Objective This time series study aimed to examine the association between daily air temperature and cause-specific cardiovascular mortality in Bavaria, Southern Germany. Methods We obtained data from ...the cities Munich, Nuremberg and Augsburg and two adjacent administrative districts (Augsburg and Aichach-Friedberg), for the period 1990–2006. Data included daily cause-specific cardiovascular death counts, mean daily meteorological variables and air pollution concentrations. In the first stage, data were analysed for Munich, Nuremberg and the Augsburg region separately using Poisson regression models combined with distributed lag non-linear models adjusting for long-term trend, calendar effects and meteorological factors. In a second stage, we combined city-specific exposure-response relationships through a multivariate meta-analysis framework. Results An increase in the 2-day average temperature from the 90th (20.0°C) to the 99th centiles (24.8°C) resulted in an increase of cardiovascular mortality by 10% (95% CI 5% to 15%) in the pooled analysis, while for a decrease from the 10th (−1.0°C) to the 1st centiles (−7.5°C) in the 15-day average temperature cardiovascular mortality increased by 8% (95% CI 2% to 14%). Strongest consistent risk estimates were seen for high 2-day average temperatures and mortality due to other heart diseases (including arrhythmias and heart failure) and cerebrovascular diseases, especially in the elderly. Conclusions Results indicate that, in addition to low temperatures, high temperatures increase cause-specific cardiovascular mortality in temperature climates. These findings may guide planning public health interventions to control and prevent the health effects of exposure to air temperature, especially for individuals at risk for mortality due to heart failure, arrhythmias or cerebrovascular diseases.
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable ...exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
•Robust PM2.5, NO2, BC and O3 hybrid LUR models at a 100x100 m resolution for Western Europe were developed•Models included large scale SAT and CTM estimates and fine scale traffic and land use and were further improved with kriging•Models were robust in time at European scale, becoming less robust at smaller spatial scales.
A growing number of epidemiological studies show associations between environmental factors and impaired cardiometabolic health. However, evidence is scarce concerning these risk factors and their ...impact on metabolic syndrome (MetS). This analysis aims to investigate associations between long-term exposure to air pollution, road traffic noise, residential greenness, and MetS.
We used data of the first (F4, 2006-2008) and second (FF4, 2013-2014) follow-up of the population-based KORA S4 survey in the region of Augsburg, Germany, to investigate associations between exposures and MetS prevalence at F4 (N = 2883) and MetS incidence at FF4 (N = 1192; average follow-up: 6.5 years). Residential long-term exposures to air pollution - including particulate matter (PM) with a diameter < 10 µm (PM
), PM < 2.5 µm (PM
), PM between 2.5 and 10 µm (PM
), absorbance of PM
(PM2.5
), particle number concentration (PNC), nitrogen dioxide (NO
), ozone (O
) - and road traffic noise were modeled by land-use regression models and noise maps. For greenness, the Normalized Difference Vegetation Index (NDVI) was obtained. We estimated Odds Ratios (OR) for single and multi-exposure models using logistic regression and generalized estimating equations adjusted for confounders. Joint Odds Ratios were calculated based on the Cumulative Risk Index. Effect modifiers were examined with interaction terms.
We found positive associations between prevalent MetS and interquartile range (IQR) increases in PM
(OR: 1.15; 95% confidence interval 95% CI: 1.02, 1.29), PM
(OR: 1.14; 95% CI: 1.02, 1.28), PM
(OR: 1.14; 95% CI: 1.02, 1.27), and PM
abs (OR: 1.17; 95% CI: 1.03, 1.32). Results further showed negative, but non-significant associations between exposure to greenness and prevalent and incident MetS. No effects were seen for exposure to road traffic noise. Joint Odds Ratios from multi-exposure models were higher than ORs from models with only one exposure.
Studies on the association between traffic noise and cardiovascular diseases have rarely considered air pollution as a covariate in the analyses. Isolated systolic hypertension has not yet been in ...the focus of epidemiological noise research.
The association between traffic noise (road and rail) and the prevalence of hypertension was assessed in two study populations with a total of 4,166 participants 25-74 years of age. Traffic noise (weighted day-night average noise level; LDN) at the facade of the dwellings was derived from noise maps. Annual average PM2.5 mass concentrations at residential addresses were estimated by land-use regression. Hypertension was assessed by blood pressure readings, self-reported doctor-diagnosed hypertension, and antihypertensive drug intake.
