Publications
197 results found
Mostafavi N, Vermeulen R, Ghantous A, et al., 2018, Acute changes in DNA methylation in relation to 24 h personal air pollution exposure measurements: a panel study in four European countries, Environment International, Vol: 120, Pages: 11-21, ISSN: 0160-4120
BackgroundOne 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.ObjectiveWe investigated the association of personal and ambient air pollution exposure measures with genome-wide DNA-methylation changes.MethodsWe 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.ResultsPersonal 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.ConclusionThis 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.
Krauskopf J, Caiment F, van Veldhoven K, et al., 2018, Short-term exposure to ambient motor vehicle emissions perturb the human circulating miRNA genome, 54th Congress of the European-Societies-of-Toxicology (EUROTOX) - Toxicology Out of the Box, Publisher: Elsevier, Pages: S85-S85, ISSN: 0378-4274
Vermeulen R, Saberi Hosnijeh F, Bodinier B, et al., 2018, Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma; univariate and functionally-informed multivariate analyses, International Journal of Cancer, Vol: 143, Pages: 1335-1347, ISSN: 0020-7136
Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years prior to diagnosis (range, 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma) and matched controls. Linear mixed models, and Partial Least Square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes, and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed modelIrrespective of the model, our analyses identified associations linking blood lower immune markerslevels of and BCL incidence. In particular, we identified growth factors, and within that family, fibroblast growth factor-2 (FGF-2,p=7.2x10-4), ) and transforming growth factor alpha (TGF-α, p=6.5x10-5) and BCL incidence.Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p=7.8x10-7), TGF-α (p=4.08x10-5),fractalkine (p=1.12x10-3), monocyte chemotactic protein-3 (p=1.36x10-4), macrophage inflammatory protein 1-alpha (p=4.6x10-4), and vascular endothelial growth factor (p=4.23x10-5). , and vascular endothelial growth factor (VEGF), to be consistently (and inversely) associated with MM incidence. Our results also provided marginal support for already reported associations between chemokines and diffuse large B-Cell lymphoma (DLBCL), and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that GM-CSF levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression.In conclusion, our study suggests a role of gr
Espin-Perez A, Portier C, Chadeau-Hyam M, et al., 2018, Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data, PLoS ONE, Vol: 13, ISSN: 1932-6203
Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.
Berger E, Delpierre C, Hosnijeh FS, et al., 2018, Association between low-grade inflammation and Breast cancer and B-cell Myeloma and Non-Hodgkin Lymphoma: findings from two prospective cohorts, Scientific Reports, Vol: 8, ISSN: 2045-2322
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.
Chadeau M, Jain P, Vineis P, et al., 2018, A multivariate approach to investigate the combined biological effects of multiple exposures, Journal of Epidemiology and Community Health, Vol: 72, Pages: 564-571, ISSN: 0143-005X
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 multi-level extension of the 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.
Guida F, Nost TH, Relton C, et al., 2018, Lung cancer risk prediction using DNA methylation markers, Annual Meeting of the American-Association-for-Cancer-Research (AACR), Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
Vlaanderen J, Portengen L, Chadeau-Hyam M, et al., 2018, Error in air pollution exposure model determinants and bias in health estimates, Journal of Exposure Science and Environmental Epidemiology, Vol: 29, Pages: 258-266, ISSN: 1559-0631
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 <2.5 μm. We assessed the impact of error in exposure determinants used in the LUR models on resultant air pollution predictions and on bias in an exposure-health-effect estimate assessed in a hypothetical cohort. We assigned error to predictors at monitoring sites (sites used to develop the LUR model) and at prediction sites (sites for which exposure predictions were needed), allowing for different error levels between site types. RESULTS: Realistic error in the exposure determinants of the selected LUR models did not induce large additional error in exposure predictions and resulted in only minor (<1%) bias in health-effect estimates. Bias in the health-effect estimates strongly increased (up to 13.6%) when exposure determinant errors were different for monitoring sites than for prediction sites. CONCLUSIONS: These results suggest that only modest reductions in bias in estimated exposure health-effects are to be expected from reducing error in exposure determinants. It is important to avoid heterogeneous errors in exposure determinants between monitoring sites and prediction sites to satisfy the transportability assumption and avoid bias in estimated exposure health-effects.
