Metabolomics最新文献

{"title":"Urinary and cellular volatile organic compounds as biomarkers for urological cancers: a systematic review of GC-MS-based volatolomics.","authors":"Boyu Xie, Qing Wen","doi":"10.1007/s11306-026-02493-7","DOIUrl":"10.1007/s11306-026-02493-7","url":null,"abstract":"<p><strong>Introduction: </strong>Volatile organic compounds (VOCs) in human biofluids are promising non-invasive cancer biomarkers. However, their potential in urological cancers, including prostate, bladder, and kidney cancers is less explored, and the relationships between urinary and cellular VOC profiles across these cancer types remain unclear.</p><p><strong>Objectives: </strong>This systematic review aimed to evaluate gas chromatography-mass spectrometry (GC-MS)-based untargeted volatolomics studies in urological cancers to investigate cancer-specific, cross-cancer, and cross-matrix (urine vs. cell culture media) VOC biomarkers, while assessing methodological factors influencing biomarker discovery.</p><p><strong>Methods: </strong>Published urinary and cellular VOC studies in prostate, bladder, and kidney cancers were systematically reviewed. VOC biomarkers, direction of change, diagnostic performance, sampling methods, analytical platforms, compound-identification strategies, and statistical approaches were extracted and compared across cancer types and sample matrices.</p><p><strong>Results: </strong>A total of 12 urinary prostate, 9 urinary bladder, 5 urinary renal, and 3 cellular VOC studies were identified. Prostate cancer profiles were mainly characterized by decreased organic acids and alcohols, whereas bladder and renal cancers more frequently showed elevated ketones and aldehydes. Several VOCs, including 2-butanone, 2-heptanone, hexanal and phenol, were identified across multiple cancer types. Cellular studies showed consistent directional changes for six VOCs, and cross-matrix analysis identified 28 shared VOCs, seven of which showed aligned trends in urine and cell models. These findings suggest that VOCs reflect both unique and shared metabolic alterations in urological cancers and may serve as useful diagnostic biomarker candidates.</p><p><strong>Conclusion: </strong>This review integrates urinary and cellular VOC data across major urological cancers and identifies unique, recurrent, and cross-matrix VOCs that warrant further mechanistic and clinical validation. Although methodological heterogeneity remains a challenge, VOC-based approaches have generally shown good sensitivity and specificity for distinguishing malignant from healthy controls. With improved and standardized analytical methods, VOC volatolomics may support the future development of non-invasive and accurate diagnostics for urological cancers.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13407726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148592264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate analysis of metabolomic data to identify biological pathways modified by a clinical intervention.","authors":"Rachel M Wood, Laura J Corbin, Jane M Blazeby, Chris A Rogers, Nicholas J Timpson, Daniel J Lawson","doi":"10.1007/s11306-026-02490-w","DOIUrl":"10.1007/s11306-026-02490-w","url":null,"abstract":"<p><strong>Introduction: </strong>High throughput metabolomic assays offer a huge opportunity to quantify the cellular processes underlying disease and intervention pathways. However, the multi-dimensional inter-relatedness between these processes coupled with the complex noisy measurement environment create a need for generation of new methods that move beyond simple pairwise associations.</p><p><strong>Objectives: </strong>To evaluate the utility of a simple, multivariate, relational comparison method called CLARITY in the context of metabolomics data.</p><p><strong>Methods: </strong>Nuclear magnetic resonance (NMR)-derived metabolomics data collected from the same individuals (N = 125) before and after a weight management intervention in By-Band-Sleeve (BBS), a clinical trial of metabolic and bariatric surgery, were used. First a traditional (univariate) linear mixed model approach was taken to identify metabolites that were changed post-intervention. The CLARITY method was then used to generate a covariance-based relational anomaly score with a view to increasing classification performance of the underlying cause of changes to the levels of and covariances between metabolites.</p><p><strong>Results: </strong>CLARITY enabled further characterisation of metabolites identified in univariate linear regression analyses, differentiating those that exhibited covariance changes from those with mean changes only. An additional cluster of metabolites were identified as undergoing a relational change that would not have been detected using traditional methods.</p><p><strong>Conclusions: </strong>Gathering insights about biological pathways from large-scale metabolomics data has the potential to inform the future design of modelling and laboratory experiments aimed at capturing the underlying biological processes relevant to disease.