Work-related stress and well-being in association with epigenetic age acceleration: a Northern Finland Birth Cohort 1966 Study

Recent evidence indicates consistent association of low socioeconomic status with epigenetic age acceleration, measured from DNA methylation. As work characteristics and job stressors are crucial components of socioeconomic status, we investigated their association with various measures of epigenetic age acceleration. The study population included employed and unemployed men and women (n=604) from the Northern Finland Birth Cohort 1966. We investigated the association of job strain, effort-reward imbalance and work characteristics with five biomarkers of epigenetic aging (Hannum, Horvath, PhenoAge, GrimAge, and DunedinPoAm). Our results indicate few significant associations between work stress indicators and epigenetic age acceleration, limited to a range of ±2 years, and smoking recording the highest effect on GrimAge age acceleration biomarker between current and no smokers (median difference 4.73 years (IQR 1.18, 8.41). PhenoAgeAA was associated with job strain active work (β=-1.301 95%CI -2.391, -0.212), slowing aging of less than 1.5 years, and working as white-collar slowed aging six months (GrimAgeAA β=-0.683, 95%CI -1.264, -0.102) when compared to blue collars. Association was found for working for more than 40 hours per week that increased the aging over 1.5 years, (HorvathAA β =2.058 95%CI 0.517,3.599, HannumAA β=1.567, 95%CI 0.415,2.719). The pattern of associations was different between women and men and some of the estimated effects are inconsistent with current literature. Our results provide the first evidence of association of work conditions with epigenetic aging biomarkers. However, further epidemiological research is needed to fully understand how work-related stress affects epigenetic age acceleration in men and women in different societies

Short-Term Nitrogen Dioxide Exposure and Emergency Hospital Admissions for Asthma in Children: A Case-Crossover Analysis in England.

BACKGROUND: There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England. METHODS: We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0-14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients' place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses. RESULTS: For each 10 µg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0-4) (OR 1.08, 95% CI 1.06-1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07-1.12) and during the cold season (OR 1.10, 95% CI 1.08-1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region. SIGNIFICANCE: Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-risk population

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

Background: Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the ‘next-generation’ epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. Results: Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). Conclusions: We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures

A note on intrinsic conditional autoregressive models for disconnected graphs

In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping

Development and Transferability of Ultrafine Particle Land Use Regression Models in London

Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm−3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016–2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from −18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC

Childhood Type 1 diabetes: an environment wide association study across England

Aims:Type 1 Diabetes is an autoimmune disease affecting ~400,000 people across the UK. Environmental factors likely trigger the disease processin genetically susceptible individuals. We assessed the associations between a wide range of environmental factors and childhood type 1 diabetesincidence in England, using an agnostic, ecological Environment Wide Association Study (EnWAS) approach, to generate hypotheses about environmental triggers. Methods:We undertook analyses at the Local Authority District (LAD) level using a national Hospital Episode Statistics (HES) based incident type 1 diabetesdataset, comprising 13,948 cases aged 0-9 years over the period April 2000-March 2011. We compiled LAD-level estimates for a range of potential demographic and environmental risk factors including meteorological, land use and environmental pollution variables. The associations between type 1 diabetesincidence and risk factors were assessed via Poisson regression, disease mapping and ecological regression. 8Results:Case counts by LAD varied from 1 to 236(median 33;inter quartile range: 24-46). Overall type 1 diabetesincidence was 21.2 (95% CI 20.9-21.6) per 100,000individuals. The EnWASand disease mapping indicated that 15out of 53 demographic and environmental risk factors were significantly associated with diabetes incidence after adjusting for multiple testing.These included air pollutants (particulate matter, nitrogen dioxide, nitrogen oxides, carbon monoxide, all inversely associated), as well as lead in soil, radon, outdoor light at night, overcrowding, population density and ethnicity. Disease mapping revealed spatial heterogeneity in type 1 diabetesrisk. The ecological regression found anassociationbetween type 1 diabetesand thelivingenvironmentdomainof the Index of Multiple Deprivation(RR 0.995 (95%Credible Interval (CrI)0.991-0.998))and radon potential class (RR 1.044 95%CrI 1.015-1.074). Conclusions:Our analysis identifiesa range of demographic and environmental factors associated with type 1 diabetesin children in England

