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Cochran D, Takis PG, Alexander JL, Mullish BH, Powell N, Marchesi JR, Powers R. Evaluating protocols for reproducible targeted metabolomics by NMR. Analyst 2024; 149:5423-5432. [PMID: 39377673 PMCID: PMC11587611 DOI: 10.1039/d4an01015a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/09/2024]
Abstract
Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.
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Affiliation(s)
- Darcy Cochran
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.
| | - Panteleimon G Takis
- Department of Chemistry, University of Ioannina, Ioannina GR 451 10, Greece.
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK.
| | - James L Alexander
- Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, South Wharf Road, Paddington London, W2 1NY, UK
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, W2 1NY, UK
- Department of Gastroenterology, St Mark's Hospital and Academic Institute, Middlesex, UK
| | - Benjamin H Mullish
- Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, South Wharf Road, Paddington London, W2 1NY, UK
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, W2 1NY, UK
| | - Nick Powell
- Departments of Gastroenterology and Hepatology, St Mary's Hospital, Imperial College Healthcare NHS Trust, South Wharf Road, Paddington London, W2 1NY, UK
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, W2 1NY, UK
| | - Julian R Marchesi
- Department of Gastroenterology, St Mark's Hospital and Academic Institute, Middlesex, UK
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.
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Trimigno A, Zhao J, Michaud WA, Paneitz DC, Chukwudi C, D’Alessandro DA, Lewis GD, Minie NF, Catricala JP, Vincent DE, Lopera Higuita M, Bolger-Chen M, Tessier SN, Li S, O’Day EM, Osho AA, Rabi SA. Metabolic Choreography of Energy Substrates During DCD Heart Perfusion. Transplant Direct 2024; 10:e1704. [PMID: 39220220 PMCID: PMC11365673 DOI: 10.1097/txd.0000000000001704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 07/02/2024] [Indexed: 09/04/2024] Open
Abstract
Background The number of patients waiting for heart transplant far exceeds the number of hearts available. Donation after circulatory death (DCD) combined with machine perfusion can increase the number of transplantable hearts by as much as 48%. Emerging studies also suggest machine perfusion could enable allograft "reconditioning" to optimize outcomes. However, a detailed understanding of the energetic substrates and metabolic changes during perfusion is lacking. Methods Metabolites were analyzed using 1-dimensional 1H and 2-dimensional 13C-1H heteronuclear spectrum quantum correlation nuclear magnetic resonance spectroscopy on serial perfusate samples (N = 98) from 32 DCD hearts that were successfully transplanted. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test for significant differences in metabolite resonances during perfusion and network analysis was used to uncover altered metabolic pathways. Results Metabolite differences were observed comparing baseline perfusate to samples from hearts at time points 1-2, 3-4, and 5-6 h of perfusion and all pairwise combinations. Among the most significant changes observed were a steady decrease in fatty acids and succinate and an increase in amino acids, especially alanine, glutamine, and glycine. This core set of metabolites was also altered in a DCD porcine model perfused with a nonblood-based perfusate. Conclusions Temporal metabolic changes were identified during ex vivo perfusion of DCD hearts. Fatty acids, which are normally the predominant myocardial energy source, are rapidly depleted, while amino acids such as alanine, glutamine, and glycine increase. We also noted depletion of ketone, β-hydroxybutyric acid, which is known to have cardioprotective properties. Collectively, these results suggest a shift in energy substrates and provide a basis to design optimal preservation techniques during perfusion.
