Metabolomics最新文献

{"title":"A machine learning approach to metabolomics identifies putative biomarker candidates and dysregulated pathways for distinguishing gout from asymptomatic hyperuricemia in the Zhuang population.","authors":"Yuxia Wei, Xiaoqiang Qiu, Li Su, Xiaofen Tang, Yuzhu Chen, Shun Liu, Dongping Huang, Xiaoyun Zeng, Yihong Xie","doi":"10.1007/s11306-026-02501-w","DOIUrl":"10.1007/s11306-026-02501-w","url":null,"abstract":"<p><strong>Introduction: </strong>Gout typically develops from hyperuricemia (HUA), but the metabolic alterations driving this transition remain poorly understood, limiting our understanding of disease pathogenesis.</p><p><strong>Objectives: </strong>To identify stage-specific putative biomarker candidates and to characterize dysregulated metabolic pathways distinguishing gout from HUA.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</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/PMC13356051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148422420","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":"Sample processing methods affect salivary metabolomics in human exercise-stress studies.","authors":"Staci Thornton, Michael R Szymanski, Gabrielle J Brewer, Douglas J Casa, Timothy E Moore, Shiying Xiao, Alexander A Green, Keith Pardee, Elaine C Lee","doi":"10.1007/s11306-026-02504-7","DOIUrl":"10.1007/s11306-026-02504-7","url":null,"abstract":"<p><strong>Introduction: </strong>Saliva biomarker research requires understanding of how saliva collection and processing techniques affect results and reproducibility. Direct comparison of the effect of processing and storage conditions on downstream assay outcomes is important to understanding the ways that delay in freezing, methods of freezing, filtration protocols, and other factors can affect results and thus, interpretations about stress response and adaptation. This type of study compliments the work that is required to understand how saliva compares to tissue and blood responses and what the implications of changes in saliva biomarkers mean to our understanding of biomarkers of stress during exercise and environmental stress exposures.</p><p><strong>Objective: </strong>In this pilot study, we evaluated the effects of different processing methods on metabolomics results and discuss the implications of these findings for future experimental design.</p><p><strong>Methods: </strong>We assessed the effects of centrifugation, time-of-day collection, filtration, and mucinase treatments and determined that there are quantifiable differences in metabolomics results with different treatments.</p><p><strong>Results: </strong>Adding processing steps did not increase the number of metabolites detected or the sensitivity and specificity of results.</p><p><strong>Conclusion: </strong>Comprehensive descriptions of methods in this area will support better interpretation and reproducibility in this field.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13350125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148422217","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":"Clinical lipidomics in bacterial sepsis: a systematic review of serum and plasma evaluations.","authors":"Nurul Saadah Ahmad, Rosni Ibrahim, Toh Leong Tan, Syarul Nataqain Baharum, Zamberi Sekawi","doi":"10.1007/s11306-026-02488-4","DOIUrl":"10.1007/s11306-026-02488-4","url":null,"abstract":"<p><strong>Background: </strong>Lipid metabolism plays a central role in host-pathogen interactions and immune regulation during bacterial sepsis, with its dysregulation contributing to organ failure and mortality. Blood-based lipidomics has emerged as a promising approach for identifying diagnostic and prognostic biomarkers in sepsis. However, discrepancies in lipid profiles between serum and plasma remain a major challenge for clinical translation. This systematic review synthesizes lipidomic evidence from serum and plasma in bacterial sepsis patients to identify reproducible lipid biomarkers, evaluate methodological heterogeneity and assess associations with clinical outcomes.</p><p><strong>Methods: </strong>A systematic search of five databases (Scopus, Pubmed, Ovid MEDLINE, Web of Science and Google Scholar) identified nine eligible studies published between 2016 and 2025 that applied untargeted or targeted lipidomics on human serum and plasma samples from adult bacterial sepsis patients (PROSPERO: CRD420251086177).</p><p><strong>Results: </strong>Across both matrices, reproducible candidate lipid biomarkers included triacylglycerols (TAG), phosphatidylcholines (PC), lysophosphatidylcholines (LPC), sphingomyelins (SM) and ceramides. Synthesized findings revealed characteristically distinct directional trajectories: consistent elevation of TAG, bidirectional shifts of PC, depletion of LPC, bidirectional shifts of SM and elevation of SM. Preliminary trend within the literature indicates that serum-based studies more frequently captured prognostic alterations, whereas plasma-based studies often focused on diagnostic discrimination. Substantial heterogeneity was observed in sample handling, lipid extraction protocols and analytical platforms.