Muwada Bashir Awad Bashir, Daniil Lisik, Saliha Selin Ozuygur Ermis, Rani Basna, Reshed Abohalaka, Selin Ercan, Helena Backman, Teet Pullerits, Roxana Mincheva, Göran Wennergren, Madeleine Rådinger, Jan Lötvall, Linda Ekerljung, Hannu Kankaanranta, Bright I. Nwaru
<p>Asthma is a heterogeneous disease, and a clear-cut characterisation of its phenotypes has historically been daunting for both clinical practice and research purposes [<span>1</span>]. Asthma phenotypes have been defined primarily based on clinical experience [<span>2</span>]. Computational science is helping to elucidate biological processes, with ongoing applications in asthma phenotyping [<span>3</span>]. In this context, clustering algorithms can learn from unlabeled data to produce distinct subgroups. This data-driven approach is believed to be less subjective and, with relevant input data, can produce clinically meaningful phenotypes [<span>4</span>]. Characterising asthma at a more granular level aligns with efforts towards precision medicine, potentially enabling improved and tailored management. By including a broad range of clinical, biological, and epidemiological parameters, we employed a machine learning approach to identify and describe asthma phenotypes in adults.</p><p>The study sample was derived from the West Sweden Asthma Study (WSAS), a longitudinal cohort study investigating different aspects of airway diseases among a representative sample of adults, which started in 2008 [<span>5</span>]. A total of 3101 individuals underwent clinical investigations, of which 1895 subjects who had ever had asthma were included in the current study. Asthma was defined as self-reported ever asthma. Age at onset was determined through the follow-up question “at what age did your asthma start?”. In total, 44 variables were selected, based on clinical experience and previous studies (see details at https://osf.io/ucrnt).</p><p>Missing data were imputed using multiple imputation with random forest. The phenotypes were derived using Deep Embedded Clustering, a novel approach that combines deep learning with clustering to discover patterns in complex data [<span>6</span>]. The R packages NbClust, Monte Carlo reference-based consensus clustering (M3C), and Adjusted Rand Index (ARI) were used to decide the optimal number of clusters based on consensus of 30 internal validation indices. The proposed numbers were evaluated in conjunction with clinical experience to determine the final optimal number of clusters (details at https://osf.io/ucrnt/). Continuous data were expressed as means and standard deviations (SD). Group comparisons were performed by one-way analysis of variance with the Tukey post hoc test or Kruskal–Wallis test, as appropriate, for continuous variables and the Chi-square for categorical variables (details at https://osf.io/ucrnt/).</p><p>Cluster 1 had the oldest average age at asthma onset (35 years), while cluster 4 had the youngest average age (13 years) (Figure 1). Cluster 1 also had the oldest average chronological age (65 years), highest average BMI (28.5 kg/m<sup>2</sup>), highest proportion of smokers and average pack-year history, most respiratory symptoms, but the lowest proportion of allergic sensitization and family hist
{"title":"Unsupervised Machine Learning Identifies Asthma Phenotypes in the Population-Based West Sweden Asthma Study","authors":"Muwada Bashir Awad Bashir, Daniil Lisik, Saliha Selin Ozuygur Ermis, Rani Basna, Reshed Abohalaka, Selin Ercan, Helena Backman, Teet Pullerits, Roxana Mincheva, Göran Wennergren, Madeleine Rådinger, Jan Lötvall, Linda Ekerljung, Hannu Kankaanranta, Bright I. Nwaru","doi":"10.1111/cea.70161","DOIUrl":"10.1111/cea.70161","url":null,"abstract":"<p>Asthma is a heterogeneous disease, and a clear-cut characterisation of its phenotypes has historically been daunting for both clinical practice and research purposes [<span>1</span>]. Asthma phenotypes have been defined primarily based on clinical experience [<span>2</span>]. Computational science is helping to elucidate biological processes, with ongoing applications in asthma phenotyping [<span>3</span>]. In this context, clustering algorithms can learn from unlabeled data to produce distinct subgroups. This data-driven approach is believed to be less subjective and, with relevant input data, can produce clinically meaningful phenotypes [<span>4</span>]. Characterising asthma at a more granular level aligns with efforts towards precision medicine, potentially enabling improved and tailored management. By including a broad range of clinical, biological, and epidemiological parameters, we employed a machine learning approach to identify and describe asthma phenotypes in adults.