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Background: One of the potential mechanisms linking air pollution to health effects is through changes in DNA-methylation, which so far has mainly been analyzed globally or at candidate sites. Objective: We investigated the association of personal and ambient air pollution exposure measures with genome-wide DNA-methylation changes. Methods: We collected repeated 24-hour personal and ambient exposure measurements of particulate matter (PM2.5), PM2.5 absorbance, and ultrafine particles (UFP) and peripheral blood samples from a panel of 157 healthy non-smoking adults living in four European countries. We applied univariate mixed-effects models to investigate the association between air pollution and genome-wide DNA-methylation perturbations at single CpG (cytosine-guanine dinucleotide) sites and in Differentially Methylated Regions (DMRs). Subsequently, we explored the association of air pollution-induced methylation alterations with gene expression and serum immune marker levels measured in the same subjects. Results: Personal exposure to PM2.5 was associated with methylation changes at 13 CpG sites and 69 DMRs. Two of the 13 identified CpG sites (mapped to genes KNDC1 and FAM50B) were located within these DMRs. In addition, 42 DMRs were associated with personal PM2.5 absorbance exposure, 16 DMRs with personal exposure to UFP, 4 DMRs with ambient exposure to PM2.5, 16 DMRs with ambient PM2.5 absorbance exposure, and 15 DMRs with ambient UFP exposure. Correlation between methylation levels at identified CpG sites and gene expression and immune markers was generally moderate. Conclusion: This study provides evidence for an association between 24-hour exposure to air pollution and DNA-methylation at single sites and regional clusters of CpGs. Analysis of differentially methylated regions provides a promising avenue to further explore the subtle impact of environmental exposures on DNA-methylation. Keywords: DNA-methylation, Air pollution, Fine particles, Ultrafine particles, Immune markers

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health. Keywords: Exposome, AOP, Systems toxicology, Systems biology, Systems chemistry, Risk assessment

Background It now established that social factors impact the quality of ageing, through the lifecourse stimulation/dysregulation of key physiological systems. Composite scores such as allostatic load, focusing on the response to stress, can be used to measure individual physiological wear-and-tear. Methods Using data from the Understanding Society study, a cross-sectional panel study including 9,088 participants representative of the UK population, we defined a synthetic biological health score (BHS) capturing the wear-and-tear of four physiological systems (endocrine, inflammatory, cardiovascular, and metabolic systems), and of two key organs (liver and kidney). We used 16 established blood-derived biomarkers of these systems to calculate the BHS and explored the relative contribution of socio-economic position to the BHS and its main components across age groups. Using data from UK biobank, including over 400,000 UK participants in whom similar biomarkers have been assayed in blood, we sought validation of our results and investigated the role of the BHS on all-cause and disease specific mortality, and disease incidence. Results We identified a systematic decreasing education-related gradient of the BHS (p < 0·001) leading to lower biological risk in participants with higher educational attainment. Education-related differences in the BHS were detected early in life, and were not attributable to lifestyle and behavioural factors. Analyses of the UK biobank data validated these findings and also showed that the BHS contributed in turn, irrespective of established health risk factors, to all-cause and disease specific mortality. Interpretation Our findings highlight the social-to-biological processes ultimately leading to health inequalities, and suggest that such disparities can already be detected in the 20-40 year age group.

Introduction/Background Persistent infection with high-risk human papillomavirus (HPV) is causally associated with cervical cancer. However, only ∼1% of women with HPV infection progress to cervical neoplasia (CIN). It is estimated that heritability may explain 25–30% of total variation in liability for cervical cancer. Common genetic variants have been detected in HLA (Human Leukocyte Antigen) regions responsible for the immune response, but this is not well understood. We aimed to conduct the largest genome-wide association study (GWAS) of cervical cancer to date, using UK Biobank data to identify genetic polymorphisms associated with CIN and cervical cancer. Methodology Using phenotypic data available from linked UK cancer registries, hospital ICD-10 records, operative procedures and participant interviews we identified 6378 women with CIN3/cervical cancer and 198,441 controls. With UK Biobank genotyping calls (Affymetrix UK Biobank Axiom® array) we conducted genome wide analyses, adjusting for population structure and potential confounders. As a stringent correction for multiple testing, we considered a pre-test significance level of 10-8 to declare single nucleotide polymorphisms (SNPs). Results In the first UK Biobank iteration we have identified potential SNPs (p Conclusion We observed genetic variants significantly associated with CIN3/cervical cancer in both cohorts. Loci within the MHC may affect susceptibility to development of CIN3/cervical cancer through altered immune responses. We will next undertake fine-mapping within the UK Biobank cohort, to further classify any novel causal variants that may explain the estimated genetic susceptibility to cervical cancer. Disclosure Funding provided by an NIHR Academic Clinical Fellowship (SL) and a Wellcome Trust Clinician Scientist Fellowship (SL). No conflicts of interest to declare.

