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Ren X, Chen J, Abraham AG, Xu Y, Siewe A, Warady BA, Kimmel PL, Vasan RS, Rhee EP, Furth SL, Coresh J, Denburg M, Rebholz CM. Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children. J Ren Nutr 2024; 34:95-104. [PMID: 37944769 PMCID: PMC10960708 DOI: 10.1053/j.jrn.2023.10.007] [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: 05/10/2023] [Revised: 09/12/2023] [Accepted: 10/21/2023] [Indexed: 11/12/2023] Open
Abstract
OBJECTIVE Evidence regarding the efficacy of a low-protein diet for patients with CKD is inconsistent and recommending a low-protein diet for pediatric patients is controversial. There is also a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein and to assess whether protein-related metabolites are associated with CKD progression. METHODS Nontargeted metabolomics was conducted in plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, nondrug metabolites and dietary intake of total protein, animal protein, plant protein, chicken, dairy, nuts and beans, red and processed meat, fish, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards models assessed the prospective association between protein-related metabolites and CKD progression defined as the initiation of kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates. RESULTS One hundred and twenty-seven (26%) children experienced CKD progression during 5 years of follow-up. Sixty metabolites were significantly associated with dietary protein intake. Among the 60 metabolites, 10 metabolites were significantly associated with CKD progression (animal protein: n = 1, dairy: n = 7, red and processed meat: n = 2, nuts and beans: n = 1), including one amino acid, one cofactor and vitamin, 4 lipids, 2 nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-glycerophosphoethanolamine (GPE, P-16:0/18:1) was positively associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 88% higher risk of CKD progression. 3-ureidopropionate was inversely associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 48% lower risk of CKD progression. CONCLUSIONS Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.
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Affiliation(s)
- Xuyuehe Ren
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison G Abraham
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Department of Epidemiology, University of Colorado School of Public Health, Aurora, Colorado
| | - Yunwen Xu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aisha Siewe
- Division of Cardiology, Department of Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Bradley A Warady
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Children's Mercy Kansas City, Kansas City, Missouri
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes, Digestive, and Kidney Disorders, National Institutes of Health, Bethesda, Maryland; Division of Renal Diseases and Hypertension, Department of Medicine, George Washington University Medical Center, Washington, District of Columbia
| | | | - Eugene P Rhee
- Nephrology Division and Endocrinology Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Susan L Furth
- Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Michelle Denburg
- Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
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Akinbule OO, Onabanjo OO, Sanni SA, Adegunwa MO, Akinbule AS, Sosanya SK, Afolabi I. Amino acid composition and protein quality of commonly consumed cooked foods in Nigeria. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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3
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Badillo-Sanchez DA, Jones DJL, Inskip SA, Scheib CL. Human Archaeological Dentin as Source of Polar and Less Polar Metabolites for Untargeted Metabolomic Research: The Case of Yersinia pestis. Metabolites 2023; 13:588. [PMID: 37233629 PMCID: PMC10223108 DOI: 10.3390/metabo13050588] [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: 03/15/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 05/27/2023] Open
Abstract
Metabolomic approaches, such as in clinical applications of living individuals, have shown potential use for solving questions regarding the past when applied to archaeological material. Here, we study for the first time the potential of this Omic approach as applied to metabolites extracted from archaeological human dentin. Dentin obtained from micro sampling the dental pulp of teeth of victims and non-victims of Yersinia pestis (plague) from a 6th century Cambridgeshire site are used to evaluate the potential use of such unique material for untargeted metabolomic studies on disease state through liquid chromatography hyphenated to high-resolution mass spectrometry (LC-HRMS). Results show that small molecules of both likely endogenous and exogenous sources are preserved for a range of polar and less polar/apolar metabolites in archaeological dentin; however, untargeted metabolomic profiles show no clear differentiation between healthy and infected individuals in the small sample analysed (n = 20). This study discusses the potential of dentin as a source of small molecules for metabolomic assays and highlights: (1) the need for follow up research to optimise sampling protocols, (2) the requirements of studies with larger sample numbers and (3) the necessity of more databases to amplify the positive results achievable with this Omic technique in the archaeological sciences.
