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.
Chronic Obstructive Pulmonary disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) are chronic pulmonary disorders with distinct pathologies but shared risk factors. Metabolomics may provide insights into mechanisms. To identify metabolites associated with COPD and IPF, and to characterize shared and disease-specific signatures. Plasma metabolomic profiling was conducted in the Lung Tissue Research Consortium (LTRC). Logistic regression identified metabolites associated with COPD and IPF, and results were replicated in an external cohort, COPDGene. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to explore disease-associated metabolite modules. We further evaluated relationships between significant metabolites and risk genes of interest. Of 1131 metabolites in LTRC, 246 (21.8%) differed between COPD and controls, and 136 (12.0%) between IPF and controls (FDR < 0.05). Among 80 shared significant metabolites in COPD and IPF, 77 showed concordant directions of effect. Shared metabolomic changes included reduced levels of steroids, triglycerides, diglycerides, phosphatidylcholines, and increased levels of carnitines. In contrast, polyunsaturated fatty acids, nicotine, and thyroxine metabolites differed between COPD and IPF; these findings were further explored by the WGCNA. External replication was performed for 120 metabolites measured in both cohorts, of which 49 (40.8%) replicated in COPD vs. control. In exploratory analyses leveraging quantitative imaging abnormalities (QIA) as a surrogate for IPF; only 4 (3.3%) metabolites replicated in the QIA vs. control model, and only 9 (7.5%) for COPD vs. QIA. Alterations in metabolomic profiles of COPD and IPF suggested shared dysregulation of several lipids. However, some metabolites pointed to disease-specific differences.
Aldric Rosario, Nicole Prince, S. Madha-Krause et al.· Metabolomics· 0 citations
Kawasaki disease (KD) is one of the most common rheumatic diseases in children and manifests with multisystem clinical features. Using untargeted metabolomics, our study investigated alterations in small-molecule metabolites in plasma of children with acute KD. Our study aimed to identify differential metabolic pathways and potential biomarkers.
Plasma samples were collected from 30 children diagnosed with KD and 30 age-matched healthy controls (HC) at Jinhua Maternal and Child Health Hospital between January 2025 and December 2025. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was applied to analyse plasma samples. Enriched pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and differential metabolic pathways were determined using MetaboAnalyst 5.0. Differential metabolites were screened using the nonparametric Mann–Whitney
U
-test and receiver operating characteristic curve area (AUC). The conservative average AUC from nested cross-validation was reported as the primary performance metric. Pearson correlation analysis was conducted to evaluate correlations between metabolites and clinical parameters.
In total, 261 differential metabolites were identified between the KD and HC groups, including 87 lipids and lipid-like molecules, 69 organic heterocyclic compounds, 38 benzenoids, 34 organic acids, 14 phenylpropanoids, and 19 other compounds. Pathway analysis of these differential metabolites revealed 30 putatively enriched metabolic pathways for exploratory analysis. Of these pathways, primary bile acid biosynthesis, arginine biosynthesis, histidine metabolism, and phenylalanine-tyrosine-tryptophan biosynthesis were nominally associated with KD. Six metabolites with exploratory discriminatory performance (AUC > 0.8) were further identified: L-tyrosine, L-tryptophan, glutamine, histidine, histamine, and taurocholic acid. A combined model incorporating these metabolites achieved an apparent AUC of 0.984 in the full dataset; nested cross-validation yielded a more conservative AUC of 0.889 (95% CI 0.798–0.968), indicating promising exploratory discriminatory performance.
Untargeted metabolomics enables identification of metabolically perturbed pathways during the acute phase of KD. L-tyrosine, L-tryptophan, glutamine, histidine, histamine, and taurocholic acid may serve as candidate biomarkers for acute phase of KD.
Hanqi Dai, Qianwen Wang, Yi Zhan· Frontiers in Medicine· 0 citations
Objective Plasma metabolomics offers insight into multiple sclerosis (MS) pathophysiology, but existing studies are limited by small sample sizes and incomplete clinical data. Methods We conducted plasma metabolomic profiling of 411 deeply phenotyped patients with MS and 46,443 controls, analyzing 162 metabolites in 30 biologically related metabolite groups. We characterized associations with MS diagnosis, disability, disease subtype, and inflammatory disease activity using regression and differential network enrichment analysis. We additionally examined 25 pre-diagnosis individuals whose samples were collected before their first demyelinating event. Results Fourteen of 30 metabolite groups were associated with MS after false discovery rate correction, with the strongest positive associations observed for atherogenic lipoproteins, glycine, cholines, and saturated fatty acids, and the strongest negative associations for aromatic amino acids, branched-chain amino acids, alanine, and citrate. Differential network enrichment analysis identified two dysregulated subnetworks encompassing amino acid and energy metabolism and lipid and lipoprotein metabolism. Five metabolite groups were negatively associated with disability: small high-density lipoprotein particles, histidine, branched-chain amino acids, albumin, and aromatic amino acids. The omega-6/omega-3 fatty acid ratio was significantly associated with recent relapse (OR = 1.92, FDR-p = 0.030) and nominally associated with future MRI activity, especially in patients on moderate or high-efficacy disease-modifying therapy. The MS metabolic signature was not detectable in pre-diagnosis samples. Interpretation These findings highlight coordinated dysregulation of amino acid and lipoprotein metabolism as hallmarks of established MS and identify a novel association of the omega-6/omega-3 ratio with inflammatory disease activity.
