Environmental Risk Factors For Disease Research Articles

Breast Cancer.

Breast cancer kills hundreds of thousands of people every year. Rapid progress over the past two decades has increased our understanding of the genetic and environmental risk factors for disease. It has also shed light on drivers of tumor progression and the molecular landscape underpinning tumor heterogeneity, as well as the role of the microenvironment and the immune system. These strides forward should lead to more effective and tailored therapies for early- and late-stage patients.

Information Retrieval Using Machine Learning for Biomarker Curation in the Exposome-Explorer.

Objective: In 2016, the International Agency for Research on Cancer, part of the World Health Organization, released the Exposome-Explorer, the first database dedicated to biomarkers of exposure for environmental risk factors for diseases. The database contents resulted from a manual literature search that yielded over 8,500 citations, but only a small fraction of these publications were used in the final database. Manually curating a database is time-consuming and requires domain expertise to gather relevant data scattered throughout millions of articles. This work proposes a supervised machine learning pipeline to assist the manual literature retrieval process.Methods: The manually retrieved corpus of scientific publications used in the Exposome-Explorer was used as training and testing sets for the machine learning models (classifiers). Several parameters and algorithms were evaluated to predict an article’s relevance based on different datasets made of titles, abstracts and metadata.Results: The top performance classifier was built with the Logistic Regression algorithm using the title and abstract set, achieving an F2-score of 70.1%. Furthermore, we extracted 1,143 entities from these articles with a classifier trained for biomarker entity recognition. Of these, we manually validated 45 new candidate entries to the database.Conclusion: Our methodology reduced the number of articles to be manually screened by the database curators by nearly 90%, while only misclassifying 22.1% of the relevant articles. We expect that this methodology can also be applied to similar biomarkers datasets or be adapted to assist the manual curation process of similar chemical or disease databases.

Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk.

There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease.

Background:A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.Objectives:Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.Discussion:There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215

Abstract 1203: Identification of 22 novel loci associated with susceptibility to testicular germ cell tumors

Abstract Background: Testicular germ cell tumors (TGCT) are the most common cancers in young men of European ancestry aged 20 to 39 years. The incidence of TGCT has doubled over the past 20 years, yet no robust environmental risk factors for disease have been identified. Although TGCTs are treatable by surgery, radiation, and platinum-based chemotherapy, multiple long-term toxicities of treatment often occur impacting morbidity and mortality. Due to their high heritability and homogenous cell of origin, TGCTs are well suited to genome-wide association methods. The Testicular Cancer Consortium (TECAC) has brought together the largest genome-wise association study (GWAS) study of TGCT to date. Methods: We conducted a GWAS of 5,602 cases and 5,006 controls aggregated from 12 locations in the US and Europe. Logistic regression models adjusted for study center and genomic ancestry. Genotypes were imputed against the Human Haplotype Reference Consortium. Meta-analysis was performed to combine GWAS results with summary statistics from five previously published TGCT studies, UK Biobank, deCODE Genetics, and an independent set of cases and controls, for a total of 10,156 cases and 179,683 controls. Biologic function of loci was explored using PAINTOR annotated with ATAC-seq data of four TGCT cell lines, SPATIAL-seq data of the NTERA2 TGCT cell-line, and publicly available data from ENCODE. Polygenic risk scores (PRS) were computed using subject-level data from the 5,602 cases and 5,006 controls, and effect sizes of the novel hits derived from the meta-analysis. Results: 22 novel and 45 previously reported loci associated with TGCT surpassed genome-wide significance (p < 5e-08). We discovered additional markers in known susceptibility loci and identified novel regions associated with germ cell development and sex determination (e.g., BCL11, AR), immune function (e.g., TNXB, ITIH5), and for the first time identified genes associated with kinetochore activity (e.g., PPP2R5A, ANAPC2). All identified risk SNPs to date account for 42.3% of heritability. Men in the highest 1% of PRS had over a 15-fold increased risk of TGCT compared to those at the median PRS, and PRS overall had an AUC of 74.29%. Conclusions: Results from our TGCT meta-analysis continue to provide insights into biological pathways affecting germ cell specification, expression, and epigenetic reprogramming, and sex determination. Our results also uniquely place TGCT as the only cancer type in which inherited variants implicating kinetochore activity, critical for chromosomal segregation, have been identified. Citation Format: John Pluta, Louisa Pyle, Timothy Bishop, Javier Benitez, Victoria Cortessis, Alberto Ferlin, Jourik Gietema, Mark Greene, Thomas Grotmol, Ramneek Gupta, Robert Hamilton, Michelle Hildebrandt, Lambertus Kiemeney, Davor Lessel, Thorunn Rafnar, Lorenzo Richiardi, Rolf Skotheim, Clare Turnbull, Fredrik Wiklund, Tongzhang Zheng, Ewa Rajpert- De Meyts, Stephen Schwartz, Katherine McGlynn, Peter Kanetsky, Katherine Nathanson. Identification of 22 novel loci associated with susceptibility to testicular germ cell tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1203.