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Affiliation(s)
| | - Donald J L Jones
- Leicester Cancer Research Centre, RKCSB, University of Leicester, Leicester LE1 7RH, UK
- The Leicester van Geest MultiOmics Facility, University of Leicester, Leicester LE1 7RH, UK
| | - Sarah A Inskip
- School of Archaeology and Ancient History, University of Leicester, Leicester LE1 7RH, UK
| | - Christiana L Scheib
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge CB2 3ER, UK
- St. John's College, University of Cambridge, Cambridge CB2 1TP, UK
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4
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García-Gavilán J, Nishi S, Paz-Graniel I, Guasch-Ferré M, Razquin C, Clish CB, Toledo E, Ruiz-Canela M, Corella D, Deik A, Drouin-Chartier JP, Wittenbecher C, Babio N, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolite Profiles Associated with the Amount and Source of Meat and Fish Consumption and the Risk of Type 2 Diabetes. Mol Nutr Food Res 2022; 66:e2200145. [PMID: 36214069 PMCID: PMC9722604 DOI: 10.1002/mnfr.202200145] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/12/2022] [Indexed: 01/18/2023]
Abstract
SCOPE Consumption of meat has been associated with a higher risk of type 2 diabetes (T2D), but if plasma metabolite profiles associated with these foods reflect this relationship is unknown. The objective is to identify a metabolite signature of consumption of total meat (TM), red meat (RM), processed red meat (PRM), and fish and examine if they are associated with T2D risk. METHODS AND RESULTS The discovery population includes 1833 participants from the PREDIMED trial. The internal validation sample includes 1522 participants with available 1-year follow-up metabolomic data. Associations between metabolites and TM, RM, PRM, and fish are evaluated with elastic net regression. Associations between the profiles and incident T2D are estimated using Cox regressions. The profiles included 72 metabolites for TM, 69 for RM, 74 for PRM, and 66 for fish. After adjusting for T2D risk factors, only profiles of TM (Hazard Ratio (HR): 1.25, 95% CI: 1.06-1.49), RM (HR: 1.27, 95% CI: 1.07-1.52), and PRM (HR: 1.27, 95% CI: 1.07-1.51) are associated with T2D. CONCLUSIONS The consumption of TM, its subtypes, and fish is associated with different metabolites, some of which have been previously associated with T2D. Scores based on the identified metabolites for TM, RM, and PRM show a significant association with T2D risk.
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Affiliation(s)
- Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Stephanie Nishi
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, ON, Canada
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Cristina Razquin
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | | | - Estefanía Toledo
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Miguel Ruiz-Canela
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Dolores Corella
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Clemens Wittenbecher
- Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,German Center for Diabetes Research, Neuherberg, Germany
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ramon Estruch
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Lipid Clinic, Department of Endocrinology and Nutrition, Agust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA,Department of Statistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Miguel A Martínez-González
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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5
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Dai L, Massy ZA, Stenvinkel P, Chesnaye NC, Larabi IA, Alvarez JC, Caskey FJ, Torino C, Porto G, Szymczak M, Krajewska M, Drechsler C, Wanner C, Jager KJ, Dekker FW, Evenepoel P, Evans M, Torp A, Iwig B, Perras B, Marx C, Drechsler C, Blaser C, Wanner C, Emde C, Krieter D, Fuchs D, Irmler E, Platen E, Schmidt-Gürtler H, Schlee H, Naujoks H, Schlee I, Cäsar S, Beige J, Röthele J, Mazur J, Hahn K, Blouin K, Neumeier K, Anding-Rost K, Schramm L, Hopf M, Wuttke N, Frischmuth N, Ichtiaris P, Kirste P, Schulz P, Aign S, Biribauer S, Manan S, Röser S, Heidenreich S, Palm S, Schwedler S, Delrieux S, Renker