A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

Association analyses between urinary concentrations of multiple trace elements and gastric precancerous lesions and gastric cancer in Anhui province, eastern China

Background

Limited epidemiological evidence suggests that exposure to trace elements adversely impacts the development of gastric precancerous lesions (GPL) and gastric cancer (GC). This study aimed to estimate the association of individual urinary exposure to multiple elements with GPL and GC.

Methods

A case-control investigation was conducted in Anhui Province from March 2021 to December 2022. A total of 528 subjects (randomly sampled from 1,020 patients with GPL, 200 patients with GC, and 762 normal controls) were included in our study. Urinary levels of iron (Fe), copper (Cu), zinc (Zn), nickel (Ni), strontium (Sr), and Cesium (Cs) were measured using inductively coupled plasma mass spectrometry (ICP-MS). Four different statistical approaches were employed to explore the risk of GPL and GC with mixed exposure, including multivariate logistic regression, weighted quantile regression (WQS), quantile g-computation (Qgcomp), and the Bayesian kernel machine regression (BKMR) model.

Results

The WQS model indicated that urinary exposure to a mixture of elements is positively correlated with both GPL and GC, with ORs for the mixture exposure of 1.34 (95% CI: 1.34-1.61) for GPL and 1.38 (95% CI: 1.27-1.50) for GC. The Qgcomp and BKMR models also demonstrated a statistically significant positive correlation between the mixture and both GPL and GC.

Conclusion

Considering the limitations of case-control studies, future prospective studies are warranted to elucidate the combined effects and mechanisms of trace elements exposure on human health.

Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Relationships between long-term exposure to major PM2.5 constituents and outpatient visits and hospitalizations in Guangdong, China

Ambient fine particulate matter (PM2.5) has attracted considerable attention due to its crucial role in the rising global disease burden. Evidence of health risks associated with exposure to PM2.5 and its major constituents is important for advancing hazard assessments and air pollution emission policies. We investigated the relationship between exposure to major constituents of PM2.5 and outpatient visits as well as hospitalizations in Guangdong Province, China, where 127 million residents live in a severe PM2.5 pollution environment. An approach that integrates the generalized weighted quantile sum (gWQS) regression with the difference-in-differences (DID) approach was used to assess the overall mixture effects and relative contributions of each constituent. We observed significant associations between long-term exposure to the mixture of PM2.5 constituents (WQS index) and outpatient visits (IR%, percentage increases in risk per unit WQS index increase:1.73, 95%CI: 1.72, 1.74) as well as hospitalizations (IR%:5.15, 95%CI: 5.11, 5.20). Black carbon (weight: 0.34) and nitrate (weight: 0.60) respectively exhibited the highest contributions to outpatient visits and hospitalizations. The overall mixture effects on outpatient visits and hospitalizations were higher with increased summer air temperatures (IR%: 7.54, 95%CI: 7.33, 7.74 and IR%: 9.55, 95%CI: 8.36, 10.75, respectively) or decreased winter air temperatures (IR%: 1.88, 95%CI: 1.68, 2.08 and IR%: 4.87, 95%CI: 3.73, 6.02, respectively). Furthermore, the overall mixture effects on outpatient visits and hospitalizations were significantly higher in populations with higher socioeconomic status (P < 0.01). It's crucial to address the primary sources of nitrate precursor substances and black carbon (mainly traffic-related and industrial-related air pollutants) and consider the complex interaction effects between air temperature and PM2.5 in the context of climate change. Of particular concern is the need to prioritize healthcare demands in economically disadvantaged regions and to address the health inequalities stemming from the uneven distribution of healthcare resources and PM2.5 pollution.

