Severe asthma is a chronic disease of airway inflammation with substantial morbidity. Deficient specialized pro-resolving mediators (SPMs) are associated with persistent airway inflammation and impaired lung function in some patients with severe asthma. Resolvin D1 (RvD1) is an SPM agonist for inflammation resolution. Plasma RvD1 was measured longitudinally over 5 years in 23 severe asthma patients in the Severe Asthma Research Program (SARP) to identify relationships between clinical parameters, type 2 inflammation, and sputum gene expression. The majority of severe asthma patients had persistently low plasma RvD1; a smaller subgroup had higher RvD1 that fluctuated over time. Correlation analysis indicated a relationship between plasma RvD1 and sputum eosinophilia. A subgroup of severe asthma patients had low plasma RvD1 and high sputum eosinophils (RvD1LoSpEosHi); a separate subgroup had high plasma RvD1 and low sputum eosinophils (RvD1HiSpEosLo). The RvD1LoSpEosHi patient cluster had increased T2 inflammation, lower lung function, and more asthma exacerbations. 42 genes were differentially expressed in RvD1LoSpEosHi severe asthma sputum, including hypoxia-inducible factor 1-alpha (HIF1A). RvD1 significantly downregulated eosinophil HIF-1α expression in vitro. These findings identify a subset of severe asthma patients with low RvD1 and increased sputum eosinophilia, and RvD1 regulation of eosinophil activation ex vivo, suggesting a counter-regulatory role for this SPM in modulating eosinophilic T2 inflammation in asthma.

Type 2 (T2) immune cells dominate the airways of patients with mild-moderate asthma (MMA) with a more complex type 1 (T1)-T2 mixed immune response evident in treatment-refractory severe asthma (SA). We hypothesized that comparing the transcriptomes of the airway epithelium of patients with SA and MMA would reveal molecular signatures associated with more severe disease in the context of a complex immune response. Using our interpretable machine learning tool, SLIDE, meaningful latent factors (context-specific gene co-expression networks) were revealed that distinguished SA from MMA. Unexpectedly, an aberrant high expression of normally host-protective, membrane-tethered, and IFN-inducible mucins, MUC1 and MUC4, was identified in SA. Gene networks in the significant latent factors discriminating SA from MMA corresponded to enrichment of a keratinization program in SA airways. Keratinization was marked by increased expression of the stress keratin KRT16, signifying squamous metaplasia suggesting adaptive reprogramming of the airway epithelium in response to chronic stress. These mucins and KRT16 were inversely associated with lung function in 2 separate asthma cohorts. Imaging of endobronchial biopsies revealed significantly higher KRT16 protein expression in SA compared with MMA that strongly correlated with MUC1 protein expression. Our study identifies dysregulated host-protective and maladaptive repair responses in SA distinguishing from MMA.

ObjectivesTo develop a contrastive learning model for lung disease classification using discriminative CT imaging embeddings.MethodsA total of 1,187 subjects were included: asthma (n = 315), COPD (n = 355), post-COVID-19 (n = 375), and healthy controls (n = 142). Of these, 1,003 subjects had a single visit with similarly protocoled CT scans acquired at total lung capacity (TLC) and residual volume (RV), and 92 (33 asthma and 59 post-COVID-19) completed a follow-up visit, with two scans per visit. We developed a modified contrastive learning model incorporating an expert-conditioned routing network and adaptive temperature scaling to learn discriminative embeddings. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The embeddings were further validated via k-means clustering, and quantitative CT (qCT) metrics were compared across the derived clusters. The embedding space was used to track disease progression or improvement in the follow-up disease subgroup and to evaluate the model's ability to predict qCT metrics, quantified by the coefficient of determination (R2).ResultsThe model achieved a macro-AUC of 89.3% (95% CI: 86.5, 91.8; P < 0.001) in differentiating the four classes. Post-COVID-19 emerged as a distinct class from asthma and COPD in the t-SNE embedding space, and its embeddings across two visits captured disease improvement. Additionally, the learned embeddings showed predictive power for several qCT metrics, particularly the Jacobian (R2=0.61).ConclusionsThe proposed model effectively differentiated these three lung diseases and provided meaningful embeddings for phenotype characterization, longitudinal assessment, and qCT metric prediction.

