Metabolomics-Based Discovery of Diagnostic Biomarkers for Dengue

• Activity Number: 352
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Carolyn Cotterman*+ and Natalia Voge and Rushika Perera and Lionel Gresh and Carol Blair and Hope Biswas and Angel Balmaseda and Eva Harris and Barry Beaty
• Companies: and Colorado State University and Colorado State University and Sustainable Sciences Institute and Colorado State University and University of California, Berkeley and Ministry of Health, Nicaragua and University of California, Berkeley and Colorado State University
• Keywords: infectious disease ; machine learning ; diagnositics

Abstract

Dengue virus is the most widespread arthropod-borne virus affecting humans, with as many as 390 million infections and 96 million cases globally each year. Of particular concern are the subset of cases which progress to life-threatening dengue hemorrhagic fever/ dengue shock syndrome. A tool that quickly and accurately differentiates dengue from other viral infections and is prognostic of progression to severe disease would be of great use for ensuring timely fluid intervention and supportive care, especially if it is low-cost and non-invasive. To assist with developing such a tool, we use liquid chromatography-mass spectrometry (LC-MS) to detect tens of thousands of molecular features in serum, saliva, and urine of suspected dengue patients in Nicaragua. We then use machine-learning techniques to identify small molecule biomarkers which, combined with routinely obtained clinical data, predict dengue diagnosis and prognosis.