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Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, socio-demographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM 10 , NO, NO2 , SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2=0.62 –0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Substantial efforts are required to limit global warming to under 2 °C, with 1.5 °C as the target (Paris Agreement goal). We set out to project future temperature-related myocardial infarction (MI) events in Augsburg, Germany, at increases in warming of 1.5 °C, 2 °C, and 3 °C. Methods: Using daily time series of MI cases and temperature projections under two climate scenarios, we projected changes in temperature-related MIs at different increases in warming, assuming no changes in population structure or level of adaptation. Results: In a low-emission scenario that limits warming to below 2 °C throughout the 21st century, temperature-related MI cases will decrease slightly by -6 (confidence interval -60; 50) per decade at 1.5 °C of warming. In a high-emission scenario going beyond the Paris Agreement goals, temperature-related MI cases will increase by 18 (-64; 117) and 63 (-83; 257) per decade with warming of 2 °C and 3 °C, respectively. Conclusion: The future burden of temperature-related MI events in Augsburg at 2 °C and 3 °C of warming will be greater than at 1.5 °C. Fulfilling the Paris Agreement goal of limiting global warming to no more than 1.5 °C is therefore essential to avoid additional MI events due to climate change. [ABSTRACT FROM AUTHOR]

Important health relevance has been suggested for ultrafine particles (UFP) and ozone, but studies on long-term effects are scarce, mainly due to the lack of appropriate spatial exposure models. We designed a measurement campaign to develop land use regression (LUR) models to predict the spatial variability focusing on particle number concentration (PNC) as indicator for UFP, ozone and several other air pollutants in the Augsburg region, Southern Germany. Three bi-weekly measurements of PNC, ozone, particulate matter (PM 10 , PM 2.5 ), soot (PM 2.5 abs) and nitrogen oxides (NO x , NO 2 ) were performed at 20 sites in 2014/15. Annual average concentration were calculated and temporally adjusted by measurements from a continuous background station. As geographic predictors we offered several traffic and land use variables, altitude, population and building density. Models were validated using leave-one-out cross-validation. Adjusted model explained variance (R 2 ) was high for PNC and ozone (0.89 and 0.88). Cross-validation adjusted R 2 was slightly lower (0.82 and 0.81) but still indicated a very good fit. LUR models for other pollutants performed well with adjusted R 2 between 0.68 (PM coarse ) and 0.94 (NO 2 ). Contrary to previous studies, ozone showed a moderate correlation with NO 2 (Pearson's r = − 0.26). PNC was moderately correlated with ozone and PM 2.5 , but highly correlated with NO x (r = 0.91). For PNC and NO x , LUR models comprised similar predictors and future epidemiological analyses evaluating health effects need to consider these similarities. [ABSTRACT FROM AUTHOR]

Background: Short-term exposure to air pollution is associated with morbidity and mortality. Metabolites are intermediaries in biochemical processes, and associations between air pollution and metabolites can yield unique mechanistic insights.Methods: We used independent cross-sectional samples with targeted metabolomics (138 metabolites across five metabolite classes) from three cohort studies, each a part of the Cooperative Health Research in the Region of Augsburg (KORA). The KORA cohorts are numbered (1 to 4) according to which survey they belong to, and lettered S or F according to whether the survey was a baseline or follow-up survey. KORA F4 (N = 3044) served as our discovery cohort, with KORA S4 (N = 485) serving as the primary replication cohort. KORA F4 and KORA S4 were primarily fasting cohorts. We used the non-fasting KORA F3 (N = 377) cohort to evaluate replicated associations in non-fasting individuals, and we performed a random effects meta-analysis of all three cohorts. Associations between the 0-4-day lags and the 5-day average of particulate matter (PM)2.5, NO2 and ozone were modelled via generalized additive models. All air pollution exposures were scaled to the interquartile range, and effect estimates presented as percent changes relative to the geometric mean of the metabolite concentration (ΔGM).Results: There were 10 discovery cohort associations, of which seven were lysophosphatidylcholines (LPCs); NO2 was the most ubiquitous exposure (5/10). The 5-day average NO2-LPC(28:0) association was associated at a Bonferroni corrected P-value threshold (P < 1.2x10-4) in KORA F4 [ΔGM = 11.5%; 95% confidence interval (CI) = 6.60, 16.3], and replicated (P < 0.05) in KORA S4 (ΔGM = 21.0%; CI = 4.56, 37.5). This association was not observed in the non-fasting KORA F3 cohort (ΔGM = -5.96%; CI = -26.3, 14.3), but remained in the random effects meta-analysis (ΔGM = 10.6%; CI = 0.16, 21).Conclusions: LPCs are associated with short-term exposure to air pollutants, in particular NO2 Further research is needed to understand the effect of nutritional/fasting status on these associations and the causal mechanisms linking air pollution exposure and metabolite profiles. [ABSTRACT FROM AUTHOR]

