Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Nov 12:2019:2612849.
doi: 10.1155/2019/2612849. eCollection 2019.

Exploring the Biomarkers of Sepsis-Associated Encephalopathy (SAE): Metabolomics Evidence from Gas Chromatography-Mass Spectrometry

Jing Zhu  1   2 , Mu Zhang  1   2 , Tingli Han  3 , Hua Wu  4 , Zhibo Xiao  5 , Shihui Lin  1   2 , Chuanjiang Wang  1   2 , Fang Xu  1   2
Affiliations

Exploring the Biomarkers of Sepsis-Associated Encephalopathy (SAE): Metabolomics Evidence from Gas Chromatography-Mass Spectrometry

Jing Zhu et al. Biomed Res Int. .

Abstract

Background: Sepsis-associated encephalopathy (SAE) is a transient and reversible brain dysfunction, that occurs when the source of sepsis is located outside of the central nervous system; SAE affects nearly 30% of septic patients at admission and is a risk factor for mortality. In our study, we sought to determine whether metabolite changes in plasma could be a potential biomarker for the early diagnosis and/or the prediction of the prognosis of sepsis.

Method: A total of 31 SAE patients and 28 healthy controls matched by age, gender, and body mass index (BMI) participated in our study. SAE patients were divided into four groups according to the Glasgow Coma Score (GCS). Plasma samples were collected and used to detect metabolism changes by gas chromatography-mass spectrometry (GC-MS). Analysis of variance was used to determine which metabolites significantly differed between the control and SAE groups.

Results: We identified a total of 63 metabolites that showed significant differences among the SAE and control groups. In particular, the 4 common metabolites in the four groups were 4-hydroxyphenylacetic acid; carbostyril, 3-ethyl-4,7-dimethoxy (35.8%); malic acid peak 1; and oxalic acid. The concentration of 4-hydroxyphenylacetic acid in sepsis patients decreased with a decrease of the GCS.

Conclusions: According to recent research on SAE, metabolic disturbances in tissue and cells may be the main pathophysiology of this condition. In our study, we found a correlation between the concentration of 4-hydroxyphenylacetic acid and the severity of consciousness disorders. We suggest that 4-hydroxyphenylacetic acid may be a potential biomarker for SAE and useful in predicting patient prognosis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
(a) The flow chart of our study. 47 septic patients and 44 paired healthy controls were in our study for metabolomic predetecting (total samples n = 91). Then, 31 out of 47 patients were diagnosed as SAE. According to the GCS, they were divided into 4 groups. (b) The PLS-DA of our study. (c) The leave-one-out cross-validation of our study.
Figure 2
Figure 2
(a) Metabolites' concentration gradient map after the ANOVA test. ∗p < 0.05, comparison of each GCS degrees of SAE groups and control groups. The name of metabolites is showing at the left, and the degree of matching with the compound in the NIST was marked in the form of n% after the name of the metabolites. The classification of the metabolites is on the right. (b) The VIP score analysis of our study shows out the best 10 metabolites, showing changes in different groups. (c) The heatmap of 4 common metabolites.
Figure 3
Figure 3
Concentration gradient map of metabolite pathways after the T test. ∗p < 0.05, comparison of each SAE groups and control groups. The classifications of the pathways are on the right.
See this image and copyright information in PMC

References

    1. Jang J. Y., Lee S. H., Shim H., Lee J. G. Serum oxygen radical activity and total antioxidation capacity are related with severities of surgical patient with sepsis: prospective pilot study. Journal of Critical Care. 2017;39:131–136. doi: 10.1016/j.jcrc.2017.01.016. - DOI - PubMed
    1. Galley H. F. Oxidative stress and mitochondrial dysfunction in sepsis. British Journal of Anaesthesia. 2011;107(1):57–64. doi: 10.1093/bja/aer093. - DOI - PubMed
    1. Kuperberg S. J., Wadgaonkar R. Sepsis-associated encephalopathy: the blood-brain barrier and the sphingolipid rheostat. Frontier in Immunology. 2017;8:p. 597. doi: 10.3389/fimmu.2017.00597. - DOI - PMC - PubMed
    1. Tauber S. C., Eiffert H., Brück W., Nau R. Septic encephalopathy and septic encephalitis‬‬. Expert Review of Anti-infective Therapy. 2017;15(2):121–132. doi: 10.1080/14787210.2017.1265448. - DOI - PubMed
    1. Tsuruta R., Oda Y. A clinical perspective of sepsis-associated delirium. Journal of Intensive Care. 2016;4:p. 18. doi: 10.1186/s40560-016-0145-4. - DOI - PMC - PubMed

MeSH terms