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•First LUR models for UFP on a national scale.•Combining targeted mobile monitoring with long-term regional background monitoring.•Different algorithms predicted external measurements ...well and correlated highly.•Deconvolution can improve large-area exposure assessment.•Models will be applied in Dutch nation-wide health studies.
Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models.
We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016–2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country.
We found similar predictive performance for the six models, with validation R2 values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20–30% in all cases, with small differences between regions and road types.
We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health studies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, ...consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.
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IJS, KILJ, NUK, PNG, UL, UM
Exposure to pesticides has been linked to Parkinson's disease (PD), although associations between specific pesticides and PD have not been well studied. Residents of rural areas can be exposed ...through environmental drift and volatilization of agricultural pesticides.
Our aim was to investigate the association between lifetime environmental exposure to individual pesticides and the risk of PD, in a national case-control study.
Environmental exposure to pesticides was estimated using a spatio-temporal model, based on agricultural crops around the residential address. Distance up to 100m from the residence was considered most relevant, considering pesticide drift potential of application methods used in the Netherlands. Exposure estimates were generated for 157 pesticides, used during the study period, of which four (i.e. paraquat, maneb, lindane, benomyl) were considered a priori relevant for PD.
A total of 352 PD cases and 607 hospital-based controls were included. No significant associations with PD were found for the a priori pesticides. In a hypothesis generating analysis, including 153 pesticides, increased risk of PD was found for 21 pesticides, mainly used on cereals and potatoes. Results were suggestive for an association between bulb cultivation and PD.
For paraquat, risk estimates for the highest cumulative exposure tertile were in line with previously reported elevated risks. Increased risk of PD was observed for exposure to (a cluster of) pesticides used on rotating crops. High correlations limited our ability to identify individual pesticides responsible for this association. This study provides some evidence for an association between environmental exposure to specific pesticides and the risk of PD, and generates new leads for further epidemiological and mechanistic research.
•Environmental exposure to four a priori selected pesticides was not significantly associated with PD.•A broad screen revealed increased PD risk for a group of 21 (related) pesticides.•Results were suggestive for an association between bulb cultivation and PD risk.•Findings should be seen a hypothesis generating and as leads for future research.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk ...factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 2.18–3.49 per S.D. 8.1 years,
p
= 2.6 × 10
–17
), male sex (OR = 1.47 1.26–1.73,
p
= 1.3 × 10
–6
) and Black versus White ethnicity (OR = 1.21 1.12–1.29,
p
= 3.0 × 10
–7
) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin–angiotensin–aldosterone system inhibitors may be explained by the aforementioned factors.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Some individuals attribute health complaints to radiofrequency electromagnetic field (RF-EMF) exposure. This condition, known as idiopathic environmental intolerance attributed to RF-EMFs (IEI-RF) or ...electromagnetic hypersensitivity (EHS), can be disabling for those who are affected. In this study we assessed factors related to developing, maintaining, or discarding IEI-RF over the course of 10 years, and predictors of developing EHS at follow-up using a targeted question without the condition of reporting health complaints attributed to RF-EMF exposure.
Participants (n = 892, mean age 50 at baseline, 52 % women) from the Dutch Occupational and Environmental Health Cohort Study AMIGO filled in questionnaires in 2011/2012 (T0), 2013 (T1), and 2021 (T4) where information pertaining to perceived RF-EMF exposure and risk, non-specific symptoms, sleep problems, IEI-RF, and EHS was collected. We fitted multi-state Markov models to represent how individuals transitioned between states (“yes”, “no”) of IEI-RF.
At each time point, about 1 % of study participants reported health complaints that they attributed to RF-EMF exposure. While this percentage remained stable, the individuals who reported such complaints changed over time: of nine persons reporting health complaints at T0, only one reported IEI-RF at both T1 and T4, and two newly reported health complaints at T4. Overall, participants had a 95 % chance of transitioning from “yes” to “no” over a time course of 10 years, and a chance of 1 % of transitioning from “no” to “yes”. Participants with high perceived RF-EMF exposure and risk had a general tendency to move more frequently between states.
We observed a low prevalence of IEI-RF in our population. Prevalence did not vary strongly over time but there was a strong aspect of change: over 10 years, there was a high probability of not attributing symptoms to RF-EMF exposure anymore. IEI-RF appears to be a more transient condition than previously assumed.
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•Time course of attribution of health complaints to RF-EMF exposure.•Predictors of electromagnetic hypersensitivity.•Multi-state Markov models to represent how individuals in the cohort transition between states of IEI-RF.•Attribution of health complaints to RF-EMF exposure appears to be a more transient condition than previously assumed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Shift work induces chronic circadian disturbance, which might result in increased health risks, including cardio-metabolic diseases. Previously, we identified sCD36 as a potential non-circadian ...biomarker of chronic circadian disturbance in mice. The aim of the current study (n = 232 individuals) was to identify whether sCD36 measured in plasma can be used as a non-circadian marker of chronic circadian disturbance in humans, which would allow its use to measure the effects of interventions and monitoring in large-scale studies. We compared levels of plasma sCD36 of day workers with recent (< 2 years) and experienced (> 5 years) night-shift workers within the Klokwerk study. We detected no differences in sCD36 levels between day workers and recent or experienced night-shift workers, measured during a day or afternoon shift. In addition, sCD36 levels measured directly after a night shift were not different from sCD36 levels measured during day or afternoon shifts, indicating no acute effect of night shifts on sCD36 levels in our study. In summary, our study does not show a relation between night-shift work experience (recent or long-term) and plasma levels of sCD36. Since we do not know if and for which time span night-shift work is associated with changes in sCD36 levels, and our study was relatively small and cross-sectional, further evidence for an association between chronic circadian disruption and this candidate biomarker sCD36 should be gathered from large cohort studies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites ...predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case–control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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