Approaches for incorporating environmental mixtures as mediators in mediation analysis

Addressing the influencing path of social noise exposure risk perception on noise mitigation behavior

Noise interference has become a common health risk in population-densified urban areas where social noise occurs frequently. However, the influence of an individual's perception of social noise exposure risk on reactive behavior remains unknown. This study developed an integrative psychosocial perspective-based model that includes constructs from two theoretical frameworks (the Theory of Planned Behavior and the Value-Belief-Norm theory) to analyze noise risk perception and behavioral intention for social noise mitigation. Haidian District, Beijing, was selected as the case study area and 300 questionnaires were distributed. The results showed that personal attributes had significant effects on residents' noise exposure risk perception and noise-mitigation behavioral intentions. Noise perception, as represented by awareness of consequences and ascription of responsibility, was significantly related to noise mitigation behavioral intention. Awareness of consequences directly positively influenced behavioral intention (β = 0.235, p < 0.001) and indirectly positively influenced behavioral intention through the mediating effect of the ascription of responsibility, which accounted for 24 % of the total effect of awareness of consequences on behavioral intention. This study provides valuable insights into the risks of social noise and encourages adaptive measures to reduce it.

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Trace elements may play an important role in obesity. This study aimed to assess the plasma and dietary intake levels of four trace elements, Mn, Cu, Zn and Se in a rural Chinese population, and analyse the relationship between trace elements and obesity. A cross-sectional study involving 2587 participants was conducted. Logistic regression models were used to analyse the association between trace elements and obesity; restricted cubic spline (RCS) models were used to assess the dose-response relationship between trace elements and obesity; the weighted quantile sum (WQS) model was used to examine the potential interaction of four plasma trace elements on obesity. Logistic regression analysis showed that plasma Se concentrations in the fourth quartile (Q4) exhibited a lower risk of developing obesity than the first quartile (Q1) (central obesity: OR = 0·634, P = 0·002; general obesity: OR = 0·525, P = 0·005). Plasma Zn concentration in the third quartile (Q3) showed a lower risk of developing obesity in general obesity compared with the first quartile (Q1) (OR = 0·625, P = 0·036). In general obesity, the risk of morbidity was 1·727 and 1·923 times higher for the second and third (Q2, Q3) quartiles of dietary Mn intake than for Q1, respectively. RCS indicated an inverse U-shaped correlation between plasma Se and obesity. WQS revealed the combined effects of four trace elements were negatively associated with central obesity. Plasma Zn and Se were negatively associated with obesity, and dietary Mn was positively associated with obesity. The combined action of the four plasma trace elements had a negative effect on obesity.

Racial and ethnic disparities in preterm birth: a mediation analysis incorporating mixtures of polybrominated diphenyl ethers

Background

Racial and ethnic disparities persist in preterm birth (PTB) and gestational age (GA) at delivery in the United States. It remains unclear whether exposure to environmental chemicals contributes to these disparities.

Objectives

We applied recent methodologies incorporating environmental mixtures as mediators in causal mediation analysis to examine whether racial and ethnic disparities in GA at delivery and PTB may be partially explained by exposures to polybrominated diphenyl ethers (PBDEs), a class of chemicals used as flame retardants in the United States.

Methods

Data from a multiracial/ethnic US cohort of 2008 individuals with low-risk singleton pregnancies were utilized, with plasma PBDE concentrations measured during early pregnancy. We performed mediation analyses incorporating three forms of mediators: (1) reducing all PBDEs to a weighted index, (2) selecting a PBDE congener, or (3) including all congeners simultaneously as multiple mediators, to evaluate whether PBDEs may contribute to the racial and ethnic disparities in PTB and GA at delivery, adjusted for potential confounders.

Results

Among the 2008 participants, 552 self-identified as non-Hispanic White, 504 self-identified as non-Hispanic Black, 568 self-identified as Hispanic, and 384 self-identified as Asian/Pacific Islander. The non-Hispanic Black individuals had the highest mean ∑PBDEs, the shortest mean GA at delivery, and the highest rate of PTB. Overall, the difference in GA at delivery comparing non-Hispanic Black to non-Hispanic White women was -0.30 (95% CI: -0.54, -0.05) weeks. This disparity reduced to -0.23 (95% CI: -0.49, 0.02) and -0.18 (95% CI: -0.46, 0.10) weeks if fixing everyone's weighted index of PBDEs to the median and the 25th percentile levels, respectively. The proportion of disparity mediated by the weighted index of PBDEs was 11.8%. No statistically significant mediation was found for PTB, other forms of mediator(s), or other racial and ethnic groups.

Conclusion

PBDE mixtures may partially mediate the Black vs. White disparity in GA at delivery. While further validations are needed, lowering the PBDEs at the population level might help reduce this disparity.

Associations of polychlorinated biphenyls and organochlorine pesticides with metabolic dysfunction-associated fatty liver disease among Chinese adults: Effect modification by lifestyle

