Search

BackgroundCOVID-19 is associated with acute risk of major adverse cardiac events (MACE), including myocardial infarction, stroke, and mortality (all-cause). However, the duration and underlying determinants of heightened risk of cardiovascular disease and MACE post-COVID-19 are not known.MethodsData from the UK Biobank was used to identify COVID-19 cases (n=10 005) who were positive for polymerase chain reaction (PCR+)-based tests for SARS-CoV-2 infection (n=8062) or received hospital-based International Classification of Diseases version-10 (ICD-10) codes for COVID-19 (n=1943) between February 1, 2020 and December 31, 2020. Population controls (n=217 730) and propensity score-matched controls (n=38 860) were also drawn from the UK Biobank during the same period. Proportional hazard models were used to evaluate COVID-19 for association with long-term (>1000 days) risk of MACE and as a coronary artery disease risk equivalent. Additional analyses examined whether COVID-19 interacted with genetic determinants to affect the risk of MACE and its components.ResultsThe risk of MACE was elevated in COVID-19 cases at all levels of severity (HR, 2.09 [95% CI, 1.94-2.25]; P

BackgroundTime-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. ObjectiveWe aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. MethodsWe applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. ResultsIn the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. ConclusionsIn a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

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.

Background: Societal changes during the COVID-19 pandemic may affect childrens health behaviors and exacerbate disparities. This study aimed to describe childrens health behaviors during the COVID-19 pandemic, how they vary by sociodemographic characteristics, and the extent to which parent coping strategies mitigate the impact of pandemic-related financial strain on these behaviors. Methods: This study used pooled data from 50 cohorts in the Environmental influences on Child Health Outcomes Program. Children or parent proxies reported sociodemographic characteristics, health behaviors, and parent coping strategies. Results: Of 3315 children aged 3-17 years, 49% were female and 57% were non-Hispanic white. Children of parents who reported food access as a source of stress were 35% less likely to engage in a higher level of physical activity. Children of parents who changed their work schedule to care for their children had 82 fewer min/day of screen time and 13 more min/day of sleep compared with children of parents who maintained their schedule. Parents changing their work schedule were also associated with a 31% lower odds of the child consuming sugar-sweetened beverages. Conclusions: Parents experiencing pandemic-related financial strain may need additional support to promote healthy behaviors. Understanding how changes in parent work schedules support shorter screen time and longer sleep duration can inform future interventions.

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with different infectivity, transmission potential, and morbidity change the characteristics of local epidemics and affect vaccine effectiveness. As part of the University of Southern California COVID-19 Pandemic Research Center's efforts to understand, control, and inform local community on coronavirus disease 2019 (COVID-19), we implemented a SARS-CoV-2 surveillance program among students, employees, and USC Keck Medical Center patients. We present the epidemiology and distribution of SARS-CoV-2 and its variants among the population.MethodsWe used digital droplet reverse-transcriptase polymerase chain reaction (PCR) to analyze in real-time remnant SARS-CoV-2 PCR-positive saliva specimens stored at the USC Keck Medicine laboratory between September 2020 and April 2022. Samples were tested for the original strain (A20) and 9 SARS-CoV-2 variants: α(B.1.1.7, Q.1-Q.8), β(B.1.351, B.1.351.2, B.1.351.3), γ(P.1, P.1.1, P.1.2), δ(B.1.617.2), δ+(or δ417N), ε(B.1.427 and B.1.429), η(B.1.525), λ(C.37) and ο(B.1.1.529, ΒΑ.1, BA.2). We reviewed deidentified health information from positive cases including demographics, history of COVID-19 (eg, symptoms, hospitalizations, and repeat infections), and COVID-19 vaccination status.ResultsWe reviewed 1169 cases and determined the variant type of 482 specimens: 77 specimens were original strain, 119 "Delta", 165 "Omicron". The original strain was detected during the third and fourth quarters of 2020. The Delta variant appeared during the second quarter of 2021, whereas Omicron appeared in the fourth quarter of 2021.ConclusionsProspectively tracking SARS-CoV-2 variants in a university population and a hospital system, utilizing a low-cost, high-throughput PCR assay, was feasible. Local variant monitoring remains important to inform prevention and control efforts among university and clinical settings.

