Metabolomics最新文献

{"title":"Parallel Offline Breath Sampling for Cross-Validated Analysis of Volatile Organic Compound Metabolites.","authors":"Eray Schulz, Mariana Maciel, Zhige Wang, Shivaum Heranjal, Xiaowen Liu, Sha Cao, Ryan F Relich, Mark Woollam, Mangilal Agarwal","doi":"10.1007/s11306-025-02340-1","DOIUrl":"10.1007/s11306-025-02340-1","url":null,"abstract":"<p><strong>Introduction: </strong>Volatile organic compounds (VOCs) in breath are potential biomarkers for medical conditions that may be used for non-invasive health monitoring. One challenge that still exists is determining the fidelity of reported VOC biomarkers. The lack of universally accepted sampling methods makes it difficult to identify reliable candidates, thus allowing for the potential of false discovery.</p><p><strong>Objectives: </strong>The purpose of this study was to robustly profile VOCs in breath samples collected from relatively healthy participants using two offline methods for collection/analysis via solid phase microextraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS).</p><p><strong>Methods: </strong>158 cross-sectional volunteers provided one-time samples using two methods, one which directly sampled breath via SPME and another which collected breath in Tedlar bags. Using both methods, 10 volunteers provided an additional nine longitudinal samples. Ambient air samples were collected routinely, and a robust data processing schematic was used to ensure high quality reporting of on-breath VOCs.</p><p><strong>Results: </strong>Data screening and processing led to the identification of > 30 unique VOCs in both methods. Hierarchical clustering and correlation analyses demonstrated volatile terpene/-oids showed homologous trends in both data sets. Of the 12 VOCs identified using both methods, 11 analytes displayed statistically significant correlations (p < 0.05) in healthy breath samples. Finally, both methods were benchmarked regarding VOC reproducibility, and analyses showed that longitudinally collected samples were more reproducible compared to cross-sectional.</p><p><strong>Conclusions: </strong>The quantitative results from both sampling methods mirrored each other, thus increasing the reliability and fidelity of VOCs reported along with the results from biostatistical analysis.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"138"},"PeriodicalIF":3.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081038","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 and machine learning-based biomarker identification for oligoasthenozoospermia.","authors":"Jinli Li, Tangzhen Zhao, Mengmeng Ma, Pengcheng Kong, Yuping Fan, Xiaoming Teng, Yi Guo","doi":"10.1007/s11306-025-02333-0","DOIUrl":"https://doi.org/10.1007/s11306-025-02333-0","url":null,"abstract":"<p><strong>Introduction and objectives: </strong>Oligoasthenozoospermia, characterized by a low sperm count and impaired progressive motility, significantly contributes to male infertility. This study examines the metabolic disparities between individuals with oligoasthenozoospermia (n = 30) and healthy controls (n = 30) utilizing ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS).</p><p><strong>Methods: </strong>A total of 1,331 metabolites were identified in positive ion mode and 870 in negative ion mode, with differential analysis indicating 211 significantly different metabolites between the two groups. Pathway analysis identified key metabolic pathways, including the pentose phosphate pathway, TCA cycle, glycerophospholipid metabolism, and fatty acid metabolism. Subsequently, various machine learning models, including Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were employed to assess the predictive capability of the identified metabolites, with 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine and [6]-gingerol demonstrating the highest predictive performance.</p><p><strong>Results: </strong>The diagnostic model, developed using LR, attained high sensitivity (0.93), specificity (1), and accuracy (0.97), with an AUC of 0.998 in the training set and 0.963 in the test set.</p><p><strong>Conclusion: </strong>These findings offer critical insights into the metabolic changes associated with oligoasthenozoospermia and establish a dependable diagnostic framework for differentiating it from controls.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"137"},"PeriodicalIF":3.