Aspirin hypersensitivity diagnostic index (AHDI): In vitro test for diagnosing of N-ERD based on urinary 15-oxo-ETE and LTE4 excretion.

BACKGROUND: 15-oxo-eicosatetraenoic acid (15-oxo-ETE), is a product of arachidonic acid (AA) metabolism in the 15-lipoxygenase-1 (15-LOX-1) pathway. 15-oxo-ETE was overproduced in the nasal polyps of patients with nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD). In this study we investigated the systemic biosynthesis of 15-oxo-ETE and leukotriene E4 (LTE4) and assessed their diagnostic value to identify patients with N-ERD. METHODS: The study included 64 patients with N-ERD, 59 asthmatics who tolerated aspirin well (ATA), and 51 healthy controls. A thorough clinical characteristics of asthmatics included computed tomography of paranasal sinuses. Plasma and urinary 15-oxo-ETE levels, and urinary LTE4 excretion were measured using high-performance liquid chromatography and tandem mass spectrometry. Repeatability and precision of the measurements were tested. RESULTS: Plasma 15-oxo-ETE levels were the highest in N-ERD (p < .001). A receiver operator characteristic (ROC) revealed that 15-oxo-ETE had certain sensitivity (64.06% in plasma, or 88.24% in urine) for N-ERD discrimination, while the specificity was rather limited. Modeling of variables allowed to construct the Aspirin Hypersensitivity Diagnostic Index (AHDI) based on urinary LTE4-to-15-oxo-ETE excretion corrected for sex and the Lund-Mackay score of chronic rhinosinusitis. AHDI outperformed single measurements in discrimination of N-ERD among asthmatics with an area under ROC curve of 0.889, sensitivity of 81.97%, specificity of 87.23%, and accuracy of 86.87%. CONCLUSIONS: We confirmed 15-oxo-ETE as a second to cysteinyl leukotrienes biomarker of N-ERD. An index based on these eicosanoids corrected for sex and Lund-Mackay score has a similar diagnostic value as gold standard oral aspirin challenge in the studied group of patients with asthma.

Increased factor XI but not factor XII is associated with enhanced inflammation and impaired fibrin clot properties in patients with eosinophilic granulomatosis with polyangiitis.

OBJECTIVES: In eosinophilic granulomatosis with polyangiitis (EGPA) a prothrombotic state, including formation of denser fibrin networks with reduced lysability has been observed. Little is known about the intrinsic pathway in EGPA. We investigated whether coagulation factors (F)XI and FXII are associated with eosinophil-driven prothrombotic state. METHODS: In 34 consecutive EGPA patients with remission we assessed FXI and FXII levels along with plasma fibrin clot permeability (Ks), fibrin clot morphology using scanning electron microscopy, and efficiency of fibrinolysis, expressed as lysis time (t50%) and maximum rate of increase in D-dimer levels (D-Drate). RESULTS: Increased FXI level (>130%, the upper reference limit) was found in 8 (23.5%) patients. Compared to patients with FXI levels ≤130%, those with increased FXI had higher eosinophil count (+365%) and reduced percentage of neutrophils (-20.4%), along with reduced Ks (-20.5%). In patients with FXI>130% clots were composed of thinner fibrin fibers (-17.5%). FXI was not associated with C-reactive protein and fibrinogen levels or anti-neutrophil cytoplasmic antibodies titers. There were no correlations between FXI and FXII levels as well as between FXII and eosinophil count (all p>0.05). CONCLUSIONS: To our knowledge, this study is the first to show association between FXI and a prothrombotic state in EGPA. Given clinical trials on FXI inhibition as an antithrombotic option, our findings suggest that this therapeutic approach could be useful in diseases with hypereosinophilia.

Non-eosinophilic asthma in nonsteroidal anti-inflammatory drug exacerbated respiratory disease.

BACKGROUND: The cellular inflammatory pattern of nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD) is heterogeneous. However, data on the heterogeneity of non-eosinophilic asthma (NEA) with aspirin hypersensitivity are scanty. By examination of N-ERD patients based on clinical data and eicosanoid biomarkers we aimed to identify NEA endotypes potentially guiding clinical management. METHODS: Induced sputum was collected from patients with N-ERD. Sixty six patients (49.6% of 133 N-ERD) with NEA were included in the hierarchical cluster analysis based on clinical and laboratory data. The quality of clustering was evaluated using internal cluster validation with different indices and a practical decision tree was proposed to simplify stratification of patients. RESULTS: The most frequent NEA pattern was paucigranulocytic (PGA; 75.8%), remaining was neutrophilic asthma (NA; 24.2%). Four clusters were identified. Cluster #3 included the highest number of NEA patients (37.9%) with severe asthma and PGA pattern (96.0%). Cluster #1 (24.2%) included severe only asthma, with a higher prevalence of NA (50%). Cluster #2 (25.8%) comprised well-controlled mild or severe asthma (PGA; 76.5%). Cluster #4 contained only 12.1% patients with well-controlled moderate asthma (PGA; 62.5%). Sputum prostaglandin D2 levels distinguished cluster #1 from the remaining clusters with an area under the curve of 0.94. CONCLUSIONS: Among identified four NEA subtypes, clusters #3 and #1 represented N-ERD patients with severe asthma but a different inflammatory signatures. All the clusters were discriminated by sputum PGD2 levels, asthma severity, and age of patients. The heterogeneity of non-eosinophilic N-ERD suggests a need for novel targeted interventions.

