Metabolomics最新文献

{"title":"Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial.","authors":"Richard D Beger, Royston Goodacre, Christina M Jones, Katrice A Lippa, Oleg A Mayboroda, Donna O'Neill, Lukas Najdekr, Ioanna Ntai, Ian D Wilson, Warwick B Dunn","doi":"10.1007/s11306-024-02155-6","DOIUrl":"10.1007/s11306-024-02155-6","url":null,"abstract":"<p><strong>Background: </strong>Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.</p><p><strong>Aim of review: </strong>In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.</p><p><strong>Key scientific concepts of review: </strong>The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897754","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 analysis reveals the benefits of ginseng berry intake on mitochondrial function and glucose metabolism in the liver of obese mice.","authors":"Kyun-Hee Lee, Moonju Hong, Haeng Jeon Hur, Mi Jeong Sung, Ae Sin Lee, Min Jung Kim, Hye Jeong Yang, Myung-Sunny Kim","doi":"10.1007/s11306-024-02152-9","DOIUrl":"10.1007/s11306-024-02152-9","url":null,"abstract":"<p><strong>Introduction: </strong>Ginseng berry (GB) has previously been demonstrated to improve systemic insulin resistance and regulate hepatic glucose metabolism and steatosis in mice with diet-induced obesity (DIO).</p><p><strong>Objectives: </strong>In this study, the role of GB in metabolism was assessed using metabolomics analysis on the total liver metabolites of DIO mice.</p><p><strong>Methods: </strong>Metabolomic profiling was performed using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS) of liver tissue from mice on a 12-wk normal chow diet (NC), high-fat diet (HFD), and HFD supplemented with 0.1% GB (HFD + GB). The detected metabolites, its pathways, and functions were analyzed through partial least square discriminant analysis (PLS-DA), the small molecular pathway database (SMPDB), and MetaboAnalyst 5.0.</p><p><strong>Results: </strong>The liver metabolite profiles of NC, HFD, and GB-fed mice (HFD + GB) were highly compartmentalized. Metabolites involved in major liver functions, such as mitochondrial function, gluconeogenesis/glycolysis, fatty acid metabolism, and primary bile acid biosynthesis, showed differences after GB intake. The metabolites that showed significant correlations with fasting blood glucose (FBG), insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) were highly associated with mitochondrial membrane function, energy homeostasis, and glucose metabolism. Ginseng berry intake increased the levels of metabolites involved in mitochondrial membrane function, decreased the levels of metabolites related to glucose metabolism, and was highly correlated with metabolic phenotypes.</p><p><strong>Conclusion: </strong>This study demonstrated that long-term intake of GB changed the metabolite of hepatosteatotic livers in DIO mice, normalizing global liver metabolites involved in mitochondrial function and glucose metabolism and indicating the potential mechanism of GB in ameliorating hyperglycemia in DIO mice.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897756","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":"Assessing the influence of sleep and sampling time on metabolites in oral fluid: implications for metabolomics studies.","authors":"Michael Scholz, Andrea Eva Steuer, Akos Dobay, Hans-Peter Landolt, Thomas Kraemer","doi":"10.1007/s11306-024-02158-3","DOIUrl":"10.1007/s11306-024-02158-3","url":null,"abstract":"<p><strong>Introduction: </strong>The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.</p><p><strong>Objectives: </strong>We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.</p><p><strong>Methods: </strong>Oral fluid specimens of 13 healthy young males were obtained in Salivette^® devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.</p><p><strong>Results: </strong>Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.</p><p><strong>Conclusion: </strong>The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141902295","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":"Characterising the urinary acylcarnitine and amino acid profiles of HIV/TB co-infection, using LC–MS metabolomics","authors":"Charles Pretorius, Laneke Luies","doi":"10.1007/s11306-024-02161-8","DOIUrl":"https://doi.org/10.1007/s11306-024-02161-8","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>The human immunodeficiency virus (HIV) and tuberculosis (TB) co-infection presents significant challenges due to the complex interplay between these diseases, leading to exacerbated metabolic disturbances. Understanding these metabolic profiles is crucial for improving diagnostic and therapeutic approaches.</p><h3 data-test=\"abstract-sub-heading\">Objective</h3><p>This study aimed to characterise the urinary acylcarnitine and amino acid profiles, including 5-hydroxyindoleacetic acid (5-HIAA), in patients co-infected with HIV and TB using targeted liquid chromatography mass spectrometry (LC–MS) metabolomics.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Urine samples, categorised into HIV, TB, HIV/TB co-infected, and healthy controls, were analysed using HPLC–MS/MS. Statistical analyses included one-way ANOVA and a Kruskal-Wallis test to determine significant differences in the acylcarnitine and amino acid profiles between groups.