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13407567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated workflow for univariate and multivariate evaluation of batch correction reliability.","authors":"Elfried Salanon, Blandine Comte, Delphine Centeno, Stéphanie Durand, Estelle Pujos-Guillot, Julien Boccard","doi":"10.1007/s11306-026-02453-1","DOIUrl":"10.1007/s11306-026-02453-1","url":null,"abstract":"<p><strong>Introduction: </strong>Assessing batch correction methods remains a major challenge in metabolomics, as no consensus currently exists for a generic and reliable evaluation strategy. Given the strong influence of batch effects on downstream statistical analyses, establishing a robust framework for their assessment is crucial to ensure result reproducibility and validity.</p><p><strong>Methods: </strong>This study presents a comprehensive workflow that combines innovative numerical indicators and diagnostic plots to assess multiple dimensions of batch correction performance. It relies on a newly developed indicator, the Batch Conformity Index (BCI), a multivariate, covariance-aware metric quantifying within- and between-batch variability. Complementary visualization tools, including single and multiblock factorization methods, hierarchical clustering and convex hull representations, provide interpretable global diagnostics. These are complemented by compound-level analyses employing classical univariate metrics such as the coefficient of variation, and intra/inter-batch dispersion indices. The workflow also integrates chemistry-based validation via isotopic ratio consistency to ensure that corrections preserve true biochemical information, enabling the detection of potential overfitting or overcorrection.</p><p><strong>Results: </strong>The benefits offered by the proposed strategy were illustrated by comparing two widely used correction methods, i.e. LOESS and ComBat, applied to a large-scale serum metabolomics dataset. The results highlighted the complementary strengths and limitations of each method, successfully captured by the proposed workflow, thus providing an objective and interpretable basis for method evaluation.</p><p><strong>Conclusion: </strong>The developed framework offers a unified strategy for evaluating batch correction reliability across multivariate, univariate, and chemical dimensions, representing a significant step toward standardized and reproducible metabolomics data harmonization.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148579235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.","authors":"Xin Sun, Baoying Gong, Ye Sun, Hui Sun, Bin Zhang, Le Yang, Wenkai Wang, Qubo Chen, Shuyun Wei, Hao Wen, Ruicheng Liu, Ling Kong, Ying Han, Jianwen Guo, Xijun Wang","doi":"10.1007/s11306-026-02509-2","DOIUrl":"10.1007/s11306-026-02509-2","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13396029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Broad-coverage targeted lipidomics reveals stage-specific lipid metabolic profiles and diagnostic and prognostic marker panels in ischemic stroke.","authors":"Haoran Huang, Liu Liu, Can Zhao, Shuying Cheng, Wenxuan Li, Anbao Xu, Xiaoqin Yin, Xin Xu","doi":"10.1007/s11306-026-02485-7","DOIUrl":"10.1007/s11306-026-02485-7","url":null,"abstract":"<p><strong>Background: </strong>Ischemic stroke (IS) is a major cause of mortality and disability globally, with challenges in early diagnosis and prognosis prediction. Dysregulated lipid metabolism is key to IS pathophysiology, but comprehensive profiling of lipid changes during disease progression remains limited.</p><p><strong>Methods: </strong>This study enrolled 223 IS patients and 57 healthy controls. Plasma lipid profiles were analyzed using broad-coverage targeted lipidomics by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Differential lipids were identified through orthogonal partial least squares discriminant analysis (OPLS-DA) with univariate analysis, and their changes across acute, subacute, convalescent, and chronic phases were examined by clustering analysis. Machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM), were used to screen diagnostic and prognostic biomarkers, followed by logistic regression models and receiver operating characteristic (ROC) curve evaluation.</p><p><strong>Results: </strong>From 607 identified lipids, 54 showed differential abundance between IS patients and healthy controls, grouped into four clusters. For diagnosis, six lipids-LPG(18:0), PE(O-16:0/18:2), TG(52:2/FA16:0), PE(O-16:0/22:6), PE(O-16:0/20:3), and PE(O-18:0/18:2)- achieved an area under the curve (AUC) of 0.984. For prognosis, six lipids-PE(O-16:0/22:5), PE(O-18:0/22:5), SM(d18:1/14:0), PG(18:0/18:1), PE(O-16:0/20:3), and LPI(16:0)-achieved an AUC of 0.925.</p><p><strong>Conclusion: </strong>This study characterizes lipid metabolism changes across IS stages reconstructed from cross-sectional data of different patient groups and establishes two six-lipid panels for diagnosis and prognosis. These findings provide insights into lipid metabolism evolution following stroke and offer candidate biomarker panels for IS management.