Contrasting theories of interaction in epidemiology and toxicology

Association between per- and poly-fluoroalkyl substances and nonalcoholic fatty liver disease: A nested case-control study in northwest China

Per- and poly-fluoroalkyl substances (PFAS) have been reported to have hepatotoxic effects. However, it is unclear whether they are linked to non-alcoholic fatty liver disease (NAFLD). This nested case-control study focused on the epidemiological links between PFAS and the prevalence of NAFLD. We selected 476 new cases of NAFLD and 952 age- and sex-matched controls from the Jinchang cohort population between 2014 and 2019. Serum concentrations of PFAS were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Only PFAS with a detection rate of ≥90 % were included for analysis, which included PFPeA, PFOA, PFNA, PFHxS, PFOS, and 9Cl-PF3ONS. The relationship between single and co-exposure to PFAS and the occurrence of NAFLD was evaluated using conditional logistic regression, Quantile g-computation (QgC), and Bayesian kernel machine regression (BKMR) model. Logistic regression indicated that PFPeA, PFOA, and 9Cl-PF3ONS were positive correlation with the incidence of NAFLD after adjusting for confounders, with odds ratios (OR) and 95 % confidence interval (CI) of 3.13 (95 % CI: 2.53, 3.86), 1.39 (95 % CI: 1.12, 1.73), and 1.41 (95 % CI: 1.20, 1.66), respectively. PFNA, PFHxS, and PFOS were nonlinearly and negatively associated with the incidence of NAFLD, with OR (95 % CI) of 0.53 (0.46, 0.62), 0.83 (0.73, 0.95), and 0.52 (0.44, 0.61), respectively. QgC showed a significant joint effect of PFAS mixture on NAFLD onset (OR: 1.52, 95 % CI: 1.24, 1.88). BKMR showed a weak positive trend between PFAS mixtures and NAFLD incidence. Positive correlations were primarily driven by PFPeA and 9Cl-PF3ONS, while negative correlations were mainly influenced by PFNA and PFOS. The BKMR model also suggested that there was an interaction between PFOS and PFNA and other four PFAS compounds. In conclusion, our findings suggest that individual and co-exposure to PFAS is associated with a risk of NAFLD onset.

Nutritional Modulation of Associations between Prenatal Exposure to Persistent Organic Pollutants and Childhood Obesity: A Prospective Cohort Study

BACKGROUND

Prenatal exposure to persistent organic pollutants (POPs) may contribute to the development of childhood obesity and metabolic disorders. However, little is known about whether the maternal nutritional status during pregnancy can modulate these associations.

OBJECTIVES

The main objective was to characterize the joint associations and interactions between prenatal levels of POPs and nutrients on childhood obesity.

METHODS

We used data from to the Spanish INfancia y Medio Ambiente-Environment and Childhood (INMA) birth cohort, on POPs and nutritional biomarkers measured in maternal blood collected at the first trimester of pregnancy and child anthropometric measurements at 7 years of age. Six organochlorine compounds (OCs) [dichlorodiphenyldichloroethylene, hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and polychlorinated biphenyls 138, 153, 180] and four per- and polyfluoroalkyl substances (PFAS) were measured. Nutrients included vitamins (D, B12, and folate), polyunsaturated fatty acids (PUFAs), and dietary carotenoids. Two POPs-nutrients mixtures data sets were established: a) OCs, PFAS, vitamins, and carotenoids (n=660), and b) OCs, PUFAs, and vitamins (n=558). Joint associations of mixtures on obesity were characterized using Bayesian kernel machine regression (BKMR). Relative importance of biomarkers and two-way interactions were identified using gradient boosting machine, hierarchical group lasso regularization, and BKMR. Interactions were further characterized using multivariate regression models in the multiplicative and additive scale.

RESULTS

Forty percent of children had overweight or obesity. We observed a positive overall joint association of both POPs-nutrients mixtures on overweight/obesity risk, with HCB and vitamin B12 the biomarkers contributing the most. Recurrent interactions were found between HCB and vitamin B12 across screening models. Relative risk for a natural log increase of HCB was 1.31 (95% CI: 1.11, 1.54, pInteraction=0.02) in the tertile 2 of vitamin B12 and in the additive scale a relative excess risk due to interaction of 0.11 (95% CI: 0.02, 0.20) was found. Interaction between perfluorooctane sulfonate and β-cryptoxanthin suggested a protective effect of the antioxidant on overweight/obesity risk.