S, Schättel S, Stephan T, Schmiedeke T, Weinreich T, Leimbach T, Stövesand T, Bahner U, Seeger W, Cupisti A, Sagliocca A, Ferraro A, Mele A, Naticchia A, Còsaro A, Ranghino A, Stucchi A, Pignataro A, De Blasio A, Pani A, Tsalouichos A, Antonio B, Iorio BRD, Alessandra B, Abaterusso C, Somma C, D'alessandro C, Torino C, Zullo C, Pozzi C, Bergamo D, Ciurlino D, Motta D, Russo D, Favaro E, Vigotti F, Ansali F, Conte F, Cianciotta F, Giacchino F, Cappellaio F, Pizzarelli F, Greco G, Porto G, Bigatti G, Marinangeli G, Cabiddu G, Fumagalli G, Caloro G, Piccoli G, Capasso G, Gambaro G, Tognarelli G, Bonforte G, Conte G, Toscano G, Del Rosso G, Capizzi I, Baragetti I, Oldrizzi L, Gesualdo L, Biancone L, Magnano M, Ricardi M, Bari MD, Laudato M, Sirico ML, Ferraresi M, Provenzano M, Malaguti M, Palmieri N, Murrone P, Cirillo P, Dattolo P, Acampora P, Nigro R, Boero R, Scarpioni R, Sicoli R, Malandra R, Savoldi S, Bertoli S, Borrelli S, Maxia S, Maffei S, Mangano S, Cicchetti T, Rappa T, Palazzo V, De Simone W, Schrander A, van Dam B, Siegert C, Gaillard C, Beerenhout C, Verburgh C, Janmaat C, Hoogeveen E, Hoorn E, Dekker F, Boots J, Boom H, Eijgenraam JW, Kooman J, Rotmans J, Jager K, Vogt L, Raasveld M, Vervloet M, van Buren M, van Diepen M, Chesnaye N, Leurs P, Voskamp P, van Esch S, Boorsma S, Berger S, Konings C, Aydin Z, Musiała A, Szymczak A, Olczyk E, Augustyniak-Bartosik H, Miśkowiec-Wiśniewska I, Manitius J, Pondel J, Jędrzejak K, Nowańska K, Nowak Ł, Szymczak M, Durlik M, Dorota S, Nieszporek T, Heleniak Z, Jonsson A, Rogland B, Wallquist C, Vargas D, Dimény E, Sundelin F, Uhlin F, Welander G, Hernandez IB, Gröntoft KC, Stendahl M, Svensson ME, Evans M, Heimburger O, Kashioulis P, Melander S, Almquist T, Woodman A, McKeever A, Ullah A, McLaren B, Harron C, Barrett C, O'Toole C, Summersgill C, Geddes C, Glowski D, McGlynn D, Sands D, Caskey F, Roy G, Hirst G, King H, McNally H, Masri-Senghor H, Murtagh H, Rayner H, Turner J, Wilcox J, Berdeprado J, Wong J, Banda J, Jones K, Haydock L, Wilkinson L, Carmody M, Weetman M, Joinson M, Dutton M, Matthews M, Morgan N, Bleakley N, Cockwell P, Roderick P, Mason P, Kalra P, Sajith R, Chapman S, Navjee S, Crosbie S, Brown S, Tickle S, Mathavakkannan S, Kuan Y. The association between TMAO, CMPF, and clinical outcomes in advanced chronic kidney disease: results from the European QUALity (EQUAL) Study. Am J Clin Nutr 2022; 116:1842-1851. [PMID: 36166845 PMCID: PMC9761748 DOI: 10.1093/ajcn/nqac278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/18/2022] [Accepted: 09/24/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Trimethylamine N-oxide (TMAO), a metabolite from red meat and fish consumption, plays a role in promoting cardiovascular events. However, data regarding TMAO and its impact on clinical outcomes are inconclusive, possibly due to its undetermined dietary source. OBJECTIVES We hypothesized that circulating TMAO derived from fish intake might cause less harm compared with red meat sources by examining the concomitant level of 3-carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF), a known biomarker of fish intake, and investigated the association between TMAO, CMPF, and outcomes. METHODS Patients were recruited from the European QUALity (EQUAL) Study on treatment in advanced chronic kidney disease among individuals aged ≥65 y whose estimated glomerular filtration rate (eGFR) had dropped for the first time to ≤20 mL/min per 1.73 m2 during the last 6 mo. The association between TMAO, CMPF, and outcomes including all-cause mortality and kidney replacement therapy (KRT) was assessed among 737 patients. Patients were further stratified by median cutoffs of TMAO and CMPF, suggesting high/low red meat and fish intake. RESULTS During a median of 39 mo of follow-up, 232 patients died. Higher TMAO was independently associated with an increased risk of all-cause mortality (multivariable HR: 1.46; 95% CI: 1.17, 1.83). Higher CMPF was associated with a reduced risk of both all-cause mortality (HR: 0.79; 95% CI: 0.71, 0.89) and KRT (HR: 0.80; 95% CI: 0.71, 0.90), independently of TMAO and other clinically relevant confounders. In comparison to patients with low TMAO and CMPF, patients with low TMAO and high CMPF had reduced risk of all-cause mortality (adjusted HR: 0.49; 95% CI: 0.31, 0.73), whereas those with high TMAO and high CMPF showed no association across adjusted models. CONCLUSIONS High CMPF conferred an independent role in health benefits and might even counteract the unfavorable association between TMAO and outcomes. Whether higher circulating CMPF concentrations are due to fish consumption, and/or if CMPF is a protective factor, remains to be verified.