</p><p><strong>Conclusions: </strong>Overall, both matrices offer valuable clinical insights, though these apparent differences in diagnostic or prognostic utility may reflect primary study designs rather than intrinsic matrix superiorities. Methodological heterogeneity limits reproducibility, highlighting the need for standardized workflows to enable translation of sepsis lipid biomarkers.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405309","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 functionality of the cysteinyl leukotriene receptor 1 (CysLTR1) in the lung by metabolomics analysis of bronchoalveolar lavage fluid.","authors":"Wilson B Adeosun, Sibongiseni K L Poswayo, Suraj P Parihar, Du Toit Loots","doi":"10.1007/s11306-026-02491-9","DOIUrl":"10.1007/s11306-026-02491-9","url":null,"abstract":"<p><strong>Introduction: </strong>The cysteinyl leukotriene receptor 1 (CysLTR1) is known as a potent lipid mediator with a well-established role in inflammatory regulation and lung disease. While its involvement in immune cell recruitment has been previously reported, its broader impact on pulmonary metabolism remains poorly understood.</p><p><strong>Objectives: </strong>The study aims to investigate the metabolic consequences of a CysLTR1 deletion in mice to elucidate its role in pulmonary metabolic homeostasis.</p><p><strong>Methods: </strong>Bronchoalveolar lavage fluid (BALF) was collected from CysLTR1 knockout (KO) and wild-type (WT) mice (n = 4 per group), and analysed using standardized untargeted gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) metabolomics.</p><p><strong>Results: </strong>Metabolomics analyses of the BALF collected from the CysLTR1 KO mice presented significantly reduced levels of glucose, glucosamine, and glyceric acid, indicating the role of the CysLTR in lung glucose uptake and consequently lung glycolysis and gluconeogenesis. This is further supported by reductions in myo-inositol and D-chiro-inositol, also supporting previous findings that this occurs due to insulin resistance. Consequential disruption of various glucose-dependent pathways, including the pentose phosphate pathway (reduced gluconic acid, sedoheptulose and xylose) and purine metabolism (reduced 1-methylinosine) indicates a consequential altered nucleotide turnover, and the significantly reduced concentrations of butanoic acid, decan-2-ol, and 1-hexadecanol, indicate changes to fatty acid metabolism in the lung, as a compensatory response to the initial glucose deficiency induced by the CysLTR1 KO. Lastly, the changes to mandelic acid, glutaric acid, tricarballylic acid, and decan-2-ol, furthermore, indicate the role of CysLTR1 in the composition/metabolism of the microbiome.</p><p><strong>Conclusion: </strong>This study expands our knowledge on the role of CysLTR1 beyond its role in immune regulation, which may contribute to a better understanding of CysLTR1 associated lung diseases and in the development of improved therapeutic strategies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405335","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":"Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry metabolomic profiling reveals harvest age dependent changes in the roots of Pelargonium sidoides DC.","authors":"Kundani Khameli, Muinat Nike Lewu, Takalani Mulaudzi, Oluwafemi Ayodeji Adebo, Oluwatobi Samuel Oluwafemi","doi":"10.1007/s11306-026-02505-6","DOIUrl":"10.1007/s11306-026-02505-6","url":null,"abstract":"<p><strong>Introduction: </strong>Cultivation of medicinal plants provides an opportunity for economic gain and health care accessibility. Ensuring consistent quality plant material is important for plants such as Pelargonium sidoides DC used for production of phytomedicines that are available in local and international markets. There is limited research on how agronomic factors affects phytochemicals in P. sidoides roots.</p><p><strong>Objective: </strong>To evaluate the effect of different irrigation regimes and harvesting age on metabolite accumulation in dried roots of P. sidoides.</p><p><strong>Methodology: </strong>Irrigation was applied at 75%, 50% and 25% plant available water (PAW) corresponding to well- watered, moderate water deficit and severe water deficit respectively. P. sidoides, which were harvested at 6, 12 and 18 months after imposing the different water deficit treatments, roots dried and subjected to ultra-performance liquid chromatography -quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with multivariate statistical analysis.