Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study

BACKGROUND

Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.

METHODS

The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.

RESULTS

The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.

CONCLUSIONS

We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Rhinitis Phenotypes Based on Multimorbidities

Chronic allergic and nonallergic rhinitis are common phenotypes of a highly prevalent disorder of the upper airways, often associated with asthma.1 Clinical data, epidemiologic studies, and mHealth-based and genomic approaches indicate the existence of 2 distinct diseases: rhinitis alone and rhinitis + asthma, which may be allergic or less often nonallergic. Both disease phenotypes need to be further characterized, because allergic conjunctivitis, food allergy, and atopic dermatitis, often present in patients with allergic rhinitis, may be considered as independent multimorbidities. Thus, the concept "multimorbid allergic disease" is more appropriate than "one-airway-one-disease." In a meta-analysis, atopic dermatitis was strongly associated with allergic and nonallergic rhinitis, but not with rhinitis and asthma.2 Asthma alone may also be associated with non-type 2 mechanisms.3 The distinction between rhinitis alone and rhinitis and asthma was found in all the 12 countries studied using an mHealth app (MASK-air) in Europe and Latin America. These data indicate that the distinction between the 2 diseases is independent of allergen exposure.4.

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.

Pediatric asthma comorbidities: Global impact and unmet needs

Real-world data on the range and impact of comorbid health conditions that affect pediatric asthma are scant, especially from developing countries. Lack of data hinders effective diagnosis, treatment, and overall management of these complex cases. We, hereby, describe the common pediatric asthma comorbid conditions in terms of evidence for association, potential mechanisms of impact on asthma control, and treatment benefit. Obesity, upper airway allergies, dysfunctional breathing, multiple sensitizations, depressive disorders, food allergy, and gastro-esophageal reflux are common associations with difficult-to-treat asthma. On the other hand, asthma symptoms and/or management may negatively impact the well-being of children through drug adverse effects, worsening of anaphylaxis symptoms, and disturbing mental health. Awareness of these ailments may be crucial for designing the optimum care for each asthmatic child individually and may ultimately improve the quality of life of patients and their families. A multidisciplinary team of physicians is required to identify and manage such comorbidities aiming to mitigate the over-use of asthma pharmacotherapy. Asthma research should target relevant real-world difficulties encountered at clinical practice and focus on interventions that would mitigate the impact of such comorbidities. Finally, policymakers and global healthcare organizations are urged to recognize pediatric asthma control as a healthcare priority and allocate resources for research and clinical interventions. In other words, global asthma control needs support by compassionate scientific partnership.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

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.

Clinical characteristics in Japanese patients with chronic rhinosinusitis who underwent endoscopic sinus surgery

OBJECTIVE

There is heterogeneity in the pathophysiology of chronic rhinosinusitis (CRS). Obtaining a detailed understanding of patient profiles in specific regions can provide valuable information not only for clinical practice but also future research plans. The aim of this study was to investigate the characteristics of patients who underwent endoscopic sinus surgery (ESS) for CRS.

METHODS

This retrospective, single-center study examined the features of 453 patients with CRS who underwent ESS in the Tokyo area of Japan. The study evaluated various factors in patients with CRS including sex and age, the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) score, the recurrence rate of CRS, comorbidities of asthma and/or allergic diseases, and IgE sensitization to 12 inhaled allergens.

RESULTS

Age-related declines in the sensitization rate to inhaled allergens were observed, and the most notable age-related decrease in specific IgE antibodies was observed for house dust mites (HDM) (p = 8.3 × 10-7). Sensitization to HDM, cat dander, and various types of fungi, including Aspergillus, was frequently observed in the CRS with asthma group, with rates of 54%, 17%, and 17%, respectively. We found that 23% of the patients had recurrence. In the recurrence group, the positive rates of specific IgE antibodies for birch and cat dander were significantly higher than in the no recurrence group. Bronchial asthma was identified as an important factor for recurrence. Among male patients, the recurrence group was younger than the no-recurrence group (p = 0.0032). Severe eosinophilic CRS (ECRS) showed early recurrence after surgery, with over the half of the patients experiencing at least one recurrence within 2 years post-surgery. Among patients with ECRS, the recurrence rate for females was 1.92 times higher than for males.

CONCLUSION

Our study revealed the influences of age and sex on various clinical phenotypes of CRS patients undergoing ESS. There was a high sensitization rate to cat dander in both the recurrence and asthma groups. Further research on diverse disease etiologies is necessary to improve therapeutic strategies for patients with CRS.

COBRAPed cohort: Do sensitization patterns differentiate children with severe asthma from those with a milder disease?

BACKGROUND

It is unclear whether sensitization patterns differentiate children with severe recurrent wheeze (SRW)/severe asthma (SA) from those with non-severe recurrent wheeze (NSRW)/non-severe asthma (NSA). Our objective was to determine whether sensitization patterns can discriminate between children from the French COBRAPed cohort with NSRW/NSA and those with SRW/SA.

METHODS

IgE to 112 components (c-sIgE) (ImmunoCAP® ISAC) were analyzed in 125 preschools (3-6 years) and 170 school-age children (7-12 years). Supervised analyses and clustering methods were applied to identify patterns of sensitization among children with positive c-sIgE.

RESULTS

We observed c-sIgE sensitization in 51% of preschool and 75% of school-age children. Sensitization to house dust mite (HDM) components was more frequent among NSRW than SRW (53% vs. 24%, p < .01). Sensitization to non-specific lipid transfer protein (nsLTP) components was more frequent among SA than NSA (16% vs. 4%, p < .01) and associated with an FEV1/FVC < -1.64 z-score. Among sensitized children, seven clusters with varying patterns were identified. The two broader clusters identified in each age group were characterized by "few sensitizations, mainly to HDM." One cluster (n = 4) with "multiple sensitizations, mainly to grass pollen, HDM, PR-10, and nsLTP" was associated with SA in school-age children.

