Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
Overall design
Urine was collected from 145 sepsis patients within 12 hours of sepsis onset. Urine was collected from 32 patients set to undergo vascular surgery within 4 hours before surgical incision.
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