</p><p>The study sample was derived from the West Sweden Asthma Study (WSAS), a longitudinal cohort study investigating different aspects of airway diseases among a representative sample of adults, which started in 2008 [<span>5</span>]. A total of 3101 individuals underwent clinical investigations, of which 1895 subjects who had ever had asthma were included in the current study. Asthma was defined as self-reported ever asthma. Age at onset was determined through the follow-up question “at what age did your asthma start?”. In total, 44 variables were selected, based on clinical experience and previous studies (see details at https://osf.io/ucrnt).</p><p>Missing data were imputed using multiple imputation with random forest. The phenotypes were derived using Deep Embedded Clustering, a novel approach that combines deep learning with clustering to discover patterns in complex data [<span>6</span>]. The R packages NbClust, Monte Carlo reference-based consensus clustering (M3C), and Adjusted Rand Index (ARI) were used to decide the optimal number of clusters based on consensus of 30 internal validation indices. The proposed numbers were evaluated in conjunction with clinical experience to determine the final optimal number of clusters (details at https://osf.io/ucrnt/). Continuous data were expressed as means and standard deviations (SD). Group comparisons were performed by one-way analysis of variance with the Tukey post hoc test or Kruskal–Wallis test, as appropriate, for continuous variables and the Chi-square for categorical variables (details at https://osf.io/ucrnt/).</p><p>Cluster 1 had the oldest average age at asthma onset (35 years), while cluster 4 had the youngest average age (13 years) (Figure 1). Cluster 1 also had the oldest average chronological age (65 years), highest average BMI (28.5 kg/m<sup>2</sup>), highest proportion of smokers and average pack-year history, most respiratory symptoms, but the lowest proportion of allergic sensitization and family hist","PeriodicalId":10207,"journal":{"name":"Clinical and Experimental Allergy","volume":"56 2","pages":"170-172"},"PeriodicalIF":5.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12879267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Subcutaneous immunotherapy (SCIT) is a well-established treatment for inducing immune tolerance in patients with allergic rhinitis (AR). However, the precise molecular mechanisms by which SCIT induces immune tolerance, particularly at the transcriptomic level over the treatment course, have not been fully elucidated. This study aimed to investigate the molecular mechanisms of SCIT by analysing changes in peripheral blood gene expression profiles in AR patients over time.
Methods: Whole blood samples were prospectively collected from 30 AR patients (16 paediatric, 14 adult) and 10 healthy controls. RNA sequencing was performed at baseline and at 3, 6 and 12 months of SCIT. Differentially expressed genes (DEGs) were identified, and pathway enrichment, immune cell deconvolution and weighted gene co-expression network analysis were conducted to explore immune regulation and tolerance mechanisms.
Results: AR patients showed 1180 DEGs compared to healthy controls, with upregulated genes related to B-cell activation and downregulated genes linked to Th1 differentiation. Both paediatric and adult cohorts exhibited consistent transcriptomic changes, characterised by progressive normalisation of gene expression, with the number of DEGs decreasing over time and significant convergence towards healthy control profiles by 12 months. SCIT enhanced type I interferon responses and antiviral pathways while reducing B-cell activation and inflammatory responses. Immune cell analysis revealed increased regulatory T cells and dendritic cells by 6 months and reduced Th2 cells and eosinophils by 12 months. Key immune-related hub genes, including CD19, CD79A, CD79B, CD22, IFIH1, STAT1, DHX58, TLR4, IL1B and TLR1, were identified as central to SCIT efficacy.
Conclusion: SCIT dynamically modulates blood gene expression profiles in AR patients, inducing immune tolerance and reducing inflammatory responses. These findings enhance understanding of the molecular mechanisms of SCIT and highlight potential biomarkers for predicting and monitoring treatment efficacy.