</p><p><strong>Results: </strong>Unsupervised multivariate analysis showed that irrigation did not affect the obtained metabolic features. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed statistically significance difference between 6 versus 12- and 18- months harvest ages. Compounds such as umckalin, epigallocatechin dimer, and gallic acid were increased in 12 and 18 months compared to the 6 months harvest. Sucrose/trehalose was increased by 0.2431- and 0.560- fold in 6 months compared to 12- and 18-months harvest ages respectively. On quantification, umckalin increased from 260.40 mg/kg DW at 6 months to 431.61 mg/kg DW at 12 months, while epigallocatechin rose from 342.98 mg/kg DW to 505.58 mg/kg DW over the same period. Umckalin sulphate was highest at 6 months under 25% PAW (7110.32 mg/kg DW) and decreased at 12 months harvest under the same irrigation level (4522.05 mg/kg DW).</p><p><strong>Conclusion: </strong>The results indicated that younger plants accumulated more primary metabolites and modified secondary metabolites, whereas older plants accumulate more secondary metabolites. Moreover, cultivation of P. sidoides in limited water is plausible.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148405343","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":"Metabolic phenotypes of doxorubicin-induced cardiotoxicity among patients with breast cancer.","authors":"Amarnath Singh, Se-Ran Jun, Katherine Wallis, Renny S Lan, Valentina Todorova, L Joseph Su, Sam Makhoul, Ping-Ching Hsu","doi":"10.1007/s11306-026-02469-7","DOIUrl":"10.1007/s11306-026-02469-7","url":null,"abstract":"<p><strong>Background: </strong>Doxorubicin (DOX)-based chemotherapy has improved survival outcomes in breast cancer patients but is often limited by doxorubicin-induced cardiotoxicity (DIC). Currently, no validated biomarkers can predict early DIC. Identifying novel biomarkers is essential for detecting patients at higher risk and enable timely interventions before irreversible cardiac injury occurs.</p><p><strong>Methods: </strong>Twenty-seven breast cancer patients treated with DOX-containing chemotherapy were stratified by change in left ventricular ejection fraction (LVEF): 19 patients who maintained normal cardiac function (normal, decline < 10%) and 8 who developed cardiotoxicity (abnormal, decline > 10%). Plasma samples were collected at baseline and after chemotherapy for untargeted metabolomic profiling. Both baseline and pre-post designs were employed to capture static and dynamic metabolic alterations associated with DIC. Stepwise logistic regression was used to filter non-informative metabolites, and predictive performance was further validated using Random Forest modeling.</p><p><strong>Results: </strong>A well-marked separation of plasma metabolomic profiles was observed between normal and abnormal cardiotoxicity groups at baseline (T0). Statistical analysis identified 100 significant metabolites at baseline (T0) and 78 metabolites after the first cycle of chemotherapy (T0-T1), with 10 metabolites common to both time-points: 3-phosphoglycerate, 2-hydroxyphenylacetate, inosine, taurine, suberate (C8-DC), sebacate (C10-DC), sphingadienine, oxindolylalanine. Machine learning models identified key metabolites (e.g., sebacate [C10-DC], 2-hydroxyhippurate, orotate, picolinate, and suberate [C8-DC]) as candidate predictors of cardiotoxicity, achieving moderate discriminatory performance in cross-validation, with higher specificity than sensitivity, indicating limited detection of abnormal cases.</p><p><strong>Conclusions: </strong>Metabolomic profiling shows potential for early detection of DIC in breast cancer patients, supporting personalized interventions to prevent irreversible cardiac damage.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148388137","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 in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.","authors":"Dyah L Dewi, Elya Manik, Ema Damayanti, Muslih Anwar, Suratno, Syam Budi Iryanto","doi":"10.1007/s11306-026-02459-9","DOIUrl":"10.1007/s11306-026-02459-9","url":null,"abstract":"<p><strong>Background: </strong>Alterations in metabolic pathways are a hallmark of cancer and play a pivotal role in breast cancer development and progression. The inherent metabolic heterogeneity of breast cancer contributes to differences in therapeutic response and patients' prognosis. Clinical metabolomics has emerged as a promising approach for identifying metabolic biomarkers that reflect tumor biology, treatment-related changes after diagnosis, and patients' outcomes.