CONCLUSION

These results support that maternal nutritional status may modulate the effect of prenatal exposure to POPs on childhood overweight/obesity. These findings may help to develop a biological hypothesis for future toxicological studies and to better interpret inconsistent findings in epidemiological studies. https://doi.org/10.1289/EHP11258.

Per- and perfluoroalkyl substances alternatives, mixtures and liver function in adults: A community-based population study in China

Experimental evidence has shown that per- and polyfluoroalkyl substances (PFAS) alternatives and mixtures may exert hepatotoxic effects in animals. However, epidemiological evidence is limited. This research aimed to explore associations of PFAS and the alternatives with liver function in a general adult population. The study participants consisted of 1,303 adults from a community-based cross-sectional investigation in Guangzhou, China, from November 2018 to August 2019. We selected 13 PFAS with detection rates > 85% in serum samples and focused on perfluorooctane-sulfonic acid (PFOS), perfluorooctanoic acid (PFOA) and their alternatives [6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), 8:2 Cl-PFESA, and perfluorohexanoic acid (PFHxA)] as predictors of outcome. Six liver function biomarkers (ALB, ALT, AST, GGT, ALP, and DBIL) were chosen as outcomes. We applied regression models with restricted cubic spline function to explore correlations between single PFAS and liver function and inspected the combined effect of PFAS mixtures on liver by applying Bayesian kernel machine regression (BKMR). We discovered positive associations among PFAS and liver function biomarkers except for ALP. For example, compared with the 25th percentile of PFAS concentration, the level of ALT increased by 12.36% (95% CI: 7.91%, 16.98%) for ln-6:2 Cl-PFESA, 5.59% (95% CI: 2.35%, 8.92%) for ln-8:2 Cl-PFESA, 3.56% (95% CI: -0.39%, 7.68%) for ln-PFHxA, 13.91% (95% CI: 8.93%, 19.13%) for ln-PFOA, and 14.25% (95% CI: 9.91%, 18.77%) for ln-PFOS at their 75th percentile. In addition, higher exposed serum PFAS was found to be correlated with greater odds of abnormal liver function. Analysis from BKMR models also showed an adverse association between PFAS mixtures and liver function. The combined effect of the PFAS mixture appeared to be non-interactive, in which PFOS was the main contributor to the overall effect. Our findings provide evidence of associations between PFAS alternatives, PFAS mixtures, and liver function in the general adult population.

Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects

OBJECTIVES

Methods exist to study exposure mixtures, but each is distinct in the research question it aims to address. We propose a new approach focused on estimating both the summed effect and individual weights of one or multiple exposure mixtures: Bayesian Weighted Sums (BWS).

METHODS

We applied BWS to simulated and real datasets with correlated exposures. The analytic context in our real-world example is an estimation of the association between polybrominated diphenyl ether (PBDE) congeners (28, 47, 99, 100, and 153) and Autism Spectrum Disorder (ASD) diagnosis and Social Responsiveness Scores (SRS).

RESULTS

Simulations demonstrate that BWS performs reliably. In adjusted models using Early Autism Risk Longitudinal Investigation (EARLI) data, the odds of ASD for a 1-unit increase in the weighted sum of PBDEs were 1.41 (95% highest posterior density 0.82, 2.50) times the odds of ASD for the unexposed and the change in z-score standardized SRS per 1 unit increase in the weighted sum of PBDEs is 0.15 (95% highest posterior density -0.08, 0.38).

CONCLUSIONS

BWS provides a means of estimating the summed effect and weights for individual components of a mixture. This approach is distinct from other exposure mixture tools. BWS may be more flexible than existing approaches and can be specified to allow multiple exposure groups based on a priori knowledge from epidemiology or toxicology.

Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare yet devastating neurodegenerative condition. The mechanisms leading to ALS are most certainly complex and likely involve a joint contribution of several factors with possible synergistic or antagonistic interactions. To provide a better understanding of the association between non-genetic factors and ALS, we evaluated the joint exposure to multiple health and environmental factors linked with ALS in our previous studies, also screening for high-dimensional interactions.