CONCLUSIONS

Although children with wheeze/asthma display frequent occurrences and high levels of sensitization, sensitization patterns did not provide strong signals to discriminate children with severe disease from those with milder disease. These results suggest that the severity of wheeze/asthma may depend on both IgE- and non-IgE-mediated 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.

From preschool wheezing to asthma: Immunological determinants

Asthma represents a chronic respiratory disease affecting millions of children worldwide. The transition from preschool wheezing to school-age asthma involves a multifaceted interplay of various factors, including immunological aspects in early childhood. These factors include complex cellular interactions among different immune cell subsets, induction of pro-inflammatory mediators and the molecular impact of environmental factors like allergens or viral infections on the developing immune system. Furthermore, the activation of specific genes and signalling pathways during this early phase plays a pivotal role in the manifestation of symptoms and subsequent development of asthma. Early identification of the propensity or risk for asthma development, for example by allergen sensitisation and viral infections during this critical period, is crucial for understanding the transition from wheeze to asthma. Favourable immune regulation during a critical 'window of opportunity' in early childhood can induce persistent changes in immune cell behaviour. In this context, trained immunity, including memory function of innate immune cells, has significant implications for understanding immune responses, potentially shaping long-term immunological outcomes based on early-life environmental exposures. Exploration of these underlying immune mechanisms that drive disease progression will provide valuable insights to understand childhood asthma development. This will be instrumental to develop preventive strategies at different stages of disease development for (i) inhibiting progression from wheeze to asthma or (ii) reducing disease severity and (iii) uncovering novel therapeutic strategies and contributing to more tailored and effective treatments for childhood asthma. In the long term, this shall empower healthcare professionals to develop evidence-based interventions that reduce the burden of asthma for children, families and society overall.

Novel, computational IgE-clustering in a population-based cross-sectional study: Mapping the allergy burden

BACKGROUND

Even though the prevalence of allergies is increasing, population-based data are still scarce. As a read-out for chronic inflammatory information, new methods are needed to integrate individual biological measurements and lifestyle parameters to mitigate the consequences and costs of allergic burden for society.

METHODS

More than 480.000 data points were collected from 1462 Luxembourg adults during the representative, cross-sectional European Health Examination Survey, spanning health and lifestyle reports. Deep IgE-profiles based on unsupervised clustering were correlated with data of the health survey.

FINDINGS

42.6% of the participants reported a physician-diagnosed allergy and 44% were found to be IgE-positive to at least one allergen or extract. The main sensitization sources were tree pollens followed by grass pollens and mites (52.4%, 51.8% and 40.3% of sensitized participants respectively), suggesting seasonal as well as perennial burden. The youngest group of participants (25-34 years old) showed the highest burden of sensitization, with 18.2% of them having IgE to 10 or more allergen groups. Unsupervised clustering revealed that the biggest cluster of 24.4% of participants was also the one with the highest medical need, marked by their multi-sensitization to respiratory sources.

INTERPRETATION

Our novel approach to analyzing large biosample datasets together with health information allows the measurement of the chronic inflammatory disease burden in the general population and led to the identification of the most vulnerable groups in need of better medical care.

Phenotype and endotype based treatment of preschool wheeze

INTRODUCTION

Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease.

AREAS COVERED

We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored.

EXPERT OPINION

Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.

Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces B and T cell responses, contributing to virus neutralization. In a cohort of 2,911 young adults, we identified 65 individuals who had an asymptomatic or mildly symptomatic SARS-CoV-2 infection and characterized their humoral and T cell responses to the Spike (S), Nucleocapsid (N) and Membrane (M) proteins. We found that previous infection induced CD4 T cells that vigorously responded to pools of peptides derived from the S and N proteins. By using statistical and machine learning models, we observed that the T cell response highly correlated with a compound titer of antibodies against the Receptor Binding Domain (RBD), S and N. However, while serum antibodies decayed over time, the cellular phenotype of these individuals remained stable over four months. Our computational analysis demonstrates that in young adults, asymptomatic and paucisymptomatic SARS-CoV-2 infections can induce robust and long-lasting CD4 T cell responses that exhibit slower decays than antibody titers. These observations imply that next-generation COVID-19 vaccines should be designed to induce stronger cellular responses to sustain the generation of potent neutralizing antibodies.

New insights into the pathophysiology and therapeutic targets of asthma and comorbid chronic rhinosinusitis with or without nasal polyposis

Asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) or without (CRSsNP) are chronic respiratory diseases. These two disorders often co-exist based on common anatomical, immunological, histopathological, and pathophysiological basis. Usually, asthma with comorbid CRSwNP is driven by type 2 (T2) inflammation which predisposes to more severe, often intractable, disease. In the past two decades, innovative technologies and detection techniques in combination with newly introduced targeted therapies helped shape our understanding of the immunological pathways underlying inflammatory airway diseases and to further identify several distinct clinical and inflammatory subsets to enhance the development of more effective personalized treatments. Presently, a number of targeted biologics has shown clinical efficacy in patients with refractory T2 airway inflammation, including anti-IgE (omalizumab), anti-IL-5 (mepolizumab, reslizumab)/anti-IL5R (benralizumab), anti-IL-4R-α (anti-IL-4/IL-13, dupilumab), and anti-TSLP (tezepelumab). In non-type-2 endotypes, no targeted biologics have consistently shown clinical efficacy so far. Presently, multiple therapeutical targets are being explored including cytokines, membrane molecules and intracellular signalling pathways to further expand current treatment options for severe asthma with and without comorbid CRSwNP. In this review, we discuss existing biologics, those under development and share some views on new horizons.

International consensus statement on allergy and rhinology: Allergic rhinitis - 2023

BACKGROUND

In the 5 years that have passed since the publication of the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis (ICAR-Allergic Rhinitis 2018), the literature has expanded substantially. The ICAR-Allergic Rhinitis 2023 update presents 144 individual topics on allergic rhinitis (AR), expanded by over 40 topics from the 2018 document. Originally presented topics from 2018 have also been reviewed and updated. The executive summary highlights key evidence-based findings and recommendation from the full document.

