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Gastrointestinal disturbances (GIDs) are relatively common in children diagnosed with autism spectrum disorder (ASD). This study assessed associations between maternal obesity, diabetes, preeclampsia, and asthma and likelihood of ASD with GIDs. This retrospective cohort study included 308,536 mother–child pairs of singletons born between 2001 and 2014 in a single integrated health care system. Social demographics, maternal health conditions during pregnancy, and child’s ASD and/or GID diagnosis by age 5 were extracted from electronic medical records. Child’s outcome was classified as: no ASD no GIDs; no ASD with GIDs; ASD no GIDs; and ASD with GIDs. Multinomial logistic regression was used to estimate odds ratios of child’s outcomes associated with maternal exposures, adjusting for covariates. Compared to children with neither disorder, each maternal condition was associated with higher odds of no ASD with GIDs, ASD no GIDs, and ASD with GIDs. Association was greatest for likelihood of ASD with GIDs (Maternal obesity odds ratio (95% confidence interval): 1.37 (1.22–1.54); Diabetes: 1.50 (1.28–1.76); Preeclampsia: 1.63 (1.36–1.95); Asthma: 1.39 (1.17–1.67); relative to no ASD no GIDs). Associations were robust to mutual adjustment for all maternal exposures. Maternal obesity, diabetes, preeclampsia, and asthma exposure during pregnancy may increase likelihood of both GIDs and ASD with or without co-occurring GIDs in offspring. This association was greatest in children diagnosed with ASD with co-occurring GIDs. Lay abstract Autism spectrum disorder is heterogeneous and often accompanied by co-occurring conditions. Previous studies have shown that maternal health conditions during pregnancy including obesity, diabetes, preeclampsia, and asthma were associated with increased likelihood of autism. However, little has been done examining the likelihood associated with autism with co-occurring conditions. This study assessed these maternal health conditions in relationship to autism and gastrointestinal disturbances, a common co-occurring condition in children diagnosed with autism. Data included 308,536 mother-child pairs from one integrated health care system with comprehensive electronic medical records. Among the study cohort, 5,131 (1.7%) children had a diagnosis of autism by age 5. Gastrointestinal disturbances were present in 35.4% of children diagnosed with autism and 25.1% of children without autism diagnoses. Our results showed that each of the four maternal health conditions during pregnancy was associated with increased likelihood of gastrointestinal disturbances, autism without gastrointestinal disturbances, and autism with gastrointestinal disturbances. For all four maternal health conditions, the association was greatest for likelihood of autism with gastrointestinal disturbances. Given that children diagnosed with autism are more likely to have gastrointestinal disturbances and over 80% of gastrointestinal disturbances in this cohort were diagnosed prior to autism diagnosis, this study suggests that there may be common biological pathways between autism and gastrointestinal disturbances impacted by these maternal exposures. Future studies are warranted to assess associations between different exposures and autism with other co-occurring conditions to increase our understanding of autism heterogeneity.

Exploration is considered essential to infant learning, but few studies have quantified infants’ task exploration. The purpose of this study was to quantify how infants explored task space with their feet while learning to activate a kick-activated mobile. Data were analyzed from fifteen 4-month-old infants who participated in a 10-min mobile task on 2–3 consecutive days. Infants learned that their vertical leg movements above a systematically increased threshold height activated the mobile. Five kinematic variables were analyzed: (a) exploration space volume, (b) exploration path length, (c) duration of time in the region of interest around the threshold that activated the mobile, (d) task-specific vertical variance of kicks, and (e) non-task-specific horizontal variance of kicks. The infants increased their general spatial exploration, volume, and path, and the infants adapted their exploration by maintaining their feet within the region of interest, although the task-specific region increased in height as the threshold increased. The infants used task-specific strategies quantified by the increased variance of kicks in the vertical direction and no change in the horizontal variance of kicks. Quantifying infants’ task exploration may provide critical insights into how learning emerges in infancy and enable researchers to more systematically describe, interpret, and support learning.

BACKGROUND: The development of motor skills in infancy is a vital neurodevelopmental milestone. Although previous studies have explored the neurotoxic effects of agricultural pesticides on infants’ motor development, limited research has examined early postnatal household pesticide use on infants’ motor development, particularly among urban communities. OBJECTIVE: This study examined the association between early postnatal household pesticide use and infants’ gross and fine motor development at 6 months of age. METHODS: Questionnaires were administered via telephone to 296 mother-infant dyads in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort. Early life household pesticide use was assessed via questionnaire administered when infants turned 3 months old and gross and fine motor development was assessed by the Ages and Stages Questionnaire (ASQ3) at 6 months old. Infant gross motor scores were reverse coded so that higher scores indicated lower gross motor performance. Negative binomial regressions were performed to assess the relationship between household pesticide use and infant gross motor development. RESULTS: Infants were predominantly Hispanic (78.7%) and full term (gestational age at birth: 39.0 ±1.9 weeks), with 22.3% of maternal participants reporting household use of rodent and insect pesticides. Adjusting for recruitment site, maternal age, ethnicity, household income, education, infant corrected age, infant sex, and home type, infants with maternal reported household use of rodent and insect pesticides had 1.30 times higher expected gross motor scores (95% CI: 1.05,1.61) than infants with no reported use of household pesticides, with higher scores indicating reduced gross motor performance. CONCLUSIONS: Our results suggest household use of rodent and insect pesticides may harm infants’ gross motor development in early childhood. Future research should evaluate the impact of specific household chemicals in infant biospecimens and their associations with infant motor development to confirm these findings.

Background/objective Prevalence of pre-pregnancy obesity and excessive gestational weight gain (GWG) are higher among women of color with low SES. Dysregulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis and its end-product, cortisol, during pregnancy is hypothesized to be associated with excessive GWG. However, past studies have produced inconsistent findings and often did not include health disparities populations. This study examined the association between pre-pregnancy body mass index (BMI), third trimester diurnal cortisol, and GWG in low-income, predominantly Hispanic women. Subjects/methods The MADRES study is an ongoing prospective cohort study of primarily Hispanic, low-income pregnant women and their children in Los Angeles, California. Data from 176 participants were included in this study. Total cortisol secretion (area under the curve, AUC) was quantified using four salivary cortisol samples (awakening, 30 min after awakening, afternoon, and bedtime) that were collected at home on one day during the third trimester of pregnancy. Moderation of the association between total cortisol and GWG by pre-pregnancy BMI was tested using multiple linear regression with a multiplicative interaction term. Results There was no association between total cortisol secretion and GWG overall (p = 0.82), but the association between total cortisol and GWG was stronger for women with class 1 pre-pregnancy obesity compared to women with normal pre-pregnancy BMI (interaction term p = 0.04). Conclusions Results suggest that obesity status before pregnancy may be exacerbating the physiological impact of cortisol on GWG.

