The urinary microbiome distinguishes symptomatic urinary tract infection from asymptomatic older adult patients presenting to the Emergency department.

Journal: Virulence
Published Date: Aug 9, 2025

Abstract

Older adults suffer from a high rate of asymptomatic bacteriuria (ASB), in which urinalysis may appear positive (presence of bacteria, white blood cells, and nitrates), often triggering initiation of antibiotics in acute care settings, without actual urinary tract infection (UTI) present. To investigate the urinary microbiome of older adults being tested for UTI, we enrolled a convenience sample of 250 older adult Emergency Department patients who had microscopic urinalysis ordered as part of their routine clinical care. Urinalysis results were classified as positive or negative, and patients were classified as being symptomatic or asymptomatic based on established diagnostic guidelines. We sought to determine if features of the urinary microbiome differed between positive and negative urinalysis (UAs) and symptomatic and asymptomatic patients with positive UAs. The same urine sample used for clinical testing was sequenced and analyzed for bacterial taxa, metabolic pathways, and known bacterial virulence factors. After exclusion for anatomical abnormalities and filtering for sequencing quality, 152 samples were analyzed (5 negative UAs, 147 positive UAs, among which 68 were asymptomatic, and 79 symptomatic). Positive UA samples showed significantly lower alpha diversity (2.29 versus 0.086,  < 0.01) and distinct community composition based on beta-diversity (PERMANOVA on Bray-Curtis distance  < 0.01). Alpha and beta diversity did not significantly differ between asymptomatic and symptomatic patients. Machine learning classifiers combining clinical covariates other than specific signs and symptoms and microbiome features (taxa, metabolic pathways, or virulence factors) revealed mostly microbiome features as predictive of symptomatic UTI over clinical features.

Authors

  • Evan S Bradley
    Department of Emergency Medicine, UMass Memorial Medical Center, Worcester, MA, USA.
  • Celina Stansky
    Department of Emergency Medicine, UMass Memorial Medical Center, Worcester, MA, USA.
  • Abigail L Zeamer
    Program in Microbiome Dynamics, UMass Chan Medical School, Worcester, MA, USA.
  • Ziyuan Huang
    Data Science, Harrisburg University of Science and Technology, Harrisburg, PA, United States of America.
  • Lindsey Cincotta
    Department of Emergency Medicine, UMass Memorial Medical Center, Worcester, MA, USA.
  • Abigail Lopes
    Department of Emergency Medicine, UMass Memorial Medical Center, Worcester, MA, USA.
  • Linda Potter
    Clinical Renal Laboratory, UMass Memorial Medical Center, Worcester, MA, USA.
  • Theresa Fontes
    Clinical Renal Laboratory, UMass Memorial Medical Center, Worcester, MA, USA.
  • Doyle V Ward
    Program in Microbiome Dynamics, UMass Chan Medical School, Worcester, MA, USA.
  • Vanni Bucci
    Department of Microbiology, UMass Chan Medical School, Worcester, Massachusetts, USA.
  • Beth A McCormick
    Program in Microbiome Dynamics, UMass Chan Medical School, Worcester, MA, USA.
  • John P Haran
    Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals