Predictors Of Asthma Severity Research Articles

IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis.

To determine the role of inflammation-related proteins in predicting asthma severity and outcome, 92 inflammation-related proteins were measured in the asthmatic serum using Olink analysis. Different bioinformatics algorithms were developed to cross analyze with the single-cell or transcriptome data sets from the Gene Expression Omnibus database to explore the role of IL18R1 and related genes in asthma and idiopathic pulmonary fibrosis (IPF). Olink identified 52 differentially expressed proteins in asthma. They were strongly linked to the cytokine-cytokine receptor interaction, TNF, and NF-κB signaling pathway. Seven proteins were found in both single-cell RNA and Olink analyses. Among them, IL18R1 was predominantly expressed in mast cells, and the results suggested enhanced communication between mast cells and CD 8+ T cells. IL18R1 was upregulated in serum and induced sputum and bronchoalveolar lavage fluid of patients with uncontrolled or severe asthma. IL18R1 was positively correlated with TNFSF1 and OSM and S100A12. The diagnostic efficacy of these serum IL18R1-related molecules for asthma ranged from 0.839 to 0.921. Moreover, high levels of IL18R1, TNFSF1, OSM, and S100A12 were significantly associated with shorter survival times and worse lung function. IL18R1-related molecules may serve as biomarkers for monitoring uncontrolled or severe asthma and as prognostic markers for IPF.

Biomarkers associated with severity classification of asthma identified by comprehensive bioinformatics analysis

Using an integrated bioinformatics approach to find novel biomarkers that can predict asthma severity. From June 2022 to December 2022, this clinical medical study was conducted and completed in the Department of Allergy, Zhongnan Hospital of Wuhan University. The gene chip dataset GSE43696 was screened and downloaded from the high-throughput Gene Expression Omnibus (GEO) database, and the gene chip data preprocessing was completed using package "affy" in R and "rma" algorithm in turn. Use the the "edgeR" and "limma" packages to screen out the differentially expressed genes (DEGs) between normal controls, mild to moderate asthma patients and severe asthma patients, and then use the "clusterProfiler" package to perform GO enrichment analysis and KEGG pathway enrichment analysis of DEGs, finally use the STRING website to construct a protein-protein interaction (PPI) network of DEGs to further screen key genes. Using the R language "WGCNA" package, the weighted gene co-expression network analysis (WGCNA) was performed on the dataset GSE43696, and the modules significantly related to the severity of asthma were screened out, then the hub genes were obtained by intersecting the WGCNA analysis results with the DEGs screened by PPI. Datasets GSE43696 and GSE63142 were used to verify the expression of hub genes, and the diagnostic value was evaluated according to the ROC curve, then the potential function of hub genes in dataset GSE43696 was further clarified by gene set enrichment analysis (GSEA). The results showed that a total of 251 DEGs were screened, including 39 in the normal group and mild to moderate asthma group, 178 in the normal group and severe asthma group, and 34 in the mild to moderate asthma group and severe asthma group, mainly involved in biological processes such as response to toxic substance, response to oxidative stress, extracellular structure organization, extracellular matrix organization. Two modules significantly correlated with asthma severity were screened out (red module, P=7e-6, r=0.43; pink module, P=5e-8, r=-0.51), and finally six hub genes were obtained, including B3GNT6, CEACAM5, CCK, ERBB2, CSH1 and DPPA5. The comparison of gene expression levels and ROC curve analysis of datasets GSE43696 and GSE63142 further verified the six hub genes, which may associated with o-glycan biosynthesis, alpha linolenic acid metabolism, linoleic acid metabolism, pentose and glucoronate interconversions. In conclusion, through a variety of bioinformatics analysis methods, this study identified six hub genes significantly related to the severity of asthma, which potentially provided a new direction for the prediction and targeted therapy of asthma.

