Rima Habre

The effects of PM 2.5 and its components from indoor and outdoor sources on cough and wheeze symptoms in asthmatic children

Particulate matter with aerodynamic diameter <2.5 μm (PM2.5) is associated with asthma exacerbation. In the Children’s Air Pollution Asthma Study, we investigated the longitudinal association of PM2.5 and its components from indoor and outdoor sources with cough and wheeze symptoms in 36 asthmatic children. The sulfur tracer method was used to estimate infiltration factors. Mixed proportional odds models for an ordinal response were used to relate daily cough and wheeze scores to PM2.5 exposures. The odds ratio associated with being above a given symptom score for a SD increase in PM2.5 from indoor sources (PMIS) was 1.24 (95% confidence interval: 0.92–1.68) for cough and 1.63 (1.11–2.39) for wheeze. Ozone was associated with wheeze (1.82, 1.19–2.80), and cough was associated with indoor PM2.5 components from outdoor sources (denoted with subscript “OS”) bromine (BrOS: 1.32, 1.05–1.67), chlorine (ClOS: 1.27, 1.02–1.59) and pyrolyzed organic carbon (OPOS: 1.49, 1.12–1.99). The highest effects were seen in the winter for cough with sulfur (SOS: 2.28, 1.01–5.16) and wheeze with organic carbon fraction 2 (OC2OS: 7.46, 1.19–46.60). Our results indicate that exposure to components originating from outdoor sources of photochemistry, diesel and fuel oil combustion is associated with symptom’s exacerbation, especially in the winter. PM2.5 mass of indoor origin was more strongly associated with wheeze than with cough.

Sources of indoor air pollution in New York City residences of asthmatic children.

Individuals spend ∼90% of their time indoors in proximity to sources of particulate and gaseous air pollutants. The sulfur tracer method was used to separate indoor concentrations of particulate matter (PM) PM2.5 mass, elements and thermally resolved carbon fractions by origin in New York City residences of asthmatic children. Enrichment factors relative to sulfur concentrations were used to rank species according to the importance of their indoor sources. Mixed effects models were used to identify building characteristics and resident activities that contributed to observed concentrations. Significant indoor sources were detected for OC1, Cl, K and most remaining OC fractions. We attributed 46% of indoor PM2.5 mass to indoor sources related to OC generation indoors. These sources include cooking (NO2, Si, Cl, K, OC4 and OP), cleaning (most OC fractions), candle/incense burning (black carbon, BC) and smoking (K, OC1, OC3 and EC1). Outdoor sources accounted for 28% of indoor PM2.5 mass, mainly photochemical reaction products, metals and combustion products (EC, EC2, Br, Mn, Pb, Ni, Ti, V and S). Other indoor sources accounted for 26% and included re-suspension of crustal elements (Al, Zn, Fe, Si and Ca). Indoor sources accounted for ∼72% of PM2.5 mass and likely contributed to differences in the composition of indoor and outdoor PM2.5 exposures.

Ambient Air Pollutants Have Adverse Effects on Insulin and Glucose Homeostasis in Mexican Americans

OBJECTIVE Recent studies suggest that air pollution plays a role in type 2 diabetes (T2D) incidence and mortality. The underlying physiological mechanisms have yet to be established. We hypothesized that air pollution adversely affects insulin sensitivity and secretion and serum lipid levels. RESEARCH DESIGN AND METHODS Participants were selected from BetaGene (n = 1,023), a study of insulin resistance and pancreatic β-cell function in Mexican Americans. All participants underwent DXA and oral and intravenous glucose tolerance tests and completed dietary and physical activity questionnaires. Ambient air pollutant concentrations (NO2, O3, and PM2.5) for short- and long-term periods were assigned by spatial interpolation (maximum interpolation radius of 50 km) of data from air quality monitors. Traffic-related air pollution from freeways (TRAP) was estimated using the dispersion model as NOx. Variance component models were used to analyze individual and multiple air pollutant associations with metabolic traits. RESULTS Short-term (up to 58 days cumulative lagged averages) exposure to PM2.5 was associated with lower insulin sensitivity and HDL-to-LDL cholesterol ratio and higher fasting glucose and insulin, HOMA-IR, total cholesterol, and LDL cholesterol (LDL-C) (all P ≤ 0.036). Annual average PM2.5 was associated with higher fasting glucose, HOMA-IR, and LDL-C (P ≤ 0.043). The effects of short-term PM2.5 exposure on insulin sensitivity were largest among obese participants. No statistically significant associations were found between TRAP and metabolic outcomes. CONCLUSIONS Exposure to ambient air pollutants adversely affects glucose tolerance, insulin sensitivity, and blood lipid concentrations. Our findings suggest that ambient air pollutants may contribute to the pathophysiology in the development of T2D and related sequelae.

