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

Prenatal exposure to air pollutant mixtures and birthweight in the upstate KIDS cohort

BACKGROUND

Single-pollutant models have linked prenatal PM2.5 exposure to lower birthweight. However, analyzing air pollutant mixtures better captures pollutant interactions and total effects. Unfortunately, strong correlations between pollutants restrict traditional methods.

OBJECTIVES

We explored the association between exposure to a mixture of air pollutants during different gestational age windows of pregnancy and birthweight.

METHODS

We included 4,635 mother-infant dyads from a New York State birth cohort born 2008-2010. Air pollution data were sourced from the EPA's Community Multiscale Air Quality model and matched to the census tract centroid of each maternal home address. Birthweight and gestational age were extracted from vital records. We applied linear regression to study the association between prenatal exposure to PM2.5, PM10, NOX, SO2, and CO and birthweight during six sensitive windows. We then utilized Bayesian kernel machine regression to examine the non-linear effects and interactions within this five-pollutant mixture. Final models adjusted for maternal socio-demographics, infant characteristics, and seasonality.

RESULTS

Single-pollutant linear regression models indicated that most pollutants were associated with a decrement in birthweight, specifically during the two-week window before birth. An interquartile range increase in PM2.5 exposure (IQR: 3.3 µg/m3) from the median during this window correlated with a 34 g decrement in birthweight (95 % CI: -54, -14), followed by SO2 (IQR: 2.0 ppb; β: -31), PM10 (IQR: 4.6 µg/m3; β: -29), CO (IQR: 60.8 ppb; β: -27), and NOX (IQR: 7.9 ppb; β: -26). Multi-pollutant BKMR models revealed that PM2.5, NOX, and CO exposure were negatively and non-linearly linked with birthweight. As the five-pollutant mixture increased, birthweight decreased until the median level of exposure.

DISCUSSION

Prenatal exposure to air pollutants, notably PM2.5, during the final two weeks of pregnancy may negatively impact birthweight. The non-linear relationships between air pollution and birthweight highlight the importance of studying pollutant mixtures and their interactions.

Association of phthalate exposure with type 2 diabetes and the mediating effect of oxidative stress: A case-control and computational toxicology study

Phthalic acid esters (PAEs) are widely used as plasticizers and have been suggested to engender adverse effects on glucose metabolism. However, epidemiological data regarding the PAE mixture on type 2 diabetes (T2DM), as well as the mediating role of oxidative stress are scarce. This case-control study enrolled 206 T2DM cases and 206 matched controls in Guangdong Province, southern China. The concentrations of eleven phthalate metabolites (mPAEs) and the oxidative stress biomarker 8-hydroxy-2'-deoxyguanosine (8-OHdG) in urine were determined. Additionally, biomarkers of T2DM in paired serum were measured to assess glycemic status and levels of insulin resistance. Significantly positive associations were observed for mono-(2-ethylhexyl) phthalate (MEHP) and Mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) with T2DM (P < 0.001). Restricted cubic spline modeling revealed a non-linear dose-response relationship between MEHHP and T2DM (Pnon-linear = 0.001). The Bayesian kernel machine regression and quantile g-computation analyses demonstrated a significant positive joint effect of PAE exposure on T2DM risk, with MEHHP being the most significant contributor. The mediation analysis revealed marginal evidence that oxidative stress mediated the association between the mPAEs mixture and T2DM, while 8-OHdG respectively mediated 26.88 % and 12.24 % of MEHP and MEHHP on T2DM risk individually (Pmediation < 0.05). Di(2-ethylhexyl) phthalate (DEHP, the parent compound for MEHP and MEHHP) was used to further examine the potential molecular mechanisms by in silico analysis. Oxidative stress may be crucial in the link between DEHP and T2DM, particularly in the reactive oxygen species metabolic process and glucose import/metabolism. Molecular simulation docking experiments further demonstrated the core role of Peroxisome Proliferator Activated Receptor alpha (PPARα) among the DEHP-induced T2DM. These findings suggest that PAE exposure can alter oxidative stress via PPARα, thereby increasing T2DM risk.

Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women

The use of high-dimensional data has expanded in many fields, including in clinical research, thus making variable selection methods increasingly important compared to traditional statistical approaches. The work aims to compare the performance of three supervised Bayesian variable selection methods to detect the most important predictors among a high-dimensional set of variables and to provide useful and practical guidelines of their use. We assessed the variable selection ability of: (1) Bayesian Kernel Machine Regression (BKMR), (2) Bayesian Semiparametric Regression (BSR), and (3) Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) regression on simulated data of different dimensions and under three scenarios with disparate predictor-response relationships and correlations among predictors. This is the first study describing when one model should be preferred over the others and when methods achieve comparable results. BKMR outperformed all other models with small synthetic datasets. BSR was strongly dependent on the choice of its own intrinsic parameter, but its performance was comparable to BKMR with large datasets. BLASSO should be preferred only when it is reasonable to hypothesise the absence of synergies between predictors and the presence of monotonous predictor-outcome relationships. Finally, we applied the models to a real case study and assessed the relationships among anthropometric, biochemical, metabolic, cardiovascular, and inflammatory variables with weight loss in 755 hospitalised obese women from the Follow Up OBese patients at AUXOlogico institute (FUOBAUXO) cohort.

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.

