Objective Post-thrombotic syndrome (PTS), a common complication of deep vein thrombosis, lacks objective diagnostic biomarkers and its molecular mechanisms remain poorly understood. This study aimed to identify plasma biomarkers and clarify pathways using integrated multi-omics and machine learning.

Methods Proteomic and metabolomic profiling of 75 PTS patients and 75 controls was performed. Differential expression analysis, pathway enrichment, and protein-metabolite network analysis were conducted. A multi-algorithm machine learning with 8 feature selection methods prioritized biomarkers. Validations and 14 models were assessed.

Results 1,104 proteins and 1,891 metabolites were differentially expressed. Citrate cycle and unsaturated fatty acid biosynthesis were enriched. Three proteins, namely DIP2B, KNG1, and SUCLG2, were consistently selected as core biomarkers. All of these proteins were significantly downregulated in PTS and externally validated. A random forest model utilizing these proteins achieved an accuracy of 97.7% in independent testing, with SUCLG2 being the most influential predictor.

Conclusion This study identifies a novel three - protein biomarker panel for the diagnosis of PTS and reveals an immunometabolic axis in the pathogenesis of PTS, which links inflammatory regulation with mitochondrial energy metabolism. These findings provide valuable insights into the development of diagnostic tools and targeted therapeutic approaches.

The authors have declared no competing interest.

This work was supported by grants from the National Natural Science Foundation of China (No. 82470517, 82070496)

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Ethics Committee of the Affiliated Drum Tower Hospital of Nanjing University Medical School (Ethics number: 2024-294-02, 2024-05-22) and the First Affiliated Hospital of Anhui Medical University (Ethics number: PJ-2024-11-23, 2024-11-23).

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