Childhood lead exposure remains a significant public health concern, particularly in urban settings with persistent socioeconomic disadvantage. Although prior studies have linked lead exposure to adverse neurodevelopmental outcomes, evidence using clinically diagnosed neurobehavioral disorders at contemporary low blood lead levels (BLLs) is limited. We conducted a retrospective cohort study of children aged 11 years or younger who received care through the Temple University Health System in Philadelphia, Pennsylvania, between 2010 and 2020. BLLs were obtained from electronic health records and analyzed as continuous measures and dichotomized at 3.5µg/dL. Attention-deficit/hyperactivity disorder (ADHD) and conduct disorders were identified using international classification diagnoses (ICD) codes, and only incident diagnoses occurring after blood lead measurement were considered. Multivariable logistic regression models were fit adjusted for individual- and neighborhood-level sociodemographic factors selected using a directed acyclic graph framework. Sex-stratified analyses were conducted to assess effect modification. Among 13,989 children included in the ADHD analysis, 751 (5.4%) were diagnosed with ADHD. Among 14,098 children included in the conduct disorder analysis, 247 (1.8%) were diagnosed with a conduct disorder. Higher log10-transformed continuous BLLs were associated with increased odds of ADHD diagnosis (adjusted OR 1.51; 95% CI 1.37, 1.67). Children with BLLs ≥ 3.5µg/dL had higher odds of ADHD (adjusted OR 1.84; 95% CI 1.56, 2.16) compared with those with BLLs < 3.5µg/dL, with stronger associations observed among males than females. Associations between log10-transformed continuous BLLs and conduct disorders were positive but not statistically significant (adjusted OR 1.12; 95% CI 0.95, 1.31), and no sex-specific associations were observed. Childhood lead exposure was associated with increased risk of ADHD in this urban clinical population, including at blood lead levels at or above the current reference value set by the Centers for Disease Control and Prevention, emphasizing the importance of continued prevention efforts. These findings underscore the ongoing neurodevelopmental risks of low-level lead exposure and highlight the disproportionate burden placed on children residing in socioeconomically disadvantaged urban communities.

Gulf War Illness (GWI) has been identified in more than 20% of Veterans following military service in the 1990-1991 Gulf War (GW). Unfortunately, after several decades, GWI pathogenesis remains unclear and the patterns of GWI symptom co-occurrence are still poorly understood. This population-based case-control study included 34,179 (16% females; 34% GW deployed; mean age 61years) Veterans enrolled in the Million Veteran Program (MVP) who were active-duty military personnel in 1990-1991. We analyzed 14 GWI symptoms in GW-deployed and non-deployed GW-Era Veterans. Multivariable generalized linear models were used to estimate GWI symptom associations with demographic factors and deployment status. Correlation, factor, and network analyses were performed to examine the underlying structure of GWI symptoms. In addition, polygenic risk score and one-sample Mendelian randomization analyses were conducted to evaluate the genetically inferred effects of health-related traits on symptom factors. Among 14 GWI symptoms, joint pain had the highest prevalence in both GW deployed (86%) and non-deployed GW-Era Veterans (79%). The median age of symptom onset ranged from 38 to 46years among deployed Veterans and from 45 to 54years among non-deployed Veterans. Factor analysis identified a five-factor latent structure as the best-fit model for GWI symptoms, and latent class analysis classified Veterans into seven symptom classes. The network of GWI symptoms showed that fatigue had the largest closeness and betweenness and had more edges in deployed Veterans. Younger age, female sex, non-European descent, lower educational attainment, enlisted rank, and GW deployment, as well as multiple GW-related exposures in deployed Veterans, were associated with increased GWI symptom burden. Genetic liability to multiple health-related outcomes, including type 2 diabetes (T2D) and posttraumatic stress disorder (PTSD), showed putative causal effects on GWI-symptom factors, with the T2D effect being larger in deployed Veterans and the PTSD effect being larger in non-deployed Veterans. The present findings demonstrate differences in symptom onset between deployed and non-deployed GW Veterans. They also reinforce the importance of symptom clusters of relevance to GWI, associations between deployment-specific exposures and symptom burden, and different genetic linkages underlying symptoms for deployed and non-deployed groups. Not applicable.