Association of Interleukin-4 in Immunoglobulin-E Mediated Asthma: A Cross Sectional Study

Backgrounds: Interleukin 4 (IL 4) is a cytokine associated with the cause of several allergic diseases such as asthma due to their function in the differentiation of T helper type 2 lymphocytes and induction of the IgE isotype switch. Object: The study aims to determine the association IL-4 level with total serum IgE levels and asthma severity. Methods: A cross-section study was performed. Eighty-seven subjects were recruited from Karbala Teaching Hospital for Children in the period extending from January 25 to May 24, 2022, including children with asthma, were subjected to measure IL 4 level using Elabscience ELISA kit and measured total IgE level using AccuBind IgE ELISA kit. Results: Eighty-seven subjects of asthmatic children in which the mean age was 7.833 ± 3.652. There are 65.52% and 34.48% of asthmatic children (male and female, respectively). Total serum IgE was 398.889 ± 227.156 IU/ml, while the IL-4 level was 5.18 ± 8.224. There was a significant difference in IL 4 levels depending on asthma severity (P= 0.037). IL-4 levels in severe persistent asthma were higher than IL-4 levels in mild and moderate persistent asthma. Further, there was a highly significant difference in IL 4 levels depending on asthma controlled (P= 0.004). IL-4 levels in not well-controlled asthma were higher than IL-4 levels in well and partial controlled asthma. In addition, there was no significant correlation between IL-4 levels and total serum IgE levels (P=0.436). Conclusion: IL 4 has an important role in predicting asthma severity and asthma control in children. These findings have important implications for the treatment of IgE-mediated asthma. Despite this, IL 4 levels have no significant correlation with total serum IgE levels. J MEDICINE 2023; 24(2): 89-95

Glucocorticoid Receptor Polymorphism A3669G Is Associated with Airflow Obstruction in Mild-to-Severe Asthma

Background: Glucocorticoids (GCs) represent the mainstay therapy for asthmatics. A subset of severe asthmatics fails to respond to steroid-based therapies, leading to important healthcare costs. Single nucleotide polymorphisms (SNPs) of glucocorticoid receptor genes were associated with a response to GC. We evaluate the possible relation of BclI and A3669G SNPs to clinical, biological and functional characteristics of asthmatics. Methods: We recruited 172 mild-to-severe asthmatic outpatients referring to the Severe Asthma and Rare Lung Disease Unit at San Luigi University Hospital. Clinical data were obtained at recruitment when spirometry tests and peripheral blood sampling were performed. Patients were genotyped for BclI and A3669G through the pyrosequencing assay results. Results: Patients with the A3669G AG genotype were younger, allergic and had higher IgE levels compared to AA genotype (p < 0.05). Moreover, asthmatics with the AA genotype had a lower post-bronchodilator FEV1/FVC ratio than the GG genotype (p < 0.05), and a higher RV/TLC ratio than the AG genotype (p < 0.05). Conclusions: The A3669G AG genotype might be related to type-2 allergic asthma; in particular, allele A of A3669G SNP was associated with GC response in our asthmatics. In conclusion, this observational cross-sectional study suggests a possible role of A3669G SNP as a predictor of asthma severity and phenotype.

Evaluation of Total Serum Immunoglobulin E level in Asthmatic Children and its Relation to Disease Severity

Background Asthma is a multifaceted disease marked by recurrent airway inflammation. The aim of the study is to evaluate the significance of total immunoglobulin E (IgE) in asthmatic children and find the relation of its level to the disease severity Patients and methods Forty-five children with acute bronchial asthma aged between 5 and 16 years, and 15 control children with comparable age and sex distribution were included in the study. All children will be subjected to complete blood picture, Total IgE, and spirometry test Forced Expiratory Volume (FEV1), Forced Vital Capacity (FVC), FEV1/FVC. Results Among the 45 patients, 57.8% were male, with a median age of 10 years. There were statistically significant differences between the asthmatic groups and the control group as regards sex, weight, total IgE level, FEV1, FVC, and FEV1/FVC ratio. There was a significant positive correlation between IgE level and age, weight and height (r=0.465, P< 0.001, r=0.349, P=0.019, r=0.314, P=0.036, respectively) Conclusion Total IgE level was shown to have a role in the diagnosis of asthma and can be used as a predictor of asthma severity in children

MicroRNA-146a-5p and microRNA-210-3p Correlate with T Regulatory Cells Frequency and Predict Asthma Severity in Egyptian Pediatric Population.

