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Background: Per-and polyfluoroalkyl substances (PFAS) contamination may disrupt sleep through disrupted metabolic and immune functions. The study aims to investigate the association and potential mechanism between PFAS and sleep. Methods: We included 136 young adults recruited between 2014-2018 and 76 were re-assessed between 2020-2022. Additional 8 participants only had complete data between 2020-2022. Plasma PFAS (PFOS, PFOA, PFHxS, PFHpS, PFPeS, PFNA, PFDA) were measured at both visits using liquid-chromatography high-resolution mass spectrometry. Plasma proteins were measured by Olink® Explore 384 Cardiometabolic and Inflammation Panel I. Sleep duration was self-reported at both visits along with follow-up sleep disturbance and sleep-related impairment using validated instruments. We utilized multiple linear regression to explore the association between individual PFAS (in tertile) and these sleep outcomes. PFAS associated with sleep outcomes were subjected to computational toxicology analysis using the Comparative Toxicogenomics Database and Toxicology in the 21st Century database to identify potential genetic links between them. Mediation analysis using proteomic data was then performed to confirm the findings from computational toxicology analysis. Results: At baseline, one tertile increase in PFDA was associated with 0.39 (95 % CI: 0.05, 0.73) hours of shorter nightly sleep duration, and, at follow-up, PFHxS and PFOA were associated with 0.39 (95 % CI: 0.05, 0.72) and 0.32 (95 % CI: 0.01, 0.63) hours shorter sleep duration, respectively. One tertile increase in PFOS exposure was associated with a 2.99-point increase in sleep disturbance scores (95 % CI: 0.67, 5.31) and a 3.35-point increase in sleep-related impairment scores (95 % CI: 0.51, 6.20). Computational toxicology and mediation analyses identified potential mediating roles for several proteins in the PFAS-sleep associations, including 11-beta-dehydrogenase isozyme 1 (HSD11B1), cathepsin B (CTSB) and several immune system-related proteins. Conclusion: Future large scale epidemiological and mechanistic studies should confirm our findings and test effect measure modification of the associations by age.

Abstract Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Abstract Background Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. Results Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. Conclusion We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single “optimal” pubertal growth pattern.

Background: Strong epidemiological evidence shows positive associations between exposure to per- and polyfluoroalkyl substances (PFAS) and adverse cardiometabolic outcomes (e.g., diabetes, hypertension, and dyslipidemia). However, the underlying cardiometabolic-relevant biological activities of PFAS in humans remain largely unclear. Aim: We evaluated the associations of PFAS exposure with high-throughput proteomics in Hispanic youth. Material and Methods: We included 312 overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) between 2001 and 2012, along with 137 young adults from the Metabolic and Asthma Incidence Research (Meta-AIR) between 2014 and 2018. Plasma PFAS (i.e., PFOS, PFOA, PFHxS, PFHpS, PFNA) were quantified using liquid-chromatography high-resolution mass spectrometry. Plasma proteins (n = 334) were measured utilizing the proximity extension assay using an Olink Explore Cardiometabolic Panel I. We conducted linear regression with covariate adjustment to identify PFAS-associated proteins. Ingenuity Pathway Analysis, protein-protein interaction network analysis, and protein annotation were used to investigate alterations in biological functions and protein clusters. Results: Results after adjusting for multiple comparisons showed 13 significant PFAS-associated proteins in SOLAR and six in Meta-AIR, sharing similar functions in inflammation, immunity, and oxidative stress. In SOLAR, PFNA demonstrated significant positive associations with the largest number of proteins, including ACP5, CLEC1A, HMOX1, LRP11, MCAM, SPARCL1, and SSC5D. After considering the mixture effect of PFAS, only SSC5D remained significant. In Meta-AIR, PFAS mixtures showed positive associations with GDF15 and IL6. Exploratory analysis showed similar findings. Specifically, pathway analysis in SOLAR showed PFOA- and PFNA-associated activation of immune-related pathways, and PFNA-associated activation of inflammatory response. In Meta-AIR, PFHxS-associated activation of dendric cell maturation was found. Moreover, PFAS was associated with common protein clusters of immunoregulatory interactions and JAK-STAT signaling in both cohorts. Conclusion: PFAS was associated with broad alterations of the proteomic profiles linked to pro-inflammation and immunoregulation. The biological functions of these proteins provide insight into potential molecular mechanisms of PFAS toxicity.

