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Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease

Background: Kawasaki disease (KD) is the most common cause of acquired heart disease. A proportion of patients were resistant to intravenous immunoglobulin (IVIG), the primary treatment of KD, and the mechanism of IVIG resistance remains unclear. The accuracy of current models predictive of IVIG res...

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Autores principales: Chen, Liqin, Song, Sirui, Ning, Qianqian, Zhu, Danying, Jia, Jia, Zhang, Han, Zhao, Jian, Hao, Shiying, Liu, Fang, Chu, Chen, Huang, Meirong, Chen, Sun, Xie, Lijian, Xiao, Tingting, Huang, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746618/
https://www.ncbi.nlm.nih.gov/pubmed/33344378
http://dx.doi.org/10.3389/fped.2020.462367
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author Chen, Liqin
Song, Sirui
Ning, Qianqian
Zhu, Danying
Jia, Jia
Zhang, Han
Zhao, Jian
Hao, Shiying
Liu, Fang
Chu, Chen
Huang, Meirong
Chen, Sun
Xie, Lijian
Xiao, Tingting
Huang, Min
author_facet Chen, Liqin
Song, Sirui
Ning, Qianqian
Zhu, Danying
Jia, Jia
Zhang, Han
Zhao, Jian
Hao, Shiying
Liu, Fang
Chu, Chen
Huang, Meirong
Chen, Sun
Xie, Lijian
Xiao, Tingting
Huang, Min
author_sort Chen, Liqin
collection PubMed
description Background: Kawasaki disease (KD) is the most common cause of acquired heart disease. A proportion of patients were resistant to intravenous immunoglobulin (IVIG), the primary treatment of KD, and the mechanism of IVIG resistance remains unclear. The accuracy of current models predictive of IVIG resistance is insufficient and doesn't meet the clinical expectations. Objectives: To develop a scoring model predicting IVIG resistance of patients with KD. Methods: We recruited 330 KD patients (50 IVIG non-responders, 280 IVIG responders) and 105 healthy children to explore the susceptibility loci of IVIG resistance in Kawasaki disease. A next generation sequencing technology that focused on 4 immune-related pathways and 472 single nucleotide polymorphisms (SNPs) was performed. An R package SNPassoc was used to identify the risk loci, and student's t-test was used to identify risk factors associated with IVIG resistance. A random forest-based scoring model of IVIG resistance was built based on the identified specific SNP loci with the laboratory data. Results: A total of 544 significant risk loci were found associated with IVIG resistance, including 27 previous published SNPs. Laboratory test variables, including erythrocyte sedimentation rate (ESR), platelet (PLT), and C reactive protein, were found significantly different between IVIG responders and non-responders. A scoring model was built using the top 9 SNPs and clinical features achieving an area under the ROC curve of 0.974. Conclusions: It is the first study that focused on immune system in KD using high-throughput sequencing technology. Our findings provided a prediction of the IVIG resistance by integrating the genotype and clinical variables. It also suggested a new perspective on the pathogenesis of IVIG resistance.
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spelling pubmed-77466182020-12-19 Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease Chen, Liqin Song, Sirui Ning, Qianqian Zhu, Danying Jia, Jia Zhang, Han Zhao, Jian Hao, Shiying Liu, Fang Chu, Chen Huang, Meirong Chen, Sun Xie, Lijian Xiao, Tingting Huang, Min Front Pediatr Pediatrics Background: Kawasaki disease (KD) is the most common cause of acquired heart disease. A proportion of patients were resistant to intravenous immunoglobulin (IVIG), the primary treatment of KD, and the mechanism of IVIG resistance remains unclear. The accuracy of current models predictive of IVIG resistance is insufficient and doesn't meet the clinical expectations. Objectives: To develop a scoring model predicting IVIG resistance of patients with KD. Methods: We recruited 330 KD patients (50 IVIG non-responders, 280 IVIG responders) and 105 healthy children to explore the susceptibility loci of IVIG resistance in Kawasaki disease. A next generation sequencing technology that focused on 4 immune-related pathways and 472 single nucleotide polymorphisms (SNPs) was performed. An R package SNPassoc was used to identify the risk loci, and student's t-test was used to identify risk factors associated with IVIG resistance. A random forest-based scoring model of IVIG resistance was built based on the identified specific SNP loci with the laboratory data. Results: A total of 544 significant risk loci were found associated with IVIG resistance, including 27 previous published SNPs. Laboratory test variables, including erythrocyte sedimentation rate (ESR), platelet (PLT), and C reactive protein, were found significantly different between IVIG responders and non-responders. A scoring model was built using the top 9 SNPs and clinical features achieving an area under the ROC curve of 0.974. Conclusions: It is the first study that focused on immune system in KD using high-throughput sequencing technology. Our findings provided a prediction of the IVIG resistance by integrating the genotype and clinical variables. It also suggested a new perspective on the pathogenesis of IVIG resistance. Frontiers Media S.A. 2020-12-04 /pmc/articles/PMC7746618/ /pubmed/33344378 http://dx.doi.org/10.3389/fped.2020.462367 Text en Copyright © 2020 Chen, Song, Ning, Zhu, Jia, Zhang, Zhao, Hao, Liu, Chu, Huang, Chen, Xie, Xiao and Huang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pediatrics
Chen, Liqin
Song, Sirui
Ning, Qianqian
Zhu, Danying
Jia, Jia
Zhang, Han
Zhao, Jian
Hao, Shiying
Liu, Fang
Chu, Chen
Huang, Meirong
Chen, Sun
Xie, Lijian
Xiao, Tingting
Huang, Min
Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title_full Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title_fullStr Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title_full_unstemmed Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title_short Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease
title_sort prediction for intravenous immunoglobulin resistance combining genetic risk loci identified from next generation sequencing and laboratory data in kawasaki disease
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746618/
https://www.ncbi.nlm.nih.gov/pubmed/33344378
http://dx.doi.org/10.3389/fped.2020.462367
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