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical and metabolic phenotypes of Oxford Biobank subjects with variations in human flavin-containing monooxygenase 5 (FMO5).","authors":"Jeremy R Everett, Fredrik Karpe, Adrien Le Guennec, Matt Neville, Christina Redfield","doi":"10.1007/s11306-025-02308-1","DOIUrl":"10.1007/s11306-025-02308-1","url":null,"abstract":"<p><strong>Introduction: </strong>Knockout of the Fmo5 gene in mice led to a lean, slow-ageing phenotype characterised by the presence of 2,3-butanediol isomers in their urine and plasma. Oral treatment of wildtype mice with 2,3-butanediol led to a low cholesterol, low epididymal fat phenotype.</p><p><strong>Objectives: </strong>Determine if significant, heterozygous coding variations in human FMO5 would give rise to similar clinical and metabolic phenotypes in humans, as in C57BL/6J mice with knockout of the Fmo5 gene and in particular, increased excretion of 2,3-butanediol.</p><p><strong>Methods: </strong>Recruitment of 12 female, Oxford Biobank volunteers with heterozygous coding variations in FMO5, associated with changed clinical traits, and 12 age- and gender-matched controls. Analysis of the key NMR-based, urine and plasma, metabolic phenotypes of these volunteers to determine if there were any statistically significant differences.</p><p><strong>Results: </strong>Some clinical parameters of the female volunteers with heterozygous coding variations in FMO5 were altered in a direction consistent with our hypothesis viz; lower insulin levels and lower waist circumference, but no consistent elevation of urinary 2,3-butanediol was found in the subjects with heterozygous coding variations in FMO5.</p><p><strong>Conclusion: </strong>Heterozygous coding variations in human FMO5 appeared to have some impact on the clinical phenotype of the females in this study but the natural variation in the levels of 2,3-butanediol was higher than any inter-group differences between women with heterozygous coding variations in human FMO5 and the women in the control group with wildtype FMO5.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"135"},"PeriodicalIF":3.3,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12420700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023686","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":"MetabOCT: a clinical trial looking for a metabolomic signature predicting the onset of Leber's hereditary optic neuropathy in healthy MtDNA mutations carriers.","authors":"Christophe Orssaud, Pascal Reynier","doi":"10.1007/s11306-025-02328-x","DOIUrl":"10.1007/s11306-025-02328-x","url":null,"abstract":"<p><strong>Introduction: </strong>The definition of Leber's hereditary optic neuropathy (LHON) does not take into account a preclinical phase during which the thickness of retinal nerve fiber layer (RNFL) is increased, prior to optic nerve atrophy, reducing the chances of visual recovery.</p><p><strong>Objectives: </strong>Search for a metabolomic signature characterizing this preclinical phase and identify biomarkers predicting the risk of LHON onset.</p><p><strong>Methods and results: </strong>The blood and tear metabolomic profiles of 90 asymptomatic LHON mutation carriers followed for one year will be explored as a function of RNFL thickness and compared to those of a healthy control.</p><p><strong>Conclusion: </strong>Identifying pre-clinical biomarkers would open a window for clinical trials.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"134"},"PeriodicalIF":3.3,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12420721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023634","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":"Simulated metabolic profiles reveal biases in pathway analysis methods.","authors":"Juliette Cooke, Cecilia Wieder, Nathalie Poupin, Clément Frainay, Timothy Ebbels, Fabien Jourdan","doi":"10.1007/s11306-025-02335-y","DOIUrl":"10.1007/s11306-025-02335-y","url":null,"abstract":"<p><strong>Introduction: </strong>Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's \"true\" metabolic disruption is unknown.</p><p><strong>Objectives: </strong>This study aims to show that PA can lead to non-specific enrichment, potentially resulting in false assumptions about the true cause of perturbed metabolic states.</p><p><strong>Methods: </strong>Using in silico metabolic modelling, we can create disruptions in metabolic networks. SAMBA, a constraint-based modelling approach, simulates metabolic profiles for entire pathway knockouts, providing both a known disruption site as well as a simulated metabolic profile for PA methods. PA should be able to detect the known disrupted pathway among the significantly enriched pathways for that profile.</p><p><strong>Results: </strong>Through network-level statistics, visualisation, and graph-based metrics, we show that even when a given pathway is completely blocked, it may not be significantly enriched when using PA methods with its corresponding simulated metabolic profile. This can be due to various reasons such as the chosen PA method, the initial pathway set definition, or the network's inherent structure.