您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2026, Vol. 64 ›› Issue (9): 36-42.doi: 10.6040/j.issn.1671-7554.0.2026.0666

• 临床医学 • 上一篇    

代谢组学揭示的急性高原病关键分子特征

丁文静1,杨一帆1,胡忻雨1,陈小兰1,薛新颖2,潘磊1   

  1. 1.首都医科大学附属北京世纪坛医院呼吸与危重症医学科, 北京 100038;2.首都医科大学宣武医院呼吸与危重症医学科, 北京 100053
  • 发布日期:2026-09-09
  • 通讯作者: 潘磊. E-mail:PL1386@bjsjth.cn
  • 基金资助:
    北京市医院管理中心“登峰”人才培养计划(DFL20240703)

Key molecular features of acute mountain sickness revealed by metabolomics

DING Wenjing1, YANG Yifan1, HU Xinyu1, CHEN Xiaolan1, XUE Xinying2, PAN Lei1   

  1. 1. Department of Respiratory and Critical Care, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China;
    2. Department of Respiratory and Critical Care, Xuanwu Hospital Capital Medical University, Beijing 100053, China
  • Published:2026-09-09

PDF (PC)

4

摘要： 目的 探讨高原暴露后急性高原病(acute mountain sickness, AMS)与无AMS人群生理参数和尿液代谢谱的差异,筛选AMS病相关候选尿液代谢物及代谢通路。 方法 纳入2024年在西藏地区招募的高原暴露后男性受试者70例,18~45(20.93±1.55)岁,分为无AMS组(n=41)和AMS组(n=29)。比较两组人口学特征及高原暴露后外周血氧饱和度(peripheral oxygen saturation, SpO2)、心率、血压、平均动脉压和心率血压乘积等生理参数差异。采用非靶向尿液代谢组学分析两组尿液代谢谱差异,并进行正交偏最小二乘判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA)、差异代谢物筛选、京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)通路富集及集成特征选择(ensemble feature selection, EFS)分析。 结果 与无AMS组比较,AMS组SpO2降低,收缩压和心率血压乘积升高(均P<0.05)。尿液代谢组学共检测到5 587个代谢物,OPLS-DA显示两组尿液代谢谱存在分离趋势。以变量投影重要性(variable importance in projection, VIP)>1并结合P<0.05筛选得到544个差异代谢物,其中上调267个、下调277个。KEGG分析显示,差异代谢物主要富集于甘氨酸、丝氨酸和苏氨酸代谢、色氨酸代谢、戊糖磷酸途径、精氨酸和脯氨酸代谢、碳代谢及支链氨基酸相关代谢等通路。进一步EFS分析显示,S-(2-羧丙基)谷胱甘肽、核糖、N-乙酰色氨酸、2-羟基戊二酸、谷氨酸、4-羟脯氨酸等代谢物具有较高分组贡献。 结论 AMS人群高原暴露后表现为更明显的氧合下降和心血管负荷增加,并伴随尿液代谢谱改变。差异代谢物主要涉及氨基酸代谢、糖代谢、氧化还原调节和脂质相关代谢过程,尿液代谢组学可为AMS相关机制研究和候选标志物筛选提供参考。