Severe bronchial asthma (BA) affects 5-10% of children, which imposes socioeconomic burden. Therefore, it is crucial to identify biomarkers for risk stratification in children with BA. T regulatory cells (Tregs) play a balancing role in allergic response regulation. We aimed to investigate the relationship between Treg, miR-210-3p, and miR-146a-5p in relation to asthma phenotypes in search of novel biomarkers of disease severity. This study included 50 children with BA classified into Group 1 (n = 25) children with mild to moderate asthma and Group 2 (n = 25) children with severe asthma. In addition to 26 control subjects. Flow cytometry was used to detect Tregs. Plasma miR-210-3p and miR-146a levels were determined using quantitative real-time PCR. Patients' FEV1 (Forced Expiratory Volume in the first second) was measured. miR-210-3p level correlated negatively with Treg frequency (r = -0.828, P < 0.001) and FEV1 (r = -0.621, P < 0.001). The level of miR-146a-5p positively correlated positively with Treg% (r = 0.303, P = 0.032). ROC curve analysis revealed that miR-210-3p was the most sensitive biomarker of severity, with the area under curve (AUC) = 0.923, 96% sensitivity, and 60% specificity. According to multivariate analysis, miR-210-3p is an independent risk factor for BA severity [OR =3.119, P = 0.030], while miR-146a-5p is a protective factor [OR =0.811, P = 0.049]. Treg frequency is linked to FEV1, miR-146a-5p and miR-210-3p in childhood BA. Upregulation of miR-210-3p is a sensitive biomarker and an independent risk factor for BA severity in Egyptian children.

A study of nasal epithelial cell gene expression in a sample of mild to severe asthmatic children and healthy controls

BackgroundAirway epithelium contributes to the natural history of bronchial asthma through the production of various cytokines and chemokines. The purpose of this study was to assess nasal epithelial cell genes (TMEM178, FKBP5, CLCA1, SERPINB2 and periostin) in childhood asthma and their utility in predicting asthma severity, and atopic status. Seventy asthmatic children were included and further subdivided into mild, moderate and severe persistent asthma together with 30 apparently healthy children as a control group. All children were subjected to medical history taking, clinical examination. Nasal epithelial samples were collected for detection of epithelial cell genes (TMEM178, FKBP5, CLCA1, SERPINB2 and periostin) by real-time PCR.ResultsTMEM178 showed significant down-regulation in asthmatic children and its expression levels decreased significantly with the progression of asthma severity. CLCA1, SERPINB2 and periostin showed statistically significant up-regulation in asthmatic children, whereas FKBP5 was increased in asthmatic children but with non-significant up-regulation when compared with the control group. Regarding atopic status, relative gene expression levels of CLCA1, SERPINB2 and periostin were significantly up-regulated in atopic asthma.ConclusionOur findings suggest the role of nasal airways epithelial cells in predicting asthma severity and atopic status, as TMEM178 expression gained attention as a predictor of asthma severity. CLCA1, SERPINB2 and periostin expression were up-regulated not only in asthmatic children, but also in atopic asthma.