Methylated polycyclic aromatic hydrocarbons from household coal use across the life course and risk of lung cancer in a large cohort of 42,420 subjects in Xuanwei, China

BACKGROUND

We previously showed that exposure to 5-methylchrysene (5MC) and other methylated polycyclic aromatic hydrocarbons (PAHs) best explains lung cancer risks in a case-control study among non-smoking women using smoky coal in China. Time-related factors (e.g., age at exposure) and non-linear relations were not explored.

OBJECTIVE

We investigated the relation between coal-derived air pollutants and lung cancer mortality using data from a large retrospective cohort.

METHODS

Participants were smoky (bituminous) or smokeless (anthracite) coal users from a cohort of 42,420 subjects from four communes in XuanWei. Follow-up was from 1976 to 2011, during which 4,827 deaths from lung-cancer occurred. Exposures were predicted for 43 different pollutants. Exposure clusters were identified using hierarchical clustering. Cox regression was used to estimate exposure-response relations for 5MC, while effect modification by age at exposure was investigated for cluster prototypes. A Bayesian penalized multi-pollutant model was fitted on a nested case-control sample, with more restricted models fitted to investigate non-linear exposure-response relations.

RESULTS

We confirmed the strong exposure-response relation for 5MC (Hazard Ratio [95% Confidence Interval] = 2.5 [2.4, 2.6] per standard-deviation (SD)). We identified four pollutant clusters, with all but two PAHs in a single cluster. Exposure to PAHs in the large cluster was associated with a higher lung cancer mortality rate (HR [95%CI] = 2.4 [2.2, 2.6] per SD), while exposure accrued before 18 years of age appeared more important than adulthood exposures. Results from the multi-pollutant model identified anthanthrene (ANT) and benzo(a)chrysene (BaC) as risk factors. 5MC remained strongly associated with lung cancer in models that included ANT and BaC and also benzo(a)pyrene (BaP).

CONCLUSION

We confirmed the link between PAH exposures and lung cancer in smoky coal users and found exposures before age 18 to be especially important. We found some evidence for the carcinogen 5MC and non-carcinogens ANT and BaC.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling

Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.

Prenatal exposure to mixtures of persistent environmental chemicals and fetal growth outcomes in Western Australia

BACKGROUND

Environmental chemicals have been implicated in the etiology of impaired fetal growth. However, few studies have assessed the effects of chemical mixtures or considered the possibility of non-monotonic exposure-response relationships for chemicals that act through the endocrine system.

METHODS

We assessed exposure to polybrominated diphenyl ethers, organochlorine pesticides, metals, and perfluorinated alkyl substances in blood and urine samples collected approximately two weeks prior to delivery in 166 non-smoking pregnant women, and subsequent birth weight, length, and head circumference of neonates who were part of the Australian Maternal Exposures to Toxic Substances (AMETS) study. We used Bayesian structured additive regression models with spike-slab priors to estimate mixture effects, identify important exposures, and model non-linearity in exposure-response relationships.

RESULTS

Mixtures of polybrominated diphenyl ethers, organochlorine pesticides, metals, and perfluorinated alkyl substances were not associated with fetal growth outcomes. Estimated change in fetal growth outcomes for an increase in exposure from the 25th to 75th percentile suggested no meaningful associations; the strongest evidence was for a small inverse association between birth weight and cesium exposure measured in whole blood (-124 g, 90% credible interval: -240 to -3 g). We identified several chemicals that may be associated with fetal growth non-linearly; however, 90% credible intervals contained small values consistent with no meaningful association.

CONCLUSIONS

Using a Bayesian penalized regression method, we assessed the shapes of exposure-response relationships, controlled for confounding by co-exposure, and estimated the single and combined effects of a large mixture of correlated environmental chemicals on fetal growth. Our findings, based on a small sample of mother-neonate pairs, suggest that mixtures of persistent chemicals are not associated with birth weight, length, and head circumference. The potential for non-monotonic relationships between environmental chemicals and fetal growth outcomes warrants further study.

Associations between human internal chemical exposure to Persistent Organic Pollutants (POPs) and In Vitro Fertilization (IVF) outcomes: Systematic review and evidence map of human epidemiological evidence

The impact of environmental chemicals like persistent organic pollutants (POPs) on reproductive health is still poorly understood, despite the high societal and economical costs. The aim of the present study was to systematically review and evaluate the human evidence on the associations between internal levels of POPs and in vitro Fertilization (IVF) outcomes among women. We applied a protocol based on the National Toxicology Program Office of Health Assessment and Translation's guidelines for the study search, selection and quality assessment. Fifteen studies were finally retained in the present work. The results showed that main families of POPs are still pervasive in follicular fluid and serum of women undergoing IVF treatments. Globally, we found inconsistent findings across studies for specific exposure-outcome dyads, suggesting that adverse effects of POPs on IVF outcomes cannot be ruled out. Specifically, there is evidence that POPs, notably some polychlorinated biphenyls and organochlorine pesticides, may impair embryo quality and pregnancy rates. Most studies have been performed in small cohorts (n<50) and focused on PCBs and OCPs, whereas major research gaps remain for emerging compounds (e.g. perfluoroalkylated substances) and the most clinically relevant outcome, live birth rate. The overall evidence presented 'serious' or 'very serious' risk of bias, mainly due to the lack of consideration of relevant confounding variables, low sample size or underreporting of methods. Globally, we judged the level of evidence being "low". Given the high economical and societal costs associated to infertility and IVF, further well-designed research is urged to fill the highlighted gaps.