Background: Asthma is a heterogeneous disease of phenotypes that differ by age at onset. At the same time, there is no consensus on the correct cut-off to determine late-onset asthma. This study aimed to characterize phenotypes of late-onset asthma in the general population using different minimum onset ages. Methods: We used survey and clinical data from two population-based studies. Cluster analysis with a novel deep clustering algorithm was performed in subjects with reported asthma onset at age ≥12 years ( n=3,103), ≥20 years ( n=2,431), and ≥40 years ( n=1,269) including data on anthropometrics/demographics, risk factors, asthma triggers, respiratory symptoms, asthma control, lung function, allergy, inflammation, and comorbidities. The clustering models were interpreted with decision trees, random forests, and a large language model. Potential risk factors, comorbidities, and clinical outcomes were evaluated descriptively. Results: Four clusters were identified in the ≥12 years group and three in the ≥20 years and ≥40 years groups. A partly/uncontrolled asthma cluster with dyspnea, cough, and cardiovascular comorbidity as well as an asthma cluster with broadly environmentally-triggered symptoms were identified in all age groups. A mild/moderate asthma cluster with allergic components was identified in the two younger onset age groups, while a mild asthma cluster was identified within the ≥12 years and ≥40 years groups. The clusters differed substantially by risk factors/exposures, comorbidity patterns, and clinical outcomes. Conclusions: Similar sets of asthma phenotypes arise across the course of adulthood, which are relatively easily identified. However, they are characterized by notable differences in clinical presentation, inflammation, comorbidities, and prognosis.