</p><p><strong>Conclusion: </strong>This work highlights how some metabolomics data may not be suited to typical PA methods, and serves as a benchmark for analysing, improving and potentially developing new PA tools.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"136"},"PeriodicalIF":3.3,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12420739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030119","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":"Identification of potential biomarkers of triton WR-1339 induced hyperlipidemia: NMR-based plasma metabolomics approach and gene expression analysis.","authors":"Mohammad Alwahsh, Rahaf Alejel, Lama Hamadneh, Shereen M Aleidi, Rosemarie Marchan, Aya Hasan, Suhair Jasim, Fadi G Saqallah, Sameer Al-Kouz, Buthaina Hussein, Ala A Alhusban, Yusuf Al-Hiari, Tariq Al-Qirim, Roland Hergenröder","doi":"10.1007/s11306-025-02318-z","DOIUrl":"https://doi.org/10.1007/s11306-025-02318-z","url":null,"abstract":"<p><strong>Background: </strong>Hyperlipidemia is a complex lipid metabolism disorder defined as an abnormal increase in circulating levels of one or more plasma lipids and lipoproteins. Triton WR-1339-induced hyperlipidemia model is one of the most commonly used acute models for hyperlipidemia induction in research. However, the metabolic alteration induced by Triton WR-1339 remains unclear.</p><p><strong>Aims: </strong>This study aimed to identify potential biomarkers associated with the Triton WR-1339-induced hyperlipidemia model. In addition, it aims to explore the underlying mechanisms of metabolic disturbances associated with hyperlipidemia.</p><p><strong>Methods: </strong>Male Wistar rats were administered Triton WR-1339 to induce hyperlipidemia. Plasma samples were collected for lipid assays and for metabolomics analysis using nuclear magnetic resonance spectroscopy. Gene expression in liver, cardiac, and kidney tissues of key associated transporters including SLC16A1, SLC25A10, SLC5A3, and SLC7A8 and SDHA enzyme subunit was assessed using RT-PCR. In-silico analysis complemented experimental data using NEBION Genevestigator and STITCH databases for molecular interactions.</p><p><strong>Results: </strong>Triton WR-1339 administration significantly elevated plasma triglycerides. Orthogonal partial least squares-discriminant analysis (OPLS-DA) demonstrated distinct metabolic profiles between control and model groups. Metabolomics results identified potential biomarkers (p < 0.05), including myo-inositol, succinate, creatine, glycine, serine, isoleucine and creatine phosphate, which all showed higher levels in hyperlipidemia group compared to control group while xanthine showed lower levels in hyperlipidemia group. Potential biomarkers were associated with inflammatory, oxidative stress responses, and abnormal lipid metabolism. Gene expression analysis revealed significant tissue-specific alterations including changes in the expression of SDHA in the liver, an upregulated SLC16A1 in cardiac tissue (in-silico and in-vivo), a downregulated SLC5A3 in cardiac tissue (in-vivo), an upregulated SLC25A10 in cardiac tissue (in-vivo) and differential in-silico expression of SLC25A10 across liver and kidney tissues. Further network analysis indicates that Triton WR-1339 may induce hyperlipidemia by significantly elevating triglyceride levels through the inhibition of LPL.</p><p><strong>Conclusions: </strong>Our findings identify a set of metabolites as potential biomarkers of hyperlipidemia development in the Triton WR-1339 model. Correlation between gene expression analysis and metabolic profiling results demonstrates a possible mechanism in which Triton WR-1339 leads to metabolic disruption during hyperlipidemia induction.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"132"},"PeriodicalIF":3.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring salivary metabolites as biomarkers in chronic craniofacial and orofacial pain: a metabolomic analysis.","authors":"Weronika Jasinska, Yonatan Birenzweig, Yair Sharav, Doron J Aframian, Yariv Brotman, Yaron Haviv","doi":"10.1007/s11306-025-02336-x","DOIUrl":"10.1007/s11306-025-02336-x","url":null,"abstract":"<p><strong>Introduction: </strong>Chronic facial pain (CFP) includes a range of conditions such as musculoskeletal, neurovascular, and neuropathic disorders affecting the facial and jaw regions, often causing significant distress to patients.</p><p><strong>Objectives: </strong>This study aims to investigate the metabolomic profile of patients with CFP, focusing on salivary metabolites as potential biomarkers for pain diagnosis and management.