METHODS

ICAR-Allergic Rhinitis 2023 employed established evidence-based review with recommendation (EBRR) methodology to individually evaluate each topic. Stepwise iterative peer review and consensus was performed for each topic. The final document was then collated and includes the results of this work.

RESULTS

ICAR-Allergic Rhinitis 2023 includes 10 major content areas and 144 individual topics related to AR. For a substantial proportion of topics included, an aggregate grade of evidence is presented, which is determined by collating the levels of evidence for each available study identified in the literature. For topics in which a diagnostic or therapeutic intervention is considered, a recommendation summary is presented, which considers the aggregate grade of evidence, benefit, harm, and cost.

CONCLUSION

The ICAR-Allergic Rhinitis 2023 update provides a comprehensive evaluation of AR and the currently available evidence. It is this evidence that contributes to our current knowledge base and recommendations for patient evaluation and treatment.

Environmental influences on childhood asthma: Allergens

Allergen exposure is associated with the development of allergen-specific sensitization, but their relationship is influenced by other contemporaneous exposures (such as microbial exposure) and the genetic predisposition of the host. Clinical outcomes of the primary prevention studies that tested the effectiveness of allergen avoidance in pregnancy and early life on the subsequent development of sensitization and asthma published to date are inconsistent. Therefore, we cannot provide any evidence-based advice on the use of allergen avoidance for the primary prevention of these conditions. The evidence about the impact of allergen exposure among and among sensitized children with asthma is more consistent, and the combination of sensitization and high exposure to sensitizing allergen increases airway inflammation, triggers symptoms, adversely impacts upon disease control, and is associated with poorer lung function in preschool age. However, there are differing opinions about the role of inhalant allergen avoidance in asthma management, and recommendations differ in different guidelines. Evidence from more recent high-quality trials suggests that mite allergen-impermeable bed encasings reduce hospital attendance with asthma attacks and that multifaceted targeted environmental control improves asthma control in children. We therefore suggest a pragmatic approach to allergen avoidance in the management of childhood asthma for clinical practice, including the recommendations to: (1) tailor the intervention to the patient's sensitization and exposure status by using titer of allergen-specific IgE antibodies and/or the size of the skin test as indicators of potential response; (2) use a multifaceted allergen control regime to reduce exposure as much as possible; and (3) start intervention as early as possible upon diagnosis.

Utility of biomarkers in the diagnosis and monitoring of asthmatic children

Asthma imposes a heavy morbidity burden during childhood; it affects over 10% of children in Europe and North America and it is estimated to exceed 400 million people worldwide by the year 2025. In clinical practice, diagnosis of asthma in children is mostly based on clinical criteria; nevertheless, assessment of both physiological and pathological processes through biomarkers, support asthma diagnosis, aid monitoring, and further lead to better treatment outcomes and reduced morbidity. Recently, identification and validation of biomarkers in pediatric asthma has emerged as a top priority across leading experts, researchers, and clinicians. Moreover, the implementation of non-invasive biomarkers for the assessment and monitoring of paediatric patients with asthma, has been prioritized; however, only a proportion of them are currently included in the clinical practise. Although, the use of non-invasive biomarkers is highly supported in recent asthma guidelines for documenting diagnosis and supporting monitoring of asthmatic patients, data on the Pediatric population are limited. In the present report, the Pediatric Asthma Committee of the World Allergy Organization (WAO), aims to summarize and discuss available data for the implementation of non-invasive biomarkers in the diagnosis and monitoring in children with asthma. Information on the most studied biomarkers, including spirometry, oscillometry, markers of allergic sensitization, fractional exhaled nitric oxide, and the most recent exhaled breath markers and "omic" approaches, will be reviewed. Practical limitations and considerations based on both experts' opinion and critical review of the literature, on the utility of all "well-known" and newly introduced non-invasive biomarkers will be presented. A critical commentary on biomarkers' use in diagnosing and monitoring asthma during the COVID-19 pandemic, cost and availability of biomarkers in different settings and in developing countries, the differences on the biomarkers use between Primary Practitioners, Pediatricians, and Specialists and their role on the longitudinal aspect of asthma is provided.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Allergic Asthma in the Era of Personalized Medicine

Allergic asthma is the most common asthma phenotype and is characterized by IgE sensitization to airborne allergens and subsequent typical asthmatic symptoms after exposure. A form of type 2 (T2) airway inflammation underlies allergic asthma. It usually arises in childhood and is accompanied by multimorbidity presenting with the occurrence of other atopic diseases, such as atopic dermatitis and allergic rhinitis. Diagnosis of the allergic endotype is based on in vivo (skin prick tests) and/or in vitro (allergen-specific IgE levels, component-resolved diagnosis (CRD)) documentation of allergic sensitization. Biomarkers identifying patients with allergic asthma include total immunoglobulin E (IgE) levels, fractional exhaled nitric oxide (FeNO) and serum eosinophil counts. The treatment of allergic asthma is a complex procedure and requires a patient-tailored approach. Besides environmental control involving allergen avoidance measurements and cornerstone pharmacological interventions based on inhaled drugs, allergen-specific immunotherapy (AIT) and biologics are now at the forefront when it comes to personalized management of asthma. The current review aims to shed light on the distinct phenotype of allergic asthma, ranging over its current definition, clinical characteristics, pathophysiology and biomarkers, as well as its treatment options in the era of precision medicine.

Early priming of asthma and respiratory allergies: Future aspects of prevention: A statement by the European Forum for Education and Research in Allergy and Airway Disease (EUFOREA) and the EAACI-Clemens von Pirquet Foundation

In order to summarize recent research on the prevention of allergies-particularly asthma-and stimulate new activities for future initiatives, a virtual workshop sponsored by the EAACI Clemens von Pirquet foundation and EUFOREA was held in October 2021. The determinants of the "allergic march" as well as the key messages from intervention studies were reviewed by an international faculty of experts. Several unmet needs were identified, and a number of priorities for future studies were proposed.