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Affiliation(s)
| | | | - William A. Michaud
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - Dane C. Paneitz
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - Chijioke Chukwudi
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - David A. D’Alessandro
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - Greg D. Lewis
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - Nathan F. Minie
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - Joseph P. Catricala
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | | | - Manuela Lopera Higuita
- Center for Engineering in Medicine and Surgery, Harvard Medical School and Massachusetts General Hospital, Shriner Children’s Boston, Boston, MA
| | - Maya Bolger-Chen
- Center for Engineering in Medicine and Surgery, Harvard Medical School and Massachusetts General Hospital, Shriner Children’s Boston, Boston, MA
| | - Shannon N. Tessier
- Center for Engineering in Medicine and Surgery, Harvard Medical School and Massachusetts General Hospital, Shriner Children’s Boston, Boston, MA
| | - Selena Li
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | | | - Asishana A. Osho
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
| | - S. Alireza Rabi
- Division of Cardiac Surgery, Corrigan Minehan Heart Center, Massachusetts General Hospital, Corrigan Minehan Heart Center, Boston, MA
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Llambrich M, Satorra P, Correig E, Gumà J, Brezmes J, Tebé C, Cumeras R. Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem. Res Synth Methods 2024; 15:687-699. [PMID: 38480474 DOI: 10.1002/jrsm.1713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/12/2024] [Accepted: 02/25/2024] [Indexed: 07/13/2024]
Abstract
Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.
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Affiliation(s)
- Maria Llambrich
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili, IISPV, Tarragona, Spain
- Metabolomics Interdisciplinary Laboratory, Department of Nutrition and Metabolism, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Pau Satorra
- Biostatistics Unit, Bellvitge Institute for Biomedical Research (IDIBELL), Hospitalet de Llobregat, Spain
| | - Eudald Correig
- Department of Biostatistics, Universitat Rovira i Virgili, Reus, Spain
| | - Josep Gumà
- Oncology Department, Hospital Universitari Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | - Jesús Brezmes
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili, IISPV, Tarragona, Spain
- Metabolomics Interdisciplinary Laboratory, Department of Nutrition and Metabolism, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Cristian Tebé
- Biostatistics Unit, Bellvitge Institute for Biomedical Research (IDIBELL), Hospitalet de Llobregat, Spain
| | - Raquel Cumeras
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili, IISPV, Tarragona, Spain
- Metabolomics Interdisciplinary Laboratory, Department of Nutrition and Metabolism, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Oncology Department, Hospital Universitari Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
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Ovbude ST, Sharmeen S, Kyei I, Olupathage H, Jones J, Bell RJ, Powers R, Hage DS. Applications of chromatographic methods in metabolomics: A review. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1239:124124. [PMID: 38640794 PMCID: PMC11618781 DOI: 10.1016/j.jchromb.2024.124124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/11/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.
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Affiliation(s)
- Susan T Ovbude
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Sadia Sharmeen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Isaac Kyei
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Harshana Olupathage
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Jacob Jones
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Richard J Bell
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA; Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - David S Hage
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA.
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Trimigno A, Holderman NR, Dong C, Boardman KD, Zhao J, O’Day EM. NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples. Metabolites 2024; 14:275. [PMID: 38786752 PMCID: PMC11122845 DOI: 10.3390/metabo14050275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.
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Bhinderwala F, Roth HE, Filipi M, Jack S, Powers R. Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids. ACS Chem Neurosci 2024; 15:1110-1124. [PMID: 38420772 PMCID: PMC11586083 DOI: 10.1021/acschemneuro.3c00678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Current Affiliation - University of Pittsburgh School of Medicine, Department of Structural Biology, Pittsburgh, PA 15213
| | - Heidi E. Roth
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
| | - Mary Filipi
- Multiple Sclerosis Clinic, Saunders Medical Center, Wahoo, NE 68066
| | - Samantha Jack
- Multiple Sclerosis Clinic, Saunders Medical Center, Wahoo, NE 68066
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln NE 68588-0304
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln NE 68588-0304
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Prince N, Liang D, Tan Y, Alshawabkeh A, Angel EE, Busgang SA, Chu SH, Cordero JF, Curtin P, Dunlop AL, Gilbert-Diamond D, Giulivi C, Hoen AG, Karagas MR, Kirchner D, Litonjua AA, Manjourides J, McRitchie S, Meeker JD, Pathmasiri W, Perng W, Schmidt RJ, Watkins DJ, Weiss ST, Zens MS, Zhu Y, Lasky-Su JA, Kelly RS. Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium. Metabolomics 2024; 20:16. [PMID: 38267770 PMCID: PMC11099615 DOI: 10.1007/s11306-023-02082-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/12/2023] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. OBJECTIVE The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. METHODS We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure. RESULTS Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. CONCLUSIONS Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.