ObjectiveThe heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.MethodsAs of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS).ResultsClusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV1/FVC, FEV1 percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time.ConclusionUpon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

RationaleAsthma and obesity often co-occur. It has been hypothesized that asthma may contribute to childhood obesity onset.ObjectivesTo determine if childhood asthma is associated with incident obesity and examine the role of asthma medication in this association.MethodsWe studied 8,716 children between ages 6 and 18.5 years who were nonobese at study entry participating in 18 US cohorts of the Environmental influences on Child Health Outcomes program (among 7,299 children with complete covariate data mean [SD] study entry age = 7.2 [1.6] years and follow up = 5.3 [3.1] years).Measurements and main resultsWe defined asthma based on caregiver report of provider diagnosis. Incident obesity was defined as the first documented body mass index ≥95th percentile for age and sex following asthma status ascertainment. Over the study period, 26% of children had an asthma diagnosis and 11% developed obesity. Cox proportional hazards models with sex-specific baseline hazards were fitted to assess the association of asthma diagnosis with obesity incidence. Children with asthma had a 23% (95% confidence intervals [CI] = 4, 44) higher risk for subsequently developing obesity compared with those without asthma. A novel mediation analysis was also conducted to decompose the total asthma effect on obesity into pathways mediated and not mediated by asthma medication use. Use of asthma medication attenuated the total estimated effect of asthma on obesity by 64% (excess hazard ratios = 0.64; 95% CI = -1.05, -0.23).ConclusionsThis nationwide study supports the hypothesis that childhood asthma is associated with later risk of obesity. Asthma medication may reduce this association and merits further investigation as a potential strategy for obesity prevention among children with asthma.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (nitrogen oxides (NOx) and PM2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV1) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4−16.8) with mean (SD) morning %predicted FEV1 of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O3 exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV1 (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of “feeling scared of trouble breathing” symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O3, NOx, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and “feeling scared of trouble breathing.” Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

Rationale: The 17q12-21.1 locus is one of the most highly replicated genetic associations with asthma. Individuals of African descent have lower linkage disequilibrium in this region, which could facilitate identifying causal variants.Objectives: To identify functional variants at 17q12-21.1 associated with early-onset asthma among African American individuals.Methods: We evaluated African American participants from SAPPHIRE (Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity) (n = 1,940), SAGE II (Study of African Americans, Asthma, Genes and Environment) (n = 885), and GCPD-A (Study of the Genetic Causes of Complex Pediatric Disorders-Asthma) (n = 2,805). Associations with asthma onset at ages under 5 years were meta-analyzed across cohorts. The lead signal was reevaluated considering haplotypes informed by genetic ancestry (i.e., African vs. European). Both an expression-quantitative trait locus analysis and a phenome-wide association study were performed on the lead variant.Measurements and Main Results: The meta-analyzed results from SAPPHIRE, SAGE II, and the GCPD-A identified rs11078928 as the top association for early-onset asthma. A haplotype analysis suggested that the asthma association partitioned most closely with the rs11078928 genotype. Genetic ancestry did not appear to influence the effect of this variant. In the expression-quantitative trait locus analysis, rs11078928 was related to alternative splicing of GSDMB (gasdermin-B) transcripts. The phenome-wide association study of rs11078928 suggested that this variant was predominantly associated with asthma and asthma-associated symptoms.Conclusions: A splice-acceptor polymorphism appears to be a causal variant for asthma at the 17q12-21.1 locus. This variant appears to have the same magnitude of effect in individuals of African and European descent.

IntroductionEstimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.MethodsUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.ResultsEnsemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV1), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV1 on chromosome 12 in 867 African American children with asthma (P = 1.26 × 10-8, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV1 Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with KITLG (KIT ligand, also known as SCF), and their minor alleles were associated with increased expression of the KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure (P = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV1, possibly mediated through KITLG, in African American children with asthma. This is the first study that has identified a genetic association between lung function and KITLG, which has established a role in orchestrating allergic inflammation in asthma.

