Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help?

Asthma Inception: Epidemiologic Risk Factors and Natural History Across the Life Course

Asthma is a descriptive label for an obstructive inflammatory disease in the lower airways manifesting with symptoms including breathlessness, cough, difficulty in breathing, and wheezing. From a clinician's point of view, asthma symptoms can commence at any age, although most patients with asthma-regardless of their age of onset-seem to have had some form of airway problems during childhood. Asthma inception and related pathophysiologic processes are therefore very likely to occur early in life, further evidenced by recent lung physiologic and mechanistic research. Herein, we present state-of-the-art updates on the role of genetics and epigenetics, early viral and bacterial infections, immune response, and pathophysiology, as well as lifestyle and environmental exposures, in asthma across the life course. We conclude that early environmental insults in genetically vulnerable individuals inducing abnormal, pre-asthmatic airway responses are key events in asthma inception, and we highlight disease heterogeneity across ages and the potential shortsightedness of treating all patients with asthma using the same treatments. Although there are no interventions that, at present, can modify long-term outcomes, a precision-medicine approach should be implemented to optimize treatment and tailor follow-up for all patients with asthma.

European Respiratory Society statement on preschool wheezing disorders: updated definitions, knowledge gaps and proposed future research directions

Since the publication of the European Respiratory Society (ERS) task force reports on the management of preschool wheezing in 2008 and 2014, a large body of evidence has accumulated suggesting that the clinical phenotypes that were proposed (episodic (viral) wheezing and multiple-trigger wheezing) do not relate to underlying airway pathology and may not help determine response to treatment. Specifically, using clinical phenotypes alone may no longer be appropriate, and new approaches that can be used to inform clinical care are needed for future research. This ERS task force reviewed the literature published after 2008 related to preschool wheezing and has suggested that the criteria used to define wheezing disorders in preschool children should include age of diagnosis (0 to <6 years), confirmation of wheezing on at least one occasion, and more than one episode of wheezing ever. Furthermore, diagnosis and management may be improved by identifying treatable traits, including inflammatory biomarkers (blood eosinophils, aeroallergen sensitisation) associated with type-2 immunity and differential response to inhaled corticosteroids, lung function parameters and airway infection. However, more comprehensive use of biomarkers/treatable traits in predicting the response to treatment requires prospective validation. There is evidence that specific genetic traits may help guide management, but these must be adequately tested. In addition, the task force identified an absence of caregiver-reported outcomes, caregiver/self-management options and features that should prompt specialist referral for this age group. Priorities for future research include a focus on identifying 1) mechanisms driving preschool wheezing; 2) biomarkers of treatable traits and efficacy of interventions in those without allergic sensitisation/eosinophilia; 3) the need to include both objective outcomes and caregiver-reported outcomes in clinical trials; 4) the need for a suitable action plan for children with preschool wheezing; and 5) a definition of severe/difficult-to-treat preschool wheezing.

Artificial intelligence and wheezing in children: where are we now?

Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs. Moreover, in the last decade, ML algorithms have been applied in wheezing phenotyping, also contributing to identifying specific genes, and have been proven to even predict asthma in preschoolers. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.

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.

Component-specific clusters for diagnosis and prediction of allergic airway diseases

BACKGROUND

Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes.

OBJECTIVE

To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms.

METHODS

In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models.

RESULTS

Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04).

CONCLUSION

Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.

Understanding the heterogeneity of childhood allergic sensitization and its relationship with asthma

PURPOSE OF REVIEW

To review the current state of knowledge on the relationship between allergic sensitization and asthma; to lay out a roadmap for the development of IgE biomarkers that differentiate, in individual sensitized patients, whether their sensitization is important for current or future asthma symptoms, or has little or no relevance to the disease.

RECENT FINDINGS

The evidence on the relationship between sensitization and asthma suggests that some subtypes of allergic sensitization are not associated with asthma symptoms, whilst others are pathologic. Interaction patterns between IgE antibodies to individual allergenic molecules on component-resolved diagnostics (CRD) multiplex arrays might be hallmarks by which different sensitization subtypes relevant to asthma can be distinguished. These different subtypes of sensitization are associated amongst sensitized individuals at all ages, with different clinical presentations (no disease, asthma as a single disease, and allergic multimorbidity); amongst sensitized preschool children with and without lower airway symptoms, with different risk of subsequent asthma development; and amongst sensitized patients with asthma, with differing levels of asthma severity.

SUMMARY

The use of machine learning-based methodologies on complex CRD data can help us to design better diagnostic tools to help practising physicians differentiate between benign and clinically important sensitization.