Gut microbiome dynamics and associations with mortality in critically ill patients

Tarik J Salameh; Katharine Roth; Lisa Schultz; Zhexi Ma; Anthony S Bonavia; James R Broach; Bin Hu; Judie A Howrylak

doi:10.1186/s13099-023-00567-8

Gut microbiome dynamics and associations with mortality in critically ill patients

Gut Pathog. 2023 Dec 19;15(1):66. doi: 10.1186/s13099-023-00567-8.

Authors

Tarik J Salameh¹, Katharine Roth², Lisa Schultz¹, Zhexi Ma¹, Anthony S Bonavia³, James R Broach^{4

5}, Bin Hu², Judie A Howrylak^{6

7}

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Milton S. Hershey Medical Center, Hershey, Penn State, PA, 17033, USA.
² Los Alamos National Laboratory, Los Alamos, USA.
³ Department of Anesthesiology and Perioperative Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA, 17033, USA.
⁴ Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, 17033, USA.
⁵ Department of Biochemistry and Molecular Biology, Penn State College of Medicine, 500 University Drive, Hershey, PA, 17033, USA.
⁶ Division of Pulmonary and Critical Care Medicine, Milton S. Hershey Medical Center, Hershey, Penn State, PA, 17033, USA. [email protected].
⁷ Department of Biochemistry and Molecular Biology, Penn State College of Medicine, 500 University Drive, Hershey, PA, 17033, USA. [email protected].

Abstract

Background: Critical illness and care within the intensive care unit (ICU) leads to profound changes in the composition of the gut microbiome. The impact of such changes on the patients and their subsequent disease course remains uncertain. We hypothesized that specific changes in the gut microbiome would be more harmful than others, leading to increased mortality in critically ill patients.

Methods: This was a prospective cohort study of critically ill adults in the ICU. We obtained rectal swabs from 52 patients and assessed the composition the gut microbiome using 16 S rRNA gene sequencing. We followed patients throughout their ICU course and evaluated their mortality rate at 28 days following admission to the ICU. We used selbal, a machine learning method, to identify the balance of microbial taxa most closely associated with 28-day mortality.

Results: We found that a proportional ratio of four taxa could be used to distinguish patients with a higher risk of mortality from patients with a lower risk of mortality (p = .02). We named this binarized ratio our microbiome mortality index (MMI). Patients with a high MMI had a higher 28-day mortality compared to those with a low MMI (hazard ratio, 2.2, 95% confidence interval 1.1-4.3), and remained significant after adjustment for other ICU mortality predictors, including the presence of the acute respiratory distress syndrome (ARDS) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score (hazard ratio, 2.5, 95% confidence interval 1.4-4.7). High mortality was driven by taxa from the Anaerococcus (genus) and Enterobacteriaceae (family), while lower mortality was driven by Parasutterella and Campylobacter (genera).

Conclusions: Dysbiosis in the gut of critically ill patients is an independent risk factor for increased mortality at 28 days after adjustment for clinically significant confounders. Gut dysbiosis may represent a potential therapeutic target for future ICU interventions.

Keywords: Gut microbiome; ICU mortality; Predictive modeling.

Grants and funding

UL1 TR000127/TR/NCATS NIH HHS/United States