Metabolomics最新文献

{"title":"Harnessing NMR technology for enhancing field crop improvement: applications, challenges, and future perspectives.","authors":"Vedant Gautam, Vibhootee Garg, Nitesh Meena, Sunidhi Kumari, Shubham Patel, Mukesh, Himanshu Singh, Shreyashi Singh, R K Singh","doi":"10.1007/s11306-025-02229-z","DOIUrl":"https://doi.org/10.1007/s11306-025-02229-z","url":null,"abstract":"<p><strong>Introduction: </strong>Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as a transformative technology in agricultural research, offering powerful analytical capabilities for field crop improvement. With global challenges such as food security and climate change intensifying, there is an urgent need for innovative methodologies to enhance our understanding of plant health, metabolic pathways, and crop-environment interactions. NMR's ability to provide nondestructive, real-time analysis of plant metabolites and soil chemistry positions it as a critical tool for addressing these pressing concerns.</p><p><strong>Objective: </strong>This review aims to elucidate the potential of NMR spectroscopy in advancing field crop improvement by highlighting its applications, challenges, and future perspectives in agricultural methodologies. The focus is on the evolution and application of NMR in agricultural research, particularly in metabolomics, phenotyping, and quality assessment.</p><p><strong>Method: </strong>A comprehensive literature review was conducted to analyze recent advancements in NMR applications in agriculture. Particular emphasis was given to high-resolution magic angle spinning (HR-MAS) and time-domain NMR techniques, which have been instrumental in elucidating plant metabolites and soil chemistry. Studies showcasing the integration of NMR with complementary technologies for enhanced metabolic profiling and genetic marker identification were reviewed.</p><p><strong>Results: </strong>Findings indicate that NMR spectroscopy is an indispensable tool in agriculture due to its ability to identify biomarkers indicative of crop resilience, monitor soil composition, and contribute to food safety and quality assessments. The integration of NMR with other technologies has accelerated metabolic profiling, aiding in the breeding of high-yielding and stress-resistant crop varieties. However, challenges such as sensitivity limitations and the need for standardization remain.</p><p><strong>Conclusion: </strong>NMR spectroscopy holds immense potential for revolutionizing agricultural research and crop improvement. Overcoming existing challenges, such as sensitivity and standardization, is crucial for its broader application in practical agricultural settings. Collaborative efforts among researchers, agronomists, and policymakers will be essential for leveraging NMR technology to address global food security challenges and promote sustainable agricultural practices.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 2","pages":"27"},"PeriodicalIF":3.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468130","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":"An untargeted metabolome-wide association study of maternal perinatal tobacco smoking in newborn blood spots.","authors":"Di He, Qi Yan, Karan Uppal, Douglas I Walker, Dean P Jones, Beate Ritz, Julia E Heck","doi":"10.1007/s11306-025-02225-3","DOIUrl":"10.1007/s11306-025-02225-3","url":null,"abstract":"<p><strong>Introduction: </strong>Maternal tobacco smoking in the perinatal period increases the risk for adverse outcomes in offspring.</p><p><strong>Objective: </strong>To better understand the biological pathways through which maternal tobacco use may have long-term impacts on child metabolism, we performed a high-resolution metabolomics (HRM) analysis in newborns, following an untargeted metabolome-wide association study workflow.</p><p><strong>Methods: </strong>The study population included 899 children without cancer diagnosis before age 6 and born between 1983 and 2011 in California. Newborn dried blood spots were collected by the California Genetic Disease Screening Program between 12 and 48 h after birth and stored for later research use. Based on HRM, we considered mothers to be active smokers if they were self- or provider-reported smokers on birth certificates or if we detected any cotinine or high hydroxycotinine intensities in newborn blood. We used partial least squares discriminant analysis and Mummichog pathway analysis to identify metabolites and metabolic pathways associated with maternal tobacco smoking.</p><p><strong>Results: </strong>A total of 26,183 features were detected with HRM, including 1003 that were found to be associated with maternal smoking late in pregnancy and early postpartum (Variable Importance in Projection (VIP) scores > = 2). Smoking affected metabolites and metabolic pathways in neonatal blood including vitamin A (retinol) metabolism, the kynurenine pathway, and tryptophan and arachidonic acid metabolism.</p><p><strong>Conclusion: </strong>The smoking-associated metabolites and pathway perturbations that we identified suggested inflammatory responses and have also been implicated in chronic diseases of the central nervous system and the lung. Our results suggest that infant metabolism in the early postnatal period reflects smoking specific physiologic responses to maternal smoking with strong biologic plausibility.