Associations of Four sensitization patterns revealed by Latent Class Analysis with Clinical symptoms: A multi-center study of China

BACKGROUND

Because of the significant regional differences in the distribution of allergens, the relationship between anaphylaxis and allergic sensitization is complex in China. Using this large-scale epidemiologic survey, we explore the potential patterns of sensitization to common allergens in mainland China and investigate their relationship with various clinical symptoms.

METHOD

The participants were recruited from 13 medical centers in mainland China from October 2019 to June 2021. Skin prick test (SPT) results that cover 18 common allergens were utilized to diagnose atopic sensitization. The demographic characteristics and clinical information were collected through questionnaires during routine medical follow-up. Latent class analysis (LCA) was conducted to determine the optimal sensitization patterns. The logistic regression was used to assess the associations of different sensitization patterns with allergy symptoms.

FINDINGS

A total of 1089 patients who had a positive SPT to at least one of 18 allergens were included for formal analysis. An optimal LCA model with 4 classes was obtained in this study, and the corresponding labels were as follows: Class1, house dust mite sensitization; Class2, low pollen sensitization; Class3, middle pollen sensitization; Class4, high pollen sensitization. The prevalence of different classes varied widely in geographical distribution, which was characterized by Class1 being very common in south and east as well as Class2 in north and west of China. Compared with patients in Class1, those in middle and high pollen sensitization clusters had the higher odds ratios (ORs) of allergic rhinitis and allergic conjunctivitis when controlling for other confounders. However, there was no significant difference between low pollen sensitization and house dust mite sensitization groups in the risks for various clinical performances except dermatitis. Additionally, the adjusted ORs (95% confidence interval) of allergic conjunctivitis and dermatitis for participants in pollen sensitization clusters (Class2, 3 and 4) were 1.56 (1.18, 2.06) and 1.43 (1.09, 1.88) respectively compared with those in Class1.

INTERPRETATION

In this study, we identified four sensitization clusters with specific risks of various clinical symptoms using common allergens by adopting LCA. Our findings may contribute to improved diagnosis and potential immunotherapy approaches to allergy in mainland China.

FUNDING

This study was supported by the National Natural Science Foundation of China (81802076 and 81871736), the Guangzhou Science and Technology Foundation (202102010327), the Foundation of SKLRD (MS-2019-06 and Z-2022-09), and the Foundation of GYYY (ZH201904) and ZNSA-2020012.

Trajectories of IgE sensitization to allergen molecules from childhood to adulthood and respiratory health in the EGEA cohort

BACKGROUND

Longitudinal studies assessing the association of profiles of allergen-specific IgE (sIgE) sensitization to a large range of allergen molecules and respiratory health are rare. We aimed to assess trajectories of molecular sIgE sensitization profiles from childhood to adulthood and their associations with respiratory health.

METHODS

IgE reactivity to microarrayed allergen molecules were measured in childhood (EGEA1) and 12 years later in adult life (EGEA2) among 291 EGEA participants (152 with asthma). At each time point, sIgE sensitization profiles were identified by latent class analysis (LCA) by considering IgE-reactivity to the 38 most prevalent respiratory allergens. The LCA-defined profiles were then studied in association with respiratory health.

RESULTS

At baseline, the mean (min-max) age of the population was 11 (4.5-16) years. The LCA identified four sIgE sensitization profiles which were very similar at both time points (% at EGEA1 and EGEA2); A: "no/few allergen(s)" (48%, 39%), B: "pollen/animal allergens" (18%, 21%), C: "most prevalent house dust mite allergens" (22%, 27%) and D: "many allergens" (12%, 13%). Overall, 73% of the participants remained in the same profile from childhood to adulthood. The profiles were associated with asthma and rhinitis phenotypes. Participants of profiles C and D had lower FEV₁ % and FEF_25-75 % as compared to profile A. Similar patterns of associations were observed for participants with asthma. There was no association with change in lung function.

CONCLUSION

Using high-resolution sIgE longitudinal data, the LCA identified four molecular sensitization profiles, mainly stable from childhood to adulthood, that were associated with respiratory health.

Precision medicine in the allergy clinic: the application of component resolved diagnosis

INTRODUCTION

A precise diagnosis is key for the optimal management of allergic diseases and asthma. In vivo or in vitro diagnostic methods that use allergen extracts often fail to identify the molecules eliciting the allergic reactions.

AREAS COVERED

Component-resolved diagnosis (CRD) has solved most of the limitations of extract-based diagnostic procedures and is currently valuable tool for the precision diagnosis in the allergy clinic, for venom and food allergy, asthma, allergic rhinitis, and atopic dermatitis. Its implementation in daily practice facilitates: a) the distinction between genuine multiple sensitizations and cross-reactive sensitization in polysensitized patients; b) the prediction of a severe, systemic reaction in food or insect venom allergy; c) the optimal selection of allergen immunotherapy based on the patient sensitization profile. This paper describes its main advantages and disadvantages, cost-effectiveness and future perspectives.

EXPERT OPINION

The diagnostic strategy based on CRD is part of the new concept of precision immunology, which aims to improve the management of allergic diseases.