Aspirin-Exacerbated Respiratory Disease: Association Between Patient-Reported Sinus and Asthma Morbidity

The association between sinonasal and pulmonary symptoms in aspirin-exacerbated respiratory disease is not fully established. To characterize sinonasal and asthma symptomatology, and to determine whether reported sinonasal symptoms predict asthma severity. Prospectively collected data from an aspirin-exacerbated respiratory disease registry cohort were included from 2013 to 2018. Sinonasal symptomatology measured by Sino-Nasal Outcomes Test (SNOT) 22-item total scores was used as the predictor variable, with Asthma Control Test (ACT) scores and percent predicted FEV1 (FEV1% predicted) as primary outcomes. All instances of paired data on the same date were used. ACT score was also evaluated with FEV1% predicted as the outcome. Mixed effects regression was completed. From 1065 aspirin-exacerbated respiratory disease registry subjects (mean age, 48.1 ± 12.8 years; 68.0% females, 29.8% males), mean SNOT-22 score was 42.3 ± 24.12 (n= 1307 observations from 869 subjects), mean ACT score was 19.4 ± 5.2 (n= 1511 observations from 931 subjects), and mean FEV1% predicted was 82.8 ± 19.6 (n= 777 observations from 307 subjects). SNOT-22 score significantly predicted ACT scores (P < .0001, 1185 paired observations from 845 subjects) and FEV1% predicted (P= .018, 485 observations from 246 subjects). Any 10-point increase in SNOT-22 score was associated with a 0.87-point decrease in ACT score and a 0.75% decrease in FEV1% predicted. Any 1-point increase in ACT score was associated with a 1.0% increase in FEV1% predicted (P < .0001, 616 observations from 269 subjects). The most severe SNOT-22 symptoms were sense of smell/taste and blockage/congestion of nose. SNOT-22 scores significantly predict ACT scores and FEV1% predicted, and ACT scores significantly predict FEV1% predicted. This study demonstrates an association between patient-reported rhinosinusitis and asthma symptom severity and subjective and objective measures of asthma severity.

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

BackgroundThe pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data.ResultsHere, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine regression.ConclusionWe found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

Biomarkers of Bronchial Asthma.

Asthma is a complex disease with a variable course. Efforts to identify biomarkers to predict asthma severity, the course of disease and response to treatment have not been very successful so far. Biomarker research has expanded greatly with the advancement of molecular research techniques. An ideal biomarker should be suitable to identify the disease as well the specific endotype/phenotype, useful in the monitoring of the disease and to determine the prognosis, easily to obtain with minimum discomfort or risk to the patient. An ideal biomarker should be suitable to identify the disease as well the specific endotype/phenotype, useful in the monitoring of the disease and to determine the prognosis, easily to obtain with minimum discomfort or risk to the patient - exhaled breath analysis, blood cells and serum biomarkers, sputum cells and mediators and urine metabolites could be potential biomarkers of asthma bronchiale. Unfortunately, at the moment, an ideal biomarker doesn't exist and the overlap between the biomarkers is a reality. Using panels of biomarkers could improve probably the identification of asthma endotypes in the era of precision medicine.

Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma

Allergic sensitization is associated with severe asthma, but assessment of sensitization is not recommended by most guidelines. We hypothesized that patterns of IgE responses to multiple allergenic proteins differ between sensitized participants with mild/moderate and severe asthma. IgE to 112 allergenic molecules (components, c-sIgE) was measured using multiplex array among 509 adults and 140 school-age and 131 preschool children with asthma/wheeze from the Unbiased BIOmarkers for the PREDiction of respiratory diseases outcomes cohort, of whom 595 had severe disease. We applied clustering methods to identify co-occurrence patterns of components (component clusters) and patterns of sensitization among participants (sensitization clusters). Network analysis techniques explored the connectivity structure of c-sIgE, and differential network analysis looked for differences in c-sIgE interactions between severe and mild/moderate asthma. Four sensitization clusters were identified, but with no difference between disease severity groups. Similarly, component clusters were not associated with asthma severity. None of the c-sIgE were identified as associates of severe asthma. The key difference between school children and adults with mild/moderate compared with those with severe asthma was in the network of connections between c-sIgE. Participants with severe asthma had higher connectivity among components, but these connections were weaker. The mild/moderate network had fewer connections, but the connections were stronger. Connectivity between components with no structural homology tended to co-occur among participants with severe asthma. Results were independent from the different sample sizes of mild/moderate and severe groups. The patterns of interactions between IgE to multiple allergenic proteins are predictors of asthma severity among school children and adults with allergic asthma.