</p><p><strong>Aims of review: </strong>This review summarizes the metabolomic profiles of breast cancer patients, using various biological materials and analytical methods, to assess their potential role as biomarkers for monitoring therapeutic response, adverse treatment effects, tracking disease progression, and predicting prognosis.</p><p><strong>Key scientific concept of review: </strong>Metabolomic shifts generate unique signatures with promising potential as biomarkers for evaluating treatment response, monitoring therapeutic adverse effects, disease progression, and predicting clinical outcomes in breast cancer patients. Biological matrices, such as serum, plasma, and tumor tissue, were commonly used in both untargeted and targeted metabolomics approaches. Liquid chromatography-mass spectrometry is the most commonly used analytical method in clinical metabolomics studies. Altered metabolites were identified and linked to metabolic pathways, particularly amino acids, glucose, and fatty acids metabolism. When integrated with genomic and transcriptomic data, these metabolic fingerprints offer a multidimensional perspective on disease trajectory, thereby enhancing patient stratification and informing personalized therapeutic strategies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376016","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":"Exploratory NMR-based metabolomics reveals transient lipoprotein changes during prolonged computer gaming.","authors":"Kasper Bygum Krarup, Henrik Bygum Krarup, Inge Søkilde Pedersen, Morten Mørk, Søren Risom Kristensen, Aase Handberg, Reinhard Wimmer, Hien Thi Thu Nguyen","doi":"10.1007/s11306-026-02502-9","DOIUrl":"10.1007/s11306-026-02502-9","url":null,"abstract":"<p><strong>Introduction: </strong>Extended computer gaming is characterized by prolonged sedentary activity, disrupted sleep, emotional stress, and unrestricted intake of energy-dense foods and beverages.</p><p><strong>Objectives: </strong>This study explored short-term metabolic responses and recovery following prolonged gaming in healthy young men.</p><p><strong>Methods: </strong>Nine healthy male participants (mean age 25.8 ± 2.6 years) took part in a controlled local area network (LAN) gaming event consisting of two 18-hour gaming sessions separated by a 6-hour sleep period. Serum samples were collected at multiple time points during the intervention and at a 5-day follow-up. Metabolomic profiling was performed using proton nuclear magnetic resonance (¹H-NMR)-based metabolomics.</p><p><strong>Results: </strong>Temporal variation in serum metabolomic profiles was observed during prolonged gaming, particularly in lipid-related measures, including very low-density lipoprotein (VLDL) subclasses. Exploratory multivariate analyses showed separation between early and later time points during the intervention. At five days following the gaming sessions, metabolomic profiles showed substantial overlap with baseline. Metabolites that varied during the gaming period generally returned toward baseline concentrations, although inter-individual variability was observed.</p><p><strong>Conclusions: </strong>Prolonged computer gaming was associated with transient alterations in serum metabolomic profiles in healthy young men, most notably in lipid-related measures. Metabolomic profiles at five days showed convergence toward baseline following a single gaming episode. These findings provide time-resolved insight into metabolomic variation under a real-world behavioral exposure. Further studies are required to determine whether repeated exposure to similar conditions is associated with cumulative metabolic changes over time.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376278","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 signature reveals dysregulated lipoprotein profile in m.3243A>G carriers: a case-control study.","authors":"Simone Rask Nielsen, Hien Thi Thu Nguyen, Malene Pontoppidan Stoico, Christina Brock, Kurt Højlund, Inge Søkilde Pedersen, Anja Lisbeth Frederiksen","doi":"10.1007/s11306-026-02503-8","DOIUrl":"10.1007/s11306-026-02503-8","url":null,"abstract":"<p><strong>Introduction: </strong>The pathogenic mitochondrial gene variant m.3243A>G disrupts oxidative phosphorylation and is associated with insulin resistance, both of which may be linked to unfavorable lipid metabolism. However, the metabolic alterations in m.3243A>G carriers, including what differentiates those with and without diabetes, remain incompletely understood.</p><p><strong>Objectives: </strong>To investigate metabolomic profiles in fasting serum and urine samples from m.3243A>G carriers compared to healthy controls.</p><p><strong>Methods: </strong>Metabolomic profiling of serum and urine samples using nuclear magnetic resonance-based metabolomics in m.3243A>G carriers (n = 28) was compared to healthy controls matched for age and sex. Additionally, profiles from m.3243A>G carriers with diabetes (n = 16) were compared with carriers without diabetes (n = 12) to identify potential metabolites associated with the presence of diabetes.