In the Greater Augsburg, Germany, study population, traffic noise and air pollution were not associated with hypertension. In the City of Augsburg population (n = 1,893), where the exposure assessment was more detailed, the adjusted odds ratio (OR) for a 10-dB(A) increase in noise was 1.16 (95% CI: 1.00, 1.35), and 1.11 (95% CI: 0.94, 1.30) after additional adjustment for PM2.5. The adjusted OR for a 1-μg/m3 increase in PM2.5 was 1.15 (95% CI: 1.02, 1.30), and 1.11 (95% CI: 0.98, 1.27) after additional adjustment for noise. For isolated systolic hypertension, the fully adjusted OR for noise was 1.43 (95% CI: 1.10, 1.86) and for PM2.5 was 1.08 (95% CI: 0.87, 1.34).
Traffic noise and PM2.5 were both associated with a higher prevalence of hypertension. Mutually adjusted associations with hypertension were positive but no longer statistically significant.
Abstract
Aims
The association between air temperature and mortality has been shown to vary over time, but evidence of temporal changes in the risk of myocardial infarction (MI) is lacking. We aimed ...to estimate the temporal variations in the association between short-term exposures to air temperature and MI in the area of Augsburg, Germany.
Methods and results
Over a 28-years period from 1987 to 2014, a total of 27 310 cases of MI and coronary deaths were recorded. Daily meteorological parameters were measured in the study area. A time-stratified case-crossover analysis with a distributed lag non-linear model was used to estimate the risk of MI associated with air temperature. Subgroup analyses were performed to identify subpopulations with changing susceptibility to air temperature. Results showed a non-significant decline in cold-related MI risks. Heat-related MI relative risk significantly increased from 0.93 95% confidence interval (CI): 0.78–1.12 in 1987–2000 to 1.14 (95% CI: 1.00–1.29) in 2001–14. The same trend was also observed for recurrent and non-ST-segment elevation MI events. This increasing population susceptibility to heat was more evident in patients with diabetes mellitus and hyperlipidaemia. Future studies using multicentre MI registries at different climatic, demographic, and socioeconomic settings are warranted to confirm our findings.
Conclusion
We found evidence of rising population susceptibility to heat-related MI risk from 1987 to 2014, suggesting that exposure to heat should be considered as an environmental trigger of MI, especially under a warming climate.
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary ...linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.
This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.
For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.
Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
•Multiple statistical algorithms with very different assumptions were compared.•Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement.•The use of an external evaluation dataset strengthens evaluation by cross-validation.
Epidemiological studies have reported associations between particulate matter (PM) concentrations and cancer and respiratory and cardiovascular diseases. DNA methylation has been identified as a ...possible link but so far it has only been analyzed in candidate sites.
We studied the association between DNA methylation and short- and mid-term air pollution exposure using genome-wide data and identified potential biological pathways for additional investigation.
We collected whole blood samples from three independent studies-KORA F3 (2004-2005) and F4 (2006-2008) in Germany, and the Normative Aging Study (1999-2007) in the United States-and measured genome-wide DNA methylation proportions with the Illumina 450k BeadChip. PM concentration was measured daily at fixed monitoring stations and three different trailing averages were considered and regressed against DNA methylation: 2-day, 7-day and 28-day. Meta-analysis was performed to pool the study-specific results.
Random-effect meta-analysis revealed 12 CpG (cytosine-guanine dinucleotide) sites as associated with PM concentration (1 for 2-day average, 1 for 7-day, and 10 for 28-day) at a genome-wide Bonferroni significance level (p ≤ 7.5E-8); 9 out of these 12 sites expressed increased methylation. Through estimation of I2 for homogeneity assessment across the studies, 4 of these sites (annotated in NSMAF, C1orf212, MSGN1, NXN) showed p > 0.05 and I2 < 0.5: the site from the 7-day average results and 3 for the 28-day average. Applying false discovery rate, p-value < 0.05 was observed in 8 and 1,819 additional CpGs at 7- and 28-day average PM2.5 exposure respectively.
The PM-related CpG sites found in our study suggest novel plausible systemic pathways linking ambient PM exposure to adverse health effect through variations in DNA methylation.
