Researching genetic versus nongenetic determinants of disease: a comparison and proposed unification

Whole-exome sequencing of individuals from an isolated population under extreme conditions implicates rare risk variants of schizophrenia

Schizophrenia (SCZ), which affects approximately 1% of the world's population, is a global public health concern. It is generally considered that the interplay between genes and the environment is important in the onset and/or development of SCZ. Although several whole-exome sequencing studies have revealed rare risk variants of SCZ, no rare coding variants have been strongly replicated. Assessing isolated populations under extreme conditions might lead to the discovery of variants with a recent origin, which are more likely to have a higher frequency than chance to reflect gene-environment interactions. Following this approach, we examined a unique cohort of Tibetans living at an average altitude above 4500 meters. Whole-exome sequencing of 47 SCZ cases and 53 controls revealed 275 potential novel risk variants and two known variants (12:46244485: A/G and 22:18905934: A/G) associated with SCZ that were found in existing databases. Only one gene (C5orf42) in the gene-based statistics surpassed the exome-wide significance in the cohort. Metascape enrichment analysis suggested that novel risk genes were strongly enriched in pathways relevant to hypoxia, neurodevelopment, and neurotransmission. Additionally, 47 new risk genes were followed up in Han sample of 279 patients with SCZ and 95 controls, only BAI2 variant appearing in one case. Our findings suggest that SCZ patients living at high altitudes may have a unique risk gene signature, which may provide additional information on the underlying biology of SCZ, which can be exploited to identify individuals at greater risk of exposure to hypoxia.

Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

BACKGROUND

Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses.

METHODS

We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10-14 years before the COVID-19 time periods.

RESULTS

Here we show, for example, "participant having son and/or daughter in household" was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively).

CONCLUSIONS

Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.

Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring

The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.

Software Application Profile: PXStools—an R package of tools for conducting exposure-wide analysis and deriving polyexposure risk scores

Motivation

Investigating the aggregate burden of environmental factors on human traits and diseases requires consideration of the entire ‘exposome’. However, current studies primarily focus on a single exposure or a handful of exposures at a time, without considering how multiple exposures may be simultaneously associated with each other or with the phenotype. Polyexposure risk scores (PXS) have been shown to predict and stratify risk for disease beyond or complementary to genetic and clinical risk. PXStools provides an analytical package to standardize exposome-wide studies as well as derive and validate polyexposure risk scores.

Implementation

PXStools is a package for the statistical R.

General features

The package allows users to (i) conduct exposure-wide association studies; (ii) derive and validate polyexposure risk scores with and without accounting for exposure interactions, using new approaches in regression modelling (hierarchical lasso);(iii) compare goodness of fit between models with and without multiple exposures; and (iv) visualize results. A data frame with a unique identifier, phenotype and exposures is needed as the only input. Various customizations are allowed including data preprocessing (removing missing or unwanted responses), covariates adjustment, multiple hypothesis correction and model specification (linear, logistic, survival).

Availability

The PXStools source code is freely available on Github at [https://github.com/yixuanh/PXStools ].

Nongenetic Factors Associated With Psychotic Experiences Among UK Biobank Participants: Exposome-Wide Analysis and Mendelian Randomization Analysis

Importance

Although hypothesis-driven research has identified several factors associated with psychosis, this one-exposure-to-one-outcome approach fails to embrace the multiplicity of exposures. Systematic approaches, similar to agnostic genome-wide analyses, are needed to identify genuine signals.

Objective

To systematically investigate nongenetic correlates of psychotic experiences through data-driven agnostic analyses and genetically informed approaches to evaluate associations.

Design, Setting, Participants

This cohort study analyzed data from the UK Biobank Mental Health Survey from January 1 to June 1, 2021. An exposome-wide association study was performed in 2 equal-sized split discovery and replication data sets. Variables associated with psychotic experiences in the exposome-wide analysis were tested in a multivariable model. For the variables associated with psychotic experiences in the final multivariable model, the single-nucleotide variant-based heritability and genetic overlap with psychotic experiences using linkage disequilibrium score regression were estimated, and mendelian randomization (MR) approaches were applied to test potential causality. The significant associations observed in 1-sample MR analyses were further tested in multiple sensitivity tests, including collider-correction MR, 2-sample MR, and multivariable MR analyses.

Exposures

After quality control based on a priori criteria, 247 environmental, lifestyle, behavioral, and economic variables.

Main Outcomes and Measures

Psychotic experiences.