Randomized controlled trials (RCTs) underpin evidence-based medicine, but a growing proportion of trials contain implausible, inaccurate, or fabricated data. When such studies are incorporated into systematic reviews, they distort effects estimates, inflate evidence certainty, and can mislead the guideline recommendations that the reviews aim to inform. Within eight recent American Academy of Allergy, Asthma & Immunology (AAAAI) and American College of Allergy, Asthma and Immunology (ACAAI) Joint Task Force on Practice Parameters (JTFPP) systematic reviews, 17% of trials published between 2021-2024 were problematic, and in a network meta-analysis of antihistamines for chronic urticaria, 39% of recent trials were excluded due to implausible data, statistical anomalies, and discrepancies between protocols and the final publication. The proliferation of generative AI further facilitates the ability to produce superficially credible, but flawed reports. Standardized tools have been developed to offer systematic approaches to detecting problematic trials, but robust safeguards such as verifiable ethics approval, prospective protocol registration, and adherence to structure reporting guidelines remain essential. As part of efforts to ensure quality research, the JTFPP will continue to update our systematic reviews to remove problematic trials as they are identified. We believe that this approach will promote the development of trustworthy guideline recommendations.

Network meta-analyses (NMA) are an increasingly popular method in allergy, asthma, and immunology to inform comparative effectiveness among competing treatment options. They enable comparisons among treatments when no direct comparisons (i.e., head-to-head randomized control trials) exist and strengthen inferences among direct comparisons by incorporating data from indirect comparisons. To inform optimal decision-making, allergy, asthma, and immunology clinicians, peer-reviewers, journal editors, and policymakers must understand the fundamentals of how to assess NMA credibility. Through worked examples supporting AAAAI/ACAAI Task Force on Practice Parameters guidelines, we summarize the process of conducting an NMA and how to interpret results to inform clinical decision-making.

BACKGROUND:The benefits and harms of using macrolides for asthma remain unclear. OBJECTIVE:As part of upcoming Academy of Allergy, Asthma and Immunology/American College of Allergy, Asthma and Immunology Joint Task Force on Practice Parameters guidelines addressing severe asthma, we systematically reviewed the efficacy and safety of macrolides for asthma. METHODS:We systematically searched MEDLINE, Embase, and CENTRAL to April 12, 2025, for randomized trials comparing macrolides with placebo or standard care for asthma. Paired reviewers independently screened records and extracted data. Individual patient-level data in random effects analysis of covariance models addressed asthma control and asthma-related quality of life. Random effects meta-analyses addressed severe exacerbations and harms. We used the Grading of Recommendations Assessment, Development and Evaluation approach to evaluate certainty of evidence. Our study protocol is registered in PROSPERO (CRD42023408677). RESULTS:Our meta-analysis comprised 19 trials enrolling 1825 participants. Compared with placebo, macrolides improve asthma control (6-item Asthma Control Questionnaire; score range 0-6, lower better; between-group mean difference: -0.23 [95% CI -0.32 to -0.13]; 40.6% vs 21.6% improving by minimally important difference of 0.5 point; high certainty), likely reduce severe exacerbations (incidence rate ratio: 0.75 [95% CI 0.57 to 0.98]; rate difference: 0.26 fewer events per patient-year [95% CI 0.45 to 0.02 fewer events]; moderate certainty), and likely modestly improve quality of life (Asthma Quality of Life Questionnaire; score range 1-7, higher better; mean difference: 0.11 [95% CI -0.06 to 0.29]; 47.6% vs 42.4% improving by minimally important difference of 0.5 points; moderate certainty) with little to no effect on serious adverse events and mortality (high certainty). Relative effects were similar among patients with type 2 high inflammation versus type 2 low inflammation asthma. CONCLUSIONS:Macrolides likely reduce severe exacerbations and improve asthma control and quality of life with little to no difference in serious harms among patients with type 2 high inflammation or type 2 low inflammation asthma.