</p><p><strong>Results: </strong>Twenty-five metabolites in serum and 16 in urine were identified as metabolites separating m.3243A>G carriers from healthy controls. The m.3243A>G carriers presented with increased triglycerides across lipoprotein particles and altered very-low-density lipoprotein concentrations and composition. In addition, there were alterations in metabolites from a number of metabolic pathways, including glycolysis, the tricarboxylic acid cycle, glutathione, one-carbon, and nucleotide metabolism. A three metabolite-urine signature (uracil, hypoxanthine, and 1-methylnicotinamide) demonstrated discriminating potential between m.3243A>G carriers and controls in exploratory machine learning analyses (area under the curve values 0.94-0.99 and cross-validation prediction of 0.81-0.93). Among m.3243A>G carriers, branched-chain amino acids were higher in individuals with diabetes compared with carriers without diabetes.</p><p><strong>Conclusion: </strong>Dysregulated lipoprotein metabolism represents a significant metabolic fingerprint of m.3243A>G carriers. Furthermore, higher levels of branched-chain amino acids may be associated with the presence of diabetes.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376293","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":"LC-QQQ serum metabolomics reveals disease-specific metabolic signatures and diagnostic metabolite panels distinguishing polycythaemia vera from secondary polycythaemia.","authors":"Piotr Halicki, Patrycja Mojsak, Tomasz Pienkowski, Adrian Godlewski, Dariusz Kiejza, Sandra Chmielewska, Adam Kretowki, Michal Ciborowski, Jaroslaw Piszcz","doi":"10.1007/s11306-026-02476-8","DOIUrl":"10.1007/s11306-026-02476-8","url":null,"abstract":"<p><strong>Background: </strong>Polycythaemia vera (PV) is a clonal myeloproliferative neoplasm driven by activating mutations in the JAK2 gene and is associated with an increased risk of thromboembolic events. Secondary polycythaemia (SP) comprises non-neoplastic conditions characterised by reactive erythrocytosis, most commonly caused by hypoxia or dysregulated erythropoietin signalling. Despite fundamentally different pathophysiological mechanisms, PV and SP often share overlapping haematological features, underscoring the need for additional disease-specific biomarkers. Metabolomics provides a functional readout of systemic metabolic remodelling and may help distinguish clonal from reactive erythrocytosis and identify potential diagnostic biomarkers.</p><p><strong>Methods: </strong>For the first time, targeted LC-QQQ-based serum metabolomics was performed using the MxP^® Quant 500 kit. The study included 76 participants: 33 with PV, 22 with SP, and 21 controls, matched for biochemical and anthropometric parameters. Statistical analyses comprised univariate testing (Mann-Whitney U test with Benjamini-Hochberg correction), pathway enrichment analysis, and ROC analysis with support vector machine (SVM) modelling, all performed in MetaboAnalyst Version 6.0.</p><p><strong>Results: </strong>Compared with the control group, PV was associated with significant alterations in 242 metabolites and 152 predefined metabolite sums and ratios, mainly involving amino acid metabolism and lipid remodelling. In SP, fewer metabolic changes were observed, affecting 81 metabolites and metabolite sums and ratios. In a direct comparison between PV and SP, 29 discriminant metabolites were identified, primarily carboxylic acids and derivatives, as well as phosphatidylcholines, with changes ranging from - 79.4% to + 80.4%. Metabolites that uniquely distinguished PV from SP included PC O-40:5, C3-DC (C4-OH), taurine, and PC O-40:6, showing changes of 26.4%, - 33.9%, 48.4%, and 26.1%, respectively. Among all comparisons, taurine and hypotaurine metabolism was the only pathway specifically altered in the PV vs. SP group comparison. ROC analysis revealed a five-metabolite panel (cysteine, LPC 20:4, taurine, PC O-38:4, and PC O-40:5) with strong discriminatory performance between PV and SP (AUC = 0.887; 95% CI: 0.721-1.00).</p><p><strong>Conclusion: </strong>Targeted serum metabolomics reveals distinct and overlapping metabolic pathways in clonal versus reactive erythrocytosis. PV exhibits broader pathway perturbations, highlighting its potential for disease stratification and biomarker discovery, whereas SP shows more restricted, yet functionally relevant, metabolic changes.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"22 4","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376268","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}