Urinary neonicotinoid concentrations and obesity: A cross-sectional study among Chinese adolescents

Epidemiological and toxicological studies on neonicotinoids and obesity have been relevant to adults and young children, but data are limited in adolescents. This study aimed to examine the association between urinary neonicotinoid concentrations and obesity measures among Chinese adolescent. A total of 524 urine samples from 300 boys (11.3-16.1 years) and 224 girls (12.1-15.8 years) were collected to detect the concentrations of eleven neonicotinoids. Generalized linear regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR) were used to estimate covariate-adjusted associations between detectable neonicotinoids and ten indicators of obesity. Nitenpyram concentration was associated with increased body mass index z-score (β = 0.170, 95% CI: 0.041, 0.299) and greater odds of being general obesity (OR = 2.46, 95% CI: 1.11, 5.46). N-desmethyl- acetamiprid concentration was associated with an increase in waist-to-height ratio (β = 0.102, 95% CI: 0.029, 0.176) and waist-to-hip ratio (β = 0.083, 95% CI: 0.011, 0.155). The concentrations of clothianidin (OR = 2.06, 95% CI: 1.10, 3.88) and flonicamid (OR = 2.39, 95% CI: 1.07, 5.32) were associated with greater odds of being abdominal obesity. In contrast, the concentrations of imidacloprid (OR = 0.35, 95% CI: 0.14, 0.88) and thiacloprid (OR = 0.28, 95% CI: 0.08, 0.99) were associated with lower odds of being general obesity. The estimates of general obesity and abdominal obesity increased significantly when concentrations of neonicotinoids mixture were at or above the 55th and 65th percentiles, respectively, compared to the 50th percentile concentration. Sex modified the association between nitenpyram and clothianidin and the risk of obesity with a positive association among boys, and a nonsignificant inverse association among girls. The findings suggest that these associations may be mixed and sex-specific.

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

The causal links between long-term exposure to major PM2.5 components and the burden of tuberculosis in China

BACKGROUND

We aimed to estimate the causal impacts of long-term exposure to major PM_2.5 components - including black carbon, organic matter, sulfate, nitrate, and ammonium - on the incidence and mortality of tuberculosis in China.

METHODS

We collected annual and provincial-level tuberculosis incidence and mortality, concentrations of PM_2.5 components, and socioeconomic indicators from between 2004 and 2018 in mainland China. We used the difference-in-differences (DID) causal inference approach with a generalized weighted quantile sum (gWQS) regression model to estimate the long-term effects and relative contributions of PM_2.5 components' exposure on tuberculosis incidence and mortality.

RESULTS

We found that long-term multi-components exposure was significantly associated with tuberculosis incidence (WQS index IR%:8.34 %, 95 % CI:4.54 %-12.27 %) and mortality (WQS index IR%:19.49 %, 95 % CI: 9.72 %-30.13 %). Primary pollutants, black carbon and organic matter, contributed most of the overall mixture effect (over 85 %). Nitrate showed a critical role in tuberculosis burden in not-aging provinces and in regions at the Q3 stratum (i.e., the 3rd quartile) of GDP per capita and urbanization rate. Meanwhile the contribution of sulfate to tuberculosis burden in regions at the Q1 stratum of GDP per capita and urbanization rate was the largest among the effect of secondary pollutants (i.e., sulfate, nitrate, and ammonium).

CONCLUSION

The mitigation of black carbon and organic matter pollution may significantly reduce the tuberculosis burden in China. Controlling nitrate emissions and increasing clean energy (i.e., energy sources with limited pollution emissions, such as natural gas and clean coal) may also be effective in certain regions.

Topological network properties of resting-state functional connectivity patterns are associated with metal mixture exposure in adolescents

Introduction

Adolescent exposure to neurotoxic metals adversely impacts cognitive, motor, and behavioral development. Few studies have addressed the underlying brain mechanisms of these metal-associated developmental outcomes. Furthermore, metal exposure occurs as a mixture, yet previous studies most often consider impacts of each metal individually. In this cross-sectional study, we investigated the relationship between exposure to neurotoxic metals and topological brain metrics in adolescents.

Methods

In 193 participants (53% females, ages: 15-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of four metals (manganese, lead, copper, and chromium) in multiple biological media (blood, urine, hair, and saliva) and acquired resting-state functional magnetic resonance imaging scans. Using graph theory metrics, we computed global and local efficiency (global:GE; local:LE) in 111 brain areas (Harvard Oxford Atlas). We used weighted quantile sum (WQS) regression models to examine association between metal mixtures and each graph metric (GE or LE), adjusted for sex and age.

