This short review illustrates, using two recent studies, the potential and challenges of using machine learning methods to identify phenotypes of wheezing and asthma from childhood onwards.

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.

Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both “curricular” and “aetiological” heterogeneity: that is, heterogeneity across patients both in the time‐course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise—not mapping cleanly onto underlying disease mechanisms—and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected “big data” on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term “modern data approaches” (MDAs)—grouping together machine learning, artificial intelligence, and data science—to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples’ underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly.

Flexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

ABSTRACT Introduction Eczema, allergic rhinitis, and asthma are traditionally considered atopic (or allergic) diseases. They are complex, multifactorial, and are caused by a variety of different mechanisms, which result in multiple heterogeneous clinical phenotypes. Atopic march is usually interpreted as the sequential development of symptoms from eczema in infancy, to asthma, and then allergic rhinitis. Areas covered: The authors reviewed the evidence on the multimorbidity of eczema, asthma, and rhinitis, and the implication of results of data-driven analyses on the concept framework of atopic march. A literature search was conducted in the PubMed and Web of Science for peer-reviewed articles published until July 2020. Application of Bayesian machine learning framework to rich phenotypic data from birth cohorts demonstrated that the postulated linear progression of symptoms (atopic march) does not capture the heterogeneity of allergic phenotypes. Expert opinion: Eczema, wheeze, and rhinitis co-exist more often than would be expected by chance, but their relationship can be best understood in a multimorbidity framework, rather than through atopic march sequence. The observation of their co-occurrence does not imply any specific relationship between them, and certainly not a progressive or causal one. It is unlikely that a sngle mechanism such as allergic sensitization underpins different multimorbidity manifestations.