A SARS-CoV-2 outbreak in a public order and safety training facility in England, June 2021.

BACKGROUND: The public order and safety (POS) sector remains susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, as workplace attendance is typically compulsory and close physical contact is often needed. Here, we report on a SARS-CoV-2 outbreak with an attack rate of 39% (9/23), which occurred between 19 and 29 June 2021 among a cohort of new POS recruits participating in a mandatory 18-week training programme in England. METHODS: The COVID-OUT (COVID-19 Outbreak investigation to Understand Transmission) study team undertook a multidisciplinary outbreak investigation, including viral surface sampling, workplace environmental assessment, participant viral and antibody testing, and questionnaires, at the two associated training facilities between 5 July and 24 August 2021. RESULTS: Environmental factors, such as ventilation, were deemed inadequate in some areas of the workplace, with carbon dioxide (CO2) levels exceeding 1,500 ppm on multiple occasions within naturally ventilated classrooms. Activities during safety training required close contact, with some necessitating physical contact, physical exertion, and shouting. Furthermore, most participants reported having physical contact with colleagues (67%) and more than one close work contact daily (97%). CONCLUSIONS: Our investigation suggests that site- and activity-specific factors likely contributed to the transmission risks within the POS trainee cohort. Potential interventions for mitigating SARS-CoV-2 transmission in this POS training context could include implementing regular rapid lateral flow testing, optimizing natural ventilation, using portable air cleaning devices in classrooms, and expanding use of well-fitted FFP2/FFP3 respirators during activities where prolonged close physical contact is required.

A SARS-CoV-2 outbreak investigation at a storage and distribution centre in England: an assessment of worker- and workplace-related risk factors.

An outbreak of SARS-CoV-2 (1 March to 10 May 2021) with an attack rate of 26.5% among approximately 1150 workers at a storage and distribution centre in England prompted a multidisciplinary outbreak investigation (5 May to 6 August 2021), with the aim of better understanding worker- and workplace-related risk factors for viral transmission in the warehousing sector. Overall, environmental factors (e.g., ventilation, humidity and temperature) were assessed to be appropriate at the facility. Nevertheless, 39 (51.3%) surface samples from across the site tested positive for low/ very low levels of SARS-CoV-2 RNA (Ct value ≥ 32.0 for all). Among the study participants, of whom 35.6% were confirmed or suspected cases, 95.5% reported having received COVID-19 prevention training, 100.0% reported handwashing, and 80.0% reported use of face coverings at work. Notably, 43.9% and 19.0% reported working with a symptomatic and a positive contact respectively. Furthermore, 80.5% and 46.3% had concerns regarding reduction in their income and future unemployment, respectively, due to self-isolation. The findings of this study suggest that, in addition to targeted workplace infection control measures and tailored work area specific risk assessments, an enhanced and equitable sick leave policy may help limit presenteeism and viral transmission in large workplaces.

A SARS-CoV-2 outbreak in a plastics manufacturing plant.

BACKGROUND: A SARS-CoV-2 outbreak with an attack rate of 14.3% was reported at a plastics manufacturing plant in England. METHODS: Between 23rd March and 13th May 2021, the COVID-OUT team undertook a comprehensive outbreak investigation, including environmental assessment, surface sampling, molecular and serological testing, and detailed questionnaires, to identify potential SARS-CoV-2 transmission routes, and workplace- and worker-related risk factors. RESULTS: While ventilation, indicated using real-time CO2 proxy measures, was generally adequate on-site, the technical office with the highest localized attack rate (21.4%) frequently reached peaks in CO2 of 2100ppm. SARS-CoV-2 RNA was found in low levels (Ct ≥35) in surface samples collected across the site. High noise levels (79dB) were recorded in the main production area, and study participants reported having close work contacts (73.1%) and sharing tools (75.5%). Only 20.0% of participants reported using a surgical mask and/or FFP2/FFP3 respirator at least half the time and 71.0% expressed concerns regarding potential pay decreases and/or unemployment due to self-isolation or workplace closure. CONCLUSIONS: The findings reinforce the importance of enhanced infection control measures in manufacturing sectors, including improved ventilation with possible consideration of CO2 monitoring, utilising air cleaning interventions in enclosed environments, and provision of good-quality face masks (i.e., surgical masks or FFP2/FFP3 respirators) especially when social distancing cannot be maintained. Further research on the impacts of job security-related concerns is warranted.