Castagne R, Gares V, Karimi M, et al., 2018, Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort, European Journal of Epidemiology, Vol: 33, Pages: 441-458, ISSN: 0393-2990
The concept of allostatic load (AL) refers to the idea of a global physiological ‘wear and tear’ resulting from the adaptationto the environment through the stress response systems over the life span. The link between socioeconomic position (SEP)and mortality has now been established, and there is evidence that AL may capture the link between SEP and mortality. Inorder to quantitatively assess the role of AL on mortality, we use data from the 1958 British birth cohort including elevenyear mortality in 8,113 adults. Specifically, we interrogate the hypothesis of a cumulative biological risk (allostatic load)reflecting 4 physiological systems potentially predicting future risk of death (N = 132). AL was defined using 14biomarkers assayed in blood from a biosample collected at 44 years of age. Cox proportional hazard regression analysisrevealed that higher allostatic load at 44 years old was a significant predictor of mortality 11 years later [HR = 3.56 (2.3 to5.53)]. We found that this relationship was not solely related to early-life SEP, adverse childhood experiences and youngadulthood health status, behaviours and SEP [HR = 2.57 (1.59 to 4.15)]. Regarding the ability of each physiologicalsystem and biomarkers to predict future death, our results suggest that the cumulative measure was advantageous comparedto evaluating each physiological system sub-score and biomarker separately. Our findings add some evidence of a biologicalembodiment in response to stress which ultimately affects mortality.
Campanella G, Gunter MJ, Polidoro S, et al., 2018, Epigenome-wide association study of adiposity and future risk of obesity-related diseases, INTERNATIONAL JOURNAL OF OBESITY, Vol: 42, Pages: 2022-2035, ISSN: 0307-0565
Plusquin M, Chadeau-Hyam M, Ghantous A, et al., 2018, DNA Methylome Marks of Exposure to Particulate Matter at Three Time Points in Early Life, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 52, Pages: 5427-5437, ISSN: 0013-936X
Krauskopf J, Caiment F, van Veldhoven K, et al., 2018, The human circulating miRNome reflects multiple organ disease risks in association with short-term exposure to traffic-related air pollution, Environment International, Vol: 113, Pages: 26-34, ISSN: 0160-4120
Traffic-related air pollution is a complex mixture of particulate matter (PM) and gaseous pollutants, such as nitrogen dioxide (NO2). PM exposure contributes to the pathogenesis of many diseases including several types of cancer, as well as pulmonary, cardiovascular and neurodegenerative diseases. Also exposure to NO2 has been related to increased cardiovascular mortality. In search of an early diagnostic biomarker for improved air pollution-associated health risk assessment, recent human studies have shown that certain circulating miRNAs are altered upon exposure to traffic-related air pollutants. Here, we present for the first time a global analysis of the circulating miRNA genome in an experimental cross-over study of a human population exposed to traffic-related air pollution. By utilizing next-generation sequencing technology and detailed real-time exposure measurements we identified 54 circulating miRNAs to be dose- and pollutant species-dependently associated with PM10, PM2.5, black carbon, ultrafine particles and NO2 already after 2 h of exposure. Bioinformatics analysis suggests that these circulating miRNAs actually reflect the adverse consequences of traffic pollution-induced toxicity in target tissues including the lung, heart, kidney and brain. This study shows the strong potential of circulating miRNAs as novel biomarkers for environmental health risk assessment.
Stringhini S, Carmeli C, Jokela M, et al., 2018, Socioeconomic status, non-communicable disease risk factors, and walking speed in older adults: multi-cohort population based study, BMJ: British Medical Journal, Vol: 360, ISSN: 0959-8138
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 un
Turner MC, Vineis P, Seleiro E, et al., 2018, EXPOsOMICS: final policy workshop and stakeholder consultation, BMC Public Health, Vol: 18, ISSN: 1471-2458
The final meeting of the EXPOsOMICS project "Final Policy Workshop and Stakeholder Consultation" took place 28-29 March 2017 to present the main results of the project and discuss their implications both for future research and for regulatory and policy activities. This paper summarizes presentations and discussions at the meeting related with the main results and advances in exposome research achieved through the EXPOsOMICS project; on other parallel research initiatives on the study of the exposome in Europe and in the United States and their complementarity to EXPOsOMICS; lessons learned from these early studies on the exposome and how they may shape the future of research on environmental exposure assessment; and finally the broader implications of exposome research for risk assessment and policy development on environmental exposures. The main results of EXPOsOMICS in relation to studies of the external exposome and internal exposome in relation to both air pollution and water contaminants were presented as well as new technologies for environmental health research (adductomics) and advances in statistical methods. Although exposome research strengthens the scientific basis for policy development, there is a need in terms of showing added value for public health to: improve communication of research results to non-scientific audiences; target research to the broader landscape of societal challenges; and draw applicable conclusions. Priorities for future work include the development and standardization of methodologies and technologies for assessing the external and internal exposome, improved data sharing and integration, and the demonstration of the added value of exposome science over conventional approaches in answering priority policy questions.