Background Adverse socioeconomic conditions in childhood affect systemic low-grade inflammation in adulthood. Studies in animals and humans suggest that socioeconomic conditions get under the skin from early life thus contributing to shape pro-inflammatory phenotypes. Although the existence of socioeconomic differences in gene regulation of the immune function have been described previously, no study has so far assessed the extent to which these differences actually explain socioeconomic variations in inflammatory markers. Methods First, we ran 2-sample Mendelian Randomization (MR) methods to identify putative genes whose expression drives C-reactive protein (CRP) levels in blood. We used two databases with summary statistics of associations between single nucleotide polymorphisms and gene expression (eQTLGen, N = 31,486 individuals) and CRP (CHARGE, N = 148,164 individuals) in blood. We tested 10,701 genes and retained those with FDR3mg/L) mediated by the selected genes. We estimated odds ratios and mediated proportions through counterfactual-based mediation models. Results We identified 426 genes driving CRP levels (robust to unmeasured confounding thanks to MR). They jointly mediated the effect of low father’s occupation on inflammation in adulthood for a proportion up to about 70% [95% confidence intervals (CI) 11-90%] and with an odds ratio of 1.53 [95% CI 1.05-2.15] compared to individuals with high paternal SEP. Analysis of the effects of life-course SEP trajectories (upward, downward and stable low from paternal to adult SEP) on gene regulation revealed trajectory dependent effects. Conclusions We suggest that adverse childhood SEP affects systemic inflammation in adulthood through a long-lasting effect on gene regulation.

Background This study explores (i) the relationship between socioeconomic position (SEP) across the life course and circulating C-reactive protein (CRP), a marker of systemic inflammation, in 6 European cohort studies (in up to N = 23 008) participating in the Lifepath project and (ii) interrogate the hypothesis of a cumulative biological risk (allostatic load, AL) reflecting 4 physiological systems, including inflammation, potentially predicting future risk of death in one of the cohort. Methods First, we estimated the association between measures of three time point SEP and adulthood CRP, adjusting for health behaviours and body mass index (BMI). To mimic life course experiences, we sequentially adjusted for the chronologically ordered measures of SEP. Next we used mortality data from the 1958 British birth cohort to operationalise AL from 14 biomarkers collected at age 44. Multivariate Cox proportional hazards regression was used to estimate hazard ratios for the association between AL, biological sub-scores and individual biomarkers with mortality. Results Educational attainment was most strongly related to inflammation where low educational attainment was associated with higher log-transformed levels of CRP (β = 0.30, (0.22-0.38)). Higher AL at 44 years old was a significant predictor of mortality 11 years later (HR = 3.56 (2.3 to 5.53)). Among the four physiological systems, only the immune-inflammatory and cardiovascular sub-scores were significantly related to mortality. Conclusions Socioeconomic circumstances across the life course are associated with higher levels of inflammation in adulthood which in turn predict subsequent risk of death. But our findings also suggest that the cumulative AL measure consisting of all the biomarkers was a better measure for predicting death adding evidence on the biological embodiment in response to chronic stress, suggesting that social-to-biological processes are at play, beyond the impact of health behaviours and BMI.

Background Methylation of viral DNA has been proposed as a novel biomarker for triage of human papillomavirus (HPV) positive women at screening. This systematic review and meta-analysis aims to assess how methylation levels change with disease severity and to determine diagnostic test accuracy (DTA) in detecting high-grade cervical intra-epithelial neoplasia (CIN). Methods We performed searches in MEDLINE, EMBASE and CENTRAL from inception to October 2019. Studies were eligible if they explored HPV methylation levels in HPV positive women. Data were extracted in duplicate and requested from authors where necessary. Random-effects models and a bivariate mixed-effects binary regression model were applied to determine pooled effect estimates. Findings 44 studies with 8819 high-risk HPV positive women were eligible. The pooled estimates for positive methylation rate in HPV16 L1 gene were higher for high-grade CIN (≥CIN2/high-grade squamous intra-epithelial lesion (HSIL) (95% confidence interval (95%CI:72·7% (47·8–92·2))) vs. low-grade CIN (≤CIN1/low-grade squamous intra-epithelial lesion (LSIL) (44·4% (95%CI:16·0–74·1))). Pooled difference in mean methylation level was significantly higher in ≥CIN2/HSIL vs. ≤CIN1/LSIL for HPV16 L1 (11·3% (95%CI:6·5–16·1)). Pooled odds ratio of HPV16 L1 methylation was 5·5 (95%CI:3·5–8·5) for ≥CIN2/HSIL vs. ≤CIN1/LSIL (p