Associations between an international COVID-19 job exposure matrix and SARS-CoV-2 infection among 2 million workers in Denmark.

OBJECTIVES: This study investigates the associations between the Danish version of a job exposure matrix for COVID-19 (COVID-19-JEM) and Danish register-based SARS-CoV-2 infection information across three waves of the pandemic. The COVID-19-JEM consists of four dimensions on transmission: two on mitigation measures, and two on precarious work characteristics. METHODS: The study comprised 2 021 309 persons from the Danish working population between 26 February 2020 and 15 December 2021. Logistic regression models were applied to assess the associations between the JEM dimensions and overall score and SARS-CoV-2 infection across three infection waves, with peaks in March-April 2020, December-January 2021, and February-March 2022. Sex, age, household income, country of birth, wave, residential region and during wave 3 vaccination status were accounted for. RESULTS: Higher risk scores within the transmission and mitigation dimensions and the overall JEM score resulted in higher odds ratios (OR) of a SARS-CoV-2 infection. OR attenuated across the three waves with ranges of 1.08-5.09 in wave 1, 1.06-1.60 in wave 2, and 1.05-1.45 in those not (fully) vaccinated in wave 3. In wave 3, no associations were found for those fully vaccinated. In all waves, the two precarious work dimensions showed weaker or inversed associations. CONCLUSIONS: The COVID-19-JEM is a promising tool for assessing occupational exposure to SARS-CoV-2 and other airborne infectious agents that mainly spread between people who are in close contact with each other. However, its usefulness depends on applied restrictions and the vaccination status in the population of interest.

Exploring the relationship between job characteristics and infection: Application of a COVID-19 job exposure matrix to SARS-CoV-2 infection data in the United Kingdom.

OBJECTIVE: This study aimed to assess whether workplace exposures as estimated via a COVID-19 job exposure matrix (JEM) are associated with SARS-CoV-2 in the UK. METHODS: Data on 244 470 participants were available from the Office for National Statistics Coronavirus Infection Survey (CIS) and 16 801 participants from the Virus Watch Cohort, restricted to workers aged 20-64 years. Analysis used logistic regression models with SARS-CoV-2 as the dependent variable for eight individual JEM domains (number of workers, nature of contacts, contact via surfaces, indoor or outdoor location, ability to social distance, use of face covering, job insecurity, and migrant workers) with adjustment for age, sex, ethnicity, index of multiple deprivation (IMD), region, household size, urban versus rural area, and health conditions. Analyses were repeated for three time periods (i) February 2020 (Virus Watch)/April 2020 (CIS) to May 2021), (ii) June 2021 to November 2021, and (iii) December 2021 to January 2022. RESULTS: Overall, higher risk classifications for the first six domains tended to be associated with an increased risk of infection, with little evidence of a relationship for domains relating to proportion of workers with job insecurity or migrant workers. By time there was a clear exposure-response relationship for these domains in the first period only. Results were largely consistent across the two UK cohorts. CONCLUSIONS: An exposure-response relationship exists in the early phase of the COVID-19 pandemic for number of contacts, nature of contacts, contacts via surfaces, indoor or outdoor location, ability to social distance and use of face coverings. These associations appear to have diminished over time.

Validation of a COVID-19 Job Exposure Matrix (COVID-19-JEM) for Occupational Risk of a SARS-CoV-2 Infection at Work: Using Data of Dutch Workers.

OBJECTIVES: A COVID-19 Job Exposure Matrix (COVID-19-JEM) has been developed, consisting of four dimensions on transmission, two on mitigation measures, and two on precarious work. This study aims to validate the COVID-19-JEM by (i) comparing risk scores assigned by the COVID-19-JEM with self-reported data, and (ii) estimating the associations between the COVID-19-JEM risk scores and self-reported COVID-19. METHODS: Data from measurements 2 (July 2020, n = 7690) and 4 (March 2021, n = 6794) of the Netherlands Working Conditions Survey-COVID-19 (NWCS-COVID-19) cohort study were used. Responses to questions related to the transmission risks and mitigation measures of Measurement 2 were used to calculate self-reported risk scores. These scores were compared with the COVID-19-JEM attributed risk scores, by assessing the percentage agreement and weighted kappa (κ). Based on Measurement 4, logistic regression analyses were conducted to estimate the associations between all COVID-19-JEM risk scores and self-reported COVID-19 (infection in general and infected at work). RESULTS: The agreement between the COVID-19-JEM and questionnaire-based risk scores was good (κ ≥ 0.70) for most dimensions, except work location (κ = 0.56), and face covering (κ = 0.41). Apart from the precarious work dimensions, higher COVID-19-JEM assigned risk scores had higher odds ratios (ORs; ranging between 1.28 and 1.80) on having had COVID-19. Associations were stronger when the infection were thought to have happened at work (ORs between 2.33 and 11.62). CONCLUSIONS: Generally, the COVID-19-JEM showed a good agreement with self-reported infection risks and infection rates at work. The next step is to validate the COVID-19-JEM with objective data in the Netherlands and beyond.

