TY - JOUR

T1 - Endothelial Cell Phenotypes Demonstrate Different Metabolic Patterns and Predict Mortality in Trauma Patients

AU - Henriksen, Hanne H.

AU - Marín de Mas, Igor

AU - Nielsen, Lars K.

AU - Krocker, Joseph

AU - Stensballe, Jakob

AU - Karvelsson, Sigurður T.

AU - Secher, Niels H.

AU - Rolfsson, Óttar

AU - Wade, Charles E.

AU - Johansson, Pär I.

N1 - Publisher Copyright: © 2023 by the authors.

PY - 2023

Y1 - 2023

N2 - In trauma patients, shock-induced endotheliopathy (SHINE) is associated with a poor prognosis. We have previously identified four metabolic phenotypes in a small cohort of trauma patients (N = 20) and displayed the intracellular metabolic profile of the endothelial cell by integrating quantified plasma metabolomic profiles into a genome-scale metabolic model (iEC-GEM). A retrospective observational study of 99 trauma patients admitted to a Level 1 Trauma Center. Mass spectrometry was conducted on admission samples of plasma metabolites. Quantified metabolites were analyzed by computational network analysis of the iEC-GEM. Four plasma metabolic phenotypes (A–D) were identified, of which phenotype D was associated with an increased injury severity score (p < 0.001); 90% (91.6%) of the patients who died within 72 h possessed this phenotype. The inferred EC metabolic patterns were found to be different between phenotype A and D. Phenotype D was unable to maintain adequate redox homeostasis. We confirm that trauma patients presented four metabolic phenotypes at admission. Phenotype D was associated with increased mortality. Different EC metabolic patterns were identified between phenotypes A and D, and the inability to maintain adequate redox balance may be linked to the high mortality.

AB - In trauma patients, shock-induced endotheliopathy (SHINE) is associated with a poor prognosis. We have previously identified four metabolic phenotypes in a small cohort of trauma patients (N = 20) and displayed the intracellular metabolic profile of the endothelial cell by integrating quantified plasma metabolomic profiles into a genome-scale metabolic model (iEC-GEM). A retrospective observational study of 99 trauma patients admitted to a Level 1 Trauma Center. Mass spectrometry was conducted on admission samples of plasma metabolites. Quantified metabolites were analyzed by computational network analysis of the iEC-GEM. Four plasma metabolic phenotypes (A–D) were identified, of which phenotype D was associated with an increased injury severity score (p < 0.001); 90% (91.6%) of the patients who died within 72 h possessed this phenotype. The inferred EC metabolic patterns were found to be different between phenotype A and D. Phenotype D was unable to maintain adequate redox homeostasis. We confirm that trauma patients presented four metabolic phenotypes at admission. Phenotype D was associated with increased mortality. Different EC metabolic patterns were identified between phenotypes A and D, and the inability to maintain adequate redox balance may be linked to the high mortality.

KW - endotheliopathy

KW - genome-scale metabolic model

KW - metabolomics

KW - systems biology

KW - trauma

KW - tricarboxylic acid cycle

UR - http://www.scopus.com/inward/record.url?scp=85147890500&partnerID=8YFLogxK

U2 - 10.3390/ijms24032257

DO - 10.3390/ijms24032257

M3 - Journal article

C2 - 36768579

AN - SCOPUS:85147890500

VL - 24

JO - International Journal of Molecular Sciences (Online)

JF - International Journal of Molecular Sciences (Online)

SN - 1661-6596

IS - 3

M1 - 2257

ER -