Host Genetic and Gut Microbial Signatures in Familial Inflammatory Bowel Disease.

INTRODUCTION:The family history of inflammatory bowel disease (IBD) has been strongly associated with risk of developing IBD. This study aimed to identify the host genetic and gut microbial signatures in familial IBD.METHODS:Genetic analyses using genome-wide single nucleotide polymorphism genotyping and whole exome sequencing were performed to calculate weighted genetic risk scores from known IBD-associated common variants and to identify rare deleterious protein-altering variants specific to patients with familial IBD in 8 Korean families that each included more than 2 affected first-degree relatives (FDRs) and their unaffected FDR(s). In parallel, gut microbial community was analyzed by 16S rRNA sequencing of stools from the sample individuals.RESULTS:The risk of familial IBD was not well explained by the genetic burden from common IBD-risk variants, suggesting the presence of family-shared genetic and environmental disease-risk factors. We identified 17 genes (AC113554.1, ACE, AKAP17A, AKAP9, ANK2, ASB16, ASIC3, DNPH1, DUS3L, FAM200A, FZD10, LAMA5, NUTM2F, PKN1, PRR26, WDR66, and ZC3H4) that each contained rare, potentially deleterious variants transmitted to the affected FDRs in multiple families. In addition, metagenomic analyses revealed significantly different diversity of gut microbiota and identified a number of differentially abundant taxa in affected FDRs, highlighting 22 novel familial disease-associated taxa with large abundance changes and the previously reported gut dysbiosis including low alpha diversity in IBD and 16 known IBD-specific taxa.DISCUSSION:This study identified familial IBD-associated rare deleterious variants and gut microbial dysbiosis in familial IBD.

The impact of population mobility on estimates of environmental exposure effects in a case‐control study

In many studies of environmental risk factors for disease, researchers use the location at diagnosis as a geographic reference for environmental exposures. However, many environmental pollutants change continuously over space and time. The dynamic characteristics of these pollutants coupled with population mobility in the United States suggest that for diseases with long latencies like cancer, historic exposures may be more relevant than exposure at the time of diagnosis. In this article, we evaluated to what extent the commonly used assumption of no population mobility results in increased bias in the estimates of the relationship between environmental exposures and long-latency health outcomes disease in a case-control study. We conducted a simulation study using the residential histories of a random sample from the National Institutes of Health-AARP (formerly American Association of Retired Persons) Diet and Health Study. We simulated case-control status based on subject exposure and true exposure effects that varied temporally. We compared estimates from models using only subject location at diagnosis to estimates where subjects were assumed to be mobile. Ignoring population mobility resulted in underestimates of subject exposure, with largest deviations observed at time points further away from study enrollment. In general, the effect of population mobility on the bias of the estimates of the relationship between the exposure and the outcome was more prominent with exposures that showed substantial spatial and temporal variability. Based on our results, we recommend using residential histories when environmental exposures and disease latencies span a long enough time period that mobility is important.