Results

The study included 155 247 participants (87 896 [57%] female; mean [SD] age, 55.94 [7.74] years). In the discovery data set, 162 variables (66%) were associated with psychotic experiences. Of these, 148 (91%) were replicated. The multivariable analysis identified 36 variables that were associated with psychotic experiences. Of these, 28 had significant genetic overlap with psychotic experiences. One-sample MR analyses revealed forward associations with 3 variables and reverse associations with 3. Forward associations with ever having experienced sexual assault and pleiotropy of risk-taking behavior and reverse associations without pleiotropy of experiencing a physically violent crime as well as cannabis use and the reverse association with pleiotropy of worrying too long after embarrassment were confirmed in sensitivity tests. Thus, associations with psychotic experiences were found with both well-studied and unexplored multiple correlated variables. For several variables, the direction of the association was reversed in the final multivariable and MR analyses.

Conclusions and Relevance

The findings of this study underscore the need for systematic approaches and triangulation of evidence to build a knowledge base from ever-growing observational data to guide population-level prevention strategies for psychosis.

Schizophrenia Polygenic Risk and Experiences of Childhood Adversity: A Systematic Review and Meta-analysis

BACKGROUND AND HYPOTHESIS

Schizophrenia has been robustly associated with multiple genetic and environmental risk factors. Childhood adversity is one of the most widely replicated environmental risk factors for schizophrenia, but it is unclear if schizophrenia genetic risk alleles contribute to this association.

STUDY DESIGN

In this systematic review and meta-analysis, we assessed the evidence for gene-environment correlation (genes influence likelihood of environmental exposure) between schizophrenia polygenic risk score (PRS) and reported childhood adversity. We also assessed the evidence for a gene-environment interaction (genes influence sensitivity to environmental exposure) in relation to the outcome of schizophrenia and/or psychosis. This study was registered on PROSPERO (CRD42020182812). Following PRISMA guidelines, a search for relevant literature was conducted using Cochrane, MEDLINE, PsycINFO, Web of Science, and Scopus databases until February 2022. All studies that examined the association between schizophrenia PRS and childhood adversity were included.

STUDY RESULTS

Seventeen of 650 identified studies met the inclusion criteria and were assessed against the Newcastle-Ottawa Scale for quality. The meta-analysis found evidence for gene-environment correlation between schizophrenia PRS and childhood adversity (r = .02; 95% CI = 0.01, 0.03; P = .001), but the effect was small and therefore likely to explain only a small proportion of the association between childhood adversity and psychosis. The 4 studies that investigated a gene-environment interaction between schizophrenia PRS and childhood adversity in increasing risk of psychosis reported inconsistent results.

CONCLUSIONS

These findings suggest that a gene-environment correlation could explain a small proportion of the relationship between reported childhood adversity and psychosis.

Expanding the environmental scope: an environment-wide association study for mental well-being

BACKGROUND

Identifying modifiable factors associated with well-being is of increased interest for public policy guidance. Developments in record linkage make it possible to identify what contributes to well-being from a myriad of factors. To this end, we link two large-scale data resources; the Geoscience and Health Cohort Consortium, a collection of geo-data, and the Netherlands Twin Register, which holds population-based well-being data.

OBJECTIVE

We perform an Environment-Wide Association Study (EnWAS), where we examine 139 neighbourhood-level environmental exposures in relation to well-being.

METHODS

First, we performed a generalized estimation equation regression (N = 11,975) to test for the effects of environmental exposures on well-being. Second, to account for multicollinearity amongst exposures, we performed principal component regression. Finally, using a genetically informative design, we examined whether environmental exposure is driven by genetic predisposition for well-being.

RESULTS

We identified 21 environmental factors that were associated with well-being in the domains: housing stock, income, core neighbourhood characteristics, livability, and socioeconomic status. Of these associations, socioeconomic status and safety are indicated as the most important factors to explain differences in well-being. No evidence of gene-environment correlation was found.

SIGNIFICANCE

These observed associations, especially neighbourhood safety, could be informative for policy makers and provide public policy guidance to improve well-being. Our results show that linking databases is a fruitful exercise to identify determinants of mental health that would remain unknown by a more unilateral approach.

Gene-Environment Interactions in Schizophrenia: A Literature Review

Schizophrenia is a devastating mental illness with a strong genetic component that is the subject of extensive research. Despite the high heritability, it is well recognized that non-genetic factors such as certain infections, cannabis use, psychosocial stress, childhood adversity, urban environment, and immigrant status also play a role. Whenever genetic and non-genetic factors co-exist, interaction between the two is likely. This means that certain exposures would only be of consequence given a specific genetic makeup. Here, we provide a brief review of studies reporting evidence of such interactions, exploring genes and variants that moderate the effect of the environment to increase risk of developing psychosis. Discovering these interactions is crucial to our understanding of the pathogenesis of complex disorders. It can help in identifying individuals at high risk, in developing individualized treatments and prevention plans, and can influence clinical management.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery

BACKGROUND

Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.

