Search

Background: Autoimmune diseases have been linked to an increased risk of pregnancy-related complications. A family history of autoimmune diseases may be related to the risk of childhood cancer based on similar histocompatibility antigens. We utilized data from national registries in Denmark to examine associations between maternal autoimmune disease and cancer in their offspring. Methods: We linked data from several national registries in Denmark to identify childhood cancer cases in children

Background: Air pollution is a global health concern, with fine particulate matter (PM2.5) constituents posing potential risks to human health, including children's neurodevelopment. Here we investigated associations between exposure during pregnancy and infancy to specific traffic-related PM2.5 components with Autism Spectrum Disorder (ASD) diagnosis. Methods: For exposure assessment, we estimated PM2.5 components related to traffic exposure (Barium [Ba] as a marker of brake dust and Zinc [Zn] as a tire wear marker, Black Carbon [BC]) and oxidative stress potential (OSP) markers (Hydroxyl Radical [OPOH] formation, Dithiothreitol activity [OPDTT], reactive oxygen species [ROS]) modeled with land use regression with co-kriging based on an intensive air monitoring campaign. We assigned exposures to a cohort of 444,651 children born in Southern California between 2016 and 2019, among whom 11,466 ASD cases were diagnosed between 2018 and 2022, Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained with logistic regression for single pollutant and PM2.5 mass co-adjusted models, also adjusting for sociodemographic characteristics. Results: Among PM2.5 components, we found the strongest positive association with ASD for our brake wear marker Ba (ORper IQR = 1.29, 95 % CI: 1.24, 1.34). This was followed by an increased risk for all PM2.5 oxidative stress potential markers; the strongest association was with ROS formation (ORper IQR = 1.22, 95 % CI: 1.18, 1.25). PM2.5 mass was linked to ASD in Hispanic and Black children, but not White children, while traffic-related PM2.5 and OSP markers increased ASD risk across all groups. In neighborhoods with the lowest socioeconomic status (SES), associations with ASD were stronger for all examined pollutants compared to higher SES areas. Conclusions: Our findings suggest that brake wear-related PM2.5 and PM2.5 OSP are associated with ASD diagnosis in Southern California. These results suggest that strategies aimed at reducing the public health impacts of PM2.5 need to consider specific sources.

Background: Although traffic-related air pollution is largely regulated at the federal level, congestion reduction projects may reduce local traffic and air pollution to levels that create positive co-benefits for population health. In recent years, many urban areas have implemented electronic tolling systems to improve traffic conditions. Objective: Quantify associations between implementing electronic tolling and local changes in traffic and infant health. Methods: Using a population-based birth cohort (Texas, 1999–2016), we calculated residential proximity to the nearest tolled road segment within 5 km (n = 625,279) and examined changes in local traffic before and after toll implementation. Using a difference-in-differences design, we compared four markers of adverse birth outcomes (term birth weight, term low birth weight, preterm birth, very preterm birth) among infants from pregnant people residing

Background: Both air pollution and noise exposures have separately been shown to affect cognitive impairment. Here, we examine how air pollution and noise exposures interact to influence the development of incident dementia or cognitive impairment without dementia (CIND). Methods: We used 1,612 Mexican American participants from the Sacramento Area Latino Study on Aging conducted from 1998 to 2007. Air pollution (nitrogen dioxides, particulate matter, ozone) and noise exposure levels were modeled with a land-use regression and via the SoundPLAN software package implemented with the Traffic Noise Model applied to the greater Sacramento area, respectively. Using Cox proportional hazard models, we estimated the hazard of incident dementia or CIND from air pollution exposure at the residence up to 5-years prior to diagnosis for the members of each risk set at event time. Further, we investigated whether noise exposure modified the association between air pollution exposure and dementia or CIND. Results: In total, 104 incident dementia and 159 incident dementia/CIND cases were identified during the 10 years of follow-up. For each ∼2 µg/m3 increase in time-varying 1- and 5-year average PM2.5 exposure, the hazard of dementia increased 33% (HR = 1.33, 95%CI: 1.00, 1.76). The hazard ratios for NO2-related dementia/CIND and PM2.5-related dementia were stronger in high-noise (≥65 dB) exposed than low-noise (

Background: Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson’s disease (PD). Objectives: Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases. Methods: We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays. Results: Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress. Conclusions: Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Although Parkinson's Disease (PD) is typically described in terms of motor symptoms, depression is a common feature. We explored whether depression influences blood-based genome-wide DNA methylation (DNAm) in 692 subjects from a population-based PD case-control study, using both a history of clinically diagnosed depression and current depressive symptoms measured by the geriatric depression scale (GDS). While PD patients in general had more immune activation and more accelerated epigenetic immune system aging than controls, the patients experiencing current depressive symptoms (GDS≥5) showed even higher levels of both markers than patients without current depressive symptoms (GDS

Due to regulations and technological advancements reducing tailpipe emissions, an increasing proportion of emissions arise from brake and tire wear particulate matter (PM). PM from these non-tailpipe sources contains heavy metals capable of generating oxidative stress in the lung. Although important, these particles remain understudied because the high cost of actively collecting filter samples. Improvements in electrical engineering, internet connectivity, and an increased public concern over air pollution have led to a proliferation of dense low-cost air sensor networks such as the PurpleAir monitors, which primarily measure unspeciated fine particulate matter (PM2.5). In this study, we model the concentrations of Ba, Zn, black carbon, reactive oxygen species concentration in the epithelial lining fluid, dithiothreitol (DTT) loss, and OH formation. We use a cokriging approach, incorporating data from the PurpleAir network as a secondary predictor variable and a land-use regression (LUR) as an external drift. For most pollutant species, cokriging models produced more accurate predictions than an LUR model, which did not incorporate data from the PurpleAir monitors. This finding suggests that low-cost sensors can enhance predictions of pollutants that are costly to measure extensively in the field.

