Modelling trajectories of parentally reported and physician-confirmed atopic dermatitis in a birth cohort study

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.

Early intervention and disease modification in atopic dermatitis-the current state of the field and barriers to progress

Atopic dermatitis (AD) is a highly prevalent chronic inflammatory skin disease representing a major source of global disability burden. Disease-modifying therapies are showing promise in chronic inflammatory disorders such as rheumatoid arthritis and Crohn's disease with method and timing of initial treatment impacting long-term disease outcomes. Whether disease-modifying therapies, specifically those used as an early interventional approach, impacts disease course and comorbidity development in AD is not well-understood. We reviewed the progress in disease modification strategies, emphasizing early intervention approaches in common (or proto-typical) inflammatory diseases. Although more common in other fields, disease modification approaches are becoming increasingly investigated in dermatology, though studies in AD are lacking. Despite significant limitations in ongoing and completed studies, early data are promising and suggest that both the choice and timing of early intervention approach can affect long-term disease course and comorbidity development. To best improve AD patient outcomes, more research is needed to further explore the impact of early disease-modifying therapies. Future studies should focus on identifying the most effective approaches and extend the early results to a more inclusive set of comorbidities and longer-term outcomes.

Identification of eczema clusters and their association with filaggrin and atopic comorbidities: analysis of five birth cohorts

BACKGROUND

Longitudinal modelling of the presence/absence of current eczema through childhood has identified similar phenotypes, but their characteristics often differ between studies.

OBJECTIVES

To demonstrate that a more comprehensive description of longitudinal pattern of symptoms may better describe trajectories than binary information on eczema presence.

METHODS

We derived six multidimensional variables of eczema spells from birth to 18 years of age (including duration, temporal sequencing and the extent of persistence/recurrence). Spells were defined as consecutive observations of eczema separated by no eczema across 5 epochs in five birth cohorts: infancy (first year); early childhood (age 2-3 years); preschool/early school age (4-5 years); middle childhood (8-10 years); adolescence (14-18 years). We applied Partitioning Around Medoids clustering on these variables to derive clusters of the temporal patterns of eczema. We then investigated the stability of the clusters, within-cluster homogeneity and associated risk factors, including FLG mutations.

RESULTS

Analysis of 7464 participants with complete data identified five clusters: (i) no eczema (51.0%); (ii) early transient eczema (21.6%); (iii) late-onset eczema (LOE; 8.1%); (iv) intermittent eczema (INT; 7.5%); and (v) persistent eczema (PE; 11.8%). There was very-high agreement between the assignment of individual children into clusters when using complete or imputed (n = 15 848) data (adjusted Rand index = 0.99; i.e. the clusters were very stable). Within-individual symptom patterns across clusters confirmed within-cluster homogeneity, with consistent patterns of symptoms among participants within each cluster and no overlap between the clusters. Clusters were characterized by differences in associations with risk factors (e.g. parental eczema was associated with all clusters apart from LOE; sensitization to inhalant allergens was associated with all clusters, with the highest risk in the PE cluster). All clusters apart from LOE were associated with FLG mutations. Of note, the strongest association was for PE [relative risk ratio (RRR) 2.70, 95% confidence interval (CI) 2.24-3.26; P < 0.001] followed by INT (RRR 2.29, 95% CI 1.82-2.88; P < 0.001).

CONCLUSIONS

Clustering of multidimensional variables identified stable clusters with different genetic architectures. Using multidimensional variables may capture eczema development and derive stable and internally homogeneous clusters. However, deriving homogeneous symptom clusters does not necessarily mean that these are underpinned by completely unique mechanisms.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

Atopic dermatitis across the life course

Atopic dermatitis, the most common chronic inflammatory skin disease, can occur at any age, and patterns of disease activity vary over time. Both prevalence and incidence are highest in infancy and early childhood, followed by a second peak in older adulthood. Birth cohort studies from European countries following children through adolescence have identified subgroups of patients with early-onset persistent disease, early-onset resolving disease, and later-onset disease. Parental history of atopy and genetic factors are among the most consistent predictors of more persistent disease. Studies have begun to examine whether molecular markers differ by age group, although longitudinal data are lacking. Breastfeeding, probiotics and skin-directed therapies such as emollients have been investigated as potential preventive measures, but randomized trials have not found consistent long-term benefit. Future research should focus on patterns of disease activity beyond early adulthood and the role of treatments on long-term disease activity.

Evolution of Eczema, Wheeze, and Rhinitis from Infancy to Early Adulthood: Four Birth Cohort Studies