METHODS

We used data from a nested case-control study within the Danish population, with 1086 ALS cases from 1982 to 2009, jointly investigating 4 hospital-based diagnoses - diabetes, obesity, physical/stress trauma, cardiovascular disease (CVD) during 1977-2009; and 4 environmental exposures - lead, formaldehyde, diesel exhaust, and solvents, assessed from individual occupational history. All covariates were evaluated as ever/never exposed, and we used targeted machine learning techniques to screen for important joint predictors and interactions. These were then evaluated in a final logistic regression model adjusting for potential confounders (age, SES, geography). All analyses were stratified by sex.

RESULTS

Among men, trauma and solvents were associated with higher odds of ALS (OR = 1.55, 95% CI: 1.08-2.23; OR = 1.49, 95% CI: 1.17-1.89, respectively), and presented a negative interaction (OR = 0.49, 95% CI: 0.30-0.80). A positive diesel/CVD interaction was observed (OR = 1.56, 95% CI: 0.94-2.60). Among women, solvents, trauma, lead, and CVD were associated with higher odds of ALS, and a negative lead/solvents interaction was documented (OR = 0.52, 95% CI: 0.42-0.63).

CONCLUSIONS

This study is one of the first attempts to evaluate joint and interactive effects of multiple risk factors on ALS, identifying potential synergistic and antagonistic mechanisms.

Interactions between environmental pollutants and dietary nutrients: current evidence and implications in epidemiological research

Environmental pollutants and nutrients may be present in the same foodstuffs or dietary patterns; share internal mechanisms of transport, metabolism and cellular uptake; or target the same molecular signalling pathways and biological functions. Lipophilic pollutants and nutrients, like dioxins and polyunsaturated fatty acids, may often converge at all aforementioned levels and thus the interactions become more likely. Despite this fact, the topic seems overlooked in mainstream epidemiological research. In this essay, we illustrate different levels of documented interactions between pollutants and nutrients with experimental, interventional and epidemiological evidence, paying special attention to lipophilic chemicals. We first describe common pollutants and nutrients encountered in diets and the internal lipophilic interface such as adipose tissue and serum lipids. Next, we discuss the preventive effects of nutrients against absorption and the toxic effects of pollutants, as well as the pollutant-induced perturbation of nutrient metabolism. Finally, we discuss the implications of nutrient-pollutant interactions in epidemiology, providing some examples of negative confounding, modification effect and statistical interactions reported for different outcomes including fetal growth, diabetes and cancer. The evidence discussed in this essay supports that the health impacts of chemicals have likely been underestimated due to the high risk of residual and coexposure confounding in diseases where interactions between pollutants and nutrients may occur.

Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions

Background Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neurodevelopment, even though the interactions of chemicals and nutrients may result in additive, synergistic, antagonistic, or potentiating effects on neurological endpoints. Investigating the impact of environmentally-relevant chemical mixtures, including heavy metals and endocrine disrupting chemicals (EDCs), is more reflective of human exposures and may result in more refined environmental policies to protect the public. Objective In this review, we provide a summary of epidemiological studies that have analyzed chemical mixtures of heavy metals and EDCs and neurobehavior utilizing multi-chemical models, including frequentist and Bayesian methods. Content Studies investigating chemicals and neurobehavior have the opportunity to not only examine the impact of chemical mixtures, but they can also identify chemicals from a mixture that may play a key role in neurotoxicity, investigate interactive effects, estimate non-linear dose response, and identify potential windows of susceptibility. The examination of neurobehavioral domains is particularly challenging given that traits emerge and change over time and subclinical nuances of neurobehavior are often unrecognized. To date, only a handful of epidemiological studies examining neurodevelopment have utilized multi-pollutant models in the investigation of heavy metals and EDCs. However, these studies were successful in identifying contaminants of importance from the exposure mixtures. Summary and Outlook Investigators are encouraged to broaden their focus to include more environmentally relevant mixtures of chemicals using advanced statistical approaches, particularly to aid in identifying potential mechanisms underlying associations.

Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study

Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine-disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52-0.98) and specificity (0.21-0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal-to-noise ratio scenarios (0.20-0.51). Lasso was highly sensitive (0.71-0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

BACKGROUND

Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility.

METHODS

We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models.

RESULTS

PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects.

CONCLUSIONS

We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

A Cumulative Risk Perspective for Occupational Health and Safety (OHS) Professionals

Cumulative risk assessment (CRA) addresses the combined risk associated with chemical and non-chemical exposures. Although CRA approaches are utilized in environmental and ecological contexts, they are rarely applied in workplaces. In this perspectives article, we strive to raise awareness among occupational health and safety (OHS) professionals and foster the greater adoption of a CRA perspective in practice. Specifically, we provide an overview of CRA literature as well as preliminary guidance on when to consider a CRA approach in occupational settings and how to establish reasonable boundaries. Examples of possible workplace co-exposures and voluntary risk management actions are discussed. We also highlight important implications for workplace CRA research and practice. In particular, future needs include simple tools for identifying combinations of chemical and non-chemical exposures, uniform risk management guidelines, and risk communication materials. Further development of practical CRA methods and tools are essential to meet the needs of complex and changing work environments.

The use of Logic regression in epidemiologic studies to investigate multiple binary exposures: an example of occupation history and amyotrophic lateral sclerosis

Investigating the joint exposure to several risk factors is becoming a key component of epidemiologic studies. Individuals are exposed to multiple factors, often simultaneously, and evaluating patterns of exposures and high-dimension interactions may allow for a better understanding of health risks at the individual level. When jointly evaluating high-dimensional exposures, common statistical methods should be integrated with machine learning techniques that may better account for complex settings. Among these, Logic regression was developed to investigate a large number of binary exposures as they relate to a given outcome. This method may be of interest in several public health settings, yet has never been presented to an epidemiologic audience. In this paper, we review and discuss Logic regression as a potential tool for epidemiological studies, using an example of occupation history (68 binary exposures of primary occupations) and amyotrophic lateral sclerosis in a population-based Danish cohort. Logic regression identifies predictors that are Boolean combinations of the original (binary) exposures, fully operating within the regression framework of interest (e.g. linear, logistic). Combinations of exposures are graphically presented as Logic trees, and techniques for selecting the best Logic model are available and of high importance. While highlighting several advantages of the method, we also discuss specific drawbacks and practical issues that should be considered when using Logic regression in population-based studies. With this paper, we encourage researchers to explore the use of machine learning techniques when evaluating large-dimensional epidemiologic data, as well as advocate the need of further methodological work in the area.

Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives

BACKGROUND

Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods.

OBJECTIVES

We aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges.

METHODS

We searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature.

DISCUSSION

We identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review.

CONCLUSIONS

Compelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.

Environmental exposure mixtures: questions and methods to address them

PURPOSE OF THIS REVIEW

This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful.

RECENT FINDINGS

When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection.

SUMMARY

The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.

Experimental study and mathematical modeling of toxic metals combined action as a scientific foundation for occupational and environmental health risk assessment. A summary of results obtained by the Ekaterinburg research team (Russia)

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Cumulative health risks assessment should be based on toxicology of mixtures.

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Some principal discrepancies between these domains are discussed by the authors.

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While simplification of the theory is inevitable, its vulgarization should be avoided.

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Our contribution to this theory and its practical applications is summarized here.

Assessment of cumulative health risks associated with the widely observed combined effects of two or more metals and their compounds on the organism has the toxicology of mixtures as its scientific basis although there is no full match between such assessment and this basis while some of the contradictions between them are of a fundamental nature. This state of things may be explained not only by simplifications characteristic of the generally recognized methodology of risk assessment but also by extreme complexity of the theory of combined toxicity, the most essential issues of which are considered by authors on the basis of literary and, mostly, their own previously published data.