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The study revealed significant metabolic alterations, especially in TB and co-infected groups. Elevated levels of medium-chain acylcarnitines indicated increased fatty acid oxidation, commonly associated with cachexia in TB. Altered amino acid profiles suggested disruptions in protein and glucose metabolism, indicating a shift towards diabetes-like metabolic states. Notably, TB was identified as a primary driver of these changes, affecting protein turnover, and impacting energy metabolism in co-infected patients.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The metabolic profiling of HIV/TB co-infection highlights the profound impact of TB on metabolic pathways, which may exacerbate the clinical complexities of co-infection. Understanding these metabolic disruptions can guide the development of targeted treatments and improve management strategies, ultimately enhancing the clinical outcomes for these patients. Further research is required to validate these findings and explore their implications in larger, diverse populations.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882950","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":"Metaboepigenetic regulation of gene expression in obesity and insulin resistance","authors":"Swapan K. Das, Mary E. Comeau, Carl D. Langefeld","doi":"10.1007/s11306-024-02159-2","DOIUrl":"https://doi.org/10.1007/s11306-024-02159-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Variation in DNA methylation (DNAm) in adipose tissue is associated with the pathogenesis of obesity and insulin resistance. The activity of enzymes involved in altering DNAm levels is dependent on several metabolite cofactors.</p><h3 data-test=\"abstract-sub-heading\">Objectives</h3><p>To understand the role of metabolites as mechanistic regulators of epigenetic marks, we tested the association between selected plasma metabolites and DNAm levels in the adipose tissue of African Americans.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>In the AAGMEx cohort (N = 256), plasma levels of metabolites were measured by untargeted liquid chromatography-mass spectrometry; adipose tissue DNAm and transcript levels were measured by reduced representation bisulfite sequencing, and expression microarray, respectively.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Among the 21 one-carbon metabolism pathway metabolites evaluated, six were associated with gluco-metabolic traits (P_FDR &lt; 0.05, for BMI, S_I, or Matsuda index) in AAGMEx. Methylation levels of 196, 116, and 180 CpG-sites were associated (P &lt; 0.0001) with S-adenosylhomocysteine (SAH), cystine, and hypotaurine, respectively. <i>Cis</i>-expression quantitative trait methylation (<i>cis</i> eQTM) analyses suggested the role of metabolite-level-associated CpG sites in regulating the expression of adipose tissue transcripts, including genes in G-protein coupled receptor signaling pathway. Plasma SAH level-associated CpG sites chr19:3403712 and chr19:3403735 were also associated with the expression of G-protein subunit alpha 15 (<i>GNA15</i>) in adipose. The expression of <i>GNA15</i> was significantly correlated with BMI (β = 1.87, P = 1.9 × 10^–16) and S_I (β = -1.61, P = 2.49 × 10^–5).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Our study suggests that a subset of metabolites modulates the methylation levels of CpG sites in specific loci and, in turn, regulates the expression of transcripts involved in obesity and insulin resistance.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883150","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":"Impact of dietary regime on the metabolomic profile of bovine buttermilk and whole milk powder","authors":"Claire Connolly, Mark Timlin, Sean A. Hogan, Eoin G. Murphy, Tom F. O’Callaghan, André Brodkorb, Deirdre Hennessy, Ellen Fitzpartick, Michael O’Donavan, Kieran McCarthy, John P. Murphy, Xiaofei Yin, Lorraine Brennan","doi":"10.1007/s11306-024-02157-4","DOIUrl":"https://doi.org/10.1007/s11306-024-02157-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Bovine milk contains a rich matrix of nutrients such as carbohydrates, fat, protein and various vitamins and minerals, the composition of which is altered by factors including dietary regime.</p><h3 data-test=\"abstract-sub-heading\">Objectives</h3><p>The objective of this research was to investigate the impact of dietary regime on the metabolite composition of bovine whole milk powder and buttermilk.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Bovine whole milk powder and buttermilk samples were obtained from spring-calving cows, consuming one of three diets. Group 1 grazed outdoors on perennial ryegrass which was supplemented with 5% concentrates; group 2 were maintained indoors and consumed a total mixed ration diet; and group 3 consumed a partial mixed ration diet consisting of perennial ryegrass during the day and total mixed ration maintained indoors at night.