ObjectiveTo describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma.Materials and methodsThe Biomedical REAl-Time Health Evaluation (BREATHE) platform connects different sensors and data streams, contextualizing an individual's symptoms and daily activities over time to understand pediatric asthma's presentation and its management. A smartwatch/smartphone combination serves as a hub for personal/wearable sensing devices collecting data on health (eg, heart rate, spirometry, medications), motion, and personal exposures (eg, particulate matter, ozone); securely transmitting information to BREATHE's servers; and interacting with the user (eg, ecological momentary assessments). Server-side integration of electronic health record data and spatiotemporally correlated information (eg, weather, traffic) elaborates on these observations. An initial panel study involving pediatric asthma patients was conducted to assess BREATHE.ResultsTwenty subjects were enrolled, during which BREATHE accrued seven consecutive days of continuous data per individual. The data were used to confirm knowledge about asthma (use of controller inhalers, time-activity behaviors, personal air pollution exposure), and additional analyses provided insights into within-day associations of environmental triggers and asthma exacerbations. Exit surveys focusing on mHealth usability, while positive, noted several translational challenges.DiscussionBased on these promising results, a longitudinal panel study to evaluate individual microenvironments and exposures is ongoing. Lessons learned thus far reflect the need to address various usability aspects, including convenience and ongoing engagement.ConclusionBREATHE enables multi-sensor mHealth studies, capturing new types of information alongside an evolving understanding of personal exposomes.

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

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.

Although it is well accepted that air pollution exposure exacerbates preexisting airway disease, it has not been firmly established that long-term pollution exposure increases the risk of new-onset asthma or chronic obstruction pulmonary disease (COPD). This Workshop brought together experts on mechanistic, epidemiological, and clinical aspects of airway disease to review current knowledge regarding whether air pollution is a causal factor in the development of asthma and/or COPD. Speakers presented recent evidence in their respective areas of expertise related to air pollution and new airway disease incidence, followed by interactive discussions. A writing committee summarized their collective findings. The Epidemiology Group found that long-term exposure to air pollution, especially metrics of traffic-related air pollution such as nitrogen dioxide and black carbon, is associated with onset of childhood asthma. However, the evidence for a causal role in adult-onset asthma or COPD remains insufficient. The Mechanistic Group concluded that air pollution exposure can cause airway remodeling, which can lead to asthma or COPD, as well as asthma-like phenotypes that worsen with long-term exposure to air pollution, especially fine particulate matter and ozone. The Clinical Group concluded that air pollution is a plausible contributor to the onset of both asthma and COPD. Available evidence indicates that long-term exposure to air pollution is a cause of childhood asthma, but the evidence for a similar determination for adult asthma or COPD remains insufficient. Further research is needed to elucidate the exact biological mechanism underlying incident childhood asthma, and the specific air pollutant that causes it.

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

BackgroundMitochondria support critical cellular functions, such as energy production through oxidative phosphorylation, regulation of reactive oxygen species, apoptosis, and calcium homeostasis.ObjectiveGiven the heightened level of cellular activity in patients with asthma, we sought to determine whether mitochondrial DNA (mtDNA) copy number measured in peripheral blood differed between individuals with and without asthma.MethodsWhole genome sequence data was generated as part of the Trans-Omics for Precision Medicine (TOPMed) Program on participants from the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) and the Study of African Americans, Asthma, Genes, & Environment II (SAGE II). We restricted our analysis to individuals who self-identified as African American (3,651 asthma cases and 1,344 controls). Mitochondrial copy number was estimated using the sequencing read depth ratio for the mitochondrial and nuclear genomes. Respiratory complex expression was assessed using RNA-sequencing.ResultsAverage mitochondrial copy number was significantly higher among individuals with asthma when compared with controls (SAPPHIRE: 218.60 vs. 200.47, P

