Metabolomic profiles differentiate between porto-sinusoidal vascular disorder, cirrhosis, and healthy individuals

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 vs. HVs and the taurocholic acid/aspartic acid ratio (AUROC 0.720) for PSVD vs. 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.

Keywords: ACLD; PSVD; cirrhosis; metabolomics; non-cirrhotic portal hypertension; portal hypertension.

Graphical abstract

Fig. 1

Study design. (A) Study design and patient groups included in the study. (B) Metabolomics workflow. (C) Supervised dimensionality reduction, with group variable supplied for clustering. The leave-one-out method was used in validation with n folds = 1, 10 repeats, and five components used for testing. (D) Unsupervised dimensionality reduction based on unbiased whole-metabolomics profiles with removal of the low-variance features. In panels C and D, sample grouping is reflected by between-sample distance, while color-coded areas are provided for visual convenience.

Fig. 2

Differential abundance of metabolites between the study groups. (A) Volcano plots demonstrating upregulation (right direction) or downregulation (left direction) of metabolites in the respective comparison. The presented p values and log₂ fold change were obtained using the limma’s moderated t-test with Benjamini Hochberg's adjustment, where significance thresholds were: p_adjusted < 0.05, log₂ fold change > |0.5|. (B) Venn diagram showing overlaps in significant metabolites between PSVD or cirrhosis and non-diseased profiles. (C) Heatmap showing highest up- and downregulated metabolites, abundance values were scaled and average values for each group are presented. PSVD, porto-sinusoidal vascular disease.

Fig. 3

Functional analysis of metabolic sets. (A) Overlaps in significantly altered pathways in PSVD or cirrhosis compared to non-diseased profiles. Color code indicates significance of a pathway in PSVD, cirrhosis, or both. The over-representation was tested using the hypergeometric test, and significant pathways were those with p_adjusted <0.05. Color code indicates significance of a pathway in PSVD, cirrhosis, or both. (B) Metabolic map of glycine, serine and threonine metabolic pathways, differentially regulated only in PSVD compared to non-diseased profiles. Color code indicates downregulated (blue) and upregulated (yellow) metabolites, scaled between -1-1. PSVD, porto-sinusoidal vascular disease.

Fig. 4

Class prediction based on significantly dysregulated metabolites using artificial neural networks. (A) Features selected for the model between PSVD and non-diseased metabolic profiles. (B) Confusion matrix showing the difference between real and predicted classes between PSVD and non-diseased patients. (C) Prioritized features for classification between PSVD and cirrhosis profiles. (D) Confusion matrix for patients with PSVD and cirrhosis. Feature importance values represent their impact on the classification of each patient (separate dots) in a model with optimal hyperparameters. The features are sorted from the most important (top) to the least important for the prediction (bottom). Confusion matrices are color-coded according to the patient groups, with gray indicating discordance between real and predicted values. The features were prioritized via repeated cross-validation with n = 5 repeats and n = 10 folds using caret's naive Bayes algorithm.

Fig. 5

Exploration of the validation dataset. (A) Principal component analysis reveals a similar separation pattern as in the original study dataset: patients with PSVD show a distinct metabolic profile, while some of them overlap with other groups. (B) A heatmap of 20 metabolites with the highest variance demonstrates similar perturbations in taurocholic acid and its derivatives. Each column represents the group’s mean concentration. (C) Comparative analysis of the metabolites with the highest diagnostic performance between PSVD and healthy volunteers. (D) Comparative analysis of the metabolites with the highest diagnostic performance between PSVD and cirrhosis. For C and D, normalized and Z-scaled values were used for testing. Shapiro-Wilk test for normality was performed, following Welch two sample t-test (normal distribution) or Wilcoxon rank sum test (non-normal distribution), results of which are reported on the plots. Box plots represent medians, minimum and maximum concentrations within 1.5x the interquartile range. n.s.: p >0.05, ∗∗∗p ≤0.001. (E) Metabolites prioritized for the machine learning model in the Vienna cohort (see Fig. 4) showing the best discrimination between patients with PSVD or cirrhosis, and healthy volunteers in the Barcelona cohort. PSVD, porto-sinusoidal vascular disease.