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148579163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-diagnostic metabolites in peripheral blood of Langerhans cell histiocytosis children with posterior pituitary involvement.","authors":"Na Li, Xiaoxi Zhao, Lu Shi, Honghao Ma, Hongyun Lian, Na Ren, Dong Wang, Lei Cui, Li Zhang, Yunze Zhao, Liping Zhang, Ying Yang, Chanjuan Wang, Wenqi Song, Tianyou Wang, Rui Zhang, Zhigang Li","doi":"10.1007/s11306-026-02484-8","DOIUrl":"10.1007/s11306-026-02484-8","url":null,"abstract":"<p><strong>Background: </strong>Langerhans cell histiocytosis (LCH) is an inflammatory and neoplastic disorder. The levels of metabolites in the plasma of children with LCH have not been studied and may be related to disease progression.</p><p><strong>Objectives: </strong>We committed to find novel pre-diagnostic metabolites in the plasma of LCH children with posterior pituitary involvement (PI).</p><p><strong>Methods: </strong>Non-targeted metabolomics sequencing was used to detect specific and pre-diagnostic metabolites in the plasma of children with LCH. Plasma samples from 56 children with LCH and 27 healthy volunteers were enrolled. Plasma samples of children with LCH were divided into three groups: children have no PI or central nervous system-risk (CNS-risk) bone lesions (NPC group), children with CNS-risk bone lesions but without PI (CNS-risk group), and children with PI (PI group).</p><p><strong>Results: </strong>The N-acetylneuraminic acid, lipoamide, L-Glutathione oxidized and indole-3-propionic acids were potential pre-diagnostic metabolites for LCH children with PI in comparison with the healthy volunteers. Metabolites in the plasma of LCH children with PI enriched specific signaling pathways including arachidonic acid metabolism, ferroptosis, etc. Through targeted metabolomics sequencing, we verified that specific metabolites were existed in the stratified involvements of patients with LCH, such as N-acetylneuraminic acid and Vitamin A, which may be related with alteration of blood-brain barrier permeability and LCH disease progression. Through the joint multi-omics analyses, we discovered that the peripheral dendritic cells may have ferroptosis phenomena, which was regulated by osteopontin secreted in the plasma of patients with LCH.</p><p><strong>Conclusion: </strong>Analyses and identification the differential metabolites in the plasma of children with LCH may significantly improve the early diagnosis and stratified treatment of children with pituitary invovlement.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomic analyses of amyotrophic lateral sclerosis, muscle cramps, and TJ-68 treatment.","authors":"Hiroshi Mitsumoto, Ken Cheung, Takashi Matsumoto, Ian Lanza, Björn Oskarsson, Stephen Johnson, Xuan-Mai T Petterson, Grace E Jang, Howard F Andrews","doi":"10.1007/s11306-026-02499-1","DOIUrl":"10.1007/s11306-026-02499-1","url":null,"abstract":"<p><strong>Introduction: </strong>Most patients with amyotrophic lateral sclerosis (ALS), a fatal motor neuron disease, experience painful muscle cramps. Our recent pilot trial of the Japanese Kampo medicine TJ-68 suggested its efficacy in improving muscle cramps in patients with ALS.</p><p><strong>Objectives: </strong>This study analyzed plasma metabolomic changes to identify the underlying mechanisms of muscle cramps in ALS and the effects of TJ-68.</p><p><strong>Methods: </strong>Plasma was obtained from 11 participants with ALS in the repeated crossover trial at five time points (baseline, two placebo phases, and two TJ-68 phases). Metabolites were analyzed using mass spectrometry. Linear mixed-effects models were applied to identify metabolite changes associated with muscle cramps, determine the effects of TJ-68 on metabolites, and predict which participants would respond to TJ-68.</p><p><strong>Results: </strong>Higher glutamine/glutamate, arginine, and leucine levels were associated with more severe muscle cramps. TJ-68 treatment increased tryptophan and aconitate levels but reduced serotonin and acetylcarnitine levels. Long-chain acylcarnitine levels were correlated with muscle cramp severity, and their levels tended to decrease with treatment. Uric acid, β-aminoisobutyric acid, α-aminoadipic acid, and acetylcholine emerged as predictors of the efficacy of TJ-68.</p><p><strong>Conclusion: </strong>This study identified the metabolite profile of muscle cramps in ALS and the changes in metabolite levels after TJ-68 treatment. Several baseline metabolites were associated with the prediction of the response to muscle cramps following TJ-68 treatment. Uric acid might be particularly useful because of its easy measurement in standard assays. Our study affirms the value of metabolomic technology for future pharmacotherapy and studies in ALS.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network-based integration of metabolomics data from large-scale repositories.","authors":"Cecilia Wieder, Eloisa Rocha Liedl, Thomas Payne, Ozgur Yurekten, Callum Martin, Felix Xavier Amaladoss, Noemi Tejera, Wanchang Lin, Yasin El Abiead, Pieter Dorrestein, Claire O'Donovan, Juan Antonio Vizcaíno, Warwick Dunn, Timothy Ebbels","doi":"10.1007/s11306-026-02507-4","DOIUrl":"10.1007/s11306-026-02507-4","url":null,"abstract":"<p><strong>Introduction: </strong>Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility.</p><p><strong>Objectives: </strong>This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content.