Exploring associations between prenatal solvent exposures and teenage drug and alcohol use: a retrospective cohort study

BACKGROUND

Investigating the effects of prenatal and childhood exposures on behavioral health outcomes in adolescence is challenging given the lengthy period between the exposure and outcomes. We conducted a retrospective cohort study in Cape Cod, Massachusetts to evaluate the impact of prenatal and early childhood exposure to tetrachloroethylene (PCE)-contaminated drinking water on the occurrence of risk-taking behaviors as a teenager. An increased occurrence of risk-taking behaviors, particularly illicit drug use, was observed in those highly exposed to PCE. We hypothesized that there may be other sources of prenatal solvent exposure such as maternal consumption of alcoholic beverages during pregnancy which might modify the previously observed associations between PCE and risk-taking behaviors and so we conducted an exploratory analysis using available cohort data. The current report presents the results of these analyses and describes the difficulties in conducting research on long-term behavioral effects of early life exposures.

METHODS

The exploratory analysis compared a referent group of subjects with no early life exposure to PCE or alcohol (n = 242) to subjects with only alcohol exposure (n = 201), subjects with only PCE exposure (n = 361), and subjects with exposure to both PCE and alcohol (n = 302). Surveys completed by the subject's mother included questions on prenatal alcoholic beverage consumption and available confounding variables such as cigarette smoking and marijuana use. Surveys completed by the subjects included questions on risk-taking behaviors such as alcoholic beverage consumption and illicit drug use as a teenager and available confounding variables. PCE exposure was modeled using a leaching and transport algorithm embedded in water distribution system modeling software that estimated the amount of PCE delivered to a subject's residence during gestation and early childhood.

RESULTS

Subjects with early life exposure to both PCE and alcohol had an increased risk of using two or more major drugs as a teen (RR = 1.9 (95% CI 1.2, 3.0)) compared to unexposed subjects. Increased risks for only PCE exposure (RR = 1.6 (95% CI 1.0, 2.4) and only alcohol exposure (RR = 1.3 (95% CI 0.7, 2.1)) were also evident but were smaller than the increased risk associated with both exposures. While available confounding variables were controlled, many relevant social risk factors were not obtained due to limitations in the retrospective study design.

CONCLUSIONS

This exploratory analysis found evidence for an additive effect of early life exposure to PCE and alcohol on the risk of use of multiple illicit drugs as a teenager. Because of numerous limitations in this retrospective study, further research is needed to examine longstanding behavioral effects of early life exposures. To be most informative, this research should involve long-term prospective data collection.

Hormesis of some organic solvents on Vibrio qinghaiensis sp.-Q67 from first binding to the β subunit of luciferase

Hormesis is a biphasic concentration–response relationship. During the luminescence inhibition test ofVibrio qinghaiensissp.-Q67 (Q67), some organic solvents display the hormesis phenomenon.

Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop

Quantifying the impact of exposure to environmental chemical mixtures is important for identifying risk factors for diseases and developing more targeted public health interventions. The National Institute of Environmental Health Sciences (NIEHS) held a workshop in July 2015 to address the need to develop novel statistical approaches for multi-pollutant epidemiology studies. The primary objective of the workshop was to identify and compare different statistical approaches and methods for analyzing complex chemical mixtures data in both simulated and real-world data sets. At the workshop, participants compared approaches and results and speculated as to why they may have differed. Several themes emerged: a) no one statistical approach appeared to outperform the others, b) many methods included some form of variable reduction or summation of the data before statistical analysis, c) the statistical approach should be selected based upon a specific hypothesis or scientific question, and d) related mixtures data should be shared among researchers to more comprehensively and accurately address methodological questions and statistical approaches. Future efforts should continue to design and optimize statistical approaches to address questions about chemical mixtures in epidemiological studies.

Identification of combined action types in experiments with two toxicants: a response surface linear model with a cross term

Within the framework of the response surface linear model with a cross term, i.e. a model of the type Y(x1, x2) = b0 + b1x1 + b2x2 + b12x1x2 (hyperbolic paraboloid), a complete solution of identification of combined action types of two toxicants x1 and x2 is presented. It is shown that the type of combined effect in this model is determined by two factors: the direction in which the toxicants act (unidirectional or oppositely directed), and the position of the saddle point S of a hyperbolic paraboloid. For unidirectional actions of toxicants, already-known ways to identify the type of combined effect (including a shape of the isobole: concave-up or concave-down) provided identical and unambiguous answers regarding the type of combined effect (antagonism or synergism). For oppositely directed actions of toxicants, the shape of the isobole (concave-up or concave-down) did not allow us to determine the type of combined action type unambiguously. We show that in both cases (unidirectional or oppositely directed actions of toxicants) the signs of the model coefficients b1, b2 and b12, in conjunction with the coordinates of the saddle point S help unambiguously identify the type of combined action by comparing the observed effect with the zero interaction response surface. An atlas of all possibly combined action types for two toxicants for the hyperbolic paraboloid model was created. Applications of the developed formalism to experimental data are provided.

