Effectiveness and adverse reactions to subcutaneous immunotherapy in children with allergic rhinitis/asthma

Effect and mechanism of allergen-specific immunotherapy on small airway dysfunction in children with asthma

BACKGROUND

The purpose of this study was to investigate the effectiveness of allergen-specific immunotherapy (AIT) on small airway dysfunction (SAD) and the underlying mechanism with a special focus on basophils.

METHODS

Sixty-five children with mild to moderate asthma who were under regular inhaled corticosteroid (ICS) treatment for more than 1 year but whose FEF75 remained below 65% of the predicted value and had positive results for serum Der p or Der f were enrolled. Children with asthma underwent house dust mite (HDM) subcutaneous immunotherapy (SCIT) treatment for 1 year. Clinical symptoms and lung function were evaluated every 3 months during HDM SCIT treatment. Basophil activation test (BAT) was carried out before and after HDM SCIT treatment. RNA sequencing was performed in isolated basophils from peripheral blood after 6 months of HDM SCIT treatment, followed by GO term and KEGG pathway enrichment analysis between patients with and without HDM SCIT treatment.

RESULTS

HDM AIT treatment ameliorated clinical symptoms while concurrently improved lung function parameters, such as FEV1/FVC, FEF75, FEF50 and MMEF (p < .05). It is worth noting that FEF75 values showed a highly significant, gradual and persistent increase (from 49.55 ± 1.27% at baseline to 71.89 ± 2.64% after 1 year of therapy) and 22 of 35 patients no longer had SAD after 1 year of treatment. BAT results revealed that AIT treatment significantly reduced basophil activity to the inhalant allergen mixtures containing HDM in vitro challenge from baseline. GO term and KEGG pathway enrichment analysis of basophils revealed that downregulated genes were mainly involved in immune cell activation, antigen presentation, and Th2 cell differentiation.

CONCLUSIONS

Our study demonstrated that HDM AIT not only improved SAD related lung function parameters, but also reduced basophil activity. RNA sequencing revealed the inhibition of phagocytosis and the phagosome pathway in basophils which may affect the polarization of Th2 cell differentiation after HDM AIT.

Advances in co-pathogenesis of the united airway diseases

According to the concept of "united airway diseases", the airway is a single organ in which upper and lower airway diseases are commonly comorbid. A range of inflammatory factors have been found to play an important role in the chain reaction of upper and lower airway diseases. However, the amount of research on this concept remains limited. The underlying mechanism of the relationship between typical diseases of the united airway, such as asthma, allergic rhinitis, and chronic sinusitis, also needs to be further explored. This review highlights the interaction between upper and lower respiratory diseases gathered from epidemiological, histoembryology, neural mechanistic, microbiological, and clinical studies, revealing the relationship between the upper and lower respiratory tracts.

Safety of allergen immunotherapy in children

PURPOSE OF REVIEW

The current review discusses allergen immunotherapy (AIT) safety in children.

RECENT FINDINGS

AIT is a well tolerated and effective treatment for pediatric allergic conditions. While mostly well tolerated, severe reactions and near fatal reactions may occur with subcutaneous immunotherapy (SCIT) once in every 160 000 visits. Sublingual immunotherapy (SLIT) is associated more with local side effects, but severe systemic reactions, including anaphylaxis, have been rarely reported. Providing informed consent, recognizing risk factors for severe systemic reactions, such as severe or uncontrolled asthma, and mitigating the risk of severe reactions are important components to improving the safety of AIT.

SUMMARY

Overall, AIT is well tolerated in children, and data suggest that the incidence of systemic reactions in children receiving SCIT is no less than mixed populations of adult and pediatric patients. SLIT carries less risk for systemic reactions, and local oral site-application reactions are usually mild and resolve within 15 days of treatment.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

BACKGROUND

Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions.

OBJECTIVE

The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults.

METHODS

A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

RESULTS

7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%).

CONCLUSION

Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.