Acute Effects on Blood Pressure Following Controlled Exposure to Cookstove Air Pollution in the STOVES Study.

BackgroundExposure to air pollution from solid fuel used in residential cookstoves is considered a leading environmental risk factor for disease globally, but evidence for this relationship is largely extrapolated from literature on smoking, secondhand smoke, and ambient fine particulate matter (PM 2.5).Methods and ResultsWe conducted a controlled human‐exposure study (STOVES [the Subclinical Tests on Volunteers Exposed to Smoke] Study) to investigate acute responses in blood pressure following exposure to air pollution emissions from cookstove technologies. Forty‐eight healthy adults received 2‐hour exposures to 5 cookstove treatments (three stone fire, rocket elbow, fan rocket elbow, gasifier, and liquefied petroleum gas), spanning PM 2.5 concentrations from 10 to 500 μg/m3, and a filtered air control (0 μg/m3). Thirty minutes after exposure, systolic pressure was lower for the three stone fire treatment (500 μg/m3 PM 2.5) compared with the control (−2.3 mm Hg; 95% CI, −4.5 to −0.1) and suggestively lower for the gasifier (35 μg/m3 PM 2.5; −1.8 mm Hg; 95% CI, −4.0 to 0.4). No differences were observed at 3 hours after exposure; however, at 24 hours after exposure, mean systolic pressure was 2 to 3 mm Hg higher for all treatments compared with control except for the rocket elbow stove. No differences were observed in diastolic pressure for any time point or treatment.ConclusionsShort‐term exposure to air pollution from cookstoves can elicit an increase in systolic pressure within 24 hours. This response occurred across a range of stove types and PM 2.5 concentrations, raising concern that even low‐level exposures to cookstove air pollution may pose adverse cardiovascular effects.

Evaluating the predictability of medical conditions from social media posts.

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.

Combustion Particle-Induced Changes in Calcium Homeostasis: A Contributing Factor to Vascular Disease?

Air pollution is the leading environmental risk factor for disease and premature death in the world. This is mainly due to exposure to urban air particle matter (PM), in particular, fine and ultrafine combustion-derived particles (CDP) from traffic-related air pollution. PM and CDP, including particles from diesel exhaust (DEP), and cigarette smoke have been linked to various cardiovascular diseases (CVDs) including atherosclerosis, but the underlying cellular mechanisms remain unclear. Moreover, CDP typically consist of carbon cores with a complex mixture of organic chemicals such as polycyclic aromatic hydrocarbons (PAHs) adhered. The relative contribution of the carbon core and adhered soluble components to cardiovascular effects of CDP is still a matter of discussion. In the present review, we summarize evidence showing that CDP affects intracellular calcium regulation, and argue that CDP-induced impairment of normal calcium control may be a critical cellular event through which CDP exposure contributes to development or exacerbation of cardiovascular disease. Furthermore, we highlight in vitro research suggesting that adhered organic chemicals such as PAHs may be key drivers of these responses. CDP, extractable organic material from CDP (CDP-EOM), and PAHs may increase intracellular calcium levels by interacting with calcium channels like transient receptor potential (TRP) channels, and receptors such as G protein-coupled receptors (GPCR; e.g., beta-adrenergic receptors [βAR] and protease-activated receptor 2 [PAR-2]) and the aryl hydrocarbon receptor (AhR). Clarifying a possible role of calcium signaling and mechanisms involved may increase our understanding of how air pollution contributes to CVD.

Mitigation of Particulate Matter-Induced Inflammation and Vasoactivity in Human Vascular Endothelial Cells by Omega-3 Polyunsaturated Fatty Acids.