What Can Epidemiological Studies Tell Us about the Impact of Chemical Mixtures on Human Health?

Humans are exposed to a large number of environmental chemicals: Some of these may be toxic, and many others have unknown or poorly characterized health effects. There is intense interest in determining the impact of exposure to environmental chemical mixtures on human health. As the study of mixtures continues to evolve in the field of environmental epidemiology, it is imperative that we understand the methodologic challenges of this research and the types of questions we can address using epidemiological data. In this article, we summarize some of the unique challenges in exposure assessment, statistical methods, and methodology that epidemiologists face in addressing chemical mixtures. We propose three broad questions that epidemiological studies can address: a) What are the potential health impacts of individual chemical agents? b) What is the interaction among agents? And c) what are the health effects of cumulative exposure to multiple agents? As the field of mixtures research grows, we can use these three questions as a basis for defining our research questions and for developing methods that will help us better understand the effect of chemical exposures on human disease and well-being.

Toxicodynamic and toxicokinetic descriptors of combined chromium (VI) and nickel toxicity

After repeated intraperitoneal injections of nickel and chromium (VI) salts to rats, we found, and confirmed by mathematical modeling, that their combined subchronic toxicity can either be of additive type or depart from it (predominantly toward subadditivity) depending on the effect assessed. Against the background of moderate systemic toxicity, the combination under study proved to possess a marked additive genotoxicity assessed by means of the random amplification of polymorphic DNA test. We also demonstrated that chromium and nickel reciprocally influenced the retention of these metals in some organs (especially in the spleen) but not their urinary excretion in this study.

The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees

BACKGROUND

There is a need to evaluate complex interaction effects on human health, such as those induced by mixtures of environmental contaminants. The usual approach is to formulate an additive statistical model and check for departures using product terms between the variables of interest. In this paper, we present an approach to search for interaction effects among several variables using boosted regression trees.

METHODS

We simulate a continuous outcome from real data on 27 environmental contaminants, some of which are correlated, and test the method's ability to uncover the simulated interactions. The simulated outcome contains one four-way interaction, one non-linear effect and one interaction between a continuous variable and a binary variable. Four scenarios reflecting different strengths of association are simulated. We illustrate the method using real data.

RESULTS

The method succeeded in identifying the true interactions in all scenarios except where the association was weakest. Some spurious interactions were also found, however. The method was also capable to identify interactions in the real data set.

CONCLUSIONS

We conclude that boosted regression trees can be used to uncover complex interaction effects in epidemiological studies.

Chemical mixtures and children's health

PURPOSE OF REVIEW

Humans are routinely exposed to multiple chemicals simultaneously or sequentially. There is evidence that the toxicity of individual chemicals may depend on the presence of other chemicals. Studies on chemical mixtures are limited, however, because of the lack of sufficient exposure data, limited statistical power, and difficulty in the interpretation of multidimensional interactions. This review summarizes the recent literature examining chemical mixtures and pediatric health outcomes, with an emphasis on metal mixtures.

RECENT FINDINGS

Several studies report significant interactions between metals in relation to pediatric health outcomes. Two prospective studies found interactive effects of early-life lead and manganese exposures on cognition. In two different cohorts, interactions between lead and cadmium exposures were reported on reproductive hormone levels and on neurodevelopment. Effects of lead exposure on impulsive behavior and cognition were modified by mercury exposure in studies from Canada and Denmark. However, there is little consistency related to exposure indicators and statistical approaches for evaluating interaction.

SUMMARY

Several studies suggest that metals interact to cause health effects that are different from exposure to each metal alone. Despite the nearly infinite number of possible chemical combinations, mixtures research represents real-life exposure scenarios and warrants more attention, particularly in the context of uniquely vulnerable children.