Asthma inflammatory phenotypes on four continents: most asthma is non-eosinophilic.

BACKGROUND: Most studies assessing pathophysiological heterogeneity in asthma have been conducted in high-income countries (HICs), with little known about the prevalence and characteristics of different asthma inflammatory phenotypes in low-and middle-income countries (LMICs). This study assessed sputum inflammatory phenotypes in five centres, in Brazil, Ecuador, Uganda, New Zealand (NZ) and the United Kingdom (UK). METHODS: We conducted a cross-sectional study of 998 asthmatics and 356 non-asthmatics in 2016-20. All centres studied children and adolescents (age range 8-20 years), except the UK centre which involved 26-27 year-olds. Information was collected using questionnaires, clinical characterization, blood and induced sputum. RESULTS: Of 623 asthmatics with sputum results, 39% (243) were classified as eosinophilic or mixed granulocytic, i.e. eosinophilic asthma (EA). Adjusted for age and sex, with NZ as baseline, the UK showed similar odds of EA (odds ratio 1.04, 95% confidence interval 0.37-2.94) with lower odds in the LMICs: Brazil (0.73, 0.42-1.27), Ecuador (0.40, 0.24-0.66) and Uganda (0.62, 0.37-1.04). Despite the low prevalence of neutrophilic asthma in most centres, sputum neutrophilia was increased in asthmatics and non-asthmatics in Uganda. CONCLUSIONS: This is the first time that sputum induction has been used to compare asthma inflammatory phenotypes in HICs and LMICs. Most cases were non-eosinophilic, including in settings where corticosteroid use was low. A lower prevalence of EA was observed in the LMICs than in the HICs. This has major implications for asthma prevention and management, and suggests that novel prevention strategies and therapies specifically targeting non-eosinophilic asthma are required globally.

Investigation of a SARS-CoV-2 Outbreak at an Automotive Manufacturing Site in England.

Workplace-related outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to occur globally. The manufacturing sector presents a particular concern for outbreaks, and a better understanding of transmission risks are needed. Between 9 March and 24 April 2021, the COVID-19 (coronavirus disease 2019) Outbreak Investigation to Understand Transmission (COVID-OUT) study undertook a comprehensive investigation of a SARS-CoV-2 outbreak at an automotive manufacturing site in England. The site had a total of 266 workers, and 51 SARS-CoV-2 infections. Overall, ventilation, humidity, and temperature at the site were assessed to be appropriate for the number of workers and the work being conducted. The company had implemented a number of infection control procedures, including provision of face coverings, spacing in the work, and welfare areas to allow for social distancing. However, observations of worker practices identified lapses in social distancing, although all were wearing face coverings. A total of 38 workers, including four confirmed cases, participated in the COVID-OUT study. The majority of participants received COVID-19 prevention training, though 42.9% also reported that their work required close physical contact with co-workers. Additionally, 73.7% and 34.2% had concerns regarding reductions in future income and future unemployment, respectively, due to self-isolation. This investigation adds to the growing body of evidence of SARS-CoV-2 outbreaks from the manufacturing sector. Despite a layered COVID-19 control strategy at this site, cases clustered in areas of high occupancy and close worker proximity.

Excess mortality among essential workers in England and Wales during the COVID-19 pandemic.