R. Rodin, B. Healy, Mariann Polgár-Turcsányi et al.· bioRxiv· 0 citations
Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.
Hangming Xiong, Yingying Xue, Xiaojing Zhang et al.· Journal of Proteomics· 0 citations
Uric acid metabolism is associated with the development of type 2 diabetes mellitus (T2DM), cardiometabolic, and cardiovascular diseases. Additionally, T2DM patients often exhibit mild cognitive impairment (MCI). However, the underlying mechanisms remain unclear. This study aims to identify and validate biomarkers associated with uric acid metabolism in T2DM and MCI, with the goal of discovering potential diagnostic and therapeutic targets to improve the quality of life for T2DM patients. Transcriptomic data for T2DM, MCI and uric acid metabolism-related genes were sourced from public databases. Biomarkers were screened using machine learning and validated for expression. Subsequent analyses included functional enrichment, immune infiltration, subcellular localization, and drug prediction. Three biomarkers—HP, ITGB3, and SELP—were identified. All showed significantly elevated expression in the T2DM group (p < 0.05). HP and ITGB3 were primarily enriched in ribosome-related pathways, primary immunodeficiency, and adherens junction processes. Immune infiltration analysis revealed that immature B cells and plasmacytoid dendritic cells were significantly enriched in T2DM. HP showed the strongest positive correlation with plasmacytoid dendritic cells (cor = 0.65, FDR <0.05), while ITGB3 exhibited the strongest positive correlation with immature B cells (cor = 0.76, FDR <0.05). Several potential therapeutic drugs were predicted, including calcifediol (score = −99.93) and meclofenamic acid (score = −99.89). This study identified three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism. Given the exploratory sample sizes, these findings are considered hypothesis-generating and require validation in larger independent cohorts.
Yan Liu, Dongmei Kang, Yuan Lei· Experimental biology and med...· 0 citations
BACKGROUND
Nonalcoholic fatty liver disease (NAFLD) is characterized by profound metabolic reprogramming. Recent evidence suggests that lactate, beyond its role as a metabolic waste product, may modulate histone lactylation, linking metabolic stress to the epigenetic regulation of disease progression. However, the specific lactate-related gene (LRG) signatures driving NAFLD remain to be elucidated.
METHODS
We integrated transcriptomic mining of the GSE164760 dataset with in vivo validation to decode the LRG landscape. Differentially expressed LRGs were identified using strict thresholds and subsequently intersected with the GeneCards database. Functional enrichment (GO/KEGG) and Protein-Protein Interaction (PPI) networks were constructed to screen for hub genes. Key biomarkers were subsequently corroborated in a high-fat diet (HFD)- induced rat NAFLD model via Western blotting.
RESULTS
A total of 98 differentially expressed LRGs were identified, which were primarily enriched in molecular catabolism and xenobiotic stress responses. From this network, eight pivotal hub genes were distilled: CREBBP, EP300, HDAC1, HIF1A, PARP1, SIRT1, STAT3, and TP53. Diagnostic modeling demonstrated their predictive value, with AUC scores ranging from 0.60 to 0.81, among which PARP1 and STAT3 exhibited high diagnostic potential (AUC > 0.80). Experimental validation in the rat model confirmed the disruption of this metabolic-epigenetic regulatory module, revealing significant dysregulation in protein expression that reflects a compensatory stress response to lipotoxicity.
DISCUSSION
These findings bridge the gap between metabolic lipotoxicity and epigenetic regulation, suggesting that lactylation acts as a critical driver in NAFLD pathogenesis.
CONCLUSION
This study delineates a preliminary, hypothesis-generating lactate-associated gene signature in NAFLD, highlighting a complex regulatory network governing hepatocyte metabolic plasticity and survival. These eight core biomarkers offer promising tissue-based targets for mechanistic intervention, though further validation in peripheral blood is required to establish their non-invasive diagnostic utility.
Qingxuan He, Xu Wang, Pengfei Wang et al.· Endocrine, Metabolic & Immun...· 0 citations