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炎症的综合代谢谱分析表明甘油磷脂和精氨酸代谢在冠状动脉疾病中起关键作用。

Comprehensive Metabolic Profiling of Inflammation Indicated Key Roles of Glycerophospholipid and Arginine Metabolism in Coronary Artery Disease.

作者信息

Zhu Qian, Wu Yonglin, Mai Jinxia, Guo Gongjie, Meng Jinxiu, Fang Xianhong, Chen Xiaoping, Liu Chen, Zhong Shilong

机构信息

School of Medicine, South China University of Technology, Guangzhou, China.

Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

出版信息

Front Immunol. 2022 Mar 8;13:829425. doi: 10.3389/fimmu.2022.829425. eCollection 2022.


DOI:10.3389/fimmu.2022.829425
PMID:35371012
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8965586/
Abstract

BACKGROUND: Systemic immune inflammation is a key mediator in the progression of coronary artery disease (CAD), concerning various metabolic and lipid changes. In this study, the relationship between the inflammatory index and metabolic profile in patients with CAD was investigated to provide deep insights into metabolic disturbances related to inflammation. METHODS: Widely targeted plasma metabolomic and lipidomic profiling was performed in 1,234 patients with CAD. Laboratory circulating inflammatory markers were mainly used to define general systemic immune and low-grade inflammatory states. Multivariable-adjusted linear regression was adopted to assess the associations between 860 metabolites and 7 inflammatory markers. Least absolute shrinkage and selection operator (LASSO) logistic-based classifiers and multivariable logistic regression were applied to identify biomarkers of inflammatory states and develop models for discriminating an advanced inflammatory state. RESULTS: Multiple metabolites and lipid species were linearly associated with the seven inflammatory markers [false discovery rate (FDR) <0.05]. LASSO and multivariable-adjusted logistic regression analysis identified significant associations between 45 metabolites and systemic immune-inflammation index, 46 metabolites and neutrophil-lymphocyte ratio states, 32 metabolites and low-grade inflammation score, and 26 metabolites and high-sensitivity C-reactive protein states ( < 0.05). Glycerophospholipid metabolism and arginine and proline metabolism were determined as key altered metabolic pathways for systemic immune and low-grade inflammatory states. Predictive models based solely on metabolite combinations showed feasibility (area under the curve: 0.81 to 0.88) for discriminating the four parameters that represent inflammatory states and were successfully validated using a validation cohort. The inflammation-associated metabolite, namely, β-pseudouridine, was related to carotid and coronary arteriosclerosis indicators ( < 0.05). CONCLUSIONS: This study provides further information on the relationship between plasma metabolite profiles and inflammatory states represented by various inflammatory markers in CAD. These metabolic markers provide potential insights into pathological changes during CAD progression and may aid in the development of therapeutic targets.

摘要

背景:全身免疫炎症是冠状动脉疾病(CAD)进展中的关键介质,涉及各种代谢和脂质变化。在本研究中,调查了CAD患者炎症指标与代谢谱之间的关系,以深入了解与炎症相关的代谢紊乱。 方法:对1234例CAD患者进行了广泛靶向的血浆代谢组学和脂质组学分析。实验室循环炎症标志物主要用于定义一般全身免疫和低度炎症状态。采用多变量调整线性回归评估860种代谢物与7种炎症标志物之间的关联。应用基于最小绝对收缩和选择算子(LASSO)的逻辑分类器和多变量逻辑回归来识别炎症状态的生物标志物,并建立区分晚期炎症状态的模型。 结果:多种代谢物和脂质种类与七种炎症标志物呈线性相关[错误发现率(FDR)<0.05]。LASSO和多变量调整逻辑回归分析确定了45种代谢物与全身免疫炎症指数、46种代谢物与中性粒细胞-淋巴细胞比率状态、32种代谢物与低度炎症评分以及26种代谢物与高敏C反应蛋白状态之间存在显著关联(<0.05)。甘油磷脂代谢以及精氨酸和脯氨酸代谢被确定为全身免疫和低度炎症状态的关键改变代谢途径。仅基于代谢物组合的预测模型显示出区分代表炎症状态的四个参数的可行性(曲线下面积:0.81至0.88),并使用验证队列成功进行了验证。炎症相关代谢物β-假尿苷与颈动脉和冠状动脉粥样硬化指标相关(<0.05)。 结论:本研究进一步提供了CAD中血浆代谢物谱与各种炎症标志物所代表的炎症状态之间关系的信息。这些代谢标志物为CAD进展过程中的病理变化提供了潜在见解,并可能有助于治疗靶点的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/6fe7f046a373/fimmu-13-829425-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/7d8533993fe1/fimmu-13-829425-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/3648c2df8be0/fimmu-13-829425-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/9e608d42a833/fimmu-13-829425-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/b8dfb13e7c08/fimmu-13-829425-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/d123c7ca1023/fimmu-13-829425-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/6fe7f046a373/fimmu-13-829425-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/7d8533993fe1/fimmu-13-829425-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/3648c2df8be0/fimmu-13-829425-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/9e608d42a833/fimmu-13-829425-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/b8dfb13e7c08/fimmu-13-829425-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/d123c7ca1023/fimmu-13-829425-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/391a/8965586/6fe7f046a373/fimmu-13-829425-g006.jpg

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