Airborne particulate matter (PM) exposure remains the leading environmental risk factor for disease globally. Interventions to mitigate the adverse effects of PM are required, since there is no discernible threshold for its effects, and exposure reduction approaches are limited. The mitigation of PM (specifically diesel exhaust particles (DEP))-induced release of pro-inflammatory cytokines interleukin-6 (IL-6) and interleukin-8 (IL-8) and vasoconstrictor endothelin-1 (ET-1) after 24 and 48 h of exposure by pre-treatment with individual pure, combined pure, and an oil formulation of two fish oil omega-3 polyunsaturated fatty acids (ω-3 PUFAs), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) were all tested at an equivalent concentration of 100 µM in vitro in human umbilical vein endothelial cells. The PUFAs and fish oil formulation completely mitigated or diminished the DEP-induced release of IL-6, IL-8, and ET-1 by 14–78%. DHA was more effective in reducing the levels of the DEP-induced release of the cytokines, especially IL-6 after 48 h of DEP exposure in comparison to EPA (p < 0.05), whereas EPA seemed to be more potent in reducing ET-1 levels. The potential of fish ω-3 PUFAs to mitigate PM-induced inflammation and vasoactivity was demonstrated by this study.

Elucidating the environmental risk factors for rheumatic diseases: An umbrella review of meta‐analyses

Although rheumatic diseases constitute a leading cause of disability, the environmental risk factors for these diseases are not clarified. In the present study, we aim to systematically appraise the epidemiological credibility of the environmental risk factors for rheumatic diseases. We systematically searched PubMed to capture meta-analyses of observational studies on environmental risk factors for the most prevalent rheumatic diseases. For each association, we estimated the summary effect size estimate, the 95% confidence and prediction intervals, and the I2 metric. We further examined the presence of small-study effects and excess significance bias. Overall, we identified 30 eligible papers describing 42 associations. Thirty-three associations were statistically significant at P<0.05, whereas 13 of them were statistically significant at P<1×10-6 . Thirty-two associations had large or very large between-study heterogeneity. In 12 associations, evidence of small-study effects and/or excess significance bias was found. Six risk factors (nine associations) presented convincing or highly suggestive evidence of association: smoking and pack-years of smoking for rheumatoid arthritis; BMI (per 5kg/m2 increase) for gout and hip osteoarthritis; alcohol consumption for gout; BMI (overweight vs lean, obese vs lean), knee injury and participation in heavy work for knee osteoarthritis. Our umbrella review indicated that a narrow range of risk factors has been examined for rheumatic diseases. Current evidence strongly supports that smoking, obesity, alcohol consumption, knee injury, and work activities are associated with risk for at least one rheumatic disease.

From Shortage to Surge: A Developmental Switch in Hippocampal–Prefrontal Coupling in a Gene–Environment Model of Neuropsychiatric Disorders

Cognitive deficits represent a major burden of neuropsychiatric disorders and result in part from abnormal communication within hippocampal–prefrontal circuits. While it has been hypothesized that this network dysfunction arises during development, long before the first clinical symptoms, experimental evidence is still missing. Here, we show that pre-juvenile mice mimicking genetic and environmental risk factors of disease (dual-hit GE mice) have poorer recognition memory that correlates with augmented coupling by synchrony and stronger directed interactions between prefrontal cortex and hippocampus. The network dysfunction emerges already during neonatal development, yet it initially consists in a diminished hippocampal theta drive and consequently, a weaker and disorganized entrainment of local prefrontal circuits in discontinuous oscillatory activity in dual-hit GE mice when compared with controls. Thus, impaired maturation of functional communication within hippocampal–prefrontal networks switching from hypo- to hyper-coupling may represent a mechanism underlying the pathophysiology of cognitive deficits in neuropsychiatric disorders.

The exposome concept: a challenge and a potential driver for environmental health research.