Robinson O, Keski-Rahkonen P, Chatzi L, et al., 2018, Cord blood metabolic signatures of birthweight: a population based study, Journal of Proteome Research, Vol: 3, Pages: 1235-1247, ISSN: 1535-3893
Birthweight is an important indicator of maternal and fetal health, and a predictor of health in later life. However, the determinants of variance in birthweight are still poorly understood. We aimed to identify the biological pathways, which may be perturbed by environmental exposures, that are important in determining birthweight. We applied untargeted mass-spectrometry based metabolomics to 481 cord blood samples collected at delivery in four birth cohorts from across Europe: ENVIRONAGE (Belgium), INMA (Spain), Piccolipiu (Italy) and Rhea (Greece). We performed a metabolome-wide association scan for birthweight on over 4000 metabolic features, controlling the false discovery rate at 5%. Annotation of compounds was conducted through reference to authentic standards. We identified 68 metabolites significantly associated with birthweight, including vitamin A, progesterone, docosahexaenoic acid, indolelactic acid, and multiple acylcarnitines and phosphatidylcholines. We observed enrichment (p < 0.05) of the tryptophan metabolism, prostaglandin formation, C21-steroid hormone signalling, carnitine shuttle and glycerophospholipid metabolism pathways. Vitamin A was associated with both maternal smoking and birthweight, suggesting a mediation pathway. Our findings shed new light on the pathways central to fetal growth and will have implications for antenatal and perinatal care and potentially for health in later life.
Preston GW, Plusquin M, Sozeri O, et al., 2017, Refinement of a Methodology for Untargeted Detection of Serum Albumin Adducts in Human Populations, CHEMICAL RESEARCH IN TOXICOLOGY, Vol: 30, Pages: 2120-2129, ISSN: 0893-228X
Chadeau M, van Veldhoven, Keski-Rahkonen P, et al., 2017, Effects of exposure to water Disinfection By-Products in a swimming pool: A metabolome-wide association study, Environment International, Vol: 111, Pages: 60-70, ISSN: 0160-4120
BackgroundExposure to disinfection by-products (DBPs) in drinking water and chlorinated swimming pools are associated with adverse health outcomes, but biological mechanisms remain poorly understood.ObjectivesEvaluate short-term changes in metabolic profiles in response to DBP exposure while swimming in a chlorinated pool.Materials and methodsThe PISCINA-II study (EXPOsOMICS project) includes 60 volunteers swimming 40 min 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 2 h 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.ResultsExposure 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.ConclusionOur 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.
Fiorito G, Polidoro S, Dugue P-A, et al., 2017, Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation, Scientific Reports, Vol: 7, ISSN: 2045-2322
Low socioeconomic status (SES) is associated with earlier onset of age-related chronic conditions and reduced life-expectancy, but the underlying biomolecular mechanisms remain unclear. Evidence of DNA-methylation differences by SES suggests a possible association of SES with epigenetic age acceleration (AA). We investigated the association of SES with AA in more than 5,000 individuals belonging to three independent prospective cohorts from Italy, Australia, and Ireland. Low SES was associated with greater AA (β = 0.99 years; 95% CI 0.39,1.59; p = 0.002; comparing extreme categories). The results were consistent across different SES indicators. The associations were only partially modulated by the unhealthy lifestyle habits of individuals with lower SES. Individuals who experienced life-course SES improvement had intermediate AA compared to extreme SES categories, suggesting reversibility of the effect and supporting the relative importance of the early childhood social environment. Socioeconomic adversity is associated with accelerated epigenetic aging, implicating biomolecular mechanisms that may link SES to age-related diseases and longevity.
