based on whole-cell experimental measurements, both at baseline conditions and in response to sympathetic provocations. Simulations were run using the developed mathematical models to investigate cardiac spatio-temporal dynamics. Tissue characterizations were additionally incorporated to build multi-scale computational models, which were used to relate spatio-temporal dynamics with chronological / biological age and ascertain underlying mechanisms. Genes involved in cardiac electro-mechanical activity that were differentially expressed in young vs old individuals, considering both chronological and biological age, were identified and the role of microRNAs in their regulation was ascertained.
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 spanning a wide range of ages, MODELAGE quantified changes in the proposed indices in relation to chronological age and other variables related to cardiorespiratory fitness. The association with arrhythmic risk of some of the proposed markers was subsequently demonstrated in followed-up patient populations, individually and in combination with other clinical and electrocardiographic variables.
The achieved results were disseminated in multiple publications, conferences, seminars, workshops as well as in the media.
Results from the in vivo, ex vivo and in vitro studies were integrated into mathematical models. A Bayesian methodology was developed in MODELAGE to identify the parameters and state variables of those models based on whole-cell experimental measurements, both at baseline conditions and in response to sympathetic provocations. Simulations were run using the developed mathematical models to investigate cardiac spatio-temporal dynamics. Tissue characterizations were additionally incorporated to build multi-scale computational models, which were used to relate spatio-temporal dynamics with chronological / biological age and ascertain underlying mechanisms. Genes involved in cardiac electro-mechanical activity that were differentially expressed in young vs old individuals, considering both chronological and biological age, were identified and the role of microRNAs in their regulation was ascertained.
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 spanning a wide range of ages, MODELAGE quantified changes in the proposed indices in relation to chronological age and other variables related to cardiorespiratory fitness. The association with arrhythmic risk of some of the proposed markers was subsequently demonstrated in followed-up patient populations, individually and in combination with other clinical and electrocardiographic variables.
The achieved results were disseminated in multiple publications, conferences, seminars, workshops as well as in the media.
MODELAGE made 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. Most previous studies investigating cardiac structural and functional properties, and the response to autonomic interventions, as a function of age had been conducted in rodents, which notably hampered 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.
From the computational point of view, MODELAGE made remarkable progress in developing novel stochastic approaches to integrate ex vivo and 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 allowed more accurate descriptions of cardiac temporal and spatial variability than with state of the art methods, which at most allowed recapitulating statistical distributions of experimental variability measurements when a relatively large number of such measurements was available. Of note, computational methods were proposed in MODELAGE for research on human cardiac variability not only under basal conditions but also in response to sympathetic provocations, which notably differed between young and old individuals. Next, tissue models were constructed by incorporating additional characterizations of fibrosis, cell-to-cell coupling and other tissue properties obtained by processing collected tissue samples.
The study of the relationship between cardiac electro-mechanical activity in different individuals and their corresponding chronological / biological age was evaluated and underlying mechanisms were uncovered. MicroRNAs and networks of associated gene targets orchestrating aging of the human heart were described. Also, MODELAGE significantly advanced in the proposal of more robust spatio-temporal variability markers that were non-invasively quantified from the electrocardiogram in young-to-old individuals. This represents a step forward with respect to the state of the art, as the proposed markers showed stronger association with arrhythmic risk and, importantly, the provided information was complementary to that of other common electrocardiographic and clinical variables.
From the computational point of view, MODELAGE made remarkable progress in developing novel stochastic approaches to integrate ex vivo and 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 allowed more accurate descriptions of cardiac temporal and spatial variability than with state of the art methods, which at most allowed recapitulating statistical distributions of experimental variability measurements when a relatively large number of such measurements was available. Of note, computational methods were proposed in MODELAGE for research on human cardiac variability not only under basal conditions but also in response to sympathetic provocations, which notably differed between young and old individuals. Next, tissue models were constructed by incorporating additional characterizations of fibrosis, cell-to-cell coupling and other tissue properties obtained by processing collected tissue samples.
The study of the relationship between cardiac electro-mechanical activity in different individuals and their corresponding chronological / biological age was evaluated and underlying mechanisms were uncovered. MicroRNAs and networks of associated gene targets orchestrating aging of the human heart were described. Also, MODELAGE significantly advanced in the proposal of more robust spatio-temporal variability markers that were non-invasively quantified from the electrocardiogram in young-to-old individuals. This represents a step forward with respect to the state of the art, as the proposed markers showed stronger association with arrhythmic risk and, importantly, the provided information was complementary to that of other common electrocardiographic and clinical variables.