Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches

Patients carrying pathogenic SCN8A variants with loss- and gain-of-function effects can be classified into five subgroups exhibiting varying developmental and epileptic components of encephalopathy

OBJECTIVE

Phenotypic heterogeneity presents challenges in providing clinical care to patients with pathogenic SCN8A variants, which underly a wide disease spectrum ranging from neurodevelopmental delays without seizures to a continuum of mild to severe developmental and epileptic encephalopathies (DEEs). An important unanswered question is whether there are clinically important subgroups within this wide spectrum. Using both supervised and unsupervised machine learning (ML) approaches, we previously found statistical support for two and three subgroups associated with loss- and gain- of- function vari-ants, respectively. Here, we test the hypothesis that the unsupervised subgroups (U1-U3) are distinguished by differential contributions of developmental and epileptic components.

METHODS

We predicted that patients in the U1 and U2 subgroups would differ in timing of developmental delay and seizure onset, with earlier and concurrent onset of both features for the U3 subgroup. Standard statistical procedures were used to test these predictions, as well as to investigate clinically relevant associations among all five subgroups.

RESULTS

Two-population proportion and Kruskal-Wallis tests supported the hypothesis of a reversed order of developmental delay and seizure onset for patients in U1 and U2, and nearly synchronous developmental delay/seizure onset for the U3 (termed DEE) subgroup. Association testing identified subgroup variation in treatment response, frequency of initial seizure type, and comorbidities, as well as different median ages of developmental delay onset for all five subgroups.

SIGNIFICANCE

Unsupervised ML approaches discern differential developmental and epileptic components among patients with SCN8A-related epilepsy. Patients in U1 (termed developmental encephalopathy) typically gain seizure control yet rarely experience improvements in development, whereas those in U2 (termed epileptic encephalopathy) have fewer if any developmental impairments despite difficulty in achieving seizure control. This understanding improves prognosis and clinical management and provides a framework to discover mechanisms underlying variability in clinical outcome of patients with SCN8A-related disorders.

Quercetin inhibits ferroptosis through the SIRT1/Nrf2/HO-1 signaling pathway and alleviates asthma disease

Background

Quercetin (QCT) is a bioflavonoid derived from vegetables and fruits that has anti-inflammatory and anti-ferroptosis effects against various diseases. Previous studies have shown that QCT modulates the production of cellular inflammatory factors in asthma models and delays the development of chronic airway inflammation. However, the regulatory mechanism of QCT, a traditional Chinese medicine, in the treatment of asthma has not been elucidated. The aim of the present study is to investigate whether QCT can inhibit ferroptosis via the SIRT1/Nrf2 pathway and play a therapeutic role in asthma.

Methods

An ovalbumin-induced mouse asthma model was established, and its function was verified by hematoxylin eosin staining, enzyme linked immunosorbent assay, ferric ion assay, malondialdehyde and superoxide dismutase assays, dihydroethidium staining, immunohistochemical staining, western blotting, and quantitative real-time polymerase chain reaction.

Results

Our results indicated that an ovalbumin-induced asthma mouse model had been successfully established and that QCT inhibited inflammation, reduced serum levels of inflammatory factors IL-4, IL-5 and IL-13, increased superoxide dismutase levels in lung tissue homogenates, and reduced malondialdehyde and ferric ion production in asthmatic mice. In addition, we found that QCT was able to reverse the expression of SIRT1, Nrf2 and HO-1 in an in vivo asthma mouse model.

Conclusions

The data from this study indicate that QCT can alleviate asthma, and its mechanism is related to the regulation of ferroptosis, oxidative stress, and the expression of SIRT1 protein.

Integrating Patients Into Programmes to Address the Allergy Knowledge Practice Gap

There is a wide gap between the first publication of new treatments with efficacy and their successful application in clinical practice. In many respects, the management of allergic diseases is a good exemplar of the knowledge/practice gap. It was assumed that systematic reviews and publication of guidelines would ensure timely delivery of effective care, but this has not proved to be the case. While there are many reasons to explain shortcomings in healthcare delivery, the lack of patient and carer involvement in the planning of research, evidence review, guideline development and guideline implementation is most compelling. To achieve adherence to evidence-based guidelines consistently across all levels of the health service requires the implementation of integrated care with clear pathways through which patients can navigate. Quality improvement methodology could be employed to plan and implement integrated care pathways (ICPs). There is evidence that ICPs achieve improved outcomes for acute hospital-based interventions, but less work has focussed on long-term conditions where more diverse agencies are involved. At all stages, stakeholder representation from the full range of healthcare professionals, patients, their families, social services, education, local government and employers must be involved. In this article we review the step-wise and iterative process by which knowledge is implemented into practice to improve patient experience and outcomes We argue how this process can benefit from the involvement of patients and their carers as equal partners, and we discuss how different initiatives have involved patients with allergic diseases. There currently is a gap in evidence that links patient involvement to improved outcomes. We recommend the use of the Core Outcome Sets (COS) and Patient Reported Experience Measures (PREMS) which have been developed for allergic diseases to monitor the effects of implementation research and the impact of patient and carer involvement on outcomes.

