AI Article Synopsis

  • The study aimed to identify urinary peptide biomarkers for the early detection of deep vein thrombosis (DVT) and pulmonary embolism (PE), using advanced mass spectrometry techniques.
  • Researchers analyzed urine samples from patients with suspected DVT/PE to find 62 specific urinary peptides, linked to DVT, and validated these findings using mouse models and human thrombi.
  • A classifier named DVT62 was developed from these peptides, demonstrating a 100% sensitivity and 83% specificity in identifying DVT, suggesting its potential for future clinical assessments.

Article Abstract

Purpose: Early and accurate detection of deep vein thrombosis (DVT) is an important clinical need. Based on the hypothesis that urinary peptides may hold information on DVT in conjunction with pulmonary embolism (PE), the study was aimed at identifying such peptide biomarkers using capillary electrophoresis coupled mass spectrometry.

Experimental Design: Patients with symptoms of unprovoked/idiopathic DVT and/or PE were examined by doppler-sonography or angio-computed tomography. Urinary proteome analysis allowed for identification of respective peptide biomarkers. To confirm their biological relevance, we induced PE in mice and assessed human ex vivo thrombi.

Results: We identified 62 urinary peptides as DVT-specific biomarkers, i.e. fragments of collagen type I and a fragment of fibrinogen β-chain. The presence of fibrinogen α/β in the acute thrombus, and collagen type I and osteopontin in the older, organized thrombus was demonstrated. The classifier DVT62 established through support vector machine (SVM) modeling based on the 62 identified peptides was validated in an independent cohort of 47 subjects (six cases and 41 controls) with a sensitivity of 100% and specificity of 83%.

Conclusions And Clinical Relevance: Urine proteome analysis enabled the detection of DVT-specific peptides, which were validated in human and mouse tissue. Furthermore, it allowed for the establishment of an urinary-proteome based classifier that is relatively specific for DVT. The data provide the basis for assessment of these biomarkers in a prospective clinical study.

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Source
http://dx.doi.org/10.1002/prca.201500105DOI Listing

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