Age of Asthma Onset, not Severity, Predicts Environmental Allergy Clusters

Early-onset asthma (diagnosis ≤12 years) is associated with greater environmental allergen sensitization. However, the relationship between asthma severity and specific environmental allergies is unknown. We hypothesized that specific environmental allergen clusters would associate with asthma severity and age of onset. In the Severe Asthma Research Program, 404 atopic adult asthmatics were identified based on elevated serum specific IgE to ≥1 of 15 environmental allergens. Patients were classified as severe/non-severe and early/late-onset asthma. Using specific IgE values for each of the allergens, k-means clustering identified Allergen clusters (ACs) and Patient clusters (PCs). PCs were compared for differences in severity, age of onset, serum IgE, and blood eosinophils. In adult asthma, 5PCs and 3ACs were identified. PC1 associated with high expression of AC1 (molds, trees, grasses and weeds) and moderate expression of AC2 (dust mites, cockroach) and AC3 (cat, dog, mouse and rat). PC2 had high expression of AC2. PC4 associated with AC3, and PC5 predominantly associated with AC1. PC3, the largest PC (n=348), had mild expression of all ACs, lower total IgE and the lowest blood eosinophils. Age of onset differed: PC1 and PC5 had a higher proportion of early-onset asthma (p=0.03), but severity did not differ across PCs (p=0.21). K-means clustering of serum IgE-based environmental allergy testing identified logical ACs in adult asthmatics. While early-onset asthma associated with outdoor allergens (AC1), the patterns and intensity of environmental allergens did not predict asthma severity. This suggests that environmental allergies are a poor biomarker of asthma severity in adults.

Combined immune and airway epithelial cell profiles define asthma severity phenotypes

Abstract Asthma constitutes a spectrum of conditions that affect a substantial proportion of the population in developed countries. A large segment of asthmatics are characterized by an immunologic type 2 allergic profile. A preponderance of conventional and biologically-based therapies are available to treat these subjects. However, a small minority of individuals clinically classified as severe asthmatics (SA), are highly refractory to treatment and account for a significantly disproportionate share of the clinical and economic impact of the asthma condition as a whole. In order to better define the immune response in SA with the ultimate goal of developing novel therapeutic modalities, we analyzed bronchoalveolar lavage (BAL) cells from a cohort of SA, mild to moderate asthmatics (M/M), and healthy controls by mass cytometry (CyTOF). Unbiased bioinformatic assessment revealed 34 clusters of BAL cells with assignment of subjects to one of four groupings by principal component analysis (PCA). The majority of SA (78.9%) fell into two of these groups and constituted 65.2% of their makeup, with M/M representing the remaining 34.8%. Similarly, analysis of airway bronchial epithelial cells (BECs) by RNA sequencing (RNASeq) revealed three subject groups with SA falling largely into one of these. Synthesis of CyTOF and RNASeq data in the context of relevant clinical parameters resulted in a combined profile for each subject, which was able to distinguish those with the most severe disease. Our approach demonstrates that an unbiased, multifactorial assessment of immune and epithelial profiles can predict asthma severity, but may also reveal mechanism-based clues for targeted, personalized therapeutic intervention in severe asthmatics.