OBJECTIVE: The 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. METHODS: As 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). RESULTS: Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV(1)/FVC, FEV(1) 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. CONCLUSION: Upon 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.

Much is known about the development of the whole amygdala, but less is known about its structurally and functionally diverse subregions. One notable distinguishing feature is their wide range of androgen and estrogen receptor densities. Given the rise in pubertal hormones during adolescence, sex steroid levels as well as receptor sensitivity could influence age-related subregion volumes. Therefore, our goal was to evaluate the associations between the total amygdala and its subregion volumes in relation to sex hormones – estradiol and free testosterone (FT) – as a function of age and genetic differences in androgen receptor (AR) sensitivity in a sample of 297 adolescents (46% female). In males, we found small effects of FT-by-age interactions in the total amygdala, portions of the basolateral complex, and the cortical and medial nuclei (CMN), with the CMN effects being moderated by AR sensitivity. For females, small effects were seen with increased genetic AR sensitivity relating to smaller basolateral complexes. However, none of these small effects passed multiple comparisons. Future larger studies are necessary to replicate these small, yet possibly meaningful effects of FT-by-age associations and modulation by AR sensitivity on amygdala development to ultimately determine if they contribute to known sex differences in emotional neurodevelopment.

For a Phase III randomized trial that compares survival outcomes between an experimental treatment versus a standard therapy, interim monitoring analysis is used to potentially terminate the study early based on efficacy. To preserve the nominal Type I error rate, alpha spending methods and information fractions are used to compute appropriate rejection boundaries in studies with planned interim analyses. For a one-sided trial design applied to a scenario in which the experimental therapy is superior to the standard therapy, interim monitoring should provide the opportunity to stop the trial prior to full follow-up and conclude that the experimental therapy is superior. This paper proposes a method called total control only (TCO) for estimating the information fraction based on the number of events within the standard treatment regimen. Based on theoretical derivations and simulation studies, for a maximum duration superiority design, the TCO method is not influenced by departure from the designed hazard ratio, is sensitive to detecting treatment differences, and preserves the Type I error rate compared to information fraction estimation methods that are based on total observed events. The TCO method is simple to apply, provides unbiased estimates of the information fraction, and does not rely on statistical assumptions that are impossible to verify at the design stage. For these reasons, the TCO method is a good approach when designing a maximum duration superiority trial with planned interim monitoring analyses.

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.

Importance Previous studies have reported associations between in utero exposure to regional air pollution and autism spectrum disorders (ASD). In utero exposure to components of near-roadway air pollution (NRAP) has been linked to adverse neurodevelopment in animal models, but few studies have investigated NRAP association with ASD risk. Objective To identify ASD risk associated with in utero exposure to NRAP in a large, representative birth cohort. Design, Setting, and Participants This retrospective pregnancy cohort study included 314,391 mother–child pairs of singletons born between 2001 and 2014 at Kaiser Permanente Southern California (KPSC) hospitals. Maternal and child data were extracted from KPSC electronic medical records. Children were followed until: clinical diagnosis of ASD, non-KPSC membership, death, or December 31, 2019, whichever came first. Exposure to the complex NRAP mixture during pregnancy was assessed using line-source dispersion models to estimate fresh vehicle emissions from freeway and non-freeway sources at maternal addresses during pregnancy. Vehicular traffic load exposure was characterized using advanced telematic models combining traditional traffic counts and travel-demand models with cell phone and vehicle GPS data. Cox proportional-hazard models estimated hazard ratios (HR) of ASD associated with near-roadway traffic load and dispersion-modeled NRAP during pregnancy, adjusted for covariates. Non-freeway NRAP was analyzed using quintile distribution due to nonlinear associations with ASD. Exposures Average NRAP and traffic load exposure during pregnancy at maternal residential addresses. Main Outcomes Clinical diagnosis of ASD. Results A total of 6,291 children (5,114 boys, 1,177 girls) were diagnosed with ASD. The risk of ASD was associated with pregnancy-average exposure to total NRAP [HR(95% CI): 1.03(1.00,1.05) per 5 ppb increase in dispersion-modeled NOx] and to non-freeway NRAP [HR(95% CI) comparing the highest to the lowest quintile: 1.19(1.11, 1.27)]. Total NRAP had a stronger association in boys than in girls, but the association with non-freeway NRAP did not differ by sex. The association of freeway NRAP with ASD risk was not statistically significant. Non-freeway traffic load exposure demonstrated associations with ASD consistent with those of NRAP and ASD. Conclusions In utero exposure to near-roadway air pollution, particularly from non-freeway sources, may increase ASD risk in children.

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.

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.

INTRODUCTION: Pregnant women’s daily time-activity and mobility patterns determine their environmental exposures and subsequently related health effects. Most studies ignore these and assess pregnancy exposures using static residential measures. METHODS: We conducted 4-day continuous geo-location monitoring in 62 pregnant Hispanic women, during pregnancy and early post-partum then derived trips by mode and stays, classified by context (indoor/outdoor, type). Generalized mixed-effect models were used to examine whether these patterns changed over time. RESULTS: Women spent on average 17.3 h/day at home. Commercial and service locations were the most popular non-home destinations, while parks and open spaces were seldom visited. Women made 3.5 daily (63.7 min/day and approximately 25% were pedestrian-based). Women were less likely to visit commercial and services locations and make vehicle-based trips postpartum compared to the 3(rd) trimester. CONCLUSION: Our findings suggest time-activity patterns vary across pregnancy and postpartum, thus assessing exposures at stationary locations might introduce measurement error.