Asthma is a chronic inflammatory disease of the airways with contributions from genes, environmental exposures, and their interactions. While genome-wide association studies (GWAS) in humans have identified ~200 susceptibility loci, the genetic factors that modulate risk of asthma through gene-environment (GxE) interactions remain poorly understood. Using the Hybrid Mouse Diversity Panel (HMDP), we sought to identify the genetic determinants of airway hyperreactivity (AHR) in response to diesel exhaust particles (DEP), a model traffic-related air pollutant. As measured by invasive plethysmography, AHR under control and DEP-exposed conditions varied 3-4-fold in over 100 inbred strains from the HMDP. A GWAS with linear mixed models mapped two loci significantly associated with lung resistance under control exposure to chromosomes 2 (p = 3.0x10-6) and 19 (p = 5.6x10-7). The chromosome 19 locus harbors Il33 and is syntenic to asthma association signals observed at the IL33 locus in humans. A GxE GWAS for post-DEP exposure lung resistance identified a significantly associated locus on chromosome 3 (p = 2.5x10-6). Among the genes at this locus is Dapp1, an adaptor molecule expressed in immune-related and mucosal tissues, including the lung. Dapp1-deficient mice exhibited significantly lower AHR than control mice but only after DEP exposure, thus functionally validating Dapp1 as one of the genes underlying the GxE association at this locus. In summary, our results indicate that some of the genetic determinants for asthma-related phenotypes may be shared between mice and humans, as well as the existence of GxE interactions in mice that modulate lung function in response to air pollution exposures relevant to humans.

BackgroundAccurate 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.ObjectivesIn 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.

BackgroundIncreasingly ensemble learning-based spatiotemporal models are being used to estimate residential air pollution exposures in epidemiological studies. While these machine learning models typically have improved performance, they suffer from exposure measurement error that is inherent in all models. Our objective is to develop a framework to formally assess shared, multiplicative measurement error (SMME) in our previously published three-stage, ensemble learning-based nitrogen oxides (NOx) model to identify its spatial and temporal patterns and predictors.MethodsBy treating the ensembles as an external dosimetry system, we quantified shared and unshared, multiplicative and additive (SUMA) measurement error components in our exposure model. We used generalized additive models (GAMs) with a smooth term for location to identify geographic locations with significantly elevated SMME and explain their spatial and temporal determinants.ResultsWe found evidence of significant shared and unshared multiplicative error (p

BackgroundAsthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies.ObjectiveWe sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility.MethodsWe leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups.ResultsWe identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P = 6.8 × 10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P = .002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P = .008). Our findings were replicated in an independent childhood asthma study in Latinos (P = 5.3 × 10-3, combined P = 2.6 × 10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P

Metabolic syndrome is characterized by disturbances in glucose homeostasis and the development of low-grade systemic inflammation, which increase the risk to develop type 2 diabetes mellitus (T2DM). Type-2 innate lymphoid cells (ILC2s) are a recently discovered immune population secreting Th2 cytokines. While previous studies show how ILC2s can play a critical role in the regulation of metabolic homeostasis in the adipose tissue, a therapeutic target capable of modulating ILC2 activation has yet to be identified. Here, we show that GITR, a member of the TNF superfamily, is expressed on both murine and human ILC2s. Strikingly, we demonstrate that GITR engagement of activated, but not naïve, ILC2s improves glucose homeostasis, resulting in both protection against insulin resistance onset and amelioration of established insulin- resistance. Together, these results highlight the critical role of GITR as a novel therapeutic molecule against T2DM and its fundamental role as an immune checkpoint for activated ILC2s.

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.

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis.

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-term measurement campaigns across 1-2 years and long-term central site monitoring in 1992-2013, we developed an integrated mixed-effect model with uncertainty estimates. Our statistical model incorporated nonlinear and spatial effects to reduce bias. Identified important predictors included temporal basis predictors, traffic indicators, population density, and subcounty-level mean pollutant concentrations. Substantial spatial autocorrelation (11-13%) was observed between neighboring communities. Ensemble learning and constrained optimization were used to enhance reliability of estimation over a large metropolitan area and a long period. The ensemble predictions of biweekly concentrations resulted in an R2 of 0.85 (RMSE: 4.7 ppb) for NO2 and 0.86 (RMSE: 13.4 ppb) for NOx. Ensemble learning and constrained optimization generated stable time series, which notably improved the results compared with those from initial mixed-effects models.

BACKGROUND: Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear. OBJECTIVE: We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults. METHODS: Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log- transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z-tests. RESULTS: In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta- analysis p = 0.00002) and de novo fatty acid biosynthesis (p = 0.03), among others. For example, when increasing all PFAS in the mixture from low (~ 30th percentile) to high (~ 70th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations. DISCUSSION: Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https:// doi.org/10.1289/EHP11372, Introduction Per- and polyfluoroalkyl substances (PFAS) make up a prevalent class of persistent organic pollutants with endocrine and metabolism disrupting properties. (1,2) PFAS are used in a broad range of [...]