Background Lower socioeconomic position (SEP) has consistently been associated with poorer health. Chronic inflammation has been proposed as having a prominent role in the construction of social inequalities in health. Disentangling the effects of social disadvantage along the life course on inflammation is key in elucidating biological mechanisms underlying socioeconomic disparities. In this study we investigate how life course socioeconomic conditions influence omics measures of inflammation at different molecular level traits from a subset of 173 Italian participants of the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods We used genome-wide methylation and transcriptional profiles obtained from blood samples from 178 Italian participants of the EPIC cohort. Starting from 824 genes involved in human inflammatory responses and corresponding to 11 502 CpG sites, we first identified 61 potential cis acting CpG loci whose degree of methylation was associated with gene expression (eMS) at a Bonferonni correction, out of which 78.7% were inversely associated with gene expression in cis. We further investigate the relationships between indicators of SEP at 3 life stages through father’s occupation, education and highest household occupation and the 61 cis eMS, involved in inflammation and functionnally relevant, separately and combined through an inflammatory methylome score. We finally investigated life course effects of early-life SEP experiences by sequentially controlling for time-ordered SEP. Results Our results consistently show that participants with a less advantaged SEP in young adulthood or in adulthood exhibit, later in life, a lower inflammatory methylome score (β=-0.0075, P-value=0.0067, β=-0.0076, P-value=0.0073 for educational level and highest household occupational position respectively), hence suggesting an overall increased level of expression for the corresponding inflammatory-related genes. Adjusting for either behavioural factors (smoking status, alcohol consumption and physical activity) and bmi, or all of them together only marginally affected our results: effect size estimates showed consistent signs, and associations reach statistical significance (P Conclusion Our results support the hypothesis that social inequalities impacts, independently from behavioural factors, adult physiology through inflammation and can be observed at the DNA methylation level. Understanding biological mechanisms by which social environment influences the inflammatory system has important implications in treatment and especially in prevention, by potentially identifying modifiable factors in the environment that affect physiological health.

Although smoking and oxidative stress are known contributors to lung carcinogenesis, their mechanisms of action remain poorly understood. To shed light into these mechanisms, we applied a novel approach using Cys34-adductomics in a lung cancer nested case-control study (n = 212). Adductomics profiles were integrated with DNA-methylation data at established smoking-related CpG sites measured in the same individuals. Our analysis identified 42 Cys34-albumin adducts, of which 2 were significantly differentially abundant in cases and controls: adduct of N-acetylcysteine (NAC, p = 4.15 × 10-3 ) and of cysteinyl-glycine (p = 7.89 × 10-3 ). Blood levels of the former were found associated to the methylation levels at 11 smoking-related CpG sites. We detect, for the first time in prospective blood samples, and irrespective of time to diagnosis, decreased levels of NAC adduct in lung cancer cases. Altogether, our results highlight the potential role of these adducts in the oxidative stress response contributing to lung carcinogenesis years before diagnosis.

Social position is known to play a role in the quality of ageing, notably through the stimulation/dysregulation of key physiological systems in response to external stresses. Using data from one wave of Understanding Society including 9088 participants, we defined, as an extension of the allostatic load, a synthetic Biological Health Score (BHS) capturing the wear-and-tear of four physiological systems (endocrine, inflammatory, cardiovascular and metabolic systems) and two organs (liver and kidney). We used 16 established blood-derived biomarkers of these systems to calculate the BHS and explored the relative contribution of socioeconomic position to the BHS and its main components across age groups. We identified a systematic decreasing education-related gradient of the BHS (p

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., Highlights • Two randomized crossover trials were used to assess the relationship between TRAP and metabolic features with MS-based metabolomics (MWAS) • The locations represent different exposure scenarios, with London characterized by diesel vehicles and Barcelona by normal mixed urban traffic • Levels of 17 and 30 metabolic compounds associated with different air pollutants in the studies, with little overlap in features across pollutants • No overlap found between metabolomic features identified in the two studies, possibly due to different levels of single pollutants • The acyl-carnitine pathway, involved in cardio-respiratory disease, was suggested as a potential pathway in association with NO2 in one study

Mass spectrometry and nuclear magnetic resonance spectroscopy can routinely generate high-resolution metabolic phenotyping profiles that typically have more variables than observations, and feature complex correlation structures in the data. Statistical analysis as proposed in metabolome-wide association studies (MWAS) can be achieved using univariate models, assessing the relationship between each of the spectral variables separately and an outcome of interest. We will describe here the main steps required to perform a MWAS from data preprocessing, to parameterization of the (univariate) statistical model, correction for multiple testing, accounting for the (partial) redundancies across the tests performed, and visualization and prioritization of the results.