Background: Per- and polyfluoroalkyl substances (PFAS) are pollutants linked to adverse health effects. Diet is an important source of PFAS exposure, yet it is unknown how diet impacts longitudinal PFAS levels. Objective: To determine if dietary intake and food sources were associated with changes in blood PFAS concentrations among Hispanic young adults at risk of metabolic diseases. Methods: Predominantly Hispanic young adults from the Children’s Health Study who underwent two visits (CHS; n = 123) and young adults from NHANES 2013–2018 who underwent one visit (n = 604) were included. Dietary data at baseline was collected using two 24-hour dietary recalls to measure individual foods and where foods were prepared/consumed (home/restaurant/fast-food). PFAS were measured in blood at both visits in CHS and cross-sectionally in NHANES. In CHS, multiple linear regression assessed associations of baseline diet with longitudinal PFAS; in NHANES, linear regression was used. Results: In CHS, all PFAS except PFDA decreased across visits (all p

Abstract Biomarkers such as exhaled nitric oxide (FeNO), a marker of airway inflammation, have applications in the study of chronic respiratory disease where longitudinal studies of within-participant changes in the biomarker are particularly relevant. A cutting-edge approach to assessing FeNO, called multiple flow FeNO, repeatedly assesses FeNO across a range of expiratory flow rates at a single visit and combines these data with a deterministic model of lower respiratory tract NO to estimate parameters quantifying airway wall and alveolar NO sources. Previous methodological work for multiple flow FeNO has focused on methods for data from a single participant or from cross-sectional studies. Performance of existing ad hoc two-stage methods for longitudinal multiple flow FeNO in cohort or panel studies has not been evaluated. In this paper, we present a novel longitudinal extension to a unified hierarchical Bayesian (L_U_HB) model relating longitudinally assessed multiple flow FeNO to covariates. In several simulation study scenarios, we compare the L_U_HB method to other unified and two-stage frequentist methods. In general, L_U_HB produced unbiased estimates, had good power, and its performance was not sensitive to the magnitude of the association with a covariate and correlations between NO parameters. In an application relating height to longitudinal multiple flow FeNO in schoolchildren without asthma, unified analysis methods estimated positive, statistically significant associations of height with airway and alveolar NO concentrations and negative associations with airway wall diffusivity while estimates from two-stage methods were smaller in magnitude and sometimes non-significant.

BackgroundExisting module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses.MethodsHere, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types.ResultsWe report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control.ConclusionACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.

Abstract Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO—measured non-invasively at the mouth—as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children’s Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.

Asthma is a common disease of the airways for which numerous genetic loci have been identified. Here, Han et al. carry out a genome-wide analysis for asthma to identify additional loci, report sex-stratified and genetic risk score analyses, and functionally follow-up one locus using a murine model of airway hyperreactivity.

Summary: Genetic studies on telomere length are important for understanding age-related diseases. Prior GWASs for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally diverse individuals (European, African, Asian, and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole-genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n = 109,122 individuals. We identified 59 sentinel variants (p < 5 × 10−9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated that the independent signals colocalized with cell-type-specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated that our TL polygenic trait scores (PTSs) were associated with an increased risk of cancer-related phenotypes.

Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.

Background: Air pollution exposure has been associated with increased risk of COVID-19 incidence and mortality by ecological analyses. Few studies have investigated the specific effect of traffic-related air pollution on COVID-19 severity. Objective: To investigate the associations of near-roadway air pollution (NRAP) exposure with COVID-19 severity and mortality using individual-level exposure and outcome data. Methods: The retrospective cohort includes 75,010 individuals (mean age 42.5 years, 54% female, 66% Hispanic) diagnosed with COVID-19 at Kaiser Permanente Southern California between 3/1/2020–8/31/2020. NRAP exposures from both freeways and non-freeways during 1-year prior to the COVID-19 diagnosis date were estimated based on residential address history using the CALINE4 line source dispersion model. Primary outcomes include COVID-19 severity defined as COVID-19-related hospitalizations, intensive respiratory support (IRS), intensive care unit (ICU) admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Covariates including socio-characteristics and comorbidities were adjusted for in the analysis. Result: One standard deviation (SD) increase in 1-year-averaged non-freeway NRAP (0.5 ppb NOx) was associated with increased odds of COVID-19-related IRS and ICU admission [OR (95% CI): 1.07 (1.01, 1.13) and 1.11 (1.04, 1.19) respectively] and increased risk of mortality (HR = 1.10, 95% CI = 1.03, 1.18). The associations of non-freeway NRAP with COVID-19 outcomes were largely independent of the effect of regional fine particulate matter and nitrogen dioxide exposures. These associations were generally consistent across age, sex, and race/ethnicity subgroups. The associations of freeway and total NRAP with COVID-19 severity and mortality were not statistically significant. Conclusions: Data from this multiethnic cohort suggested that NRAP, particularly non-freeway exposure in Southern California, may be associated with increased risk of COVID-19 severity and mortality among COVID-19 infected patients. Future studies are needed to assess the impact of emerging COVID-19 variants and chemical components from freeway and non-freeway NRAP.

