Identification of Metabolomics Biomarkers in Extracranial Carotid Artery Stenosis

  • Chia Ni Lin
  • , Kai Cheng Hsu
  • , Kuo Lun Huang
  • , Wen Cheng Huang
  • , Yi Lun Hung
  • , Tsong Hai Lee*
  • *Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

8 Scopus citations

Abstract

The biochemical identification of carotid artery stenosis (CAS) is still a challenge. Hence, 349 male subjects (176 normal controls and 173 stroke patients with extracranial CAS ≥ 50% diameter stenosis) were recruited. Blood samples were collected 14 days after stroke onset with no acute illness. Carotid plaque score (≥2, ≥5 and ≥8) was used to define CAS severity. Serum metabolites were analyzed using a targeted Absolute IDQ®p180 kit. Results showed hypertension, diabetes, smoking, and alcohol consumption were more common, but levels of diastolic blood pressure, HDL-C, LDL-C, and cholesterol were lower in CAS patients than controls (p < 0.05), suggesting intensive medical treatment for CAS. PCA and PLS-DA did not demonstrate clear separation between controls and CAS patients. Decision tree and random forest showed that acylcarnitine species (C4, C14:1, C18), amino acids and biogenic amines (SDMA), and glycerophospholipids (PC aa C36:6, PC ae C34:3) contributed to the prediction of CAS. Metabolite panel analysis showed high specificity (0.923 ± 0.081, 0.906 ± 0.086 and 0.881 ± 0.109) but low sensitivity (0.230 ± 0.166, 0.240 ± 0.176 and 0.271 ± 0.169) in the detection of CAS (≥2, ≥5 and ≥8, respectively). The present study suggests that metabolomics profiles could help in differentiating between controls and CAS patients and in monitoring the progression of CAS.

Original languageEnglish
Article number3022
JournalCells
Volume11
Issue number19
DOIs
StatePublished - 10 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • carotid artery stenosis
  • decision tree
  • ischemic stroke
  • metabolomics
  • random forest

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