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Serum metabolic characteristics of interstitial lung disease: a potential link between endocrine aging and gut-derived metabolites

Aug 2026 · Frontiers in Medicine · Vol 13 · 0 citations · 34 references
Medicine

TL;DR

Untargeted metabolomics has revealed significant systemic metabolic dysregulation in ILD and the biomarkers and “metabolic-immune-endocrine” interaction patterns identified offer potential leads for early diagnosis and targeted treatment, which require validation in larger cohorts.

Abstract

Objective To analyze differences in serum metabolic profiles between patients with interstitial lung disease (ILD) and healthy controls in northern China using untargeted metabolomics techniques, to identify differentially expressed metabolites and key dysregulated pathways, and to identify potential metabolic biomarkers. Methods Thirty patients with interstitial lung disease (ILD) and 30 healthy controls were enrolled. Untargeted metabolomics analysis was performed using LC-MS/MS in both positive and negative ion modes. Differentially expressed metabolites were identified using the criteria VIP > 1.0, FC > 1.2 or FC < 0.833 and P-value < 0.05. Pathway enrichment analysis was conducted using the KEGG database, and diagnostic performance was evaluated using ROC curves. Results The global metabolic profile showed significant separation between the ILD group and the control group. Pathway enrichment analysis revealed significant dysregulation in lipid metabolism (particularly linoleic acid metabolism), amino acid biosynthesis, and steroid hormone metabolism. GSEA analysis further confirmed the suppression of overall metabolic activity and the specific enrichment of tryptophan metabolism. Among the differentially expressed metabolites, DG(15:0/18:2(9Z,12Z)/0:0) (AUC = 0.914), Medroxalol (AUC = 0.912), estriol 3-sulfate (AUC = 0.890) and DHEA-S (AUC = 0.877) demonstrated high diagnostic value. Conclusion Untargeted metabolomics has revealed significant systemic metabolic dysregulation in ILD. The biomarkers and “metabolic-immune-endocrine” interaction patterns identified offer potential leads for early diagnosis and targeted treatment, which require validation in larger cohorts.

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