AI Article Synopsis

  • The study focused on identifying unique metabolomic signatures in patients with porto-sinusoidal vascular disorder (PSVD) and cirrhosis to improve diagnosis.
  • Serum samples from healthy volunteers and patients with PSVD or cirrhosis were analyzed using advanced techniques like liquid chromatography-mass spectrometry, identifying significant metabolic changes linked to PSVD.
  • Machine learning models were developed to distinguish PSVD from cirrhosis and healthy controls; key metabolites like taurocholic acid showed strong potential for non-invasive diagnostic use.

Article Abstract

Background & Aims: Porto-sinusoidal vascular disorder (PSVD) is a rare and diagnostically challenging vascular liver disease. This study aimed to identify distinct metabolomic signatures in patients with PSVD or cirrhosis to facilitate non-invasive diagnosis and elucidate perturbed metabolic pathways.

Methods: Serum samples from 20 healthy volunteers (HVs), 20 patients with histologically confirmed PSVD or 20 patients with cirrhosis were analyzed. Metabolites were measured using liquid chromatography-mass spectrometry. Differential abundance was evaluated with Limma's moderated t-statistics. Artificial neural network and support vector machine models were developed to classify PSVD against cirrhosis or HV metabolomic profiles. An independent cohort was used for validation.

Results: A total of 283 metabolites were included for downstream analysis. Clustering effectively separated PSVD from HV metabolomes, although a subset of patients with PSVD (n = 5, 25%) overlapped with those with cirrhosis. Differential testing revealed significant PSVD-linked metabolic perturbations, including pertubations in taurocholic and adipic acids, distinguishing patients with PSVD from both HVs and those with cirrhosis. Alterations in pyrimidine, glycine, serine, and threonine pathways were exclusively associated with PSVD. Machine learning models utilizing selected metabolic signatures reliably differentiated the PSVD group from HVs or patients with cirrhosis using only 4 to 6 metabolites. Validation in an independent cohort demonstrated the high discriminative ability of taurocholic acid (AUROC 0.899) for patients with PSVD HVs and the taurocholic acid/aspartic acid ratio (AUROC 0.720) for PSVD cirrhosis.

Conclusions: High-throughput metabolomics enabled the identification of distinct metabolic profiles that differentiate between PSVD, cirrhosis, and healthy individuals. Unique alterations in the glycine, serine, and threonine pathways suggest their potential involvement in PSVD pathogenesis.

Impact And Implications: Porto-sinusoidal vascular disorder (PSVD) is a vascular liver disease that can lead to pre-sinusoidal portal hypertension in the absence of cirrhosis, with poorly understood pathophysiology and no established treatment. Our study demonstrates that analyzing the serum metabolome could reveal distinct metabolic signatures in patients with PSVD, including alterations in the pyrimidine, glycine, serine and threonine pathways, potentially shedding light on the disease's underlying pathways. These findings could enable earlier and non-invasive diagnosis of PSVD, potentially reducing reliance on invasive procedures like liver biopsy and guiding diagnostic pathways.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11609546PMC
http://dx.doi.org/10.1016/j.jhepr.2024.101208DOI Listing

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  • The study focused on identifying unique metabolomic signatures in patients with porto-sinusoidal vascular disorder (PSVD) and cirrhosis to improve diagnosis.
  • Serum samples from healthy volunteers and patients with PSVD or cirrhosis were analyzed using advanced techniques like liquid chromatography-mass spectrometry, identifying significant metabolic changes linked to PSVD.
  • Machine learning models were developed to distinguish PSVD from cirrhosis and healthy controls; key metabolites like taurocholic acid showed strong potential for non-invasive diagnostic use.
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