BACKGROUND: Modern oil development frequently occurs in close proximity to human populations. Los Angeles, California is home to the largest urban oil field in the country with thousands of active oil and gas wells in very close proximity to homes, schools and parks, yet little is known about potential health impacts. The neighborhoods along the Las Cienagas oil fields are situated in South LA, densely populated by predominantly low-income Black and Latinx families, many of whom are primarily Spanish-speakers. METHODS: A cross-sectional community-based study was conducted between January 2017 and August 2019 among residents living

PurposeTo examine characteristics of coronavirus disease 2019 (COVID-19) decedents in California (CA) and evaluate for disproportionate mortality across race/ethnicity and ethnicity/nativity.MethodsCOVID-19 deaths were identified from death certificates. Age-adjusted mortality rate ratios (MRR) were compared across race/ethnicity. Proportionate mortality rates (PMR) were compared across race/ethnicity and by ethnicity/nativity.ResultsWe identified 10,200 COVID-19 deaths in CA occurring February 1 through July 31, 2020. Decedents tended to be older, male, Hispanic, foreign-born, and have lower educational attainment. MRR indicated elevated COVID-19 morality rates among Asian/Pacific Islander, Black, and Hispanic groups compared with the White group, with Black and Hispanic groups having the highest MRR at 2.75 (95%CI:2.54-2.97) and 4.18 (95%CI: 3.99-4.37), respectively. Disparities were larger at younger ages. Similar results were observed with PMR, which remained in analyses stratified by education. Elevated PMR were observed in all ethnicity/nativity groups, especially foreign-born Hispanic individuals, relative to U.S.-born non-Hispanic individuals, were generally larger at younger ages, and persisted after stratifying by education.ConclusionsDifferential COVID-19 mortality was observed in California across racial/ethnic groups and by ethnicity/nativity groups with evidence of greater disparities among younger age groups. Identifying COVID-19 disparities is an initial step towards mitigating disease impacts in vulnerable communities.

Annual average PM(2.5) and NO(2) were associated with airway inflammation as measured by FeNO in schoolchildren, adding new evidence that long-term exposure affects FeNO beyond the well-documented short-term effects.

Background Fetal growth is predictive of health later in life. Both toxic and essential metals influence fetal growth, but most studies have focused on these elements individually and used birth weight as an indicator of fetal growth. The objective of the current study was to investigate the impact of a mixture of metals on fetal size in mid-pregnancy in a predominately lower income Hispanic pregnancy cohort in Los Angeles. Methods For our primary analysis, we focused on six elements that have previously been associated individually with fetal size, including arsenic (As), barium (Ba), cadmium (Cd), mercury (Hg), molybdenum (Mo), and tin (Sn), measured in maternal urine samples collected in early pregnancy (median: 12.4 weeks gestation). In an exploratory analysis, we additionally included cobalt (Co), nickel (Ni), antimony (Sb), and thallium (Tl). Using covariate-adjusted Bayesian Kernel Machine Regression (BKMR) as our main mixture modeling approach, we examined the impact of these metals on fetal biometry measures obtained between 18 and 22 weeks gestation, with a focus on estimated fetal weight (EFW). Results BKMR identified Mo and Ba as the mixture components that contributed most to associations with EFW. Linear associations were observed for both metals. An increase in Mo from the 25th to 75th percentile was associated with a 0.114 (95% credible interval (CI): 0.019, 0.247) SD higher EFW, equivalent to a 7.4 g difference. Similar associations were observed between Mo and the other fetal measures evaluated. In contrast, an increase in Ba from the 25th to 75th percentile was associated with a −0.076 (95% CI: 0.217, 0.066) SD lower EFW, equivalent to a 4.9 g difference. Similar inverse associations were observed for Ba in relation to abdominal circumference and biparietal diameter. BKMR also identified a possible interaction between Ba and Mo in relation to head circumference, suggesting that the positive associations between Mo and this outcome may be attenuated at high levels of Ba, which was consistent with findings from linear regression (Pinteraction = 0.03). In an exploratory analysis accounting for a larger mixture of metals, Mo and Ba consistently contributed most to associations with EFW. An inverse association was also identified between Sb and EFW. Conclusions Our results suggest that Mo may promote fetal growth, while Ba and Sb may reduce fetal growth, in this population.

Infant birth weight influences numerous health outcomes throughout the life course including childhood obesity and metabolic morbidities. Maternal experience of stress, both before and during pregnancy, has been hypothesized to influence fetal growth and birth outcomes. However, these associations currently are not fully understood, due to conflicting results in the published literature. Salivary cortisol is often used as a biological biomarker to assess the diurnal pattern of the hypothalamic&ndash, pituitary&ndash, adrenal axis (HPA-axis) functioning. Cortisol metrics include both the total cortisol concentration secreted during waking hours, reflected by the area under the curve (AUC), and cortisol dynamics, which include the diurnal cortisol slope (DCS) and the cortisol awakening response (CAR). This study examined the association of these cortisol metrics measured during the third trimester of pregnancy and infant birth weight among 240 mother-infant dyads participating in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort study, which is predominately comprised of Hispanic low-income women. There were no significant associations with the maternal biological stress response and infant birth weight in this study. More research is needed in larger studies to better understand how the biological stress response influences birth weight in populations facing health disparities.

Prior research supports that infants born very preterm (PT), compared with full term (FT), have early differences in rate of learning and motor control that may hinder their ability to learn challenging motor tasks. Four-month-old infants born FT (n = 18) and PT (n = 18) participated in an infant kick-activated mobile task that was scaffolded to motivate progressively higher kicks. We found the FT group learned the association between their leg movements and mobile activation on the second day, but the PT group learned the association on the third day. Both groups of infants increased the height of their kicks on the day they learned the task, compared with their spontaneous kicking height. These findings suggest that infants born PT have the ability to learn challenging motor tasks, such as kicking high, when participating in a task environment that uses scaffolding.