Exposure to environmental pollutants and unhealthy lifestyles are key risk factors for metabolic dysfunction-associated fatty liver disease (MAFLD). While previous studies have suggested links between exposure to organochlorine pesticides (PCBs) and organochlorine pesticides (OCPs) and MAFLD, the results have been inconsistent. Furthermore, the combined effects of PCBs and OCPs on MAFLD and whether lifestyle factors can modify the associations remain unknown. Therefore, this study aimed to investigate the individual and joint effects of PCBs and OCPs on MAFLD and explore the potential modifying role of lifestyle. The study included 1923 participants from Wuhan, China. MAFLD was diagnosed based on ultrasonically diagnosed hepatic steatosis and the presence of overweight/obese, diabetes mellitus, or metabolic dysregulation. Healthy lifestyle score was determined by smoking, alcohol consumption, physical activity, and diet. Logistic regression and weighted quantile sum (WQS) were used to assess associations of individual and mixture of PCBs/OCPs with MAFLD. To explore the potential lifestyle modification, joint associations of PCBs/OCPs and lifestyle on MAFLD were conducted. Single-pollutant analysis showed positive associations of p,p'-DDE, β-HCH, PCB-153, and PCB-180 with MAFLD, with ORs (95% CIs) of 1.18 (1.05, 1.33), 1.57 (1.20, 2.05), 1.45 (1.14, 1.83), and 1.42 (1.12, 1.80), respectively. WQS regression demonstrated a harmful effect of PCBs/OCPs mixture on MAFLD (OR = 1.73, 95% CI = 1.24, 2.43), with β-HCH, p,p'-DDE, and PCB-180 being the major contributors. In the joint association analysis, participants with both high PCBs/OCPs exposure and unhealthy lifestyle have the highest odds of MAFLD. In conclusion, exposure to the mixture of PCBs and OCPs was positively correlated with MAFLD, and adopting a healthy lifestyle can mitigate the adverse impact.

Overall and individual associations between per- and polyfluoroalkyl substances and liver function indices and the metabolic mechanism

Per- and polyfluoroalkyl substances (PFAS) can disrupt liver homeostasis. Studies have shown that a single exposure to PFAS may provoke abnormal liver function; however, few studies have investigated the overall effect of PFAS mixtures. We aimed to investigate associations between exposure to PFAS mixtures and liver function indices and explore the relevant mechanisms. This study included 278 adult males from Guangzhou, China. Serum metabolite profiles were analyzed using untargeted metabolomics. We applied weighted quantile sum (WQS) regression as well as Bayesian kernel machine regression (BKMR) to analyze the association of nine PFAS mixtures with 14 liver function indices. PFAS mixtures were positively associated with apolipoprotein B (APOB) and gamma-glutamyltransferase (GGT) and negatively associated with direct bilirubin (DBIL) and total bilirubin (TBIL) in both the WQS and BKMR analyses. In addition, Spearman's correlation test showed individual PFAS correlated with APOB, GGT, TBIL, and DBIL, while there's little correlation between individual PFAS and other liver function indices. In linear regression analysis, PFHxS, PFOS, PFHpS, PFNA, PFDA, and PFUdA were associated with APOB; PFOA, PFDA, PFOS, PFNA, and PFUdA were associated with GGT. Subsequently, a metabolome-wide association study and mediation analysis were combined to explore metabolites that mediate these associations. The mechanisms linking PFAS to APOB and GGT are mainly related with amino acid and glycerophospholipid metabolism. High-dimensional mediation analysis showed that glycerophospholipids are the main markers of the association between PFAS and APOB, and that (R)-dihydromaleimide, Ile Leu, (R)-(+)-2-pyrrolidone-5-carboxylic acid, and L-glutamate are the main markers of the association between PFAS and GGT. In summary, overall associations between PFAS and specific indices of liver function were found using two statistical methods; the metabolic pathways and markers identified here may serve to prompt more detailed study in animal-based systems, as well as a similar detailed analysis in other populations.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BACKGROUND

Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods.

OBJECTS

We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data.

METHODS

Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA.

RESULTS

The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA.

CONCLUSIONS

Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

Association of exposures to serum terpenes with the prevalence of dyslipidemia: a population-based analysis

This study sought to examine hitherto unresearched relationships between serum terpenes and the prevalence of dyslipidemia. Serum terpenes such as limonene, α-pinene, and β-pinene from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) were used as independent variables in this cross-sectional study. Continuous lipid variables included total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), residual cholesterol (RC), and apolipoprotein B (Apo B). Binary lipid variables (elevated TC, ≥5.18 mmol/L; lowered HDL-C, <1.04 mmol/L in men, and <1.30 mmol/L in women; elevated non-HDL-C, ≥4.2 mmol/L; elevated TG, ≥1.7 mmol/L; elevated LDL-C, ≥3.37 mmol/L; elevated RC, ≥1.0 mmol/L; and elevated Apo B, ≥1.3 g/L) suggest dyslipidemia. The relationships between the mixture of serum terpenes with lipid variables were investigated using weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR). The study for TC, HDL-C, and non-HDL-C included a total of 1,528 people, whereas the analysis for TG, LDL-C, RC, and Apo B comprised 714 participants. The mean age of the overall participants was 47.69 years, and 48.77% were male. We found that tertiles of serum terpene were positively associated with binary (elevated TC, non-HDL-C, TG, LDL-C, RC, Apo B, and lowered HDL-C) and continuous (TC, non-HDL-C, TG, LDL-C, RC, and Apo B, but not HDL-C) serum lipid variables. WQS regression and BKMR analysis revealed that the mixture of serum terpenes was linked with the prevalence of dyslipidemia. According to our data, the prevalence of dyslipidemia was correlated with serum concentrations of three terpenes both separately and collectively.