Considering biomarkers in asthma disease severity

Amongst patients with asthma, reliance on the type/dose of prescribed medication and symptom control does not adequately capture those at risk of adverse outcomes, and we need biomarkers for risk and treatment stratification which are consistently accurate, readily quantifiable and reproducible. The majority of patients with severe asthma, regardless of age, have predominant type-2 (T2) inflammation mediated disease, making airway/blood eosinophils, FeNO, periostin and/or allergic sensitization potentially important biomarkers for severe disease. In both adult and pediatric asthma, there is scope to improve prediction of severe attacks by using a composite T2 biomarkers of blood eosinophils and FeNO. Technological advances in component-resolved diagnostics (CRD) microarray technologies coupled with the development of interpretation software offer a possibility to use CRD as biomarkers of asthma severity amongst sensitized asthmatics. Genetic predisposition and polygenic risk scores of relevant traits (e.g., lung function, host immune responses, biomarkers of exposure from the indoor and outdoor environment, infection and microbial dysbiosis) may also contribute to prediction algorithms. We challenge the idea that asthma can be accurately defined in an individual patient by a discrete and static "endotype" (e.g., T2-high asthma). As we traverse the new era of molecular endotyping in asthma, we need to understand how relevant mechanisms impact patient outcomes, and in parallel develop new tools and approaches to stratify therapies and define individual patient trajectories.

Childhood CCL18, CXCL10 and CXCL11 levels differentially relate to and predict allergy development

BACKGROUND

Chemokines are important mediators in immune cell recruitment, contributing to allergy development. However, extensive studies of chemokines in the circulation in relation to the presence and development of allergic diseases remain scarce. Our aim was to investigate associations of circulating allergy-related chemokines with the development of asthma and sensitization cross-sectionally and longitudinally in a population-based cohort.

METHODS

The chemokines CCL17, CCL22, CXCL10, CXCL11 and CCL18 were measured in plasma samples from children in the Manchester Asthma and Allergy Study. Samples were available from cord blood at birth (n = 376), age 1 (n = 195) and age 8 (n = 334). Cross-sectional and longitudinal association analyses were performed in relation to asthma and allergic sensitization, as well as allergic phenotype clusters previously derived using machine learning in the same study population.

RESULTS

In children with asthma and/or allergic sensitization, CCL18 levels were consistently elevated at 1 and/or 8 years of ages. In a longitudinal model including information on asthma from 4 time points (5, 8, 11 and 16 years of ages), we observed a significant association between increasing CCL18 levels at age 1 and a higher risk of asthma from early school age to adolescence (OR = 2.9, 95% CI 1.1-7.6, p = .028). We observed similar associations in longitudinal models for allergic sensitization. Asthma later in life was preceded by increased CXCL10 levels after birth and decreased CXCL11 levels at birth.

CONCLUSION

Elevated CCL18 levels throughout childhood precede the development of asthma and allergic sensitization. The Th1-associated chemokines CXCL10 and CXCL11 also associated with the development of both outcomes, with differential temporal effects.

The Molecular Allergen Recognition Profile in China as Basis for Allergen-Specific Immunotherapy

Approximately 30% of the world population suffers from immunoglobulin-E (IgE)-mediated allergy. IgE-mediated allergy affects the respiratory tract, the skin and the gastrointestinal tract and may lead to life-threatening acute systemic manifestations such as anaphylactic shock. The symptoms of allergy are mediated by IgE-recognition of causative allergen molecules from different allergen sources. Today, molecular allergy diagnosis allows determining the disease-causing allergens to develop allergen-specific concepts for prevention and treatment of allergy. Allergen-specific preventive and therapeutic strategies include allergen avoidance, vaccination, and tolerance induction. The implementation of these preventive and therapeutic strategies requires a detailed knowledge of the relevant allergen molecules affecting a given population. China is the world´s most populous country with around 1.4 billion inhabitants and an estimated number of more than 400 million allergic patients. Research in allergy in China has dramatically increased in the last decade. We summarize in this review article what is known about the dominating allergen sources and allergen molecules in China and what further investigations could be performed to draw a molecular map of IgE sensitization for China as a basis for the implementation of systematic and rational allergen-specific preventive and therapeutic strategies to combat allergic diseases in this country.

Combining Allergen Components Improves the Accuracy of Peanut Allergy Diagnosis

BACKGROUND

IgE to peanut often occurs in the absence of peanut allergy. Detection of allergen component specific IgE (sIgE) has improved diagnosis and birthed molecular allergen component arrays, in which sensitization to multiple allergen components can be measured simultaneously.

OBJECTIVE

To improve the diagnostic utility of serology for peanut allergy, by mapping interactions of sIgE to multiple components and IgE functional characteristics.

METHODS

A cohort of 100 children was studied, with a 60-children cohort employed for external validation. Levels of total IgE, sIgE to peanut, and peanut components were measured using singleplex ImmunoCAP and multiplex immuno solid-phase allergen chip (ISAC). Peanut IgE specific activity, avidity, and diversity were determined. Diagnostic modeling was performed using a Bayesian hierarchical model.

RESULTS

Sensitization to the 112 allergens on ISAC (model 1) demonstrated the highest accuracy to diagnose peanut allergy (area under the curve [AUC] = 0.92). Sensitization to peanut components on ISAC (model 2) reported an AUC of 0.86 and on singleplex (model 3) an AUC of 0.92, which was greater than that of Ara h 2 sIgE alone (AUC = 0.90). Functional characteristics of peanut sIgE (model 4) reported an AUC of 0.89, which was greater than that of peanut sIgE (AUC = 0.75). Model 3 offered the highest predictive value and the second highest overall diagnostic accuracy.

CONCLUSIONS

sIgE to a combination of allergen components (Ara h 1, 2, 3, and 6) is highly predictive of peanut allergy and superior to individual markers. Combining the functional characteristics of IgE was superior to peanut sIgE levels alone. These models can be applied in real time during clinical consultations using online calculators.

Allergen extract- and component-based diagnostics in children of the ALLIANCE asthma cohort

BACKGROUND

Current in vitro allergen-specific IgE (sIgE) detection assays measure IgE against allergen extracts or molecules in a single- or multiplex approach. Direct comparisons of the performance of such assays among young children with common presentations of allergic diseases regardless of sensitization status are largely missing.

OBJECTIVES

The aim of this study was a comparison of the analytical and diagnostic performance for common clinical questions of three commonly used technologies which rely upon different laboratory methodologies among children of the All Age Asthma (ALLIANCE) cohort (clinicaltrials.gov: NCT02496468).