BACKGROUND: Exposure to SARS-CoV-2, subsequent development of COVID-19 and death from COVID-19 may vary by occupation, and the risks may be higher for those categorised as 'essential workers'. METHODS: We estimated excess mortality by occupational group and sex separately for each month in 2020 and for the entire 12 months overall. RESULTS: Mortality for all adults of working age was similar to the annual average over the previous 5 years. Monthly excess mortality peaked in April, when the number of deaths was 54.2% higher than expected and was lowest in December when deaths were 30.0% lower than expected.Essential workers had consistently higher excess mortality than other groups throughout 2020. There were also large differences in excess mortality between the categories of essential workers, with healthcare workers having the highest excess mortality and social care and education workers having the lowest. Excess mortality also varied widely between men and women, even within the same occupational group. Generally, excess mortality was higher in men. CONCLUSIONS: In summary, excess mortality was consistently higher for essential workers throughout 2020, particularly for healthcare workers. Further research is needed to examine excess mortality by occupational group, while controlling for important confounders such as ethnicity and socioeconomic status. For non-essential workers, the lockdowns, encouragement to work from home and to maintain social distancing are likely to have prevented a number of deaths from COVID-19 and from other causes.

Exposure to a SARS-CoV-2 infection at work: development of an international job exposure matrix (COVID-19-JEM).

OBJECTIVE: This study aimed to construct a job exposure matrix (JEM) for risk of becoming infected with the SARS-CoV-2 virus in an occupational setting. METHODS: Experts in occupational epidemiology from three European countries (Denmark, The Netherlands and the United Kingdom) defined the relevant exposure and workplace characteristics with regard to possible exposure to the SARS-CoV-2 virus. In an iterative process, experts rated the different dimensions of the COVID-19-JEM for each job title within the International Standard Classification of Occupations system 2008 (ISCO-08). Agreement scores, weighted kappas, and variances were estimated. RESULTS: The COVID-19-JEM contains four determinants of transmission risk [number of people, nature of contacts, contaminated workspaces and location (indoors or outdoors)], two mitigation measures (social distancing and face covering), and two factors for precarious work (income insecurity and proportion of migrants). Agreement scores ranged from 0.27 [95% confidence interval (CI) 0.25-0.29] for 'migrants' to 0.76 (95% CI 0.74-0.78) for 'nature of contacts'. Weighted kappas indicated moderate-to-good agreement for all dimensions [ranging from 0.60 (95% CI 0.60-0.60) for 'face covering' to 0.80 (95% CI 0.80-0.80) for 'contaminated workspaces'], except for 'migrants' (0.14 (95% CI -0.07-0.36). As country differences remained after several consensus exercises, the COVID-19-JEM also has a country-axis. CONCLUSIONS: The COVID-19-JEM assesses the risk at population level using eight dimensions related to SARS-COV-2 infections at work and will improve our ability to investigate work-related risk factors in epidemiological studies. The dimensions of the COVID-19-JEM could also be valuable for other future communicable diseases in the workplace.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses.

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

Relationships between airborne pollutants, serum albumin adducts and short-term health outcomes in an experimental crossover study.

Exposure to air pollution can have both short-term and long-term effects on health. However, the relationships between specific pollutants and their effects can be obscured by characteristics of both the pollution and the exposed population. One way of elucidating the relationships is to link exposures and internal changes at the level of the individual. To this end, we combined personal exposure monitoring (59 individuals, Oxford Street II crossover study) with mass-spectrometry-based analyses of putative serum albumin adducts (fixed-step selected reaction monitoring). We attempted to infer adducts' identities using data from another, higher-resolution mass spectrometry method, and were able to detect a semi-synthetic standard with both methods. A generalised least squares regression method was used to test for associations between amounts of adducts and pollution measures (ambient concentrations of nitrogen dioxide and particulate matter), and between amounts of adducts and short-term health outcomes (measures of lung health and arterial stiffness). Amounts of some putative adducts (e.g., one with a positive mass shift of ∼143 Da) were associated with exposure to pollution (11 associations), and amounts of other adducts were associated with health outcomes (eight associations). Adducts did not appear to provide a link between exposures and short-term health outcomes.

Short-term exposure to traffic-related air pollution reveals a compound-specific circulating miRNA profile indicating multiple disease risks.