Monocyte exposure to fine particulate matter results in miRNA release: A link between air pollution and potential clinical complication.

Chronic exposure to PM2.5 contributes to the pathogenesis of numerous disorders, although the underlying mechanisms remain unknown. The study investigated whether exposure of human monocytes to PM2.5 is associated with alterations in miRNAs. Monocytes were exposed in vitro to PM2.5 collected during winter and summer, followed by miRNA isolation from monocytes. Additionally, in 140 persons chronically exposed to air pollution, some miRNA patterns were isolated from serum seasonally. Between-season differences in chemical PM2.5 composition were observed. Some miRNAs were expressed both in monocytes and in human serum. MiR-34c-5p and miR-223-5p expression was more pronounced in winter. Bioinformatics analyses showed that selected miRNAs were involved in the regulation of several pathways. The expression of the same miRNA species in monocytes and serum suggests that these cells are involved in the production of miRNAs implicated in the development of disorders mediated by inflammation, oxidative stress, proliferation, and apoptosis after exposure to PM2.5.

Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma. Methods: We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. Results: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. Conclusions: ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.

Local and Systemic Production of Pro-Inflammatory Eicosanoids Is Inversely Related to Sensitization to Aeroallergens in Patients with Aspirin-Exacerbated Respiratory Disease.

Aspirin-exacerbated respiratory disease (AERD) is characterized by overproduction of the pro-inflammatory eicosanoids. Although immunoglobulin E-mediated sensitization to aeroallergens is common among AERD patients, it does not belong to the defining disease characteristics. In this study of 133 AERD patients, we sought to find a relationship between sensitization to aeroallergens and local (leukotriene E4, prostaglandin E2 and prostaglandin D2) and/or systemic (leukotriene E4) production of arachidonic acid metabolites. Interestingly, a negative association between pro-inflammatory eicosanoid levels in induced sputum supernatant or urine and sensitization to aeroallergens was observed. This inverse relationship might suggest the presence of a protective effect of atopic sensitization to aeroallergens against stronger local airway inflammation and higher systemic AERD-related inflammatory activity.

Biomarkers for predicting response to long-term high dose aspirin therapy in aspirin-exacerbated respiratory disease.

INTRODUCTION: Aspirin-exacerbated respiratory disease (AERD) is a phenotype of asthma characterized by eosinophilic inflammation in the airways, mast cell activation, cysteinyl leukotriene overproduction, and acute respiratory reactions on exposure to cyclooxygenase-1 inhibitors. Aspirin desensitization followed by daily high-dose aspirin therapy is a safe and effective treatment option for the majority of patients with AERD. However, there is still some percentage of the population who do not derive benefits from daily aspirin use. METHODS: Based on the current literature, the biomarkers, which might predict aspirin treatment outcomes in AERD patients, were evaluated. RESULTS AND CONCLUSIONS: Patients with severe symptoms of chronic rhinosinusitis, type 2 asthma based on blood eosinophilia, non-neutrophilic inflammatory phenotype based on sputum cells, as well as high plasma level of 15-hydroxyeicosatetraenoic acid (15-HETE) are potentially good responders to long term high-dose aspirin therapy. Additionally, high expression of the hydroxyprostaglandin dehydrogenase gene, HPGD encoding prostaglandin-degrading enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH) and low expression of the proteoglycan 2 gene, PRG2 encoding constituent of the eosinophil granule in sputum cells might serve as a predictor of good response to aspirin therapy. Variations in the expression of cysteinyl leukotriene receptor 1 in the airways could additionally influence the response to long-term aspirin therapy. Arachidonic acid metabolites levels via the 5-lipoxygenase as well as via the cyclooxygenase pathways in induced sputum supernatant do not change during high dose long-term aspirin therapy and do not influence outcomes of aspirin treatment.

Eicosanoids and Eosinophilic Inflammation of Airways in Stable COPD.