Combined health effects of PM2.5 components on respiratory mortality in short-term exposure using BKMR: A case study in Sichuan, China

One of the major causes of global mortality is respiratory diseases. Fine particulate matter (PM2.5) increased the risk of respiratory death in short-term exposure. PM2.5 is the chemical mixture of components with different health effects. The combined health effects of PM2.5 are determined by the role of each component and the potential interaction between components, but they have not been studied in short-term exposure. Sichuan Province (SC), with high respiratory mortality and heavy PM2.5 pollution, had distinctive regional differences in four regions in sources and proportions of PM2.5, so it was divided into four regions to explore the combined health effects of PM2.5 components on respiratory mortality in short-term exposure and to identify the main hazardous components. Due to the multicollinear, interactive, and nonlinear characteristics of the associations between PM2.5 components and respiratory mortality, Bayesian kernel machine regression (BKMR) was used to characterize the combined health effects, along with quantile-based g-computation (QGC) as a reference. Positive combined effects of PM2.5 were found in all four regions of Sichuan using BKMR with excess risks (ER) of 0.0101-0.0132 (95 % CI: 0.0093-0.0158) and in the central basin and northwest basin using QGC with relative risks (RR) of 1.0064 (95 % CI: 1.0039, 1.0089) and 1.0044 (95 % CI: 1.0022, 1.0066), respectively. In addition, the adverse health effect was larger in cold seasons than that in warm seasons, so vulnerable people should reduce outdoor activities in heavily polluted days, especially in the cold season. For the components of PM2.5, the BC and OM mainly from traffic, dominated the adverse health effects on respiratory mortality. Furthermore, NO3- might aggravate the adverse health effects of BC/OM. Therefore, BC/OM and NO3- should be focused together in air pollution control.

Associations of exposure to heavy metal mixtures with kidney stone among U.S. adults: A cross-sectional study

Assessing the effects of heavy metals (HMs) on kidney stone is often limited to analyzing individual metal exposures, with studies on the effects of exposure to mixtures of HMs being scarce. To comprehensively evaluate the relationship between exposure to mixed HMs and kidney stones, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2007-2016, which included 7809 adults. We used multiple statistical methods, including multiple logistic regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp) and bayesian kernel machine regression (BKMR), to assess the association between single HM and mixed exposure to HMs and kidney stones. Firstly, in single exposure analysis, urinary cadmium (Cd) and cobalt (Co) demonstrated a positive association with the risk of kidney stones. Secondly, various other approaches consistently revealed that mixed exposure to HMs exhibited a positive association with kidney stone risk, primarily driven by Cd, Co, and barium (Ba) in urine, with these associations being particularly notable among the elderly population. Finally, both BKMR and survey-weighted generalized linear models consistently demonstrated a significant synergistic effect between urinary Co and urinary uranium (Ur) in elevating the risk of kidney stones. Overall, this study provides new epidemiological evidence that mixed exposure to HMs is associated with an increased risk of kidney stones. Further prospectively designed studies are needed to confirm these findings.

A State-of-the-Science Review on Metal Biomarkers

PURPOSE OF REVIEW

Biomarkers are commonly used in epidemiological studies to assess metals and metalloid exposure and estimate internal dose, as they integrate multiple sources and routes of exposure. Researchers are increasingly using multi-metal panels and innovative statistical methods to understand how exposure to real-world metal mixtures affects human health. Metals have both common and unique sources and routes of exposure, as well as biotransformation and elimination pathways. The development of multi-element analytical technology allows researchers to examine a broad spectrum of metals in their studies; however, their interpretation is complex as they can reflect different windows of exposure and several biomarkers have critical limitations. This review elaborates on more than 500 scientific publications to discuss major sources of exposure, biotransformation and elimination, and biomarkers of exposure and internal dose for 12 metals/metalloids, including 8 non-essential elements (arsenic, barium, cadmium, lead, mercury, nickel, tin, uranium) and 4 essential elements (manganese, molybdenum, selenium, and zinc) commonly used in multi-element analyses.

RECENT FINDINGS

We conclude that not all metal biomarkers are adequate measures of exposure and that understanding the metabolic biotransformation and elimination of metals is key to metal biomarker interpretation. For example, whole blood is a good biomarker of exposure to arsenic, cadmium, lead, mercury, and tin, but it is not a good indicator for barium, nickel, and uranium. For some essential metals, the interpretation of whole blood biomarkers is unclear. Urine is the most commonly used biomarker of exposure across metals but it should not be used to assess lead exposure. Essential metals such as zinc and manganese are tightly regulated by homeostatic processes; thus, elevated levels in urine may reflect body loss and metabolic processes rather than excess exposure. Total urinary arsenic may reflect exposure to both organic and inorganic arsenic, thus, arsenic speciation and adjustment for arsebonetaine are needed in populations with dietary seafood consumption. Hair and nails primarily reflect exposure to organic mercury, except in populations exposed to high levels of inorganic mercury such as in occupational and environmental settings. When selecting biomarkers, it is also critical to consider the exposure window of interest. Most populations are chronically exposed to metals in the low-to-moderate range, yet many biomarkers reflect recent exposures. Toenails are emerging biomarkers in this regard. They are reliable biomarkers of long-term exposure for arsenic, mercury, manganese, and selenium. However, more research is needed to understand the role of nails as a biomarker of exposure to other metals. Similarly, teeth are increasingly used to assess lifelong exposures to several essential and non-essential metals such as lead, including during the prenatal window. As metals epidemiology moves towards embracing a multi-metal/mixtures approach and expanding metal panels to include less commonly studied metals, it is important for researchers to have a strong knowledge base about the metal biomarkers included in their research. This review aims to aid metals researchers in their analysis planning, facilitate sound analytical decision-making, as well as appropriate understanding and interpretation of results.

The role of exposure to per- and polyfluoroalkyl substances in racial/ethnic disparities in hypertension: Results from the study of Women's health across the nation

BACKGROUND

Racial/ethnic disparities in hypertension are a pressing public health problem. The contribution of environmental pollutants including PFAS have not been explored, even though certain PFAS are higher in Black population and have been associated with hypertension.

