Background Asthma is a heterogenous airway disease characterized by multiple phenotypes. Unbiased identification of these phenotypes is paramount for optimizing asthma management. Objectives To identify and characterize asthma phenotypes based on a broad set of attributes using a novel machine learning approach in a representative sample of Swedish adults. Methods Deep learning clustering was used to derive asthma phenotypes in a sample of 1,895 subjects aged 16-75, drawn from the ongoing West Sweden Asthma Study. The algorithm integrated 47 variables encompassing demographics, risk factors, asthma triggers, pulmonary function, disease severity, allergy, and comorbidity profiles. The optimal clustering solution was selected by combining statistical metrics and clinical interpretation. Results A four-cluster solution was determined to reliably represent the data, resulting in distinct phenotypes described as: (1) troublesome, late-onset, non-atopic asthma with smoking ( n=458, 24.2%); 2) female-dominated early adult-onset asthma ( n=545, 28.7%); 3) adult-onset asthma with high inflammation ( n=358, 18.9%); and 4) early-onset, mild, atopic asthma ( n=534, 28.2%). The phenotypes also differed with respect to demographics, risk factors, asthma triggers, pulmonary function, symptom profiles, and markers of inflammation. Current asthma was more common in phenotypes with later age of asthma onset than phenotypes with early onset. Conclusion Four clinically meaningful asthma phenotypes, distinguishable by age of onset, severity, risk factors, and prognosis, were found in Swedish adults. This provides a setting for future research to profile the immunological basis of the phenotypes, and further our understanding of their pathophysiology, therapeutic possibilities, future clinical outcomes, and societal burden.

Background: This initiative aimed to elucidate the clinical relevance of type 2 (T2) inflammation as a driver of asthma, atopic dermatitis, chronic rhinitis, chronic rhinosinusitis with nasal polyps (CRSwNP) and eosinophilic esophagitis. Methods: A steering committee (SC) conducted a non-systematic literature search to inform the design of a Delphi questionnaire including 23 consensus statements, which was circulated to 30 experts including the SC. Experts rated their agreement with each statement on a 9-point Likert scale and provided optional feedback that was used to develop a second Delphi questionnaire. On 22 October 2020, a meeting was held to discuss the conclusions from the questionnaires and explore how this initiative may impact the management of patients with T2 inflammation-driven disease. Post meeting, a consensus statement on the role of T2 inflammation in eosinophilic esophagitis was circulated to the experts. Results: It was agreed that T2 inflammation may be an underlying driver of asthma, atopic dermatitis, chronic rhinitis, CRSwNP and eosinophilic esophagitis, and that the published evidence suggests that these diseases overlap. Some of this overlap may include related multimorbid conditions driven by T2 inflammation. Thus, in patients with multiple T2 inflammation-driven diseases, a cross-speciality approach is warranted to provide effective care. A question guide with input from relevant experts was proposed, to identify comorbidities and facilitate appropriate holistic patient management. Conclusions: These consensus recommendations should be used as a framework to further understand the extent of T2 inflammation-driven multi-organ disease and to improve the holistic management and care of these patients.

Background: The prevalence of asthma has increased both among children and adults during the latter half of the 20th century. The prevalence among adults is affected by the incidence of asthma in childhood but also in adulthood. Time trends in asthma incidence are poorly studied. The aim was to study the incidence of asthma among adults from 1996-2006 and 2006-2016, and compare the risk factor patterns. Methods: Within the Obstructive Lung Disease in Northern Sweden (OLIN) studies, two randomly selected population-based samples in ages 20-69 years participated in postal questionnaire surveys about asthma in 1996 (n=7104, 85%) and 2006 (n=6165, 77%), respectively. A 10-year follow-up of the two cohorts with the same validated questionnaire was performed, and n=5709 and n=4552, respectively, responded. Different definitions of population at risk were used in the calculations of asthma incidence. The protocol followed a study performed 1986 to 1996 in the same area. Results: The crude incidence rate of physician-diagnosed asthma was 4.4/1000/year (men 3.8, women 5.5) from 1996-2006, and 4.8/1000/year (men 3.7, women 6.2) from 2006-2016. When correcting for possible under-diagnosis at study entry, the incidence rate was 2.4/1000/year from 1996-2006 and 2.6/1000/year from 2006-2016. The incidence rates were similar across age groups. Allergic rhino-conjunctivitis was the main risk factor for incident asthma in both observation periods (risk ratios 2.4-2.6). Conclusions: The incidence of asthma among adults has been stable over the last two decades, and on similar level since the 1980s. The high incidence contributes to the increase in asthma prevalence.