</p><p><strong>Methods: </strong>Metabolomics investigation was performed using combined liquid chromatography with mass spectrometry (UPLC-MS) for metabolic profiling.</p><p><strong>Results: </strong>A comprehensive analysis was conducted, utilizing both untargeted and targeted metabolomics to examine 28 metabolites previously associated with pain conditions. The results revealed significant differences in 18 metabolites between the CFP group and a control group, with seven metabolites consistently showing elevated levels regardless of gender: DL-Isoleucine, DL-Glutamine, DL-Citrulline, D-(+)-Pyroglutamic acid, DL-Tryptophan, DL-Phenylalanine, and Spermidine.</p><p><strong>Conclusions: </strong>The findings suggest a potential link between specific salivary metabolites and CFP, highlighting the complexity of pain mechanisms. Further research is needed to understand the causality and implications of these metabolic changes, which could lead to more targeted and personalized approaches in managing pain.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"133"},"PeriodicalIF":3.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993091","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":"Decoding blood fatty acids in Crimean-Congo hemorrhagic fever.","authors":"Serkan Bolat, Seyit Ali Büyüktuna, Serra İlayda Yerlitaş, Hayrettin Yavuz, Gözde Ertürk Zararsız, Meltem Kurt Yenihan, Merve Gülşah Lafçı, Ertuğrul Keskin, Yasemin Çakır Kıymaz, Gökmen Zararsız, Halef Okan Doğan","doi":"10.1007/s11306-025-02327-y","DOIUrl":"https://doi.org/10.1007/s11306-025-02327-y","url":null,"abstract":"<p><strong>Introduction: </strong>Fatty acids (FAs) are essential for cellular structure, metabolism, and inflammatory regulation. This study investigated FA profiles in Crimean-Congo hemorrhagic fever (CCHF), a severe viral illness with high mortality rates, to explore their potential as disease progression and severity biomarkers.</p><p><strong>Methods: </strong>190 participants were included in the study, comprising 115 CCHF-positive patients, 30 CCHF-negative patients, and 45 healthy controls. FA concentrations were analyzed via gas chromatography‒mass spectrometry (GC-MS).</p><p><strong>Results: </strong>Statistically significant differences in specific FA levels were observed between the study groups. Compared with mild and moderate cases, severe cases showed distinctive FA profiles. Notably, higher omega-6/omega-3 ratios and linoleic acid to dihomo-γ-linolenic acid (LA/DGLA) ratios are associated with severe disease outcomes and poor prognosis and are correlated with inflammatory markers such as IL-6 and D-dimer. Pathway analysis was performed to identify disruptions in fatty acid biosynthesis and metabolism. Additionally, Cox regression analyses were conducted to determine key fatty acids associated with prognosis. Regression analyses identified several key fatty acids influencing prognosis, including myristic acid, phytanic acid, linoleic acid, gamma-linolenic acid, alpha-linolenic acid, oleic acid, behenic acid, cerotic acid, linoleic acid DGLA, omega-6 fatty acids, omega-9 fatty acids, and the omega-6/omega-3 ratio. Pathway analysis revealed that the disruptions in the most affected pathways were the biosynthesis of unsaturated fatty acids, α-linolenic acid metabolism, elongation, degradation, arachidonic acid metabolism, and fatty acid biosynthesis in CCHF pathogenesis.</p><p><strong>Conclusion: </strong>This study highlights significant alterations in fatty acid metabolism and laboratory markers in CCHF. These findings provide insights into the pathophysiology of this disease and may guide future research on targeted therapeutic strategies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"127"},"PeriodicalIF":3.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining clinical chemistry with metabolomics for metabolic phenotyping at population levels.","authors":"Yun Xu, Ian D Wilson, Royston Goodacre","doi":"10.1007/s11306-025-02331-2","DOIUrl":"https://doi.org/10.1007/s11306-025-02331-2","url":null,"abstract":"<p><strong>Introduction: </strong>Untargeted metabolic phenotyping (metabolomics/metabonomics), also known as metabotyping, has been shown to be able to discriminate reliably between different physiological or clinical conditions. However, we believe that standard panels of routinely collected clinical and clinical chemistry data also have the potential to provide assay panels that complement metabotyping.</p><p><strong>Objectives: </strong>To test the above hypothesis and evaluate the use of multivariate statistical analyses to provided panels of clinical/clinical chemistry data measurements that predict the age, sex and body mass index (BMI) of 977 normal subjects and compare these predictions with results acquired by metabotyping on the same healthy individuals.