METHODS

Sera from 106 paediatric study participants (mean age 4 years) were assessed for the presence of sIgE by means of the ImmunoCAP™ sx1 and fx5 mixes, the ImmunoCAP ISAC™ 112 microarray and a Euroline™ panel.

RESULTS

Total and negative concordance was high (>82%->89%), while positive concordance varied considerably (0%-100%) but was also >50% for the most common sensitizations analysed (house dust mite and birch). All three test systems showed good sensitivity and specificity (AUC consistently > 0.7). However, no significant differences with regard to identifying sIgE sensitizations associated with symptoms in children with suspected pollen- or dust-triggered wheeze or presenting with symptoms of allergic rhinoconjunctivitis or food allergy were detected. Extending the number of allergens did not change the similar performance of the three assay systems.

CONCLUSION AND CLINICAL RELEVANCE

Among young children, the three sIgE assays showed good analytical and diagnostic concordance. Our results caution that the identification of larger numbers of sensitizations by more comprehensive multiplex approaches may not improve the clinical utility of sIgE testing in this age group.

Biomarkers in Different Asthma Phenotypes

Asthma is the most common respiratory disease. It has multiple phenotypes thatcan be partially differentiated by measuring the disease’s specific characteristics—biomarkers. The pathogenetic mechanisms are complex, and it is still a challenge to choose suitable biomarkers to adequately stratify patients, which became especially important with the introduction of biologicals in asthma treatment. Usage of biomarkers and an understanding of the underlying pathobiological mechanisms lead to the definition of endotypes. Asthma can be broadly divided into two endotypes, T2-high and T2-low. The right combination of various biomarkers in different phenotypes is under investigation, hoping to help researchers and clinicians in better disease evaluation since theindividual approach and personalized medicine are imperative. Multiple biomarkers are superior to a single biomarker.

Noninvasive and minimally invasive techniques for the diagnosis and management of allergic diseases

Allergic diseases of the (upper and lower) airways, the skin and the gastrointestinal tract, are on the rise, resulting in impaired quality of life, decreased productivity, and increased healthcare costs. As allergic diseases are mostly tissue-specific, local sampling methods for respective biomarkers offer the potential for increased sensitivity and specificity. Additionally, local sampling using noninvasive or minimally invasive methods can be cost-effective and well tolerated, which may even be suitable for primary or home care sampling. Non- or minimally invasive local sampling and diagnostics may enable a more thorough endotyping, may help to avoid under- or overdiagnosis, and may provide the possibility to approach precision prevention, due to early diagnosis of these local diseases even before they get systemically manifested and detectable. At the same time, dried blood samples may help to facilitate minimal-invasive primary or home care sampling for classical systemic diagnostic approaches. This EAACI position paper contains a thorough review of the various technologies in allergy diagnosis available on the market, which analytes or biomarkers are employed, and which samples or matrices can be used. Based on this assessment, EAACI position is to drive these developments to efficiently identify allergy and possibly later also viral epidemics and take advantage of comprehensive knowledge to initiate preventions and treatments.

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of 'big data', we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.

Microarray-Based Allergy Diagnosis: Quo Vadis?

More than 30% of the world population suffers from allergy. Allergic individuals are characterized by the production of immunoglobulin E (IgE) antibodies against innocuous environmental allergens. Upon allergen recognition IgE mediates allergen-specific immediate and late-phase allergic inflammation in different organs. The identification of the disease-causing allergens by demonstrating the presence of allergen-specific IgE is the key to precision medicine in allergy because it allows tailoring different forms of prevention and treatment according to the sensitization profiles of individual allergic patients. More than 30 years ago molecular cloning started to accelerate the identification of the disease-causing allergen molecules and enabled their production as recombinant molecules. Based on recombinant allergen molecules, molecular allergy diagnosis was introduced into clinical practice and allowed dissecting the molecular sensitization profiles of allergic patients. In 2002 it was demonstrated that microarray technology allows assembling large numbers of allergen molecules on chips for the rapid serological testing of IgE sensitizations with small volumes of serum. Since then microarrayed allergens have revolutionized research and diagnosis in allergy, but several unmet needs remain. Here we show that detection of IgE- and IgG-reactivity to a panel of respiratory allergens microarrayed onto silicon elements is more sensitive than glass-based chips. We discuss the advantages of silicon-based allergen microarrays and how this technology will allow addressing hitherto unmet needs in microarray-based allergy diagnosis. Importantly, it described how the assembly of silicon microarray elements may create different microarray formats for suiting different diagnostic applications such as quick testing of single patients, medium scale testing and fully automated large scale testing.

Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

Atopic phenotypes and their implication in the atopic march

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.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.

Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort

Background

To date, there has been no reliable in vitro test to either diagnose or differentiate nonsteroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (N‐ERD). The aim of the present study was to develop and validate an artificial neural network (ANN) for the prediction of N‐ERD in patients with asthma.

Methods

This study used a prospective database of patients with N‐ERD (n = 121) and aspirin‐tolerant (n = 82) who underwent aspirin challenge from May 2014 to May 2018. Eighteen parameters, including clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant (ISS) and urine were extracted for the ANN.

Results

The validation sensitivity of ANN was 94.12% (80.32%‐99.28%), specificity was 73.08% (52.21%‐88.43%), and accuracy was 85.00% (77.43%‐92.90%) for the prediction of N‐ERD. The area under the receiver operating curve was 0.83 (0.71‐0.90).

Conclusions

The designed ANN model seems to have powerful prediction capabilities to provide diagnosis of N‐ERD. Although it cannot replace the gold‐standard aspirin challenge test, the implementation of the ANN might provide an added value for identification of patients with N‐ERD. External validation in a large cohort is needed to confirm our results.