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Metabolomic analysis of the whole milk powder (N = 27) and buttermilk (N = 29) samples was preformed using liquid chromatography-tandem mass spectrometry, with 504 and 134 metabolites identified in the samples respectively. In whole milk powder samples, a total of 174 metabolites from various compound classes were significantly different across dietary regimes (FDR adjusted p-value ≤ 0.05), including triglycerides, of which 66% had their highest levels in pasture-fed samples. Triglycerides with highest levels in pasture-fed samples were predominantly polyunsaturated with high total carbon number. Regarding buttermilk samples, metabolites significantly different across dietary regimes included phospholipids, sphingomyelins and an acylcarnitine.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>In conclusion the results reveal a significant impact of a pasture-fed dietary regime on the metabolite composition of bovine dairy products, with a particular impact on lipid compound classes.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883154","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":"Bioactivity-driven fungal metabologenomics identifies antiproliferative stemphone analogs and their biosynthetic gene cluster.","authors":"Navid J Ayon, Cody E Earp, Raveena Gupta, Fatma A Butun, Ashley E Clements, Alexa G Lee, David Dainko, Matthew T Robey, Manead Khin, Lina Mardiana, Alexandra Longcake, Manuel Rangel-Grimaldo, Michael J Hall, Michael R Probert, Joanna E Burdette, Nancy P Keller, Huzefa A Raja, Nicholas H Oberlies, Neil L Kelleher, Lindsay K Caesar","doi":"10.1007/s11306-024-02153-8","DOIUrl":"10.1007/s11306-024-02153-8","url":null,"abstract":"<p><strong>Introduction: </strong>Fungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways.</p><p><strong>Objectives: </strong>To prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences.</p><p><strong>Methods: </strong>The 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics.</p><p><strong>Results: </strong>We isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated-Omics datasets.</p><p><strong>Conclusions: </strong>This work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879005","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":"¹H NMR analysis of metabolites from leaf tissue of resistant and susceptible oil palm breeding materials against Ganoderma boninense.","authors":"Hernawan Yuli Rahmadi, Muhamad Syukur, Widodo, Willy Bayuardi Suwarno, Sri Wening, Arfan Nazhri Simamora, Syarul Nugroho","doi":"10.1007/s11306-024-02160-9","DOIUrl":"10.1007/s11306-024-02160-9","url":null,"abstract":"<p><strong>Introduction: </strong>Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using ¹H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations.</p><p><strong>Objectives: </strong>The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using ¹H NMR analysis.</p><p><strong>Methods: </strong>Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy.</p><p><strong>Results: </strong>Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma.</p><p><strong>Conclusion: </strong>A metabolomic study with ¹H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879004","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":"Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.","authors":"Alexsandro Macedo Silva, Jéssica Levy, Eduardo De Carli, Leandro Teixeira Cacau, José Fernando Rinaldi de Alvarenga, Isabela Judith Martins Benseñor, Paulo Andrade Lotufo, Jarlei Fiamoncini, Lorraine Brennan, Dirce Maria Lobo Marchioni","doi":"10.1007/s11306-024-02145-8","DOIUrl":"10.1007/s11306-024-02145-8","url":null,"abstract":"<p><strong>Introduction: </strong>Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates.</p><p><strong>Objective: </strong>This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health.</p><p><strong>Materials and methods: </strong>A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR).</p><p><strong>Results: </strong>Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population.</p><p><strong>Conclusion: </strong>A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788652","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":"Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations","authors":"Lu Li, Shi Yan, David Horner, Morten A. Rasmussen, Age K. Smilde, Evrim Acar","doi":"10.1007/s11306-024-02128-9","DOIUrl":"https://doi.org/10.1007/s11306-024-02128-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Longitudinal metabolomics data from a meal challenge test contains both <i>fasting</i> and <i>dynamic</i> signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: <i>subjects</i>, <i>metabolites</i>, and <i>time</i>. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications.</p><h3 data-test=\"abstract-sub-heading\">Objectives</h3><p>Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC<span>(_{2000})</span> cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (<i>subjects</i> by <i>metabolites</i> by <i>time</i>) is coupled with the fasting data (<i>subjects</i> by <i>metabolites</i>) in the <i>subjects</i> mode.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications—when markers are present in both fasting and dynamic states.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770601","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}