OBJECTIVES

We examined the extent to which racial/ethnic disparities in incident hypertension are explained by racial/ethnic differences in serum PFAS concentrations.

METHODS

We included 1058 hypertension-free midlife women with serum PFAS concentrations in 1999-2000 from the multi-racial/ethnic Study of Women's Health Across the Nation with approximately annual follow-up visits through 2017. Causal mediation analysis was conducted using accelerated failure time models. Quantile-based g-computation was used to evaluate the joint effects of PFAS mixtures.

RESULTS

During 11,722 person-years of follow-up, 470 participants developed incident hypertension (40.1 cases per 1000 person-years). Black participants had higher risks of developing hypertension (relative survival: 0.58, 95% CI: 0.45-0.76) compared with White participants, which suggests racial/ethnic disparities in the timing of hypertension onset. The percent of this difference in timing that was mediated by PFAS was 8.2% (95% CI: 0.7-15.3) for PFOS, 6.9% (95% CI: 0.2-13.8) for EtFOSAA, 12.7% (95% CI: 1.4-22.6) for MeFOSAA, and 19.1% (95% CI: 4.2, 29.0) for PFAS mixtures. The percentage of the disparities in hypertension between Black versus White women that could have been eliminated if everyone's PFAS concentrations were dropped to the 10th percentiles observed in this population was 10.2% (95% CI: 0.9-18.6) for PFOS, 7.5% (95% CI: 0.2-14.9) for EtFOSAA, and 17.5% (95% CI: 2.1-29.8) for MeFOSAA.

CONCLUSIONS

These findings suggest differences in PFAS exposure may be an unrecognized modifiable risk factor that partially accounts for racial/ethnic disparities in timing of hypertension onset among midlife women. The study calls for public policies aimed at reducing PFAS exposures that could contribute to reductions in racial/ethnic disparities in hypertension.

Serum Nutritional Biomarkers and All-Cause and Cause-Specific Mortality in U.S. Adults with Metabolic Syndrome: The Results from National Health and Nutrition Examination Survey 2001-2006

BACKGROUND

There is limited research on the associations between serum nutritional biomarkers and mortality risk in patients with metabolic syndrome (MetS). Existing studies merely investigated the single-biomarker effect. Thus, this study aimed to investigate the combined effect of nutritional biomarker mixtures and mortality risk using the Bayesian kernel machine regression (BKMR) model in patients with MetS.

METHODS

We included the MetS patients, defined according to the 2018 Guideline on the Management of Blood Cholesterol from the National Health and Nutrition Examination Survey (NHANES) 2001-2006. A total of 20 serum nutritional biomarkers were measured and evaluated in this study. The Cox proportional hazard model and restricted cubic spline models were used to evaluate the individual linear and non-linear association of 20 nutritional biomarkers with mortality risk. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixture of nutritional biomarkers and mortality risk.

RESULTS

A total of 1455 MetS patients had a median age of 50 years (range: 20-85). During a median of 17.1-year follow-up, 453 (24.72%) died: 146 (7.20%) caused by CVD and 87 (5.26%) by cancer. Non-linear and linear analyses indicated that, in total, eight individual biomarkers (α-carotene, β-carotene, bicarbonate, lutein/zeaxanthin, lycopene, potassium, protein, and vitamin A) were significantly associated with all-cause mortality (all p-values < 0.05). Results from BKMR showed an association between the low levels of the mixture of nutritional biomarkers and high risk of all-cause mortality with the estimated effects ranging from 0.04 to 0.14 (referent: medians). α-Carotene (PIP = 0.971) and potassium (PIP = 0.796) were the primary contributors to the combined effect of the biomarker mixture. The nutritional mixture levels were found to be negatively associated with the risk of cardiovascular disease (CVD) mortality and positively associated with the risk of cancer mortality. After it was stratified by nutrients, the mixture of vitamins showed a negative association with all-cause and CVD mortality, whereas the mixture of mineral-related biomarkers was positively associated with all-cause and cancer mortality.

CONCLUSION

Our findings support the evidence that nutritional status was associated with long-term health outcomes in MetS patients. It is necessary for MetS patients to be concerned with certain nutritional status (i.e., vitamins and mineral elements).

Environmental exposure to perchlorate, nitrate and thiocyanate, and thyroid function in Chinese adults: A community-based cross-sectional study

BACKGROUND

Evidence on environmental exposure to perchlorate, nitrate, and thiocyanate, three thyroidal sodium iodine symporter (NIS) inhibitors, and thyroid function in the Chinese population remains limited.

OBJECTIVE

To investigate the associations of environmental exposure to perchlorate, nitrate, and thiocyanate with markers of thyroid function in Chinese adults.

METHODS

A total of 2441 non-pregnant adults (mean age 50.4 years and 39.1% male) with a median urinary iodine of 180.1 μg/L from four communities in Shenzhen were included in this cross-sectional study. Urinary perchlorate, nitrate, thiocyanate, and thyroid profiles, including serum free thyroxine (FT4), total thyroxine (TT4), free triiodothyronine (FT3), total triiodothyronine (TT3), and thyroid stimulating hormone (TSH), were measured. Generalized linear model was applied to investigate the single-analyte associations. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) models were used to examine the association between the co-occurrence of three anions and thyroid profile.

RESULTS

The median levels of urinary perchlorate, nitrate, and thiocyanate were 5.8 μg/g, 76.4 mg/g, and 274.1 μg/g, respectively. After adjusting for confounders, higher urinary perchlorate was associated with lower serum FT4, TT4, and TT3, and higher serum FT3 and TSH (all P < 0.05). Comparing extreme tertiles, subjects in the highest nitrate tertile had marginally elevated TT3 (β: 0.02, 95% CI: 0.00-0.04). Each 1-unit increase in log-transformed urinary thiocyanate was associated with a 0.04 (95% CI: 0.02-0.06) pmol/L decrease in serum FT3. The WQS indices were inversely associated with serum FT4, TT4, and FT3 (all P < 0.05). In the BKMR model, the mixture of three anions was inversely associated with serum FT4, TT4, and FT3.