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Affiliation(s)
- Nicole Prince
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Youran Tan
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Akram Alshawabkeh
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
| | - Elizabeth Esther Angel
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Stefanie A Busgang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - José F Cordero
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anne L Dunlop
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
- Department of Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
- Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Cecilia Giulivi
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Anne G Hoen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Margaret R Karagas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - David Kirchner
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonary Medicine, Golisano Children's Hospital at Strong, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Susan McRitchie
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - John D Meeker
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wimal Pathmasiri
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rebecca J Schmidt
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, 95616, USA
- MIND Institute, School of Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Deborah J Watkins
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael S Zens
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350 10.1002/mrc.5350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/23/2024]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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9
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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Villalba H, Llambrich M, Gumà J, Brezmes J, Cumeras R. A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison. Metabolites 2023; 13:1167. [PMID: 38132849 PMCID: PMC10744506 DOI: 10.3390/metabo13121167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.
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Affiliation(s)
- Héctor Villalba
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Maria Llambrich
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Josep Gumà
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
- Department of Medicine and Surgery, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
| | - Jesús Brezmes
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
| | - Raquel Cumeras
- Department of Oncology, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), CERCA, 43204 Reus, Spain
- Department of Electrical Electronic Engineering and Automation, University of Rovira i Virgili (URV), 43007 Tarragona, Spain
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11
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Gill B, Schwecht I, Rahman N, Dhawan T, Verschoor C, Nazli A, Kaushic C. Metabolic signature for a dysbiotic microbiome in the female genital tract: A systematic review and meta-analysis. Am J Reprod Immunol 2023; 90:e13781. [PMID: 37766408 DOI: 10.1111/aji.13781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/06/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The vaginal microbiome (VMB) is a critical determinant of reproductive health, where a microbial shift towards a dysbiotic environment has implications for susceptibility to, and clinical presentation of sexually transmitted infections (STIs). Metabolomic profiling of the vaginal microenvironment has led to the identification of metabolic responses to clinical conditions of dysbiosis. However, no studies have examined metabolic markers that are common across conditions and can serve as a signature for vaginal dysbiosis. METHOD OF STUDY We have conducted a comprehensive systematic review and meta-analysis to identify consistently deregulated metabolites along with their impact on host and microbial metabolism during dysbiosis. We employed two complementary approaches including a vote counting analysis for all eligible studies identified in the systematic review, in addition to a meta-analysis for a subset of studies with sufficient available data. Significantly deregulated metabolites were then selected for pathway enrichment analysis. RESULTS Our results revealed a total of 502 altered metabolites reported across 10 dysbiotic conditions from 16 studies. Following a rigorous, collective analysis, six metabolites which were consistently downregulated and could be generalized to all dysbiotic conditions were identified. In addition, five downregulated and one upregulated metabolite was identified from a bacterial vaginosis (BV) focused sub-analysis. These metabolites have the potential to serve as a metabolic signature for vaginal dysbiosis. Their role in eight altered metabolic pathways indicates a disruption of amino acid, carbohydrate, and energy metabolism during dysbiosis. CONCLUSION Based on this analysis, we propose a schematic model outlining the common metabolic perturbations associated with vaginal dysbiosis, which can be potential targets for therapeutics and prophylaxis.
