Metabolomics最新文献

{"title":"Network propagation in bipartite metabolite-reaction graphs for metabolomic data exploration.","authors":"Julia Kuligowski, Marta Moreno-Torres, David Pérez-Guaita, Francesc Albert Esteve-Turrillas, Guillermo Quintás","doi":"10.1007/s11306-026-02529-y","DOIUrl":"10.1007/s11306-026-02529-y","url":null,"abstract":"<p><strong>Introduction: </strong>Interpretation of metabolomic data is frequently limited by incomplete metabolite coverage and the predefined representation of biochemical organization provided by pathway-based approaches.</p><p><strong>Objectives: </strong>This study describes and evaluates a graph-based network propagation method designed to faciitate metabolomic data exploration by diffusing node-level statistical relevance scores across a metabolic network.</p><p><strong>Methods: </strong>An undirected bipartite metabolite-reaction network was reconstructed using the KEGG database, comprising 8236 nodes (1601 metabolites and 6635 reactions). Simulated datasets with 75% sparsity mimicking two metabolic perturbations and two no-effect control groups, and real data from a previous study were used to test the strategy. Node-level statistical signal scores were distributed across the network topology using a random walk-based diffusion operator combined with a supervised clamping procedure to preserve initially observed measurements. A topological distance mask was subsequently applied to restrict propagation to nodes located within a predefined network distance from experimentally measured metabolites.</p><p><strong>Results: </strong>Network propagation redistributed statistical relevance across connected subgraphs, expanding the number of metabolic features carrying topology-informed statistical scores beyond experimentally observed metabolites in simulated and real data. In both oxidative stress and mitochondrial dysfunction simulations, significant nodes displayed non-random topological organization consistent with the simulated perturbations. Propagation of real data also identified additional unmeasured metabolites that were topologically connected to experimentally observed metabolites. In multivariate analyses, network propagation expanded the feature space and showed that it could improve clustering performance.</p><p><strong>Conclusions: </strong>The proposed bipartite metabolite-reaction network propagation provides an exploratory tool for metabolomics. By integrating topological context with statistical relevance, this approach complements established pathway analyses for hypothesis generation and candidate feature recovery in partially observed metabolic systems.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148983031","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":"The Gini coefficient as a surrogate for the regulability or homeostasis of metabolite concentrations: focus on ergothioneine.","authors":"Douglas B Kell, Warwick B Dunn, Catherine L Winder, Keerthana Anand, Benjamin Greenfield, Louise C Kenny, Abi Merriel, J Bernadette Moore, Catriona Waitt","doi":"10.1007/s11306-026-02534-1","DOIUrl":"10.1007/s11306-026-02534-1","url":null,"abstract":"<p><p>The Gini coefficient is a simple non-parametric statistic that reflects the degree of variation of a property between individuals or samples, taking a value between zero and one. We here develop the idea that if the metabolism of a metabolite is tightly regulated it should have a low Gini coefficient whereas if for instance it is exogenous and present only in a subset of samples (e.g. from individuals that have ingested the substance and/or metabolised it to various degrees) the Gini coefficient will be much greater. Analysis of published datasets shows this to be true, allowing one to suggest that the Gini coefficient might assist in assessing both regulability and the origin of a particular metabolite. Plasma amino acid levels are especially tightly regulated (some have a Gini coefficient less than 0.1, a value lower than that for any transcript), while exogenous drugs and their metabolites have the highest Gini coefficients (some over 0.8). Vitamins are essential and exogenous (but regulated) and have intermediate values (ca. 0.2-0.4). Ergothioneine is a valuable nutraceutical that is not synthesised by humans and it is also of some interest to assess the variation in its distribution. A number of studies find it normally to have a Gini coefficient in the range 0.3-0.4, and with lower levels of the Gini coefficient (as in a pilot study summarised here) suggesting a relatively weak exogenous exposure. Overall, we consider that the Gini coefficient provides a convenient means of describing the extent to which a metabolite concentration varies over a metabolomic dataset, and hence the extent to which it may be regulated.