Birth weight centiles and small for gestational age by sex and ethnicity for England and Wales

Objectives To construct UK Ethnic Birth Weight Centiles (UK-EBWC) for gestational age and cut-offs for small for gestational age (SGA) for England and Wales and to evaluate the SGA misclassification using the UK centiles. Design Analysis of national birth data. Participants All live singleton births in England and Wales in 2006 to 2012, as recorded by the Office for National Statistics (ONS) and birth registrations, linked with National Health Service (NHS) into Numbers for Babies (NN4B). Main Outcome Measures Both sex-specific and ethnicity-sex-specific birth weight centiles for gestational age, and ethnicity-sex-specific SGA cut-offs. Centiles were computed using the Generalized Additive Model for Location, Scale and Shape (GAMLSS). Results Our sex-specific centiles performed well and showed an agreement between the expected and observed number of births below the centiles. The ethnicity-sex-specific centiles for Black and Asian presented lower values compared to the White centiles. Comparisons of sex-specific and ethnicity-sex-specific centiles shows that use of sex-specific centiles increases the SGA diagnosed cases by 50% for Asian, 30% for South Asian (Indian, Pakistani and Bangladeshi) and 20% for Black ethnicity. Conclusions The centiles show important differences between ethnic groups, in particular the 10th centile used to define SGA. To account for these differences and to minimize misclassification of SGA, we recommend the use of customized birth weight centiles

Asthmatic symptoms and air pollution: a panel study on children living in the Italian Po Valley

The Po Valley (Northern Italy) has elevated levels of air-pollution due to various sources of pollution and adverse weather conditions. This study evaluates the short-term effects of exposure to particulate matter with a diameter of 10 microns or less (PM10) on asthma symptoms in school-aged children. An initial cross-sectional survey was conducted in the area to estimate asthma prevalence in children. Out of a total of 250 asthmatic children identified by the study, 69 agreed to participate in a panel study. The PM10 exposure assessment was based on a combination of geographic and environmental measurements leading to a focus on three different areas, each characterised by its own daily PM10 level. Participants were monitored daily for respiratory symptoms for eight weeks (January-March 2006). We assessed the relationship between daily PM10 exposure and occurrence of asthma symptoms with a generalised linear model based on a total of 3864 person-days of observation. Exposure to PM10 per m3 was found to be particularly associated with cough (OR=1.03, CI 95% 0.99; 1.08) and phlegm (OR=1.05, CI 95% 1.00; 1.10). In the most polluted area, exposure to PM10 was also associated with wheezing (OR=1.18, CI 95% 1.02; 1.37)

Prospective evaluation of ultrasound and biochemical\u2010based multivariable models for the prediction of late pre\u2010eclampsia

Objective: Prospective assignment at 11+0 to 13+6weeks of risk for late pre-eclampsia (PE) using eight logistic regression-based statistical models. Methods: Five hundred and fifty-four pregnancies. Uterine artery pulsatility index, parity, body mass index, mean arterial pressure, pregnancy-associated plasma protein-A, free \u3b2-human chorionic gonadotrophin and maternal age, were combined to obtain 'a posteriori risk of PE'. Results: We observed 39 cases (7%) of late PE. There were 12 cases of severe PE and 27 of mild PE. According to the models used, the estimated detection rate ranged from 38.5% to 84.6% with a false-positive rate of 10%. The median risk ratio (estimated median risk of PE in affected pregnancies divided by estimated risk of PE in unaffected pregnancies) ranged between 1.66 and 7.61. The most reproducible biochemical-based model was a mixed model encompassing maternal history and pregnancy-associated plasma protein-A. Conclusion: Some of the multivariable models drawn from the literature accurately predicted the late PE occurrence. The failure of some models may be because of the population in question not bearing several of the risk factors used to generate the models proposed. An effective combined screening at first trimester for late PE seems possible. \ua9 2011 John Wiley & Sons, Ltd