Background Short-term exposure to increased particulate matter (PM) concentration has been reported to trigger myocardial infarction (MI). However, the association with ultrafine particles remains unclear. Objectives We aimed to assess the effects of short-term air pollution and especially ultrafine particles on registry-based MI events and coronary deaths in the area of Augsburg, Germany. Methods Between 1995 and 2009, the MONICA/KORA myocardial infarction registry recorded 15,417 cases of MI and coronary deaths. Concentrations of PM < 10 μm (PM 10 ), PM < 2.5 μm (PM 2.5 ), particle number concentration (PNC) as indicator for ultrafine particles, and meteorological parameters were measured in the study region. Quasi-Poisson regression adjusting for time trend, temperature, season, and weekday was used to estimate immediate, delayed and cumulative effects of air pollutants on the occurrence of MI. The daily numbers of total MI, nonfatal and fatal events as well as incident and recurrent events were analysed. Results We observed a 1.3% risk increase (95%-confidence interval: [−0.9%; 3.6%]) for all events and a 4.4% [−0.4%; 9.4%] risk increase for recurrent events per 24.3 μg/m 3 increase in same day PM 10 concentrations. Nonfatal events indicated a risk increase of 3.1% [−0.1%; 6.5%] with previous day PM 10 . No association was seen for PM 2.5 which was only available from 1999 on. PNC showed a risk increase of 6.0% [0.6%; 11.7%] for recurrent events per 5529 particles/cm 3 increase in 5-day average PNC. Conclusions Our results suggested an association between short-term PM 10 concentration and numbers of MI, especially for nonfatal and recurrent events. For ultrafine particles, risk increases were notably high for recurrent events. Thus, persons who already suffered a MI seemed to be more susceptible to air pollution. [ABSTRACT FROM AUTHOR]

As the underlying mechanisms of the adverse effects of cold spells on cardiac events are not well understood, we explored the effects of cold spells on plasma viscosity, a blood parameter linked to cardiovascular disease. This cross-sectional study involved 3622 participants from the KORA S1 Study (1984–1985), performed in Augsburg, Germany. Exposure data was obtained from the Bavarian State Office for the Environment. Cold spells were defined as two or more consecutive days with daily mean temperatures below the 3rd, 5th, or 10th percentile of the distribution. The effects of cold spells on plasma viscosity were explored by generalized additive models with distributed lag nonlinear models (DLNM). We estimated cumulative effects at lags 0–1, 0–6, 0–13, 0–20, and 0–27 days separately. Cold spells (mean temperature <3rd, <5th or <10th percentile) were significantly associated with an increase in plasma viscosity with a lag of 0–1 days [%change of geometric mean (95% confidence interval): 1.35 (0.06–2.68), 1.35 (0.06–2.68), and 2.49 (0.34–4.69), respectively], and a lag of 0–27 days [18.81 (8.97–29.54), 17.85 (8.29–28.25), and 7.41 (3.35–11.0), respectively]. For the analysis with mean temperature <3rd or 10th percentile, we also observed significant associations at lag 0–20 days [8.34 (0.43–16.88), and 4.96 (1.68, 8.35), respectively]. We found that cold spells had significant immediate and longer lagged effects on plasma viscosity. This finding supports the complex interplay of multiple mechanisms of cold on adverse cardiac events and enriches the knowledge about how cold exposure acts on the human body. [Display omitted] • This is a cross-sectional study to explore the effect of cold spells on plasma viscosity. • We explored the effects of different cold spell definitions on plasma viscosity. • Cold spells were significantly associated with increases in plasma viscosity. [ABSTRACT FROM AUTHOR]

• Longitudinal study in the general population with a large sample size. • The repeated measurements of metabolites strengthened statistical power. • Soot, coarse particles and NO 2.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 (PM 10), coarse particles (PM coarse), fine particles, PM 2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM 2.5abs), nitrogen oxides (NO 2 , NO x), 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 PM 2.5abs 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. [ABSTRACT FROM AUTHOR]

The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000–2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes. • We estimated daily 1 × 1 km near-surface relative humidity (RH) in Germany, 2000–2021. • The random forest model achieved good performance (R2 = 0.83, RMSE = 5.07%). • Validation with external data confirmed the model's high accuracy and low errors. • We propose an RH modeling process generalizable to other study domains/countries. • We provide reliable and highly-resolved RH data for epidemiological studies. [ABSTRACT FROM AUTHOR]