Air pollution is increasingly recognized as a risk factor for progression of kidney disease; however, few studies have examined its impact among patients with primary glomerular disorders. To address this knowledge gap, we previously reported positive associations between fine particulate matter ≤ 2.5μm in aerodynamic diameter (PM2.5) and black carbon with kidney disease progression in an observational cohort of children and adults (n = 925) with primary glomerular diseases, namely minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, and IgA nephropathy. In the current study, we leverage the same cohort to (1) identify additional air pollutants that may be associated with kidney disease progression using data from the National Center for Atmospheric Research (NCAR); (2) determine whether the association between baseline air pollution exposure and kidney disease progression is maintained over a longer follow-up period; and (3) assess whether associations identified in our previously published findings remain when using NCAR pollutant data. In this retrospective cohort study, we obtained air pollutant concentration data from NCAR based on participant residential census tract at enrollment. For each census tract and pollutant, we aggregated daily pollutant concentrations to annual averages. We used Cox proportional hazards models to estimate associations between average baseline pollutant exposure and time to kidney disease progression, defined as a 40% decline in estimated glomerular filtration rate (eGFR) or occurrence of kidney failure (eGFR) < 15 ml/min/1.73 m2) during follow-up. Hazard ratios (HR) represented a doubling of exposure. Use of NCAR data supported our previous findings of adverse effects of PM2.5 and black carbon on the progression of glomerular disease. Moreover, using the NCAR data we identified novel associations with NO2 exposure, HR 1.12 [1.01, 1.26] and specific components of PM2.5, including organic matter, HR 1.17 [1.01, 1.37] and an increased risk of disease progression. When considering a longer follow-up period, associations between baseline exposures and kidney outcomes persisted, but were attenuated suggesting a need for recent, interval-specific risk assessment, inclusion of acute exposures, and enhanced spatial resolution of exposures. Our findings highlight the importance of the systematic assessment including spatial and temporal variation of a broad range of air pollution components to determine the impact of exposure on short- and long-term outcomes in patients with primary glomerular disease (word count: 368).

Humans encounter a wide range of natural and synthetic chemicals throughout their lifespan and these exposures have a complex impact on health outcomes. Epidemiological studies provide compelling evidence linking chemical exposure to neurological disorders and various cancers. However, establishing causality between specific pollutants, or their mixtures, and health outcomes is challenging due to long disease latencies. Functional biomarkers in exposed subjects provide rapid predictions of the impact of environmental exposures. Notably, genome alterations, including DNA breaks and epigenetic modifications, are hallmark features of cancers, premature aging, and neurological diseases. Biomonitoring studies show that individual chemical exposures are specific. These differences highlight disparities across socioeconomic groups. They also present opportunities to associate specific environmental factors and disease development, as well as socioeconomic determinants of exposures and their consequences. This review focuses on biomonitoring environmental chemical exposures and their effects with hair and hair follicles. Pollutants detected in the hair matrix reflect cumulative internal exposures over specific time periods, while biomarkers can be analyzed in plucked hair follicles. These mini organs contain proliferating and stem/progenitor cells, which are critical to understanding the onset of various diseases. We propose an integrated approach that combines socioeconomic indicators, chemical analyses in hair, and biological assessments of matching hair follicles to evaluate the exposome and its effects on the genome. We discuss the state of the art of these approaches, their current limitations, and the perspectives they offer.