TNF-α (rs1800629) polymorphism modifies the effect of sensitization to house dust mite on asthma and bronchial hyperresponsiveness in children

Asthma is a complex disease, with various genetic and environmental factors implicated in its development. Sensitization to the house dust mite (HDM) is closely linked with the development of respiratory allergies, including asthma. However, some children sensitized to HDM do not complain of any symptoms of respiratory allergies, even though HDM is correlated with an increased risk for developing asthma, suggesting the involvement of other factors. Tumor necrosis factor (TNF)-α is associated with the pathophysiologies of asthma in combination with its genetic polymorphism. The aim of the present study was to elucidate the associations between sensitization to HDM, polymorphism of TNF-α rs1800629, and asthma/bronchial hyperresponsiveness (BHR). Our results revealed that sensitization to HDM is associated with asthma diagnosis in lifetime, current asthma, and BHR in Korean children. Furthermore, the genetic polymorphism of TNF-a rs1800629 was found to modify and interact with these associations. This study suggests that prevention strategies for childhood asthma need to be targeted according to genetic susceptibility.

Artificial intelligence in sleep medicine: background and implications for clinicians

None

Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma

BACKGROUND

Allergic sensitization is associated with severe asthma, but assessment of sensitization is not recommended by most guidelines.

OBJECTIVE

We hypothesized that patterns of IgE responses to multiple allergenic proteins differ between sensitized participants with mild/moderate and severe asthma.

METHODS

IgE to 112 allergenic molecules (components, c-sIgE) was measured using multiplex array among 509 adults and 140 school-age and 131 preschool children with asthma/wheeze from the Unbiased BIOmarkers for the PREDiction of respiratory diseases outcomes cohort, of whom 595 had severe disease. We applied clustering methods to identify co-occurrence patterns of components (component clusters) and patterns of sensitization among participants (sensitization clusters). Network analysis techniques explored the connectivity structure of c-sIgE, and differential network analysis looked for differences in c-sIgE interactions between severe and mild/moderate asthma.

RESULTS

Four sensitization clusters were identified, but with no difference between disease severity groups. Similarly, component clusters were not associated with asthma severity. None of the c-sIgE were identified as associates of severe asthma. The key difference between school children and adults with mild/moderate compared with those with severe asthma was in the network of connections between c-sIgE. Participants with severe asthma had higher connectivity among components, but these connections were weaker. The mild/moderate network had fewer connections, but the connections were stronger. Connectivity between components with no structural homology tended to co-occur among participants with severe asthma. Results were independent from the different sample sizes of mild/moderate and severe groups.

CONCLUSIONS

The patterns of interactions between IgE to multiple allergenic proteins are predictors of asthma severity among school children and adults with allergic asthma.

Allergic Endotypes and Phenotypes of Asthma

Allergic asthma is defined as asthma associated with sensitization to aeroallergens, which leads to asthma symptoms and airway inflammation. Allergic asthma is the most common asthma phenotype. The onset of allergic asthma is most often in childhood and is usually accompanied by other comorbidities including atopic dermatitis and allergic rhinitis. It is often persistent although there is a wide variation in disease severity. It is a TH2-driven process. Biomarkers have been identified to distinguish patients with allergic asthma, particularly serum IgE levels, tests to indicate sensitization to aeroallergens such as specific IgE or skin prick test positivity, blood and sputum eosinophil levels, fraction of exhaled nitric oxide, and periostin. Treatments for allergic asthma include environmental control measures, allergen immunotherapy, and glucocorticoids. Biologics, targeting the TH2 pathway, have been shown to be effective in the treatment of allergic asthma.

Childhood Asthma: Advances Using Machine Learning and Mechanistic Studies

A paradigm shift brought by the recognition that childhood asthma is an aggregated diagnosis that comprises several different endotypes underpinned by different pathophysiology, coupled with advances in understanding potentially important causal mechanisms, offers a real opportunity for a step change to reduce the burden of the disease on individual children, families, and society. Data-driven methodologies facilitate the discovery of "hidden" structures within "big healthcare data" to help generate new hypotheses. These findings can be translated into clinical practice by linking discovered "phenotypes" to specific mechanisms and clinical presentations. Epidemiological studies have provided important clues about mechanistic avenues that should be pursued to identify interventions to prevent the development or alter the natural history of asthma-related diseases. Findings from cohort studies followed by mechanistic studies in humans and in neonatal mouse models provided evidence that environments such as traditional farming may offer protection by modulating innate immune responses and that impaired innate immunity may increase susceptibility. The key question of which component of these exposures can be translated into interventions requires confirmation. Increasing mechanistic evidence is demonstrating that shaping the microbiome in early life may modulate immune function to confer protection. Iterative dialogue and continuous interaction between experts with different but complementary skill sets, including data scientists who generate information about the hidden structures within "big data" assets, and medical professionals, epidemiologists, basic scientists, and geneticists who provide critical clinical and mechanistic insights about the mechanisms underpinning the architecture of the heterogeneity, are keys to delivering mechanism-based stratified treatments and prevention.

Environment and Host-Genetic Determinants in Early Development of Allergic Asthma: Contribution of Fungi

Asthma is a chronic debilitating airway disease affecting millions of people worldwide. Although largely thought to be a disease of the first world, it is now clear that it is on the rise in many middle- and lower-income countries. The disease is complex, and its etiology is poorly understood, which explains failure of most treatment strategies. We know that in children, asthma is closely linked to poor lung function in the first 3-years of life, when the lung is still undergoing post-natal alveolarization phase. Epidemiological studies also suggest that environmental factors around that age do play a critical part in the establishment of early wheezing which persists until adulthood. Some of the factors that contribute to early development of asthma in children in Western world are clear, however, in low- to middle-income countries this is likely to differ significantly. The contribution of fungal species in the development of allergic diseases is known in adults and in experimental models. However, it is unclear whether early exposure during perinatal or post-natal lung development influences a protective or promotes allergic asthma. Host immune cells and responses will play a crucial part in early development of allergic asthma. How immune cells and their receptors may recognize fungi and promote allergic asthma or protect by tolerance among other immune mechanisms is not fully understood in this early lung development stage. The aim of this review is to discuss what fungal species are present during early exposure as well as their contribution to the development of allergic responses. We also discuss how the host has evolved to promote tolerance to limit hyper-responsiveness to innocuous fungi, and how host evasion by fungi during early development consequentially results in allergic diseases.