OBJECTIVES

We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.

DISCUSSION

The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.

CONCLUSIONS

Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.

Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

OBJECTIVE

To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors.

RESEARCH DESIGN AND METHODS

We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA_1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively.

RESULTS

In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively.

CONCLUSIONS

For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.

Determinants of physical, mental and social well-being: a longitudinal environment-wide association study

BACKGROUND

Although the World Health Organization (WHO) has defined health as a state of physical, mental and social well-being, public health strategies have primarily focused on one domain of well-being. We sought to systematically and simultaneously identify and validate associations of behavioural patterns, psychosocial factors, mental and physical health conditions, access to and utilization of health care and anthropometrics with physical, mental and social well-being.

METHODS

We conducted a longitudinal environment-wide association study (EWAS) with a training and testing set approach, accounting for multiple testing using a false discovery rate control. We used multivariate multilevel regression to examine the association of each exposure at wave 1 with the three outcomes at wave 2 in the Hong Kong FAMILY Cohort (n = 10 484).

RESULTS

Out of 194 exposures, we identified and validated 14, 5 and 5 exposures that were individually associated with physical, mental and social well-being, respectively. We discovered three factors, namely depressive symptoms, life satisfaction and happiness, that were simultaneously associated with the three domains that define health.

CONCLUSIONS

These associations, if verified to be causal, could become intervention targets to holistically improve population health. Our findings provide empirical support for placing mental health at the forefront of the public health agenda, and also support recent calls to use life satisfaction and happiness to guide public policy.

Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.

A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease

Environmental factors, and in particular diet, are known to play a key role in the development of Coronary Heart Disease. Many of these factors were unveiled by detailed nutritional epidemiology studies, focusing on the role of a single nutrient or food at a time. Here, we apply an Environment-Wide Association Study approach to Nurses' Health Study data to explore comprehensively and agnostically the association of 257 nutrients and 117 foods with coronary heart disease risk (acute myocardial infarction and fatal coronary heart disease). After accounting for multiple testing, we identify 16 food items and 37 nutrients that show statistically significant association - while adjusting for potential confounding and control variables such as physical activity, smoking, calorie intake, and medication use - among which 38 associations were validated in Nurses' Health Study II. Our implementation of Environment-Wide Association Study successfully reproduces prior knowledge of diet-coronary heart disease associations in the epidemiological literature, and helps us detect new associations that were only marginally studied, opening potential avenues for further extensive experimental validation. We also show that Environment-Wide Association Study allows us to identify a bipartite food-nutrient network, highlighting which foods drive the associations of specific nutrients with coronary heart disease risk.

Impact of Gene-Environment Interactions on Cancer Development

Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.

Using universal kriging to improve neighborhood physical disorder measurement

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across 4 US cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 and 6.8% lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.

The exposome and health: Where chemistry meets biology

Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.

Limitations and Misinterpretations of E-Values for Sensitivity Analyses of Observational Studies

The E-value was recently introduced on the basis of earlier work as "the minimum strength of association…that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates." E-values have been proposed for wide application in observational studies evaluating causality. However, they have limitations and are prone to misinterpretation. E-values have a monotonic, almost linear relationship with effect estimates and thus offer no additional information beyond what effect estimates can convey. Whereas effect estimates are based on real data, E-values may make unrealistic assumptions. No general rule can exist about what is a "small enough" E-value, and users of the biomedical literature are not familiar with how to interpret a range of E-values. Problems arise for any measure dependent on effect estimates and their CIs-for example, bias due to selective reporting and dependence on choice of exposure contrast and level of confidence. The automation of E-values may give an excuse not to think seriously about confounding. Moreover, biases other than confounding may still undermine results. Instead of misused or misinterpreted E-values, the authors recommend judicious use of existing methods for sensitivity analyses with careful assumptions; systematic assessments of whether and how known confounders have been handled, along with consideration of their prevalence and magnitude; thorough discussion of the potential for unknown confounders considering the study design and field of application; and explicit caution in making causal claims from observational studies.