PCBs are classified as xenoestrogens and carcinogens and their health risks may be sex-specific. To identify potential sex-specific responses to PCB-exposure we established gene expression profiles in a population study subdivided into females and males. Gene expression profiles were determined in a study population consisting of 512 subjects from the EnviroGenomarkers project, 217 subjects who developed lymphoma and 295 controls were selected in later life. We ran linear mixed models in order to find associations between gene expression and exposure to PCBs, while correcting for confounders, in particular distribution of white blood cells (WBC), as well as random effects. The analysis was subdivided according to sex and development of lymphoma in later life. The changes in gene expression as a result of exposure to the six studied PCB congeners were sex- and WBC type specific. The relatively large number of genes that are significantly associated with PCB-exposure in the female subpopulation already indicates different biological response mechanisms to PCBs between the two sexes. The interaction analysis between different PCBs and WBCs provides only a small overlap between sexes. In males, cancer-related pathways and in females immune system-related pathways are identified in association with PCBs and WBCs. Future lymphoma cases and controls for both sexes show different responses to the interaction of PCBs with WBCs, suggesting a role of the immune system in PCB-related cancer development.

Background: Birthweight reflects in utero exposures and later health evolution. Despite existing studies employing high-dimensional molecular measurements, the understanding of underlying mechanisms of birthweight remains limited. Methods: To investigate the systems biology of birthweight, we integrated methylome, transcriptome, metabolome and a set of inflammatory proteins measured in cord blood samples from four birth-cohorts (n=489). We focused on two sets of 68 metabolites and 903 CpGs previously related to birthweight and investigated the correlation structures existing between these two sets and all the other variables from different groups of omic data via bipartite Pearson correlations. Findings: Results substantiated previously identified biomarkers related to birthweight in cord blood, mostly involved in the regulation of the immune system, and identified new multiple omic features. Three metabolic features [PC(34:2), plasmalogen PC(36:4)/PC(O-36:5), and a compound with m/z of 781.0545], two CpGs (on the DHCR24 and SC4MOL gene), and two proteins (periostin and CCL22), were identified as key signals related to birthweight. Due to importance of the epigenetic signals in cholesterol biosynthesis, we further studied the association of cholesterol levels in cord blood with birthweight in the ENVIRONAGE cohort (n=1,096), finding that the increase of birthweight an was associated with increment of high-density lipoprotein cholesterol. Interpretation: Our data suggested that cholesterol metabolism in cord blood is involved in the birthweight mechanism and that integration of different omic layers is a useful approach to generate new hypotheses about biological mechanisms. Funding Statement: This work is supported by the Bijzonder Onderzoeksfonds (BOF) Hasselt University through a PhD fellowship [to RA], the “EXPOsOMICS” grant [grant number 308610-FP7 European Commission to PV], and the “STOP” grant [grant number 774548-European Commission H2020 to PV]. The ENVIRONAGE birth-cohort is supported by the EU Program “Ideas” (ERC-2012-StG 310898) and the FWO (G082317N). Piccolipiu cohort has been approved and initially funded by the CCM grant 2010 and by the Italian Ministry of Health. Declaration of Interests: The authors declare that they have no conflict of interests. Ethics Approval Statement: Piccolipiu cohort has been approved and initially funded by the CCM grant 2010 and by the Italian Ministry of Health.

Aims To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and results We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10−14 to 1.0 × 10−6 (discovery) and P = 5.6 × 10−10 to 1.1 × 10−2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P Conclusion Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled = 4 × 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled = 2 × 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled = 7 × 10-16 and 1 × 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled = 2 × 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled = 5 × 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity ≤ 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

Prediagnostic serum/plasma concentrations of B-cell activation markers have been associated with future risk of B-cell lymphomas (BCL) in HIV-infected patients and in the general population. Current evidence for the general population is however limited and relies on relatively small numbers of observations, especially for specific histologies. We carried out a nested case-control study, including 218 BCL and 218 matched controls, within two prospective cohorts, to investigate the association between plasma levels of soluble (s)CD27 and sCD30 and future risk of BCL, and main histologic subtypes separately. To expand the evidence further, we performed meta-analyses of the published data on these associations from prospective studies among the general population. Our study revealed a significant relationship between sCD30 concentration and BCL risk (OR = 0.86, 1.53, 1.76, for the 2nd-4th quartiles respectively, p trend = 0.01). Similar increased risks were observed for diffuse large B-cell lymphoma and follicular lymphoma. Analyses of sCD27 blood concentrations did not show significant associations with BCL, (OR = 0.90, 1.26, 1.65 for the 2nd-4th quartiles, respectively, p trend = 0.17), but significant associations were observed for chronic lymphocytic leukaemia and for the group of "other BCL" subtypes. Our findings involving sCD30 were confirmed within our meta-analyses of five prospective cohorts, while results were more heterogeneous for sCD27 with the exception of CLL which was found consistently in all studies. Data to date suggest that chronic B-cell stimulation might be an important mechanism involved in B-cell lymphomagenesis both in HIV-infected and in the general population.