</p><p><strong>Methods: </strong>Metabotyping involved serum metabolomics using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) previously reported in our HUSERMET study (Dunn et al., 2015), while clinical chemistry data were obtained in clinic for 19 measurements assessing liver and kidney function, blood pressure, serum glucose, cations, as well as lipids. Multivariate analyses involved using support vector machines, random forest and partial least squares, to predict sex, age and BMI. These models used as inputs: (i) the clinical chemistry data alone; (ii) three metabolomics datasets; (iii) combinations of clinical chemistry with the metabolomics data. Model predictions were rigorously validated using 1,000 bootstrapping re-sampling coupled with permutation tests.</p><p><strong>Results: </strong>Multivariate statistical analyses on the clinical chemistry data obtained for these healthy participants could be used to predict: their sex, based on creatinine; their age, based on systolic blood pressure, total serum protein and serum glucose; as well as BMI using alanine transaminase, total cholesterol (Total-c) to high-density lipoprotein cholesterol (HDL-c) ratio and diastolic blood pressure. Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics. Moreover, this powerful combination allowed for quantitative predictions of age and BMI.</p><p><strong>Conclusion: </strong>Multivariate statistical analysis on clinical chemistry data from the HUSERMET study obtained similar predictions of age, sex or BMI, compared to metabotyping using GC-MS and LC-MS. These predictions from clinical chemistry data were between 71 and 85% accurate (depending on the MVA used) and compared favourably with metabolomics (71-91 depending on analytical method). Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics to 77-93% accuracy, suggesting that this augmentation of methods may be a useful approach in the search for clinical biomarkers.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"126"},"PeriodicalIF":3.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960408","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-partum feeding strategies affect colostrum metabolite levels related to nitrogen and energy metabolism in Holstein dairy cows.","authors":"Paraskevi Tsermoula, Niels Bastian Kristensen, Bekzod Khakimov","doi":"10.1007/s11306-025-02329-w","DOIUrl":"https://doi.org/10.1007/s11306-025-02329-w","url":null,"abstract":"<p><strong>Introduction: </strong>Cow colostrum synthesis takes place during the last month of pregnancy. Its composition is influenced by individual and environmental factors, such as cow parity, feeding, season and environmental conditions. Therefore, colostrum metabolomic profiling may provide information about the physiological status of cows around calving.</p><p><strong>Objectives: </strong>The cow colostrum metabolome was analyzed to determine whether its variability could be used to elucidate the cows' physiological status around calving and provide insights into the outcomes of cow transition programs.</p><p><strong>Methods: </strong>The factors assessed included a control feeding based on grass-clover silage and barley straw (FAR), two phase feedings based on acidified corn silage and canola cake, supplemented with magnesium chloride (MGC) or magnesium chloride and ammonium chloride (NH₄) and a feeding consisting of one week of grass-diluted MGC followed by two weeks of the NH₄. Colostrum was collected from 89 dairy cows, which were randomly allocated to the feedings three weeks before the expected calving date during spring, summer and autumn. Cow colostrum samples were analyzed using proton nuclear magnetic resonance spectroscopy.</p><p><strong>Results: </strong>Our results show that calving season influenced the levels of 14 metabolites. Independent of seasonal variation, acidified corn silage diets resulted in consistent decreased levels of tryptophan, acetate and cytidine, while the non-acidified grass-based diet resulted in increased concentrations of fucose.</p><p><strong>Conclusions: </strong>Although colostrum is physiologically regulated, our findings, for the first time, indicate that the four feeding strategies induce shifts in fucose, tryptophan, acetate and cytidine levels, reflecting the energy and nitrogen metabolism of cows before parturition.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"128"},"PeriodicalIF":3.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960446","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}