Objective: To examine the prospective associations between exposure to perfluoroalkyl substances (PFASs) and longitudinal measurements of glucose metabolism in high-risk overweight and obese Hispanic children. Methods: Forty overweight and obese Hispanic children (8–14 years) from urban Los Angeles underwent clinical measures and 2-hour oral glucose tolerance tests (OGTT) at baseline and a follow-up visit (range: 1–3 years after enrollment). Baseline plasma perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorohexane sulfonic acid (PFHxS), and the plasma metabolome were measured by liquid-chromatography with high-resolution mass spectrometry. Multiple linear regression models were used to assess the association between baseline PFASs and changes in glucose homeostasis over follow-up. A metabolome-wide association study coupled with pathway enrichment analysis was performed to evaluate metabolic dysregulation associated with plasma PFASs concentrations. We performed a structural integrated analysis aiming to characterize the joint impact of all factors and to identify latent clusters of children with alterations in glucose homeostasis, based on their exposure and metabolomics profile. Results: Each ln (ng/ml) increase in PFOA and PFHxS concentrations was associated with a 30.6 mg/dL (95% CI: 8.8–52.4) and 10.2 mg/dL (95% CI: 2.7–17.7) increase in 2-hour glucose levels, respectively. A ln (ng/ml) increase in PFHxS concentrations was also associated with 17.8 mg/dL increase in the glucose area under the curve (95% CI: 1.5–34.1). Pathway enrichment analysis showed significant alterations of lipids (e.g., glycosphingolipids, linoleic acid, and de novo lipogenesis), and amino acids (e.g., aspartate and asparagine, tyrosine, arginine and proline) in association to PFASs exposure. The integrated analysis identified a cluster of children with increased 2-h glucose levels over follow up, characterized by increased PFAS levels and altered metabolite patterns. Conclusions: This proof-of-concept analysis shows that higher PFAS exposure was associated with dysregulation of several lipid and amino acid pathways and longitudinal alterations in glucose homeostasis in Hispanic youth. Larger studies are needed to confirm these findings and fully elucidate the underlying biological mechanisms. Keywords: Perfluoroalkyl substances, Type 2 diabetes, Glucose metabolism, Metabolomics, Children

Abstract Background Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms. Methods Schoolchildren from the population-based southern California Children’s Health Study were visited annually from 2003 to 2012. Bronchitic symptoms over the prior 12 months were assessed by questionnaire. A gradient boosting model was fit using groups of risk factors (including traffic/air pollution exposures) for all children and by asthma status. Training data consisted of one observation per participant in a random study year (for 50% of participants). Validation data consisted of: (1) a random (later) year in the same participants (within-participant); (2) a random year in participants excluded from the training data (across-participant). Results At baseline, 13.2% of children had asthma and 18.1% reported bronchitic symptoms. Models performed similarly within- and across-participant. Previous year symptoms/medication use provided much of the predictive ability (across-participant area under the receiver operating characteristic curve (AUC): 0.76 vs 0.78 for all risk factors, in all participants). Traffic/air pollution exposures added modestly to prediction as did body mass index percentile, age and parent stress. Conclusions Regardless of asthma status, previous symptoms were the most important predictors of current symptoms. Traffic/air pollution variables contribute modest predictive information, but impact large populations. Methods proposed here could be generalized to personalized exacerbation predictions in future longitudinal studies to support targeted prevention efforts.

Type-2 innate lymphoid cells (ILC2s) are an immune population secreting Th2 cytokines playing a role in the regulation of adipose metabolic homeostasis. Here the authors show that engagement of GITR, a member of the TNF superfamily, in activated ILC2s is protective against insulin resistance in both a preventive and a therapeutic manner in the context of obesity.

