BACKGROUND:Ultrafine particles (≤100 nm diameter) may have a higher toxicity than larger particles but are still not regulated nor part of routine air pollution monitoring. So far, health effects of long-term exposure to ambient ultrafine particles are not well understood, owing to a lack of exposure data and epidemiological studies. METHODS:We conducted a systematic review and meta-analysis on the health effects of long-term exposure to ultrafine particles, including studies published until December 2024. A meta-analysis was conducted for outcomes with at least four available effect estimates. Confidence in the body of evidence was evaluated using the Office of Health Assessment and Translation method. RESULTS:We identified 85 studies investigating various mortality, morbidity and subclinical outcomes. In meta-analyses of single-pollutant models, we found positive associations with natural mortality (hazard ratio 1.06, 95% CI 1.04-1.08) and C-reactive protein (10.14% increase (95% CI -0.51-21.99%) per 10 000 pt·cm-3 increase in long-term exposure to ultrafine particles, with low and inadequate levels of evidence, respectively. The remaining studies revealed overall limited evidence for adverse effects on a wide range of outcomes. Less than half of the studies adjusted for co-pollutants. CONCLUSION:The evidence base on long-term health effects of ultrafine particles has increased substantially in the past decade, while the overall evidence for independent effects of long-term ultrafine particle exposure remains inadequate to low. More studies are needed to draw firm conclusions about the independent adverse effects of long-term ultrafine particles on various health end-points, with a special focus on the influence of co-pollutant adjustment.

OBJECTIVES:We developed and evaluated a risk-based Air Quality Index (AQI) for Germany that harmonizes pollutant categories on risk equivalence, incorporates hourly concentrations, and improves health-relevant communication. METHODS:We identified pollutant-outcome pairs with causal or likely causal evidence, extracted effect estimates from systematic reviews and meta-analyses, and transformed daily mean effects into hourly effect estimates using German monitoring data. Equivalence coefficients were derived via Monte Carlo simulations and standardized to PM2.5 as the reference pollutant. PM2.5 thresholds were anchored in WHO Air Quality Guideline 2021 and the EU information threshold; risk-equivalent thresholds for PM10, NO2, O3, and SO2 were generated accordingly. An additional module was developed to account for concurrent elevation of multiple pollutants. The index was empirically evaluated using hourly data from German monitoring stations for 2019 and 2022, and the results were compared with the previous AQI. In sensitivity analyses, we assessed the effects of incorporating updated evidence and excluding asthma-related outcomes. RESULTS:Application of the risk-based AQI resulted in a marked shift from "very good" to "good" and "moderate" categories in comparison with the previous AQI, particularly for PM2.5 and O3, reflecting stricter concentration thresholds. "Poor" classifications increased modestly, while "very poor" remained rare. The multipollutant episodes module affected less than 1% of hours. CONCLUSION:Overall, the risk-based AQI represents a significant step toward health-based communication of air quality in Germany. By integrating scientific evidence into its design and providing tailored guidance, it enhances preventive public health protection. Its implementation may improve risk awareness and support behavioural changes that reduce exposure.

Environmental risk factors-air pollution, noise, heat, chemical contamination, and light pollution-are increasingly recognized as key contributors to cardiovascular disease but remain underrepresented in clinical guidelines and public health strategies. This comprehensive review, developed under the auspices of the European Society of Cardiology (ESC), synthesizes current evidence on the cardiovascular consequences of environmental exposures. Building on prior ESC recommendations on air pollution, the consensus statement extends the focus to include climate change, urban heat islands, chemical pollutants, noise, and light pollution, highlighting their shared pathophysiological mechanisms: oxidative stress, inflammation, endothelial dysfunction, and circadian disruption. Epidemiological and experimental studies confirm that these exposures exacerbate the incidence of coronary artery disease, stroke, heart failure, arrhythmias, and hypertension-even at levels below existing regulatory thresholds. It is proposed the exposome framework as a conceptual tool to understand the cumulative lifetime impact of environmental hazards on cardiovascular health. Special attention is given to vulnerable populations, including children, the elderly, socioeconomically disadvantaged groups, and patients with pre-existing cardiovascular disease. The document outlines urgent research needs, such as the need for high-resolution exposure data, exploration of gene-environment interactions and molecular pathways, and the development of real-world and mechanistic studies assessing interventions. Mitigation strategies are discussed across individual, clinical, and policy levels, with a call for heart-healthy urban design, stricter emissions legislation, and equitable access to clean environments. Cardiologists are uniquely positioned to advocate for environmental cardiovascular health, bridging the gap between science, clinical care, and policy. This statement aims to accelerate that translation by raising awareness and promoting action across disciplines.