CONCLUSIONS

Our study provides evidence that individual and combined environmental exposure to perchlorate, nitrate, and thiocyanate are associated with significant changes in thyroid function markers in the Chinese population with adequate iodine intake.

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.

How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis

Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants' residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: -0.09, -0.18-0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36-0.98) and obesity (RR, 95 %CI: 0.81, 0.64-1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health.

A review of practical statistical methods used in epidemiological studies to estimate the health effects of multi-pollutant mixture

Environmental risk factors have been implicated in adverse health effects. Previous epidemiological studies on environmental risk factors mainly analyzed the impact of single pollutant exposure on health, while in fact, humans are constantly exposed to a complex mixture consisted of multiple pollutants/chemicals. In recent years, environmental epidemiologists have sought to assess adverse health effects of exposure to multi-pollutant mixtures based on the diversity of real-world environmental pollutants. However, the statistical challenges are considerable, for instance, multicollinearity and interaction among components of the mixture complicate the statistical analysis. There is currently no consensus on appropriate statistical methods. Here we summarized the practical statistical methods used in environmental epidemiology to estimate health effects of exposure to multi-pollutant mixture, such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regressions, shrinkage methods (least absolute shrinkage and selection operator, elastic network model, adaptive elastic-net model, and principal component analysis), environment-wide association study (EWAS), etc. We sought to review these statistical methods and determine the application conditions, strengths, weaknesses, and result interpretability of each method, providing crucial insight and assistance for addressing epidemiological statistical issues regarding health effects from multi-pollutant mixture.

Sporoderm-broken spores of Ganoderma lucidum alleviates liver injury induced by DBP and BaP co-exposure in rat

Dibutyl phthalate (DBP) and Benzo(a)pyrene (BaP) are ubiquitous contaminants in environment and foodstuffs, which increase the chance of their combined exposure to humans in daily life. However, the combined effects of DBP and BaP on liver and the underlying mechanisms are still unclear. In this study, we explored the combined effects of DBP and BaP on liver and the potential mechanisms in a rat model. We found that DBP and BaP co-exposure activated the MyD88/NF-κB pathway through increasing TLR4 acetylation (TLR4ac) level, leading to the imbalance of pro-inflammatory factors (CXCL-13, IL-6 and TNF-α) and anti-inflammatory factors (IL-10), ultimately resulting in liver tissue damage and functional changes. Sporoderm-broken spores of Ganoderma lucidum (SSGL) had strong alleviating effects on liver injury induced by DBP and BaP co-exposure. Our study found that SSGL suppressed TLR4ac-regulated MyD88/NF-κB signaling to reduce the release of pro-inflammatory factors, and promote the secretion of IL-10, thus alleviating liver injury caused by DBP and BaP co-exposure. In conclusion, SSGL contributed to liver protection against DBP and BaP-induced liver injury in rats via suppressing the TLR4ac-regulated MyD88/NF-κB signaling.

Pre- and postnatal exposure to multiple ambient air pollutants and child behavioral problems at five years of age

Ambient air pollution is emerging as a risk factor for adverse neurological symptoms and early childhood diseases. This study aimed to evaluate the association between pre- and postnatal exposure to air pollutants and childhood behavior by using MOCEH prospective birth cohort data. In total, 353 mother-child pairs at birth, who completed child behavioral assessments using the Korean version of the Child Behavior Checklist at five years of age, were included in the study. Multivariate linear regression (MLR) for single pollutant and Bayesian kernel machine regression (BKMR) for multiple pollutants were conducted. MLR analysis showed that air pollutant exposures during the first trimester were significantly associated with the internalizing problems score after adjusting for covariates. The estimates were 0.19 (0.05-0.32) per 1 μg/m³ increase in PM_2.5, 0.13 (0.04-0.22) per 1 μg/m³ increase in PM₁₀, and 0.20 (0.02-0.37) per 1 ppb increase in NO₂. The BKMR model analysis revealed that the overall effects of multiple air pollutants during the first trimester of pregnancy and 0-6 months of the infantile period were significantly associated with behavioral problems. Boys showed a stronger associations than girls. Taken together, these results showed that the first trimester of pregnancy and 0-6 months of the infantile period were important for air pollutant exposure because exposure at these periods was associated with behavioral problems in 5-year-old children. Future efforts are required to control air pollution levels and reduce the health burden of vulnerable populations, including pregnant women and children.

Estimating the health effects of environmental mixtures using principal stratification

The control of ambient air quality in the United States has been a major public health success since the passing of the Clean Air Act, with particulate matter (PM) reductions resulting in an estimated 160 000 premature deaths prevented in 2010 alone. Currently, public policy is oriented around lowering the levels of individual pollutants and this focus has driven the nature of much epidemiological research. Recently, attention has been given to viewing air pollution as a complex mixture and to developing a multi-pollutant approach to controlling ambient concentrations. We present a statistical approach for estimating the health impacts of complex environmental mixtures using a mixture-altering contrast, which is any comparison, intervention, policy, or natural experiment that changes a mixture's composition. We combine the notion of mixture-altering contrasts with sliced inverse regression, propensity score matching, and principal stratification to assess the health effects of different air pollution chemical mixtures. We demonstrate the application of this approach in an analysis of the health effects of wildfire PM air pollution in the Western US.

Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR)

It is well documented that fine particles matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are associated with a range of adverse health outcomes. However, most epidemiologic studies have focused on understanding their additive effects, despite that individuals are exposed to multiple air pollutants simultaneously that are likely correlated with each other. Therefore, we applied a novel method - Bayesian Kernel machine regression (BKMR) and conducted a population-based cohort study to assess the individual and joint effect of air pollutant mixtures (PM2.5, O3, and NO2) on all-cause mortality among the Medicare population in 15 cities with 656 different ZIP codes in the southeastern US. The results suggest a strong association between pollutant mixture and all-cause mortality, mainly driven by PM2.5. The positive association of PM2.5 with mortality appears stronger at lower percentiles of other pollutants. An interquartile range change in PM2.5 concentration was associated with a significant increase in mortality of 1.7 (95% CI: 0.5, 2.9), 1.6 (95% CI: 0.4, 2.7) and 1.4 (95% CI: 0.1, 2.6) standard deviations (SD) when O3 and NO2 were set at the 25th, 50th, and 75th percentiles, respectively. BKMR analysis did not identify statistically significant interactions among PM2.5, O3, and NO2. However, since the small sub-population might weaken the study power, additional studies (in larger sample size and other regions in the US) are in need to reinforce the current finding.

Updating the European Union's regulation on classification, labelling and packaging of substances and mixtures (CLP): A key opportunity for consumers, workers and stakeholders with interests in the legislation and toxicology of hazardous chemicals

Recent advancements in toxicology and the European Union's Green Deal, with its Chemicals Strategy for Sustainability, have paved the way for major changes in EU legislation on the control of environmental chemicals for a cleaner and safer environment. Another substantial legislative advancement underway is the update of the "Regulation on Classification, Labelling and Packaging of Substances and Mixtures (CLP)," an ambitious piece of EU legislation with exceptional scientific toxicological background in identifying a hazard, aiming at better protecting its citizens and the environment from the risk of chemical substances and products, the occupational settings included. Update of CLP legislation additionally aims at facilitating the free exchange of chemicals in the European Internal Market, provided that proper labelling and packaging processes are implemented. Participation in the ongoing online public consultation on these issues, ending on November 15, 2021, is of key relevance to ensure a transparent and effective definition of such an important piece of legislation, fully compliant with current EU priorities in terms of human and environmental protection and animal welfare.

Data analysis strategies for the characterization of chemical contaminant mixtures. Fish as a case study

Thousands of chemicals are potentially contaminating the environment and food resources, covering a wide spectrum of molecular structures, physico-chemical properties, sources, environmental behavior and toxic profiles. Beyond the description of the individual chemicals, characterizing contaminant mixtures in related matrices has become a major challenge in ecological and human health risk assessments. Continuous analytical developments, in the fields of targeted (TA) and non-targeted analysis (NTA), have resulted in ever larger sets of data on associated chemical profiles. More than ever, the implementation of advanced data analysis strategies is essential to elucidate profiles and extract new knowledge from these large data sets. Specifically focusing on the data analysis step, this review summarizes the recent progress in integrating data analysis tools into TA and NTA workflows to address the challenging characterization of chemical mixtures in environmental and food matrices. As fish matrices are relevant in both aquatic pollution and consumer exposure perspectives, fish was chosen as the main theme to illustrate this review, although the present document is equally relevant to other food and environmental matrices. The key features of TA and NTA data sets were reviewed to illustrate the challenges associated with their analysis. Advanced filtering strategies to mine NTA data sets are presented, with a particular focus on chemical filters and discriminant analysis. Further, the applications of supervised and unsupervised multivariate analysis methods to characterize exposure to chemical mixtures, and their associated challenges, is discussed.

Praegnatio Perturbatio-Impact of Endocrine-Disrupting Chemicals

The burden of adverse pregnancy outcomes such as preterm birth and low birth weight is considerable across the world. Several risk factors for adverse pregnancy outcomes have been identified. One risk factor for adverse pregnancy outcomes receiving considerable attention in recent years is gestational exposure to endocrine-disrupting chemicals (EDCs). Humans are exposed to a multitude of environmental chemicals with known endocrine-disrupting properties, and evidence suggests exposure to these EDCs have the potential to disrupt the maternal-fetal environment culminating in adverse pregnancy and birth outcomes. This review addresses the impact of maternal and fetal exposure to environmental EDCs of natural and man-made chemicals in disrupting the maternal-fetal milieu in human leading to adverse pregnancy and birth outcomes-a risk factor for adult-onset noncommunicable diseases, the role lifestyle and environmental factors play in mitigating or amplifying the effects of EDCs, the underlying mechanisms and mediators involved, and the research directions on which to focus future investigations to help alleviate the adverse effects of EDC exposure.

Endocrine-disrupting chemicals: implications for human health

Since reports published in 2015 and 2016 identified 15 probable exposure-outcome associations, there has been an increase in studies in humans of exposure to endocrine-disrupting chemicals (EDCs) and a deepened understanding of their effects on human health. In this Series paper, we have reviewed subsequent additions to the literature and identified new exposure-outcome associations with substantial human evidence. Evidence is particularly strong for relations between perfluoroalkyl substances and child and adult obesity, impaired glucose tolerance, gestational diabetes, reduced birthweight, reduced semen quality, polycystic ovarian syndrome, endometriosis, and breast cancer. Evidence also exists for relations between bisphenols and adult diabetes, reduced semen quality, and polycystic ovarian syndrome; phthalates and prematurity, reduced anogenital distance in boys, childhood obesity, and impaired glucose tolerance; organophosphate pesticides and reduced semen quality; and occupational exposure to pesticides and prostate cancer. Greater evidence has accumulated than was previously identified for cognitive deficits and attention-deficit disorder in children following prenatal exposure to bisphenol A, organophosphate pesticides, and polybrominated flame retardants. Although systematic evaluation is needed of the probability and strength of these exposure-outcome relations, the growing evidence supports urgent action to reduce exposure to EDCs.