A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction. Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction. Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression. Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

Dust-mite inducing asthma: what advice can be given to patients?

Introduction: Amongst allergic asthmatics, high allergen exposure increases asthma severity. However, there is no consensus on the role of mite allergen avoidance in the management of asthma, and various guidelines differ in their recommendations. Areas covered: Several systematic reviews/meta-analyses on mite avoidance in the management of asthma have been published, and their findings have been used for a call to provide a recommendation in British guidelines that dust-mite control measures should not be recommended. However, there are several problems with such analysis (such as combining studies in adults and children), and we question whether these are appropriate tools to evaluate available evidence about mite allergen avoidance, and whether it is correct to rely disproportionately on the results of meta-analyses/systematic reviews to inform clinical practice in this area. Recent evidence in children suggests that mite-impermeable bed encasings reduce emergency hospital attendance with severe asthma exacerbations. Expert opinion: The practical questions include how to achieve a sufficient real-life reduction allergen exposure, and how to identify patients who will benefit from effective intervention. The intervention should start early in the natural history of asthma, and consideration for choosing patients should include using the titre of allergen-specific IgE antibodies or the size of skin test wheal as an indicator.

Blood Biomarkers of Sensitization and Asthma

Biomarkers are essential to determine different phenotypes of childhood asthma, and for the prediction of response to treatments. In young preschool children with asthma, aeroallergen sensitization, and blood eosinophil count of 300/μL or greater may identify those who can benefit from the daily use of inhaled corticosteroids (ICS). We propose that every preschool child who is considered for ICS treatment should have these two features measured as a minimum before a decision is made on the commencement of long-term preventive treatment. In practice, IgE-mediated sensitization should be considered as a quantifiable variable, i.e., we should use the titer of sIgE antibodies or the size of skin prick test response. A number of other blood biomarkers may prove useful (e.g., allergen-specific IgG/IgE antibody ratios amongst sensitized individuals, component-resolved diagnostics which measures sIgE response to a large number of allergenic molecules, assessment of immune responses to viruses, level of serum CC16, etc.), but it remains unclear whether these can be translated into clinically useful tests. Going forward, a more integrated approach which takes into account multiple domains of asthma, from the pattern of symptoms and blood biomarkers to genetic risk and lung function measures, is needed if we are to move toward a stratified approach to asthma management.

Does understanding endotypes translate to better asthma management options for all?

Despite the development of novel treatments, improvement in the design of delivery devices, and new technologies for monitoring and improving adherence, the burden of asthma is not decreasing. Predicting an individual patient's response to asthma drugs remains challenging, and the provision of personalized treatment remains elusive. Although biomarkers, such as allergic sensitization and blood eosinophilia, might be important predictors of response to inhaled corticosteroids in preschool children, these relatively cheap and available investigations are seldom used in clinical practice to select patients for corticosteroid prescription. However, for the majority of patients, response to different treatments cannot be accurately predicted. One of the key factors preventing further advances is the reductionist view of asthma as a single disease, which is forcing patients with different asthma subtypes into a single group for empiric treatment. This inevitably results in treatment failures and, for some, an unacceptable risk/benefit ratio. The approach to asthma today is an example of the traditional symptom (diagnosis)-based, one-size-fits-all approach rather than a stratified approach, and our guidelines-driven management based on a unitary diagnosis might not be the optimal way to deliver care. The only way to deliver stratified medicine and find a cure is through the understanding of asthma endotypes. We propose that the way to discover endotypes, biomarkers, and personalized treatments is through the iterative process based on interpretation of big data analytics from birth and patient cohorts, responses to treatments in randomized controlled trials, and in vitro mechanistic studies using human samples and experimental animal models, with technological and methodological advances at its core.

Epidemiology of Asthma in Children and Adults

Asthma is a globally significant non-communicable disease with major public health consequences for both children and adults, including high morbidity, and mortality in severe cases. We have summarized the evidence on asthma trends, environmental determinants, and long-term impacts while comparing these epidemiological features across childhood asthma and adult asthma. While asthma incidence and prevalence are higher in children, morbidity, and mortality are higher in adults. Childhood asthma is more common in boys while adult asthma is more common in women, and the reversal of this sex difference in prevalence occurs around puberty suggesting sex hormones may play a role in the etiology of asthma. The global epidemic of asthma that has been observed in both children and adults is still continuing, especially in low to middle income countries, although it has subsided in some developed countries. As a heterogeneous disease, distinct asthma phenotypes, and endotypes need to be adequately characterized to develop more accurate and meaningful definitions for use in research and clinical settings. This may be facilitated by new clustering techniques such as latent class analysis, and computational phenotyping methods are being developed to retrieve information from electronic health records using natural language processing (NLP) algorithms to assist in the early diagnosis of asthma. While some important environmental determinants that trigger asthma are well-established, more work is needed to define the role of environmental exposures in the development of asthma in both children and adults. There is increasing evidence that investigation into possible gene-by-environment and environment-by-environment interactions may help to better uncover the determinants of asthma. Therefore, there is an urgent need to further investigate the interrelationship between environmental and genetic determinants to identify high risk groups and key modifiable exposures. For children, asthma may impair airway development and reduce maximally attained lung function, and these lung function deficits may persist into adulthood without additional progressive loss. Adult asthma may accelerate lung function decline and increase the risk of fixed airflow obstruction, with the effect of early onset asthma being greater than late onset asthma. Therefore, in managing asthma, our focus going forward should be firmly on improving not only short-term symptoms, but also the long-term respiratory and other health outcomes.