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Urinary Metabolomics and One-Class Classification To Discover Children Affected by Bile Acid Synthetic Disorders: A

Matteo Stocchero1,2, Paola Pirillo1,2, Gabriele Poloniato1,2

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|April 24, 2026
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Summary

Machine learning can now distinguish rare bile acid synthetic disorders (BASD) like BASD1, BASD2, and cerebrotendinous xanthomatosis (CTX) from other liver conditions using urinary metabolomics. This approach aids in early diagnosis for these complex metabolic diseases.

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Area of Science:

  • Biochemistry
  • Metabolomics
  • Machine Learning

Background:

  • Bile acid synthetic disorders (BASDs) are rare metabolic conditions impacting bile acid synthesis.
  • Symptoms overlap with other liver disorders, necessitating early diagnosis, especially in children.
  • 3β-Dehydrogenase deficiency (BASD1), 5β-reductase deficiency (BASD2), and cerebrotendinous xanthomatosis (CTX) are common BASDs.

Purpose of the Study:

  • To develop a machine learning model for differentiating BASD1, BASD2, and CTX from other liver diseases.
  • To utilize urinary metabolome data quantified by liquid chromatography-mass spectrometry (LC-MS) for classification.
  • To identify specific metabolites linked to each type of BASD.

Main Methods:

  • Employed a one-class classification (OCC) machine learning approach.
  • Combined principal component analysis (PCA) and K-nearest neighbors (KNN) within model population analysis (MPA).
  • Integrated targeted and untargeted metabolomics data for enhanced classifier performance.

Main Results:

  • Successfully developed an OCC model to distinguish BASD patients from those with other liver issues.
  • Identified key urinary metabolites associated with BASD1, BASD2, and CTX.
  • The model's interpretation strategy revealed metabolite sets specific to each BASD type.

Conclusions:

  • The proposed machine learning strategy effectively identifies rare bile acid synthetic disorders using urinary metabolomics.
  • This approach shows potential for diagnosing other rare diseases with overlapping symptoms.
  • Early and accurate diagnosis of BASDs is crucial for patient outcomes.