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Affiliation(s)
- Lu Dai
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden,Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Ziad A Massy
- Division of Nephrology, Ambroise Paré University Hospital, Boulogne-Billancourt, France,Centre for Research in Epidemiology and Population Health (CESP), Inserm UMRS 1018, Team 5, University Versailles-Saint Quentin, University Paris-Saclay, Paris, France
| | - Peter Stenvinkel
- Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Nicholas C Chesnaye
- ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Islam Amine Larabi
- Laboratory of Pharmacology and Toxicology, CHU, Raymond Poincare, Garches, France,INSERM U1173, UFR des Sciences de la Santé Simone Veil, Montigny le Bretonneux, Université de Versailles-Saint-Quentin-en-Yvelines, Versailles, France
| | - Jean Claude Alvarez
- Laboratory of Pharmacology and Toxicology, CHU, Raymond Poincare, Garches, France,INSERM U1173, UFR des Sciences de la Santé Simone Veil, Montigny le Bretonneux, Université de Versailles-Saint-Quentin-en-Yvelines, Versailles, France
| | - Fergus J Caskey
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Claudia Torino
- IFC-CNR, Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Reggio Calabria, Italy
| | - Gaetana Porto
- G.O.M., Bianchi Melacrino Morelli, Reggio Calabria, Italy
| | - Maciej Szymczak
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Magdalena Krajewska
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | | | - Christoph Wanner
- Division of Nephrology, University Hospital of Würzburg, Würzburg, Germany
| | - Kitty J Jager
- ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Friedo W Dekker
- ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Pieter Evenepoel
- Department of Microbiology, Immunology, and Transplantation, Nephrology and Renal Transplantation Research Group, KU Leuven, Leuven, Belgium,Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium
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6
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Koeken VACM, Qi C, Mourits VP, de Bree LCJ, Moorlag SJCFM, Sonawane V, Lemmers H, Dijkstra H, Joosten LAB, van Laarhoven A, Xu CJ, van Crevel R, Netea MG, Li Y. Plasma metabolome predicts trained immunity responses after antituberculosis BCG vaccination. PLoS Biol 2022; 20:e3001765. [PMID: 36094960 PMCID: PMC9499240 DOI: 10.1371/journal.pbio.3001765] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 09/22/2022] [Accepted: 07/27/2022] [Indexed: 11/30/2022] Open
Abstract
The antituberculosis vaccine Bacillus Calmette–Guérin (BCG) induces nonspecific protection against heterologous infections, at least partly through induction of innate immune memory (trained immunity). The amplitude of the response to BCG is variable, but the factors that influence this response are poorly understood. Metabolites, either released by cells or absorbed from the gut, are known to influence immune responses, but whether they impact BCG responses is not known. We vaccinated 325 healthy individuals with BCG, and collected blood before, 2 weeks and 3 months after vaccination, to assess the influence of circulating metabolites on the immune responses induced by BCG. Circulating metabolite concentrations after BCG vaccination were found to have a more pronounced impact on trained immunity responses, such as the increase in IL-1β and TNF-α production upon Staphylococcus aureus stimulation, than on specific adaptive immune memory, assessed as IFN-γ production in response to Mycobacterium tuberculosis. Circulating metabolites at baseline were able to predict trained immunity responses at 3 months after vaccination and enrichment analysis based on the metabolites positively associated with trained immunity revealed enrichment of the tricarboxylic acid (TCA) cycle and glutamine