The Complexities of Evaluating the Exposome in Psychiatry: A Data-Driven Illustration of Challenges and Some Propositions for Amendments

Identifying modifiable factors through environmental research may improve mental health outcomes. However, several challenges need to be addressed to optimize the chances of success. By analyzing the Netherlands Mental Health Survey and Incidence Study-2 data, we provide a data-driven illustration of how closely connected the exposures and the mental health outcomes are and how model and variable specifications produce "vibration of effects" (variation of results under multiple different model specifications). Interdependence of exposures is the rule rather than the exception. Therefore, exposure-wide systematic approaches are needed to separate genuine strong signals from selective reporting and dissect sources of heterogeneity. Pre-registration of protocols and analytical plans is still uncommon in environmental research. Different studies often present very different models, including different variables, despite examining the same outcome, even if consistent sets of variables and definitions are available. For datasets that are already collected (and often already analyzed), the exploratory nature of the work should be disclosed. Exploratory analysis should be separated from prospective confirmatory research with truly pre-specified analysis plans. In the era of big-data, where very low P values for trivial effects are detected, several safeguards may be considered to improve inferences, eg, lowering P-value thresholds, prioritizing effect sizes over significance, analyzing pre-specified falsification endpoints, and embracing alternative approaches like false discovery rates and Bayesian methods. Any claims for causality should be cautious and preferably avoided, until intervention effects have been validated. We hope the propositions for amendments presented here may help with meeting these pressing challenges.

Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment

Many factors affect the variation in the exposome. We examined the influence of shared household and partner's sex in relation to the variation in 128 endocrine disrupting chemical (EDC) exposures among couples. In a cohort comprising of 501 couples trying for pregnancy, we measured 128 (13 chemical classes) persistent and nonpersistent EDCs and estimated 1) sex-specific differences; 2) variance explained by shared household; and 3) Spearman's rank correlation coefficients ( r_s) for females, males, and couples' exposures. Sex was correlated with 8 EDCs including per- and polyfluoroalkyl substances (PFASs) ( p < 0.05). Shared household explained 43% and 41% of the total variance for PFASs and blood metals, respectively, but less than 20% for the remaining 11 EDC classes. Coexposure patterns of the exposome were similar between females and males, with within-class r_s higher for persistent than for nonpersistent chemicals. Median r_ss of polybrominated compounds and urine metalloids were 0.45 and 0.09, respectively, for females (0.41 and 0.08 for males; 0.21 and 0.04 for couples). Our findings suggest that individual, rather than shared environment, could be a major factor influencing the covariation of the exposome. Understanding the correlations of exposures has important analytical and sampling implications for exposomics research.

Perspective: Limiting Dependence on Nonrandomized Studies and Improving Randomized Trials in Human Nutrition Research: Why and How

A large majority of human nutrition research uses nonrandomized observational designs, but this has led to little reliable progress. This is mostly due to many epistemologic problems, the most important of which are as follows: difficulty detecting small (or even tiny) effect sizes reliably for nutritional risk factors and nutrition-related interventions; difficulty properly accounting for massive confounding among many nutrients, clinical outcomes, and other variables; difficulty measuring diet accurately; and suboptimal research reporting. Tiny effect sizes and massive confounding are largely unfixable problems that narrowly confine the scenarios in which nonrandomized observational research is useful. Although nonrandomized studies and randomized trials have different priorities (assessment of long-term causality compared with assessment of treatment effects), the odds for obtaining reliable information with the former are limited. Randomized study designs should therefore largely replace nonrandomized studies in human nutrition research going forward. To achieve this, many of the limitations that have traditionally plagued most randomized trials in nutrition, such as small sample size, short length of follow-up, high cost, and selective reporting, among others, must be overcome. Pivotal megatrials with tens of thousands of participants and lifelong follow-up are possible in nutrition science with proper streamlining of operational costs. Fixable problems that have undermined observational research, such as dietary measurement error and selective reporting, need to be addressed in randomized trials. For focused questions in which dietary adherence is important to maximize, trials with direct observation of participants in experimental in-house settings may offer clean answers on short-term metabolic outcomes. Other study designs of randomized trials to consider in nutrition include registry-based designs and "N-of-1" designs. Mendelian randomization designs may also offer some more reliable leads for testing interventions in trials. Collectively, an improved randomized agenda may clarify many things in nutrition science that might never be answered credibly with nonrandomized observational designs.

Another Round of "Clue" to Uncover the Mystery of Complex Traits

A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections

The exposome is defined as "the totality of environmental exposures encountered from birth to death" and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on "Lifetime Exposures and Human Health: The Exposome," held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

Update on the State of the Science for Analytical Methods for Gene-Environment Interactions

The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.