Background Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure. Objective To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021. Methods The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM2.5, NO2, and O3) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020–5/31/2020, 6/1/202–9/30/2020, 10/1/2020–12/31/2020, and 1/1/2021–2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation. Results The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM2.5, NO2, and O3 were 1.11 (1.04, 1.18) per 2.3 μg/m3,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 μg/m3 for PM2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO2 and 0.96 (0.85, 1.08) per 12.0 ppb for O3. Conclusion Long-term PM2.5 and NO2 exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM2.5 and NO2 exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

A growing number of studies report associations between air pollution and COVID-19 mortality. Most were ecological studies at the county or regional level which disregard important local variability and relied on data from only the first few months of the pandemic. Using COVID-19 deaths identified from death certificates in California, we evaluated whether long-term ambient air pollution was related to weekly COVID-19 mortality at the census tract-level during the first ∼12 months of the pandemic. Weekly COVID-19 mortality for each census tract was calculated based on geocoded death certificate data. Annual average concentrations of ambient particulate matter, Graphical abstract Image 1

Background: Exposure to ultrafine particles (UFP, particles with aerodynamic diameter

Background: Ambient air pollution and maternal diabetes may affect common biological pathways underlying adverse neurodevelopmental effects. However, joint effects of maternal diabetes and air pollution on autism spectrum disorder (ASD) have not been studied. Objective: We evaluated whether prenatal and early-life air pollution exposure interacts with maternal diabetes status to affect ASD risk. Methods: This retrospective cohort study included 246,420 singleton children born in Kaiser Permanente Southern California hospitals in 1999–2009. Children were followed from birth until age 5, during which 2471 ASD cases were diagnosed. Ozone (O3), particulate matter

Residents of the Imperial Valley, a rural, agricultural border region in California, have raised concerns over high rates of pediatric asthma symptoms. There is an urgent need to understand the influences and predictors of children&rsquo, s respiratory health in Imperial Valley. We assessed the impacts of sociodemographic, lifestyle, and household factors on children&rsquo, s respiratory health and asthma prevalence by administering a survey to parents of elementary school children (n = 357) in northern Imperial Valley. We observed an overall asthma prevalence of 22.4% and respiratory symptoms and allergies were widely reported, including wheezing (35.3%), allergies (36.1%), bronchitic symptoms (28.6%), and dry cough (33.3%). Asthmatics were significantly more likely to report respiratory symptoms, but high rates of wheezing, allergies, and dry cough were observed among nonasthmatics, suggesting the possibility for underdiagnosis of respiratory impairment in our school-age population. Having an asthmatic mother and exposure to environmental tobacco smoke were also associated with greater odds of asthma. Our findings provide evidence to support community concerns about children&rsquo, s respiratory health, while also suggesting that household and demographic characteristics have limited explanatory power for assessing asthma in this population. This work provides critical baseline data with which to evaluate local environmental factors and their influence on asthma and respiratory symptoms.

Background: Studies of effects of air pollution on gestational diabetes mellitus (GDM) have not been consistent, and there has been little investigation of effects of exposure preceding pregnancy. In previous studies, the temporal relationship between exposure and GDM onset has been difficult to establish. Methods: Data were obtained for 239,574 pregnancies between 1999 and 2009 in a population-based health care system with comprehensive electronic medical records. Concentrations of ambient nitrogen dioxide (NO2), particulate matter (PM) ≤2.5 μm in aerodynamic diameter (PM2.5) and ≤10 μm (PM10), and ozone (O3) during preconception and the first trimester of pregnancy at the residential birth address were estimated from regulatory air monitoring stations. Odds ratios (ORs) of GDM diagnosed in the second and third trimesters in association with pollutant exposure were estimated using generalized estimating equation models adjusted for birth year, medical center service areas, maternal age, race/ethnicity, education, census-tract household income, and parity. Results: In single-pollutant models, preconception NO2 was associated with increased risk of GDM (OR = 1.10 per 10.4 ppb, 95% confidence interval [CI]: 1.07, 1.13). First trimester NO2 was weakly associated with GDM, and this was not statistically significant (OR = 1.02 per 10.4 ppb, 95% CI: 0.99, 1.05). Preconception NO2 associations were robust in multi-pollutant models adjusted for first trimester NO2 with another co-pollutant from both exposure windows. In single-pollutant models, preconception PM2.5 and PM10 associations were associated with increased risk of GDM (OR = 1.04 per 6.5 μg/m3, 95% CI: 1.01, 1.06; OR = 1.03 per 16.1 μg/m3, 95% CI: 1.00, 1.06, respectively), but these effect estimates were not robust to adjustment for other pollutants. In single-pollutant models, preconception and first trimester O3 were associated with reduced risk of GDM (OR = 0.94 per 15.7 ppb, 95% CI: 0.92, 0.95; OR = 0.95 per 15.7 ppb, 95% CI: 0.94, 0.97), associations that were robust to adjustment for co-pollutants. Conclusions: Maternal exposure to NO2 during the preconception trimester may increase risk of GDM. Keywords: Air pollution, Gestational diabetes mellitus, Preconception, Pregnancy

Autism spectrum disorder (ASD) affects more boys than girls. Recent animal studies found that early life exposure to ambient particles caused autism-like behaviors only in males. However, there has been little study of sex-specificity of effects on ASD in humans. We evaluated ASD risk associated with prenatal and first year of life exposures to particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) by child sex. This retrospective cohort study included 246,420 singleton children born in Kaiser Permanente Southern California (KPSC) hospitals between 1999 and 2009. The cohort was followed from birth through age five to identify 2471 ASD cases from the electronic medical record. Ambient PM2.5 and other regional air pollution measurements (PM less than 10 μm, ozone, nitrogen dioxide) from regulatory air monitoring stations were interpolated to estimate exposure during each trimester and first year of life at each geocoded birth address. Hazard ratios (HRs) were estimated using Cox regression models to adjust for birth year, KPSC medical center service areas, and relevant maternal and child characteristics. Adjusted HRs per 6.5 μg/m3 PM2.5 were elevated during entire pregnancy [1.17 (95% confidence interval (CI), 1.04–1.33)]; first trimester [1.10 (95% CI, 1.02–1.19)]; third trimester [1.08 (1.00–1.18)]; and first year of life [1.21 (95% CI, 1.05–1.40)]. Only the first trimester association remained robust to adjustment for other exposure windows, and was specific to boys only (HR = 1.18; 95% CI, 1.08–1.27); there was no association in girls (HR = 0.90; 95% CI, 0.76–1.07; interaction p-value 0.03). There were no statistically significant associations with other pollutants. PM2.5-associated ASD risk was stronger in boys, consistent with findings from recent animal studies. Further studies are needed to better understand these sexually dimorphic neurodevelopmental associations.