关键词: 急性高原病, 尿液代谢组学, 低氧, 差异代谢物, 集成特征选择

Abstract: Objective To compare physiological parameters and urinary metabolomic profiles between individuals with acute mountain sickness(AMS)and those without AMS after high-altitude exposure, and to screen AMS-related candidate urinary metabolites and metabolic pathways. Methods In 2024, seventy male participants recruited in Xizang after high-altitude exposure, aged 18-45(20.93±1.55)years, were included, comprising 41 without AMS and 29 with AMS(AMS group). Demographic characteristics and post-exposure physiological parameters, including peripheral oxygen saturation(SpO2), heart rate, blood pressure, mean arterial pressure, and rate pressure product, were compared between groups. Untargeted urinary metabolomics was performed to characterize metabolic differences. Orthogonal partial least squares discriminant analysis(OPLS-DA), differential metabolite screening, Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment, and ensemble feature selection(EFS)were further conducted. Results Compared with the non-AMS group, the AMS group had lower SpO2 and higher systolic blood pressure and rate pressure product(all P<0.05). A total of 5,587 urinary metabolites were detected. OPLS-DA showed a tendency toward separation between the two groups. Using variable importance in projection(VIP)>1 combined with P<0.05, 544 differential metabolites were identified, including 267 upregulated and 277 downregulated metabolites. KEGG analysis showed that these metabolites were mainly enriched in glycine, serine and threonine metabolism, tryptophan metabolism, the pentose phosphate pathway, arginine and proline metabolism, carbon metabolism and branched-chain amino acid-related pathways. EFS highlighted S-(2-carboxypropyl)glutathione, ribose, N-acetyltryptophan, 2-hydroxyglutaric acid, glutamate and 4-hydroxyproline as candidate metabolites with high discriminatory contributions. Conclusion Individuals with AMS exhibit more pronounced oxygen desaturation and increased cardiovascular load after high-altitude exposure, accompanied by altered urinary metabolic profiles. The differential metabolites were mainly involved in amino acid metabolism, carbohydrate metabolism, redox balance regulation, and lipid-related metabolism, suggesting that urinary metabolomics may provide useful clues for AMS-related mechanistic studies and candidate biomarker screening.

Key words: Acute mountain sickness, Urinary metabolomics, Hypoxia, Differential metabolites, Ensemble feature selection

中图分类号: 

