Air Pollution and Autism Spectrum Disorder in Israel: A Negative Control Analysis

Particulate matter 2.5 causally increased genetic risk of autism spectrum disorder

BACKGROUND

Growing evidence suggested that particulate matter (PM) exhibit an increased risk of autism spectrum disorder (ASD). However, the causal association between PM and ASD risk remains unclear.

METHODS

We performed two-sample Mendelian randomization (MR) analyses, using instrumental variables (IVs) sourced from the largest genome-wide association studies (GWAS) databases. We employed three MR methods: inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, with IVW method serving as our primary MR method. Sensitivity analyses were performed to ensure the stability of these findings.

RESULTS

The MR results suggested that PM2.5 increased the genetic risk of ASD (β = 2.41, OR = 11.13, 95% CI: 2.54-48.76, P < 0.01), and similar result was found for PM2.5 absorbance (β = 1.54, OR = 4.67, 95% CI: 1.21-18.01, P = 0.03). However, no such association was found in PM10 (β = 0.27, OR = 1.30, 95% CI: 0.72-2.36, P = 0.38). After adjusting for the false discovery rate (FDR) correction, our MR results remain consistent. Sensitivity analyses did not find significant heterogeneity or horizontal pleiotropy.

CONCLUSIONS

Our findings indicate that PM2.5 is a potential risk factor for ASD. Effective strategies to mitigate air pollutants might lead to a reduced incidence of ASD.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Particulate matter exposure during pregnancy and infancy and risks of autism spectrum disorder in children: A systematic review and meta-analysis

PURPOSE

This meta-analysis aimed to clarify the relationship between particulate matter (PM) and autism spectrum disorder (ASD) in detail.

METHODS

A systematic literature search was performed using eight databases before April 9, 2022. The estimated effects were combined separately according to the PM type. Subgroup analyses were conducted in terms of the study design type, study location, exposure window, birth year, and sex.

RESULTS

PM_2.5 was associated with an increased risk of ASD, while PM₁₀ was not. PMc, PM₁, and diesel particulate matter (DPM) were also associated with an increased risk of ASD. Specifically, a 10 μg/m³ increase in PM_2.5 was associated with a 1.337-fold increased risk of ASD in children, and a 10 μg/m³ increase in PMc and PM₁ may increase the risk of ASD by 1.062 and 3.643 times, respectively. PM_2.5 exposure may increase the risk of ASD in boys. Exposure to PMc might increase the risk of ASD in children born after the year 2000. The combined results of different PM differed between studies with continuous and non-continuous data for different study design type, study location, and birth year. The sensitive window for PM_2.5 exposure to increase the risk of ASD may be from the first, second, and third trimesters to the first year of the postnatal period. Exposure to PMc during pregnancy was significantly associated with ASD.

CONCLUSION

Exposure to PM_2.5 may increase the risk of ASD in boys. Exposure to PM_2.5 during the first, second, and third trimesters and postnatally increased the risk of ASD.

Causal effect of PM1 on morbidity of cause-specific respiratory diseases based on a negative control exposure

BACKGROUND

Extensive studies have linked PM_2.5 and PM₁₀ with respiratory diseases (RD). However, few is known about causal association between PM₁ and morbidity of RD. We aimed to assess the causal effects of PM₁ on cause-specific RD.

METHODS

Hospital admission data were obtained for RD during 2014 and 2019 in Beijing, China. Negative control exposure and extreme gradient boosting with SHapley Additive exPlanation was used to explore the causality and contribution between PM₁ and RD. Stratified analysis by gender, age, and season was conducted.

RESULTS

A total of 1,183,591 admissions for RD were recorded. Per interquartile range (28 μg/m³) uptick in concentration of PM₁ corresponded to a 3.08% [95% confidence interval (CI): 1.66%-4.52%] increment in morbidity of total RD. And that was 4.47% (95% CI: 2.46%-6.52%) and 0.15% (95% CI: 1.44%-1.78%), for COPD and asthma, respectively. Significantly positive causal associations were observed for PM₁ with total RD and COPD. Females and the elderly had higher effects on total RD, COPD, and asthma only in the warm months (Z = 3.03, P = 0.002; Z = 4.01, P < 0.001; Z = 3.92, P < 0.001; Z = 2.11, P = 0.035; Z = 2.44, P = 0.015). Contribution of PM₁ ranked first, second and second for total RD, COPD, and asthma among air pollutants.

CONCLUSION

PM₁ was causally associated with increased morbidity of total RD and COPD, but not causally associated with asthma. Females and the elderly were more vulnerable to PM₁-associated effects on RD.

