Chronological and biological aging of the human left ventricular myocardium: Analysis of microRNAs contribution

Aging is the main risk factor for cardiovascular diseases. In humans, cardiac aging remains poorly characterized. Most studies are based on chronological age (CA) and disregard biological age (BA), the actual physiological age (result of the aging rate on the organ structure and function), thus yielding potentially imperfect outcomes. Deciphering the molecular basis of ventricular aging, especially by BA, could lead to major progresses in cardiac research. We aim to describe the transcriptome dynamics of the aging left ventricle (LV) in humans according to both CA and BA and characterize the contribution of microRNAs, key transcriptional regulators. BA is measured using two CA-associated transcriptional markers: CDKN2A expression, a cell senescence marker, and apparent age (AppAge), a highly complex transcriptional index. Bioinformatics analysis of 132 LV samples shows that CDKN2A expression and AppAge represent transcriptomic changes better than CA. Both BA markers are biologically validated in relation to an aging phenotype associated with heart dysfunction, the amount of cardiac fibrosis. BA-based analyses uncover depleted cardiac-specific processes, among other relevant functions, that are undetected by CA. Twenty BA-related microRNAs are identified, and two of them highly heart-enriched that are present in plasma. We describe a microRNA-gene regulatory network related to cardiac processes that are partially validated in vitro and in LV samples from living donors. We prove the higher sensitivity of BA over CA to explain transcriptomic changes in the aging myocardium and report novel molecular insights into human LV biological aging. Our results can find application in future therapeutic and biomarker research

The fluctuation behavior of heart and respiratory system signals as a quantitative tool for studying long-term environmental exposures and chronic diseases

Background: Several studies over the last decades have suggested that a wide range of disease states, as well as the aging process itself, are marked by progressive impairment of the involved physiological processes to adapt, resulting in a loss of complexity in the dynamics of physiological functions. Therefore, measuring complexity from physiological system signals holds enormous promise for providing a new understanding of the mechanisms underlying physiological systems and how they change with diseases and aging. Furthermore, since physiological systems are continuously exposed to environmental factors, measuring how physiological complexity changes during exposure to environmental elements might also provide new insights into their effects. Indeed, this approach may be able to unveil subtle but important changes in the regulatory mechanisms of physiological systems not detectable by traditional analysis methods. Objectives: The overall objective of this PhD thesis was to quantify the complexity of the dynamics of heart and respiratory system signals, in order to investigate how this complexity changes with long-term environmental exposures and chronic diseases, using data from large epidemiological and clinical studies, in order to control for most potential confounders of the fluctuation behavior of systems signals (e.g., demographic, environmental, clinical, and lifestyle factors). We specifically aimed (1) at assessing the influence, first, of long-term smoking cessation, and second, of long-term exposure to traffic-related particulate matter of less than 10 micrometers in diameter (TPM10), on the regulation of the autonomic cardiovascular system and heart rate dynamics in an aging general population, using data from the SAPALDIA cohort study; (2) to assess whether the subgrouping of patients with recurrent obstructive airway diseases, including mild-to-moderate asthma, severe asthma, and COPD, according to their pattern of lung function fluctuation, allows for the identification of phenotypes with specific treatable traits, using data from the BIOAIR study. Methods: In the SAPALDIA cohort, a population-based Swiss cohort, 1608 participants ≥ 50 years of age underwent ambulatory 24-hr electrocardiogram monitoring and reported on lifestyle and medical history. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyses of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with (1) smoking status and time elapsed since smoking cessation; (2) long-term exposure to TPM10. The models were adjusted for known confounding factors. In the BIOAIR study, we conducted a time series clustering analysis based on the fluctuation of twice-daily FEV1 measurements recorded over a one year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or COPD from the longitudinal Pan-European BIOAIR study. Results: In the SAPALDIA cohort, our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover. Regarding long-term exposure to TPM10, we did not observe an overall association with heart rate variability/heart rate dynamics in the entire study population. However, significant changes in the heart rate dynamics were found in subjects without cardiovascular morbidity and significant changes, both in the heart rate dynamics and in the heart rate variability, were found in non-obese subjects without cardiovascular morbidity. Furthermore, subjects with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM10. In the BIOAIR study, we identified five phenotypes, of those three distinct phenotypes of severe asthma, in which the progressive functional alteration of the lung corresponded to a gradually increasing clinical severity and translated into specific risks of exacerbation and treatment response features. Conclusions: This thesis hopes to demonstrate the importance of multidimensional approaches to gain understanding in the complex functioning of the human physiological system and of disease processes. Characterization of the complexity in the fluctuation behavior of system signals holds enormous promise for providing new understandings of the regulatory mechanisms of physiological systems and how they change with diseases. However, it is important to combine this kind of approach with classical epidemiological approaches in order to disentangle the various contributions of the intrinsic physiological dynamics, aging, diseases and comorbidities, lifestyle, and environment. In the SAPALDIA cohort study, we were able to disentangle the influence of specific environmental exposures, such as particulate matter air pollution and smoking exposure, on the heart rate variability and heart rate dynamics, and thus to unveil long-term alterations in former heavy smokers, as well as adverse effects of low level, but long-term, exposure to TPM10 in healthy subjects and in subjects with homozygous GSTM1 gene deletion. In the BIOAIR study, we provide evidence that airway dynamics contain substantial information, which enables the identification of clinically meaningful phenotypes, in which the functional alteration of the lung translates into specific treatable traits