Results

We observed significant negative associations between the metal mixture and GE and LE [βGE = -0.076, 95% CI (-0.122, -0.031); βLE= -0.051, 95% CI (-0.095, -0.006)]. Lead and chromium measured in blood contributed most to this association for GE, while chromium measured in hair contributed the most for LE.

Discussion

Our results suggest that exposure to this metal mixture during adolescence reduces the efficiency of integrating information in brain networks at both local and global levels, informing potential neural mechanisms underlying the developmental toxicity of metals. Results further suggest these associations are due to combined joint effects to different metals, rather than to a single metal.

Detoxification Role of Metabolic Glutathione S-Transferase (GST) Genes in Blood Lead Concentrations of Jamaican Children with and without Autism Spectrum Disorder

Glutathione S-transferases (GST) are involved in the detoxification of exogenous chemicals including lead (Pb). Using data from 344 pairs of autism spectrum disorder (ASD) cases and age- and sex-matched typically developing (TD) controls (2−8 years old) from Jamaica, we investigated the interaction between three GST genes and ASD status as determinants of blood Pb concentrations (BPbCs). We found that ASD cases had lower geometric mean BPbCs than TD children (1.74 vs. 2.27 µg/dL, p < 0.01). Using a co-dominant genetic model, ASD cases with the Ile/Val genotype for the GSTP1 Ile105Val polymorphism had lower GM BPbCs than TD controls, after adjusting for a known interaction between GSTP1 and GSTT1, child’s parish, socioeconomic status, consumption of lettuce, fried plantains, and canned fish (Ile/Val: 1.78 vs. 2.13 µg/dL, p = 0.03). Similarly, among carriers of the I/I or I/D (I*) genotype for GSTT1 and GSTM1, ASD cases had lower adjusted GM BPbCs than TD controls (GSTT1 I*: 1.61 vs. 1.91 µg/dL, p = 0.01; GSTM1 I*: 1.71 vs. 2.04 µg/dL, p = 0.01). Our findings suggest that genetic polymorphisms in GST genes may influence detoxification of Pb by the enzymes they encode in Jamaican children with and without ASD.

Repeated holdout validation for weighted quantile sum regression

Weighted Quantile Sum (WQS) regression is a method commonly used in environmental epidemiology to assess the impact of chemical mixtures in relation to a health outcome of interest. Data are partitioned into a single training and test set to reduce sample-specific chemical weights. However, in typical epidemiology sample sizes, this may produce unstable chemical weights and WQS index estimates, and investigators may resort to training and testing on the same data. To solve this problem, we propose repeated holdout validation whereby data are randomly partitioned 100 times, producing a distribution of validated results. Taking the mean as the final estimate, confidence estimates may also be calculated for inference. Further, this method helps characterize the variability in chemical weights, aiding in the identification of chemicals of concern. This is important since it may direct future research into specific chemicals.

Using data from 718 mother-child pairs in the Swedish Environmental Longitudinal, Mother and Child, Asthma and Allergy (SELMA) study, we assessed the association between prenatal exposure to 26 endocrine disrupting chemicals and child Intelligence Quotient (IQ). Results using a single partition were unstable, varying by random seed. The WQS index estimate was significant when all data was used (e.g. no partition) (β = −2.2 CI = −3.43, −0.98), but attenuated and nonsignificant using repeated holdout validation (β = −0.82 CI = −2.11, 0.45). When implementing WQS in epidemiologic studies with limited sample sizes, repeated holdout validation is a viable alternative to using a single, or no partitioning. Repeated holdout can both stabilize results and help characterize the uncertainty in identifying chemicals of concern, while maintaining some of the the rigor of holdout validation.

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Repeated holdout validation improves the stability of WQS estimates in finite study samples

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Uncertainty in identifying toxic chemicals of concern is acknowledged and characterized