Epigenetic mechanisms of particulate matter exposure: air pollution and hazards on human health.

Environmental pollution nowadays has not only a direct correlation with human health changes but a direct social impact. Epidemiological studies have evidenced the increased damage to human health on a daily basis because of damage to the ecological niche. Rapid urban growth and industrialized societies importantly compromise air quality, which can be assessed by a notable accumulation of air pollutants in both the gas and the particle phases. Of them, particulate matter (PM) represents a highly complex mixture of organic and inorganic compounds of the most variable size, composition, and origin. PM being one of the most complex environmental pollutants, its accumulation also varies in a temporal and spatial manner, which challenges current analytical techniques used to investigate PM interactions. Nevertheless, the characterization of the chemical composition of PM is a reliable indicator of the composition of the atmosphere, the quality of breathed air in urbanized societies, industrial zones and consequently gives support for pertinent measures to avoid serious health damage. Epigenomic damage is one of the most promising biological mechanisms of air pollution-derived carcinogenesis. Therefore, this review aims to highlight the implication of PM exposure in diverse molecular mechanisms driving human diseases by altered epigenetic regulation. The presented findings in the context of pan-organic cancer, fibrosis, neurodegeneration and metabolic diseases may provide valuable insights into the toxicity effects of PM components at the epigenomic level and may serve as biomarkers of early detection for novel targeted therapies.

Ventricular Dysfunction in Obese and Nonobese Rats with Metabolic Syndrome.

Obesity and dyslipidemias are both signs of metabolic syndrome, usually associated with ventricular arrhythmias. Here, we tried to identify cardiac electrical alteration and biomarkers in nonobese rats with metabolic syndrome (MetS), and these findings might lead to more lethal arrhythmias than obese animals. The MetS model was developed in Wistar rats with high-sucrose diet (20%), and after twenty-eight weeks were obtained two subgroups: obese (OMetS) and nonobese (NOMetS). The electrocardiogram was used to measure the ventricular arrhythmias and changes in the heart rate variability. Also, we measured ventricular hypertrophy and its relationship with electrical activity alterations of both ventricles, using micro-electrode and voltage clamp techniques. Also, we observed alterations in the contraction force of ventricles where a transducer was used to record mechanical and electrical papillary muscle, simultaneously. Despite both subgroups presenting long QT syndrome (0.66 ± 0.05 and 0.66 ± 0.07 ms with respect to the control 0.55 ± 0.1 ms), the changes in the heart rate variability were present only in OMetS, while the NOMetS subgroup presented changes in QT interval variability (NOMetS SD = 1.8, SD2 = 2.8; SD1/SD2 = 0.75). Also, the NOMetS revealed tachycardia (10%; p < 0.05) with changes in action potential duration (63% in the right papillary and 50% in the left papillary) in the ventricular papillary which are correlated with certain alterations in the potassium currents and the force of contraction. The OMetS showed an increase in action potential duration and the force of contraction in both ventricles, which are explained as bradycardia. Our results revealed lethal arrhythmias in both MetS subgroups, irrespectively of the presence of obesity. Consequently, the NOMetS showed mechanical-electrical alterations regarding ventricle hypertrophy that should be at the NOMetS, leading to an increase of CV mortality.

The Role of the Autonomic Nervous System on Cardiac Rhythm during the Evolution of Diabetes Mellitus Using Heart Rate Variability as a Biomarker.

Heart rate variability (HRV) is highly influenced by the Autonomic Nervous System (ANS). Several illnesses have been associated with changes in the ANS, thus altering the pattern of HRV. However, the variability of the heart rhythm is originated within the Sinus Atrial Node (SAN) which has its own variability. Still, although both oscillators produce HRV, the influence of the SAN on HRV has not yet been exhaustively studied. On the other hand, the complications of diabetes mellitus (DM), for instance, nephropathy, retinopathy, and neuropathy, increase cardiovascular morbidity and mortality. Traditionally, these complications are diagnosed only when the patient is already suffering from the negative symptoms these complications implicate. Consequently, it is of paramount importance to develop new techniques for early diagnosis prior to any deterioration on healthy patients. HRV has been proved to be a valuable, noninvasive clinical evidence for evaluating diseases and even for describing aging and behavior. In this study, several ECGs were recorded and their RR and PP intervals were analyzed to detect the interpotential interval (ii) of the SAN. Additionally, HRV reduction was quantified to identify alterations in the nervous system within the nodal tissue via measuring the SD1/SD2 ratio in a Poincaré plot. With 15 years of DM development, the data showed an age-dependent increase in HRV due to the axon retraction of ANS neurons from its effectors. In addition, these alterations modify the heart rhythm-producing fatal arrhythmias. Therefore, it is possible to avoid the consequences of DM identifying alterations in SAN previous to its symptomatic appearance. This could be used as an early diagnosis indicator.