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148983108","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":"Urinary metabolomics may improve prediction of overall survival beyond tumor stage in colorectal cancer: results from the ColoCare study.","authors":"Tengda Lin, Boyi Guo, Victoria M Bandera, David B Liesenfeld, Jincheng Shen, Benjamin Haaland, Paul A Stewart, Kenneth M Boucher, Patricia A Erickson, Sheetal Hardikar, Victoria Damerell, Doratha A Byrd, Jane C Figueiredo, Adetunji T Toriola, David Shibata, Erin M Siegel, Christopher I Li, Alexis B Ulrich, Christoph Kahlert, Daniel O Scharfstein, Biljana Gigic, Cornelia M Ulrich, Jennifer Ose","doi":"10.1007/s11306-026-02516-3","DOIUrl":"10.1007/s11306-026-02516-3","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is a leading cause of cancer-related mortality. Prognosis is primarily guided by tumor stage despite substantial molecular heterogeneity. Urinary metabolomics may capture systemic and tumor-related biology beyond staging and could improve prognostic assessment. We hypothesized that incorporating urinary metabolomic profiles would improve overall survival (OS) prediction performance compared with a stage- and age-based reference model.</p><p><strong>Method: </strong>A total of n = 76 stage I-IV CRC patients recruited as part of the ColoCare Study in Heidelberg Germany with pre-surgery urinary metabolomics were included (23 deaths; median follow-up 3.03 years). Four metabolomics-based penalized Cox models adjusted for tumor stage and age at diagnosis were developed using LASSO, adaptive LASSO, spike-and-slab LASSO, and iterative sure independence screening (iSIS)-LASSO. Model discrimination was assessed using Harrell's C-index and time-dependent AUC based on the nested cross-validation.</p><p><strong>Results: </strong>Compared with the reference model (Cox model including only tumor stage and age at diagnosis), all metabolomics-based models provided better discrimination. The spike-and-slab LASSO Cox model demonstrated the best performance, achieving a C-index of 0.75 (vs. 0.68) and consistently higher time-dependent AUCs at 1-5 years of follow-up, with a peak AUC of 0.76 at year 3 (vs. 0.68). Three urinary metabolites were consistently selected across all metabolomics-based models: indolelactate, 2-hydroxyisobutyrate and a uridine-like metabolite.</p><p><strong>Conclusions: </strong>Urinary metabolomics may improve CRC OS prediction beyond tumor stage and age at diagnosis, especially with the spike-and-slab LASSO Cox model. These results support urinary metabolomics as a promising noninvasive prognostic tool that merits external validation.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13569605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148945352","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":"Comparative metabolomic profiling reveals salinity tolerance mechanisms in a rice introgression line.","authors":"Chander Kant Chaudhary, Praveen Kumar Guttula, Kirti Agrawal, Prasanta Kumar Subudhi, Manas Ranjan Gartia","doi":"10.1007/s11306-026-02525-2","DOIUrl":"10.1007/s11306-026-02525-2","url":null,"abstract":"<p><strong>Introduction: </strong>Rice (Oryza sativa) is extremely sensitive to salinity, yet the metabolic mechanisms underlying salt tolerance remains incompletely understood.</p><p><strong>Objective: </strong>In this study, we performed leaf tissue-specific untargeted metabolomic profiling of the salt-tolerant introgression line JN100 (JN), its donor parent Nona Bokra (NB), and its recurrent parent Jupiter (JU) to characterize metabolic responses to salt stress.</p><p><strong>Results and conclusions: </strong>Comparative analysis identified differentially accumulated metabolites (DAMs) spanning diverse chemical classes, including amino acids, sugars and carbohydrates, lipids, organic acids, cofactors, electron carriers, and nucleotides. Under salt stress (SS), 201 DAMs (89 upregulated and 112 downregulated) were detected in JN relative to JU. Notably, metabolites such as allantoin, glycitin, nicotinamide ribotide, D-arabinono-1,4-lactone, violanthin, L-methionine S-oxide, ribitol, lysine, rutin, glutamine, pantothenic acid, and quinic acid, showed significant differential accumulation. Pathway enrichment analysis revealed significant enrichment of arginine biosynthesis, purine metabolism, and alanine, aspartate, and glutamate metabolism, indicating extensive reprogramming of nitrogen and energy-associated metabolic pathways under salinity stress. Integration of transcriptomic and metabolomic datasets from the SS experiments further identified ten differentially expressed genes (DEGs) associated with the metabolite network in the JN vs. JU comparison. Among these, OsDHQDT/SDH, OsFd-GOGAT, phenylalanyl-tRNA synthetase, OsP5CS1, OsP5CS2, and a pyridoxal phosphate-dependent transferase were linked to metabolites involved in shikimate, amino acid, and proline metabolism.