Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction

Yan Cao; Zhengyu Liu; Wenfeng Ma; Chen Fang; Yanfang Pei; Yingxia Jing; Jie Huang; Xiaotong Han; Weiwei Xiao

doi:10.3389/fendo.2023.1060470

Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction

Front Endocrinol (Lausanne). 2023 Feb 16:14:1060470. doi: 10.3389/fendo.2023.1060470. eCollection 2023.

Authors

Yan Cao^{1

2}, Zhengyu Liu^{3

4}, Wenfeng Ma⁵, Chen Fang⁶, Yanfang Pei¹, Yingxia Jing¹, Jie Huang¹, Xiaotong Han^{1

2}, Weiwei Xiao¹

Affiliations

¹ Department of Emergency, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.
² Clinical Research Center for Emergency and Critical Care in Hunan Province, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
³ Department of Cardiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
⁴ Clinical Research Center for Heart Failure of Hunan Province, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
⁵ Emergency Department, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
⁶ Institute of Emergency Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.

Abstract

Objective: Sepsis 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.

Method: Plasma 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.

Results: Two 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.

Conclusion: Metabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.

Keywords: cardiac dysfunction; diagnosis; metabolomics; prognosis; sepsis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Chromatography, Liquid
Discriminant Analysis
Humans
Kynurenic Acid*
Metabolomics
Sepsis*

Substances

Kynurenic Acid

Grants and funding

This work was supported by Clinical Medical Technology Innovation Guidance Program of Hunan Province [2020SK50909], Traditional Chinese Medicine Research Program of Hunan Province [C2023020], Natural Science Foundation of Hunan Province [2020JJ8082], Program of Clinical Research Center for Emergency and Critical Care in Hunan Province [2021SK4011], Major Program of Hunan Province Science and Technology Innovation [2020SK1010]