Maternal per- and polyfluoroalkyl substances (PFAS) exposure has been linked to adverse health effects on offspring, but the mechanisms remain unclear. The present study investigates the relationship between maternal PFAS exposure and the expression of placental cytochrome P450 enzymes (CYP19A1, CYP2J2, and CYP2E1), and explores the potential role of these enzymes in linking maternal PFAS exposure to offspring development. We included 350 mother-infant pairs from the Jiashan birth cohort. Thirteen PFAS compounds were measured in maternal plasma collected at 8-16weeks of gestation, while the expression levels of the three CYP genes were quantified in placental tissues collected at delivery. Offspring weight and length were measured at birth and at 1, 3, 6, 8, 12, and 24months of age, and their ponderal index (PI) was computed. Multivariable linear regression was used to examine associations between plasma concentrations of individual PFAS and placental CYP gene expression. Quantile-based g-computation was used to examine the association of the PFAS mixture with placental CYP gene expression. A linear mixed model was used to examine the associations of maternal PFAS and placental CYP expression levels with repeated PI measurements from birth to 24months. Multilevel mediation analysis was conducted to explore the potential mediating role of CYP genes. Consistent inverse associations between maternal PFAS exposure and the three placental CYP genes expression in female placentas were observed. Specifically, statistically significant decreases were observed in the associations between all PFAS compounds and CYP2J2 expression with β- estimates for the highest exposure versus the lowest from -0.255 to -0.174, as well as between the highest exposure of perfluorotridecanoic acid (PFTrDA) and CYP19A1 expression (β = -0.279, 95% CI: -0.55, -0.007). In contrast, for male fetuses, significant associations were found between the highest exposure of perfluorohexane sulfonate (PFHxS) and increased CYP19A1 expression (β = 0.404, 95%CI: 0.131,0.677), and between the middle of perfluoroundecanoic acid (PFUdA), perfluorododecanoic acid (PFDoA), PFTrDA, and perfluoroheptanoic acid (PFHPA) and decreased CYP2E1 expression (βPFUdA = -0.286, 95% CI: -0.511,-0.061; βPFDoA = -0.270, 95% CI: -0.495,-0.045;βPFTrDA = -0.236, 95% CI: -0.466,-0.006; and βPFHpA = -0.344, 95% CI: -0.571,-0.117, respectively). The PFAS mixture was associated with decreased the three CYP genes expression in the placenta of female fetuses, but no association was observed in male placentas. Additionally, placental CYP19A1 expression was associated with a decrease in girls' PI and an increase in boys' PI, respectively. However, no mediating role of CYP19A1 in the relationship between PFTrDA and PI in girls or between PFHxS and PI in boys was observed, as the indirect effect was not statistically significant. These results indicate that maternal PFAS exposure may disrupt placental CYP expression and the implications of these disruptions for offspring development warrant further investigation.

Prenatal depression shows diverse patterns in severity and progression. While air pollution has been linked to depression risk, its impact on the course of prenatal depression remains understudied. The study objectives were to (1) identify distinct trajectories of depressive symptoms across pregnancy, and (2) to evaluate whether preconception and early-pregnancy air pollution exposure impacts membership in trajectory groups. This study followed 542 predominantly low-income Hispanic/Latina participants in the MADRES cohort. We assessed depressive symptoms using the Center for Epidemiologic Studies-Depression (CES-D) scale at 1st, 2nd, and 3rd trimester visits. Daily residential concentrations of PM2.5, PM10, NO2, and O3 were estimated using inverse-distance squared spatial interpolation from monitoring data. We used latent growth mixture modeling (LGMM) to identify trajectory classes and multinomial logistic regression to estimate adjusted relative risk ratios (RRRs) of membership in each class relative to the lowest group with averaged air pollution levels over preconception and early pregnancy. We identified four distinct trajectories: moderate-to-high symptoms (7.4%), consistently-moderate symptoms (14.6%), moderate-to-low symptoms (34.7%), and consistently-low symptoms (43.4%). Higher proportions of Black mothers and those not cohabitating were found in the two highest symptom trajectories. Participants with an interquartile range higher early-pregnancy NO2 (10.28 ppb) had a 3.82-fold higher relative risk (95% CI: 1.50-9.73) of following the moderate-to-high versus consistently-low-symptom trajectory. Similar elevated risks were observed for PM2.5 (RRR = 1.61; 95%CI: 1.08-2.40) and PM10 (RRR = 1.84; 95% CI: 1.01-3.35). Early-pregnancy exposure to ambient air pollution is associated with increased risk of moderate-to-high depressive symptoms across pregnancy.