Background Advances in untargeted metabolomic technologies have great potential for insight into adverse metabolic effects underlying exposure to environmental chemicals. However, important challenges need to be addressed, including how biological response corresponds to the environmental chemical burden in different target tissues. Aim We performed a pilot study using state-of-the-art ultra-high-resolution mass spectrometry (UHRMS) to characterize the burden of lipophilic persistent organic pollutants (POPs) in metabolic tissues and associated alterations in the plasma metabolome. Methods We studied 11 adolescents with severe obesity at the time of bariatric surgery. We measured 18 POPs that can act as endocrine and metabolic disruptors (i.e. 2 dioxins, 11 organochlorine compounds [OCs] and 5 polybrominated diphenyl ethers [PBDEs]) in visceral and subcutaneous abdominal adipose tissue (vAT and sAT), and liver samples using gas chromatography with UHRMS. Biological pathways were evaluated by measuring the plasma metabolome using high-resolution metabolomics. Network and pathway enrichment analysis assessed correlations between the tissue-specific burden of three frequently detected POPs (i.e. p,p’-dichlorodiphenyldichloroethene [DDE], hexachlorobenzene [HCB] and PBDE-47) and plasma metabolic pathways. Results Concentrations of 4 OCs and 3 PBDEs were quantifiable in at least one metabolic tissue for > 80% of participants. All POPs had the highest median concentrations in adipose tissue, especially sAT, except for PBDE-154, which had comparable average concentrations across all tissues. Pathway analysis showed high correlations between tissue-specific POPs and metabolic alterations in pathways of amino acid metabolism, lipid and fatty acid metabolism, and carbohydrate metabolism. Conclusions Most of the measured POPs appear to accumulate preferentially in adipose tissue compared to liver. Findings of plasma metabolic pathways potentially associated with tissue-specific POPs concentrations merit further investigation in larger populations.

Fractional exhaled nitric oxide (FENO50), a marker of allergic airway inflammation, is used in respiratory research and asthma clinical care; however, its trajectory with increasing age during childhood has not been well characterised. We examined FENO50 longitudinally during a period of important somatic growth to describe trajectories across childhood and adolescence in healthy participants and evaluate clinical factors as potential determinants of trajectories.FENO50 was collected at six visits over 8 years in a population-based cohort of 1791 schoolchildren without asthma (median age at entry 8.4 years). Smooth sex-specific FENO50 trajectories were estimated using generalised additive mixed models, with participant-level random effects. We evaluated whether sex-specific trajectories were influenced by race/ethnicity, body mass index (BMI) percentile, allergic rhinitis or puberty.Different FENO50 patterns were observed by sex in later childhood and several factors were associated with either FENO50 level or change in FENO50 as participants aged. FENO50–age trajectories were similar by sex until age ∼11.5 years, after which males had greater FENO50 change than females. This divergence in FENO50–age trajectories coincides with puberty. Males with higher starting BMI percentile had attenuated FENO50–age slopes. Among males, FENO50 levels were lower in non-Hispanic white subjects. Among both sexes, participants with rhinitis had higher FENO50. FENO50 levels within individuals tracked over time; however, there was considerable variation in FENO50 patterns across participants.FENO50 trajectories from longitudinal data provide evidence of sex differences coinciding with puberty, suggesting potential hormone link. Improved understanding of determinants of FENO50 trajectories is needed to realise the potential for using individualised predicted FENO50 trajectories.

Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.

Prenatal exposure to air pollution has been associated with autism spectrum disorder (ASD) risk but no study has examined associations with ASD severity or functioning. Cognitive ability, adaptive functioning, and ASD severity were assessed in 327 children with ASD from the Childhood Autism Risks from Genetics and the Environment study using the Mullen Scales of Early Learning (MSEL), the Vineland Adaptive Behavior Scales (VABS), and the Autism Diagnostic Observation Schedule calibrated severity score. Estimates of nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10), ozone, and near-roadway air pollution were assigned to each trimester of pregnancy and first year of life. Increasing prenatal and first year NO2 exposures were associated with decreased MSEL and VABS scores. Increasing PM10 exposure in the third trimester was paradoxically associated with improved performance on the VABS. ASD severity was not associated with air pollution exposure.

Human motor cortex can activate pelvic floor muscles (PFM), but the motor cortical representation of the PFM is not well characterized. PFM representation is thought to be focused in the supplementary motor area (SMA). Here we examine the degree to which PFM representation is distributed between SMA and the primary motor cortex (M1), and how this representation is utilized to activate the PFM in different coordination patterns. We show that two types of coordination patterns involving PFM can be voluntarily accessed: one activates PFM independently of synergists and a second activates PFM prior to and in proportion with synergists (in this study, the gluteus maximus muscle – GMM). Functional magnetic resonance imaging (fMRI) showed that both coordination patterns involve overlapping activation in SMA and M1, suggesting the presence of intermingled but independent neural populations that access the different patterns. Transcranial magnetic stimulation (TMS) confirmed SMA and M1 representation for the PFM. TMS also showed that, equally for SMA and M1, PFM can be activated during rest but GMM can only be activated after voluntary drive to GMM, suggesting that these populations are distinguished by activation threshold. We conclude that PFM representation is broadly distributed in SMA and M1 in humans.

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing realtime environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache Kafka, Spark and Hadoop frameworks and PostgreSQL DBMS. A main contribution of this work is extending the Spark framework with a mediation layer, based on logical schema mappings and query rewriting, to facilitate data analysis over a consistent harmonized schema. The system provides both batch and stream analytic capabilities over the massive data generated by wearable and fixed sensors. Demo Video: https://www.youtube.com/watch?v=6ntm4C29L-I.

