Researchers at the University of Calgary (AB, Canada) have demonstrated the use of plasma metabolomic profiles to identify H1N1 influenza pneumonia, facilitating earlier diagnosis that could potentially reduce the mortality rates associated with H1N1 influenza pneumonia.
The study, published online in Critical Care, utilized 1H-NMR and GC-MS to create metabolomic profiles for patients who were admitted into various intensive care units, using plasma samples collected within 24 hours of admission.
The samples obtained for the study consisted of: 42 patients with confirmed H1N1 influenza pneumonia; 30 patients with bacterial community acquired pneumonia; and 31 controls. The control individuals were non-infected patients admitted to intensive care units after elective procedures, chosen through age and sex matching with patients with H1N1 influenza pneumonia.
It was observed that metabolomic profiles from patients with H1N1 influenza pneumonia were distinguishable from the metabolomic profiles of the other two cohorts, with characteristic metabolites associated with the H1N1 influenza pneumonia cohort identified. The results also demonstrated that the analytical platforms utilized for creating the metabolomic profiles, 1H-NMR and GC-MS, could distinguish H1N1 influenza pneumonia with a high sensitivity and specificity.
The team also investigated the hypothesis that the same metabolomic profiles could aid prognosis of mortality as a result of H1N1 influenza pneumonia. This was tested by comparing the metabolomic profiles of 14 surviving and seven non-surviving patients diagnosed with H1N1 pneumonia.
It was noted that the metabolomic profiles from surviving and non-surviving patients of H1N1 influenza pneumonia were also distinguishable. This indicated that these metabolomic profiles could also potentially be used for the prognosis of H1N1 influenza pneumonia related mortality.
Source: Banoei MM, Vogel HJ, Weljie AM et al. Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia. Crit. Care. 21:97 (2017)