Air pollution, epigenetics, and asthma

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.�This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.�Steps required for mediation analyses in the seasonality model are shown.�The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study

A distributed geospatial approach to describe community characteristics for multisite studies

Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions

cole-brokamp/DeGAUSS v0.2

added memoisation support use separate images for each geomarker, not tags fix bug where a missing county would cause a failur

cole-brokamp/rize: initial release

code moved from automagi

geocodeCAGIS: First Relase

Initial relase of geocodeCAGIS software package for R

cole-brokamp/dep_index v0.1

a nationwide community deprivation inde

CB v0.1

My personal R packag