Computational Derivation of Core, Dynamic Human Blunt Trauma Inflammatory Endotypes

Lukas Schimunek, Haley Lindberg, Maria Cohen, Rami A. Namas, Qi Mi, Jinling Yin, Derek Barclay, Fayten El-Dehaibi, Andrew Abboud, Ruben Zamora, Timothy Robert Billiar, Yoram Vodovotz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Systemic inflammation ensues following traumatic injury, driving immune dysregulation and multiple organ dysfunction (MOD). While a balanced immune/inflammatory response is ideal for promoting tissue regeneration, most trauma patients exhibit variable and either overly exuberant or overly damped responses that likely drive adverse clinical outcomes. We hypothesized that these inflammatory phenotypes occur in the context of severe injury, and therefore sought to define clinically distinct endotypes of trauma patients based on their systemic inflammatory responses. Using Patient-Specific Principal Component Analysis followed by unsupervised hierarchical clustering of circulating inflammatory mediators obtained in the first 24 h after injury, we segregated a cohort of 227 blunt trauma survivors into three core endotypes exhibiting significant differences in requirement for mechanical ventilation, duration of ventilation, and MOD over 7 days. Nine non-survivors co-segregated with survivors. Dynamic network inference, Fisher Score analysis, and correlations of IL-17A with GM-CSF, IL-10, and IL-22 in the three survivor sub-groups suggested a role for type 3 immunity, in part regulated by Th17 and γδ 17 cells, and related tissue-protective cytokines as a key feature of systemic inflammation following injury. These endotypes may represent archetypal adaptive, over-exuberant, and overly damped inflammatory responses.

Original languageEnglish
Article number589304
JournalFrontiers in Immunology
Volume11
DOIs
StatePublished - 18 Jan 2021
Externally publishedYes

Keywords

  • biomarker
  • critical illness
  • inflammation
  • network analysis
  • systems biology

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