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906464","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":"Acute-phase serum metabolomics signatures for predicting post-stroke cognitive impairment after ischemic stroke: a prospective cohort study.","authors":"Xiangwen Hao, Bianying Feng, Xinyu Zhang, Yanyang Li, Jingqi Chen, Xiaoyan Zheng, Li Tian, Qiu Hong Man","doi":"10.1007/s11306-026-02521-6","DOIUrl":"10.1007/s11306-026-02521-6","url":null,"abstract":"<p><strong>Background: </strong>Post-stroke cognitive impairment (PSCI) is a common long-term complication of acute ischemic stroke often accompanied by metabolic disorder, yet early metabolomic predictors remain poorly characterized. This study aimed to identify acute-phase serum metabolomic signatures associated with PSCI and develop a prediction model for early PSCI risk stratification.</p><p><strong>Methods: </strong>In this prospective study, 130 acute ischemic stroke patients were enrolled. Serum samples collected within 24 h of stroke onset were subjected to untargeted metabolomic profiling. Cognitive status was assessed at 3 months after stroke. KEGG pathway and MECNA analyses were performed to assess pathway-level and metabolite-metabolite correlation patterns. Bootstrap-LASSO stability selection and logistic regression were used to develop a prediction model, which was evaluated by stratified 10-fold cross-validation. Calibration curves and decision curve analysis assessed model performance.</p><p><strong>Results: </strong>A total of 51 candidate differential metabolites were identified between PSCI and Non-PSCI patients, involving purine/caffeine-related metabolism, bile acid metabolism, lipid and fatty acid metabolism, and amino acid-related metabolism. MECNA identified 15,120 differential metabolite-metabolite correlation pairs, suggesting distinct acute-phase serum correlation patterns in patients who later developed PSCI. Six metabolites were retained in the final model: 6-Hydroxymellein, 21-Deoxycortisol, Inosine, 2-Hydroxy-3-methylbutyric acid, Isoleucyl-Arginine, and Propylparaben. The metabolite-only model achieved an AUC of 0.774 (95% CI, 0.690-0.853), showing higher discrimination than the core clinical-only model and the combined clinical-metabolomic model.</p><p><strong>Conclusions: </strong>Acute-phase serum metabolomic profiling identified multi-domain metabolic features associated with PSCI. The internally validated six-metabolite model showed moderate performance for early PSCI risk stratification.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13529855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148865617","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":"Metabolomics of cerebrospinal fluid in pediatric neuroborreliosis: unraveling candidate immunometabolic signatures and diagnostic potential.","authors":"Agata Serrafi, Adam El Idrissi, Andrzej Wasilewski, Hanna Czapor-Irzabek, Fatima Chegdani, Małgorzata Pupek, Agnieszka Matera-Witkiewicz, Tomasz Zatoński, Katarzyna Połtyn-Zaradna, Milena Ściskalska, Anna Janicka-Kłos, Jolanta Jasonek, Leszek Szemborn","doi":"10.1007/s11306-026-02524-3","DOIUrl":"10.1007/s11306-026-02524-3","url":null,"abstract":"<p><strong>Background: </strong>Neuroborreliosis (LNB) poses a significant diagnostic challenge in paediatrics due to its non-specific clinical presentation and the limited sensitivity of standard tests. Metabolomics offers the possibility of direct insight into the biochemical changes occurring in the central nervous system during infection.</p><p><strong>Objective: </strong>The aim of the study was to characterise the metabolic profile of paired serum and cerebrospinal fluid (CSF) in children with LNB to identify candidate immunometabolic signatures associated with the infection.</p><p><strong>Methods: </strong>Samples were analysed using NMR spectroscopy and LC-MS/MS. Data gaps were filled using a Random Forest algorithm, followed by univariate and multivariate statistical tests (PLS-DA) and pathway enrichment analysis based on the KEGG database.</p><p><strong>Results: </strong>Profound dysregulation of purine metabolism was demonstrated, manifested by elevated concentrations of hypoxanthine, xanthine and uric acid, which may reflect a combination of non-specific infection-related responses, oxidative stress, and potentially the pathogen's metabolic demand for host purines. Significant changes were observed in the biosynthesis of aromatic amino acids (particularly L-tyrosine) and in glycerophospholipid metabolism, suggesting damage to the blood-brain barrier. In CSF, alterations in choline and glutamate levels were identified as candidate metabolic signatures potentially associated with neuroinflammation and excitotoxicity.