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Related Experiment Video

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Identifying Platelet Lipidomic Networks and Evaluating Machine-Learning Models to Identify Distinctive Features

Vivek Nandhan Kanpa1,2,3, Suzy Whoriskey4, Ana Le Chevillier1,2,5

  • 1UCD Conway SPHERE Research Group, Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland.

Cells
|July 13, 2026
PubMed
Summary

Platelet lipidomics reveals distinct lipid profiles in acute coronary syndromes (ACS) and chronic coronary syndromes (CCS). Ceramides show significant associations, suggesting potential for targeted diagnostic panels in coronary artery disease.

Keywords:
coronary diseasemachine learningmulti-omicsplatelets

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LC-MS Analysis of Human Platelets as a Platform for Studying Mitochondrial Metabolism
06:04

LC-MS Analysis of Human Platelets as a Platform for Studying Mitochondrial Metabolism

Published on: April 4, 2016

Area of Science:

  • Cardiovascular Research
  • Metabolomics
  • Molecular Diagnostics

Background:

  • Platelet lipidomics is crucial for understanding thromboinflammation in acute coronary syndromes (ACS) and chronic coronary syndromes (CCS).
  • Differences in platelet lipid profiles between ACS and CCS remain largely uncharacterized.
  • Existing research lacks detailed comparisons of platelet lipidomes in these distinct coronary artery disease subtypes.

Purpose of the Study:

  • To investigate and compare the platelet lipidomic profiles of patients with ACS and CCS.
  • To identify specific lipid biomarkers associated with ACS and CCS.
  • To explore the potential of platelet lipidomics for classifying coronary artery disease subtypes.

Main Methods:

  • Untargeted platelet lipidomics and miRNA transcriptomics on samples from 19 ACS and 57 CCS patients.
  • Integration of lipidomics data with clinical and hematological traits.
  • Statistical network analyses and cross-validated machine learning models for data interpretation.

Main Results:

  • Identified 81 significantly altered lipid features between ACS and CCS (FDR q < 0.05), primarily phosphatidylcholines, lysophosphatidylcholines, and ceramides.
  • Specific ceramides (e.g., Cer 18:2;O2/24:0) showed strong inverse or promotive associations with ACS.
  • Oxidized phospholipid species were identified as key nodes in ACS-specific lipid networks through differential correlation analysis.

Conclusions:

  • Platelet lipidomics reveals distinct molecular signatures differentiating ACS from CCS.
  • Specific ceramide profiles in platelets are mechanistically distinct from plasma ceramides and hold promise as biomarkers.
  • Further validation of a ceramide-focused platelet lipid panel in larger cohorts is warranted for improved coronary artery disease diagnostics.