The exposome encompasses all environmental exposures humans undergo from conception. We frame exposome studies according to four broad aims essential for environmental health research. First, a descriptive aim, consisting in assessing exposure patterns, including correlations between exposures and their within-subject variability. This descriptive aim includes "environmental justice" studies of associations between the exposome and sociodemographic factors. A second - etiologic - aim is to describe the subclinical and clinical effects of the exposome (hazard identification). A third aim is the quantification of the exposome health impact (e.g., in life years lost at the population level) and the ranking of exposures in terms of population disease burden (exposome disease burden or risk assessment). A fourth (intervention) aim corresponds to the identification of approaches to modify the exposome, as a way to improve health. With the large increase in the number of chemicals quantifiable in a small volume of a biospecimen, the main challenge of descriptive exposome studies is population representativeness. Regarding the etiologic aim, by simultaneously showing associations of a given biological parameter with hundreds of exposures, exposome studies effectively limit publication bias and selective reporting of results, which are a strong concern in single-exposure studies. They nonetheless face several challenges, related to the curse of dimensionality, the correlation between exposures, the breadth vs. depth tension… Increasing the number of exposures considered implies to simultaneously increase study population sizes, aiming for cohorts of 100,000 subjects or more. The accuracy of exposure assessment should simultaneously be improved, e.g., by collecting repeated biospecimens within each subject, assessing exposures at various ages and decreasing limits of quantification. Classical exposome statistical designs such as ExWAS (exposome-wide association studies) are subject to a high false positive rate. Models adapted to sparse data allowing to control for confounding by co-exposures appear more efficient. The results from exposome descriptive and etiologic studies can be combined to feed exposome disease burden assessments. These can in turn help prioritize exposures for which efficient interventions need to be identified. The approaches outlined in this work could help exposome research contribute more strongly to environmental health research and to the associated risk management decisions.

Radon is a known environmental carcinogen associated with thousands of lung cancers each year. Understanding characteristics associated with testing is critical to design interventions to increase acceptance and promote mitigation. The objective of this study was to evaluate socioeconomic factors and cancer beliefs associated with previous home air radon testing, mitigation, and-for those who had not yet tested-acceptance of a free test. A population-based survey was administered to adults residing in New Hampshire and Vermont to understand previous home air radon testing and mitigation patterns, and among those who had not previously tested, acceptance of information on how to request a free test. The proportions of test kits requested and returned are reported. Of 1,717 survey respondents, 513 (29.7%) had previously tested for radon, 767 (44.7%) had not, and 437 (25.5%) did not know. Those who had not previously tested (767/1,280; 60%) had lower educational attainment, were less likely to be married, were less likely to be homeowners, and were more likely to express negative attitudes towards cancer prevention than those who had. Of 108 respondents whose previous test had shown levels ≥2.0 pCi/L, only 59 (55%) had installed a new mitigation system. Of those with a steady place to live, 996 had not previously tested for radon or did not know. Those 996 were offered information on how to obtain a free test kit and 66% accepted (659/996); 49% of those (324/659) used the information to request a kit, and 38% of those (124/324) returned the kit for testing. In multivariable models, not previously testing the home for radon was significantly associated with being divorced/separated, household income <$150,000, and lack of employment-based health insurance. Among those without a prior test, declining information for a free radon test was significantly associated with educational attainment of high school or less, and inversely associated with being divorced/separated. Resident differences in socioeconomic factors and cancer beliefs regarding home air radon testing may offer potential opportunities for targeted interventions designed to decrease radon-associated lung cancer deaths.

