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.

Introduction: Following the “hygiene hypothesis” and the increase in prevalence of atopic diseases such as allergic rhinitis, a plethora of studies have investigated the role of sibship composition as a protective factor, but findings are conflicting. Aim: To synthesize the global literature linking birth order and sibship size (number of siblings) to the risk of allergic rhinitis. Methods: Fifteen databases were systematically searched, with no restrictions on publication date or language. Observational studies with defined sibship composition (birth order or sibship size) as exposure and allergic rhinitis or allergic rhinoconjunctivitis (self-reported or clinically diagnosed) as outcome were eligible. Study selection, data extraction, and quality assessment was performed independently in pairs. Relevant data were summarized in tables. Comparable numerical data were analyzed using meta-analysis with robust variance estimation (RVE). Results: Seventy-six reports with >2 million subjects were identified. Being second- or later-born child was associated with protection against both current (pooled risk ratio [RR] 0.79, 95%CI 0.73-0.86) and ever (RR 0.77, 95%CI 0.68-0.88) allergic rhinitis. Having siblings, regardless of birth order, was associated with a decreased risk of current allergic rhinitis (RR 0.89, 95% CI 0.83-0.95) and allergic rhinoconjunctivitis (RR 0.92, 95%CI 0.86-0.98). These effects were unchanged across age, time period, and geographical regions. Conclusion: Our findings indicate that primarily, a higher birth order, and to a lesser extent the number of siblings, is associated with a lower risk of developing allergic rhinitis.