Modeling Unobserved Heterogeneity in Susceptibility to Ambient Benzo[a]pyrene Concentration among Children with Allergic Asthma Using an Unsupervised Learning Algorithm

Effects of Benzo[α]pyrene on Mucus Secretion and Tissue Remodeling in a Rat Model of Allergic Rhinitis

OBJECTIVES

Exposure to benzo[α]pyrene (BaP) increases the incidence and severity of allergic rhinitis (AR), but the underlying mechanisms remain unclear. Thus, we investigated the in vivo effects of BaP exposure on mucus hypersecretion and tissue remodeling in a rat model of AR.

METHODS

Female Sprague-Dawley rats were randomly divided into 4 groups: a negative control group, a group of healthy rats exposed to BaP, a group of rats with ovalbumin (OVA)-induced AR, and a group of AR model rats exposed to BaP. Nasal symptoms and levels of OVA-specific serum immunoglobulin E (IgE) were measured in each individual rat. Moreover, examination of goblet cell hyperplasia and collagen deposition was carried out with periodic acid-Schiff (PAS) staining and Masson trichrome (MT) staining. Mucin 5AC (MUC5AC) expression was assessed by immunohistochemistry.

RESULTS

BaP significantly increased the number of sneezes, the number of nasal rubs and the levels of OVA-specific serum IgE in rats with AR. Statistically significant differences in goblet cell hyperplasia and collagen deposition were observed between the BaP-exposed AR model group and the AR model group. Immunohistochemical results showed that the nasal mucosa of AR model rats displayed markedly elevated MUC5AC expression after BaP exposure.

CONCLUSION

Our data indicate that mucus hypersecretion and the development of nasal remodeling might be pathophysiologic mechanisms underlying increased susceptibility to AR after exposure to BaP.

Children's exposure to polycyclic aromatic hydrocarbon (PAHs): a review on urinary 1-hydroxypyrene and associated health effects

This article reviewed the published studies on the environmental exposure to polycyclic aromatic hydrocarbons (PAHs) among children and assessed the urinary 1-hydroxypyrene (1-OHP) level as a biomarker of exposure to PAHs. The current knowledge of the potential health effects of increased 1-OHP in children was reviewed. Additionally, the influence of genetic polymorphism on the urinary 1-OHP level was discussed in this review. The assembled data showed that children who are attending schools or living close to industrial and polluted urban areas might have greater exposure to higher concentrations of PAHs with a higher level of urinary 1-OHP when compared to those children living in rural areas. Urinary 1-OHP may be a reliable biomarker for determining the genotoxic effects, oxidative stress and inflammation caused by exposure to PAHs. Strong research evidence indicated that the total body burden of PAHs should be evaluated by biomonitoring of 1-OHP in line with other urinary PAHs metabolites (with 2-3 rings) to evaluate recent total exposure to PAHs. Overall, the study suggests implementing a mitigation plan to combat air pollution to provide a cleaner environment for children.

A scoping review of asthma and machine learning

OBJECTIVE

The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature.

DATA SOURCES

We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020.

STUDY SELECTION

DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented.

RESULTS

A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status).

CONCLUSIONS

The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence).

Supplemental data for this article is available online at http://dx.doi.org/ .

Airborne Benzo[a]Pyrene may contribute to divergent Pheno-Endotypes in children

BACKGROUND

Asthma represents a syndrome for which our understanding of the molecular processes underlying discrete sub-diseases (i.e., endotypes), beyond atopic asthma, is limited. The public health needs to characterize etiology-associated endotype risks is becoming urgent. In particular, the roles of polyaromatic hydrocarbon (PAH), globally distributed combustion by-products, toward the two known endotypes - T helper 2 cell high (Th2) or T helper 2 cell low (non-Th2) - warrants clarification.

OBJECTIVES

To explain ambient B[a]P association with non-atopic asthma (i.e., a proxy of non-Th2 endotype) is markedly different from that with atopic asthma (i.e., a proxy for Th2-high endotype).

METHODS

In a case-control study, we compare the non-atopic as well as atopic asthmatic boys and girls against their respective controls in terms of the ambient Benzo[a]pyrene concentration nearest to their home, plasma 15-Ft2-isoprostane (15-Ft2-isoP), urinary 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG), and lung function deficit. We repeated the analysis for i) dichotomous asthma outcome and ii) multinomial asthma-overweight/obese (OV/OB) combined outcomes.

RESULTS

The non-atopic asthma cases are associated with a significantly higher median B[a]P (11.16 ng/m3) compared to that in the non-atopic controls (3.83 ng/m3; P-value < 0.001). In asthma-OV/OB stratified analysis, the non-atopic girls with lean and OV/OB asthma are associated with a step-wisely elevated B[a]P (median,11.16 and 18.00 ng/m3, respectively), compared to the non-atopic lean control girls (median, 4.28 ng/m3, P-value < 0.001). In contrast, atopic asthmatic children (2.73 ng/m3) are not associated with a significantly elevated median B[a]P, compared to the atopic control children (2.60 ng/m3; P-value > 0.05). Based on the logistic regression model, on ln-unit increate in B[a]P is associated with 4.7-times greater odds (95% CI, 1.9-11.5, P = 0.001) of asthma among the non-atopic boys. The same unit increase in B[a]P is associated with 44.8-times greater odds (95% CI, 4.7-428.2, P = 0.001) among the non-atopic girls after adjusting for urinary Cotinine, lung function deficit, 15-Ft2-isoP, and 8-oxodG.

CONCLUSIONS

Ambient B[a]P is robustly associated with non-atopic asthma, while it has no clear associations with atopic asthma among lean children. Furthermore, lung function deficit, 15-Ft2-isoP, and 8-oxodG are associated with profound alteration of B[a]P-asthma associations among the non-atopic children.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.