Abstract Background Disproportionately high rates of maternal overweight and obesity among the Hispanic population before, during, and after pregnancy pose serious health concerns for both mothers (e.g., preeclampsia, gestational diabetes, weight retention) and children (e.g., elevated lifelong obesity risk). A growing body of evidence implicates environmental exposures (e.g., air pollution, metals) and social stressors (e.g., poverty, violence) in contributing to obesity-related biobehavioral processes, such as physical activity, dietary intake, perceived stress, and cortisol regulation. However, current understanding of the role of environmental exposures and social stressors on obesity-related biobehavioral processes is limited by infrequent, inter-individual measurement, and lack of personal exposure monitoring. Methods The “Maternal and Developmental Risks from Environmental and Social Stressors” (MADRES) real-time and personal sampling study examines the within-subject day-level effects of environmental and social stressors on maternal pre- and post-partum obesity-related biobehavioral responses. Among a cohort of 65 low-income, Hispanic women in urban Los Angeles, this study uses innovative personal, real-time data capture strategies (e.g., ecological momentary assessment [EMA], personal exposure monitoring, geolocation monitoring, accelerometry) to repeatedly assess obesity-related processes during the 1st and 3rd trimester, and at 4–6 months postpartum. Day-level effects of environmental exposures and social stressors on women’s physical activity, diet, perceived stress and salivary cortisol measured across repeated days will be tested using multilevel modeling. Discussion Hispanic women of childbearing age bear a disproportionately high burden of obesity, and this population is also unduly exposed to numerous obesogenic settings. By using innovative real-time data capture strategies, the current study will uncover the daily impacts of environmental and social stressor exposures on women’s obesity-related biobehavioral responses, which over time can lead to excessive gestational weight gain, postpartum weight retention and can pose serious consequences for both mother and child. Findings from the real-time and personal sampling study will identify key mechanistic targets for policy, clinical, and programmatic interventions, with the potential for broad-reaching public health impacts.

(1) Background: The developmental origins of health and disease (DOHaD) hypothesis links adverse fetal exposures with developmental mal-adaptations and morbidity later in life. Short- and long-term exposures to air pollutants are known contributors to health outcomes; however, the potential for developmental health effects of air pollution exposures during gestation or early-childhood have yet to be reviewed and synthesized from a DOHaD lens. The objective of this study is to summarize the literature on cardiovascular and metabolic, respiratory, allergic, and neuropsychological health outcomes, from prenatal development through early childhood, associated with early-life exposures to outdoor air pollutants, including traffic-related and wildfire-generated air pollutants. (2) Methods: We conducted a search using PubMed and the references of articles previously known to the authors. We selected papers that investigated health outcomes during fetal or childhood development in association with early-life ambient or source-specific air pollution exposure. (3) Results: The current literature reports that prenatal and early-childhood exposures to ambient and traffic-related air pollutants are associated with a range of adverse outcomes in early life, including cardiovascular and metabolic, respiratory and allergic, and neurodevelopmental outcomes. Very few studies have investigated associations between wildfire-related air pollution exposure and health outcomes during prenatal, postnatal, or childhood development. (4) Conclusion: Evidence from January 2000 to January 2022 supports a role for prenatal and early-childhood air pollution exposures adversely affecting health outcomes during development. Future studies are needed to identify both detrimental air pollutants from the exposure mixture and critical exposure time periods, investigate emerging exposure sources such as wildfire, and develop feasible interventional tools.

Background: Per- and polyfluoroalkyl substances (PFASs) exposure is ubiquitous among the US population and has been linked to adverse health outcomes including cardiometabolic diseases, immune dysregulation and endocrine disruption. However, the metabolic mechanism underlying the adverse health effect of PFASs exposure is unknown. Objective: The aim of this project is to investigate the association between PFASs exposure and altered metabolic pathways linked to increased cardiometabolic risk in young adults. Methods: A total of 102 young adults with 82% overweight or obese participants were enrolled from Southern California between 2014 and 2017. Cardiometabolic outcomes were assessed including oral glucose tolerance test (OGTT) measures, body fat and lipid profiles. High-resolution metabolomics was used to quantify plasma exposure levels of three PFAS congeners and intensity profiles of the untargeted metabolome. Fasting concentrations of 45 targeted metabolites involved in fatty acid and lipid metabolism were used to verify untargeted metabolomics findings. Bayesian Kernel Machine Regression (BKMR) was used to examine the associations between PFAS exposure mixture and cardiometabolic outcomes adjusting for covariates. Mummichog pathway enrichment analysis was used to explore PFAS-associated metabolic pathways. Moreover, the effect of PFAS exposure on the metabolic network, including metabolomic profiles and cardiometabolic outcomes, was investigated. Results: Higher exposure to perfluorooctanoic acid (PFOA) was associated with higher 30-minute glucose levels and glucose area under the curve (AUC) during the OGTT (p