Perspective: Improving Nutritional Guidelines for Sustainable Health Policies: Current Status and Perspectives

A large body of evidence supports the notion that incorrect or insufficient nutrition contributes to disease development. A pivotal goal is thus to understand what exactly is appropriate and what is inappropriate in food ingestion and the consequent nutritional status and health. The effective application of these concepts requires the translation of scientific information into practical approaches that have a tangible and measurable impact at both individual and population levels. The agenda for the future is expected to support available methodology in nutrition research to personalize guideline recommendations, properly grading the quality of the available evidence, promoting adherence to the well-established evidence hierarchy in nutrition, and enhancing strategies for appropriate vetting and transparent reporting that will solidify the recommendations for health promotion. The final goal is to build a constructive coalition among scientists, policy makers, and communication professionals for sustainable health and nutritional policies. Currently, a strong rationale and available data support a personalized dietary approach according to personal variables, including sex and age, circulating metabolic biomarkers, food quality and intake frequency, lifestyle variables such as physical activity, and environmental variables including one's microbiome profile. There is a strong and urgent need to develop a successful commitment among all the stakeholders to define novel and sustainable approaches toward the management of the health value of nutrition at individual and population levels. Moving forward requires adherence to well-established principles of evidence evaluation as well as identification of effective tools to obtain better quality evidence. Much remains to be done in the near future.

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health

The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)

Background: Few older adults achieve recommended physical activity levels. We conducted a "neighborhood environment-wide association study (NE-WAS)" of neighborhood influences on physical activity among older adults, analogous, in a genetic context, to a genome-wide association study.Methods: Physical Activity Scale for the Elderly (PASE) and sociodemographic data were collected via telephone survey of 3,497 residents of New York City aged 65 to 75 years. Using Geographic Information Systems, we created 337 variables describing each participant's residential neighborhood's built, social, and economic context. We used survey-weighted regression models adjusting for individual-level covariates to test for associations between each neighborhood variable and (i) total PASE score, (ii) gardening activity, (iii) walking, and (iv) housework (as a negative control). We also applied two "Big Data" analytic techniques, LASSO regression, and Random Forests, to algorithmically select neighborhood variables predictive of these four physical activity measures.Results: Of all 337 measures, proportion of residents living in extreme poverty was most strongly associated with total physical activity [-0.85; (95% confidence interval, -1.14 to -0.56) PASE units per 1% increase in proportion of residents living with household incomes less than half the federal poverty line]. Only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood meaZsures were associated with housework after accounting for multiple comparisons.Conclusions: This systematic approach revealed patterns in the domains of neighborhood measures associated with physical activity.Impact: The NE-WAS approach appears to be a promising exploratory technique. Cancer Epidemiol Biomarkers Prev; 26(4); 495-504. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."

Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era

PURPOSE OF REVIEW

Mixtures, or combinations and interactions between multiple environmental exposures, are hypothesized to be causally linked with disease and health-related phenotypes. Established and emerging molecular measurement technologies to assay the exposome, the comprehensive battery of exposures encountered from birth to death, promise a new way of identifying mixtures in disease in the epidemiological setting. In this opinion, we describe the analytic complexity and challenges in identifying mixtures associated with phenotype and disease.

RECENT FINDINGS

Existing and emerging machine-learning methods and data analytic approaches (e.g., "environment-wide association studies" [EWASs]), as well as large cohorts may enhance possibilities to identify mixtures of correlated exposures associated with phenotypes; however, the analytic complexity of identifying mixtures is immense.

SUMMARY

If the exposome concept is realized, new analytical methods and large sample sizes will be required to ascertain how mixtures are associated with disease. The author recommends documenting prevalent correlated exposures and replicated main effects prior to identifying mixtures.

Pharmacokinetics, Pharmacodynamics, and Pharmacogenomics of Immunosuppressants in Allogeneic Hematopoietic Cell Transplantation: Part II

Part I of this article included a pertinent review of allogeneic hematopoietic cell transplantation (alloHCT), the role of postgraft immunosuppression in alloHCT, and the pharmacokinetics, pharmacodynamics, and pharmacogenomics of the calcineurin inhibitors and methotrexate. In this article (Part II), we review the pharmacokinetics, pharmacodynamics, and pharmacogenomics of mycophenolic acid (MPA), sirolimus, and the antithymocyte globulins (ATG). We then discuss target concentration intervention (TCI) of these postgraft immunosuppressants in alloHCT patients, with a focus on current evidence for TCI and on how TCI may improve clinical management in these patients. Currently, TCI using trough concentrations is conducted for sirolimus in alloHCT patients. Several studies demonstrate that MPA plasma exposure is associated with clinical outcomes, with an increasing number of alloHCT patients needing TCI of MPA. Compared with MPA, there are fewer pharmacokinetic/dynamic studies of rabbit ATG and horse ATG in alloHCT patients. Future pharmacokinetic/dynamic research of postgraft immunosuppressants should include '-omics'-based tools: pharmacogenomics may be used to gain an improved understanding of the covariates influencing pharmacokinetics as well as proteomics and metabolomics as novel methods to elucidate pharmacodynamic responses.