metabolism, both of which were previously found to be important for trained immunity. Several new metabolic pathways that influence trained immunity were identified, among which taurine metabolism associated with BCG-induced trained immunity, a finding validated in functional experiments. In conclusion, circulating metabolites are important factors influencing BCG-induced trained immunity in humans. Modulation of metabolic pathways may be a novel strategy to improve vaccine and trained immunity responses. The response to the BCG vaccine, which provides protection against tuberculosis as well as unrelated pathogens, is variable. This study shows that the baseline plasma metabolome is able to partially predict trained immunity upon BCG vaccination, identifying the metabolome as a potential source of this heterogeneity.
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Affiliation(s)
- Valerie A. C. M. Koeken
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Cancan Qi
- Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Vera P. Mourits
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - L. Charlotte J. de Bree
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Simone J. C. F. M. Moorlag
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Vidhisha Sonawane
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Heidi Lemmers
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Helga Dijkstra
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Leo A. B. Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Arjan van Laarhoven
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Cheng-Jian Xu
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Reinout van Crevel
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Yang Li
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- TWINCORE, a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
- * E-mail:
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Cui J, Sachaphibulkij K, Teo WS, Lim HM, Zou L, Ong CN, Alberts R, Chen J, Lim LHK. Annexin-A1 deficiency attenuates stress-induced tumor growth via fatty acid metabolism in mice: an Integrated multiple omics analysis on the stress- microbiome-metabolite-epigenetic-oncology (SMMEO) axis. Theranostics 2022; 12:3794-3817. [PMID: 35664067 PMCID: PMC9131274 DOI: 10.7150/thno.68611] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/28/2022] [Indexed: 11/22/2022] Open
Abstract
Background: High emotional or psychophysical stress levels have been correlated with an increased risk and progression of various diseases. How stress impacts the gut microbiota to influence metabolism and subsequent cancer progression is unclear. Methods: Feces and serum samples from BALB/c ANXA1+/+ and ANXA1-/- mice with or without chronic restraint stress were used for 16S rRNA gene sequencing and GC-MS metabolomics analysis to investigate the effect of stress on microbiome and metabolomics during stress and breast tumorigenesis. Breast tumors samples from stressed and non-stressed mice were used to perform Whole-Genome Bisulfite Sequencing (WGBS) and RNAseq analysis to construct the potential network from candidate hub genes. Finally, machine learning and integrated analysis were used to map the axis from chronic restraint stress to breast cancer development. Results: We report that chronic stress promotes breast tumor growth via a stress-microbiome-metabolite-epigenetic-oncology (SMMEO) axis. Chronic restraint stress in mice alters the microbiome composition and fatty acids metabolism and induces an epigenetic signature in tumors xenografted after stress. Subsequent machine learning and systemic modeling analyses identified a significant correlation among microbiome composition, metabolites, and differentially methylated regions in stressed tumors. Moreover, silencing Annexin-A1 inhibits the changes in the gut microbiome and fatty acid metabolism after stress as well as basal and stress-induced tumor growth. Conclusions: These data support a physiological axis linking the microbiome and metabolites to cancer epigenetics and inflammation. The identification of this axis could propel the next phase of experimental discovery in further understanding the underlying molecular mechanism of tumorigenesis caused by physiological stress.
