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KMID : 1195620210140010093
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Clinical and Experimental Otorhinolaryngology
2021 Volume.14 No. 1 p.93 ~ p.99
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Cluster Analysis of Inhalant Allergens in South Korea: A Computational Model of Allergic Sensitization
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Kim Dong-Kyu
Park Young-Sun
Cha Kyung-Joon
Jang Dae-Il
Ryu Seung-Ho
Kim Kyung-Rae
Kim Sang-Heon
Yoon Ho-Joo
Cho Seok-Hyun
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Abstract
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Objectives: Sensitization to specific inhalant allergens is a major risk factor for the development of atopic diseases, which impose a major socioeconomic burden and significantly diminish quality of life. However, patterns of inhalant allergic sensitization have yet to be precisely described. Therefore, to enhance the understanding of aeroallergens, we performed a cluster analysis of inhalant allergic sensitization using a computational model.
Methods: Skin prick data were collected from 7,504 individuals. A positive skin prick response was defined as an allergen-to-histamine wheal ratio ≥1. To identify the clustering of inhalant allergic sensitization, we performed computational analysis using the four-parameter unified-Richards model.
Results: Hierarchical cluster analysis grouped inhalant allergens into three clusters based on the Davies-Bouldin index (0.528): cluster 1 (Dermatophagoides pteronyssinus and Dermatophagoides farinae), cluster 2 (mugwort, cockroach, oak, birch, cat, and dog), and cluster 3 (Alternaria tenus, ragweed, Candida albicans, Kentucky grass, and meadow grass). Computational modeling revealed that each allergen cluster had a different trajectory over the lifespan. Cluster 1 showed a high level (>50%) of sensitization at an early age (before 19 years), followed by a sharp decrease in sensitization. Cluster 2 showed a moderate level (10%?20%) of sensitization before 29 years of age, followed by a steady decrease in sensitization. However, cluster 3 revealed a low level (<10%) of sensitization at all ages.
Conclusion: Computational modeling suggests that allergic sensitization consists of three clusters with distinct patterns at different ages. The results of this study will be helpful to allergists in managing patients with atopic diseases.
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KEYWORD
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Allergen, Skin Test, Cluster Analysis, Computational Biology
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