Decreased Neuroautonomic Complexity in Men during an Acute Major Depressive Episode: Analysis of Heart Rate Dynamics

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (P<0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression

Gait variability: methods, modeling and meaning

The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal) features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting

Double symbolic joint entropy in nonlinear dynamic complexity analysis

Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transformations of Wessel N. symbolic entropy and base-scale entropy, and two local ones, namely symbolizations of permutation and differential entropy, constitute four double symbolic joint entropies that have accurate complexity detections in chaotic models, logistic and Henon map series. In nonlinear dynamical analysis of different kinds of heart rate variability, heartbeats of healthy young have higher complexity than those of the healthy elderly, and congestive heart failure (CHF) patients are lowest in heartbeats' joint entropy values. Each individual symbolic entropy is improved by double symbolic joint entropy among which the combination of base-scale and differential symbolizations have best complexity analysis. Test results prove that double symbolic joint entropy is feasible in nonlinear dynamic complexity analysis.Comment: 7 pages, 4 figure

Long-range dependencies in heart rate signals- revisited

The RR series extracted from human electrocardiogram signal (ECG) is considered as a fractal stochastic process. The manifestation of long-range dependencies is the presence of power laws in scale dependent process characteristics. Exponents of these laws: $\beta$ - describing power spectrum decay, $\alpha$ - responsible for decay of detrended fluctuations or $H$ related to, so-called, roughness of a signal, are known to differentiate hearts of healthy people from hearts with congestive heart failure. There is a strong expectation that resolution spectrum of exponents, so-called, local exponents in place of global exponents allows to study differences between hearts in details. The arguments are given that local exponents obtained in multifractal analysis by the two methods: wavelet transform modulus maxima (WTMM) and multifractal detrended fluctuation analysis (MDFA), allow to recognize the following four stages of the heart: healthy and young, healthy and advance in years, subjects with left ventricle systolic dysfunction (NYHA I--III class) and characterized by severe congestive heart failure (NYHA III-IV class).Comment: 24 page

Emergence of Complex Dynamics in a Simple Model of Signaling Networks

A variety of physical, social and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by $1/f$-scaling of their power spectra, can emerge in networks of simple signaling units. We find that under general conditions, complex dynamics can be generated by systems fulfilling two requirements: i) a ``small-world'' topology and ii) the presence of noise. Our findings support two notable conclusions: first, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions (i) and (ii) are robust to some degree of degradation, i.e., they will still be able to generate $1/f$-dynamics