Exploring blood transcriptional signatures for thrombosis diagnosis in critically ill patients with COVID-19

Cosimo Cristella1,2, Matthijs P Raadsen3, Hugo D G van Willigen1,2

  • 1Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Insights

Severe COVID-19 patients with blood clots show distinct gene expression patterns. The maltase-glucoamylase (MGAM) gene may predict thrombotic complications better than standard markers.

Area of Science:

  • Genomics
  • Critical Care Medicine
  • Molecular Biology

Background:

  • COVID-19 is associated with hypercoagulability and increased thrombotic risk in critically ill patients.
  • Understanding the molecular mechanisms of thrombosis in severe COVID-19 is crucial for patient management.

Purpose of the Study:

  • To identify transcriptional signatures linked to thrombosis in intensive care unit patients with COVID-19.
  • To evaluate the predictive capability of these signatures for thrombotic complications.

Main Methods:

  • Whole-blood transcriptional profiling was performed on 57 mechanically ventilated COVID-19 patients.
  • Differential gene expression and machine learning (orthogonal partial least squares discriminant analysis) were used to compare patients with and without thrombotic complications (TC).

Main Results:

  • Patients with TC exhibited greater transcriptome disruption and 283 differentially expressed genes.
  • Key findings included heightened neutrophil and monocyte activation, inflammatory responses, and suppressed lymphocyte function.
  • A machine learning model achieved high classification performance (AUC = 0.961).
  • The maltase-glucoamylase (MGAM) gene was identified as a top discriminatory biomarker (AUC = 0.94), outperforming D-dimer and C-reactive protein.

Conclusions:

  • Distinct transcriptional signatures characterize thrombotic complications in critically ill COVID-19 patients, reflecting immune dysregulation.
  • MGAM is a promising novel biomarker for identifying high thrombotic risk, potentially improving personalized risk stratification and management.

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