Taiwan is one of the fastest-warming regions globally. As climate change intensifies, understanding how vulnerability influences health outcomes critical. This study aimed to identify regional vulnerability factors for temperature-related respiratory mortality and effective region-specific adaptation policies. A two-stage time-series study was conducted using daily respiratory mortality counts aggregated by county and day. This study employed a distributed lag non-linear model to estimate the temperature-attributable mortality burden from respiratory diseases across all counties and cities in Taiwan. A two-stage meta-analysis was conducted to estimate temperature-mortality associations and quantify cold- and heat-related mortality burdens by county. Meta-regression was used to identify regional vulnerability factors modifying temperature-related mortality risk, and geographically weighted regression (GWR) was applied to characterize the spatial heterogeneity of these effects across counties. Cold exposure was linked to a higher burden of respiratory disease mortality (attributable fraction [AF]: 2.03%, 95% CI: 1.10-2.95) than heat exposure (AF: 1.02%, 95% CI: 0.65-1.40). For cold-related AFs, higher proportions of Indigenous populations (3.27, 0.79-5.75), low-income populations (2.11, 0.67-3.55), greater population density (2.21, 0.46-3.96), and children (0.98, 0.35-1.61) were significantly associated with increased risk, suggesting vulnerability factors. GWR further showed that hospital bed availability had statistically significant protective effects against cold-related AF in 10 of 19 counties (β = - 5.24 to - 6.78), most pronounced in remote mountainous counties (Hualien, Taitung, Kaohsiung). Higher proportions of Indigenous populations, low-income population, and children amplify cold-related respiratory mortality. Hospital bed availability confers the strongest protection against cold-related mortality in remote, mountainous counties. Climate adaptation policies for cold-related respiratory health should therefore be tailored to local vulnerability profiles, prioritizing healthcare expansion in geographically remote counties rather than applying uniform investment across all regions.

The contribution of environmental determinants in the etiology of amyotrophic lateral sclerosis (ALS) is still unclear. Among the various environmental factors, exposure to green spaces, also known as greenness, is attracting considerable interest as many studies have reported its beneficial associations to health outcomes, particularly to neurodegenerative diseases. To investigate the relation between greenness and ALS risk, we conducted a population-based case-control study in a Northern Italy population (from Modena, Reggio Emilia and Parma provinces), including 499 cases of ALS newly-diagnosed from 1998 to 2011 and 1,935 sex-, age-, and province-matched controls randomly selected from study provinces residents. We evaluated the association between greenness in the proximity of residence and ALS risk, assessing exposure through multiple satellite-based and land-use derived indices, both conventional and novel devised, for a total of six indices, each providing specific information, including annual and seasonal Normalized Difference Vegetation Index (NDVI), NDVI-weighted to green areas, green cover ratio, accessibility index, and their combined Green Exposure Index (GEI). We used conditional logistic regression models to evaluate disease risk for increasing exposure through both fixed-categories and non-linear restricted cubic splines. We observed a non-linear U-shaped association between greenness and ALS risk with increased odds ratios at both low and high levels. Results were more defined when using NDVI-based indices, while the associations were smoother when considering GEI. The higher risk at low levels may be related to lower accessibility to green spaces with lower physical activity and higher exposure to outdoor air pollutants, whilst elevated greenness may reflect higher exposure to neurotoxic pesticides. These results were confirmed also after adjustment for potential confounders, namely magnetic fields and light at night. Sex stratified analysis yielded similar results, except for more distinct associations in females for GEI. Despite the limitations due to possible unmeasured confounding and exposure misclassification related to the use of residential data, our results provide evidence of an inverse association between intermediate residential greenness and ALS risk, and may have public health implications including disease prevention and urban planning.