Effects of Childhood Asthma on the Development of Obesity among School-aged Children

Rationale: Asthma and obesity often occur together in children. It is unknown whether asthma contributes to the childhood obesity epidemic. Objectives: We aimed to investigate the effects of asthma and asthma medication use on the development of childhood obesity. Methods: The primary analysis was conducted among 2,171 nonobese children who were 5‐8 years of age at study enrollment in the Southern California Childrens Health Study (CHS) and were followed for up to 10 years. A replication analysis was performed in an independent sample of 2,684 CHS children followed from a mean age of 9.7 to 17.8 years. Measurements and Main Results: Height and weight were measured annually to classify children into normal, overweight, and obese categories. Asthma status was ascertained by parent‐ or self‐reported physician‐diagnosed asthma. Cox proportional hazards models were fitted to assess associations of asthma history with obesity incidence during follow‐up. We found that children with a diagnosis of asthma at cohort entry were at 51% increased risk of developing obesity during childhood and adolescence compared with children without asthma at baseline (hazard ratio, 1.51; 95% confidence interval, 1.08‐2.10) after adjusting for confounders. Use of asthma rescue medications at cohort entry reduced the risk of developing obesity (hazard ratio, 0.57; 95% confidence interval, 0.33‐0.96). In addition, the significant association between a history of asthma and an increased risk of developing obesity was replicated in an independent CHS sample. Conclusions: Children with asthma may be at higher risk of obesity. Asthma rescue medication use appeared to reduce obesity risk independent of physical activity.

Longitudinal Associations Between Ambient Air Pollution with Insulin Sensitivity, β-Cell Function, and Adiposity in Los Angeles Latino Children.

Evidence suggests that ambient air pollution (AAP) exposure may contribute to the development of obesity and type 2 diabetes. The objective of this study was to determine whether exposure to elevated concentrations of nitrogen dioxide (NO2) and particulate matter with aerodynamic diameter <2.5 (PM2.5) had adverse effects on longitudinal measures of insulin sensitivity (SI), β-cell function, and obesity in children at high risk for developing diabetes. Overweight and obese Latino children (8–15 years; n = 314) were enrolled between 2001 and 2012 from Los Angeles, CA, and followed for an average of 3.4 years (SD 3.1 years). Linear mixed-effects models were fitted to assess relationships between AAP exposure and outcomes after adjusting for covariates including body fat percent. Higher NO2 and PM2.5 were associated with a faster decline in SI and a lower SI at age 18 years, independent of adiposity. NO2 exposure negatively affected β-cell function, evidenced by a faster decline in disposition index (DI) and a lower DI at age 18 years. Higher NO2 and PM2.5 exposures over follow-up were also associated with a higher BMI at age 18 years. AAP exposure may contribute to development of type 2 diabetes through direct effects on SI and β-cell function.

Effects of air pollution exposure on glucose metabolism in Los Angeles minority children.

Growing evidence indicates that ambient (AAP: NO2, PM2.5 and O3) and traffic‐related air pollutants (TRAP) contribute to metabolic disease risk in adults; however, few studies have examined these relationships in children.

Traffic-related air pollution and alveolar nitric oxide in southern California children.

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. Indoor exposure to traffic-related air pollution is associated with distal airway inflammation in schoolchildren http://ow.ly/V98mT

Lifetime prevalence of childhood eczema and the effect of indoor environmental factors: Analysis in Hispanic and non-Hispanic white children.

BACKGROUND The prevalence of eczema varies markedly across the globe. It is unclear whether the geographic variation is due to race and/or ethnic differences, environmental exposures, or genetic factors. OBJECTIVE We investigated the effects of ethnicity and environmental exposures on eczema in Hispanic white and non-Hispanic white children who participated in the Southern California Childrens Health Study. METHODS We performed a cross-sectional study with sociodemographic predictors and environmental exposures among Hispanic white and non-Hispanic white children ages 4-8 years enrolled in the Childrens Health Study, 2002-2003. RESULTS Eczema prevalence differed by ethnicity: Hispanic whites showed lower prevalence (13.8%) compared with non-Hispanic whites (20.2%), and adjustment for sociodemographic factors did not account for the ethnic difference (odds ratio [OR] 0.79 [95% confidence interval {CI}, 0.65-0.95]). Parental history of allergic disease had a larger effect in Hispanic whites than in non-Hispanic whites (p for interaction = 0.005). High maternal education level (OR 1.46 [95% CI, 1.14-1.87]), parental history of allergic disease (OR 2.21 [95% CI, 1.78-2.76]), and maternal smoking during pregnancy (OR 1.44 [95% CI, 1.06-1.95]) increased the risk of eczema. Indoor environmental factors (e.g., mold, water damage, humidifier use) increased the risk of eczema in non-Hispanic whites independent of a parental history of allergic disease, but, in Hispanic whites, increased risks were observed, primarily in children without a parental history of allergic disease. CONCLUSION Hispanic white children in southern California had a lower prevalence of eczema than non-Hispanic whites, and this ethnic difference was not accounted for by sociodemographic differences. The effects of a parental history of allergic disease and indoor environmental exposures on eczema varied by ethnicity, which indicated that the etiology of eczema may differ in Hispanic whites and in non-Hispanic whites.

A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma

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.

Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution

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.