Particulate matter (PM) air pollution exposure has been associated with cancer incidence and mortality especially with lung cancer. The liver is another organ possibly affected by PM due to its role in detoxifying xenobiotics absorbed from PM. Various studies have investigated the mechanistic pathways between inhaled pollutants and liver damage, cancer incidence, and tumor progression. However, little is known about the effects of PM on liver cancer survival. Twenty thousand, two hundred and twenty-one California Cancer Registry patients with hepatocellular carcinoma (HCC) diagnosed between 2000 and 2009 were used to examine the effect of exposure to ambient PM with diameter2.5 μm (PM

INTRODUCTION: The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation. Repeat FeNO maneuvers at multiple fixed exhalation flow rates (extended NO analysis) can be used to estimate parameters quantifying proximal and distal sources of NO in mathematical models of lower respiratory tract NO. A growing number of studies use extended NO analysis, but there is no official standard flow rate sampling protocol. In this paper, we provide information for study planning by deriving theoretically optimal flow rate sampling designs. METHODS: First, we reviewed previously published designs. Then, under a nonlinear regression framework for estimating NO parameters in the steady-state two compartment model of NO, we identified unbiased optimal four flow rate designs (within the range of 10 to 400 ml/s) using theoretical derivations and simulation studies. Optimality criteria included NO parameter standard errors (SEs). A simulation study was used to estimate sample sizes required to detect associations with NO parameters estimated from studies with different designs. RESULTS: Most designs (77%) were unbiased. NO parameter SEs were smaller for designs with: more target flows, more replicate maneuvers per target flow, and a larger range of target flows. High flows were most important for estimating alveolar NO concentration, while low flows were most important for the proximal NO parameters. The Southern California Children's Health Study design (30, 50, 100 and 300 ml/s) had ���1.8 fold larger SEs and required 1.1 to 3.2 fold more subjects to detect the association of a determinant with each NO parameter as compared to an optimal design of 10, 50, 100 and 400 ml/s. CONCLUSIONS: There is a class of reasonable flow rate sampling designs with good theoretical performance. In practice, designs should be selected to balance the tradeoffs between optimality and feasibility of the flow range and total number of maneuvers.

BACKGROUND: The fraction of exhaled nitric oxide (Feno) value is a biomarker of eosinophilic airway inflammation and is associated with childhood asthma. Identification of common genetic variants associated with childhood Feno values might help to define biological mechanisms related to specific asthma phenotypes. OBJECTIVE: We sought to identify the genetic variants associated with childhood Feno values and their relation with asthma. METHODS: Feno values were measured in children age 5 to 15 years. In 14 genome-wide association studies (N = 8,858), we examined the associations of approximately 2.5 million single nucleotide polymorphisms (SNPs) with Feno values. Subsequently, we assessed whether significant SNPs were expression quantitative trait loci in genome-wide expression data sets of lymphoblastoid cell lines (n = 1,830) and were related to asthma in a previously published genome-wide association data set (cases, n = 10,365; control subjects: n = 16,110). RESULTS: We identified 3 SNPs associated with Feno values: rs3751972 in LYR motif containing 9 (LYRM9; P = 1.97 × 10(-10)) and rs944722 in inducible nitric oxide synthase 2 (NOS2; P = 1.28 × 10(-9)), both of which are located at 17q11.2-q12, and rs8069176 near gasdermin B (GSDMB; P = 1.88 × 10(-8)) at 17q12-q21. We found a cis expression quantitative trait locus for the transcript soluble galactoside-binding lectin 9 (LGALS9) that is in linkage disequilibrium with rs944722. rs8069176 was associated with GSDMB and ORM1-like 3 (ORMDL3) expression. rs8069176 at 17q12-q21, but not rs3751972 and rs944722 at 17q11.2-q12, were associated with physician-diagnosed asthma. CONCLUSION: This study identified 3 variants associated with Feno values, explaining 0.95% of the variance. Identification of functional SNPs and haplotypes in these regions might provide novel insight into the regulation of Feno values. This study highlights that both shared and distinct genetic factors affect Feno values and childhood asthma. C.O. is supported by a NIH grant that supported the Hutterite studies (R01 HL085197). D.E. is supported by UK Medical Research Council Centre (G0600705). G.S. is supported by UK Medical Research Council Centre (G0600705). J.K. is funded by a Wellcome Trust 4-year PhD studentship in molecular, genetic, and life course epidemiology (WT083431MA). L.D. is supported by a European Respiratory Society/Marie Curie Joint Research Fellowship of the European Respiratory Society and the European Community’s Seventh Framework Programme FP7/2007-2013–Marie Curie Actions under grant agreement RESPIRE, PCOFUND-GA-2008-229571 (no. MC 1226- 2009). N.T. is supported by UK Medical Research Council Centre (G0600705). R.V. was partly supported by an unrestricted personal grant from GlaxoSmithKline, NL. V.J. is supported by the Netherlands Organization for Health Research and Development (ZonMw 90700303, 916.10159). L. Duijts is supported by a European Respiratory Society/Marie Curie Joint Research Fellowship of the European Respiratory Society and the European Community’s Seventh Framework Programme (FP7/2007-2013-Marie Curie Actions under grant agreement RESPIRE, PCOFUND-GA-2008-229571 [no. MC 1226-2009]). M. T. Salam has received research support from the National Heart, Lung, and Blood Institute (NHLBI; 5R01HL61768 and 5R01HL76647); the Southern California Environmental Health Sciences Center funded by the National Institute of Environmental Health Sciences (5P30ES007048); and the Environmental Protection Agency (EPA; 5P01ES009581, R826708-01, and RD831861-01), the National Institute of Environmental Health Sciences (5P01ES011627), and the Hastings Foundation. J. Genuneit has received grant EuFP6 (018996 under IP LSH-2004-1.25- 1). Evans has received a grant in the form of the MRC New Investigator Award G0600705. B. St Pourcain has received research support with Autism Speaks (7132). O. Fuchs has received research support from the European Commission within Seventh Framework Programme (theme FP7-KBBE-2007- 1) as part of EFRAIM (Impact of exogenous factors in the development of Allergy, contract no. 211911), the European Respiratory Society for a long-term research fellowship (no. 675). F. D. Gilliand has received research support from the NHLBI (5R01HL61768 and 5R01HL76647), Southern California Environmental Health Sciences Center funded by the National Institute of Environmental Health Sciences (5P30ES007048), the Children’s Environmental Health Center funded by the National Institute of Environmental Health Sciences and the Environmental Protection Agency (5P01Es009581, R826708-01, and RD831861-01), the National Institute of Environmental Health Sciences (5P01ES011627)