The exposome concept was defined in 2005 as encompassing all environmental exposures from conception onwards, as a new strategy to evidence environmental disease risk factors. Although very appealing, the exposome concept is challenging in many respects. In terms of assessment, several hundreds of time-varying exposures need to be considered, but increasing the number of exposures assessed should not be done at the cost of increased exposure misclassification. Accurately assessing the exposome currently requires numerous measurements, which rely on different technologies; resulting in an expensive set of protocols. In the future, high-throughput 'omics technologies may be a promising technique to integrate a wide range of exposures from a small numbers of biological matrices. Assessing the association between many exposures and health raises statistical challenges. Due to the correlation structure of the exposome, existing statistical methods cannot fully and efficiently untangle the exposures truly affecting the health outcome from correlated exposures. Other statistical challenges relate to accounting for exposure misclassification or identifying synergistic effects between exposures. On-going exposome projects are trying to overcome technical and statistical challenges. From a public health perspective, a better understanding of the environmental risk factors should open the way to improved prevention strategies.

Epigenetic drift, epigenetic clocks and cancer risk.

It is well-established that the DNA methylation landscape of normal cells undergoes a gradual modification with age, termed as 'epigenetic drift'. Here, we review the current state of knowledge of epigenetic drift and its potential role in cancer etiology. We propose a new terminology to help distinguish the different components of epigenetic drift, with the aim of clarifying the role of the epigenetic clock, mitotic clocks and active changes, which accumulate in response to environmental disease risk factors. We further highlight the growing evidence that epigenetic changes associated with cancer risk factors may play an important causal role in cancer development, and that monitoring these molecular changes in normal cells may offer novel risk prediction and disease prevention strategies.

Genetic Epidemiology and Public Health: The Evolution From Theory to Technology.

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done.

The role of epigenetics in genetic and environmental epidemiology.

Epidemiology is the branch of science that investigates the causes and distribution of disease in populations in order to provide preventative measures and promote human health. The fields of genetic and environmental epidemiology primarily seek to identify genetic and environmental risk factors for disease, respectively. Epigenetics is emerging as an important piece of molecular data to include in these studies because it can provide mechanistic insights into genetic and environmental risk factors for disease, identify potential intervention targets, provide biomarkers of exposure, illuminate gene-environment interactions and help localize disease-relevant genomic regions. Here, we describe the importance of including epigenetics in genetic and environmental epidemiology studies, provide a conceptual framework when considering epigenetic data in population-based studies and touch upon the many challenges that lie ahead.

Shared burden: public health impact of nine environmental pollutants in Europe.

Airborne particulate matter may be the most significant environmental risk factor for disease across much of Western Europe, according to a new analysis published this month in EHP.1 The study compares the disease burden associated with nine environmental exposures across six European countries—Finland, Germany, France, Italy, Belgium, and the Netherlands. Few studies have sought to rank environmental risk factors on an international level by their public health impact—a critical aspect for developing effective policy measures, according to the team led by Otto Hanninen, a pollution exposure researcher at Finland’s National Institute for Health and Welfare. “Comparability across countries is very useful in promoting abatement that requires international policies—such as those pertaining to long-range transported particulate matter or vehicle emissions,” Hanninen says. The research team evaluated the potential public health impact for each of the nine exposures by estimating years of life lost due to death and disability. For each exposure, they selected one or more health outcomes that had been causally associated with it—for instance, cardiac death and chronic bronchitis associated with fine particulate matter (PM2.5), sleep disturbance associated with traffic noise, onset of asthma associated with secondhand smoke exposure, and cancer resulting from exposure to dioxins and dioxin-like polychlorinated biphenyls (PCBs).2 Other environmental risk factors assessed included benzene, formaldehyde, lead, ozone, and radon. Hanninen and colleagues concluded that about 3–7% of the total annual disease burden across all six countries could be associated with these nine environmental exposures. PM2.5 was the environmental risk factor with the highest public health significance, accounting for 68% of the total estimated health impacts. Secondhand smoke and traffic noise tied for second place, each estimated to account for 8% of the environmental disease burden, while radon accounted for an estimated 7%. Homes bordering Ruhrschnellweg, a highway in Essen, Germany. It’s not surprising that airborne particles dominated the disease burden, says C. Arden Pope III, an environmental economist at Brigham Young University who was not involved in the current study. “Everyone is exposed to particulates. These findings reflect the ubiquity of that exposure, even in developed countries, compared to other risk factors such as radon or even secondhand smoke, where exposures may be more limited,” he says. The World Health Organization (WHO) has estimated that outdoor air pollution was responsible for 3.7 million deaths in 2012—roughly 1 in 15 deaths globally.3 Most of those deaths were in low- and middle-income countries that often struggle with high levels of ambient air pollution. However, the current analysis suggested PM2.5 was the dominant risk factor even in Finland, a clean and affluent country by WHO standards, and the country with the lowest air pollution impacts of the six studied. “We found PM to be roughly twice as harmful as fatal traffic accidents in Finland,” Hanninen says. Also surprising, he says, was the evidence of a high impact of traffic noise, which the authors evaluated as a risk factor for severe sleep disturbance and ischemic heart events. The researchers estimated that traffic noise cost Europeans 400–1,500 years of life per million people due to death and disability. “We actually expect that estimate for noise to be too low,” Hanninen says, because the researchers did not include all known health end points for noise exposure, such as hypertension and heart disease.4 The researchers were able to obtain exposure data collected with standardized methods in all countries only for secondhand smoke, PM2.5, and ozone. They excluded suspected health effects that weren’t sufficiently researched or monitored. For instance, they did not account for developmental impacts of endocrine disruption, which have been associated with dioxin and PCB exposures.5 “The study is ambitious in its attempt to integrate the literature and apply it to an area of real concern,” Pope says. “The limitations of the paper reflect the limitations of our own knowledge.” The next important step, says Hanninen, is to identify preventable risks and then analyze potential policies for feasibility and cost effectiveness.

