Identification of potential biomarkers and pathways involved in high-altitude pulmonary edema using GC-MS and LC-MS metabolomic methods

Abstract

High-altitude pulmonary edema (HAPE) is a life-threatening altitude sickness afflicting certain individuals after rapid ascent to high altitude above 2500 m. In the setting of HAPE, an early diagnosis is critical and currently based on clinical evaluation. The aim of this study was to utilize the metabolomics to identify the altered metabolic patterns and potential biomarkers for HAPE. Serum samples from HAPE patients (n = 24) and healthy controls (n = 21) were analyzed by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to profile differential metabolites and explore dysregulated metabolic pathways. The correlation analysis and receiver operating characteristic (ROC) curve analysis were further performed to screen biomarkers for HAPE. A total of 119 differential metabolites between the control and HAPE groups were identified. Top dysregulated pathways included pyrimidine metabolism, citrate cycle, sulfur metabolism, phenylalanine metabolism and purine metabolism. After correlation analysis with clinical indices, 39 differential metabolites were obtained as potential biomarkers related to HAPE. Finally, 7 biomarkers, specifically S-nitroso-N-acetylcysteine, aminocaproic acid, emodin, threo-hydroxyaspartic acid, 6-hydroxynicotinic acid, 3-acetylphenol sulfate and cis-aconitic acid, were screened out through ROC analysis, which displayed high diagnostic accuracy for HAPE. Taken together, the altered serum metabolic profile is associated with the occurrence of HAPE. Diagnostic tests based on the biomarkers from metabolomics may hold promise as a strategy for early detection of HAPE.

Keywords: Biomarkers; Disturbed metabolism; High-altitude pulmonary edema; Metabolomics.

Fig. 1

Schematic diagram of the study design. Serum samples from HAPE patients (n = 24) and healthy controls (n = 21) were analyzed by GC-MS and LC-MS to profile differential metabolites and explore dysregulated metabolic pathways. The correlation analysis and ROC curve analysis were further performed to screen biomarkers for HAPE.

Fig. 2

Score plots of PCA and PLS-DA for the control and HAPE groups in serum GC-MS and LC-MS. (A) PCA score plot of GC-MS. (B) PCA score plot of LC-MS. (C) PLS-DA score plot of GC-MS. (D) PLS-DA score plot of LC-MS.

Fig. 3

Score plots and Validation plots of OPLS-DA for the control and HAPE groups in serum GC-MS and LC-MS. (A) OPLS-DA score plot of GC-MS. (B) OPLS-DA score plot of LC-MS. (C) Validation plot of GC-MS. (D) Validation plot of LC-MS.

Fig. 4

Volcano plots and heatmaps of serum differential metabolites between the control and HAPE groups obtained from GC-MS and LC-MS analysis. (A) Volcano plot of differential metabolites obtained from GC-MS. (B) Heatmap of differential metabolites obtained from GC-MS. (C) Volcano plot of differential metabolites obtained from LC-MS. (D) Heatmap of differential metabolites obtained from LC-MS.

Fig. 5

Metabolic pathway analysis of serum differential metabolites between the control and HAPE groups. (A) Metabolic pathway analysis was performed using MetaboAnalyst with differential metabolites. (B) Schematic scheme of the disturbed metabolic pathways associated with HAPE. Metabolites marked in red represent up-regulation and blue represent down-regulation in the HAPE group compared with the control group.

Fig. 6

Correlation analysis of serum different metabolites and clinical indices (HR, RR, SpO₂ and LLS). Boxes in heatmap marked in orange represent positive correlation and blue negative correlation. (^***P < 0.001)

Fig. 7

ROC curves and level boxplots of 7 differential serum metabolites with AUC ≥ 0.95 between the control and HAPE groups. (A) S-nitroso-N-acetylcysteine. (B) Aminocaproic acid. (C) Emodin. (D) Threo-hydroxyaspartic acid. (E) 6-Hydroxynicotinic acid. (F) 3-Acetylphenol sulfate. (G) cis-Aconitic acid.