Respiratory infection- and asthma-prone, low vaccine responder children demonstrate distinct mononuclear cell DNA methylation pathways

BACKGROUND

Infants with frequent viral and bacterial respiratory infections exhibit compromised immunity to routine immunizations. They are also more likely to develop chronic respiratory diseases in later childhood. This study investigated the feasibility of epigenetic profiling to reveal endotype-specific molecular pathways with potential for early identification and immuno-modulation. Peripheral blood mononuclear cells from respiratory infection allergy/asthma-prone (IAP) infants and non-infection allergy/asthma prone (NIAP) were retrospectively selected for genome-wide DNA methylation and single nucleotide polymorphism analysis. The IAP infants were enriched for the low vaccine responsiveness (LVR) phenotype (Fisher's exact p-value = 0.02).

RESULTS

An endotype signature of 813 differentially methylated regions (DMRs) comprising 238 lead CpG associations (FDR < 0.05) emerged, implicating pathways related to asthma, mucin production, antigen presentation and inflammasome activation. Allelic variation explained only a minor portion of this signature. Stimulation of mononuclear cells with monophosphoryl lipid A (MPL), a TLR agonist, partially reversed this signature at a subset of CpGs, suggesting the potential for epigenetic remodeling.

CONCLUSIONS

This proof-of-concept study establishes a foundation for precision endotyping of IAP children and highlights the potential for immune modulation strategies using adjuvants for future investigation.

Distinct Adverse Reactions to mRNA, Inactivated Virus, and Adenovirus Vector COVID-19 Vaccines: Insights from a Cohort Study on Atopic and Non-Atopic Subjects in Brazil

The emergence of COVID-19 caused by SARS-CoV-2 prompted an unprecedented global response to develop vaccines at an accelerated pace. Messenger RNA (mRNA) and adenovirus vector vaccines emerged as the frontrunners in global immunization efforts, significantly reducing hospitalization, severity, and mortality, supplemented by inactivated virus-based vaccines in developing countries. However, concerns regarding adverse effects, including allergic reactions, have been raised. This study aimed to investigate the adverse effects following COVID-19 vaccination, particularly in atopic and non-atopic individuals. A cohort of 305 volunteers receiving BNT162, ChAdOx1, or CoronaVac vaccines were assessed based on a Skin Prick Test (SPT), specific IgE levels, and clinical history of asthma and rhinitis. Adverse effects were self-reported and scored across the different vaccination shots. The results indicated a notable presence of mild adverse effects following the first and third doses, regardless of vaccine type. ChAdOx1 recipients experienced more adverse effects compared to those receiving BNT162 and CoronaVac, including headaches, muscle pain, fever, chills, nausea, and flu-like symptoms. Atopic individuals receiving ChAdOx1 reported more adverse effects, such as muscle pain, fever, and chills, compared to non-atopic individuals. Conversely, headaches were more frequently reported in non-atopic individuals receiving BNT162 compared to atopic individuals. No anaphylaxis or allergic reactions were reported, indicating valuable evidence supporting the safety of COVID-19 vaccination in individuals with respiratory allergies. This study highlights the importance of understanding vaccine-related adverse effects, particularly in vulnerable populations, to inform vaccination strategies and address safety concerns in global immunization campaigns.

Respiratory Infection- and Asthma-prone, Low Vaccine Responder Children Demonstrate Distinct Mononuclear Cell DNA Methylation Pathways

Background

Infants with frequent viral and bacterial respiratory infections exhibit compromised immunity to routine immunisations. They are also more likely to develop chronic respiratory diseases in later childhood. This study investigated the feasibility of epigenetic profiling to reveal endotype-specific molecular pathways with potential for early identification and immuno-modulation. Peripharal immune cells from respiratory infection allergy/asthma prone (IAP) infants were retrospectively selected for genome-wide DNA methylation and single nucleotide polymorphism analysis. The IAP infants were enriched for the low vaccine responsiveness (LVR) phenotype (Fishers Exact p-value = 0.01).

Results

An endotype signature of 813 differentially methylated regions (DMRs) comprising 238 lead CpG associations (FDR < 0.05) emerged, implicating pathways related to asthma, mucin production, antigen presentation and inflammasome activation. Allelic variation explained only a minor portion of this signature. Stimulation of mononuclear cells with monophosphoryl lipid A (MPLA), a TLR agonist, partially reversing this signature at a subset of CpGs, suggesting the potential for epigenetic remodelling.

