Combined early and adult life risk factor associations for mid-life obesity in a prospective birth cohort: assessing potential public health impact.

OBJECTIVE: The combined effect of life-course influences on obesity development and thus their potential public health impact is unclear. We evaluated combined associations and predicted probabilities for early and adult life risk factors with central and general obesity in mid-adulthood. SETTING: 1958 British birth cohort. PARTICIPANTS: 4629 males and 4670 females with data on waist circumference. OUTCOME MEASURES: 45 year obesity measured via waist circumference, waist-hip ratio (WHR) and BMI. RESULTS: At 45 years, approximately a third of the population were centrally obese and a quarter were generally obese. Three factors (parental overweight, maternal smoking during pregnancy and adult inactivity) were consistently associated with central and general obesity. Predicted probabilities for waist obesity increased from those with none to all three risk factors (0.15-0.33 in men; 0.19-0.39 in women (ptrend<0.001)), with a similar trend for general obesity. Additional factors (adult smoking, low fibre and heavy alcohol consumption) were associated with WHR obesity, although varying by gender. Prevalence of risk factors was higher in manual than non-manual groups: for example, in men 38% versus 25%, respectively, had ≥2 risk factors for waist and general obesity. CONCLUSIONS: Early-life and adult factors that are amenable to change are highly prevalent and accumulate in association with central and general obesity in mid-adulthood. The increase in probabilities for mid-adult obesity associated with cumulative levels of risk factors suggests the potential for public health impact

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

Refinement of a Methodology for Untargeted Detection of Serum Albumin Adducts in Human Populations.

Covalently modified blood proteins (e.g., serum albumin adducts) are increasingly being viewed as potential biomarkers via which the environmental causes of human diseases may be understood. The notion that some (perhaps many) modifications have yet to be discovered has led to the development of untargeted adductomics methods, which attempt to capture entire populations of adducts. One such method is fixed-step selected reaction monitoring (FS-SRM), which analyses distributions of serum albumin adducts via shifts in the mass of a tryptic peptide [Li et al. (2011) Mol. Cell. Proteomics 10, M110.004606]. Working on the basis that FS-SRM might be able to detect biological variation due to environmental factors, we aimed to scale the methodology for use in an epidemiological setting. Development of sample preparation methods led to a batch workflow with increased throughput and provision for quality control. Challenges posed by technical and biological variation were addressed in the processing and interpretation of the data. A pilot study of 20 smokers and 20 never-smokers provided evidence of an effect of smoking on levels of putative serum albumin adducts. Differences between smokers and never-smokers were most apparent in putative adducts with net gains in mass between 105 and 114 Da (relative to unmodified albumin). The findings suggest that our implementation of FS-SRM could be useful for studying other environmental factors with relevance to human health

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

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

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

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

Life-course socioeconomic status and DNA methylation of genes regulating inflammation

Background: In humans, low socioeconomic status (SES) across the life course is associated with greater diurnal cortisol production, increased inflammatory activity and higher circulating antibodies for several pathogens, all suggesting a dampened immune response. Recent evidence suggests that DNA methylation of pro-inflammatory genes may be implicated in the biological embedding of the social environment. Methods: The present study examines the association between life-course SES and DNA methylation of candidate genes, selected on the basis of their involvement in SES-related inflammation, in the context of a genome-wide methylation study. Participants were 857 healthy individuals sampled from the EPIC Italy prospective cohort study. Results: Indicators of SES were associated with DNA methylation of genes involved in inflammation. NFATC1, in particular, was consistently found to be less methylated in individuals with low vs high SES, in a dose-dependent manner. IL1A, GPR132 and genes belonging to the MAPK family were also less methylated among individuals with low SES. In addition, associations were found between SES and CXCL2 and PTGS2, but these genes were consistently more methylated among low SES individuals. Conclusions: Our findings support the hypothesis that the social environment leaves an epigenetic signature in cells. Although the functional significance of SES-related DNA methylation is still unclear, we hypothesize that it may link SES to chronic disease ris

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

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

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