Markers of biological ageing have potential utility in primary care and public health. We developed an elastic net regression model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum (almost 100,000 features assayed), within a large sample (N=2,239) from the UK occupational Airwave cohort. We investigated the determinants of accelerated ageing, including genetic, lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (r=0.85 in independent test set). Increased metabolomic age acceleration (mAA) was associated (p

PURPOSE OF REVIEW: This systematic review evaluated existing evidence linking air pollution exposure in humans to major epigenetic mechanisms: DNA methylation, microRNAs, long noncoding RNAs, and chromatin regulation. RECENT FINDINGS: Eighty-two manuscripts were eligible, most of which were observational (85%), conducted in adults (66%) and based on DNA methylation (79%). Most observational studies, except panel, demonstrated modest effects of air pollution on the methylome. Panel and experimental studies revealed a relatively large number of significant methylome alterations, though based on smaller sample sizes. Particulate matter levels were positively associated in several studies with global or LINE-1 hypomethylation, a hallmark of several diseases, and with decondensed chromatin structure. Several air pollution species altered the DNA methylation clock, inducing accelerated biological aging. The causal nature of identified associations is not clear, however, especially that most originate from countries with low air pollution levels. Existing evidence, gaps, and perspectives are highlighted herein. ispartof: Curr Environ Health Rep vol:5 issue:4 pages:544-578 ispartof: location:Switzerland status: published

Chronic inflammation may be involved in cancer development and progression. Using 28 inflammatory-related proteins collected from prospective blood samples from two case-control studies nested in the Italian component of the European Prospective Investigation into Cancer and nutrition (n = 261) and in the Northern Sweden Health and Disease Study (n = 402), we tested the hypothesis that an inflammatory score is associated with breast cancer (BC) and Β-cell Non-Hodgkin Lymphoma (B-cell NHL, including 68 multiple myeloma cases) onset. We modelled the relationship between this inflammatory score and the two cancers studied: (BC and B-cell NHL) using generalised linear models, and assessed, through adjustments the role of behaviours and lifestyle factors. Analyses were performed by cancer types pooling both populations, and stratified by cohorts, and time to diagnosis. Our results suggested a lower inflammatory score in B-cell NHL cases (β = −1.28, p = 0.012), and, to lesser, extent with BC (β = −0.96, p = 0.33) compared to controls, mainly driven by cancer cases diagnosed less than 6 years after enrolment. These associations were not affected by subsequent adjustments for potential intermediate confounders, notably behaviours. Sensitivity analyses indicated that our findings were not affected by the way the inflammatory score was calculated. These observations call for further studies involving larger populations, larger variety of cancer types and repeated measures of larger panel of inflammatory markers.

BackgroundObesity is an established risk factor for several common chronic diseases such as breast and colorectal cancer, metabolic and cardiovascular diseases; however, the biological basis for these relationships is not fully understood. To explore the association of obesity with these conditions, we investigated peripheral blood leucocyte (PBL) DNA methylation markers for adiposity and their contribution to risk of incident breast and colorectal cancer and myocardial infarction.MethodsDNA methylation profiles (Illumina Infinium® HumanMethylation450 BeadChip) from 1941 individuals from four population-based European cohorts were analysed in relation to body mass index, waist circumference, waist-hip and waist-height ratio within a meta-analytical framework. In a subset of these individuals, data on genome-wide gene expression level, biomarkers of glucose and lipid metabolism were also available. Validation of methylation markers associated with all adiposity measures was performed in 358 individuals. Finally, we investigated the association of obesity-related methylation marks with breast, colorectal cancer and myocardial infarction within relevant subsets of the discovery population.ResultsWe identified 40 CpG loci with methylation levels associated with at least one adiposity measure. Of these, one CpG locus (cg06500161) in ABCG1 was associated with all four adiposity measures (P = 9.07×10−8 to 3.27×10−18) and lower transcriptional activity of the full-length isoform of ABCG1 (P = 6.00×10−7), higher triglyceride levels (P = 5.37×10−9) and higher triglycerides-to-HDL cholesterol ratio (P = 1.03×10−10). Of the 40 informative and obesity-related CpG loci, two (in IL2RB and FGF18) were significantly associated with colorectal cancer (inversely, P P ConclusionOur results suggest that epigenetic changes, in particular altered DNA methylation patterns, may be an intermediate biomarker at the intersection of obesity and obesity-related diseases, and could offer clues as to underlying biological mechanisms.