Rationale: The relationship between eczema, wheeze or asthma, and rhinitis is complex, and epidemiology and mechanisms of their comorbidities is unclear. Objectives: To investigate within-individual patterns of morbidity of eczema, wheeze, and rhinitis from birth to adolescence/early adulthood. Methods: We investigated onset, progression, and resolution of eczema, wheeze, and rhinitis using descriptive statistics, sequence mining, and latent Markov modeling in four population-based birth cohorts. We used logistic regression to ascertain if early-life eczema or wheeze, or genetic factors (filaggrin [FLG] mutations and 17q21 variants), increase the risk of multimorbidity. Measurements and Main Results: Single conditions, although the most prevalent, were observed significantly less frequently than by chance. There was considerable variation in the timing of onset/remission/persistence/intermittence. Multimorbidity of eczema+wheeze+rhinitis was rare but significantly overrepresented (three to six times more often than by chance). Although infantile eczema was associated with subsequent multimorbidity, most children with eczema (75.4%) did not progress to any multimorbidity pattern. FLG mutations and rs7216389 were not associated with persistence of eczema/wheeze as single conditions, but both increased the risk of multimorbidity (FLG by 2- to 3-fold, rs7216389 risk variant by 1.4- to 1.7-fold). Latent Markov modeling revealed five latent states (no disease/low risk, mainly eczema, mainly wheeze, mainly rhinitis, multimorbidity). The most likely transition to multimorbidity was from eczema state (0.21). However, although this was one of the highest transition probabilities, only one-fifth of those with eczema transitioned to multimorbidity. Conclusions: Atopic diseases fit a multimorbidity framework, with no evidence for sequential atopic march progression. The highest transition to multimorbidity was from eczema, but most children with eczema (more than three-quarters) had no comorbidities.

Data-driven research on eczema: Systematic characterization of the field and recommendations for the future

Background

The past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods.

Methods

We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.

Results

Five key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers.

Conclusions

Research areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.

Associations between child filaggrin mutations and maternal diet with the development of allergic diseases in children

BACKGROUND

Filaggrin (FLG) loss-of-function mutations in children and maternal diet in pregnancy have been implicated in child allergy outcomes. This paper studies the questions: "do FLG mutations modify the effect of maternal diet on the odds of development of allergic diseases?" and "which factor leads to the highest rate of diagnosis allergic diseases over time, maternal diet, or FLG mutations?".

METHODS

Exact logistic regressions studied effect modification. Cox proportional hazard models compared the rate of allergic disease development in three groups (N = 624): (1) children with FLG mutation, (2) children without FLG mutation whose mothers did not eat an allergy preventive diet, and (3) children without FLG mutation whose mothers ate an allergy preventive diet. Maternal diet was classified using a validated index.

RESULTS

Cox models showed the development of atopic dermatitis, asthma, and wheeze was significantly higher for children in group 1 versus 3 (HR = 2.40 [1.32, 4.37], HR = 2.29 [1.05, 4.97], and HR 2.10 [1.004, 4.38], respectively), but not significantly higher for children in group 1 versus 2 (HR = 1.30 [0.74, 2.29], HR = 1.27 [0.61, 2.63], and HR = 1.29 [0.65, 2.58], respectively). Development of allergic rhinitis was significantly higher for group 1 versus 2 and 3 (1 vs. 2: HR = 2.29 [1.10, 4.76]; 1 vs. 3: HR = 3.21 [1.46, 7.08]). There was no significant effect modification for any outcome.

CONCLUSION

Children with FLG mutation had similar risk of atopic dermatitis, asthma, and wheeze as children without an FLG mutation whose mothers did not eat an allergy preventive diet during pregnancy. Child FLG mutation did not modify the effect of maternal diet. The results suggest that maternal diet in pregnancy, a modifiable risk factor, could be a target for preventive interventions.

Early-life predictors and risk factors of peanut allergy, and its association with asthma in later-life: Population-based birth cohort study

BACKGROUND

Understanding risk factors for peanut allergy (PA) is essential to develop effective preventive measures.

OBJECTIVE

To ascertain associates and predictors of PA, and the relationship between PA and asthma severity.

METHODS

In a population-based birth cohort, we investigated the association between objectively confirmed PA with early-life environmental exposures, filaggrin (FLG)-loss-of-function mutations and other atopic disease. We then examined the association of PA with longitudinal trajectories of sensitisation, wheeze and allergic comorbidities, which were previously derived using machine learning. Finally, we ascertained the relationship between PA and asthma severity.

RESULTS

PA was confirmed in 30/959 participants with evaluable data. In the multivariate analysis, eczema in infancy (OR=4.4, 95% CI 1.5-13.2, p=0.007), egg sensitisation at age 3 years (OR=9.7, 95% CI 3.3-29.9, p<0.001) and early-life cat ownership (OR=3.0, 95% CI 1.1-8.4, p=0.04) were independent associates of PA. In the stratified analysis among 700 participants with genetic information, in children with early-life eczema there was no difference in FLG mutations between children with and without PA (3/18 [16.7%] vs. 42/220 [19.1%], p=1.00). In contrast, among children without eczema, those with PA were almost 8-times more likely to have FLG mutations (2/6 [33.3%] vs. 27/456 [5.9%], p=0.049). We observed associations between PA and multiple allergic sensitisation profiles derived using machine learning, with ~60-fold increase in risk amongst individuals assigned to multiple early sensitisation. PA was significantly associated with persistent wheeze (but not other wheeze phenotypes), and with trajectories of atopic disease characterised by co-morbid persistent eczema and wheeze (but not with transient phenotypes). Children with PA were more likely to have asthma, but among asthmatics we found no evidence of an association between PA and asthma severity.

CONCLUSIONS

Peanut allergy is associated with multiple IgE-sensitisation and early-onset persistent eczema and wheeze. FLG loss-of-function mutations were associated with peanut allergy in children without eczema.