Background: Within the Multiethnic Cohort (MEC), we examined the association between air pollution and mortality among African American, European American, Japanese American, and Latina American women diagnosed with breast cancer. Methods: We used a land use regression (LUR) model and kriging interpolation to estimate nitrogen oxides (NOx , NO2) and particulate matter (PM2.5, PM10) exposures for 3,089 breast cancer cases in the MEC, who were diagnosed from 1993 through 2013 and resided largely in Los Angeles County, California. Cox proportional hazards models were used to examine the association of time-varying air pollutants with all-cause, breast cancer, cardiovascular disease (CVD), and non-breast cancer/non-CVD mortality, accounting for key covariates. Results: We identified 1,125 deaths from all causes (474 breast cancer, 272 CVD, 379 non-breast cancer/non-CVD deaths) among the 3,089 breast cancer cases with 8.1 years of average follow-up. LUR and kriged NOX (per 50 ppb) and NO2 (per 20 ppb), PM2.5 (per 10 µg/m3), and PM10 (per 10 µg/m3) were positively associated with risks of all-cause (Hazard Ratio (HR) range = 1.13–1.25), breast cancer (HR range = 1.19–1.45), and CVD mortality (HR range = 1.37–1.60). Associations were statistically significant for LUR NOX and CVD mortality (HR = 1.60; 95% CI: 1.08–2.37) and kriged NO2 and breast cancer mortality (HR = 1.45; 95% CI 1.02–2.07). Gaseous and PM pollutants were positively associated with breast cancer mortality across racial/ethnic group. Conclusion: In this study, air pollutants have a harmful impact on breast cancer survival. Additional studies should evaluate potential confounding by socioeconomic factors. These data support maintaining clean air laws to improve survival for women with breast cancer.

Background: Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. Objectives: In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. Methods: We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. Results: We found an optimal combination of four temporal basis functions and 200 m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62 ppb (0.28) and 9.63 ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R2 of 0.87–0.90; RMSE of 3.97–9.69 ppb; RMSEIQR of 0.21–0.27), illustrating minimal over-fitting. Conclusions: Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region. Keywords: Air pollution, Nitrogen oxides, Spatiotemporal variability, Generalization, Machine learning, Cluster methods

Background: Previous studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population. Methods: A total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants’ residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components. Results: Each per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04–1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01–1.35). Conclusion: Policies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations. Keywords: Air pollution, noise, Metabolic syndrome, Mexican-Americans

Background: Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms. Methods: We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other. Keywords: Air pollution, Bayesian, Clustering, Low birth weight, Pollutant profile, Profile regression

Pathologic features of Parkinson's disease (PD) include death of dopaminergic neurons in the substantia nigra, presence of α-synuclein containing Lewy bodies, and iron accumulation in PD-related brain regions. The observed iron accumulation may be contributing to PD etiology but it also may be a byproduct of cell death or cellular dysfunction. To elucidate the possible role of iron accumulation in PD, we investigated genetic variation in 16 genes related to iron homeostasis in three case–control studies from the United States, Australia, and France. After screening 90 haplotype tagging single nucleotide polymorphisms (SNPs) within the genes of interest in the US study population, we investigated the five most promising gene regions in two additional independent case–control studies. For the pooled data set (1289 cases, 1391 controls) we observed a protective association (OR = 0.83, 95% CI: 0.71–0.96) between PD and a haplotype composed of the A allele at rs1880669 and the T allele at rs1049296 in transferrin (TF; GeneID: 7018). Additionally, we observed a suggestive protective association (OR = 0.87, 95% CI: 0.74-1.02) between PD and a haplotype composed of the G allele at rs10247962 and the A allele at rs4434553 in transferrin receptor 2 (TFR2; GeneID: 7036). We observed no associations in our pooled sample for haplotypes in SLC40A1, CYB561, or HFE. Taken together with previous findings in model systems, our results suggest that TF or a TF-TFR2 complex may have a role in the etiology of PD, possibly through iron misregulation or mitochondrial dysfunction within dopaminergic neurons.

Parkinson's disease (PD) is acknowledged as the second most common neurodegenerative disorder after Alzheimer's Disease. Older age may be the only unequivocal risk factor for PD although the male to female ratio is consistently greater than 1 in populations of European ancestry. Characteristic features of PD include dopaminergic neuron death in the substantia nigra (SN) pars compacta, accumulation of α-synuclein inclusions known as Lewy bodies in the SN, and brain iron accumulation beyond that observed in non-PD brains of a similar age. In this review article, we will provide an overview of human and animal studies investigating the contributions of iron in PD, a summary of human studies of iron-related genes in PD, a review of the literature on the genetics of iron metabolism, and some hypotheses on possible roles for iron in the pathogenic processes of PD including potential interactions between iron and other factors associated with Parkinson's disease.