Pooling samples for “top-down” molecular exposomics research: the methodology

BackgroundExposomics is the cutting-edge concept of screening the environmental risk factors for disease. In the novel “top-down” approach, we estimate the molecular exposome by measuring all body fluid analytes in a case-controlled study. However, to detect diverse pollutants, a sufficient sample size and multiple analytical methods are required. This may lead to dramatically increased costs and research workload.MethodsTo help reduce complexity, we suggest a sample pooling strategy along with a scheme for combining both general unknown or multi-targeted screening with targeted analysis. The sample pooling method was tested using computer simulations.ResultsBy comprehensively analysis of pooled samples, it is possible to identify environmental risk factors. Factors are initially screened in the pooled case and control population samples, then in the randomized grouped and pooled case and control subpopulation samples. In the sample grouping, five or more pools were suggested for groups having 30 individuals per pool.ConclusionsThis study suggests that sample pooling is a useful strategy for exposomics research, which provides a hypothesis-free method for pollutant risk screening.

Age-associated epigenetic drift: implications, and a case of epigenetic thrift?

It is now well established that the genomic landscape of DNA methylation (DNAm) gets altered as a function of age, a process we here call ‘epigenetic drift’. The biological, functional, clinical and evolutionary significance of this epigenetic drift, however, remains unclear. We here provide a brief review of epigenetic drift, focusing on the potential implications for ageing, stem cell biology and disease risk prediction. It has been demonstrated that epigenetic drift affects most of the genome, suggesting a global deregulation of DNAm patterns with age. A component of this drift is tissue-specific, allowing remarkably accurate age-predictive models to be constructed. Another component is tissue-independent, targeting stem cell differentiation pathways and affecting stem cells, which may explain the observed decline of stem cell function with age. Age-associated increases in DNAm target developmental genes, overlapping those associated with environmental disease risk factors and with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated age DNAm signatures. Epigenetic drift is also influenced by genetic factors. Thus, drift emerges as a promising biomarker for premature or biological ageing, and could potentially be used in geriatrics for disease risk prediction. Finally, we propose, in the context of human evolution, that epigenetic drift may represent a case of epigenetic thrift, or bet-hedging. In summary, this review demonstrates the growing importance of the ‘ageing epigenome’, with potentially far-reaching implications for understanding the effect of age on stem cell function and differentiation, as well as for disease prevention.