Bronchial Remodeling-based Latent Class Analysis Predicts Exacerbations in Severe Preschool Wheezers

Preventing progression of preschool wheezing to asthma: Opportunities for intervention

Recurrent wheezing in preschool children is heterogeneous and results from numerous genetic and environmental risk factors, which result in the same final clinical manifestation of acute episodes of wheezing but have distinct underlying mechanisms. Effective disease-modifying approaches, therefore, need to target the pathways driving the symptoms. We have good evidence to show that targeting airway eosinophilia alone in early-life preschool wheezing and using inhaled corticosteroids is not disease-modifying. Although airway remodelling develops early in preschool wheezing, the challenge is identifying suitable treatments for structural airway changes. There is increasing evidence for the role of lower airway bacterial infection contributing to wheeze episodes, but clinical trials investigating the impact of targeted antibiotic treatment on disease modification are needed. There is also increasing data supporting an association between lower airway neutrophilia and wheezing in a subgroup of preschool children, but direct causation and the role of neutrophil function remain unknown. Finally, there is encouraging preliminary data for the role of inactivated mixed bacterial lysates in children with non-allergic, infection-associated wheeze episodes, but the impact on longer-term outcomes and their mechanism of action is unknown. This review outlines a range of potential novel targets and approaches that may enable secondary prevention of asthma from preschool wheezing. In parallel, the potential for harm when interventions are introduced indiscriminately is highlighted. Some of the challenges that need to be addressed, including trial designs allowing tailored interventions, the need for non-invasive biomarkers for targeted interventions, and ensuring extended and long-term follow-up after intervention, are highlighted.

Update on asthma biology

This is an exciting time to be conducting asthma research. The recent development of targeted asthma biologics has validated the power of basic research to discover new molecules amenable to therapeutic intervention. Advances in high-throughput sequencing are providing a wealth of "omics" data about genetic and epigenetic underpinnings of asthma, as well as about new cellular interacting networks and potential endotypes in asthma. Airway epithelial cells have emerged not only as key sensors of the outside environment but also as central drivers of dysregulated mucosal immune responses in asthma. Emerging data suggest that the airway epithelium in asthma remembers prior encounters with environmental exposures, resulting in potentially long-lasting changes in structure and metabolism that render asthmatic individuals susceptible to subsequent exposures. Here we summarize recent insights into asthma biology, focusing on studies using human cells or tissue that were published in the past 2 years. The studies are organized thematically into 6 content areas to draw connections and spur future research (on genetics and epigenetics, prenatal and early-life origins, microbiome, immune and inflammatory pathways, asthma endotypes and biomarkers, and lung structural alterations). We highlight recent studies of airway epithelial dysfunction and response to viral infections and conclude with a framework for considering how bidirectional interactions between alterations in airway structure and mucosal immunity can lead to sustained lung dysfunction in asthma.

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England

Background

Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems.

Methods

We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events.

Results

We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease.

Conclusion

Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.

Early Prediction of Asthma

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.