Espín-Pérez A, Font-Ribera L, van Veldhoven K, et al., 2017, Blood transcriptional and microRNA responses to short-term exposure to disinfection by-products in a swimming pool., Environment International, Vol: 110, Pages: 42-50, ISSN: 0160-4120
BACKGROUND: Swimming in a chlorinated pool results in high exposure levels to disinfection by-products (DBPs), which have been associated with an increased risk of bladder cancer. OBJECTIVES: By studying molecular responses at the blood transcriptome level we examined the biological processes associated with exposure to these compounds. METHODS: Whole-genome gene expression and microRNA analysis was performed on blood samples collected from 43 volunteers before and 2h after 40min swimming in an indoor chlorinated pool (PISCINAII study). Exposure to THMs was measured in exhaled breath. Heart rate and kcal expenditure were measured as proxies for physical activity. Associations between exposure levels and gene expression were assessed using multivariate normal models (MVN), correcting for age, body mass index and sex. A Bonferroni threshold at 5% was applied. RESULTS: MVN-models for the individual exposures identified 1778 genes and 23 microRNAs that were significantly associated with exposure to at least one DBP. Due to co-linearity it was not possible to statistically disentangle responses to DBP exposure from those related to physical activity. However, after eliminating previously reported transcripts associated with physical activity a large number of hits remained associated with DBP exposure. Among those, 9 were linked with bladder and 31 with colon cancer. Concordant microRNA/mRNA expressions were identified in association with DBP exposure for hsa-mir-22-3p and hsa-miR-146a-5p and their targets RCOR1 and TLR4, both related to colon cancer in association with DBP exposure. CONCLUSIONS: Short-term exposure to low levels of DBPs shows genomics responses that may be indicative of increased cancer risk.
Vineis P, Avendano-Pabon M, Barros H, et al., 2017, The biology of inequalities in health: the LIFEPATH project, Longitudinal and Life Course Studies, Vol: 8, Pages: 417-449, ISSN: 1757-9597
Socioeconomic differences in health have been consistently observed worldwide. Physical health deteriorates more rapidly with age among men and women with lower socioeconomic status (SES) than among those with higher SES. The biological processes underlying these differences are best understood by adopting a life-course approach. In this paper we introduce the pan-European LIFEPATH project which uses the revised Strachan-Sheikh (2004) model to describe ageing across the life-course. This model presents ageing as a phenomenon with two broad stages across life: build-up and decline. The ‘build-up’ stage, from conception and early intra-uterine life to late adolescence or early twenties, is characterised by rapid successions of developmentally and socially sensitive periods. The second stage, starting in early adulthood, is a period of 'decline' from maximum attained health to loss of function, overt disease and death.LIFEPATH adopts a study design that integrates social science and public health approaches with biology (including molecular epidemiology), using well-characterised population cohorts and omics measurements (particularly epigenomics). The specific objectives of the project are: (a) to show that healthy ageing is an achievable goal for society; (b) to improve the understanding of the mechanisms through which healthy ageing pathways diverge by SES, by investigating life-course biological pathways using omic technologies; (c) to examine the consequences of the current economic recession on health and the biology of ageing (and the consequent increase in social inequalities); (d) to provide updated, relevant and innovative evidence for healthy ageing policies (particularly “health in all policies”) using both observational studies and an experimental approach based on a reanalysis of data from a "conditional cash transfer" randomised experiment in New York and new data collected as part of an earned income tax credit randomis
McCrory C, O'Leary N, Fraga S, et al., 2017, Socioeconomic differences in children's growth trajectories from infancy to early adulthood: evidence from four European countries, JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH, Vol: 71, Pages: 981-989, ISSN: 0143-005X
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Georgiadis P, Liampa I, Hebels DG, et al., 2017, Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis., BMC Genomics, Vol: 18, ISSN: 1471-2164
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 < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while
Chadeau M, Plusquin M, Guida F, et al., 2017, DNA methylation and exposure to ambient air pollution in two prospective cohorts, Environment International, Vol: 108, Pages: 127-136, ISSN: 0160-4120
Long-term exposure to air pollution has been associated with several adverse health effects including cardiovascular, respiratory diseases and cancers. However, underlying molecular alterations remain to be further investigated. The aim of this study is to investigate the effects of long-term exposure to air pollutants on (a) average DNA methylation at functional regions and, (b) individual differentially methylated CpG sites. An assumption is that omic measurements, including the methylome, are more sensitive to low doses than hard health outcomes.This study included blood-derived DNA methylation (Illumina-HM450 methylation) for 454 Italian and 159 Dutch participants from the European Prospective Investigation into Cancer and Nutrition (EPIC). Long-term air pollution exposure levels, including NO2, NOx, PM2.5, PMcoarse, PM10, PM2.5 absorbance (soot) were estimated using models developed within the ESCAPE project, and back-extrapolated to the time of sampling when possible. We meta-analysed the associations between the air pollutants and global DNA methylation, methylation in functional regions and epigenome-wide methylation. CpG sites found differentially methylated with air pollution were further investigated for functional interpretation in an independent population (EnviroGenoMarkers project), where (N = 613) participants had both methylation and gene expression data available.Exposure to NO2 was associated with a significant global somatic hypomethylation (p-value = 0.014). Hypomethylation of CpG island's shores and shelves and gene bodies was significantly associated with higher exposures to NO2 and NOx. Meta-analysing the epigenome-wide findings of the 2 cohorts did not show genome-wide significant associations at single CpG site level. However, several significant CpG were found if the analyses were separated by countries. By regressing gene expression levels against methylation levels of the exposure-related CpG sites, we identified several significant CpG-
castagne R, Boulange CL, Karaman I, et al., 2017, Improving visualisation and interpretation of metabolome-wide association studies (MWAS): an application in a population-based cohort using untargeted 1H NMR metabolic profiling., Journal of Proteome Research, Vol: 16, Pages: 3623-3633, ISSN: 1535-3893
1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.