PURPOSE: Lipid mediators, particularly eicosanoids, are associated with airway inflammation, especially with the eosinophilic influx. This study aimed to measure lipid mediators and cells in induced sputum, that could possibly reflect the inflammatory process in the bronchial tree of COPD subjects. PATIENTS AND METHODS: Eighty patients diagnosed with COPD and 37 healthy controls participated in the study. Induced sputum samples were ascertained for differential cell count and induced sputum supernatant concentrations of selected eicosanoids by the means of gas chromatography/mass spectrometry and high-performance liquid chromatography/tandem mass spectrometry. RESULTS: Increased sputum eosinophilia was associated with higher concentrations of selected proinflammatory eicosanoids. In COPD subjects prostaglandin D2 and 11-dehydro-thromboxane B2 correlated negatively with airway obstruction measured by FEV1 and FEV1/FVC values. COPD subjects with disease exacerbations during past 12 months had significantly higher concentrations of prostaglandin D2, 12-oxo-eicosatetraenoic acid and 5-oxo-eicosatetraenoic acid. CONCLUSION: Stable COPD is often associated with eosinophil influx in the lower airways and elevated concentrations of eicosanoids that is reflected by some disease characteristics.

Biomarkers for predicting response to aspirin therapy in aspirin-exacerbated respiratory disease.

BACKGROUND: Aspirin desensitization followed by daily aspirin use is an effective treatment for aspirin-exacerbated respiratory disease (AERD). OBJECTIVE: To assess clinical features as well as genetic, immune, cytological and biochemical biomarkers that might predict a positive response to high-dose aspirin therapy in AERD. METHODS: We enrolled 34 AERD patients with severe asthma who underwent aspirin desensitization followed by 52-week aspirin treatment (650 mg/d). At baseline and at 52 weeks, clinical assessment was performed; phenotypes based on induced sputum cells were identified; eicosanoid, cytokine and chemokine levels in induced sputum supernatant were determined; and induced sputum expression of 94 genes was assessed. Responders to high-dose aspirin were defined as patients with improvement in 5-item Asthma Control Questionnaire score, 22-item Sino-Nasal Outcome Test (SNOT-22) score and forced expiratory volume in 1 second at 52 weeks. RESULTS: There were 28 responders (82%). Positive baseline predictors of response included female sex (p = .002), higher SNOT-22 score (p = .03), higher blood eosinophil count (p = .01), lower neutrophil percentage in induced sputum (p = .003), higher expression of the hydroxyprostaglandin dehydrogenase gene, HPGD (p = .004) and lower expression of the proteoglycan 2 gene, PRG2 (p = .01). The best prediction model included Asthma Control Test and SNOT-22 scores, blood eosinophils and total serum immunoglobulin E. Responders showed a marked decrease in sputum eosinophils but no changes in eicosanoid levels. CONCLUSIONS AND CLINICAL RELEVANCE: Female sex, high blood eosinophil count, low sputum neutrophil percentage, severe nasal symptoms, high HPGD expression and low PRG2 expression may predict a positive response to long-term high-dose aspirin therapy in patients with AERD.

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.

Subphenotypes of nonsteroidal antiinflammatory disease-exacerbated respiratory disease identified by latent class analysis.

BACKGROUND: Induced sputum (IS) allows to measure mediators of asthmatic inflammation in bronchial secretions. NSAID-exacerbated respiratory disease (NERD) is recognized as a distinct asthma phenotype, usually with a severe course, eosinophilic airway inflammation, and increased production of pro-inflammatory eicosanoids. A more insightful analysis of NERD patients has shown this phenotype to be nonhomogeneous. OBJECTIVE: We aimed to identify possible subphenotypes in a cohort of NERD patients with the means of latent class analysis (LCA). METHODS: A total of 95 asthma patients with aspirin hypersensitivity underwent sputum induction. High-performance liquid chromatography or gas chromatography coupled with mass spectrometry was used to profile eicosanoids in induced sputum supernatant (ISS). Sixteen variables covering clinical characteristics, IS inflammatory cells, and eicosanoids were considered in the LCA. RESULTS: Three classes (subphenotypes) were distinguished within the NERD cohort. Class 1 subjects had mild-to-moderate asthma, an almost equal distribution of inflammatory cell patterns, the lowest concentrations of eicosanoids, and logLTE4 /logPGE2 ratio. Class 2 represented severe asthma with impaired lung function despite high doses of steroids. High sputum eosinophilia was in line with higher pro-inflammatory LTE4 in ISS and the highest logLTE4 /logPGE2 ratio. Class 3 subjects had mild-to-moderate asthma and were also characterized by eosinophilic airway inflammation, yet increased production of pro- (LTE4 , PGD2 and 11-dehydro-TBX2 ) was balanced by anti-inflammatory PGE2 . The value of logLTE4 /logPGE2 was between values calculated for classes 1 and 3, similarly to disease control and severity. CONCLUSIONS: LCA revealed three distinct NERD subphenotypes. Our results support a more complex pathobiology of aspirin hypersensitivity. Considering NERD heterogeneity, the relationship between inflammatory pathways and clinical manifestations of asthma may lead to more individualized treatment in difficult to treat patients in the future.