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. (C) 2018 Published by Elsevier Ltd.

Maternal exposure to airborne particulate matter (PM) has been associated with restricted fetal growth and reduced birthweight. Here, we performed methylome-wide analyses of cord and children's blood DNA in relation to residential exposure to PM smaller than 10 mu m (PM10). This study included participants of the Avon Longitudinal Study of Pregnancy and Childhood (ALSPAC, cord blood, n = 780; blood at age 7, n = 757 and age 15-17, n = 850) and the EXPosOMICS birth cohort consortium including cord blood from ENVIRON-AGE (n = 197), INMA (n = 84), Piccolipiu (n = 99) and Rhea (n = 75). We could not identify significant CpG sites, by meta-analyzing associations between maternal PM10 exposure during pregnancy and DNA methlation in cord blood, nor by studying DNA methylation and concordant annual exposure at 7 and 15-17 years. The CpG cg21785536 was inversly associated with PM10 exposure using a longitudinal model integrating the three studied age groups (-1.2% per 10 mu g/m(3); raw p-value = 3.82 X 10(-8)). Pathway analyses on the corresponding genes of the 100 strongest associated CpG sites of the longitudinal model revealed enriched pathways relating to the GABAergic synapse, p53 signaling and NOTCH1. We provided evidence that residential PM10 exposure in early life affects methylation of the CpG cg21785536 located on the EGF Domain Specific O-Linked N-Acetylglucosamine Transferase gene. This work was carried out within the scope of the European Commission seventh Framework program grant EXPOsOMICS (308610-FP7 to PV). M.P. received support for the was supported by the European Commission seventh Framework program Marie Curie Actions grant (628858). The Centre for Environment and Health is supported by the Medical Research Council and Public Health England (MR/L01341X/1). M.C.-H. acknowledges support from the "Mechanomics" Population Research Committee project (22184). The ENVIRONAGE birth cohort is supported by grants from the European Research Council (ERC-2012-StG310898) and the Flemish Scientific Fund (FWO, 1516112N/G.0873.11.N.10). ARIES was funded by the BBSRC (BBI025751/1 and BB/I025263/1). The ALSPAC study was core-supported by the Medical Research Council, the Wellcome Trust (Grant ref: 102215/2/13/1), and the University of Bristol. Financial support for the generation of DNA methylation data within the ARIES study was provided by MRC, National Institutes for Health. ARIES is maintained and hosted within the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/2 and MC_UU_12013/8). Piccolipiu cohort funded by the Italian National Centre for Disease Prevention and Control (CCM grant 2010) and by the Italian Ministry of Health (art 12 and 12bis Dl.gs.vo 502/92). A.G. acknowledges support from Plan Cancer-Eva-Inserm research grant. Piccolipiu cohort acknowledges Andrea Ranzi for his contribution to the assessment of exposure to air pollution.

BACKGROUND: Land use regression (LUR) models are commonly used in environmental epidemiology to assign spatially resolved estimates of air pollution to study participants. In this setting, estimated LUR model parameters are assumed to be transportable to a main study (the ''transportability assumption''). We provide an empirical illustration of how violation of this assumption can affect exposure predictions and bias health-effect estimates. METHODS: We based our simulation on two existing LUR models, one for nitrogen dioxide, the other for particulate matter with aerodynamic diameter