Stability of peripheral blood immune markers in patients with asthma

BackgroundAsthma is a complex disease with variable course. Efforts to identify biomarkers to predict asthma severity, the course of disease and response to treatment have not been very successful so far. We have previous suggested that PAR-2 and CRTh2 expression on specific peripheral blood cell subtypes may be biomarkers of asthma severity. We reasoned that parameters that remain stable when asthma symptoms are controlled would be the most appropriate to evaluate for their utility to predict loss of asthma control and/or severity of the disease.MethodsNineteen stable asthmatics were recruited from the University of Alberta Asthma clinic and followed in clinic every 3 months for a total of 4 visits. Patients had spirometry and completed the ACQ questionnaire in every visit. Blood was drawn in every visit and analyzed for a number of immune parameters by flow cytometry. These parameters included PAR-2 and CRTh2 expression on monocyte subgroups and T lymphocytes respectively, as well as numbers of eosinophils, innate lymphoid type-2 cells (ILC2) and dendritic cells. Within person stability of immune and physiological parameters was calculated using the intraclass correlation (ICC) using R version 3.4.0.ResultsFEV1 (% predicted), FEV1/FVC ratio, ACQ5 and ACQ7 did not differ significantly over the 4 visits, as would be expected for patients with stable asthma. Peripheral blood eosinophil numbers by Kimura stain and by flow cytometry showed ICC scores of 0.44 and 0.52 respectively, indicating moderate stability. The % of ILC2 cells in peripheral blood also showed moderate stability [ICC score of 0.45 (0.14–0.67)]. The stability for all other immune parameters was poor.ConclusionAmong the peripheral blood immune parameters we studied, only numbers of eosinophils and ILC2 in peripheral blood were moderately stable over a year in stable asthmatics. Further studies are required to understand the reasons for the variability of the other cell types.

Diagnostic Value of Serum Baseline Tryptase Levels in Childhood Asthma and Its Correlation With Disease Severity

S Gao, J Fan, Z Wang. Int Arch Allergy Immunol. 2016;171(3–4):194–202 To determine if the measurement of serum baseline tryptase (sBT) levels can accurately diagnose pediatric asthma and predict asthma severity. The study included 114 asthmatic children between the ages of 5 and 12 years. Within the cohort, 36 children had mild intermittent asthma, 38 had mild persistent asthma, and 40 had moderate to severe persistent asthma. In addition, 34 age-matched healthy children were included as controls. Serum baseline tryptase …

Obstruction phenotype as a predictor of asthma severity and instability in children

Small-airways instability resulting in premature airway closure has been recognized as a risk for asthma severity and poor control. Although spirometry has limited sensitivity for detecting small-airways dysfunction, a focus on the air-trapping component of obstruction might identify a risk factor for asthma instability. We sought to use spirometric measurements to identify patterns of airway obstruction in children and define obstruction phenotypes that relate to asthma instability. Prebronchodilation and postbronchodilation spirometric data were obtained from 560 children in the Asthma Phenotypes in the Inner City study. An air-trapping obstruction phenotype (A Trpg) was defined as a forced vital capacity (FVC) z score of less than -1.64 or an increase in FVC of 10% of predicted value or greater with bronchodilation. The airflow limitation phenotype (A Limit) had an FEV1/FVC z score of less than -1.64 but not ATrpg. The no airflow limitation or air-trapping criteria (None) phenotype had neither ATrpg nor ALimit. The 3 obstruction phenotypes were assessed as predictors of number of exacerbations, asthma severity, and airway lability. Patients with the ATrpg phenotype (14% of the cohort) had more exacerbations during the 12-month study compared with those with the ALimit (P<.03) and None (P<.001) phenotypes. Patients with the ATrpg phenotype also had the highest Composite Asthma Severity Index score, the highest asthma treatment step, the greatest variability in FEV1 over time, and the greatest sensitivity to methacholine challenge. A Trpg and ALimit patterns of obstruction, as defined by using routine spirometric measurements, can identifyobstruction phenotypes that are indicators of risk for asthma severity and instability.