Recent studies have suggested a potential association of particulate matter (PM) and noise with diabetes and obesity, but studies examining other environmental exposures and their sex-specific and joint associations remain limited. Therefore, we investigated sex-specific individual and joint associations of annual exposure to multiple environmental factors with diabetes and obesity-related measures using cross-sectional data from the population-based multi-center German National Cohort (NAKO). Outcomes included self-reported diabetes mellitus, body mass index (BMI), obesity (BMI ≥30 kg/m2), and waist circumference. Annual mean residential exposures included air pollutants, air temperature, day-evening-night road traffic noise (Lden) and surrounding greenness (normalized difference vegetation index (NDVI)). We used sex-stratified linear and logistic regression models to assess individual associations and quantile g-computation to assess joint associations. Among 174,955 adult participants (50.4% women), 5.6% reported a diabetes diagnosis and 20.9% were obese. An interquartile range increase in PM2.5 and Lden was consistently associated with diabetes and obesity-related measures (e.g., PM2.5-diabetes for men: odds ratio (OR) [95% confidence interval] = 1.12 [1.02; 1.22]; Lden-BMI for women: 0.22 kg/m2 [0.16; 0.27]). Greenness showed non-linear (inverted U-shaped) with all outcomes. An interquartile range increase in multiple exposures simultaneously was associated with higher odds of diabetes, obesity and higher obesity-related measures (e.g., mixture (PM2.5,Lden, lack of NDVI)-diabetes: OR = 1.20 [1.09; 1.33] for men; mixture (PM2.5,Lden, lack of NDVI)-BMI: 0.33 kg/m2 [0.21; 0.44] for women). While longitudinal studies need to confirm these findings, the study highlights that reducing multiple adverse environmental exposures could be potential targets for the prevention of diabetes and obesity.

Persons with diabetes mellitus have complex healthcare needs. Existing disease management programmes (DMPs) are based on a one-size-fits-all approach. However, individuals might require more individualised care. This study aims to identify groups with different patterns of healthcare utilization among people with diabetes in Germany and factors associated with these different patterns. A cross-sectional survey was conducted among a random sample from a statutory health insurance (SHI) with diabetes (n = 1332) and linked to longitudinal SHI data. Latent class analysis was used to identify subgroups with similar patterns of healthcare utilization and factors associated with different patterns. Four patterns of healthcare utilization were identified among people with diabetes: ‘low users’ (20.8% of the total sample); ‘low users with ophthalmologist visit’ (45.2%); ‘high users’ (26.5%); and ‘high users with mental health care’ (7.5%). The classes differed significantly in age, sex, type, duration and severity of diabetes, DMP membership, diabetes training, health-related quality of life, and prevalence of depression. The ‘high users with mental health care’ class was for example younger, more female, had a lower quality of life and the highest prevalence of depression. This study may provide a first basis for thinking about targeted care in Germany beyond DMPs.

Naturkontakte im Kontext der betrieblichen Gesundheitsförderung können Gesundheit, sozialen Zusammenhalt, Umweltbewusstsein und organisationale Gerechtigkeit fördern, insbesondere, wenn Beschäftigte am Prozess beteiligt werden. Die cluster-randomisierte Studie NatFair untersucht multidisziplinär die Implementierung und die Wirksamkeit arbeitsplatzbezogener naturbasierter Interventionen (ANBI) auf Wohlbefinden, Naturverbundenheit und soziale Kohäsion in Betrieben in Deutschland.

Supplementary Table S1 shows Spearman correlations per (sub) cohort between NO2, PM2.5, BC, and O3 (warm season) among participants with full information in the main model

Supplementary Figure S1 shows box plots of exposures by individual (sub-) cohort study.

Supplementary Figure S4 shows the natural cubic splines for air pollutants and breast cancer incidence

Supplementary Figure S3 shows the results from single- and multi-pollutant models and the cumulative risk index for breast cancer

BACKGROUND:Ambient ultrafine particles (UFP, <100 nm) are suspected to cause adverse health effects independent from larger particle fractions. Accordingly, there is increasing interest in their health effects. This paper describes a systematic review and meta-analysis of studies investigating the association of short-term concentrations of UFP with natural, cardiovascular, and respiratory mortality. METHODS:We systematically searched for epidemiological studies published between January 2011 and December 2024 in the databases PubMed and LUDOK and added studies from before 2011 from a previous review. We assessed heterogeneity and risk of bias and performed random-effects meta-analyses when at least four estimates were available. RESULTS:We identified 21 studies in total, with 17, 17, and 15 studies on natural, cardiovascular, and respiratory mortality, respectively. Meta-analytic summary estimates were 1.000 (95 % CI: 0.993, 1.007), 0.996 (0.990, 1.002) and 1.005 (0.979, 1.032) for natural, cardiovascular, and respiratory mortality, respectively, per 10,000 pt/cm3 increase in UFP at lag 0, which had most available estimates. Overall, associations were non-significant and close to null across most lags. We found heterogeneity in UFP monitoring and lag reporting. CONCLUSION:The number of studies on the association between short-term UFP concentrations and mortality has increased substantially in the last years. However, the current evidence does not clearly support an association between short-term concentrations of UFP and mortality. Future studies should improve and harmonize UFP monitoring to improve investigation of health effects and inform policymaking.

