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.