Barrera-Gomez J, Agier L, Portengen L, et al., 2017, A systematic comparison of statistical methods to detect interactions in exposome-health associations, ENVIRONMENTAL HEALTH, Vol: 16, ISSN: 1476-069X
BackgroundThere is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions.MethodsSimulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate.ResultsGLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects.ConclusionsGLINTERNET and DSA provided better perf
Vlaanderen J, van Veldhoven K, Font-Ribera L, et al., 2017, Acute changes in serum immune markers due to swimming in a chlorinated pool, Environment International, Vol: 105, Pages: 1-11, ISSN: 0160-4120
BackgroundExposure to disinfectants and disinfection byproducts (DBPs) due to swimming in chlorinated water has been associated with allergic and respiratory health effects, including asthma.ObjectivesBiological mechanisms contributing to these associations are largely unknown. We hypothesized a potential pathway involving modulation of the immune system.MethodsWe assessed levels of immune markers (CCL11, CCL22, CXCL10, CRP, EGF, GCSF, IL-8, IL-17, IL-1RA, MPO, VEGF, Periostin) in serum collected from 30 women and 29 men before and after 40 min of swimming in a chlorinated pool. Exposure to DBPs was assessed by measuring bromodichloromethane, bromoform, chloroform, and dibromochloromethane in exhaled breath before and after swimming. Covariate data including information on physical activity was available through questionnaires and measurements. We assessed the association between indicators of swimming in a chlorinated pool and changes in serum immune marker concentrations using linear regression with bivariate normal distributions and adjusted for multiple comparisons by applying the Benjamini-Hochberg procedure.ResultsWe observed a significant decrease in serum concentrations of IL-8 (− 12.53%; q = 2.00e-03), CCL22 (− 7.28%; q = 4.00e-04), CCL11 (− 7.15%; q = 9.48e-02), CRP (− 7.06%; q = 4.68e-05), and CXCL10 (− 13.03%; q = 6.34e-14) and a significant increase in IL-1RA (20.16%; q = 4.18e-06) from before to after swimming. Associations with quantitative measurements of DBPs or physical activity were similar in direction and strength. Most of the observed associations became non-significant when we adjusted the effects of exposure to DBPs for physical activity or vice-versa.ConclusionsOur study indicates that swimming in a chlorinated pool induces perturbations of the immune response through acute alterations of patterns of cytokine and chemokine secretion. The observed effects could not be uniquely attributed to either exposure to DBP
Mostafavi N, Vlaanderen J, Portengen L, et al., 2017, Associations between genome-wide gene expression and ambient nitrogen oxides, Epidemiology, Vol: 28, Pages: 320-328, ISSN: 1044-3983
Background: We hypothesize that biological perturbations due to exposure to ambient air pollution are reflected in gene expression levels in peripheral blood mononuclear cells. Methods: We assessed the association between exposure to ambient air pollution and genome-wide gene expression levels in peripheral blood mononuclear cells collected from 550 healthy subjects participating in cohorts from Italy and Sweden. Annual air pollution estimates of nitrogen oxides (NOx) at time of blood collection (1990-2006) were available from the ESCAPE study. In addition to univariate analysis and two variable selection methods to investigate the association between expression and exposure to NOx, we applied gene set enrichment analysis to assess overlap between our most perturbed genes and gene sets hypothesized to be related to air pollution and cigarette smoking. Finally, we assessed associations between NOx and CpG island methylation at the identified genes. Results: Annual average NOx exposure in the Italian and Swedish cohorts was 94.2 and 6.7 μg/m3, respectively. Long-Term exposure to NOx was associated with seven probes in the Italian cohort and one probe in the Swedish (and combined) cohorts. For genes AHCYL2 and MTMR2, changes were also seen in the methylo me. Genes hypothesized to be downregulated due to cigarette smoking were enriched among the most strongly downregulated genes from our study. Conclusion: This study provides evidence of subtle changes in gene expression related to exposure to long-Term NOx. On a global level, the observed changes in the transcriptome may indicate similarities between air pollution and tobacco induced changes in the transcriptome.