Animal work indicates exposure to air pollutants may alter the composition of the gut microbiota. This study examined relationships between air pollutants and the gut microbiome in young adults residing in Southern California. Our results demonstrate significant associations between exposure to air pollutants and the composition of the gut microbiome using whole-genome sequencing. Higher exposure to 24-hour O3 was associated with lower Shannon diversity index, higher Bacteroides caecimuris, and multiple gene pathways, including L-ornithine de novo biosynthesis as well as pantothenate and coenzyme A biosynthesis I. Among other pollutants, higher NO2 exposure was associated with fewer taxa, including higher Firmicutes. The percent variation in gut bacterial composition that was explained by air pollution exposure was up to 11.2% for O3 concentrations, which is large compared to the effect size for many other covariates reported in healthy populations. This study provides the first evidence of significant associations between exposure to air pollutants and the compositional and functional profile of the human gut microbiome. These results identify O3 as an important pollutant that may alter the human gut microbiome. Keywords: Air pollution, Gut microbiome, Whole genome sequencing

Abstract Background Evidence suggests that childhood near-roadway air pollution (NRAP) exposures contribute to increased body mass index (BMI); however, effects of NRAP exposure during the vulnerable periods including in utero and first year of life have yet to be established. In this study, we examined whether exposure to elevated concentrations of NRAP during in utero and/or first year of life increase childhood BMI growth. Methods Participants in the Children’s Health Study enrolled from 2002 to 2003 with annual visits over a four-year period and who changed residences before study entry were included (n = 2318). Annual height and weight were measured and lifetime residential NRAP exposures including in utero and first year of life periods were estimated by nitrogen oxides (NOx) using the California line-source dispersion model. Linear mixed effects models assessed in utero or first year near-road freeway and non-freeway NOx exposures and BMI growth after adjusting for age, sex, race/ethnicity, parental education, Spanish questionnaire, and later childhood near-road NOx exposure. Results A two-standard deviation difference in first year of life near-road freeway NOx exposure was associated with a 0.1 kg/m2 (95% confidence interval (CI): 0.03, 0.2) faster increase in BMI growth per year and a 0.5 kg/m2 (95% CI: 0.02, 0.9) higher attained BMI at age 10 years. Conclusions Higher exposure to early life NRAP increased the rate of change of childhood BMI and resulted in a higher attained BMI at age 10 years that were independent of later childhood exposures. These findings suggest that elevated early life NRAP exposures contribute to increased obesity risk in children.

Objective: Growing evidence indicates exposure to air pollution contributes to obesity and cardiometabolic disease risk in children and adults, however studies are lacking in young adulthood, an important transitional period in the life course. The aim of this study was to examine the associations of short- and long-term regional ambient and near-roadway air pollution (NRAP) exposures on adiposity and cardiometabolic health in young adults aged 17–22 years. Methods: From 2014 to 2018, a subset of participants (n = 158) were recruited from the Children's Health Study to participate in the Meta-AIR (Metabolic and Asthma Incidence Research) study to assess obesity (body composition and abdominal adiposity) and cardiometabolic health (fasting glucose, fasting insulin and lipid profiles) measures. Prior 1-month and 1-year average air pollution exposures were calculated from residential addresses. This included nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.

