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Background <p>Wilson disease (WD) is an autosomal recessive disorder caused by variants in the ATP7B gene, leading to copper metabolism dysfunction and multi-organ damage. Early diagnosis is critical for improving clinical outcomes, but current screening methods have limitations. Metabolomics can reveal early metabolic disturbances in disease; however, the metabolic profile of newborns with WD remains unexplored. This study aimed to identify potential metabolic biomarkers for early WD detection through untargeted metabolomic analysis.</p> Methods <p>Dried blood spot (DBS) samples from six genetically confirmed WD positive newborns and 84 healthy controls were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis was employed to identify differentially abundant metabolites. Pathway enrichment analysis and receiver operating characteristic (ROC) curve evaluation were performed to assess diagnostic performance.</p> Results <p>A total of 29 significantly altered metabolites (21 upregulated, 8 downregulated) were identified in WD positive newborns, primarily associated with tyrosine metabolism. ROC analysis revealed 11 metabolites with an area under the curve (AUC) &gt; 90%. Additionally, two pairs of isomers also demonstrated exhibited high diagnostic sensitivity and specificity and were closely linked to WD pathogenesis. In positive group, tyrosine metabolism pathway was most significantly affected, as evidenced by increased levels of 3,4-Dihydroxyphenylacetic acid and Homogentisic acid, alongside a decreased level of Gentisaldehyde.</p> Conclusion <p>WD positive newborns exhibit distinct metabolic reprogramming prior to copper accumulation, with tyrosine metabolism dysregulation as a potential early feature. The identified differential metabolites may serve as promising biomarkers for newborn WD screening, providing a foundation for metabolomics-based early diagnostic strategies.</p>

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Discovery of newborn Wilson disease biomarkers via integrated next-generation sequencing and untargeted metabolomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • Xianwei Guan,
  • Yun Sun,
  • Yanyun Wang,
  • Yahong Li,
  • Zhilei Zhang,
  • Dongyang Hong,
  • Peiying Yang,
  • Xiaowei Liang,
  • Xin Wang,
  • Bin Yu

摘要

Background

Wilson disease (WD) is an autosomal recessive disorder caused by variants in the ATP7B gene, leading to copper metabolism dysfunction and multi-organ damage. Early diagnosis is critical for improving clinical outcomes, but current screening methods have limitations. Metabolomics can reveal early metabolic disturbances in disease; however, the metabolic profile of newborns with WD remains unexplored. This study aimed to identify potential metabolic biomarkers for early WD detection through untargeted metabolomic analysis.

Methods

Dried blood spot (DBS) samples from six genetically confirmed WD positive newborns and 84 healthy controls were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis was employed to identify differentially abundant metabolites. Pathway enrichment analysis and receiver operating characteristic (ROC) curve evaluation were performed to assess diagnostic performance.

Results

A total of 29 significantly altered metabolites (21 upregulated, 8 downregulated) were identified in WD positive newborns, primarily associated with tyrosine metabolism. ROC analysis revealed 11 metabolites with an area under the curve (AUC) > 90%. Additionally, two pairs of isomers also demonstrated exhibited high diagnostic sensitivity and specificity and were closely linked to WD pathogenesis. In positive group, tyrosine metabolism pathway was most significantly affected, as evidenced by increased levels of 3,4-Dihydroxyphenylacetic acid and Homogentisic acid, alongside a decreased level of Gentisaldehyde.

Conclusion

WD positive newborns exhibit distinct metabolic reprogramming prior to copper accumulation, with tyrosine metabolism dysregulation as a potential early feature. The identified differential metabolites may serve as promising biomarkers for newborn WD screening, providing a foundation for metabolomics-based early diagnostic strategies.