Traffic-related air pollution (TRAP) is a complex mixture of compounds that contributes to the pathogenesis of many diseases including several types of cancer, pulmonary, cardiovascular and neurodegenerative diseases, and more recently also diabetes mellitus. In search of an early diagnostic biomarker for improved environmental health risk assessment, recent human studies have shown that certain extracellular miRNAs are altered upon exposure to TRAP. Here, we present a global circulating miRNA analysis in a human population exposed to different levels of TRAP. The cross-over study, with sampling taking place during resting and physical activity in two different exposure scenarios, included for each subject personal exposure measurements of PM10,PM2.5, NO, NO2, CO, CO2, BC and UFP. Next-generation sequencing technology was used to identify global circulating miRNA levels across all subjects. We identified 8 miRNAs to be associated with the mixture of TRAP and 27 miRNAs that were associated with the individual pollutants NO, NO2, CO, CO2, BC and UFP. We did not find significant associations between miRNA levels and PM10 or PM2.5. Integrated network analysis revealed that these circulating miRNAs are potentially involved in processes that are implicated in the development of air pollution-induced diseases. Altogether, this study demonstrates that signatures consisting of circulating miRNAs present a potential novel biomarker to be used in health risk assessment.

Impact of short-term traffic-related air pollution on the metabolome - Results from two metabolome-wide experimental studies.

Exposure to traffic-related air pollution (TRAP) has been associated with adverse health outcomes but underlying biological mechanisms remain poorly understood. Two randomized crossover trials were used here, the Oxford Street II (London) and the TAPAS II (Barcelona) studies, where volunteers were allocated to high or low air pollution exposures. The two locations represent different exposure scenarios, with Oxford Street characterized by diesel vehicles and Barcelona by normal mixed urban traffic. Levels of five and four pollutants were measured, respectively, using personal exposure monitoring devices. Serum samples were used for metabolomic profiling. The association between TRAP and levels of each metabolic feature was assessed. All pollutant levels were significantly higher at the high pollution sites. 29 and 77 metabolic features were associated with at least one pollutant in the Oxford Street II and TAPAS II studies, respectively, which related to 17 and 30 metabolic compounds. Little overlap was observed across pollutants for metabolic features, suggesting that different pollutants may affect levels of different metabolic features. After observing the annotated compounds, the main pathway suggested in Oxford Street II in association with NO2 was the acyl-carnitine pathway, previously found to be associated with cardio-respiratory disease. No overlap was found between the metabolic features identified in the two studies.

Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data.

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding "treatment" and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.

Short-term transcriptome and microRNAs responses to exposure to different air pollutants in two population studies.

Diesel vehicle emissions are the major source of genotoxic compounds in ambient air from urban areas. These pollutants are linked to risks of cardiovascular diseases, lung cancer, respiratory infections and adverse neurological effects. Biological events associated with exposure to some air pollutants are widely unknown but applying omics techniques may help to identify the molecular processes that link exposure to disease risk. Most data on health risks are related to long-term exposure, so the aim of this study is to investigate the impact of short-term exposure (two hours) to air pollutants on the blood transcriptome and microRNA expression levels. We analyzed transcriptomics and microRNA expression using microarray technology on blood samples from volunteers participating in studies in London, the Oxford Street cohort, and, in Barcelona, the TAPAS cohort. Personal exposure levels measurements of particulate matter (PM10, PM2.5), ultrafine particles (UFPC), nitrogen oxides (NO2, NO and NOx), black carbon (BC) and carbon oxides (CO and CO2) were registered for each volunteer. Associations between air pollutant levels and gene/microRNA expression were evaluated using multivariate normal models (MVN). MVN-models identified compound-specific expression of blood cell genes and microRNAs associated with air pollution despite the low exposure levels, the short exposure periods and the relatively small-sized cohorts. Hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p and hsa-miR-92a-3p are found significantly expressed in association with exposures. These microRNAs target also relevant transcripts, indicating their potential relevance in the research of omics-biomarkers responding to air pollution. Furthermore, these microRNAs are also known to be associated with diseases previously linked to air pollution exposure including several cancers such lung cancer and Alzheimer's disease. In conclusion, we identified in this study promising compound-specific mRNA and microRNA biomarkers after two hours of exposure to low levels of air pollutants during two hours that suggest increased cancer risks.

A multivariate approach to investigate the combined biological effects of multiple exposures.

Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.

The human circulating miRNome reflects multiple organ disease risks in association with short-term exposure to traffic-related air pollution.