</p><p><strong>Conclusions: </strong>Metabolomic analysis enables precise mapping of the biochemical response in paediatric LNB. The identified abnormalities in purine metabolism, neuroinflammation and cell membrane integrity represent a promising starting point for future biomarker discovery research.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13529893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148865659","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":"ExpoLib: a framework for an MS/MS exposome library of anthropogenic and natural toxicants and their biotransformation products.","authors":"Vinicius Verri Hernandes, Miguel A Aguilar Ramos, Rolf Breinbauer, Philipp Fruhmann, Hannes Mikula, Monika Ehling-Schulz, Ellen L Zechner, Emily P Balskus, Benedikt Warth","doi":"10.1007/s11306-026-02481-x","DOIUrl":"10.1007/s11306-026-02481-x","url":null,"abstract":"<p><strong>Introduction: </strong>Despite technological advancements over the last three decades in small-molecule omics, compound annotation remains a major bottleneck in untargeted metabolomics and non-targeted environmental analysis. This is especially true for exposomics applications, which remain significantly affected by the limited chemical space coverage.</p><p><strong>Objectives: </strong>This work aims at describing the development of an MS/MS spectral library containing > 170 relevant xenobiotics from different classes of food, environmental, and microbial toxicants.</p><p><strong>Methods: </strong>LC-MS/MS data was acquired using collision-induced dissociation in data dependent acquisition mode under 13 different single collision energies with four additional collision energy spread experiments. A diverse set of compounds including natural toxins produced by bacteria, fungi (mycotoxins), and plants (phytotoxins), as well as anthropogenic chemicals such as bisphenols, phthalates, PFAS chemicals, drugs, consumer care products ingredients, and pesticides, and additional toxicologically relevant chemical classes were screened. Metabolic products for which commercially available reference standards and/or MS/MS spectra are not available in any public or commercial database have been included (e.g. colibactin-DNA-adduct, cereulide, deoxynivalenol-3-glucuronide). Library generation was performed in mzmine.</p><p><strong>Results: </strong>Open-format data based on representative spectra are provided.This new resource, available at https://zenodo.org/records/20715576 , is aimed at providing a ready-to-use tool for the annotation of key exogenous compounds which are frequently overlooked in clinical metabolomics but may exert potent biological effects. A detailed discussion from a user perspective is provided regarding the library generation workflow in mzmine, aiming at facilitating the work of fellow researchers in the creation of their own in-house libraries.</p><p><strong>Conclusion: </strong>We intend to provide the metabolomics community with better tools for exposomics research and to reduce perceived barriers in developing specialized MS/MS libraries for widening chemical space coverage and increasing quality and confidence.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148829976","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":"From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation.","authors":"Dipendra Bhandari, Henry A Paz, Keith Henderson, Kiran Kumar Adepu, Ahmad Mani-Varnosfaderani, Hailemariam Abrha Assress, Brian D Piccolo, Renny S Lan, Elisabet Børsheim, Colin D Kay, Sree V Chintapalli","doi":"10.1007/s11306-026-02520-7","DOIUrl":"10.1007/s11306-026-02520-7","url":null,"abstract":"<p><strong>Introduction: </strong>Untargeted metabolomics often results in a significant portion of unannotated metabolites, or \"metabolic dark matter,\" which hinders biological interpretation.</p><p><strong>Objectives: </strong>A two-step analytical approach was developed to systematically prioritize and interpret unannotated metabolites using plasma LC-MS/MS data from pregnant women with obesity as a biologically relevant test dataset.</p><p><strong>Methods: </strong>The first step involved clustering 1,021 known metabolites into ten structurally coherent groups based on the Tanimoto similarity, thus defining the biologically relevant chemical space of the dataset. These metabolites were further characterized by Absorption, Distribution, Metabolism, and Excretion (ADME) profiling, protein target prediction, molecular docking and Kyoto Encyclopedia of Genes and Genomes pathway mapping analysis, to establish biological plausibility and functional perspective. Candidate structures for 1,836 unannotated features were retrieved from PubChem using molecular formula and molecular weight matching within a ±0.5 Da tolerance.