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Affiliation(s)
- Biban Gill
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
| | - Ingrid Schwecht
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
| | - Nuzhat Rahman
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
| | - Tushar Dhawan
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
| | - Chris Verschoor
- Health Sciences North Research Institute, Northern Ontario School of Medicine, Sudbury, ON, Canada
| | - Aisha Nazli
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
| | - Charu Kaushic
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- McMaster Immunology Research Center, Michael G. DeGroote Center for Learning and Discovery, McMaster University, Hamilton, ON, Canada
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12
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Song G, Wang L, Tang J, Li H, Pang S, Li Y, Liu L, Hu J. Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers. Metabolomics 2023; 19:36. [PMID: 37014438 PMCID: PMC10073066 DOI: 10.1007/s11306-023-02002-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/22/2023] [Indexed: 04/05/2023]
Abstract
BACKGROUND AND AIMS Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current "gold standard" techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management. METHODS Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity). RESULTS GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions. CONCLUSION Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research.
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Affiliation(s)
- Guodong Song
- The Second Hospital of Tianjin Medical University, No 23. Pingjiang Road, Hexi District, 300211, Tianjin, China
| | - Li Wang
- The Second Hospital of Tianjin Medical University, No 23. Pingjiang Road, Hexi District, 300211, Tianjin, China
| | - Junlong Tang
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China
| | - Haohui Li
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China
| | - Shuyu Pang
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China
| | - Yan Li
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China
| | - Li Liu
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China.
| | - Junyuan Hu
- Metanotitia Inc, No 59. Gaoxin South 9Th Road, Yuehai Street, Nanshan District, Shenzhen, 518056, Guangdong, China.
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13
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Chen S, He W. Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Bone Mineral Density. Calcif Tissue Int 2023; 112:543-562. [PMID: 36877247 DOI: 10.1007/s00223-023-01069-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/01/2023] [Indexed: 03/07/2023]
Abstract
Mounting evidence has supported osteoporosis (OP) as a metabolic disorder. Recent metabolomics studies have discovered numerous metabolites related to bone mineral density (BMD). However, the causal effects of metabolites on BMD at distinct sites remained underexplored. Leveraging genome-wide association datasets, we conducted two-sample Mendelian randomization (MR) analyses to investigate the causal relationship between 486 blood metabolites and bone mineral density at five skeletal sites including heel (H), total body (TB), lumbar spine (LS), femoral neck (FN), and ultra-distal forearm (FA). Sensitivity analyses were performed to test the presence of the heterogeneity and the pleiotropy. To exclude the influences of reverse causation, genetic correlation, and linkage disequilibrium (LD), we further performed reverse MR, linkage disequilibrium regression score (LDSC), and colocalization analyses. In the primary MR analyses, 22, 10, 3, 7, and 2 metabolite associations were established respectively for H-BMD, TB-BMD, LS-BMD, FN-BMD, and FA-BMD at the nominal significance level (IVW, P < 0.05) and passing sensitivity analyses. Among these, one metabolite, androsterone sulfate showed a strong effect on four out of five BMD phenotypes (Odds ratio [OR] for H-BMD = 1.045 [1.020, 1.071]; Odds ratio [OR] for TB-BMD = 1.061 [1.017, 1.107]; Odds ratio [OR] for LS-BMD = 1.088 [1.023, 1.159]; Odds ratio [OR] for FN-BMD = 1.114 [1.054, 1.177]). Reverse MR analysis provided no evidence for the causal effects of BMD measurements on these metabolites. Colocalization analysis have found that several metabolite associations might be driven by shared genetic variants such as mannose for TB-BMD. This study identified some metabolites causally related to BMD at distinct sites and several key metabolic pathways, which shed light on predictive biomarkers and drug targets for OP.
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Affiliation(s)
- Shuhong Chen
- Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, China.
| | - Weiman He
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Cao YY, Guo K, Zhao R, Li Y, Lv XJ, Lu ZP, Tian L, Ren S, Wang ZQ. Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma. Digit Health 2023; 9:20552076231179007. [PMID: 37312938 PMCID: PMC10259126 DOI: 10.1177/20552076231179007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/12/2023] [Indexed: 06/15/2023] Open
Abstract
Background Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics. Methods Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC. Results A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850). Conclusions Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.
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Affiliation(s)
- Ying-Ying Cao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yuan Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiao-Jing Lv
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zi-Peng Lu
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Tian
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhong-Qiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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