METEORIN AS A BIOMARKER TO PREDICT ASTHMA SEVERITY

RespirologyVolume 22, Issue S3 p. 93-93 APSR Poster Abstracts METEORIN AS A BIOMARKER TO PREDICT ASTHMA SEVERITY First published: 20 November 2017 https://doi.org/10.1111/resp.13207_18Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume22, IssueS3Supplement: 22nd Congress of the Asian Pacific Society of Respirology, International Convention Centre, Sydney, Australia, 23–26 November 2017November 2017Pages 93-93 RelatedInformation

Asthma, allergy, and obesity: Examining the relationship among Nevada children

ABSTRACTObjective: The majority of pediatric asthma is atopic, but whether pediatric obese asthma is atopic is indeterminate in the literature. In Nevada, children become sensitized to aeroallergens, a risk factor for asthma, at young ages. Additionally, Nevada children have high rates of obesity. Our objective is to determine whether elevated body mass index (BMI) is associated with asthma severity, allergen sensitization, and polysensitization. Methods: Medical records from a pediatric allergy clinic provided BMI percentile, physician-diagnosed asthma severity, skin prick test data, and sociodemographics such as age, race, sex, and insurance status from asthmatic patients. Descriptive statistics and binary and multinomial logistic regression were conducted. Results: In this population (N = 125) aged 1–16, 61% were male, 65% were white, and 74% had private health insurance. Sixty-five percent of children were under/healthy weight and 29% were overweight/obese. Asthma symptoms were moderate in 66% of the population, and severe in 18%. Nearly 85% of this population was atopic, and 82% were polysensitized. Sensitization and polysensitization occurred in all weight categories. Asthma severity and elevated BMI were not associated significantly. Overweight/obese children (≥85th percentile) had lower odds of allergen sensitization (adjusted odds ratio 0.26, 95% CI = 0.85–0.78, p = 0.016) and polysensitization (adjusted odds ratio 0.30, 95% CI = 0.11–0.85, p = 0.023) than healthy weight children (<85th percentile). Conclusion: Although overweight children did show allergen sensitization, those who were overweight had lower odds of allergen sensitization and lower odds of polysensitization, as compared to normal weight asthmatic children. Elevated BMI was not a significant predictor of asthma severity.

Classification of Asthma Severity and Medication Using TensorFlow and Multilevel Databases

Escalating cost of treating chronic diseases demand that they be, to the extent possible, self-managed by the patients. In self-management of disease an imperative is to predict, the possible future state of morbidity (at time, T¹), given the present precursor conditions (at time, Tº) and expected precursor condition (at time, T¹).This paper reports the results of a study to evaluate the potential use of using TensorFlow and Inpatient Databases at national level and hospital level for predicting the asthma severity. Methods of Deep Neural Networks (DNN) have been deployed in classification of morbidity conditions, as well as treatment options. The results indicate that training a DNN to predict asthma severity level or the imminence of an asthma attack is possible.

Nasal lavage fluid nuclear factor kappa B and cytology in asthmatic children and their correlation with severity and control

BackgroundAsthma is the most common chronic inflammatory disease in childhood. The relevance of NFκB which is known to be an inflammatory marker in upper airway epithelium and its relation to lower airway inflammation has not been fully studied in childhood asthma. Aim of studyThe study aimed at evaluating the diagnostic value of nasal lavage nuclear factor kappa B and cells as a less-invasive bench-side maneuver and inflammatory biomarkers in asthmatic children and correlating with asthma severity. MethodsThis case-control study recruited 60 asthmatic children from Pediatric Chest Clinic, Children’s Hospital; Ain Shams University. Thirty healthy non-asthmatic children-age and sex-matched were included as a control group. Nasal lavage cytology, nasal lavage NFκB and forced expiratory volume in 1s (FEV1) % of predicted for age and sex were estimated. ResultsNasal lavage NFκB levels were significantly higher in asthmatics than in controls with a mean of 0.129±0.113μg/μg nuclear proteins and 0.0176±0.013μg/μg nuclear proteins, respectively. Nasal lavage NFκB and eosinophil levels were significantly higher with increasing asthma severity and with worsening levels of asthma control. Nasal lavage NFκB showed a sensitivity of 87% and a specificity of 87% in predicting asthma severity. ConclusionsDespite that spirometry and clinical classification are the gold standards for grading of asthma, Nasal lavage NFκB and cells can be considered as a new less-invasive non-subjective inflammatory marker for assessment of different grades of asthma severity and control.