</p><p><strong>Results: </strong>This search yielded 569,115 candidate structures, of which 368,197 unique structures were retained after curation. Tanimoto coefficient filtering reduced the candidate pool to 19,868 structurally plausible candidates, and retention time-based prioritization further refined this set to 418 high confidence candidate annotations, including 83 database-supported candidates identified through HMDB and LIPID MAPS structure database cross-referencing. RT-based prioritization effectively distinguished positional isomers sharing the same molecular formula by incorporating agreement between predicted and experimentally observed retention times.</p><p><strong>Conclusion: </strong>This improved discrimination among structurally similar candidates, expanded metabolite annotation confidence, and provided a scalable framework for prioritizing dark matter metabolites in untargeted metabolomics.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148829958","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":"Serum metabolomic profiling reveals load-specific adaptations to resistance training.","authors":"Diego Salgueiro, Matthews Martins, Guilherme Scherrer, Denis Valério, Alex Castro, Renato Barroso, Richard Leite, Valerio Barauna","doi":"10.1007/s11306-026-02514-5","DOIUrl":"10.1007/s11306-026-02514-5","url":null,"abstract":"<p><strong>Introduction: </strong>Resistance training (RT) imposes repeated mechanical stimuli triggering systemic metabolic reprogramming whose molecular underpinnings remain incompletely characterized. Univariate approaches fail to capture pathway-level shifts defining the metabolomic response to RT in protocols differing in load.</p><p><strong>Objectives: </strong>To evaluate whether high-load (HL) and low-load (LL) RT protocols performed to volitional failure induce serum biochemical patterns specific to training status and loading condition, undetectable by univariate approaches.</p><p><strong>Methods: </strong>Seventeen healthy young men completed an 8-week RT intervention (HL: 80% 1-RM, n = 9; LL: 30% 1-RM, n = 8). Fasting serum profiles were acquired by untargeted ¹H-NMR spectroscopy. Univariate comparisons used paired t-tests and one-way ANOVA with Benjamini-Hochberg correction. Random Forest models were validated by stratified 5 × 5-fold cross-validation and 1000-iteration permutation testing, with group separation assessed by sensitivity, specificity, and AUC. Discriminant metabolites were independently mapped onto established metabolic pathways to assess biochemical plausibility.</p><p><strong>Results: </strong>Of 10 metabolites significantly altered, five were consistently modulated across both protocols (3-hydroxyisovalerate, 3-hydroxybutyrate, acetone, isobutyrate, and lactate), reflecting shared adaptations in amino acid turnover and ketone body metabolism. Training-status separation achieved AUC = 1.00 (p < 0.001), with 3-hydroxyisovalerate as the dominant feature. Load-specific divergence was captured only by an exploratory multivariate signature of choline, glucose, and alanine (AUC = 0.94; p = 0.008), none of which was individually significant in univariate testing. Pathway integration demonstrated that discriminant metabolites are consistently related to well-established metabolic pathways: leucine catabolism, ketone body turnover, and glycolytic-oxidative rebalancing.</p><p><strong>Conclusions: </strong>RT induces biologically coherent, load-modulated serum metabolomic shifts detectable only through multivariate analysis. These findings are hypothesis-generating and require external validation in independent cohorts before applied implementation.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148808723","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 profiling reveals distinct and shared biology in COPD and IPF.","authors":"Aldric Rosario, Nicole Prince, Shariq Madha-Krause, Sharon M Lutz, Julian E Hecker, Jeong H Yun, Michael H Cho, Arda Halu, George R Washko, Frank C Sciurba, Lucas Barwick, Andrew H Limper, Kevin R Flaherty, Gerard J Criner, Kevin K Brown, Robert Wise, Fernando J Martinez, Russell P Bowler, Robert E Gerszten, Clary B Clish, Edwin K Silverman, Rachel S Kelly","doi":"10.1007/s11306-026-02508-3","DOIUrl":"10.1007/s11306-026-02508-3","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Objectives: </strong>To identify metabolites associated with COPD and IPF, and to characterize shared and disease-specific signatures.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>Alterations in metabolomic profiles of COPD and IPF suggested shared dysregulation of several lipids. However, some metabolites pointed to disease-specific differences.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148794563","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}