Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.
Background: Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis. Methods: Septic patients and healthy individ...
| Publicado en: | BMC Anesthesiology Vol. 23; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
BioMed Central
11/9/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173514898&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173514898 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712253 1CH3 jtl: BMC Anesthesiology issn: 14712253 maglogo: N pubinfo: dt: 11/9/2023 vid: 23 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 173514898 173514898 173514898 10.1186/s12871-023-02317-4 173514898 ppf: 1 ppct: 13 formats: tig: atl: Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis. aug: au: She, Han Du, Yuanlin Du, Yunxia Tan, Lei Yang, Shunxin Luo, Xi Li, Qinghui Xiang, Xinming Lu, Haibin Hu, Yi Liu, Liangming Li, Tao affil: Department of Anesthesiology, Daping Hospital, Army Medical University, 400042, Chongqing, China sug: subj: Sepsis Diagnosis Sepsis Prognosis Metabolites Machine Learning Biological Markers Health Screening Animal Studies Rats Models, Biological Mass Spectrometry Chromatography, High Pressure Liquid Cox Proportional Hazards Model Descriptive Statistics Comparative Studies Amino Acids Metabolism Phenylalanine Metabolism Tyrosine Metabolism Glycine Metabolism Serine Metabolism Threonine Metabolism Arginine Metabolism Proline Metabolism Algorithms Random Forest Sensitivity and Specificity Funding Source ab: Background: Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis. Methods: Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis. Results: A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis. Conclusions: This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis. Trial registration: This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021–179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772). pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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