Long-term exposure to a mixture of industrial SO2, NO2, and PM2.5 and anti-citrullinated protein antibody positivity

BACKGROUND

Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and fine particles matter (PM_2.5) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA).

METHODS

Serum ACPA was determined for 7600 randomly selected CARTaGENE general population subjects in Quebec, Canada. Industrial SO₂, NO₂, and PM_2.5 concentrations, estimated by the California Puff (CALPUFF) atmospheric dispersion model, were assigned based on residential postal codes at the time of sera collection. Single-exposure logistic regressions were performed for ACPA positivity defined by 20 U/ml, 40 U/ml, and 60 U/ml thresholds, adjusting for age, sex, French Canadian origin, smoking, and family income. Associations between regional overall PM_2.5 exposure and ACPA positivity were also investigated. The associations between the combined three industrial exposures and the ACPA positivity were assessed by weighted quantile sum (WQS) regressions.

RESULTS

Significant associations between individual industrial exposures and ACPA positivity defined by the 20 U/ml threshold were seen with single-exposure logistic regression models, for industrial emissions of PM_2.5 (odds ratio, OR = 1.19, 95% confidence intervals, CI: 1.04-1.36) and SO₂ (OR = 1.03, 95% CI: 1.00-1.06), without clear associations for NO₂ (OR = 1.01, 95% CI: 0.86-1.17). Similar findings were seen for the 40 U/ml threshold, although at 60 U/ml, the results were very imprecise. The WQS model demonstrated a positive relationship between combined industrial exposures and ACPA positivity (OR = 1.36, 95% CI: 1.10-1.69 at 20 U/ml) and suggested that industrial PM_2.5 may have a closer association with ACPA positivity than the other exposures. Again, similar findings were seen with the 40 U/ml threshold, though 60 U/ml results were imprecise. No clear association between ACPA and regional overall PM_2.5 exposure was seen.

CONCLUSIONS

We noted positive associations between ACPA and industrial emissions of PM_2.5 and SO₂. Industrial PM_2.5 exposure may play a particularly important role in this regard.

Per- and poly-fluoroalkyl substances and bone mineral density: Results from the Bayesian weighted quantile sum regression

Per- and poly-fluoroalkyl substances (PFAS) are chemicals, detected in 95% of Americans, that induce osteotoxicity and modulate hormones, thereby influencing bone health. Previous studies found associations between individual PFAS and bone mineral density in adults but did not analyze their combined effects.

OBJECTIVE

To extend weighted quantile sum (WQS) regression to a Bayesian framework (Bayesian extension of the WQS regression [BWQS]) and determine the association between a mixture of serum PFAS and mineral density in lumbar spine, total, and neck femur in 499 adults from the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES).

METHODS

We used BWQS to assess the combined association of eight PFAS, as a mixture, with bone mineral density in adults. As secondary analyses, we focused on vulnerable populations (men over 50 years and postmenopausal women). Analyses were adjusted for sociodemographic factors. Sensitivity analyses included bone mineral density associations with individual compounds and results from WQS regressions.

RESULTS

The mean age was 55 years old (SD = 1) with average spine, total, and neck femur mineral densities of 1.01 (SD = 0.01), 0.95 (SD = 0.01), and 0.78 (SD = 0.01) gm/cm2, respectively. PFAS mixture levels showed no evidence of association with mineral density (spine: β = -0.004; 95% credible interval [CrI] = -0.04, 0.04; total femur: β = 0.002; 95% CrI = -0.04, 0.05; femur neck: β = 0.005; 95%CrI = -0.03, 0.04) in the overall population. Results were also null in vulnerable populations. Findings were consistent across sensitivity analyses.

CONCLUSIONS

We introduced a Bayesian extension of WQS and found no evidence of the association between PFAS mixture and bone mineral density.

Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Challenges Raised by Mediation Analysis in a High-Dimension Setting

BACKGROUND

Mediation analysis is used in epidemiology to identify pathways through which exposures influence health. The advent of high-throughput (omics) technologies gives opportunities to perform mediation analysis with a high-dimension pool of covariates.

OBJECTIVE

We aimed to highlight some biostatistical issues of this expanding field of high-dimension mediation.

DISCUSSION

The mediation techniques used for a single mediator cannot be generalized in a straightforward manner to high-dimension mediation. Causal knowledge on the relation between covariates is required for mediation analysis, and it is expected to be more limited as dimension and system complexity increase. The methods developed in high dimension can be distinguished according to whether mediators are considered separately or as a whole. Methods considering each potential mediator separately do not allow efficient identification of the indirect effects when mutual influences exist among the mediators, which is expected for many biological (e.g., epigenetic) parameters. In this context, methods considering all potential mediators simultaneously, based, for example, on data reduction techniques, are more adapted to the causal inference framework. Their cost is a possible lack of ability to single out the causal mediators. Moreover, the ability of the mediators to predict the outcome can be overestimated, in particular because many machine-learning algorithms are optimized to increase predictive ability rather than their aptitude to make causal inference. Given the lack of overarching validated framework and the generally complex causal structure of high-dimension data, analysis of high-dimension mediation currently requires great caution and effort to incorporate a priori biological knowledge. https://doi.org/10.1289/EHP6240.