Panni T, Mehta AJ, Schwartz JD, Baccarelli AA, Just AC, Wolf K, Wahl S, Cyrys J, Kunze S, Strauch K, Waldenberger M, Peters A. 2016. A genome-wide analysis of DNA methylation and fine particulate matter air pollution in three study populations: KORA F3, KORA F4, and the Normative Aging Study. Environ Health Perspect 124:983-990; http://dx.doi.org/10.1289/ehp.1509966.
Air temperature has been shown to be associated with mortality; however, only very few studies have been conducted in Germany. This study examined the association between daily air temperature and ...cause-specific mortality in Bavaria, Southern Germany. Moreover, we investigated effect modification by age and ambient air pollution.
We obtained data from Munich, Nuremberg as well as Augsburg, Germany, for the period 1990 to 2006. Data included daily cause-specific death counts, mean daily meteorology and air pollution concentrations (particulate matter with a diameter<10 μm PM10 and maximum 8-h ozone). We used Poisson regression models combined with distributed lag non-linear models adjusting for long-term trend, calendar effects, and meteorological factors. Air pollutant concentrations were categorized into three levels, and an interaction term was included to quantify potential effect modification of the air temperature effects.
The temperature-mortality relationships were non-linear for all cause-specific mortality categories showing U- or J-shaped curves. An increase from the 90th (20.0 °C) to the 99th percentile (24.8 °C) of 2-day average temperature led to an increase in non-accidental mortality by 11.4% (95% CI: 7.6%-15.3%), whereas a decrease from the 10th (-1.0 °C) to the 1st percentile (-7.5 °C) in the 15-day average temperature resulted in an increase of 6.2% (95% CI: 1.8%-10.8%). The very old were found to be most susceptible to heat effects. Results also suggested some effect modification by ozone, but not for PM10.
Results indicate that both very low and very high air temperature increase cause-specific mortality in Bavaria. Results also pointed to the importance of considering effect modification by age and ozone in assessing temperature effects on mortality.
•Longitudinal study in the general population with a large sample size.•The repeated measurements of metabolites strengthened statistical power.•Soot, coarse particles and NO2.were negatively ...associated with some phosphatidylcholines.•Physically inactive participants were more susceptible.
Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear.
To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics.
We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999–2001) and two follow-up examinations (F4: 2006–08 and FF4: 2013–14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 μm (PM10), coarse particles (PMcoarse), fine particles, PM2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM2.5abs), nitrogen oxides (NO2, NOx), and ozone at individuals’ residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites.
Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m3 (interquartile range) in PM2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations percent change in the geometric mean: −2.5% (95% confidence interval: −3.6%, −1.5%).
Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.
Associations between several persistent organic pollutants (POPs) and type 2 diabetes have been found in humans, but the relationship has rarely been investigated in the general population. The ...current nested case-control study examined internal exposure to polychlorinated biphenyls (PCB) and pesticides and the incidence of type 2 diabetes among participants of two population-based German cohort studies.
We retrospectively selected 132 incident cases of type 2 diabetes and 264 age- and sex-matched controls from the CARdiovascular Living and Aging in Halle (CARLA) study (2002–2006, East Germany) and the Cooperative Health Research in the Region of Augsburg (KORA) study (1999–2001, South Germany) based on diabetes status at follow-up examinations in 2007–2010 and 2006–08, respectively (60% male, mean age 63 and 54 years). We assessed the association between baseline POP concentrations and incident diabetes by conditional logistic regression adjusted for cohort, BMI, cholesterol, alcohol, smoking, physical activity, and parental diabetes. Additionally, we examined effect modification by sex, obesity, parental diabetes and cohort.
In both cohorts, diabetes cases showed a higher BMI, a higher frequency of parental diabetes, and higher levels of POPs. We observed an increased chance for incident diabetes for PCB-138 and PCB-153 with an odds ratio (OR) of 1.50 (95%CI: 1.07–2.11) and 1.53 (1.15–2.04) per interquartile range increase in the respective POP. In addition, explorative results suggested higher OR for women and non-obese participants.
Our results add to the evidence on diabetogenic effects of POPs in the general population, and warrant both policies to prevent human exposure to POPs and additional research on the adverse effects of more complex chemical mixtures.
•Longitudinal studies on the association between POPs and diabetes in the general population are scarce.•We investigated 132 incident cases of type 2 diabetes and 264 age- and sex-matched controls.•Internal exposure to 3 polychlorinated biphenyls and 3 pesticides was determined at baseline.•PCB-138 and PCB-153 were positively associated with incident type 2 diabetes.•Women and non-obese participants indicated higher OR.
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