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 2","pages":"30"},"PeriodicalIF":3.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468456","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":"Phosphorylated glycosphingolipids are commonly detected in Caenorhabditis elegans lipidomes.","authors":"Michael Witting, Liesa Salzer, Sven W Meyer, Aiko Barsch","doi":"10.1007/s11306-024-02216-w","DOIUrl":"10.1007/s11306-024-02216-w","url":null,"abstract":"<p><strong>Introduction: </strong>The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipid species for which no reference spectra are available is tedious and often involves a lot of manual work. Integrating high-resolution mass spectrometry with enhancements from chromatographic and ion mobility separation enables the in-depth investigation of intact lipids.</p><p><strong>Objectives: </strong>We investigated phosphorylated glycosphingolipids from the nematode Caenorhabditis elegans, a biomedical model organism, and aimed to identify different species from this class of lipids, which have been described in one particular publication only. We checked if these lipids can be detected in lipid extracts of C. elegans.</p><p><strong>Methods: </strong>We used UHPLC-UHR-TOF-MS and UHPLC-TIMS-TOF-MS in combination with dedicated data analysis to check for the presence of phosphorylated glycosphingolipids. Specifically, candidate features were identified in two datasets using Mass Spec Query Language (MassQL) to search fragmentation data. The additional use of retention time (RT) and collisional cross section (CCS) information allowed to filter false positive annotations.</p><p><strong>Results: </strong>As a result, we detected all previously described phosphorylated glycosphingolipids and novel species as well as their biosynthetic precursors in two different lipidomics datasets. MassQL significantly speeds up the process by saving time that would otherwise be spent on manual data investigations. In total over 20 sphingolipids could be described.</p><p><strong>Conclusion: </strong>MassQL allowed us to search for phosphorylated glycosphingolipids and their potential biosynthetic precursors systematically. Using orthogonal information such as RT and CCS helped filter false positive results. With the detection in two different datasets, we demonstrate that these sphingolipids are a general part of the C. elegans lipidome.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 2","pages":"29"},"PeriodicalIF":3.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468134","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 predictive model for neoadjuvant therapy response in breast cancer.","authors":"Rafael Nambo-Venegas, Virginia Isabel Enríquez-Cárcamo, Marcela Vela-Amieva, Isabel Ibarra-González, Lourdes Lopez-Castro, Sara Aileen Cabrera-Nieto, Juan E Bargalló-Rocha, Cynthia M Villarreal-Garza, Alejandro Mohar, Berenice Palacios-González, Juan P Reyes-Grajeda, Fernanda Sarahí Fajardo-Espinoza, Marlid Cruz-Ramos","doi":"10.1007/s11306-025-02230-6","DOIUrl":"https://doi.org/10.1007/s11306-025-02230-6","url":null,"abstract":"<p><p>Neoadjuvant therapy is a standard treatment for breast cancer, but its effectiveness varies among patients. This highlights the importance of developing accurate predictive models. Our study uses metabolomics and machine learning to predict the response to neoadjuvant therapy in breast cancer patients.</p><p><strong>Objective: </strong>To develop and validate predictive models using machine learning and circulating metabolites for forecasting responses to neoadjuvant therapy among breast cancer patients, enhancing personalized treatment strategies.</p><p><strong>Methods: </strong>Based on pathological analysis after neoadjuvant chemotherapy and surgery, this retrospective study analyzed 30 young women breast cancer patients from a single institution, categorized as responders or non-responders. Utilizing liquid chromatography-tandem mass spectrometry, we investigated the plasma metabolome, explicitly targeting 40 metabolites, to identify relevant biomarkers linked to therapy response, using machine learning to generate a predictive model and validate the results.</p><p><strong>Results: </strong>Eighteen significant biomarkers were identified, including specific acylcarnitines and amino acids. The most effective predictive model demonstrated a remarkable accuracy of 90.7% and an Area Under the Curve (AUC) of 0.999 at 95% confidence, illustrating its potential utility as a web-based application for future patient management. This model's reliability underscores the significant role of circulating metabolites in predicting therapy outcomes.</p><p><strong>Conclusion: </strong>Our study's findings highlight the crucial role of metabolomics in advancing personalized medicine for breast cancer treatment by effectively identifying metabolite biomarkers correlated with neoadjuvant therapy response. This approach signifies a critical step towards tailoring treatment plans based on individual metabolic profiles, ultimately improving patient outcomes in breast cancer care.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 2","pages":"28"},"PeriodicalIF":3.