Arsenic and benzo[a]pyrene co-exposure acts synergistically in inducing cancer stem cell-like property and tumorigenesis by epigenetically down-regulating SOCS3 expression

Arsenic and benzo[a]pyrene (BaP) are among the most common environmental carcinogens causing lung cancer. Millions of people are exposed to arsenic through consuming arsenic-contaminated drinking water. High levels of BaP are found in well-done barbecued meat and other food in addition to cigarette smoke. Hence, arsenic and BaP co-exposure in humans is common. However, the combined health effect and the underlying mechanism of arsenic and BaP co-exposure have not been well-understood. In this study we investigate the combined tumorigenic effect of arsenic and BaP co-exposure and the mechanism using both cell culture and mouse models. It was found that arsenic (sodium arsenite, 1.0 µM) and BaP (2.5 µM) co-exposure for 30 weeks synergizes in inducing malignant transformation of immortalized non-tumorigenic human bronchial epithelial cells and cancer stem cell (CSC)-like property to enhance their tumorigenicity. In animal studies, A/J mice were exposed to arsenic in drinking water (sodium arsenite, 20 ppm) starting from gestation day 18. After birth, the dams continuously received arsenic water throughout lactation. At weaning (3 weeks of age), male offspring were exposed to either arsenic alone via drinking the same arsenic water or exposed to arsenic plus BaP. BaP was administered via oral gavage (3 µmol per mouse per week) once a week starting from 3 weeks of age for 8 weeks. All mice were euthanized 34-weeks after the first BaP exposure. It was found that mice in control and arsenic exposure alone group did not develop lung tumors. All mice in BaP exposure alone group developed lung adenomas. However, arsenic and BaP co-exposure synergized in increasing lung tumor multiplicity and tumor burden. Furthermore, 30% of mice in arsenic and BaP co-exposure group also developed lung adenocarcinomas. Mechanistic studies revealed that arsenic and BaP co-exposure does not produce more BPDE-DNA adducts than BaP exposure alone; but acts synergistically in activating aryl hydrocarbon receptor (AhR) to up-regulate the expression of a histone H3 lysine 9 methyltransferase SUV39H1 and increase the level of suppressive H3 lysine 9 dimethylation (H3K9me2), which down-regulates the expression of tumor suppressive SOCS3 leading to enhanced activation of Akt and Erk1/2 to promote cell transformation, CSC-like property and tumorigenesis. Together, these findings suggest that arsenic and BaP co-exposure synergizes in causing epigenetic dysregulation to enhance cell transformation, CSC-like property and tumorigenesis.

Environmental mixtures and children's health: identifying appropriate statistical approaches

PURPOSE OF REVIEW

Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context.

RECENT FINDINGS

New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows.

SUMMARY

Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.

Investigating associations between anti-nuclear antibody positivity and combined long-term exposures to NO2, O3, and PM2.5 using a Bayesian kernel machine regression approach

BACKGROUND

Air pollution has many adverse health effects, but the combined or synergistic effects of multiple ambient air pollutants on anti-nuclear antibodies (ANA, a serologic marker of systemic autoimmune rheumatic disease, SARDs) have never been assessed.

OBJECTIVE

To flexibly model ANA and individual and joint associations of long-term exposures to nitrogen dioxide (NO₂), ozone (O₃), and fine particles matter (PM_2.5) using a Bayesian Kernel machine regression (BKMR) approach and to compare the results to those from individual logistic regressions.

METHODS

Serum ANA positivity was determined for randomly selected CARTaGENE general population subjects in Quebec, Canada. CARTaGENE is a public research platform created for investigating the associations of environmental, genomic, and lifestyle factors on chronic diseases. Ambient NO₂, O₃, and PM_2.5 estimates, derived from ground-measurement and chemical-transport-model simulated concentrations, were assigned to subjects based on residential postal codes at the time of blood collection. Our models adjusted for age, sex, French Canadian origin, smoking, and family income.

RESULTS

Concentrations of NO₂, O₃, and PM_2.5 were closely correlated in space. In the 5485 CARTaGENE subjects studied, we did not see clear associations between NO₂, PM_2.5 or O₃ and ANA positivity, with either the BKMR or logistic models.

CONCLUSIONS

BKMR did not uncover associations between ANA positivity and individual levels or combined exposures of NO₂, O₃, and PM_2.5; neither did simpler logistic models. Additional studies (in younger populations, in distinct race/ethnicity groups, and/or in jurisdictions with high air pollution levels) would be helpful to reinforce current findings.

Effects of environmental stressors on stem cells

Environmental toxicants are ubiquitous, and many are known to cause harmful health effects. However, much of what we know or think we know concerning the targets and long-term effects of exposure to environmental stressors is sadly lacking. Toxicant exposure may have health effects that are currently mischaracterized or at least mechanistically incompletely understood. While much of the recent excitement about stem cells (SCs) focuses on their potential as therapeutic agents, they also offer a valuable resource to give us insight into the mechanisms and risks of toxicant effects. Not only as a response to the increasing ethical pressure to reduce animal testing, SC studies allow us valuable insight into the true effects of human exposure to environmental stressors under controlled conditions. We present a review of the history of publications on the effects of environmental stressors on SCs, followed by a consolidation of the literature over the past five years on a subset of key environmental stressors of importance to human health and their effects on both embryonic and tissue SCs. The review will make constructive suggestions as to areas of toxicant research where further studies are needed, as well as making indications of the potential utility for advancing knowledge and directing research on environmental toxicology.

Data Science in Environmental Health Research

PURPOSE OF REVIEW

Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research.

RECENT FINDINGS

We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research.

SUMMARY

In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.