首页|Early prediction of growth patterns after pediatric kidney transplantation based on height-related single-nucleotide polymorphisms

Early prediction of growth patterns after pediatric kidney transplantation based on height-related single-nucleotide polymorphisms

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Background:Growth retardation is a common complication of chronic kidney disease in children,which can be partially relieved after renal transplantation.This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease(ESRD)after kidney transplantation using machine learning algorithms based on genomic and clinical variables.Methods:A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing(WES),and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation.Based on previous studies,we comprehensively collected 729 height-related single-nucleotide polymorphisms(SNPs)in exon regions.Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction.Results:The 110 children were divided into two groups according to change in height-for-age Z-score.After univariate analysis,age and 19 SNPs were incorporated into the model and validated.The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve(AUC)of 0.924,and also performed well in the external validation cohort(accuracy,0.7949;AUC,0.796).Conclusions:A model with good performance forpredicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms.The model.is expected to guide clinicians in the management of children after renal transplantation,including the use of growth hormone,glucocorticoid withdrawal,and nutritional supplementation,to alleviate growth retardation in children with ESRD.

GrowthMachine learningPediatric kidney transplantationSingle-nucleotide polymorphism

Yi Feng、Yonghua Feng、Mingyao Hu、Hongen Xu、Zhigang Wang、Shicheng Xu、Yongchuang Yan、Chenghao Feng、Zhou Li、Guiwen Feng、Wenjun Shang

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Department of Renal Transplantation,The First Affiliated Hospital of Zhengzhou University,Zhengzhou,Henan 450052,China

Precision Medicine Center,Academy of Medical Science,Zhengzhou University,Zhengzhou,Henan 450052,China

国家自然科学基金

82270792

2024

中华医学杂志(英文版)
中华医学会

中华医学杂志(英文版)

CSTPCD
影响因子:0.838
ISSN:0366-6999
年,卷(期):2024.137(10)
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