Air Pollution and Maximum Temperature Are Associated with Neurodevelopmental Regressive Events in Autism Spectrum Disorder

Neurodevelopmental regression (NDR) is an enigmatic event associated with autism spectrum disorder (ASD) during which a child loses previously acquired skills and develops ASD symptoms. In some, a trigger which precedes the NDR event, such as a fever, can be identified, but in many cases no trigger is obvious. We hypothesize that air pollution (PM_2.5) may trigger NDR, especially in those children without an identified trigger. Average daily PM_2.5, ozone, precipitation and maximum temperature (T_max) were derived from Environmental Protection Agency models and National Oceanic and Atmospheric Administration monitors based on zip-code information from 83 ASD participants during the six-weeks following the onset month of an NDR event and a reference period defined as one year before and one year after the event. Seasonally adjusted logistic regression (LR) and linear mixed models (LMM) compared cases (with a history of NDR) and matched controls (without a history of NDR). LR models found that the risk of NDR was related to higher PM_2.5 during 3 to 6 weeks of the NDR event period, particularly in those without a trigger. Overall, both models converged on NDR being related to a higher PM_2.5 and lower T_max both during the NDR event period as well as the reference period, particularly in those without a known trigger. This temporal pattern suggests that environmental triggers, particularly PM_2.5, could be related to NDR, especially in those without an identifiable trigger. Further studies to determine the underlying biological mechanism of this observation could help better understand NDR and provide opportunities to prevent NDR.

The epidemiological evidence linking exposure to ambient particulate matter with neurodevelopmental disorders: A systematic review and meta-analysis

BACKGROUND

There has been increasing attention on the associations between ambient particulate matter (PM) in early-life and neurodevelopmental disorders (NDDs). However, the associations remained unclear when considering different types of NDDs and different sizes of PM, and vulnerable exposure windows during early-life were not identified yet.

OBJECTIVE

To synthesize the published literature on the associations between ambient particulate matter (PM) and risk of different types of neurodevelopmental disorders (NDDs) in a systematic review and meta-analysis.

METHODS

A systematic search of Medline, Embase, PubMed, Cochrane Library, and Web of Science was performed from inception through 24 January 2022. Two reviewers conducted the study selection, data extraction, and quality appraisal. A random-effects model was used for meta-analyses with two quality-of-evidence assessments (the Grading of Recommendations Assessment, Development, and Evaluation system and the best evidence synthesis system).

RESULTS

A total of 6554 articles were screened, of which 31 were included in the review, and 20 provided adequate data for meta-analyses. Exposures to particulate matter of 2.5 μm or less (PM_2.5) during prenatal periods (OR, 1.32 [95%CI, 1.03-1.69]), the first year after birth (OR, 1.62 [95%CI, 1.22-2.15]) and the second year after birth (OR, 3.13 [95%CI, 1.47-6.67]) were associated with increased risk of autism spectrum disorders (ASD) in children. The quality of evidence for these associations during early postnatal periods was somewhat moderate with limited studies. We found inconsistent evidence when considering other types of NDDs and different sizes of PM.

CONCLUSIONS AND RELEVANCE

Current evidence indicated that there might be an association between PM_2.5 exposure and higher risk of ASD, and early postnatal periods appeared to be the critical exposure window. High-quality studies are needed to assess the evidence for other types of NDDs.

Air pollution and mental health: associations, mechanisms and methods

PURPOSE OF REVIEW

There is increasing interest in the links between exposure to air pollution and a range of health outcomes. The association with mental health however is much less established. This article reviews developments in the field over the past 12 months, highlighting the evidence for causation, associations between multiple air pollutants and mental health outcomes, and assesses the challenges of researching this topic.

RECENT FINDINGS

Increasingly rigorous methods are being applied to the investigation of a broader range of mental health outcomes. These methods include basic science, neuroimaging, and observational studies representing diverse geographical locations. Cohort studies with linked high-resolution air pollutant exposure data are common, facilitating advanced analytic methods. To date, meta-analyses have demonstrated small and significant positive associations between long-term exposure to fine particulate matter and depressive symptoms and cognitive decline. Methodological complexities in measuring exposure and outcome pose ongoing difficulties for the field.

SUMMARY

Literature on this topic has recently seen an appreciable expansion. Work that better estimates daily exposure, controls for complex confounders, and is driven by hypotheses founded in candidate causal mechanisms would help clarify associations, and inform targeted interventions and policymakers.