BACKGROUND:Asthma is a heterogeneous disease influenced by genetic and environmental factors. Fine particulate matter (PM2.5) exacerbates asthma, likely through oxidative stress pathways, but whether genetic variation modifies this effect remains unclear. METHODS:We analysed data on 948 adults with asthma from the Severe Asthma Research Program (SARP), linking ZIP-code-level PM2.5 exposure with whole-genome sequencing data. We tested 4337 single nucleotide polymorphisms (SNPs) in 120 oxidative stress pathway genes for gene-environment (GxE) interactions with PM2.5 on lung function (forced expiratory volume in 1 s [FEV1] % predicted) using weighted linear regression. Gene expression data from bronchial epithelial cells (n = 170) were used to assess cis-expression quantitative trait loci (eQTLs). FINDINGS:Higher PM2.5 exposure was associated with lower FEV1% predicted (β per μg/m3 = -0.7, p = 0.01). We identified 20 SNPs across seven genes (OXSR1, PXDN, TPO, LRRK2, APP, MSRA, MSRB2) with significant GxE interactions after multiple-testing correction. Five SNPs were also eQTLs, linking PM2.5-modified gene expression to lung function. Minor alleles in OXSR1 and PXDN were associated with reduced gene expression and worsened FEV1% under high PM2.5 exposure. Conversely, TPO variants were associated with higher baseline expression and lower lung function, but under increasing PM2.5 exposure, minor allele carriers showed suppressed TPO expression and improved FEV1%. INTERPRETATION:This study identified 20 SNPs in oxidative stress pathway genes that modify the effect of PM2.5 on lung function in asthma. These findings highlight the importance of integrating environmental context in genetic studies and suggest potential therapeutic targets for pollution-sensitive asthma phenotypes. FUNDING:Supported by NIH grants.

BACKGROUND:The National Institutes of Health initiated the Precision Medicine in Severe and/or Exacerbation Prone Asthma (PrecISE) program with the objective of implementing adaptive design strategies to test multiple new treatments in biomarker-identified patient subsets. OBJECTIVE:The PrecISE study sought to recruit a cohort of patients with severe asthma for a biomarker-stratified adaptive trial of 5 different interventions. METHODS:Patients aged 12 years and older with a clinical diagnosis of asthma who were adhering to a stable medical regimen consisting of at least medium-dose inhaled corticosteroids and a second controller were enrolled if their diagnosis could be confirmed by bronchodilator responsiveness on spirometry or airway hyperreactivity to methacholine. Protocol adaptations over the course of the study to biomarker sampling and enrichment, interventions studied, the statistical analysis plan, and sample size are described. The baseline characteristics of the cohort were analyzed. RESULTS:A total of 358 participants were randomized. The 4 predictive biomarkers used for intervention randomization assignments were blood eosinophil count (median = 180; interquartile range (IQR) = 100-290 cells/mL), fractional exhaled nitric oxide measurement (median = 18; IQR = 11-27 ppb); plasma IL-6 level (median = 2.5; IQR = 1.6-3.6 pg/mL), and ADH5 risk genotype (present in 253 participants [71%]). Blood eosinophil counts weakly correlated with fractional exhaled nitric oxide measures (Rs = 0.13; P = .02) and IL-6 levels (Rs = 0.17; P = .002); otherwise, these biomarkers were independent from each other. Specific intervention results will be reported separately. CONCLUSION:The PrecISE adaptive study design with multiperiod crossovers is an efficient and novel way to study multiple interventions simultaneously in a heterogeneous disease such as asthma.

Background:The efficacy and safety of bronchial thermoplasty (BT) for severe asthma remains unclear. Objective:We systematically synthesized the efficacy and safety of BT in patients with severe asthma. Methods:As part of the upcoming American Academy of Allergy, Asthma & Immunology/American College of Allergy, Asthma & Immunology Joint Task Force on Practice Parameters severe asthma guidelines, we searched the Medline, Embase, and Central databases to July 29, 2025, for randomized trials comparing BT to control for asthma. Paired reviewers independently screened records and extracted data. Random-effects meta-analyses addressed asthma control (Asthma Control Questionnaire 6 [scale, 0-6; lower better]), asthma-related quality of life (Asthma Quality of Life Questionnaire [range, 1-7; higher better]), severe asthma exacerbations, and harms, stratified by procedural, postprocedural, and overall. The GRADE approach informed certainty-of-evidence ratings (PROSPERO: CRD42023408565). Results:Six trials randomized 573 adults. Procedurally, BT likely increases severe exacerbations (incidence rate ratio [IRR] 2.95; 95% CI, 1.20-7.25; risk difference [RD] 0.08 more events per patient-year; moderate certainty) and serious respiratory adverse events (IRR 5.98; 95% CI, 1.07-33.29; RD 0.18 more events per patient-year; moderate certainty). Postprocedurally, BT may reduce severe exacerbations (IRR 0.67; 95% CI, 0.50-0.89; RD 0.67 fewer events per patient-year; low certainty). Overall, BT may improve asthma control (mean difference -0.37; 95% CI, -0.67 to -0.07; RD for 0.5-point decrease: 15.9% more; low certainty), asthma-related quality of life (mean difference 0.55; 95% CI, 0.28-0.82; RD for 0.5-point increase: 19.1% more; low certainty), and severe exacerbations (IRR 0.81; 95% CI, 0.68-0.97; RD 0.11 fewer events per patient-year; low certainty). Conclusion:Among adults with severe asthma, BT may improve asthma control and asthma-related quality of life and may reduce overall severe exacerbations, but it likely increases procedural severe exacerbations and serious respiratory harms.