</p><p><strong>Methods: </strong>We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main .</p><p><strong>Results: </strong>As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected.</p><p><strong>Conclusion: </strong>Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148456314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of metabolomics and machine learning algorithm for discovery of early diagnostic biomarkers of osteoporosis.","authors":"Jing Liu, Jialong Wang, Zhijun Bao, Jingkun Jia, Jinru Jia, Lifeng Han, Jinghui Du, Erwei Liu","doi":"10.1007/s11306-026-02506-5","DOIUrl":"10.1007/s11306-026-02506-5","url":null,"abstract":"<p><strong>Background: </strong>Osteoporosis (OP) is a prevalent metabolic bone disorder and a major public health concern characterized by reduced bone mass and bone microstructural deterioration. Early identification of osteoporosis and implementation of preventive interventions remain critical for reducing fracture risk and disease burden. Identifying plasma biomarkers reflecting metabolic alterations related to OP may facilitate early detection and risk assessment.</p><p><strong>Methods: </strong> Untargeted metabolomics and lipidomics profiling based on ultra-performance liquid chromatography-mass spectrometry (UHPLC-MS) was performed in a discovery cohort comprising 75 patients with OP and 140 healthy controls. Differential features were identified using multivariate statistical analysis (PCA, OPLS-DA), and FDR-adjusted univariate analysis. Multivariable logistic regression and LASSO-regularized logistic regression was employed, with age and sex incorporated as mandatory covariates to identify independent lipid predictors. A Random Forest (RF) model was further evaluated in an independent validation cohort consisting of 20 OP patients and 42 healthy controls.</p><p><strong>Result: </strong> Sixty-one differential metabolites were identified, primarily enriched in lipid metabolism pathways. Further targeted lipidomics identified four diagnostic lipid biomarkers, including LPA(16:0), LPI(16:0), LPI(18:0), and LPI(20:0). Following covariate adjustment for age and sex, key lipid species remained independently associated with OP. The RF-based diagnostic model maintained robust performance in the validation cohort, yielding an AUC of 0.916 (95% CI: 0.842-0.990), with high sensitivity and specificity.</p><p><strong>Conclusions: </strong>LPA(16:0), LPI(16:0), LPI(18:0), and LPI(20:0) in plasma were negatively correlated with the T value of bone mineral density. These associations persisted after stringent adjustment for age and sex, suggesting that lysophospholipid dysregulation is an independent metabolic hallmark of OP. The multi-metabolites model based on four biomarkers showed promising predictive performance for OP and may provide a potential tool for early risk assessment.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13369700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148448045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A metabolomic signatures in hyperuricemia: a systematic review.","authors":"Yingnan Wu, Xu Han, Jiazhu Jin, Yunhua Chen, Danyang Cui, Xiaoxia Ma, Hongtao Guo, Miao Jiang","doi":"10.1007/s11306-026-02475-9","DOIUrl":"10.1007/s11306-026-02475-9","url":null,"abstract":"<p><strong>Background: </strong>Hyperuricemia (HUA) is traditionally viewed as a disorder of purine metabolism. However, its broader metabolic alterations remain incompletely understood. Metabolomics provides a useful approach for exploring metabolite changes associated with HUA, but a comprehensive synthesis of existing findings is still lacking.</p><p><strong>Aim of review: </strong>This systematic review and meta-analysis aimed to characterize the systemic metabolic signature of HUA beyond purine pathways. By synthesizing data from 27 metabolomics studies involving 12,335 participants, the study sought to identify consistent metabolite biomarkers and key dysregulated pathways to provide new insights for diagnosis and therapeutic targeting.</p><p><strong>Key scientific concepts of review: </strong>This review included 27 metabolomics studies involving 12,335 participants and identified 1,187 metabolites reported in association with HUA. Qualitative synthesis showed 54 consistently elevated and 20 consistently decreased blood metabolites, mainly involving amino acids, lipid-related metabolites, energy-related compounds, vitamins and their derivatives, and purine nucleoside metabolites. The meta-analysis was limited to two eligible studies, with one study contributing most of the statistical weight; it suggested higher levels of Alanine, Leucine, Phenylalanine, and Tyrosine and lower Histidine levels in HUA. Pathway enrichment analysis highlighted \"One carbon pool by folate,\" \"Arginine biosynthesis,\" \"Glutathione metabolism,\" and related amino acid and energy metabolism pathways. Overall, these findings suggest that HUA may be associated with metabolic perturbations beyond purine metabolism alone, but the candidate metabolites and pathways require further validation in longitudinal, standardized, and mechanistic studies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}