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Background: The therapeutic landscape of chronic myeloid leukaemia (CML) has been transformed by tyrosine kinase inhibitors (TKI). Nilotinib, showed higher rates of major molecular response than imatinib, however associated with higher cardiovascular (CV) toxicity. We sought to describe the CV events associated with nilotinib in a real-world population and assess the predictive value of the HFA-ICOS risk score., Methods: The HFA-ICOS baseline risk was calculated for patients with CML treated with nilotinib beween 2006 and 2021. The primary end point was the incidence of all CV events. The secondary end point was the incidence of ischaemic events. Survival analysis evaluated the risk (hazard ratio [HR]) of events stratified by baseline risk category, whilst on nilotinib therapy., Results: Two hundred and twenty-nine eligible patients were included. The incidence of CV events was 20.9% (95% CI: 15.7-26.2%) following a median duration of treatment of 34.4 months. The secondary end point occurred in 12.7% (95% CI: 8.4-16.9%) of the population. Patients with higher HFA-ICOS baseline score had higher rates of CV events (low: 11.2%, medium: 28.2% [HR: 2.51, 95% CI: 1.17-5.66], high/very high: 32.4% [HR: 3.57, 95% CI: 1.77-7.20]) and ischaemic events (low: 5.20%, medium: 17.9% [HR: 2.19, 95% CI: 0.97-4.96], high/very high: 21.6% [HR: 3.9, 95% CI: 1.91-7.89]). In patients who did not have a CV event, the median total dose at last follow up or cessation of nilotinib therapy was lower when compared to the total daily median dose of nilotinib in patients who had a CV event (450 mg vs. 600 mg, p = 0.0074)., Conclusions: The HFA-ICOS risk stratification tool is an efficient discriminator at low, medium and high/very high risk of developing cardiovascular events, with an overall positive trend towards increasing cardiotoxicity rates with rising risk catergories. This study provides evidence to support the use of this predictive tool in nilotinib treated patients., (© 2024. The Author(s).)

Purpose: Asthma is a clinical syndrome with various underlying pathomechanisms and clinical phenotypes. Genetic, ethnic, and geographic factors may influence the differences in clinical presentation, severity, and prognosis. We compared the characteristics of asthma based on the geographical background by analyzing representative cohorts from the United States, Europe, South America, and Asia using the Severe Asthma Research Program (SARP), Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED), Program for Control of Asthma in Bahia (ProAR), and Cohort for Reality and Evolution of Adult Asthma in Korea (COREA), respectively., Methods: The clinical characteristics and medications for the SARP (n = 669), U-BIOPRED (n = 509), ProAR (n = 996), and COREA (n = 3,748) were analyzed. Subgroup analysis was performed for severe asthma., Results: The mean age was highest and lowest in the COREA and SARP, respectively. The asthma onset age was lowest in the ProAR. The mean body mass index was highest and lowest in the SARP and COREA, respectively. Baseline pulmonary function was lowest and highest in the U-BIOPRED and COREA, respectively. The number of patients with acute exacerbation in the previous year was highest in U-BIOPRED. The mean blood eosinophil count was highest in COREA. The total immunoglobulin E was highest in the ProAR. The frequency of atopy was highest in the SARP. The principal component analysis plot revealed differences among all cohorts., Conclusions: The cohorts from 4 different continents exhibited different clinical and physiological characteristics, probably resulting from the interplay between genetic susceptibility and geographical factors., Competing Interests: There are no financial or other issues that might lead to conflict of interest., (Copyright © 2024 The Korean Academy of Asthma, Allergy and Clinical Immunology • The Korean Academy of Pediatric Allergy and Respiratory Disease.)

Background: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development., Objectives: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma., Methods: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics., Results: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy., Conclusion: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes., Key Points: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22., (© 2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)