Conclusions

This proof-of-concept study establishes a foundation for precision endotyping of IAP children and highlights the potential for immune modulation strategies using adjuvants for furture investigation.

Building digital patient pathways for the management and treatment of multiple sclerosis

Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

Looking beyond Self-Protection: The Eyes Instruct Systemic Immune Tolerance Early in Life

The eyes provide themselves with immune tolerance. Frequent skin inflammatory diseases in young blind people suggest, nonetheless, that the eyes instruct a systemic immune tolerance that benefits the whole body. We tested this premise by using delayed skin contact hypersensitivity (DSCH) as a tool to compare the inflammatory response developed by sighted (S) and birth-enucleated (BE) mice against oxazolone or dinitrofluorobenzene at the ages of 10, 30 and 60 days of life. Adult mice enucleated (AE) at 60 days of age were also assessed when they reached 120 days of life. BE mice displayed exacerbated DSCH at 60 but not at 10 or 30 days of age. AE mice, in contrast, show no exacerbated DSCH. Skin inflammation in 60-day-old BE mice was hapten exclusive and supported by distinct CD8+ lymphocytes. The number of intraepidermal T lymphocytes and migrating Langerhans cells was, however, similar between S and BE mice by the age of 60 days. Our observations support the idea that the eyes instruct systemic immune tolerance that benefits organs outside the eyes from an early age. The higher prevalence of inflammatory skin disorders reported in young people might then reflect reduced immune tolerance associated with the impaired functional morphology of the eyes.

A review of current biomarkers in chronic rhinosinusitis with or without nasal polyps

INTRODUCTION

Chronic rhinosinusitis (CRS) is a heterogeneous disease with a variety of cellular and molecular pathophysiologic mechanisms. Biomarkers have been explored in CRS using various phenotypes, such as polyp recurrence after surgery. Recently, the presence of regiotype in CRS with nasal polyps (CRSwNP) and the introduction of biologics for the treatment of CRSwNP has indicated the importance of endotypes, and there is a need to elucidate endotype-based biomarkers.

AREAS COVERED

Biomarkers for eosinophilic CRS, nasal polyps, disease severity, and polyp recurrence have been identified. Additionally, endotypes are being identified for CRSwNP and CRS without nasal polyps using cluster analysis, an unsupervised learning technique.

EXPERT OPINION

Endotypes in CRS have still being established, and biomarkers capable of identifying endotypes of CRS are not yet clear. When identifying endotype-based biomarkers, it is necessary to first identify endotypes clarified by cluster analysis for outcomes. With the application of machine learning, the idea of predicting outcomes using a combination of multiple integrated biomarkers, rather than a single biomarker, will become mainstream.

Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans

BACKGROUND

Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility.

OBJECTIVE

We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations.

METHODS

We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry.

RESULTS

The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases.

CONCLUSIONS

GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Biomarkers in oral immunotherapy

Food allergy (FA) is a global health problem that affects a large population, and thus effective treatment is highly desirable. Oral immunotherapy (OIT) has been showing reasonable efficacy and favorable safety in most FA subjects. Dependable biomarkers are needed for treatment assessment and outcome prediction during OIT. Several immunological indicators have been used as biomarkers in OIT, such as skin prick tests, basophil and mast cell reactivity, T cell and B cell responses, allergen-specific antibody levels, and cytokines. Other novel indicators also could be potential biomarkers. In this review, we discuss and assess the application of various immunological indicators as biomarkers for OIT.

Understanding human mast cells: lesson from therapies for allergic and non-allergic diseases

Mast cells have crucial roles in allergic and other inflammatory diseases. Preclinical approaches provide circumstantial evidence for mast cell involvement in many diseases, but these studies have major limitations - for example, there is still a lack of suitable mouse models for some mast cell-driven diseases such as urticaria. Some approaches for studying mast cells are invasive or can induce severe reactions, and very few mediators or receptors are specific for mast cells. Recently, several drugs that target human mast cells have been developed. These include monoclonal antibodies and small molecules that can specifically inhibit mast cell degranulation via key receptors (such as FcεRI), that block specific signal transduction pathways involved in mast cell activation (for example, BTK), that silence mast cells via inhibitory receptors (such as Siglec-8) or that reduce mast cell numbers and prevent their differentiation by acting on the mast/stem cell growth factor receptor KIT. In this Review, we discuss the existing and emerging therapies that target mast cells, and we consider how these treatments can help us to understand mast cell functions in disease.