Mostafavi N, Vlaanderen J, Portengen L, et al., 2017, Associations between genome-wide gene expression and ambient nitrogen Oxides, EPIDEMIOLOGY, Vol: 28, Pages: 320-328, ISSN: 1044-3983
Background: We hypothesize that biological perturbations due to exposure to ambient air pollution are reflected in gene expression levels in peripheral blood mononuclear cells.Methods: We assessed the association between exposure to ambient air pollution and genome-wide gene expression levels in peripheral blood mononuclear cells collected from 550 healthy subjects participating in cohorts from Italy and Sweden. Annual air pollution estimates of nitrogen oxides (NOx) at time of blood collection (1990–2006) were available from the ESCAPE study. In addition to univariate analysis and two variable selection methods to investigate the association between expression and exposure to NOx, we applied gene set enrichment analysis to assess overlap between our most perturbed genes and gene sets hypothesized to be related to air pollution and cigarette smoking. Finally, we assessed associations between NOx and CpG island methylation at the identified genes.Results: Annual average NOx exposure in the Italian and Swedish cohorts was 94.2 and 6.7 µg/m3, respectively. Long-term exposure to NOx was associated with seven probes in the Italian cohort and one probe in the Swedish (and combined) cohorts. For genes AHCYL2 and MTMR2, changes were also seen in the methylome. Genes hypothesized to be downregulated due to cigarette smoking were enriched among the most strongly downregulated genes from our study.Conclusion: This study provides evidence of subtle changes in gene expression related to exposure to long-term NOx. On a global level, the observed changes in the transcriptome may indicate similarities between air pollution and tobacco induced changes in the transcriptome.
Stringhini S, Carmeli C, Jokela M, et al., 2017, Socioeconomic status and the 25 x 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women, The Lancet, Vol: 389, Pages: 1229-1237, ISSN: 0140-6736
Background:In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors.Methods:We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors.Findings:During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attrib
Vlaanderen J, Leenders M, Chadeau-Hyam M, et al., 2017, Exploring the nature of prediagnostic blood transcriptome markers of chronic lymphocytic leukemia by assessing their overlap with the transcriptome at the clinical stage, BMC GENOMICS, Vol: 18, ISSN: 1471-2164
Background:We recently identified 700 genes whose expression levels were predictive of chronic lymphocytic leukemia (CLL) in a genome-wide gene expression analysis of prediagnostic blood from future cases and matched controls. We hypothesized that a large fraction of these markers were likely related to early disease manifestations. Here we aim to gain a better understanding of the natural history of the identified markers by comparing results from our prediagnostic analysis, the only prediagnostic analysis to date, to results obtained from a meta-analysis of a series of publically available transcriptomics profiles obtained in incident CLL cases and controls.Results:We observed considerable overlap between the results from our prediagnostic study and the clinical CLL signals (p-value for overlap Bonferroni significant markers 0.01; p-value for overlap nominal significant markers < 2.20e-16). We observed similar patterns with time to diagnosis and similar functional annotations for the markers that were identified in both settings compared to the markers that were only identified in the prediagnostic study. These results suggest that both gene sets operate in similar pathways.Conclusion:An overlap exists between expression levels of genes predictive of CLL identified in prediagnostic blood and expression levels of genes associated to CLL at the clinical stage. Our analysis provides insight in a set of genes for which expression levels can be used to follow the time-course of the disease; providing an opportunity to study CLL progression in more detail in future studies.
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