Objective To assess the association of low socioeconomic status and risk factors for non-communicable diseases (diabetes, high alcohol intake, high blood pressure, obesity, physical inactivity, smoking) with loss of physical functioning at older ages. Design Multi-cohort population based study. Setting 37 cohort studies from 24 countries in Europe, the United States, Latin America, Africa, and Asia, 1990-2017 Participants 109 107 men and women aged 45-90 years. Main outcome measure Physical functioning assessed using the walking speed test, a valid index of overall functional capacity. Years of functioning lost was computed as a metric to quantify the difference in walking speed between those exposed and unexposed to low socioeconomic status and risk factors. Results According to mixed model estimations, men aged 60 and of low socioeconomic status had the same walking speed as men aged 66.6 of high socioeconomic status (years of functioning lost 6.6 years, 95% confidence interval 5.0 to 9.4). The years of functioning lost for women were 4.6 (3.6 to 6.2). In men and women, respectively, 5.7 (4.4 to 8.1) and 5.4 (4.3 to 7.3) years of functioning were lost by age 60 due to insufficient physical activity, 5.1 (3.9 to 7.0) and 7.5 (6.1 to 9.5) due to obesity, 2.3 (1.6 to 3.4) and 3.0 (2.3 to 4.0) due to hypertension, 5.6 (4.2 to 8.0) and 6.3 (4.9 to 8.4) due to diabetes, and 3.0 (2.2 to 4.3) and 0.7 (0.1 to 1.5) due to tobacco use. In analyses restricted to high income countries, the number of years of functioning lost attributable to low socioeconomic status by age 60 was 8.0 (5.7 to 13.1) for men and 5.4 (4.0 to 8.0) for women, whereas in low and middle income countries it was 2.6 (0.2 to 6.8) for men and 2.7 (1.0 to 5.5) for women. Within high income countries, the number of years of functioning lost attributable to low socioeconomic status by age 60 was greater in the United States than in Europe. Physical functioning continued to decline as a function of unfavourable risk factors between ages 60 and 85. Years of functioning lost were greater than years of life lost due to low socioeconomic status and non-communicable disease risk factors. Conclusions The independent association between socioeconomic status and physical functioning in old age is comparable in strength and consistency with those for established non-communicable disease risk factors. The results of this study suggest that tackling all these risk factors might substantially increase life years spent in good physical functioning. This study was supported by the European Commission (Horizon 2020 grant No 633666) and the Swiss State Secretariat for Education, Research and Innovation SERI. SS was supported by an Ambizione grant (PZ00P3_167732) from the Swiss National Science Foundation. Silvia Fraga is supported by the Portuguese Foundation for Science and Technology (SFRH/BPD/97015/2013). Various sources have supported recruitment, follow-up, and measurements in the 48 cohort studies contributing to this collaborative analysis. MK is supported by the UK Medical Research Council (K013351), NordForsk, the Nordic Programme on Health and Welfare, the Academy of Finland (311492), and the Finnish Work Environment Fund. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of this manuscript.

Background Indian Asians, who make up a quarter of the world's population, are at high risk of developing type 2 diabetes. We investigated whether DNA methylation is associated with future type 2 diabetes incidence in Indian Asians and whether differences in methylation patterns between Indian Asians and Europeans are associated with, and could be used to predict, differences in the magnitude of risk of developing type 2 diabetes. Methods We did a nested case-control study of DNA methylation in Indian Asians and Europeans with incident type 2 diabetes who were identified from the 8-year follow-up of 25 372 participants in the London Life Sciences Prospective Population (LOLIPOP) study. Patients were recruited between May 1, 2002, and Sept 12, 2008. We did epigenome-wide association analysis using samples from Indian Asians with incident type 2 diabetes and age-matched and sex-matched Indian Asian controls, followed by replication testing of top-ranking signals in Europeans. For both discovery and replication, DNA methylation was measured in the baseline blood sample, which was collected before the onset of type 2 diabetes. Epigenome-wide significance was set at p

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 risk.

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.

Background B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. Results We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0–15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p

Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.

The use of omics represents a shift in approach for environmental epidemiology and exposure science. In this article, the aspects of the use of omics that will require further development in the near future are discussed, including (a) the underlying causal interpretation and models; (b) the “meet-in-the-middle” concept, with examples; (c) the role of “calibration” of measurements; and (d) the role of life-course epidemiology and the related development of adequate biostatistical models. Environ. Mol. Mutagen. 54:461-467, 2013. © 2013 Wiley Periodicals, Inc.

Introduction Land use regression (LUR) models are frequently used to assign spatially resolved estimates of air pollution to cohort and case-control studies. Effect sizes in air pollution studies a...

Introduction Lung cancer is the first cause of death from cancer worldwide. Survival improvement is hampered by the lack of the lack of understanding of the molecular mechanisms driving lung carcin...

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Technological advances in molecular biology over the past decade have given rise to high dimensional and complex datasets offering the possibility to investigate biological associations between a range of genomic features and complex phenotypes. The analysis of this novel type of data generated unprecedented computational challenges which ultimately led to the definition and implementation of computationally efficient statistical models that were able to scale to genome-wide data, including Bayesian variable selection approaches. While extensive methodological work has been carried out in this area, only few methods capable of handling hundreds of thousands of predictors were implemented and distributed. Among these we recently proposed GUESS, a computationally optimised algorithm making use of graphics processing unit capabilities, which can accommodate multiple outcomes. In this paper we propose R2GUESS, an R package wrapping the original C++ source code. In addition to providing a user-friendly interface of the original code automating its parametrisation, and data handling, R2GUESS also incorporates many features to explore the data, to extend statistical inferences from the native algorithm (e.g., effect size estimation, significance assessment), and to visualize outputs from the algorithm. We first detail the model and its parametrisation, and describe in details its optimised implementation. Based on two examples we finally illustrate its statistical performances and flexibility.

