Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783624830167285760 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7746618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenliqin predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT songsirui predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT ningqianqian predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT zhudanying predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT jiajia predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT zhanghan predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT zhaojian predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT haoshiying predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT liufang predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT chuchen predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT huangmeirong predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT chensun predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT xielijian predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT xiaotingting predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease AT huangmin predictionforintravenousimmunoglobulinresistancecombininggeneticrisklociidentifiedfromnextgenerationsequencingandlaboratorydatainkawasakidisease |