Multivariate analysis and model building for classifying patients in the peroxisomal disorders X-linked adrenoleukodystrophy and Zellweger syndrome in Chinese pediatric patients

Orphanet Journal of Rare Diseases

Research

Georgi Z. Genchev¹  Sira Sriswasdi¹  Xiaofen Zhang²  Wei Ji²  Guoli Tian²  Yanmin Wang²  Zhixing Zhu³  Hui Lu⁴ 

[1] Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand;Newborn Screening Center, Shanghai Children’s Hospital; School of Medicine, Shanghai Jiao Tong University, Shanghai, China;Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine; Center for Biomedical Informatics, Shanghai Children’s Hospital; School of Medicine, Shanghai Jiao Tong University, Shanghai, China;Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine; Center for Biomedical Informatics, Shanghai Children’s Hospital; School of Medicine, Shanghai Jiao Tong University, Shanghai, China;SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China;

关键词: X-linked adrenoleukodystrophy;    X-ALD;    Zellweger syndrome;    Very long chain fatty acids;    C26: carnitine;    Hexacosanoylcarnitine;    Newborn screening;    Metabolomic signature;    PLS-DA;    Sparse PLS-DA;    PCA;    t-SNE;   

DOI  :  10.1186/s13023-023-02673-x

 received in 2022-11-08, accepted in 2023-03-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe peroxisome is a ubiquitous single membrane-enclosed organelle with an important metabolic role. Peroxisomal disorders represent a class of medical conditions caused by deficiencies in peroxisome function and are segmented into enzyme-and-transporter defects (defects in single peroxisomal proteins) and peroxisome biogenesis disorders (defects in the peroxin proteins, critical for normal peroxisome assembly and biogenesis). In this study, we employed multivariate supervised and non-supervised statistical methods and utilized mass spectrometry data of neurological patients, peroxisomal disorder patients (X-linked adrenoleukodystrophy and Zellweger syndrome), and healthy controls to analyze the role of common metabolites in peroxisomal disorders, to develop and refine a classification models of X-linked adrenoleukodystrophy and Zellweger syndrome, and to explore analytes with utility in rapid screening and diagnostics.ResultsT-SNE, PCA, and (sparse) PLS-DA, operated on mass spectrometry data of patients and healthy controls were utilized in this study. The performance of exploratory PLS-DA models was assessed to determine a suitable number of latent components and variables to retain for sparse PLS-DA models. Reduced-features (sparse) PLS-DA models achieved excellent classification performance of X-linked adrenoleukodystrophy and Zellweger syndrome patients.ConclusionsOur study demonstrated metabolic differences between healthy controls, neurological patients, and peroxisomal disorder (X-linked adrenoleukodystrophy and Zellweger syndrome) patients, refined classification models and showed the potential utility of hexacosanoylcarnitine (C26:0-carnitine) as a screening analyte for Chinese patients in the context of a multivariate discriminant model predictive of peroxisomal disorders.

【 授权许可】

CC BY   
© The Author(s) 2023. corrected publication 2023

【 图 表 】

40517_2023_252_Article_IEq133.gif

40517_2023_252_Article_IEq127.gif

40517_2023_252_Article_IEq124.gif

40517_2023_252_Article_IEq122.gif

40517_2023_252_Article_IEq103.gif

40517_2023_252_Article_IEq86.gif