Accurate airway segmentation is essential for quantitative assessment of pulmonary diseases but remains challenging in expiratory computed tomography (CT) because airways become thinner and less distinguishable from adjacent vessels, resulting in severe class imbalance and increased peripheral leakages. Conventional manual or rule-based methods are time-consuming and limited reproducibility, while most deep learning studies have focused exclusively on inspiratory scans. To address these limitations, we propose a three-dimensional (3D) segmentation framework with an attention gate, termed Averaged Multi-Gaussian Response (AMGR), integrated into a U-Net architecture and tailored for expiratory airways. The AMGR gate stabilizes feature fusion by averaging multiple Gaussian responses per channel, suppressing peripheral noise and improving continuity in distal airway. The model is trained using a composite loss that combines cross-entropy, intersection over union, and centerline, jointly enhancing overlap accuracy and structural completeness. A total of 120 subjects from four cohorts (asthma, COPD, post-COVID-19, and healthy subjects) were used for training, validation, and independent testing. Quantitative evaluation demonstrated a high Dice score of 0.9592 relative to existing approaches, while maintaining balanced Precision (0.9416) and Recall (0.9780). Qualitatively, the model effectively suppresses false-positive leakages and noise. These results confirm that the AMGR framework provides a robust and generalizable solution for airway segmentation in expiratory CT, enabling accurate geometric analysis for future phenotype- and biomarker-based pulmonary studies.

Rationale: The club cell secretory protein (CC16), encoded by the SCGB1A1 gene, has an anti-inflammatory role in airways diseases, including asthma (Li, AJRCCM 2023; Voraphani, AJRCCM 2023). We hypothesize that additional pathway genes regulated or co-expressed with CC16 have cumulative effects on asthma severity outcomes by modulating airway inflammation and maintaining lung homeostasis. Methods: As published previously, four genes were downregulated in CC16 knockout experiments and correlated with CC16 levels: BPIFA1, SFTPD, LTF, and LYZ (Iannuzo, Front. Immunol 2023). We developed a four-gene biomarker score in 94 patients with asthma from the NHLBI-sponsored Severe Asthma Research Program (SARP3) who underwent research bronchoscopy for lower airway epithelial brushings, RNA isolation, and bulk RNA sequencing. We log-transformed the raw values of each transcript; obtained standardized residuals from regression models that included sex, age, and batch effects; and summed the standardized residuals of all four proteins to estimate a standardized sum score (Zhai, JACI Allergy 2024). Logistic regression models tested the four-gene score, three-gene score without LTF, and individual transcripts for associations with exacerbations during 12-month follow-up and zero-inflated negative binomial regression for number of exacerbations. Results: 30.85% percent of 94 patients experienced at least one exacerbation at the end of follow-up. The CC16-associated four-gene biomarker score showed protective effects on asthma exacerbation risk with a 54% percent reduction in exacerbation risk for every one-fold higher score (OR=0.46, 95% CI: 0.27-0.78, p =0.0040, Figure 1). Transcript expression of BPIFA1 (OR=0.87, 95% CI:0.66-1.16, p=0.337), SFTPD (OR=0.33, 95% CI:0.04-2.52, p=0.283), LYZ (OR=0.62, 95%CI:0.28-1.39, p=0.248), and LTF (OR=0.36, 95% CI:0.18-0.74, p=0.0053) all indicated protective directionality with respect to exacerbation risk. However, only LTF demonstrated a statistically significant association. When we excluded LTF, the remaining three-gene score showed a trend toward association with risk (OR=0.64, 95% CI:0.40-1.03, p=0.068) and was significantly associated with number of exacerbations (RR=0.49, 95% CI:0.26-0.95, p=0.034). Compared to LTF alone (OR=0.48, PPV=0.50, NPV=0.71, Sensitivity=0.14, AUC=0.65), the combined four-gene score (OR=0.46, PPV=0.60, NPV= 0.73, Sensitivity=0.21, AUC=0.68) showed improved predictive performance. Conclusions: We identified a CC16-associated transcriptomic predictive score for asthma exacerbations, with LTF showing the strongest effect. Although the other three genes showed individually weak and non-significant associations with exacerbations, the three gene score was significant for number of exacerbations.We demonstrate the potential of this transcriptomic score as a predictive biomarker for asthma severity-related outcomes. Further research investigating these genes and their interaction with other CC16-associated immune pathways could provide insight into therapeutic targets.