  • R594.3
[1] Jin J. Acute mountain sickness[J]. JAMA, 2017, 318(18): 1840. DOI:10.1001/jama.2017.16077
[2] Hackett P, Rennie D. Acute mountain sickness[J]. Lancet, 1977, 1(8009): 491-492.
[3] 张月文, 王甜, 姜璐瑶, 等. 高原低氧环境对机体损伤的研究进展[J]. 联勤军事医学, 2024, 38(9): 813-817.
[4] Roach R C, Bärtsch P, Hackett P H, et al. The Lake Louise acute mountain sickness scoring system[M] //Sutton J R, Houston C S, Coates G. Hypoxia and Molecular Medicine. Burlington, VT: Queen City Press, 1993: 272-274.
[5] Luks A M, Beidleman B A, Freer L, et al. Wilderness medical society clinical practice guidelines for the prevention, diagnosis, and treatment of acute altitude illness: 2024 update[J]. Wilderness Environ Med, 2024, 35(S1): 2-19.
[6] Luks A M, Auerbach P S, Freer L, et al. Wilderness medical society clinical practice guidelines for the prevention and treatment of acute altitude illness: 2019 update[J]. Wilderness Environ Med, 2019, 30(S4): 3-18.
[7] Hackett P H, Roach R C. High-altitude illness[J]. N Engl J Med, 2001, 345(2): 107-114.
[8] 王宇平, 郭长江, 杨继军, 等. 急性低氧暴露小鼠外周血代谢组变化分析[J]. 中国应用生理学杂志, 2009, 25(2): 177-180. DOI:10.13459/j.cnki.cjap.2009.02.016 Wang Yuping, Guo Changjiang, Yang Jijun, et al. Effects of acute hypoxia on plasma metabolome in mice[J]. Chinese Journal of Applied Physiology, 2009, 25(2): 177-180.
[9] Nicholson J K, Lindon J C, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[J]. Xenobiotica, 1999, 29(11): 1181-1189.
[10] Gao J Y, Zhao M, Cheng X, et al. Metabolomic analysis of human plasma sample after exposed to high altitude and return to sea level[J]. PLoS One, 2023, 18(3): e0282301. DOI: 10.1371/journal.pone.0282301
[11] 王馨, 刘兴龙, 曹雪, 等. 基于LC-MS/MS的电揿针干预高海拔暴露人群的代谢组学研究[J]. 昆明理工大学学报(自然科学版), 2024, 49(6): 121-132. Wang Xin, Liu Xinglong, Cao Xue, et al. LC-MS/MS-based metabolomics of electro-thumbtackneedle in high-altitude exposed population[J]. Journal of Kunming University of Science and Technology(Natural Science), 2024, 49(6): 121-132.
[12] Sibomana I, Foose D P, Raymer M L, et al. Urinary metabolites as predictors of acute mountain sickness severity[J]. Front Physiol, 2021, 12: 709804. DOI: 10.3389/fphys.2021.709804
[13] 赵明亮, 王相阳, 李欣欣, 等. 高原缺氧代谢组学研究进展[J]. 中草药, 2018, 49(4): 948-954. Zhao Mingliang, Wang Xiangyang, Li Xinxin, et al. Metabolomics approach to evaluation of plateau hypoxia[J]. Chinese Traditional and Herbal Drugs, 2018, 49(4): 948-954.
[14] Zhu G Y, Yin C L, Tian Z, et al. Metabolomic analysis of plasma from patients with acute mountain sickness using chromatography-mass spectrometry[J]. Medicine(Baltimore), 2015, 94(45): e1988. DOI: 10.1097/MD.0000000000001988
[15] Liao W T, Liu B, Chen J, et al. Metabolite modulation in human plasma in the early phase of acclimatization to hypobaric hypoxia[J]. Sci Rep, 2016, 6: 22589. DOI: 10.1038/srep22589
[16] OBrien K A, Atkinson R A, Richardson L, et al. Metabolomic and lipidomic plasma profile changes in human participants ascending to Everest Base Camp[J]. Sci Rep, 2019, 9(1): 2297. DOI: 10.1038/s41598-019-38832-z
[17] Tan Z, Shen P, Wen Y, et al. Assessment of metabolomic variations among individuals returning to plain areas after exposure to high altitudes: a metabolomic analysis of human plasma samples with high-altitude de-acclimatization syndrome.l[J]. Front Mol Biosci, 2024, 11: 1375360. DOI:10.3389/fmolb.2024.1375360
[18] Koundal S, Gandhi S, Kaur T, et al. "Omics" of high altitude biology: a urinary metabolomics biomarker study of rats under hypobaric hypoxia[J]. OMICS, 2015, 19(12): 757-765.
