» Articles » PMID: 32882869

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Overview
Journal Metabolites
Publisher MDPI
Date 2020 Sep 5
PMID 32882869
Citations 7
Authors
Affiliations
Soon will be listed here.
Abstract

Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical methods were used to determine lipids present in patient plasma. The obtained lipid signatures revealed 355 features for the negative ion mode and 297 for the positive ion mode, which were relevant for differential diagnosis of sepsis and SIRS. These lipids were also tested as prognosis predictors. Lastly, L-octanoylcarnitine was found to be the most promising lipid signature for both the diagnosis and prognosis of critically ill patients, with accuracies of 75% for both purposes. In short, we presented the determination of lipid signatures as a potential tool for differential diagnosis of sepsis and SIRS and prognosis of these patients.

Citing Articles

The shifting lipidomic landscape of blood monocytes and neutrophils during pneumonia.

Schuurman A, Chouchane O, Butler J, Peters-Sengers H, Joosten S, Brands X JCI Insight. 2024; 9(4).

PMID: 38385743 PMC: 10967382. DOI: 10.1172/jci.insight.164400.


Healthcare data quality assessment for improving the quality of the Korea Biobank Network.

Kim K, Oh S, Ko S, Lee K, Choi W, Choi I PLoS One. 2023; 18(11):e0294554.

PMID: 37983215 PMC: 10659164. DOI: 10.1371/journal.pone.0294554.


Unraveling the Metabolic Changes in Acute Pancreatitis: A Metabolomics-Based Approach for Etiological Differentiation and Acute Biomarker Discovery.

Dancu G, Tarta C, Socaciu C, Bende F, Danila M, Sirli R Biomolecules. 2023; 13(10).

PMID: 37892240 PMC: 10605849. DOI: 10.3390/biom13101558.


Lipid oxidation dysregulation: an emerging player in the pathophysiology of sepsis.

Muniz-Santos R, Lucieri-Costa G, de Almeida M, Moraes-de-Souza I, Brito M, Silva A Front Immunol. 2023; 14:1224335.

PMID: 37600769 PMC: 10435884. DOI: 10.3389/fimmu.2023.1224335.


Mass-Spectrometry-Based Lipidomics Discriminates Specific Changes in Lipid Classes in Healthy and Dyslipidemic Adults.

Sanchez-Vinces S, Garcia P, Silva A, de Piloto Fernandes A, Barreto J, Duarte G Metabolites. 2023; 13(2).

PMID: 36837840 PMC: 9964724. DOI: 10.3390/metabo13020222.


References
1.
ODonnell V, Ekroos K, Liebisch G, Wakelam M . Lipidomics: Current state of the art in a fast moving field. Wiley Interdiscip Rev Syst Biol Med. 2019; 12(1):e1466. DOI: 10.1002/wsbm.1466. View

2.
Singer M, Deutschman C, Seymour C, Shankar-Hari M, Annane D, Bauer M . The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016; 315(8):801-10. PMC: 4968574. DOI: 10.1001/jama.2016.0287. View

3.
Smilde A, van der Werf M, Bijlsma S, van der Werff-van der Vat B, Jellema R . Fusion of mass spectrometry-based metabolomics data. Anal Chem. 2005; 77(20):6729-36. DOI: 10.1021/ac051080y. View

4.
Ho K, Dobb G, Knuiman M, Finn J, Lee K, Webb S . A comparison of admission and worst 24-hour Acute Physiology and Chronic Health Evaluation II scores in predicting hospital mortality: a retrospective cohort study. Crit Care. 2005; 10(1):R4. PMC: 1550848. DOI: 10.1186/cc3913. View

5.
Pang Z, Chong J, Li S, Xia J . MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics. Metabolites. 2020; 10(5). PMC: 7281575. DOI: 10.3390/metabo10050186. View