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Updated: Feb 12, 2026

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
Published on: December 9, 2022
Endothelial biomarkers in human sepsis: pathogenesis and prognosis for ARDS
Carolyn M Hendrickson1, Michael A Matthay2,3,4
11 Department of Medicine, Division of Pulmonary and Critical Care Medicine, Zuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, CA, USA.
Abstract:
Experimental models of sepsis in small and large animals and a variety of in vitro preparations have established several basic mechanisms that drive endothelial injury. This review is focused on what can be learned from the results of clinical studies of plasma biomarkers of endothelial injury and inflammation in patients with sepsis. There is excellent evidence that elevated plasma levels of several biomarkers of endothelial injury, including von Willebrand factor antigen (VWF), angiopoietin-2 (Ang-2), and soluble fms-like tyrosine kinase 1 (sFLT-1), and biomarkers of inflammation, especially interleukin-8 (IL-8) and soluble tumor necrosis factor receptor (sTNFr), identify sepsis patients with a higher mortality. There are also some data that elevated levels of endothelial biomarkers can identify which patients with non-pulmonary sepsis will develop acute respiratory distress syndrome (ARDS). If ARDS patients are divided among those with indirect versus direct lung injury, then there is an association of elevated levels of endothelial biomarkers in indirect injury and markers of inflammation and alveolar epithelial injury in patients with direct lung injury. New research suggests that the combination of biologic and clinical markers may make it possible to segregate patients with ARDS into hypo- versus hyper-inflammatory phenotypes that may have implications for therapeutic responses to fluid therapy. Taken together, the studies reviewed here support a primary role of the microcirculation in the pathogenesis and prognosis of ARDS after sepsis. Biological differences identified by molecular patterns could explain heterogeneity of treatment effects that are not explained by clinical factors alone.
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