Wang Shan, Liang Jifang, Shi Haipeng, Xia Yanmei, Li Jing, Wu Wenjing, Wang Hongxiong, Wu Weidong
Shanxi University of Traditional Chinese Medicine, Jinzhong 030619, Shanxi, China.
Department of Intensive Care Unit, Shanxi Bethune Hospital, Taiyuan 030032, Shanxi, China. Corresponding author: Wu Weidong, Email:
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2022 Apr;34(4):346-351. doi: 10.3760/cma.j.cn121430-20210612-00875.
To analyze the changes of serum lipidomics in patients with sepsis and healthy controls, search for the differences of lipid metabolites, and reveal the changes of lipidomics in the process of sepsis.
A prospective observational study was conducted. From September 2019 to April 2020, morning blood samples of upper extremity superficial veins were collected from 30 patients with definite sepsis diagnosed in intensive care unit (ICU) of Shanxi Bethune Hospital and 30 age-matched healthy subjects during the same period. Serum lipid metabolites were analyzed by ultra-high performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS), and the quality control samples were analyzed by base peak spectroscopy (BPC) and verified experimental repetition. Student t-test and fold change (FC) were used for screening significant differences in lipid metabolites and determining their expression changes. Principal component analysis (PCA) and orthogonal projectionto latent structure discriminant analysis (OPLS-DA) were used to determine the entire allocation of experimental groups apiece, access the quality of being near to the true value of model, and screen the differential lipid metabolites with variable importance of projection (VIP). Finally, Metabo Analyst platform database was used to analyze lipid molecular metabolic pathways.
BPC results showed that the experimental repeatability was good and the experimental data was reliable. The main parameter model interpretation rate of PCA model RX = 0.511, indicating that the model was reliable. The main parameter model interpretation rate of OPLS-DA model RY = 0.954, Q = 0.913, indicating that the model was stable and reliable. With FC > 2.0 or FC < 0.5, P < 0.05, a total of 72 differential lipid metabolites were obtained based on VIP > 1. Based on Metabo Analyst 5.0, 24 distinguishable lipid metabolites were identified including 8 phosphatidylethanolamine (PE), 7 lysophosphatidylcholine (LPC), 6 phosphatidylcholine (PC), 2 lysophosphatidylethanolamine (LPE) and 1 phosphatidylserine (PS). Compared with healthy volunteers, the lipid molecules expression proved down-regulated in most sepsis patients, including PC, LPC, LPE, and some PE, while some PE and PS were up-regulated, which was mainly related to the PE (18:0p/20:4), PC (16:0/16:0) and LPC (18:1) metabolic pathways in glycerophospholipids.
There are significant differences in lipid metabolites between the sera of sepsis patients and healthy volunteers. PE (18:0p/20:4), PC (16:0/16:0) and LPC (18:1) may be new targets for sepsis prediction and intervention.
分析脓毒症患者与健康对照者血清脂质组学的变化，寻找脂质代谢物的差异，揭示脓毒症过程中脂质组学的变化。
进行一项前瞻性观察性研究。2019年9月至2020年4月，采集山西白求恩医院重症监护病房（ICU）确诊的30例脓毒症患者及同期30例年龄匹配的健康受试者上肢浅静脉的清晨血样。采用超高效液相色谱-质谱联用仪（UPLC-MS/MS）分析血清脂质代谢物，通过基峰色谱（BPC）分析质量控制样品并验证实验重复性。采用学生t检验和变化倍数（FC）筛选脂质代谢物的显著差异并确定其表达变化。采用主成分分析（PCA）和正交投影到潜在结构判别分析（OPLS-DA）确定各实验组的整体分布情况，评估模型接近真实值的程度，并筛选具有变量重要性投影（VIP）的差异脂质代谢物。最后，使用Metabo Analyst平台数据库分析脂质分子代谢途径。
BPC结果显示实验重复性良好，实验数据可靠。PCA模型的主要参数模型解释率RX = 0.511，表明该模型可靠。OPLS-DA模型的主要参数模型解释率RY = 0.954，Q = 0.913，表明该模型稳定可靠。以FC>2.0或FC<0.5，P<0.05为标准，基于VIP>1共获得72种差异脂质代谢物。基于Metabo Analyst 5.0，鉴定出24种可区分的脂质代谢物，包括8种磷脂酰乙醇胺（PE）、7种溶血磷脂酰胆碱（LPC）、6种磷脂酰胆碱（PC）、2种溶血磷脂酰乙醇胺（LPE）和1种磷脂酰丝氨酸（PS）。与健康志愿者相比，大多数脓毒症患者的脂质分子表达下调，包括PC、LPC、LPE和部分PE，而部分PE和PS上调，这主要与甘油磷脂中的PE（18:0p/20:4）、PC（16:0/16:0）和LPC（18:1）代谢途径有关。
脓毒症患者血清与健康志愿者血清中的脂质代谢物存在显著差异。PE（18:0p/20:4）、PC（16:0/16:0）和LPC（18:1）可能是脓毒症预测和干预的新靶点。