Little is known about the relation between traffic noise exposure, an established environmental risk factor for cardiovascular disease, and early obesity-related risk markers such as adipose tissue (AT) and hepatic fat. Therefore, we aimed to assess associations of long-term road traffic noise exposure with AT depots measures from whole-body magnetic resonance imaging (MRI). We analyzed cross-sectional data from 11,343 participants from the population-based German National Cohort (NAKO) who underwent MRI examination between 2014 and 2016, considering visceral (VAT), subcutaneous abdominal (SCAAT), subcutaneous thoracic AT (SCTAT) and hepatic fat content as outcomes. Annual road traffic noise (Lden) data from the year 2017 (source: central EIONET data repository) was used to calculate weighted mean noise levels on a continuous scale within 10 and 100-meter buffers of participants' residencies. Among 11,101 participants with complete outcome data, 48.7 % were women, and the mean age was 51.9 years. Higher annual Lden was associated with increased AT depots and hepatic fat content in men (e.g., VAT: 1.72 %-change [95 % confidence interval: [0.14 %; 3.30 %]; SCAAT: 2.18 %-change [0.43 %; 3.93 %], hepatic fat content: 3.57 %-change [1.41 %; 5.78 %] per 10 dB(A) increase in Lden (10 m)) and women (e.g., VAT: 3.13 %-change [1.09 %; 5.18 %]; SCAAT: 2.38 %-change [0.55 %; 4.20 %], hepatic fat content: 3.08 %-change [1.00 %; 5.21 %] per 10 dB(A) increase in Lden (10 m)). Associations were robust with all outcomes after adjusting for air pollutants and surrounding greenness, and effect modification by obesity and hypertension was observed for SCAAT, SCTAT and hepatic fat content. Our findings indicate that annual exposure to road traffic noise is associated with increased adipose tissue depots and hepatic fat content, and thus present novel evidence for the cross-sectional association between noise and early MRI-derived metabolic health markers.

Supplementary Figure S5 shows the results for air pollutants and breast cancer of models including confounders violating the assumption of proportional hazards as strata

Supplementary Figure S2 shows the results of the two-pollutant models of single and co-pollutants and breast cancer

Supplementary Table S3 shows air pollution exposure extrapolated back to the time of enrolment and time-varying exposure analysis using residential history between enrolment and end-of follow-up based on the Danish Eulerian Hemispheric Model (DEHM). Extrapolation was performed using the absolute difference and the ratio between the baseline and 2010 periods.

We aimed to assess the exposure to multiple environmental indicators and compare the spatial variation across participants of the German National Cohort (NAKO) to lay the foundation for health analyses. We collected highly resolved German-wide data to capture the following environmental drivers: urbanisation by population density; outdoor air pollution by particulate matter (PM2.5), nitrogen dioxide (NO2), ozone; road traffic noise; meteorology by air temperature, relative humidity; and the built environment by greenspace and land cover. All assessed exposures were assigned to the NAKO participants based on their baseline residential addresses. The NAKO study regions ranged from highly urbanised areas (Berlin, Hamburg) to rural regions (Neubrandenburg). This large variation is reflected in the individual environmental exposures at the place of residence. In 2019, annual PM2.5 and NO2 levels ranged from 6.0 to 14.6 and 3.7-33.6 mu g/m(3), respectively. Annual mean air temperature ranged between 7.8 and 12.7 degrees C. Noise data was available for a subset of urban residents (22 %), of which 42 % fell into the lowest and 1.8 % into the highest category of Lden 55-59 and Lden >75 dB(A), respectively. Greenspace also showed considerable differences (Normalised Difference Vegetation Index between 0.08 and 0.84). Spearman correlation was moderate to high within the different exposure groups, but mostly low to moderate between the groups. For the first time, a comprehensive population-based dataset with high quality environmental indicators is available for the whole of Germany. Expanding the database by adding innovative indicators such as light pollution, walkability, biodiversity as well as contextual socioeconomic factors will further increase its usefulness for science and public health.