Rationale Exposure to ambient air pollutants has been associated with increased lung cancer incidence and mortality, but due to the high case fatality rate, little is known about the impacts of air pollution exposures on survival after diagnosis. This study aimed to determine whether ambient air pollutant exposures are associated with the survival of patients with lung cancer. Methods Participants were 352 053 patients with newly diagnosed lung cancer during 1988–2009 in California, ascertained by the California Cancer Registry. Average residential ambient air pollutant concentrations were estimated for each participant9s follow-up period. Cox proportional hazards models were used to estimate HRs relating air pollutant exposures to all-cause mortality overall and stratified by stage (localised only, regional and distant site) and histology (squamous cell carcinoma, adenocarcinoma, small cell carcinoma, large cell carcinoma and others) at diagnosis, adjusting for potential individual and area-level confounders. Results Adjusting for histology and other potential confounders, the HRs associated with 1 SD increases in NO 2 , O 3 , PM 10 , PM 2.5 for patients with localised stage at diagnosis were 1.30 (95% CI 1.28 to 1.32), 1.04 (95% CI 1.02 to 1.05), 1.26 (95% CI 1.25 to 1.28) and 1.38 (95% CI 1.35 to 1.41), respectively. Adjusted HRs were smaller in later stages and varied by histological type within stage (p 3 ). The largest associations were for patients with early-stage non-small cell cancers, particularly adenocarcinomas. Conclusions These epidemiological findings support the hypothesis that air pollution exposures after lung cancer diagnosis shorten survival. Future studies should evaluate the impacts of exposure reduction.

Mechanisms for the adverse respiratory effects of traffic-related air pollution (TRAP) have yet to be established. We evaluated the acute effects of TRAP exposure on proximal and distal airway inflammation by relating indoor nitric oxide (NO), a marker of TRAP exposure in the indoor microenvironment, to airway and alveolar sources of exhaled nitric oxide (FeNO).FeNO was collected online at four flow rates in 1635 schoolchildren (aged 12–15 years) in southern California (USA) breathing NO-free air. Indoor NO was sampled hourly and linearly interpolated to the time of the FeNO test. Estimated parameters quantifying airway wall diffusivity (DawNO) and flux (J′awNO) and alveolar concentration (CANO) sources of FeNO were related to exposure using linear regression to adjust for potential confounders.We found that TRAP exposure indoors was associated with elevated alveolar NO. A 10 ppb higher indoor NO concentration at the time of the FeNO test was associated with 0.10 ppb higher average CANO (95% CI 0.04–0.16) (equivalent to a 7.1% increase from the mean), 4.0% higher J′awNO (95% CI −2.8–11.3) and 0.2% lower DawNO (95% CI −4.8–4.6).These findings are consistent with an airway response to TRAP exposure that was most marked in the distal airways.

For traffic-related pollutants like ultrafine particles (UFP), a significant fraction of overall exposure occurs within or close to the transit microenvironment. Therefore, understanding exposure to these pollutants in such microenvironments is crucial to accurately assessing overall UFP exposure. The aim of this study was to develop models for predicting in-cabin UFP concentrations if roadway concentrations are known, quantifying the effect of vehicle characteristics, ventilation settings, driving conditions and air exchange rates (AER). Particle concentrations and AER were measured in 43 and 73 vehicles, respectively, under various ventilation settings and driving speeds. Multiple linear regression (MLR) and generalized estimating equation (GEE) regression models were used to identify and quantify the factors that determine inside-to-outside (I/O) UFP ratios and AERs across a full range of vehicle types and ages. AER was the most significant determinant of UFP I/O ratios, and was most strongly influenced by ventilation setting (recirculation or outside air intake). Further inclusion of ventilation fan speed, vehicle age or mileage, and driving speed explained greater than 79% of the variability in measured UFP I/O ratios.

Bronchitic symptoms in children pose a significant clinical and public health burden. Exposures to criteria air pollutants affect bronchitic symptoms, especially in children with asthma. Less is known about near-roadway exposures.Bronchitic symptoms (bronchitis, chronic cough, or phlegm) in the past 12 months were assessed annually with 8 to 9 years of follow-up on 6757 children from the southern California Children's Health Study. Residential exposure to freeway and non-freeway near-roadway air pollution was estimated using a line-source dispersion model. Mixed-effects logistic regression models were used to relate near-roadway air pollutant exposures to bronchitic symptoms among children with and without asthma.Among children with asthma, a two standard deviation increase in non-freeway exposures (odds ratio [OR]: 1.44; 95% confidence interval [CI]: 1.17-1.78) and freeway exposures (OR: 1.31; 95% CI: 1.06-1.60) were significantly associated with increased risk of bronchitic symptoms. Among children without asthma, only non-freeway exposures had a significant association (OR: 1.14; 95% CI: 1.00-1.29). Associations were strongest among children living in communities with lower regional particulate matter.Near-roadway air pollution was associated with bronchitic symptoms, especially among children with asthma and those living in communities with lower regional particulate matter. Better characterization of traffic pollutants from non-freeway roads is needed since many children live in close proximity to this source.

Clinical and research settings often require sequencing multiple respiratory tests in a brief visit. Guidelines recommend measuring the concentration of exhaled nitric oxide (FeNO) before spirometry, but evidence for a spirometry carryover effect on FeNO is mixed. Only one study has investigated spirometry carryover effects on multiple flow FeNO analysis. The objective of this study was to evaluate evidence for carryover effects of recent spirometry on three exhaled NO summary measures: FeNO at 50 ml/s, airway wall NO flux [J'awNO] and alveolar NO concentration [CANO] in a population-based sample of schoolchildren.Participants were 1146 children (191 with asthma), ages 12-15, from the Southern California Children's Health Study who performed spirometry and multiple flow FeNO on the same day. Approximately, half the children performed spirometry first. Multiple linear regression was used to estimate differences in exhaled NO summary measures associated with recent spirometry testing, adjusting for potential confounders.In the population-based sample, we found no evidence of spirometry carryover effects. However, for children with asthma, there was a suggestion that exhaled NO summary measures assessed ≤6 min after spirometry were lower (FeNO: 25.8% lower, 95% CI: -6.2%, 48.2%; J'awNO: 15.1% lower 95% CI: -26.5%, 43.0%; and CANO 0.43 parts per billion lower, 95% CI: -0.12, 0.98).In clinical settings, it is prudent to assess multiple flow FeNO before spirometry. In studies of healthy subjects, it may not be necessary to assess FeNO first.