Rationale: We previously derived three asthma phenotypes from clinical data, quantitative computed tomography (qCT), and computational fluid dynamics (CFD)-incorporated cluster analysis: old, obese, and intermediate groups (AJRCCM 2024;209:A2776). The obese asthma cluster demonstrated worse asthma control test (ACT) and asthma quality of life (AQLQ). We hypothesize that obese asthmatics may experience greater aerodynamic force and pressure in airways, which contribute to poorer asthma control in the setting of less mucus plugging. Methods: Inspiratory and expiratory CTs, clinical data, and mucus plug scores were collected from 172 asthma patients in SARP III. For 97 subjects with high-quality image segmentation, we used CFD airflow simulations of breathing (tidal volume: 6 ml/kg) to compute air flowrate and pressure in individual branches throughout the entire conducting airways. Average aerodynamic force applied around segmental branches were analyzed from pressure and cross-sectional area of each branch at airway generation 3-6. To discriminate effects of obesity and aging, 172 patients were classified into four groups based on age (>65 years old) and obesity (BMI>30). Kruskal-Wallis with Dunn's test and Spearman correlation analysis were used, with statistical significance by p<0.05. Results: CFD-derived peak expiratory aerodynamic force and air pressure around segmental airways were greater in the obese cluster (105±40 µN, 22.7±9.2 Pa, p<0.001 for all) than those in the others (old: 43±13 µN, 8.5±2.8 Pa; intermediate: 37±15 µN, 7.3±3.2 Pa), where force was highly associated with BMI (r=0.81, p<0.001). The obese cluster had higher flowrate (0.004±0.001 L/s, p<0.001) than the others (old: 0.003±0.001 L/s; intermediate: 0.002±0.001 L/s) but did not exhibit different airway luminal area. Greater force and pressure were associated with worse ACT (force r=-0.34, p<0.001; pressure r=0.35, p<0.001) and AQLQ scores (force r=-0.32, p=0.001; pressure r=-0.30, p=0.003). Elderly obese asthmatics had less mucus plugging (1.25±2.2, p=0.004), less functional small airway disease percentage (fSAD%, 7.1±9.0%, p=0.002), and higher post-bronchodilator FEV1/FVC (89.5±12.2%, p=0.004) than the elderly non-obese group (mucus score=9.3±7.2, fSAD%=22.1±18.0%, FEV1/FVC=71.5±7.3%), without significant difference from non-elderly obese asthmatics. Conclusion: qCT-CFD analysis showed that obese patients with asthma may experience higher aerodynamic force and pressure during normal breathing than nonobese asthmatics. This may contribute to greater remodeling and healthcare utilization despite preserved lung function and less mucus plugging in elderly obese asthmatics than in elderly nonobese asthmatics.

Determining spatial location of cells within tissues gives vital insight into the interactions between resident and inflammatory cells and is a critical factor for uncoupling the mechanisms driving disease. Here, we apply single-cell spatial transcriptomics to reveal the airway wall landscape in health and during asthma. We identified proinflammatory cellular ecosystems that exist within discrete spatial niches in healthy and asthma samples. These cellular hubs are characterized by a high level of chemokine and alarmin expression, along with unique combinations of stromal cells. Mechanistically, we demonstrated that receptors, such as ACKR1, retain immune mediators locally, while amphiregulin-expressing mast cells are prominent within these proinflammatory hubs. Despite anti-inflammatory treatments, the asthma airway mucosa exhibited a distinct remodeling program within these cellular ecosystems, marked by increased proximity between key cell types. This study provides an unprecedented view of the topography of the airway wall, revealing distinct, specific ecosystems within spatial niches to target for therapeutic intervention.