Plasma Metabolomics and Machine Learning Reveals Metabolic Alterations and Diagnostic Biomarkers for Deep Venous Thrombosis in Hypertensive Patients after Traumatic Fracture.

Journal: Journal of proteome research
Published Date: Jul 15, 2025

Abstract

We aimed to explore the metabolic dysregulations and diagnostic biomarkers for post-traumatic deep venous thrombosis (pt-DVT) in hypertensive (HPT) patients after fracture. An untargeted ultraperformance liquid chromatography-mass spectrometry-based metabolomics approach was employed to perform a comprehensive metabolic analysis of plasma samples of 80 patients with post-traumatic deep venous thrombosis and hypertension (pt-DVT&HPT) and 117 patients with hypertension only (HPT). Thirty-seven (37) differential metabolites were identified between pt-DVT&HPT and HPT patients. Purine metabolism, citric acid cycle, sphingolipid metabolism, histidine metabolism, aminoacyl-tRNA biosynthesis, valine, leucine, and isoleucine degradation were the most significantly altered metabolic pathways in pt-DVT in HPT patients. Metabolite-protein interaction network analysis unveils ten (10) proteins/genes that could serve as therapeutic targets. Multivariate methods with Unbiased Variable selection in the R package (MUVR) algorithm identified four metabolites as novel biomarkers for pt-DVT&HPT, and receiver operating characteristics (ROC) analysis showed that a predictive model based on the four biomarkers (l-carnitine, lactic acid, adenine, and l-acetylcarnitine) exhibited better predictive capability for pt-DVT than the D-Dimer. The integration of these diagnostic biomarkers and D-Dimer increased its diagnostic potential. The generalization ability of this biomarker panel was validated in an independent cohort. This study contributed to our understanding of metabolic alterations associated with pt-DVT and paved the way for early diagnosis of pt-DVT in HPT patients.

Authors

  • Mukhtar Aliyu
    Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multi-omics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Shi-Hao Tang
    Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multi-omics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China.
  • Jia-Hao Wang
    College of Science, North China University of Science and Technology, Tangshan 063210, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Pengfei Wang
    Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Hanzhong Xue
    Department of Trauma Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710054, P. R. China.
  • Tie-Lin Yang
    Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multi-omics and Diseases in Shaanxi Province Higher Education Institutions, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China.
  • Wei Huang
    Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.

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