Objectives To assess the effects of long-term variations in ambient air pollutants on longitudinal changes in exhaled nitric oxide (FeNO), a potentially useful biomarker of eosinophilic airway inflammation, based on data from the southern California Children9s Health Study. Methods Based on a cohort of 1211 schoolchildren from eight Southern California communities with FeNO measurements in 2006–2007 and 2007–2008, regression models adjusted for short-term effects of air pollution were fitted to assess the association between changes in annual long-term exposures and changes in FeNO. Results Increases in annual average concentrations of 24-h average NO 2 and PM 2.5 (scaled to the IQR of 1.8 ppb and 2.4 μg/m 3 , respectively) were associated with a 2.29 ppb (CI 0.36 to 4.21; p=0.02) and a 4.94 ppb (CI 1.44 to 8.47; p=0.005) increase in FeNO, respectively, after adjustments for short-term effects of the respective pollutants. In contrast, changes in annual averages of PM 10 and O 3 were not significantly associated with changes in FeNO. These findings did not differ significantly by asthma status. Conclusions Changes in annual average exposure to current levels of ambient air pollutants are significantly associated with changes in FeNO levels in children, independent of short-term exposures and asthma status. Use of this biomarker in population-based epidemiological research has great potential for assessing the impact of changing real world mixtures of ambient air pollutants on children9s respiratory health.

"Extended" (multiple-flow) measurements of exhaled nitric oxide (FeNO) potentially can distinguish proximal and distal airway inflammation, but have not been evaluated previously in large populations. We performed extended NO testing within a longitudinal study of a school-based population, to relate bronchial flux (J'awNO) and peripheral NO concentration (CalvNO) estimates with respiratory health status determined from questionnaires. We measured FeNO at 30, 50, 100, and 300 ml/sec in 1640 subjects aged 12–15 from 8 communities, then estimated J'awNO and CalvNO from linear and nonlinear regressions of NO output vs. flow. J'awNO, as well as FeNO at all flows, showed influences of asthma, allergy, Asian or African ancestry, age, and height (positive), and of weight (negative), generally corroborating past findings. By contrast, CalvNO results were inconsistent across different extended NO regression models, and appeared more sensitive to small measurement artifacts. Conclusions: Extended NO testing is feasible in field surveys of young populations. In interpreting results, size, age, and ethnicity require attention, as well as instrumental and environmental artifacts. J'awNO and conventional FeNO provide similar information, probably reflecting proximal-airway inflammation. CalvNO may give additional information relevant to peripheral-airway, alveolar, or systemic pathology. However, it needs additional research, including testing of populations with independently verifiable peripheral or systemic pathology, to optimize measurement technique and interpretation.

Background and aims: Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized, semiquantitative questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women’s Health and Aging Study (WHAS). Methods: Using data on men and women ages 65 and older from the CHS, we applied logistic regression models to rank activities by “relative influence” in predicting low physical activity.We considered subsets of the most influential activities as inputs to potential surrogate models (logistic regressions). We evaluated predictive accuracy and predictive validity using the area under receiver operating characteristic curves and assessed criterion validity using proportional hazards models relating frailty status (defined using the surrogate) to mortality. Results: Walking for exercise and moderately strenuous household chores were highly influential for both genders. Women required fewer activities than men for accurate classification. The WHAS model (8 CHS activities) was an effective surrogate, but a surrogate using 6 activities (walking, chores, gardening, general exercise, mowing and golfing) was also highly predictive. Conclusions: We recommend a 6 activity questionnaire to assess physical activity for men and women. If efficiency is essential and the study involves only women, fewer activities can be included.

The fractional concentration of exhaled nitric oxide (FeNO) has been used as a non-invasive biomarker of airway response to inhaled toxic exposures in both population-based and occupational studies. Results have been mixed, but a compelling finding is the 60% decrease (95% CI 54 to 66) and subsequent 130% increase (95% CI 63 to 225) in FeNO observed in a quasi-experimental study of FeNO in a group of healthy young adult volunteers before, during and after the 2008 Beijing Olympic games air pollution control measures.1 Methodological developments now allow for the assessment of NO from proximal and distal airway compartment sources. The ability to non-invasively differentiate inflammation in two lower respiratory tract compartments may provide a practical method to gain insights into the effects of inhaled toxins. Using this approach, …

Background Inducible nitric oxide synthase (iNOS; encoded by nitric oxide synthase isoform 2 [NOS2] ) is the major enzyme for nitric oxide synthesis in airways. As such, measurement of fractional concentration of exhaled nitric oxide (Feno) provides an in vivo assessment of iNOS activity. Short-term exposure to air pollution, haplotypes, and DNA methylation in the NOS2 promoter has been associated independently with iNOS expression, Feno levels, or both. Objective We aimed to examine the effects of ambient air pollutants, NOS2 promoter haplotypes, and NOS2 promoter methylation on Feno levels in children. Methods We selected 940 participants in the Children's Health Study who provided buccal samples and had undergone Feno measurement on the same day. DNA methylation was measured with a bisulfite-PCR Pyrosequencing assay. Seven single nucleotide polymorphisms captured the haplotype diversity in the NOS2 promoter. Average particulate matter with an aerodynamic diameter of 2.5 μm or less (PM 2.5 ) and 10 μm (PM 10 ) or less and ozone and nitrogen dioxide levels 7 days before Feno measurement were estimated based on air pollution data obtained at central monitoring sites. Results We found interrelated effects of PM 2.5 , NOS2 promoter haplotypes, and iNOS methylation on Feno levels. Increased 7-day average PM 2.5 exposure was associated with lower iNOS methylation ( P = .01). NOS2 promoter haplotypes were globally associated with NOS2 promoter methylation ( P = 6.2 × 10 −8 ). There was interaction among 1 common promoter haplotype, iNOS methylation level, and PM 2.5 exposure on Feno levels ( P interaction = .00007). Conclusion Promoter variants in NOS2 and short-term PM 2.5 exposure affect iNOS methylation. This is one of the first studies showing contributions of genetic and epigenetic variations in air pollution–mediated phenotype expression.