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Cluster Analysis of Inhalant Allergens in South Korea: A Computational Model of Allergic Sensitization

Clinical and Experimental Otorhinolaryngology 2021년 14권 1호 p.93 ~ 99
김동규, 박영순, 차경준, 장대일, 류승호, 김경래, 김상헌, 윤호주, 조석현,
소속 상세정보
김동규 ( Kim Dong-Kyu ) - Hallym University College of Medicine Chuncheon Sacred Heart Hospital Department of Otorhinolaryngology-Head and Neck Surgery
박영순 ( Park Young-Sun ) - Hanyang University College of Natural Sciences Department of Mathematics
차경준 ( Cha Kyung-Joon ) - Hanyang University College of Natural Sciences Department of Mathematics
장대일 ( Jang Dae-Il ) - Hanyang University College of Natural Sciences Department of Mathematics
류승호 ( Ryu Seung-Ho ) - Sungkyunkwan University School of Medicine Kangbuk Samsung Hospital Department of Occupational and Environmental Medicine
김경래 ( Kim Kyung-Rae ) - Hanyang University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery
김상헌 ( Kim Sang-Heon ) - Hanyang University College of Medicine Department of Internal Medicine
윤호주 ( Yoon Ho-Joo ) - Hanyang University College of Medicine Department of Internal Medicine
조석현 ( Cho Seok-Hyun ) - Hanyang University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery

Abstract


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.

키워드

Allergen; Skin Test; Cluster Analysis; Computational Biology

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