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Purpose <p>This study aimed to explore metabolite pathways and identify residual metabolites during the post-neonatal intrahepatic cholestasis caused by citrin deficiency (post-NICCD) phase, while developing a predictive model for failure to thrive (FTT) using selected metabolites.</p> Method <p>A case-control study was conducted from October 2020 to July 2024, including 16 NICCD patients, 31 NICCD-matched controls, 34 post-NICCD patients, and 70 post-NICCD-matched controls. Post-NICCD patients were further stratified into two groups based on growth outcomes. Biomarkers for FTT were identified using Lasso regression and random forest analysis. A non-invasive predictive model was developed, visualized as a nomogram, and internally validated using the enhanced bootstrap method. The model’s performance was evaluated with receiver operating characteristic curves and calibration curves. Metabolite concentrations (amino acids, acylcarnitines, organic acids, and free fatty acids) were measured using liquid chromatography or ultra-performance liquid chromatography-tandem mass spectrometry.</p> Results <p>The biosynthesis of unsaturated fatty acids was identified as the most significantly altered pathway in post-NICCD patients. Twelve residual metabolites altered during both NICCD and post-NICCD phases were identified, including: 2-hydroxyisovaleric acid, alpha-ketoisovaleric acid, C5:1, 3-methyl-2-oxovaleric acid, C18:1OH, C20:4, myristic acid, eicosapentaenoic acid, carnosine, hydroxylysine, phenylpyruvic acid, and 2-methylcitric acid. Lasso regression and random forest analysis identified kynurenine, arginine, alanine, and aspartate as the optimal biomarkers for predicting FTT in post-NICCD patients. The predictive model constructed with these four biomarkers demonstrated an AUC of 0.947.</p> Conclusion <p>While post-NICCD patients recover clinically and biochemically, their metabolic profiles remain incompletely restored. The predictive model based on kynurenine, arginine, alanine, and aspartate provides robust diagnostic performance for detecting FTT in post-NICCD patients.</p>

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Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月12日

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  • Peiyao Wang,
  • Duo Zhou,
  • Lingwei Hu,
  • Pingping Ge,
  • Ziyan Cen,
  • Zhenzhen Hu,
  • Qimin He,
  • Kejun Zhou,
  • Benqing Wu,
  • Xinwen Huang

摘要

Purpose

This study aimed to explore metabolite pathways and identify residual metabolites during the post-neonatal intrahepatic cholestasis caused by citrin deficiency (post-NICCD) phase, while developing a predictive model for failure to thrive (FTT) using selected metabolites.

Method

A case-control study was conducted from October 2020 to July 2024, including 16 NICCD patients, 31 NICCD-matched controls, 34 post-NICCD patients, and 70 post-NICCD-matched controls. Post-NICCD patients were further stratified into two groups based on growth outcomes. Biomarkers for FTT were identified using Lasso regression and random forest analysis. A non-invasive predictive model was developed, visualized as a nomogram, and internally validated using the enhanced bootstrap method. The model’s performance was evaluated with receiver operating characteristic curves and calibration curves. Metabolite concentrations (amino acids, acylcarnitines, organic acids, and free fatty acids) were measured using liquid chromatography or ultra-performance liquid chromatography-tandem mass spectrometry.

Results

The biosynthesis of unsaturated fatty acids was identified as the most significantly altered pathway in post-NICCD patients. Twelve residual metabolites altered during both NICCD and post-NICCD phases were identified, including: 2-hydroxyisovaleric acid, alpha-ketoisovaleric acid, C5:1, 3-methyl-2-oxovaleric acid, C18:1OH, C20:4, myristic acid, eicosapentaenoic acid, carnosine, hydroxylysine, phenylpyruvic acid, and 2-methylcitric acid. Lasso regression and random forest analysis identified kynurenine, arginine, alanine, and aspartate as the optimal biomarkers for predicting FTT in post-NICCD patients. The predictive model constructed with these four biomarkers demonstrated an AUC of 0.947.

Conclusion

While post-NICCD patients recover clinically and biochemically, their metabolic profiles remain incompletely restored. The predictive model based on kynurenine, arginine, alanine, and aspartate provides robust diagnostic performance for detecting FTT in post-NICCD patients.