Toward the integration of Omics data in epidemiological studies: still a "long and winding road"

Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the nonhost will enable to build more accurate risk prediction models. Epidemiologists aim to integrate omics data along with important information coming from other sources (questionnaires, candidate markers) that has been proved to be relevant in the discrimination risk assessment of complex diseases. However, the integrative models in large-scale epidemiologic research are still in their infancy and they face numerous challenges, some of them at the analytical stage. So far, there are a small number of studies that have integrated more than two omics data sets, and the inclusion of non-omics data in the same models is still missing in most of studies. In this contribution, we aim at approaching the omics and non-omics data integration from the epidemiology scope by considering the "massive" inclusion of variables in the risk assessment and predictive models. We also provide already available examples of integrative contributions in the field, propose analytical strategies that allow considering both omics and non-omics data in the models, and finally review the challenges imbedding this type of research.

Systematic review of the association between dietary acid load, alkaline water and cancer

OBJECTIVES

To evaluate the evidence for a causal relationship between dietary acid/alkaline and alkaline water for the aetiology and treatment of cancer.

DESIGN

A systematic review was conducted on published and grey literature separately for randomised intervention and observational studies with either varying acid-base dietary intakes and/or alkaline water with any cancer outcome or for cancer treatment.

OUTCOME MEASURES

Incidence of cancer and outcomes of cancer treatment.

RESULTS

8278 citations were identified, and 252 abstracts were reviewed; 1 study met the inclusion criteria and was included in this systematic review. No randomised trials were located. No studies were located that examined dietary acid or alkaline or alkaline water for cancer treatment. The included study was a cohort study with a low risk of bias. This study revealed no association between the diet acid load with bladder cancer (OR=1.15: 95% CI 0.86 to 1.55, p=0.36). No association was found even among long-term smokers (OR=1.72: 95% CI 0.96 to 3.10, p=0.08).

CONCLUSIONS

Despite the promotion of the alkaline diet and alkaline water by the media and salespeople, there is almost no actual research to either support or disprove these ideas. This systematic review of the literature revealed a lack of evidence for or against diet acid load and/or alkaline water for the initiation or treatment of cancer. Promotion of alkaline diet and alkaline water to the public for cancer prevention or treatment is not justified.

Analytical Complexity in Detection of Gene Variant-by-Environment Exposure Interactions in High-Throughput Genomic and Exposomic Research

It seems intuitive that disease risk is influenced by the interaction between inherited genetic variants and environmental exposure factors; however, we have few documented interactions between variants and exposures. Advances in technology may enable the simultaneous measurement (i.e., on the same individuals in an epidemiological study) of millions of genome variants with thousands of environmental "exposome" factors, significantly increasing the number of possible factor pairs available for testing for the presence of interactions. The burden of analytic complexity, or sheer number of genetic and exposure factors measured, poses a considerable challenge for discovery of interactions in population-scale data. Advances in analytic approaches, large sample sizes, less conservative methods to mitigate multiple testing, and strong biological priors will be required to prune the search space to find reproducible and robust gene-by-environment interactions in observational data.

Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study

BACKGROUND

There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one).

OBJECTIVES

We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk.

METHODS

We conducted a population-based case-control study of NHL in four National Cancer Institute-Surveillance, Epidemiology, and End Results centers--Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington--from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL.

RESULTS

The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa.

CONCLUSIONS

An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk.

Epidemiology research in rheumatology-progress and pitfalls

Epidemiology research is a vital component of clinical studies in all medical fields. This Review provides a brief introduction to the methodology and interpretation of population and clinical epidemiology studies of musculoskeletal disorders. Data sources (including 'big data' and the issue of missing data), study design (cross-sectional, case-control and cohort studies, including clinical trial design) and the interpretation of study results are discussed with examples from the field of rheumatology, particularly using findings in patients with rheumatoid arthritis. Two or more treatments can be compared in clinical trials using a variety of study designs including superiority, noninferiority or equivalence. The different types of risk in epidemiological studies-absolute, attributable, background and relative-are important concepts in epidemiological research and their relative usefulness to clinicians and patients should be considered carefully. The potential pitfalls and challenges of generalizing the results of epidemiological studies to understanding disease aetiology and to clinical practice are also emphasized. The aim of the Review is to help readers to critically appraise published articles that use epidemiological designs or methods.

