Abstract

Although intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) presents with persistent inflammatory stimulation of the blood vessels and an increased risk of coronary artery dilatation. However, the pathogenesis of this disease is unclear, with no established biomarkers to predict its occurrence. This study intends to explore the utility of S100A12/TLR2-related signaling molecules and clinical indicators in the predictive modeling of IVIG-resistant KD. The subjects were classified according to IVIG treatment response: 206 patients in an IVIG-sensitive KD group and 49 in an IVIG-resistant KD group. Real-time PCR was used to measure the expression of S100A12, TLR2, MYD88, and NF-κB in peripheral blood mononuclear cells of patients, while collecting demographic characteristics, clinical manifestations, and laboratory test results of KD children. Multi-factor binary logistic regression analysis identified procalcitonin (PCT) level (≥ 0.845 ng/mL), Na level (≤ 136.55 mmol/L), and the relative expression level of S100A12 (≥ 10.224) as independent risk factors for IVIG-resistant KD and developed a new scoring model with good predictive ability to predict the occurrence of IVIG-resistant KD.

Details

Title
Combination of S100A12/TLR2 signaling molecules and clinical indicators in a new predictive model for IVIG-resistant Kawasaki disease
Author
Wu, Yali 1 ; Liu, Pan 2 ; Zhou, Yang 1 ; Yang, Youjun 1 ; Li, Shiyu; Yin, Wei 1 ; Liu, Fan 1 ; Ding, Yan 1 

 Huazhong University of Science and Technology, Department of Rheumatology and Immunology, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Wuhan, China (GRID:grid.33199.31) (ISNI:0000 0004 0368 7223) 
 Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Department of Pediatrics, Wuhan, China (GRID:grid.412787.f) (ISNI:0000 0000 9868 173X) 
Pages
7261
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3003351972
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.