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468454","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":"Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome.","authors":"Filipa Amaro, Márcia Carvalho, Carina Carvalho-Maia, Carmen Jerónimo, Rui Henrique, Maria de Lourdes Bastos, Paula Guedes de Pinho, Joana Pinto","doi":"10.1007/s11306-024-02212-0","DOIUrl":"https://doi.org/10.1007/s11306-024-02212-0","url":null,"abstract":"<p><strong>Introduction: </strong>Despite considerable advances in cancer research, the increasing prevalence and high mortality rate of clear cell renal cell carcinoma (ccRCC) remain a significant challenge. A more detailed comprehension of the distinctive metabolic characteristics of ccRCC is vital to enhance diagnostic, prognostic, and therapeutic strategies.</p><p><strong>Objectives: </strong>This study aimed to investigate the metabolic signatures of ccRCC tumours and, for the first time, their correlation with the urinary metabolome of the same patients.</p><p><strong>Methods: </strong>We applied a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach to analyse matched tissue and urine samples from a cohort of 18 ccRCC patients and urine samples from 18 cancer-free controls. Multivariate and univariate statistical methods, as well as pathway and correlation analyses, were performed to assess metabolic dysregulations and correlations between tissue and urine.</p><p><strong>Results: </strong>The results showed a ccRCC metabolic signature characterized by reprogramming in amino acid, energy, and sugar and inositol phosphate metabolisms. Our study identified, for the first time, significantly decreased levels of asparagine, proline, gluconate, 3-aminoisobutanoate, 4-aminobutanoate and urea in ccRCC tumours, highlighting the involvement of arginine biosynthesis, β-alanine metabolism and purine and pyrimidine metabolism in ccRCC. The correlations between tissue and urine metabolomes provide evidence for the potential usefulness of urinary metabolites in understanding systemic metabolic changes driven by RCC tumours.</p><p><strong>Conclusions: </strong>These findings significantly advance our understanding of metabolic reprogramming in ccRCC and the systemic metabolic changes associated with the disease. Future research is needed to validate these findings in larger cohorts and to determine their potential implications for diagnosis and targeted therapies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 2","pages":"26"},"PeriodicalIF":3.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414533","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":"The role of salivary metabolomics in chronic periodontitis: bridging oral and systemic diseases.","authors":"Jawaher Albahri, Heather Allison, Kathryn A Whitehead, Howbeer Muhamadali","doi":"10.1007/s11306-024-02220-0","DOIUrl":"10.1007/s11306-024-02220-0","url":null,"abstract":"<p><strong>Background: </strong>Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.</p><p><strong>Aim of review: </strong>In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.</p><p><strong>Key scientific concepts of the review: </strong>Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"24"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11805826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370873","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 for coronary slow flow using untargeted metabolomics.","authors":"Yunxian Chen, Jiarong Liang, Sujuan Chen, Baofeng Chen, Fenglei Guan, Xiangying Liu, Xiangyang Liu, Yuanlin Zhao, Liangqiu Tang","doi":"10.1007/s11306-025-02223-5","DOIUrl":"10.1007/s11306-025-02223-5","url":null,"abstract":"<p><strong>Background: </strong>Coronary slow flow (CSF) is associated with poor cardiovascular prognosis. However, its pathogenesis is unclear. This study aimed to identify potential characteristic biomarkers in patients with CSF using untargeted metabolomics.</p><p><strong>Methods: </strong>We prospectively enrolled 30 patients with CSF, 30 with coronary artery disease (CAD), and 30 with normal coronary arteries (NCA), all of whom were age-matched, according to the results of coronary angiography. Serum metabolomics were analyzed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Differentially expressed metabolites were identified through orthogonal partial least squares-discriminant analysis (OPLS-DA) combined with univariate fold-change and VIP value analysis. Pathway enrichment of these metabolites was performed using the KEGG database, and ROC curves were plotted to assess the diagnostic value of the metabolites in CSF patients.</p><p><strong>Results: </strong>Compared to the CAD and NCA groups, 256 metabolites showed specific expression in CSF, with 18 meeting stringent screening criteria (VIP > 1, FC ≥ 2, or FC ≤ 0.5, and P < 0.05). Seven metabolites demonstrated high diagnostic value for CSF: inositol 1,3,4-trisphosphate (AUC: 1.0), Cer (d24:1/18:0 (2OH)) (AUC: 0.984), Creosol (AUC: 0.976), Chaps (AUC: 0.904), Arg-Thr-Lys-Arg (AUC: 0.929), Ser-Tyr-Arg (AUC: 0.912), and Methyl Indole-3-Acetate (AUC: 0.909). Pathway analysis highlighted the HIF-1 signaling pathway as the most significant metabolic pathway.</p><p><strong>Conclusions: </strong>We identified seven metabolites that may serve as serum biomarkers for predicting and diagnosing CSF through untargeted metabolomics. The HIF-1 signaling pathway appears to be crucial in the development of CSF.