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Background <p>Polycythaemia vera (PV) is a clonal myeloproliferative neoplasm driven by activating mutations in the JAK2 gene and is associated with an increased risk of thromboembolic events. Secondary polycythaemia (SP) comprises non-neoplastic conditions characterised by reactive erythrocytosis, most commonly caused by hypoxia or dysregulated erythropoietin signalling. Despite fundamentally different pathophysiological mechanisms, PV and SP often share overlapping haematological features, underscoring the need for additional disease-specific biomarkers. Metabolomics provides a functional readout of systemic metabolic remodelling and may help distinguish clonal from reactive erythrocytosis and identify potential diagnostic biomarkers.</p> Methods <p>For the first time, targeted LC–QQQ–based serum metabolomics was performed using the MxP<sup>®</sup> Quant 500 kit. The study included 76 participants: 33 with PV, 22 with SP, and 21 controls, matched for biochemical and anthropometric parameters. Statistical analyses comprised univariate testing (Mann–Whitney U test with Benjamini–Hochberg correction), pathway enrichment analysis, and ROC analysis with support vector machine (SVM) modelling, all performed in MetaboAnalyst Version 6.0.</p> Results <p>Compared with the control group, PV was associated with significant alterations in 242 metabolites and 152 predefined metabolite sums and ratios, mainly involving amino acid metabolism and lipid remodelling. In SP, fewer metabolic changes were observed, affecting 81 metabolites and metabolite sums and ratios. In a direct comparison between PV and SP, 29 discriminant metabolites were identified, primarily carboxylic acids and derivatives, as well as phosphatidylcholines, with changes ranging from − 79.4% to + 80.4%. Metabolites that uniquely distinguished PV from SP included PC O-40:5, C3-DC (C4-OH), taurine, and PC O-40:6, showing changes of 26.4%, − 33.9%, 48.4%, and 26.1%, respectively. Among all comparisons, taurine and hypotaurine metabolism was the only pathway specifically altered in the PV vs. SP group comparison. ROC analysis revealed a five-metabolite panel (cysteine, LPC 20:4, taurine, PC O-38:4, and PC O-40:5) with strong discriminatory performance between PV and SP (AUC = 0.887; 95% CI: 0.721–1.00).</p> Conclusion <p>Targeted serum metabolomics reveals distinct and overlapping metabolic pathways in clonal versus reactive erythrocytosis. PV exhibits broader pathway perturbations, highlighting its potential for disease stratification and biomarker discovery, whereas SP shows more restricted, yet functionally relevant, metabolic changes.</p>

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LC–QQQ serum metabolomics reveals disease-specific metabolic signatures and diagnostic metabolite panels distinguishing polycythaemia vera from secondary polycythaemia

  • 期刊论文

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  • 发表日期：

    2026年7月3日

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  • Piotr Halicki,
  • Patrycja Mojsak,
  • Tomasz Pienkowski,
  • Adrian Godlewski,
  • Dariusz Kiejza,
  • Sandra Chmielewska,
  • Adam Kretowki,
  • Michal Ciborowski,
  • Jaroslaw Piszcz

摘要

Background

Polycythaemia vera (PV) is a clonal myeloproliferative neoplasm driven by activating mutations in the JAK2 gene and is associated with an increased risk of thromboembolic events. Secondary polycythaemia (SP) comprises non-neoplastic conditions characterised by reactive erythrocytosis, most commonly caused by hypoxia or dysregulated erythropoietin signalling. Despite fundamentally different pathophysiological mechanisms, PV and SP often share overlapping haematological features, underscoring the need for additional disease-specific biomarkers. Metabolomics provides a functional readout of systemic metabolic remodelling and may help distinguish clonal from reactive erythrocytosis and identify potential diagnostic biomarkers.

Methods

For the first time, targeted LC–QQQ–based serum metabolomics was performed using the MxP® Quant 500 kit. The study included 76 participants: 33 with PV, 22 with SP, and 21 controls, matched for biochemical and anthropometric parameters. Statistical analyses comprised univariate testing (Mann–Whitney U test with Benjamini–Hochberg correction), pathway enrichment analysis, and ROC analysis with support vector machine (SVM) modelling, all performed in MetaboAnalyst Version 6.0.

Results

Compared with the control group, PV was associated with significant alterations in 242 metabolites and 152 predefined metabolite sums and ratios, mainly involving amino acid metabolism and lipid remodelling. In SP, fewer metabolic changes were observed, affecting 81 metabolites and metabolite sums and ratios. In a direct comparison between PV and SP, 29 discriminant metabolites were identified, primarily carboxylic acids and derivatives, as well as phosphatidylcholines, with changes ranging from − 79.4% to + 80.4%. Metabolites that uniquely distinguished PV from SP included PC O-40:5, C3-DC (C4-OH), taurine, and PC O-40:6, showing changes of 26.4%, − 33.9%, 48.4%, and 26.1%, respectively. Among all comparisons, taurine and hypotaurine metabolism was the only pathway specifically altered in the PV vs. SP group comparison. ROC analysis revealed a five-metabolite panel (cysteine, LPC 20:4, taurine, PC O-38:4, and PC O-40:5) with strong discriminatory performance between PV and SP (AUC = 0.887; 95% CI: 0.721–1.00).

Conclusion

Targeted serum metabolomics reveals distinct and overlapping metabolic pathways in clonal versus reactive erythrocytosis. PV exhibits broader pathway perturbations, highlighting its potential for disease stratification and biomarker discovery, whereas SP shows more restricted, yet functionally relevant, metabolic changes.