Aug 2026· Frontiers in Medicine· Vol 13· 0 citations· 36 references
TL;DR
Untargeted metabolomics enables identification of metabolically perturbed pathways during the acute phase of KD, and L-tyrosine, L-tryptophan, glutamine, histidine, histamine, and taurocholic acid may serve as candidate biomarkers for acute phase of KD.
Abstract
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.
Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.
Xin Sun, Baoying Gong, Ye Sun et al.· Metabolomics· 0 citations
PURPOSE
Vogt-Koyanagi-Harada (VKH) disease is a sight-threatening autoimmune disorder whose early systemic metabolic alterations remain poorly defined. We aimed to characterize plasma metabolomic and lipidomic signatures in new-onset acute VKH disease and to explore their clinical relevance.
DESIGN
Retrospective case-control study.
PARTICIPANTS
Twenty-six patients with new-onset acute VKH disease and 39 healthy controls were included.
METHODS
Untargeted metabolomics and quantitative lipidomics were performed in plasma from treatment-naïve VKH patients and healthy controls. An elastic net model was applied to identify a discriminative glycerophospholipid signature.
MAIN OUTCOME MEASURES
Differential metabolites and lipids and their correlations with cerebrospinal fluid (CSF) white blood cell (WBC) count and subfoveal choroidal thickness (SFCT) were analyzed.
RESULTS
Metabolomics identified 74 significantly altered metabolites, with lipids as the predominate class and glycerophospholipid metabolism as a core perturbed pathway. Lipidomics further revealed 262 significantly altered lipids, with 254 downregulated in the VKH group. Glycerophospholipids predominated among the altered lipids (67.2%), representing a marked enrichment compared with their proportion in the overall lipidome (43.9%, p < 0.0001). Nine glycerophospholipids correlated negatively with CSF WBC count (all p < 0.05), with PE(16:0_22:4) showing the strongest association (r = -0.59, p = 0.003). Three phosphatidylcholine species correlated negatively with SFCT (all p < 0.05). An elastic net model identified a 25-glycerophospholipid signature that achieved an area under the curve of 0.953 in discriminating VKH patients from healthy controls.
CONCLUSIONS
These findings highlight dysregulated glycerophospholipid metabolism as a key metabolic feature of new-onset acute VKH disease and provide additional insights into disease pathogenesis.
Yang Deng, Ziqian Huang, Xiaoran Jiang et al.· American journal of ophthalm...· 0 citations
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.
Lu Liu, Xinyi Wang, Jinling Xiao et al.· Frontiers in Medicine· 0 citations
ABSTRACT Neglected tropical diseases (NTDs) affect more than 1.6 billion people worldwide and remain major contributors to morbidity, disability, stigma, and poverty. Metabolomics has emerged as a promising approach for investigating disease-associated biochemical alterations and identifying biomarkers for diagnosis, prognosis, and treatment monitoring. This systematic review evaluated the available evidence on metabolomic profiles in WHO-defined NTDs and their potential clinical applications. MEDLINE/PubMed, Web of Science, and Scopus were searched through March 2026. Observational studies investigating metabolomic profiles in human subjects with NTDs were included, and methodological quality was assessed using a modified QUADOMICS tool. Seventy-four studies met the inclusion criteria. Most were conducted in China and Brazil and focused on dengue, onchocerciasis, schistosomiasis, leprosy, and Chagas disease. Across diseases, metabolomic alterations were predominantly observed in amino acid, lipid, carbohydrate, and energy metabolism pathways. Several candidate biomarkers were identified, including N-acetyltyramine-O-β-glucuronide for onchocerciasis, bile acid derivatives for schistosomiasis, amino acids and peptides for Chagas disease, lipid mediators for leprosy, and lipid- and serotonin-related metabolites for dengue. However, most studies remained exploratory, and only two validated biomarkers in independent populations. Risk-of-bias assessment revealed frequent methodological limitations, including inadequate reporting of pre-analytical procedures, insufficient characterization of study populations, and lack of strategies to address overfitting. Human metabolomics studies were absent for several WHO-defined NTDs, highlighting major research gaps. Future research should prioritize standardized methodologies, multicenter validation studies, and greater inclusion of underrepresented diseases and endemic populations to translate metabolomic discoveries into clinically useful tools for NTD diagnosis, monitoring, and control.
J. B. Maciel, R. L. Abreu Netto, V. I. Mwangi et al.· Revista da Sociedade Brasile...· 1 citation
Gout typically develops from hyperuricemia (HUA), but the metabolic alterations driving this transition remain poorly understood, limiting our understanding of disease pathogenesis. To identify stage-specific putative biomarker candidates and to characterize dysregulated metabolic pathways distinguishing gout from HUA. We conducted a targeted metabolomics assay on the baseline plasma samples from a Zhuang minority cohort using LC-MS/MS. The analyzed sample set comprised 38 HUA patients, 47 gout patients, and 52 healthy controls. Sex-stratified differential metabolite analysis was performed across all participants, as well as in female and male subgroups. Pathway enrichment analysis was carried out using the KEGG database. Machine learning approaches, including the Boruta algorithm and support vector machine (SVM), were employed for putative biomarker discovery and model evaluation in male participants. Among all participants, 24 metabolites reached nominal significance (P < 0.05), but only uric acid remained significant after FDR correction. In sex-stratified analyses, no metabolite survived FDR correction in females, whereas in males, seven metabolites (flavone, glutamine, L-2-aminoadipic acid, L-pipecolic acid, N1-methyl-2-pyridone-5-carboxamide, phenyllactic acid, and uric acid) showed significant differences among healthy controls, HUA patients, and gout patients (FDR < 0.1). These metabolites were primarily involved in nitrogen metabolism, arginine biosynthesis, D-amino acid metabolism, nicotinate and nicotinamide metabolism, and purine metabolism. Machine learning identified four metabolites (N1-methyl-2-pyridone-5-carboxamide, flavone, glutamine, and phenyllactic acid) that distinguished gout from healthy controls, with AUCs of 0.902 and 0.800 in the training and validation sets, respectively. A second model (L-pipecolic acid, glutamine, phenyllactic acid, and flavone) discriminated gout from HUA, achieving AUCs of 0.850 and 1.000. Sensitivity analyses excluding obese or hypertriglyceridemic participants confirmed the robust performance of both models. This study suggests sex-specific metabolic alterations in gout and provides robust machine learning-based models for male participants. The identified metabolite signatures appear to extend purine metabolism to involve amino acid and energy metabolic pathways. These findings provide a basis for mechanism-targeted strategies in HUA management. External validation remains essential.
Yu Wei, X. Qiu, Li Su et al.· Metabolomics· 1 citation
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
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