|本期目录/Table of Contents|

[1]林家仕,费小小.不同CRF水平的MS风险因素人群血浆代谢物差异[J].体育科学研究,2024,28(5):46-55.
　LIN Jiashi,FEI Xiaoxiao.Differences in Plasma Metabolites among MS Risk Factors with Different CRF Levels[J].sports science research,2024,28(5):46-55.
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不同CRF水平的MS风险因素人群血浆代谢物差异(PDF)
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《体育科学研究》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2024年5期
页码:
46-55
栏目:
体育生物科学
出版日期:
2024-11-20

文章信息/Info

Title:
Differences in Plasma Metabolites among MS Risk Factors with Different CRF Levels
作者:
林家仕; 费小小
集美大学体育学院，福建 厦门361021
Author(s):
LIN Jiashi; FEI Xiaoxiao
Physical Education Institute of Jimei University,Xiamen 361021,China
关键词:
心肺耐力; 代谢综合征风险因素; 代谢通路; 血液代谢组学
Keywords:
cardiorespiratory fitness; metabolic syndrome risk factors; metabolic pathways; blood metabolomics
分类号:
-
DOI:
-
文献标志码:
A
摘要:
通过对不同心肺耐力（CRF）水平的代谢综合征（MS）风险因素人群血浆差异代谢物进行分析，探讨CRF水平变化影响MS风险因素的代谢组学机制，为CRF改善MS风险因素人群机体代谢提供理论基础。选取90名40~65岁受试者，采用3分位数法划分CRF等级：低（L）、中等（M）、高（H）。基于高效化学同位素标记-液质联用（LC-MS）非靶向代谢组学技术分析血浆样本，使用IsoMS Pro 14.1、SPSS 26.0等软件工具对采集的数据进行预处理，应用三层级代谢物鉴定方法对代谢物进行结构鉴定。格式转换后经SIMCAP14.1软件进行多元统计分析，采用火山图分析、变量投影重要度值（VIP）筛选和鉴定差异代谢物，使用MetaboAnalyst 5.0软件进行代谢通路分析，并根据KEGG数据库对代谢途径进行注释。结果显示：（1）H组与L组存在8种差异代谢物，每种代谢物都表现出特定的变化趋势，包括a-酮戊二酸（↑）、顺式乌头酸（↑）、精氨酸（↑）、γ-氨基丁酸（↑）、丝氨酸（↑）、缬氨酸（↓）、蛋氨酸（↓）、谷氨酰胺（↓）；（2）代谢通路显示CRF主要影响精氨酸生物合成、三羧酸（TCA）循环以及半胱氨酸和蛋氨酸代谢等11条生物代谢途径。得出结论：研究确定了8种代谢物，作为区分不同CRF水平的MS风险因素人群潜在生物标志物，此外精氨酸生物合成、TCA循环等生物代谢途径阐明CRF对MS风险因素的影响机制，有助于解释高CRF水平是MS风险因素的保护性因素。
Abstract:
This study aims to analyze plasma differential metabolites related to metabolic syndrome (MS) risk factors across different cardiorespiratory fitness (CRF) levels,and to explore the metabolomic mechanisms through which variations in CRF levels influence MS risk factors,providing a theoretical basis for improving metabolic health in populations at risk of MS through CRF enhancement.Methods:Ninety subjects aged 40~65 were selected and categorized into low (L),moderate (M),and high (H) CRF levels using tertile classification. Plasma samples were analyzed using untargeted metabolomics based on highperformance liquid chromatography coupled with mass spectrometry (LC-MS). Data were preprocessed using IsoMS Pro 14.1 and SPSS 26.0 software. Three-tiered metabolite identification methods were employed for structural identification of metabolites. Multivariate statistical analysis was conducted using SIMCAP 14.1 software following data format conversion. Differential metabolites were identified and characterized using volcano plot analysis and variable importance in projection (VIP) values. Metabolic pathway analysis was performed using MetaboAnalyst 5.0 software,with pathway annotations based on the KEGG database.Results:(1) Eight differential metabolites were identified between the H and L groups,each exhibiting specific trends:increased levels of alphaketoglutarate (↑),succinic acid (↑),arginine (↑),gammaaminobutyric acid (↑),serine (↑),and decreased levels of valine (↓),methionine (↓),and glutamine (↓). (2) Metabolic pathway analysis revealed that CRF primarily influences arginine biosynthesis,tricarboxylic acid (TCA) cycle,and cysteine and methionine metabolism,among 11 biological pathways. Conclusion:This study identifies eight metabolites as potential biomarkers distinguishing MS risk factors among individuals with different CRF levels. Furthermore,elucidation of pathways such as arginine biosynthesis and TCA cycle highlights the mechanisms through which CRF impacts MS risk factors,suggesting that high CRF levels may act protectively against MS risk factors.

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备注/Memo:
更新日期/Last Update: 2024-11-11