Enhancement of early warning properties in the Kuramoto model and in an atrial fibrillation model due to an external perturbation of the system.

When a complex dynamical system is externally disturbed, the statistical moments of signals associated to it can be affected in ways that depend on the nature and amplitude of the perturbation. In systems that exhibit phase transitions, the statistical moments can be used as Early Warnings (EW) of the transition. A natural question is thus to wonder what effect external disturbances have on the EWs of system. In this work we study the impact of external noise added to the system on the EWs, with particular focus on understanding the importance of the amplitude and complexity of the noise. We do this by analyzing the EWs of two computational models related to biology: the Kuramoto model, which is a paradigm of synchronization for biological systems, and a cellular automaton model of cardiac dynamics which has been used as a model for atrial fibrillation. For each model we first characterize the EWs. Then, we introduce external noise of varying intensity and nature to observe what effect this has on the EWs. In both cases we find that the introduction of noise amplified the EWs, with more complex noise having a greater effect. This both offers a way to improve the chance of detection of EWs in real systems and suggests that natural variability in the real world does not have a detrimental effect on EWs, but the opposite.

Heart Rate Variability as Early Biomarker for the Evaluation of Diabetes Mellitus Progress.

According to the American Diabetes Association (ADA), the side effects of diabetes mellitus have recently increased the global health expenditure each year. Of these, the early diagnostic can contribute to the decrease on renal, cardiovascular, and nervous systems complications. However, the diagnostic criteria, which are commonly used, do not suggest the diabetes progress in the patient. In this study, the streptozotocin model in mice (cDM) was used as early diagnostic criterion to reduce the side effects related to the illness. The results showed some clinical signs similarly to five-year diabetes progress without renal injury, neuropathies, and cardiac neuropathy autonomic in the cDM-model. On the other hand, the electrocardiogram was used to determine alterations in heart rate and heart rate variability (HRV), using the Poincaré plot to quantify the HRV decrease in the cDM-model. Additionally, the SD1/SD2 ratio and ventricular arrhythmias showed increase without side effects of diabetes. Therefore, the use of HRV as an early biomarker contributes to evaluating diabetes mellitus complications from the diagnostic.

Metabolic syndrome remodels electrical activity of the sinoatrial node and produces arrhythmias in rats.

In the last ten years, the incidences of metabolic syndrome and supraventricular arrhythmias have greatly increased. The metabolic syndrome is a cluster of alterations, which include obesity, hypertension, hypertriglyceridemia, glucose intolerance and insulin resistance, that increase the risk of developing, among others, atrial and nodal arrhythmias. The aim of this study is to demonstrate that metabolic syndrome induces electrical remodeling of the sinus node and produces arrhythmias. We induced metabolic syndrome in 2-month-old male Wistar rats by administering 20% sucrose in the drinking water. Eight weeks later, the rats were anesthetized and the electrocardiogram was recorded, revealing the presence of arrhythmias only in treated rats. Using conventional microelectrode and voltage clamp techniques, we analyzed the electrical activity of the sinoatrial node. We observed that in the sinoatrial node of "metabolic syndrome rats", compared to controls, the spontaneous firing of all cells decreased, while the slope of the diastolic depolarization increased only in latent pacemaker cells. Accordingly, the pacemaker currents If and Ist increased. Furthermore, histological analysis showed a large amount of fat surrounding nodal cardiomyocytes and a rise in the sympathetic innervation. Finally, Poincaré plot denoted irregularity in the R-R and P-P ECG intervals, in agreement with the variability of nodal firing potential recorded in metabolic syndrome rats. We conclude that metabolic syndrome produces a dysfunction SA node by disrupting normal architecture and the electrical activity, which could explain the onset of arrhythmias in rats.