Framingham risk score modifies the effect of PM10 on heart rate variability

Associations of forest negative air ions exposure with cardiac autonomic nervous function and the related metabolic linkages: A repeated-measure panel study

Forest environment has many health benefits, and negative air ions (NAI) is one of the major forest environmental factors. Many studies have explored the effect of forest environment on cardiac autonomic nervous function, while forest NAI in the among function and the underlying mechanism still remain unclear. To explore the associations and molecular linkages between short-term exposure to forest NAI and heart rate variability (HRV), a repeated-measure panel study was conducted among 31 healthy adults. Participants were randomly selected to stay in a forest park for 3 days and 2 nights. Individual exposures including NAI were monitored simultaneously and HRV indices were measured repeatedly at the follow-up period. Urine samples were collected for non-targeted metabolomics analysis. Mixed-effect models were adopted to evaluate associations among NAI, HRV indices and metabolites. The median of NAI concentration was 68.11 (138.20) cm^-3 during the study period. Short-term exposure to forest NAI was associated with the ameliorative HRV indices, especially the excitatory parasympathetic nerve. For instance, per interquartile range increase of 5-min moving average of NAI was associated with 9.99 % (95%CI: 8.95 %, 11.03 %) increase of power in high frequency. Eight metabolites were associated with NAI exposure. The down-regulated tyrosine metabolism was firstly observed, followed by other amino acid metabolic alterations. The NAI-related metabolic changes reflect the reduction of inflammation and oxidative stress. HRV indices were associated with 25 metabolites, mainly including arginine, proline and histidine metabolism. Short-term exposure to forest NAI is beneficial to HRV, especially to the parasympathetic nerve activity, by successively disturbing different metabolic pathways which mainly reflect the increased anti-inflammation and the reduced inflammation. The results will provide epidemiological evidences for developing forest therapy and improving cardiac autonomic nervous function.

The influence of urban haze pollution on urban shrinkage in China-an analysis of the mediating effect of the labor supply

Panel data of 234 cities in China from 2011 to 2018 are used to measure the urban shrinkage index. PM_2.5 is used as an indicator of haze pollution, and labor supply is the mediator. On this basis, the influence mechanism of haze pollution on urban shrinkage is analyzed theoretically. Next, using the dynamic panel model and the mediating effect model, we empirically examine the impact of urban shrinkage on haze pollution and the mediating effect of labor supply. The main findings are as follows: haze pollution increases the degree of urban shrinkage. Labor supply plays a regulatory role in the process of haze pollution affecting urban shrinkage. The influence of haze pollution on labor supply is significantly negative, that is, haze pollution will result in a decline of the city labor supply. Every 1 percentage point increase in smog pollution will reduce the labor supply by 1.4585 percentage points. The effect of labor supply on urban contraction is significantly negative. According to our research, pertinent policies and suggestions are proposed to reduce both urban shrinkage and haze pollution.

A new buffer selection strategy for land use regression model of PM2.5 in Xi'an, China

In order to calculate the spatial distribution of high-resolution air-pollutant levels, the land use regression (LUR) model can be an effective method due to the comprehensive consideration of various factors. Traditional LUR models mostly use predefined buffers, which have the disadvantage of not matching high-resolution data well. In order to get a better-fitting model, a few researches have proposed new buffer selection methods. To solve this problem, we propose a new optimal buffer selection method based on the dichotomy to improve the correlation between predicted variables and pollutant concentration. For some socioeconomic data with high spatial resolution that cannot be obtained, for example, building data is used instead of population density data. Compared with the model with the predefined buffers, the model with our buffer selection strategy explained additional 5% variability in measured concentrations, in terms of the R² of the final model. Our model explained 98% of the samples, and the deviation (1.78%) and root mean square error (5.17 μg/m) were small. It means that the LUR model with our buffer selection strategy can be used as a fit method to better describe spatial variability in atmospheric pollutant levels, which will be conducive to epidemiological research and urban environmental planning.

Air pollution and cardiovascular disease: car sick

The cardiovascular effects of inhaled particle matter (PM) are responsible for a substantial morbidity and mortality attributed to air pollution. Ultrafine particles, like those in diesel exhaust emissions, are a major source of nanoparticles in urban environments, and it is these particles that have the capacity to induce the most significant health effects. Research has shown that diesel exhaust exposure can have many detrimental effects on the cardiovascular system both acutely and chronically. This review provides an overview of the cardiovascular effects on PM in air pollution, with an emphasis on ultrafine particles in vehicle exhaust. We consider the biological mechanisms underlying these cardiovascular effects of PM and postulate that cardiovascular dysfunction may be implicated in the effects of PM in other organ systems. The employment of multiple strategies to tackle air pollution, and especially ultrafine particles from vehicles, is likely to be accompanied by improvements in cardiovascular health.

Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction

BACKGROUND

Ambient air pollutants can increase cardiovascular mortality. One possible mechanism is the effect on the autonomic balance of the cardiovascular system. Studies on acute effects of particulate matter (PM) exposure on heart rate variability (HRV), a surrogate marker for autonomic balance, in patients with prior myocardial infarction (MI) revealed inconsistent results.

METHOD

We prospectively enrolled participants with acute MI. These participants received a 24-hour Holter electrocardiography examination and echocardiography six months after the index MI. Linear [standard deviation of all normal to normal intervals, standard deviation of NN intervals (SDNN), and a low-frequency to high-frequency ratio (LF/HF)] and non-linear parameters of heart rate variability [multiscale entropy (MSE)] were calculated to show autonomic balance. Data for PM2.5, PM2.5-10, and PM10, were obtained from a fixed-site station in Taiwan. Linear mixed effect models were used to estimate acute effects (within 0-3 days) of PM exposure (per 10 μg/m3) on heart rate variability.

RESULTS

A total of 90 participants were enrolled in this study with a mean age of 58.7 (13.3) and 83 (92.2%) male participants. Traditional HRV parameters, SDNN and LF/HF, were positively correlated with two-day lagged PM2.5-10 and PM10 [adjusted beta coefficient: SDNN: 130.3 and 58.5; LH/HF: 0.32 and 0.21 (all p < or = 0.01)]. MSE slopes 1-5 were negatively correlated with same-day PM2.5-10 and PM10 (adjusted beta coefficient -0.011 (p = 0.01) and -0.005 (p = 0.02), respectively). The left ventricular ejection fraction was negatively correlated with one-day lagged PM2.5-10, and PM10 (adjusted beta coefficient -0.49 and -0.4, respectively; both p < 0.05), after adjusting for MI size.

CONCLUSION

Our results suggest that coarse PM may acutely affect cardiac autonomic balance. MSE is a sensitive marker for detecting changes in autonomic imbalance in patients with prior MI following acute PM exposure.

Association of long-term exposure to traffic-related PM10 with heart rate variability and heart rate dynamics in healthy subjects

BACKGROUND

Epidemiological evidence on the influence of long-term exposure to traffic-related particulate matter (TPM₁₀) on heart rate variability (HRV) is weak.

OBJECTIVE

To evaluate the association of long-term exposure (10 years) with TPM₁₀ on the regulation of the autonomic cardiovascular system and heart rate dynamics (HRD) in an aging general population, as well as potential modifying effects by the a priori selected factors sex, smoking status, obesity, and gene variation in selected glutathione S-transferases (GSTs).

METHODS

We analyzed data from 1593 SAPALDIA cohort participants aged ≥ 50 years. For each participant, various HRV and HRD parameters were derived from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable mixed linear regression models in order to evaluate the association with TPM₁₀. Potential modifying effects were assessed using interaction terms.

RESULTS

No association between long-term exposure to TPM₁₀ and HRV/HRD was observed in the entire study population. However, HRD changes were found in subjects without cardiovascular morbidity and both HRD and HRV changes in non-obese subjects without cardiovascular morbidity. Subjects without cardiovascular morbidity with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM₁₀.

CONCLUSION

This study suggests that long-term exposure to TPM₁₀ triggers adverse changes in the regulation of the cardiovascular system. These adverse effects were more visible in the subjects without cardiovascular disease, in whom the overall relationship between TPM₁₀ and HRV/HRD could not be masked by underlying morbidities and the potential counteracting effects of related drug treatments.

Spatio-temporal variations of PM2.5 emission in China from 2005 to 2014

With the rapid development of economy, air pollution has become increasingly serious nowadays in China, especially for the PM2.5. In this paper, the Spatio-temporal variations of PM2.5 emission over the past decade, from 2005 to 2014, were researched by cartograms. Meanwhile, a complex network technology was adopted to study the spatial auto-correlation of PM2.5 emission. The results showed that every province in China suffered a disparate increment in PM2.5 emission during the past ten years and also indicated that provinces in the same region had a huge influence on each other. There were three sectors including the thermal power, biomass burning and building materials that constituted the major sources of PM2.5 emission and they had different changing trends. There existed a dramatic difference in the east and west of China considering that the amount of PM2.5 was closely related to gross domestic product (GDP) and population. With higher GDP and population, eastern provinces emitted the most amount of PM2.5. Normalization results proposed that most of the provinces were PM2.5 exporting provinces in the southeast of China while most in the northwest were importing provinces. This study can help the policy-makers understand the distribution characteristics of PM2.5 emission and propose the effective strategy to mitigate the pollution of haze.