• First study on the effects of air temperature on epigenetic age acceleration. • Medium-term exposures to high temperatures are associated with increased HorvathAA, HannumAA, GrimAA, and SkinBloodAA. • Long-term exposures to high temperatures are associated with increased HorvathAA, HannumAA, PhenoAA, GrimAA, and SkinBloodAA. • More pronounced associations between air temperature and epigenetic age acceleration are observed among women and people with obesity or diabetes. Climate change poses a serious threat to human health worldwide, while aging populations increase. However, no study has ever investigated the effects of air temperature on epigenetic age acceleration. This study involved 1,725 and 1,877 participants from the population-based KORA F4 (2006–2008) and follow-up FF4 (2013–2014) studies, respectively, conducted in Augsburg, Germany. The difference between epigenetic age and chronological age was referred to as epigenetic age acceleration and reflected by Horvath's epigenetic age acceleration (HorvathAA), Hannum's epigenetic age acceleration (HannumAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Epigenetic Skin and Blood Age acceleration (SkinBloodAA). Daily air temperature was estimated using hybrid spatiotemporal regression-based models. To explore the medium- and long-term effects of air temperature modeled in time and space on epigenetic age acceleration, we applied generalized estimating equations (GEE) with distributed lag non-linear models, and GEE, respectively. We found that high temperature exposure based on the 8-week moving average air temperature (97.5th percentile of temperature compared to median temperature) was associated with increased HorvathAA, HannumAA, GrimAA, and SkinBloodAA: 1.83 (95% CI: 0.29–3.37), 11.71 (95% CI: 8.91–14.50), 2.26 (95% CI: 1.03–3.50), and 5.02 (95% CI: 3.42–6.63) years, respectively. Additionally, we found consistent results with high temperature exposure based on the 4-week moving average air temperature was associated with increased HannumAA, GrimAA, and SkinBloodAA: 9.18 (95% CI: 6.60–11.76), 1.78 (95% CI: 0.66–2.90), and 4.07 (95% CI: 2.56–5.57) years, respectively. For the spatial variation in annual average temperature, a 1 °C increase was associated with an increase in all five measures of epigenetic age acceleration (HorvathAA: 0.41 [95% CI: 0.24–0.57], HannumAA: 2.24 [95% CI: 1.95–2.53], PhenoAA: 0.32 [95% CI: 0.05–0.60], GrimAA: 0.24 [95%: 0.11–0.37], and SkinBloodAA: 1.17 [95% CI: 1.00–1.35] years). In conclusion, our results provide first evidence that medium- and long-term exposures to high air temperature affect increases in epigenetic age acceleration. [ABSTRACT FROM AUTHOR]

The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (T air), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of T air data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (T min), mean (T mean), maximum (T max) T air and diurnal T air range during 2000–2020. We used weather station T air observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate T air from cells with both T air and LST available. In the second stage, we used this model to predict T air for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated T air values to obtain T air countrywide. All models achieved high accuracy (0.91 ≤ R2 ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R2 ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the T mean model (0.71 ≤ R2 ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual T mean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader T air variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide T air at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes. • We mapped daily T min , T mean , T max and DTR in 1 × 1 km over Germany during 2000–2020. • All models achieved high accuracy (R2 ≥ 0.91) and low errors (RMSE ≤ 2.02 °C). • Extensive validation using external data confirmed the models' good performance. • We detected significant increases in T air averages over the observed period. • Our dataset is critical for impact analyses of T air changes on health. [ABSTRACT FROM AUTHOR]

In order to assess the personal exposure to ultrafine particles (UFP) during individual day-time activities and to investigate the impact of different microenvironments on exposure, we measured personal exposure to particle number concentrations (PNC), a surrogate for UFP, among 112 non-smoking participants in Augsburg, Germany over a nearly two-year period from March 2007 to December 2008. We obtained 337 personal PNC measurements from 112 participants together with dairies of their activities and locations. The measurements lasted on average 5.5 h and contained on average 330 observations. In addition, ambient PNC were measured at an urban background stationary monitoring site. Personal PNC were highly variable between measurements (IQR of mean: 11 780–24 650 cm −3 ) and also within a single measurement. Outdoor personal PNC in traffic environments were about two times higher than in non-traffic environments. Higher indoor personal PNC were associated with activities like cooking, being in a bistro or exposure to passive smoking. Overall, personal and stationary PNC were weakly to moderately correlated ( r < 0.41). Personal PNC were much higher than stationary PNC in traffic (ratio: 1.5), when shopping (ratio: 2.4), and indoors with water vapor (ratio: 2.5). Additive mixed models were applied to predict personal PNC by participants’ activities and locations. Traffic microenvironments were significant determinants for outdoor personal PNC. Being in a bistro, passive smoking, and cooking contributed significantly to an increased indoor personal PNC. [ABSTRACT FROM AUTHOR]

• We analyzed associations between air pollution, noise, greenness, and metabolic syndrome. • PM 10 , PM coarse , PM 2.5 , PM 2.5 abs were positively associated with prevalent metabolic syndrome. • No significant associations were observed for incident metabolic syndrome. 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 10), PM < 2.5 µm (PM 2.5), PM between 2.5 and 10 µm (PM coarse), absorbance of PM 2.5 (PM2.5 abs), particle number concentration (PNC), nitrogen dioxide (NO 2), ozone (O 3) – 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 10 (OR: 1.15; 95% confidence interval [95% CI]: 1.02, 1.29), PM 2.5 (OR: 1.14; 95% CI: 1.02, 1.28), PM coarse (OR: 1.14; 95% CI: 1.02, 1.27), and PM 2.5 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. [ABSTRACT FROM AUTHOR]