[19] Chang Y, Zhang W, Chen K, et al.Metabonomics window into plateau hypoxia[J]. J Int Med Res, 2019, 47(11): 5441-5452.
[20] Kumari M, Sharma D, Kumari A, et al. Urine metabolite profiling in Indian males exposed to high-altitude: a longitudinal pilot study[J]. Sci Rep, 2025, 15(1):16981. DOI: 10.1038/s41598-025-00312-y
[21] Lou B S, Wu P S, Liu Y, et al. Effects of acute syste-matic hypoxia on human urinary metabolites using LC-MS-based metabolomics[J]. High Alt Med Biol, 2014, 15(2): 192-202.
[22] Gandhi S, Chinnadurai V, Bhadra K, et al. Urinary metabolic modulation in human participants residing in Siachen: a 1H NMR metabolomics approach[J]. Sci Rep, 2022, 12(1): 9070. DOI: 10.1038/s41598-022-13031-5
[23] Li W, Zhang M, Hu Y Y, et al. Acute mountain sickness prediction: a concerto of multidimensional phenotypic data and machine learning strategies in the framework of predictive, preventive, and personalized medicine[J]. EPMA J, 2025,16(2): 265-284.
[24] Pena E, EI Alam S, Siques P, et al. Oxidative stress and diseases associated with high-altitude exposure[J]. Antioxidants(Basel), 2022, 11(2): 267. DOI: 10.3390/antiox11020267
[25] Richalet J P, Hermand E, Lhuissier F J. Cardiovascular physiology and pathophysiology at high altitude[J]. Nat Rev Cardiol, 2024, 21(2): 75-88.
[26] 潘镜余, 陈玲, 刘鸿春, 等. 高原低压低氧环境所致机体代谢重编程的研究进展[J]. 兵团医学, 2025, 23(4): 110-113.
[27] TeSlaa T, Ralser M, Fan J, et al. The pentose phosphate pathway in health and disease[J]. Nat Metab, 2023, 5(8): 1275-1289.
[28] Bowser J L, Lee J W, Yuan X, et al. The hypoxia-adenosine link during inflammation[J]. J Appl Physiol(1985), 2017, 123(5): 1303-1320.
[29] Ducker G S, Rabinowitz J D. One-carbon metabolism in health and disease[J]. Cell Metab, 2017, 25(1): 27-42.
[30] Neumann U, Genze N, Heider D. EFS: an ensemble feature selection tool implemented as R-package and web-application[J]. BioData Min, 2017, 10: 21. DOI: 10.1186/s13040-017-0142-8
[31] Abeel T, Helleputte T, Van de Peer Y, et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods[J]. Bioinformatics, 2010, 26(3): 392-398.
[32] Lawler N G, Abbiss C R, Gummer J P A, et al. Characterizing the plasma metabolome during 14 days of live-high, train-low simulated altitude: a metabolomic approach[J]. Exp Physiol, 2019, 104(1): 81-92.
[1] 丁文静,杨一帆,胡忻雨,爱丽菲拉·艾力,张昊芃,薛新颖,潘磊. 急性高原病发生相关因素及生理参数变化特征[J]. 山东大学学报 (医学版), 2026, 64(8): 64-70.
[2] 高玉杰,龙启福,胡英,许玉珍,王茹,永胜. 生物信息学鉴定低氧诱导小鼠肾脏线粒体损伤的Hub基因及其作用机制[J]. 山东大学学报 (医学版), 2023, 61(9): 57-68.
[3] 王海鹏,邹娟娟,高春苗,王孝,王岩,李延忠. OSAHS慢性间歇性低氧大鼠模型的建立及意义[J]. 山东大学学报 (医学版), 2021, 59(2): 7-13.
[4] 薛源,林雪艳,徐歌,田永杰. 低氧诱导因子-1α在子宫内膜异位症患者血清中的表达和对在位子宫内膜间质细胞上皮-间质转化的影响[J]. 山东大学学报 (医学版), 2021, 59(2): 41-47.
[5] 王凯民,谭娟娟,严志强,李志强. Sirtuin在低氧诱导人脐带间充质干细胞增殖中的作用[J]. 山东大学学报（医学版）, 2017, 55(7): 23-30.
[6] 王立轩,张璐,许新,李思雪,刘敏,王亚萍,马慧娟. 慢性间歇性低压低氧通过PI3K依赖的eNOS活化增强大鼠胸主动脉舒张[J]. 山东大学学报（医学版）, 2016, 54(2): 11-15.
[7] 李勉贤，陈弹，李红霞. GATA-4基因增加骨髓间充质干细胞抗缺氧能力的探讨[J]. 山东大学学报(医学版), 2013, 51(7): 6-9.
[8] 张忠霞1，张宏2，崔冬生1，王涛3，牛静亚1，张睿1，周洁3，裴运海3，李江静3，许顺江1. 左旋肉碱对大鼠缺血性脑损伤的神经保护作用[J]. 山东大学学报(医学版), 2012, 50(8): 14-19.
[9] 刘金峰1，张楠2，徐超3，李哲2，刘杰2，孙玉萍2. HIF-1α与HGF在非小细胞肺癌中的共表达及其与淋巴管生成的关系[J]. 山东大学学报(医学版), 2011, 49(11): 112-.
[10] 崔晓萍1，林航1，陈建梅2，穆军山1，叶建新1，殷红兵1，应大君3. 低氧对体外培养的神经干细胞中Wnt/β-catenin 信号通路相关分子表达的影响[J]. 山东大学学报(医学版), 2010, 48(9): 44-48.
[11] 赵鹏 路瑛丽 冯连世 徐建方 朱珂. 低氧训练对大鼠EPO/EPOR及其表达的影响[J]. 山东大学学报(医学版), 2009, 47(5): 5-15.
[12] 贾晓妍,许冬梅,张成银,张慧. 结缔组织生长因子和低氧诱导因子-1α在大鼠慢性马兜铃酸肾病模型肾小管间质中的表达[J]. 山东大学学报(医学版), 2008, 46(3): 268-271.
[13] 郭文菁,曲迅,张彬彬,邵倩倩,宋丙凤,高文娟,刘佳,王春娥,孔北华. 低氧微环境对MDA-435细胞株MMP2、9及其抑制剂TIMP21表达的影响[J]. 山东大学学报(医学版), 2006, 44(10): 978-980.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发