Traffic-related air pollution is a complex mixture of particulate matter (PM) and gaseous pollutants, such as nitrogen dioxide (NO2). PM exposure contributes to the pathogenesis of many diseases including several types of cancer, as well as pulmonary, cardiovascular and neurodegenerative diseases. Also exposure to NO2 has been related to increased cardiovascular mortality. In search of an early diagnostic biomarker for improved air pollution-associated health risk assessment, recent human studies have shown that certain circulating miRNAs are altered upon exposure to traffic-related air pollutants. Here, we present for the first time a global analysis of the circulating miRNA genome in an experimental cross-over study of a human population exposed to traffic-related air pollution. By utilizing next-generation sequencing technology and detailed real-time exposure measurements we identified 54 circulating miRNAs to be dose- and pollutant species-dependently associated with PM10, PM2.5, black carbon, ultrafine particles and NO2 already after 2 h of exposure. Bioinformatics analysis suggests that these circulating miRNAs actually reflect the adverse consequences of traffic pollution-induced toxicity in target tissues including the lung, heart, kidney and brain. This study shows the strong potential of circulating miRNAs as novel biomarkers for environmental health risk assessment.

Effects of exposure to water disinfection by-products in a swimming pool: A metabolome-wide association study.

BACKGROUND: Exposure to disinfection by-products (DBPs) in drinking water and chlorinated swimming pools are associated with adverse health outcomes, but biological mechanisms remain poorly understood. OBJECTIVES: Evaluate short-term changes in metabolic profiles in response to DBP exposure while swimming in a chlorinated pool. MATERIALS AND METHODS: The PISCINA-II study (EXPOsOMICS project) includes 60 volunteers swimming 40min in an indoor pool. Levels of most common DBPs were measured in water and in exhaled breath before and after swimming. Blood samples, collected before and 2h after swimming, were used for metabolic profiling by liquid-chromatography coupled to high-resolution mass-spectrometry. Metabolome-wide association between DBP exposures and each metabolic feature was evaluated using multivariate normal (MVN) models. Sensitivity analyses and compound annotation were conducted. RESULTS: Exposure levels of all DBPs in exhaled breath were higher after the experiment. A total of 6,471 metabolic features were detected and 293 features were associated with at least one DBP in exhaled breath following Bonferroni correction. A total of 333 metabolic features were associated to at least one DBP measured in water or urine. Uptake of DBPs and physical activity were strongly correlated and mutual adjustment reduced the number of statistically significant associations. From the 293 features, 20 could be identified corresponding to 13 metabolites including compounds in the tryptophan metabolism pathway. CONCLUSION: Our study identified numerous molecular changes following a swim in a chlorinated pool. While we could not explicitly evaluate which experiment-related factors induced these associations, molecular characterization highlighted metabolic features associated with exposure changes during swimming.

Blood transcriptional and microRNA responses to short-term exposure to disinfection by-products in a swimming pool.

BACKGROUND: Swimming in a chlorinated pool results in high exposure levels to disinfection by-products (DBPs), which have been associated with an increased risk of bladder cancer. OBJECTIVES: By studying molecular responses at the blood transcriptome level we examined the biological processes associated with exposure to these compounds. METHODS: Whole-genome gene expression and microRNA analysis was performed on blood samples collected from 43 volunteers before and 2h after 40min swimming in an indoor chlorinated pool (PISCINAII study). Exposure to THMs was measured in exhaled breath. Heart rate and kcal expenditure were measured as proxies for physical activity. Associations between exposure levels and gene expression were assessed using multivariate normal models (MVN), correcting for age, body mass index and sex. A Bonferroni threshold at 5% was applied. RESULTS: MVN-models for the individual exposures identified 1778 genes and 23 microRNAs that were significantly associated with exposure to at least one DBP. Due to co-linearity it was not possible to statistically disentangle responses to DBP exposure from those related to physical activity. However, after eliminating previously reported transcripts associated with physical activity a large number of hits remained associated with DBP exposure. Among those, 9 were linked with bladder and 31 with colon cancer. Concordant microRNA/mRNA expressions were identified in association with DBP exposure for hsa-mir-22-3p and hsa-miR-146a-5p and their targets RCOR1 and TLR4, both related to colon cancer in association with DBP exposure. CONCLUSIONS: Short-term exposure to low levels of DBPs shows genomics responses that may be indicative of increased cancer risk.