Cluster analysis of inhalant allergens in south korea: A computational model of allergic sensitization

Dong Kyu Kim, Young Sun Park, Kyung Joon Cha, Daeil Jang, Seungho Ryu, Kyung Rae Kim, Sang Heon Kim, Ho Joo Yoon, Seok Hyun Cho

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)93-99
Number of pages7
JournalClinical and Experimental Otorhinolaryngology
Volume14
Issue number1
DOIs
StatePublished - 2021 Feb

Keywords

  • Allergen
  • Cluster analysis
  • Computational biology
  • Skin test

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