Lower socioeconomic position (SEP) has consistently been associated with poorer health. To explore potential biological embedding and the consequences of SEP experiences from early life to adulthood, we investigate how SEP indicators at different points across the life course may be related to a combination of 28 inflammation markers. Using blood-derived inflammation profiles measured by a multiplex array in 268 participants from the Italian component of the European Prospective Investigation into Cancer and Nutrition cohort, we evaluate the association between early life, young adulthood and later adulthood SEP with each inflammatory markers separately, or by combining them into an inflammatory score. We identified an increased inflammatory burden in participants whose father had a manual occupation, through increased plasma levels of CSF3 (G-CSF; β = 0.29; P = 0.002) and an increased inflammatory score (β = 1.96; P = 0.029). Social mobility was subsequently modelled by the interaction between father’s occupation and the highest household occupation, revealing a significant difference between “stable Non-manual” profiles over the life course versus “Manual to Non-manual” profiles (β = 2.38, P = 0.023). Low SEP in childhood is associated with modest increase in adult inflammatory burden; however, the analysis of social mobility suggests a stronger effect of an upward social mobility over the life course.

In the following paper we consider a simulation technique for stochastic trees. One of the most important areas in computational genetics is the calculation and subsequent maximization of the likelihood function associated to such models. This typically consists of using importance sampling (IS) and sequential Monte Carlo (SMC) techniques. The approach proceeds by simulating the tree, backward in time from observed data, to a most recent common ancestor (MRCA). However, in many cases, the computational time and variance of estimators are often too high to make standard approaches useful. In this paper we propose to stop the simulation, subsequently yielding biased estimates of the likelihood surface. The bias is investigated from a theoretical point of view. Results from simulation studies are also given to investigate the balance between loss of accuracy, saving in computing time and variance reduction., Comment: 22 Pages, 5 Figures

Biomarkers play a central role in chronic disease epidemiology, providing insights into pathways related to the relationship between environmental exposures and disease risk. Recent developments in both data acquisition techniques and laboratory approaches advocate for a more extensive and refined use of biomarkers.We review some issues related to biomarker identification and validation techniques as well as the main methodologies to measure biomarkers in existing biobank data. Finally, we describe analytical strategies recently proposed to include the time component into biomarker research.This review suggests that some of the technical issues to identify, validate, and analyze biomarkers have been partly addressed in epidemiological studies. The inclusion of biomarker analyses into longitudinal frameworks provides a promising potential to analyze the role of different types of biomarkers and to refine the 'causal' models linking exposure to disease risk. These kinds of approaches can be implemented based on existing cohort data, at the cost of some approximation, but their generalization would ideally require advancements in study design, such as routinely allowing for the collection of several biological samples at different time points.

Rates of heart disease and stroke vary markedly between north and south China. A 1H NMR spectroscopy-based metabolome-wide association approach was used to identify urinary metabolites that discriminate between southern and northern Chinese population samples, to investigate population biomarkers that might relate to the difference in cardiovascular disease risk. NMR spectra were acquired from two 24-h urine specimens per person for 523 northern and 244 southern Chinese participants in the INTERMAP Study of macro/micronutrients and blood pressure. Discriminating metabolites were identified using orthogonal partial least squares discriminant analysis and assessed for statistical significance with conservative family wise error rate < 0.01 to minimize false positive findings. Urinary metabolites significantly ((P < 1.2 × 10-16 to 2.9 × 10-69) higher in northern than southern Chinese populations included dimethylglycine, alanine, lactate, branched-chain amino acids (isoleucine, leucine, valine), N-acetyls of glycoprotein fragments (including uromodulin), N-acetyl neuraminic acid, pentanoic/heptanoic acid, and methylguanidine; metabolites significantly (P < 1.1 × 10-12 to 2 × 10-127) higher in the south were gut microbial cometabolites (hippurate, 4-cresyl sulfate, phenylacetylglutamine, 2-hydroxyisobutyrate), succinate, creatine, scyllo-inositol, prolinebetaine, and trans-aconitate. These findings indicate the importance of environmental influences (e.g., diet), endogenous metabolism, and mammalian-gut microbial cometabolism, which may help explain north-south China differences in cardiovascular disease risk. © 2010 American Chemical Society.