{"title":"Longitudinal Blood Transcriptome Analysis Reveals Dynamic Gene Expression Patterns in Patients With Allergic Rhinitis Following House Dust Mite Subcutaneous Immunotherapy.","authors":"Chang Liu, Shikun He, Jinxiu Zhang, Jincai Zhu, Jianxia Rao, Kanghua Wang, Yunping Fan, Yueqi Sun","doi":"10.1111/cea.70158","DOIUrl":"https://doi.org/10.1111/cea.70158","url":null,"abstract":"<p><strong>Introduction: </strong>Subcutaneous immunotherapy (SCIT) is a well-established treatment for inducing immune tolerance in patients with allergic rhinitis (AR). However, the precise molecular mechanisms by which SCIT induces immune tolerance, particularly at the transcriptomic level over the treatment course, have not been fully elucidated. This study aimed to investigate the molecular mechanisms of SCIT by analysing changes in peripheral blood gene expression profiles in AR patients over time.</p><p><strong>Methods: </strong>Whole blood samples were prospectively collected from 30 AR patients (16 paediatric, 14 adult) and 10 healthy controls. RNA sequencing was performed at baseline and at 3, 6 and 12 months of SCIT. Differentially expressed genes (DEGs) were identified, and pathway enrichment, immune cell deconvolution and weighted gene co-expression network analysis were conducted to explore immune regulation and tolerance mechanisms.</p><p><strong>Results: </strong>AR patients showed 1180 DEGs compared to healthy controls, with upregulated genes related to B-cell activation and downregulated genes linked to Th1 differentiation. Both paediatric and adult cohorts exhibited consistent transcriptomic changes, characterised by progressive normalisation of gene expression, with the number of DEGs decreasing over time and significant convergence towards healthy control profiles by 12 months. SCIT enhanced type I interferon responses and antiviral pathways while reducing B-cell activation and inflammatory responses. Immune cell analysis revealed increased regulatory T cells and dendritic cells by 6 months and reduced Th2 cells and eosinophils by 12 months. Key immune-related hub genes, including CD19, CD79A, CD79B, CD22, IFIH1, STAT1, DHX58, TLR4, IL1B and TLR1, were identified as central to SCIT efficacy.</p><p><strong>Conclusion: </strong>SCIT dynamically modulates blood gene expression profiles in AR patients, inducing immune tolerance and reducing inflammatory responses. These findings enhance understanding of the molecular mechanisms of SCIT and highlight potential biomarkers for predicting and monitoring treatment efficacy.</p>","PeriodicalId":10207,"journal":{"name":"Clinical and Experimental Allergy","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Morten Borg, Ole Hilberg, Rikke Ibsen, Anders Løkke
{"title":"Adherence to Grass Pollen Allergen Immunotherapy and Allergy Medication Use in Patients With Allergic Rhinitis","authors":"Morten Borg, Ole Hilberg, Rikke Ibsen, Anders Løkke","doi":"10.1111/cea.70159","DOIUrl":"10.1111/cea.70159","url":null,"abstract":"","PeriodicalId":10207,"journal":{"name":"Clinical and Experimental Allergy","volume":"56 1","pages":"103-105"},"PeriodicalIF":5.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tunn Ren Tay, Mon Hnin Tun, Sudev Suthendran, Nicole Yu-Fang Sieow, Yan Cao, Soyah Binti Mohamed Noor, Hui Ye, Haijuan Chen, Xiao Ling Li, Norlidah Binte Mohd Noor, Nuraini Binte Mohamed Razali, Chee Wei Tan, Choon How How
{"title":"Direct and Indirect Pathways Between Patient, Health System and Socioeconomic Factors and Medication Adherence in Asthma","authors":"Tunn Ren Tay, Mon Hnin Tun, Sudev Suthendran, Nicole Yu-Fang Sieow, Yan Cao, Soyah Binti Mohamed Noor, Hui Ye, Haijuan Chen, Xiao Ling Li, Norlidah Binte Mohd Noor, Nuraini Binte Mohamed Razali, Chee Wei Tan, Choon How How","doi":"10.1111/cea.70160","DOIUrl":"10.1111/cea.70160","url":null,"abstract":"","PeriodicalId":10207,"journal":{"name":"Clinical and Experimental Allergy","volume":"56 2","pages":"167-169"},"PeriodicalIF":5.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. T. Ma, C. E. Fan, X. Tong, C. Y. An, H. J. Cai, L. Y. Ai, Y. F. Li, D. Y. Wang, X. D. Wang, G. L. Shang, Y. D. Hu, Y. F. Bai, Y. L. Chen, H. T. Wang, H. Y. Ning, L. Zhang, J. J. Zhang, X. Y. Wang
The cover image is based on the article Epidemiology, Diagnosis and Prevention of Allergic Rhinitis in High-Pollen Areas of Northern China by T. T. Ma et al., https://doi.org/10.1111/cea.70087.