Exploratory analysis of plasma cytokine/chemokine levels in 6-year-old children from a birth cohort study

Kiwako Yamamoto-Hanada; Eiryo Kawakami; Mayako Saito-Abe; Miori Sato; Hiroshi Mitsubuchi; Masako Oda; Takahiko Katoh; Masafumi Sanefuji; Shouichi Ohga; Mari Kuwajima; Nathan Mise; Akihiko Ikegami; Fujio Kayama; Ayako Senju; Masayuki Shimono; Koichi Kusuhara; Shin Yamazaki; Shoji F Nakayama; Kenji Matsumoto; Hirohisa Saito; Yukihiro Ohya

doi:10.1016/j.cyto.2020.155051

Exploratory analysis of plasma cytokine/chemokine levels in 6-year-old children from a birth cohort study

Cytokine. 2020 Mar 6:130:155051. doi: 10.1016/j.cyto.2020.155051. Online ahead of print.

Authors

Kiwako Yamamoto-Hanada¹, Eiryo Kawakami², Mayako Saito-Abe³, Miori Sato³, Hiroshi Mitsubuchi⁴, Masako Oda⁴, Takahiko Katoh⁴, Masafumi Sanefuji⁵, Shouichi Ohga⁵, Mari Kuwajima⁵, Nathan Mise⁶, Akihiko Ikegami⁶, Fujio Kayama⁶, Ayako Senju⁷, Masayuki Shimono⁷, Koichi Kusuhara⁷, Shin Yamazaki⁸, Shoji F Nakayama⁹, Kenji Matsumoto¹⁰, Hirohisa Saito¹⁰, Yukihiro Ohya³

Affiliations

¹ Allergy Center, Medical Support Center for the Japan Environment and Children's Study, National Center for Child Health and Development, Tokyo, Japan; Medical Support Center for the Japan Environment and Children's Study, National Research Institute for Child Health and Development, Tokyo, Japan. Electronic address: yamamoto-k@ncchd.go.jp.
² RIKEN Medical Sciences Innovation Hub Program, Kanagawa, Japan; Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
³ Allergy Center, Medical Support Center for the Japan Environment and Children's Study, National Center for Child Health and Development, Tokyo, Japan; Medical Support Center for the Japan Environment and Children's Study, National Research Institute for Child Health and Development, Tokyo, Japan.
⁴ Regional Center for Pilot Study of Japan Environment and Children's Study, Kumamoto University, Kumamoto, Japan.
⁵ Regional Center for Pilot Study of Japan Environment and Children's Study, Kyushu University, Fukuoka, Japan.
⁶ Regional Center for Pilot Study of Japan Environment and Children's Study, Jichi Medical University, Tochigi, Japan.
⁷ Regional Center for Pilot Study of Japan Environment and Children's Study, University of Occupational and Environmental Health, Fukuoka, Japan.
⁸ Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Ibaraki, Japan.
⁹ Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Ibaraki, Japan. Electronic address: fabre@nies.go.jp.
¹⁰ Allergy Center, Medical Support Center for the Japan Environment and Children's Study, National Center for Child Health and Development, Tokyo, Japan; Department of Allergy and Clinical Immunology, National Research Institute for Child Health and Development, Tokyo, Japan.

PMID: 32151964
DOI: 10.1016/j.cyto.2020.155051

Abstract

This study aimed to reveal a new dimension of allergy profiles in the general population by using machine learning to explore complex relationships among various cytokines/chemokines and allergic diseases (asthma and atopic dermatitis; AD). We examined the symptoms related to asthma and AD and the plasma levels of 72 cytokines/chemokines obtained from a general population of 161 children at 6 years of age who participated in a pilot birth cohort study of the Japan Environment and Children's Study (JECS). The children whose signs and symptoms fulfilled the criteria of AD, which are mostly based on questionnaire including past symptoms, tended to have higher levels of the two chemokine ligands, CCL17 and CCL27, which are used for diagnosis of AD. On the other hand, another AD-related chemokine CCL22 level in plasma was higher only in children with visible flexural eczema, which is one of AD diagnostic criteria but was judged on the same day of blood examination unlike other criteria. Here, we also developed an innovative method of machine learning for elucidating the complex cytokine/chemokine milieu related to symptoms of allergic diseases by using clustering analysis based on the random forest dissimilarity measure that relies on artificial intelligence (AI) technique. To our surprise, the majority of children showing at least any asthma-related symptoms during the last month were divided by AI into the two clusters, either cluster-2 having elevated levels of IL-33 (related to eosinophil activation) or cluster-3 having elevated levels of CXCL7/NAP2 (related to neutrophil activation), among the total three clusters. Future studies will clarify better approach for allergic diseases by endotype classification.

Keywords: Asthma; Atopic dermatitis; Child; Cohort study; Machine learning.