Biomarkers for hemorrhagic shock at high-altitude based on metabolomics and machine learning: Identification and validation of D-inositol-4-phosphate, phosalone and methionine

ZHOU Yuanqun; XIANG Xinming; OUYANG Xingnan; ZHANG Jie; ZHANG Zisen; LIU Liangming; LI Tao

doi:10.16016/j.2097-0927.202511028

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Biomarkers for hemorrhagic shock at high-altitude based on metabolomics and machine learning: Identification and validation of D-inositol-4-phosphate, phosalone and methionine

Basic Medicine | 更新时间：2026-01-14

- Biomarkers for hemorrhagic shock at high-altitude based on metabolomics and machine learning: Identification and validation of D-inositol-4-phosphate, phosalone and methionine
- Journal of Army Medical University Vol. 48, Issue 1, Pages: 75-85(2026)
- 作者机构：
  
  陆军军医大学（第三军医大学）大坪医院战伤休克与输血研究室，创伤与化学中毒全国重点实验室，重庆
- 作者简介：
  
  LI Tao E-mail：lt200132@tmmu.edu.cn
- 基金信息：
  
  the National Natural Science Foundation for Young Scholars of China(82402549)
- DOI：10.16016/j.2097-0927.202511028
  CLC： R339.54;R363.21;R605.971
- Received：05 November 2025，
  
  Revised：2025-12-10，
  
  Accepted：14 December 2025，
  
  Published：15 January 2026
- 稿件说明：
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周远群，向鑫明，欧阳杏楠，等.基于代谢组学与机器学习筛选高原失血性休克诊断标志物：D-inositol-4-phosphate、Phosalone与Methionine的鉴定与验证［J］.陆军军医大学学报，2026，48（01）：75-85.DOI：10.16016/j.2097-0927.202511028.

ZHOU Y Q，XIANG X M，OUYANG X N，et al.Biomarkers for hemorrhagic shock at high-altitude based on metabolomics and machine learning： Identification and validation of D-inositol-4-phosphate， phosalone and methionine［J］.J Army Med Univ，2026，48（01）：75-85.DOI：10.16016/j.2097-0927.202511028.
周远群，向鑫明，欧阳杏楠，等.基于代谢组学与机器学习筛选高原失血性休克诊断标志物：D-inositol-4-phosphate、Phosalone与Methionine的鉴定与验证［J］.陆军军医大学学报，2026，48（01）：75-85.DOI：10.16016/j.2097-0927.202511028. DOI：

ZHOU Y Q，XIANG X M，OUYANG X N，et al.Biomarkers for hemorrhagic shock at high-altitude based on metabolomics and machine learning： Identification and validation of D-inositol-4-phosphate， phosalone and methionine［J］.J Army Med Univ，2026，48（01）：75-85.DOI：10.16016/j.2097-0927.202511028. DOI：

摘要

目的

整合代谢组学与机器学习算法研究模拟高原条件下失血性休克的代谢特征，筛选高原环境失血性休克的潜在诊断生物标志物。

方法

10~12周龄、体质量180~220 g SPF级雄性SD大鼠80只按随机数字表法分为（

=40）：假手术（Sham）组和休克（uncontrolled hemorrhagic shock，UHS）组。大鼠放置于低压缺氧舱（模拟海拔5 000 m高原环境）喂养48 h，出舱后立即经尾静脉注射油酸，30 min后，离断脾动脉使其自由出血，待平均动脉血压（mean arterial pressure，MAP）降至40 mmHg，建立模拟高原条件下的失血性休克模型。采集2组大鼠的血清，通过代谢组学分析鉴定差异代谢物。通过加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）识别与休克相关的代谢物，利用

LASSO（least absolute shrinkage and selection operator）回归、随机森林（random forest，RF）、支持向量机递归特征消除（support vector machine-recursive feature elimination，SVM-RFE）3种机器学习方法鉴定失血性休克的生物标志物。基于十折交叉验证进行受试者工作特征曲线（receiver operating characteristic curve，ROC）分析，计算平均曲线下面积（area under the curve，AUC），以评估生物标志物的诊断性能。

结果

代谢组学数据主成分分析结果显示Sham组样本和UHS组样本明显分离，正交偏最小二乘法-判别分析（orthogonal projections to latent structures-discriminant analysis，OPLS-DA）进一步证实2组血清样本的代谢模式存在明显差异。共鉴定到5 398个代谢物，与Sham组相比，UHS组有391个代谢物显著下调（VIP

1，FC

1/1.5，

0.05），1 181个代谢物显著上调（VIP

1，FC

1.5，

0.05）。其中，变化最多的是脂质类代谢物，包括FFA（18：2）、FFA（18：1）、FFA（12：0）、FFA（14：0）、Ergosterol、Mesaconic acid、Suberic acid、gamma-Linolenic acid和 PC［22：5（4Z，7Z，10Z，13Z，16Z）/22：6（4Z，7Z，10Z，13Z，16Z，19Z）］等，其次是有机酸及衍生物类代谢物，包括Methionine、Citrulline、Creatine、L-Lactic acid、Oxalacetic acid和2-Acetolactate等。代谢通路富集分析显示这些差异代谢物主要参与谷氨酰胺代谢，牛磺酸代谢，丙氨酸、天冬氨酸和谷氨酸代谢以及甘油磷脂代谢等。WGCNA分析结果显示MEturquoise模块与UHS呈最强正相关（

