Genomic biomarkers from urine cells indicate a unique metabolomic difference between sepsis and sterile inflammation (discovery cohort)

Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset. Urine was collected from 145 sepsis patients within 12 hours of sepsis onset. Urine was collected from 39 SIRS patients within 4 hours after end of surgery.