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

TH17 cells and corticosteroid insensitivity in severe asthma

Asthma is classically described as having either a type 2 (T2) eosinophilic phenotype or a non-T2 neutrophilic phenotype. T2 asthma usually responds to classical bronchodilation therapy and corticosteroid treatment. Non-T2 neutrophilic asthma is often more severe. Patients with non-T2 asthma or late-onset T2 asthma show poor response to the currently available anti-inflammatory therapies. These therapeutic failures result in increased morbidity and cost associated with asthma and pose a major health care problem. Recent evidence suggests that some non-T2 asthma is associated with elevated T_H17 cell immune responses. T_H17 cells producing Il-17A and IL-17F are involved in the neutrophilic inflammation and airway remodeling processes in severe asthma and have been suggested to contribute to the development of subsets of corticosteroid-insensitive asthma. This review explores the pathologic role of T_H17 cells in corticosteroid insensitivity of severe asthma and potential targets to treat this endotype of asthma.

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Achieving Precision Medicine in Allergic Disease: Progress and Challenges

Allergic diseases (atopic dermatitis, food allergy, eosinophilic esophagitis, asthma and allergic rhinitis), perhaps more than many other traditionally grouped disorders, share several overlapping inflammatory pathways and risk factors, though we are still beginning to understand how the relevant patient and environmental factors uniquely shape each disease. Precision medicine is the concept of applying multiple levels of patient-specific data to tailor diagnoses and available treatments to the individual; ideally, a patient receives the right intervention at the right time, in order to maximize effectiveness but minimize morbidity, mortality and cost. While precision medicine in allergy is in its infancy, the recent success of biologics, development of tools focused on large data set integration and improved sampling methods are encouraging and demonstrates the utility of refining our understanding of allergic endotypes to improve therapies. Some of the biggest challenges to achieving precision medicine in allergy are characterizing allergic endotypes, understanding allergic multimorbidity relationships, contextualizing the impact of environmental exposures (the “exposome”) and ancestry/genetic risks, achieving actionable multi-omics integration, and using this information to develop adequately powered patient cohorts and refined clinical trials. In this paper, we highlight several recently developed tools and methods showing promise to realize the aspirational potential of precision medicine in allergic disease. We also outline current challenges, including exposome sampling and building the “knowledge network” with multi-omics integration.

Milestones of Precision Medicine: An Innovative, Multidisciplinary Overview

Although the concept of precision medicine, in which healthcare is tailored to the molecular and clinical characteristics of each individual, is not new, its implementation in clinical practice has been heterogenous. In some medical specialties, precision medicine has gone from being just a promise to a reality that achieves better patient outcomes. This is a fact if we consider, for example, the great advances made in the genetic diagnosis and subsequent treatment of countless hereditary diseases, such as cystic fibrosis, which have improved the life expectancy of many of the affected children. In the field of oncology, the development of targeted therapies has prolonged the survival of patients with breast, lung, colorectal, melanoma, and hematological malignancies. In other disciplines, clinical milestones are perhaps less well known, but no less important. The current challenge is to expand and generalize the use of technologies that are central to precision medicine, such as massively parallel sequencing, to improve the management (prevention and treatment) of complex conditions such as cardiovascular, kidney, or autoimmune diseases. This process requires investment in specialized expertise, multidisciplinary collaboration, and the nationwide organization of genetic laboratories for diagnosis of specific diseases.

MicroRNA Targets for Asthma Therapy

Asthma is a chronic inflammatory obstructive lung disease that is stratified into endotypes. Th2 high asthma is due to an imbalance of Th1/Th2 signaling leading to abnormally high levels of Th2 cytokines, IL-4, IL-5, and IL-13 and in some cases a reduction in type I interferons. Some asthmatics express Th2 low, Th1/Th17 high phenotypes with or without eosinophilia. Most asthmatics with Th2 high phenotype respond to beta-adrenergic agonists, muscarinic antagonists, and inhaled corticosteroids. However, 5-10% of asthmatics are not well controlled by these therapies despite significant advances in lung immunology and the pathogenesis of severe asthma. This problem is being addressed by developing novel classes of anti-inflammatory agents. Numerous studies have established efficacy of targeting pro-inflammatory microRNAs in mouse models of mild/moderate and severe asthma. Current approaches employ microRNA mimics and antagonists designed for use in vivo. Chemically modified oligonucleotides have enhanced stability in blood, increased cell permeability, and optimized target specificity. Delivery to lung tissue limits clinical applications, but it is a tractable problem. Future studies need to define the most effective microRNA targets and effective delivery systems. Successful oligonucleotide drug candidates must have adequate lung cell uptake, high target specificity, and efficacy with tolerable off-target effects.

Artificial intelligence in the diagnosis of pediatric allergic diseases

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.