=0.95，

=8e-40），MEsalmon模块与UHS呈最强负相关（

=-0.67，

=2e-11）。进一步以|基因显著性（gene significance，GS）|

0.2和|模块隶属度（module membership，MM）|

0.8为标准进行筛选，共获得344个与高原环境失血性休克显著相关的特征代谢物。通过LASSO回归、RF和SVM-RFE 3种机器学习算法筛选出3种生物标志物，分别是Methionine、D-inositol-4-phosphate和Phosalone。与Sham组相比，UHS组中D-inositol-4-phosphate和Phosalone的含量显著升高，而Methionine的含量显著降低。ROC曲线分析结果显示D-inositol-4-phosphate、Phosalone和Methionine的平均AUC分别为0.988、0.950和0.988，表明这3种代谢物对模拟高原条件下失血性休克具有良好的预测能力。

结论

模拟高原条件下失血性休克大鼠的代谢特征明显紊乱，多条代谢途径发生显著改变，代谢物D-inositol-4-phosphate、Phosalone和Methionine可能成为预测高原环境下失血性休克发生或判断休克程度的生物标志物。

Abstract

Objective

To systematically investigate the metabolic characteristics of hemorrhagic shock (HS) under simulated high-altitude conditions by integrating metabolomics and machine learning algorithms

and screen the biomarkers for predicting HS under these conditions.

Methods

Eighty SPF-grade male SD rats (10 to 12 weeks old

weighing 180 to 220 g) were randomly divided into a sham operation group (Sham) and an uncontrolled HS group (UHS)

with 40 animals in each group. All rats were placed in a hypobaric hypoxia chamber to simulate an altitude of 5 000 m for 48 h. After removal from the chamber

the rats were given an injection of oleic acid immediately

via

tail vein

and then in 30 min latter

those from the UHS group were induced to free bleeding by splenic artery transection

with the endpoint set at a mean arterial pressure (MAP) of 40 mmHg. Thus

a rat model of HS under stimulated high-altitude conditions was established. Metabolomic analysis was performed on the serum samples to identify differential metabolites between the Sham and UHS groups. Weighted gene co-expression network analysis (WGCNA) was carried out to identify metabolites associated with UHS. Three machine learning methods

including least absolute shrinkage and selection operator (Lasso) regression

random forest (RF)

and support vector machine-recursive feature elimination (SVM-RFE) were employed to identify relevant biomarkers for HS at high-altitude. Based on 10-fold cross-validation

the diagnostic performance of the biomarkers was evaluated with receiver operating characteristic (ROC) curve analysis

and the area under the curve (AUC) was calculated.

Results

Principal component analysis (PCA) of the metabolomic data showed clear separation between the samples from the Sham and UHS groups. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further confirmed significant differences in metabolic profiles between the 2 groups. There were 5 398 metabolites identified

with the UHS group having 391 metabolites significantly down-regulated (VIP

1/1.5

0.05) and 1 181 metabolites obviously up-regulated (VIP

1.5

0.05) when compared to the Sham group. Among the metabolites

the most alter

ed metabolites were lipids

including FFA (18:2)

FFA (18:1)

FFA (12:0)

FFA (14:0)

ergosterol

mesaconic acid

suberic acid

gamma-linolenic acid

and PC [22:5 (4Z

10Z

13Z

16Z)/22:6 (4Z

10Z

13Z

16Z

19Z)

]

and then followed by organic acids and derivatives

such as methionine

citrulline

creatine

L-lactic acid

oxalacetic acid

and 2-acetolactate. Pathway enrichment analysis showed that these differential metabolites were mainly involved in glutamine metabolism

taurine metabolism

alanine

aspartate and glutamate metabolism

as well as glycerophospholipid metabolism. WGCNA analysis found that MEturquoise module exhibited the strongest positive correlation with UHS (

=0.95

=8e-40)

while the MEsalmon module showed the strongest negative correlation with UHS (

=-0.67

=2e-11). With thresholds of |Gene Significance (GS)|

0.2 and |Module Membership (MM)|

0.8

344 characteristic metabolites were identified

and all of them were significantly associated with HS in a high-altitude environment. The 3 machine learning algorithms yielded 3 biomarkers

that is

methionine

D-inositol-4-phosphate and phosalone. Significantly increased D-inositol-4-phosphate and phosalone while decreased methionine were observed in the UHS group than the Sham group. ROC curve analysis revealed that the AUC values of D-inositol-4-phosphate

phosalone and methionine were 0.988

0.950 and 0.988

respectively

indicating that the 3 biomarkers having good predictive efficiency for HS under simulated high-altitude condition.

Conclusion

HS rats under simulated high-altitude conditions present significantly disturbed metabolic profiles

characterized by substantial changes across multiple pathways. The metabolites D-inositol-4-phosphate

phosalone and methionine may serve as potential biomarkers for predicting the occurrence or evaluating the severity of HS in high-altitude environments.

关键词

Keywords

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