Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction

被引:6
作者
Cao, Yan [1 ,2 ]
Liu, Zhengyu [3 ,4 ]
Ma, Wenfeng [5 ]
Fang, Chen [6 ]
Pei, Yanfang [1 ]
Jing, Yingxia [1 ]
Huang, Jie [1 ]
Han, Xiaotong [1 ,2 ]
Xiao, Weiwei [1 ]
机构
[1] Hunan Normal Univ, Hunan Prov Peoples Hosp, Affiliated Hosp 1, Dept Emergency, Changsha, Hunan, Peoples R China
[2] Hunan Normal Univ, Hunan Prov Peoples Hosp, Affiliated Hosp 1, Clin Res Ctr Emergency & Crit Care Hunan Prov, Changsha, Peoples R China
[3] Hunan Normal Univ, Hunan Prov Peoples Hosp, Affiliated Hosp 1, Dept Cardiol, Changsha, Peoples R China
[4] Hunan Normal Univ, Hunan Prov Peoples Hosp, Affiliated Hosp 1, Clin Res Ctr Heart Failure Hunan Prov, Changsha, Peoples R China
[5] Hunan Normal Univ, Affiliated Hosp 1, Emergency Dept, Changsha, Peoples R China
[6] Hunan Normal Univ, Hunan Prov Peoples Hosp, Affiliated Hosp 1, Inst Emergency Med, Changsha, Peoples R China
关键词
sepsis; cardiac dysfunction; metabolomics; diagnosis; prognosis; MYOCARDIAL DYSFUNCTION;
D O I
10.3389/fendo.2023.1060470
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
ObjectiveSepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction. MethodPlasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group. ResultsTwo metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism. ConclusionMetabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.
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页数:13
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