Abstract Background AXL is one of the TAM (TYRO3, AXL and MERTK) receptor tyrosine kinases and may affect numerous immune-related health conditions. However, the role for AXL in asthma, including its epigenetic regulation, has not been extensively studied. Methods We investigated the association between AXL DNA methylation at birth and risk of childhood asthma symptoms at age 6 years. DNA methylation of multiple CpG loci across the regulatory regions of AXL was measured in newborn bloodspots using the Illumina HumanMethylation450 array on a subset of 246 children from the Children’s Health Study (CHS). Logistic regression models were fitted to assess the association between asthma symptoms and DNA methylation. Findings were evaluated for replication in a separate population of 1038 CHS subjects using Pyrosequencing on newborn bloodspot samples. AXL genotypes were extracted from genome-wide data. Results Higher average methylation of CpGs in the AXL gene at birth was associated with higher risk of parent-reported wheezing, and the association was stronger in girls than in boys. This relationship reflected the methylation status of the gene-body region near the 5′ end, for which a 1% higher methylation level was significantly associated with a 72% increased risk of ever having wheezed by 6 years. The association of one CpG locus, cg00360107 was replicated using Pyrosequencing. Increased AXL methylation was also associated with lower mRNA expression level of this gene in lung tissue from the Cancer Genome Atlas (TCGA) dataset. Furthermore, AXL DNA methylation was strongly linked to underlying genetic polymorphisms. Conclusions AXL DNA methylation at birth was associated with higher risk for asthma-related symptoms in early childhood.

Abstract Background The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation that has proved to be useful in investigations of genetic and epigenetic airway susceptibility to ambient air pollutants. For example, susceptibility to airway inflammation from exposure to particulate matter with aerodynamic diameter

Group 2 Innate lymphoid cells (ILC2) contribute significantly to allergic inflammation. However, the role of microbiota on ILC2s remains to be unraveled. Here we show that short chain fatty acids (SCFAs), such as butyrate, derived from fermentation of dietary fibers by the gut microbiota inhibit pulmonary ILC2 functions and subsequent development of airway hyperreactivity (AHR). We further show that SCFAs modulate GATA3, oxidative phosphorylation, and glycolytic metabolic pathways in pulmonary ILC2s. The observed phenotype is associated with increased IL-17a secretion by lung ILC2s and linked to enhanced neutrophil recruitment to the airways. Finally, we show that butyrate-producing gut bacteria in germ-free mice effectively suppress ILC2-driven AHR. Collectively, our results demonstrate a previously unrecognized role for microbial-derived SCFAs on pulmonary ILC2s in the context of AHR. The data suggest strategies aimed at modulating metabolomics and microbiota in the gut, not only to treat, but to prevent lung inflammation and asthma.

Background: Air pollution exposure has been shown to increase the risk of obesity and metabolic dysfunction in animal models and human studies. However, the metabolic pathways altered by air pollution exposure are unclear, especially in adolescents and young adults who are at a critical period in the development of cardio-metabolic diseases. Objectives: The aim of this study was to examine the associations between air pollution exposure and indices of fatty acid and amino acid metabolism. Methods: A total of 173 young adults (18–23 years) from eight Children's Health Study (CHS) Southern California communities were examined from 2014 to 2018. Near-roadway air pollution (NRAP) exposure (freeway and non-freeway) and regional air pollution exposure (nitrogen dioxide, ozone and particulate matter) during one year before the study visit were estimated based on participants' residential addresses. Serum concentrations of 64 targeted metabolites including amino acids, acylcarnitines, non-esterified fatty acid (NEFA) and glycerol were measured in fasting serum samples. Principal component analysis of metabolites was performed to identify metabolite clusters that represent key metabolic pathways. Mixed effects models were used to analyze the associations of air pollution exposure with metabolomic principal component (PC) scores and individual metabolite concentrations adjusting for potential confounders. Results: Higher lagged one-year averaged non-freeway NRAP exposure was associated with higher concentrations of NEFA oxidation byproducts and higher NEFA-related PC score (all p's ≤ 0.038). The effect sizes were larger among obese individuals (interaction p = 0.047). Among females, higher freeway NRAP exposure was also associated with a higher NEFA-related PC score (p = 0.042). Among all participants, higher freeway NRAP exposure was associated with a lower PC score for lower concentrations of short- and median-chain acylcarnitines (p = 0.044). Conclusions: Results of this study indicate that NRAP exposure is associated with altered fatty acid metabolism, which could contribute to the metabolic perturbation in obese youth. Keywords: Traffic, Air pollution, Obesity, Metabolic diseases, Metabolomics

BackgroundThe effect of air pollution exposure on atherosclerosis severity or incident clinical events in patients with coronary artery disease is not known. Methods and ResultsWe conducted a prospective longitudinal cohort study of 6575 Ohio residents undergoing elective diagnostic coronary angiography. Multinomial regression and Cox proportional hazards models were used to assess the relationship between exposure to fine particulate matter