Placing epidemiological results in the context of multiplicity and typical correlations of exposures

Epidemiological studies evaluate multiple exposures, but the extent of multiplicity often remains non-transparent when results are reported. There is extensive debate in the literature on whether multiplicity should be adjusted for in the design, analysis, and reporting of most epidemiological studies, and, if so, how this should be done. The challenges become more acute in an era where the number of exposures that can be studied (the exposome) can be very large. Here, we argue that it can be very insightful to visualize and describe the extent of multiplicity by reporting the number of effective exposures for each category of exposures being assessed, and to describe the distribution of correlation between exposures and/or between exposures and outcomes in epidemiological datasets. The results of new proposed associations can be placed in the context of this background information. An association can be assigned to a percentile of magnitude of effect based on the distribution of effects seen in the field. We offer an example of how such information can be routinely presented in an epidemiological study/dataset using data on 530 exposure and demographic variables classified in 32 categories in the National Health and Nutrition Examination Survey (NHANES). Effects that survive multiplicity considerations and that are large may be prioritized for further scrutiny.

Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United States national health and nutrition examination survey

BACKGROUND

Environmental and behavioural factors are thought to contribute to all-cause mortality. Here, we develop a method to systematically screen and validate the potential independent contributions to all-cause mortality of 249 environmental and behavioural factors in the National Health and Nutrition Examination Survey (NHANES).

METHODS

We used Cox proportional hazards regression to associate 249 factors with all-cause mortality while adjusting for sociodemographic factors on data in the 1999-2000 and 2001-02 surveys (median 5.5 follow-up years). We controlled for multiple comparisons with the false discovery rate (FDR) and validated significant findings in the 2003-04 survey (median 2.8 follow-up years). We selected 249 factors from a set of all possible factors based on their presence in both the 1999-2002 and 2003-04 surveys and linkage with at least 20 deceased participants. We evaluated the correlation pattern of validated factors and built a multivariable model to identify their independent contribution to mortality.

RESULTS

We identified seven environmental and behavioural factors associated with all-cause mortality, including serum and urinary cadmium, serum lycopene levels, smoking (3-level factor) and physical activity. In a multivariable model, only physical activity, past smoking, smoking in participant's home and lycopene were independently associated with mortality. These three factors explained 2.1% of the variance of all-cause mortality after adjusting for demographic and socio-economic factors.

CONCLUSIONS

Our association study suggests that, of the set of 249 factors in NHANES, physical activity, smoking, serum lycopene and serum/urinary cadmium are associated with all-cause mortality as identified in previous studies and after controlling for multiple hypotheses and validation in an independent survey. Whereas other NHANES factors may be associated with mortality, they may require larger cohorts with longer time of follow-up to detect. It is possible to use a systematic association study to prioritize risk factors for further investigation.

An environmental wide association study (EWAS) approach to the metabolic syndrome

BACKGROUND

Environmental contaminants have previously been linked to components of the Metabolic Syndrome (MetS). However, exposure to environmental contaminants is in part determined by various lifestyle factors.

OBJECTIVE

Using an "Environmental Wide Association Study" (ELWAS) integrating environmental contaminants and lifestyle factors, we aimed to evaluate a possible additive role of both contaminants and lifestyle factors regarding MetS.

METHODS

1016 subjects aged 70years were investigated in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. 43 environmental contaminants were measured in the circulation. Dietary records were used to evaluate 21 nutrients and the proportions of 13 fatty acids were determined in serum cholesterol esters to further quantify fat quality intake. Adding 5 other important lifestyle factors yielded together 76 environmental and lifestyle factors. MetS was defined by the NCEP/ATPIII-criteria.

RESULTS

23% had MetS. Using cross-validation within the sample, fourteen environmental contaminants or lifestyle factors consistently showed a false discovery rate <0.05. When the major variables entered a multiple model, only p, p'-DDE levels (positive), PCB209 (inverse) and exercise habits (inverse) were together with a fatty acid pattern, with high levels of palmitic acid and oleic acid and low levels of linoleic acid, related to MetS (p<0.002 for all variables).

CONCLUSION

Using a cross-sectional EWAS approach, certain environmental contaminants and lifestyle factors were found to be associated with prevalent metabolic syndrome in an additive fashion in an elderly population.

Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach

To address the complex nature of cancer occurrence and outcomes, approaches have been developed to simultaneously assess the role of two or more etiologic agents within hierarchical levels including the: (i) macroenvironment level (e.g., health care policy, neighborhood, or family structure); (ii) individual level (e.g., behaviors, carcinogenic exposures, socioeconomic factors, and psychologic responses); and (iii) biologic level (e.g., cellular biomarkers and inherited susceptibility variants). Prior multilevel approaches tend to focus on social and environmental hypotheses, and are thus limited in their ability to integrate biologic factors into a multilevel framework. This limited integration may be related to the limited translation of research findings into the clinic. We propose a "Multi-level Biologic and Social Integrative Construct" (MBASIC) to integrate macroenvironment and individual factors with biology. The goal of this framework is to help researchers identify relationships among factors that may be involved in the multifactorial, complex nature of cancer etiology, to aid in appropriate study design, to guide the development of statistical or mechanistic models to study these relationships, and to position the results of these studies for improved intervention, translation, and implementation. MBASIC allows researchers from diverse fields to develop hypotheses of interest under a common conceptual framework, to guide transdisciplinary collaborations, and to optimize the value of multilevel studies for clinical and public health activities.

Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease

Motivation: Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possible interaction in context of disease. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies. Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D.

Contact:abutte@stanford.edu

Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus

Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be due to gene–environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS [environment-wide association study (EWAS)], we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene–environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (corrected p = 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-β-carotene were 40 % greater than the marginal effect size [odds ratio (OR) 1.8, 95 % CI 1.3–2.6]. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.

A nutrient-wide association study on blood pressure

BACKGROUND

A nutrient-wide approach may be useful to comprehensively test and validate associations between nutrients (derived from foods and supplements) and blood pressure (BP) in an unbiased manner.

METHODS AND RESULTS

Data from 4680 participants aged 40 to 59 years in the cross-sectional International Study of Macro/Micronutrients and Blood Pressure (INTERMAP) were stratified randomly into training and testing sets. US National Health and Nutrition Examination Survey (NHANES) four cross-sectional cohorts (1999-2000, 2001-2002, 2003-2004, 2005-2006) were used for external validation. We performed multiple linear regression analyses associating each of 82 nutrients and 3 urine electrolytes with systolic and diastolic BP in the INTERMAP training set. Significant findings were validated in the INTERMAP testing set and further in the NHANES cohorts (false discovery rate <5% in training, P<0.05 for internal and external validation). Among the validated nutrients, alcohol and urinary sodium-to-potassium ratio were directly associated with systolic BP, and dietary phosphorus, magnesium, iron, thiamin, folacin, and riboflavin were inversely associated with systolic BP. In addition, dietary folacin and riboflavin were inversely associated with diastolic BP. The absolute effect sizes in the validation data (NHANES) ranged from 0.97 mm Hg lower systolic BP (phosphorus) to 0.39 mm Hg lower systolic BP (thiamin) per 1-SD difference in nutrient variable. Inclusion of nutrient intake from supplements in addition to foods gave similar results for some nutrients, though it attenuated the associations of folacin, thiamin, and riboflavin intake with BP.

CONCLUSIONS

We identified significant inverse associations between B vitamins and BP, relationships hitherto poorly investigated. Our analyses represent a systematic unbiased approach to the evaluation and validation of nutrient-BP associations.

Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans

We propose guidelines to evaluate the cumulative evidence of gene-environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.

Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels

Background Both genetic and environmental factors contribute to triglyceride, low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C) levels. Although genome-wide association studies are currently testing the genetic factors systematically, testing and reporting one or a few factors at a time can lead to fragmented literature for environmental chemical factors. We screened for correlation between environmental factors and lipid levels, utilizing four independent surveys with information on 188 environmental factors from the Centers of Disease Control, National Health and Nutrition Examination Survey, collected between 1999 and 2006.

Methods We used linear regression to correlate each environmental chemical factor to triglycerides, LDL-C and HDL-C adjusting for age, age², sex, ethnicity, socio-economic status and body mass index. Final estimates were adjusted for waist circumference, diabetes status, blood pressure and survey. Multiple comparisons were controlled for by estimating the false discovery rate and significant findings were tentatively validated in an independent survey.

Results We identified and validated 29, 9 and 17 environmental factors correlated with triglycerides, LDL-C and HDL-C levels, respectively. Findings include hydrocarbons and nicotine associated with lower HDL-C and vitamin E (γ-tocopherol) associated with unfavourable lipid levels. Higher triglycerides and lower HDL-C were correlated with higher levels of fat-soluble contaminants (e.g. polychlorinated biphenyls and dibenzofurans). Nutrients and vitamin markers (e.g. vitamins B, D and carotenes), were associated with favourable triglyceride and HDL-C levels.

Conclusions Our systematic association study has enabled us to postulate about broad environmental correlation to lipid levels. Although subject to confounding and reverse causality bias, these findings merit evaluation in additional cohorts.