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"23"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370872","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":"Unveiling the metabolomic profile of growth hormone deficiency children using NMR spectroscopy.","authors":"Eftychia A Aggelaki, Aristeidis Giannakopoulos, Panagiota D Georgiopoulou, Styliani A Chasapi, Alexandra Efthymiadou, Dimitra Kritikou, Dionisios Chrysis, Georgios A Spyroulias","doi":"10.1007/s11306-024-02217-9","DOIUrl":"10.1007/s11306-024-02217-9","url":null,"abstract":"<p><strong>Introduction: </strong>The diagnosis of Growth Hormone Deficiency (GHD) during childhood has been the subject of much controversy over the last few years. Aiming to accurate medical treatment, there is a need for biomarker discovery.</p><p><strong>Objective: </strong>To characterize the metabolic profile of GHD children, examine the effect of GH administration on the metabolic signature, and investigate the correlations between metabolites and IGF-1.</p><p><strong>Methods: </strong>Nuclear Magnetic Resonance (NMR)-based untargeted and targeted metabolomic approach applied to study the metabolic profiles of children with GHD. Plasma, serum, and urine samples were collected from twenty-two children diagnosed with GHD and forty-eight age matched controls from the Pediatric Endocrinology Unit of the University Hospital of Patras. Experimental data were examined by both multivariate and univariate statistical analysis.</p><p><strong>Results: </strong>The results of this pilot study revealed a different metabolic fingerprint of children with GHD in comparison to age-matched healthy individuals. However, the detected alterations in the metabolite patterns before and after GH treatment were subtle and of minor discriminative statistical power.</p><p><strong>Conclusions: </strong>This study provides evidence that metabolome plays a pivotal role in GHD, but large-scale multicenter studies are warranted to validate the results.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"25"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11805833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370874","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 profiles of meconium in preeclamptic and normotensive pregnancies.","authors":"Elli Toivonen, Jutta Sikkinen, Anne Salonen, Olli Kärkkäinen, Ville Koistinen, Anton Klåvus, Topi Meuronen, Tuomas Heini, Arina Maltseva, Mikael Niku, Tiina Jääskeläinen, Hannele Laivuori","doi":"10.1007/s11306-025-02224-4","DOIUrl":"10.1007/s11306-025-02224-4","url":null,"abstract":"<p><strong>Introduction: </strong>Preeclampsia (PE) is a common vascular pregnancy disorder affecting maternal and fetal metabolism with severe immediate and long-term consequences in mothers and infants. During pregnancy, metabolites in the maternal circulation pass through the placenta to the fetus. Meconium, a first stool of the neonate, offers a view to maternal and fetoplacental unit metabolism and could add to knowledge on the effects of PE on the fetus and newborn.</p><p><strong>Objectives: </strong>To compare meconium metabolome of infants from PE and normotensive pregnancies.</p><p><strong>Methods: </strong>A cohort of preeclamptic parturients and normotensive controls were recruited in Tampere University Hospital during 2019-2022. Meconium was sampled and its metabolome analyzed using liquid chromatography- mass spectrometry in 48 subjects in each group.</p><p><strong>Results: </strong>Differences in abundances of 1263 compounds, of which 19 could be annotated, were detected between the two groups. Several acylcarnitines, androsterone sulfate, three bile acids, amino acid derivatives (phenylacetylglutamine, epsilon-(gamma-glutamyl)lysine and N-(phenylacetyl)glutamic acid), as well as caffeine and paraxanthine were lower in the PE group compared to the control group. Urea and progesterone were higher in the PE group.</p><p><strong>Conclusion: </strong>PE is associated with alterations in the meconium metabolome of infants. The differing abundances of several metabolites show alterations in the interaction between the fetoplacental unit and mother in PE, but whether they are a cause or an effect of the disorder remains to be further investigated.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"21"},"PeriodicalIF":3.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11762436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039934","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":"Correction: High-quality identification of volatile organic compounds (VOCs) originating from breath.","authors":"Wisenave Arulvasan, Hsuan Chou, Julia Greenwood, Madeleine L Ball, Owen Birch, Simon Coplowe, Patrick Gordon, Andreea Ratiu, Elizabeth Lam, Ace Hatch, Monika Szkatulska, Steven Levett, Ella Mead, Chloe Charlton-Peel, Louise Nicholson-Scott, Shane Swann, Frederik-Jan van Schooten, Billy Boyle, Max Allsworth","doi":"10.1007/s11306-024-02203-1","DOIUrl":"10.1007/s11306-024-02203-1","url":null,"abstract":"","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"22"},"PeriodicalIF":3.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11762595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039929","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}