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Affiliation(s)
- Jianzhou Cui
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore
- NUS Immunology Program, Life Sciences Institute, NUS, Singapore
| | - Karishma Sachaphibulkij
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore
- NUS Immunology Program, Life Sciences Institute, NUS, Singapore
| | - Wen Shiun Teo
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore
- NUS Immunology Program, Life Sciences Institute, NUS, Singapore
| | - Hong Meng Lim
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore
- NUS Immunology Program, Life Sciences Institute, NUS, Singapore
| | - Li Zou
- Saw Swee Hock School of Public Health, NUS, Singapore
| | - Choon Nam Ong
- Saw Swee Hock School of Public Health, NUS, Singapore
- NUS Environmental Research Institute, NUS, Singapore
| | - Rudi Alberts
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, NUS, Singapore
| | - Jinmiao Chen
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Lina H. K. Lim
- Immunology Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore
- NUS Immunology Program, Life Sciences Institute, NUS, Singapore
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Plasma Metabolite Profiles Following Consumption of Animal Protein and Soybean-Based Diet in Hypercholesterolemic Postmenopausal Women. Metabolites 2022; 12:metabo12030209. [PMID: 35323651 PMCID: PMC8952012 DOI: 10.3390/metabo12030209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/09/2022] [Accepted: 02/23/2022] [Indexed: 02/05/2023] Open
Abstract
Subjective reporting of food intake can be unreliable. No objective method is available to distinguish between diets differing in protein type. To address this gap, a secondary analysis of a randomized controlled cross-over feeding trial was conducted. Assessed were fasting plasma metabolite profiles and their associations with cardiometabolic risk factors (CMRFs). Hypercholesterolemic post-menopausal women (N = 11) were provided with diets containing predominantly animal protein (AP) and soy protein (SP). Untargeted metabolomics were used to determine the plasma metabolite profiles at the end of each diet phase. Concentrations of identified metabolites (N = 829) were compared using paired t-tests adjusted for false discovery rate, partial least square-discrimination analysis (PLS-DA) and receiver operating characteristics (ROC). Among the identified metabolites, 58 differed significantly between the AP and SP diets; the majority were phospholipids (n = 36), then amino acids (n = 10), xenobiotics (n = 7), vitamin/vitamin-related (n = 3) and lipids (n = 2). Of the top 10 metabolites, amino acid-derived metabolites, phospholipids and xenobiotics comprised the main categories differing due to dietary protein type. ROC curves confirmed that the top 10 metabolites were potential discriminating biomarkers for AP- and SP-rich diets. In conclusion, amino acid-derived metabolites, phosphatidylethanolamine-derived metabolites and isoflavones were identified as potential metabolite biomarkers distinguishing between dietary protein type.
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Plasma Metabolite Profiles of Red Meat, Poultry, and Fish Consumption, and Their Associations with Colorectal Cancer Risk. Nutrients 2022; 14:nu14050978. [PMID: 35267954 PMCID: PMC8912563 DOI: 10.3390/nu14050978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Red and processed meat consumption has been consistently associated with increased risk of colorectal cancer (CRC), but the association for fish intake is unclear. Evidence using objective dietary assessment approaches to evaluate these associations is sparse. OBJECTIVES We aim to investigate the plasma metabolite profiles related to red meat, poultry, and fish consumption and examine their associations with CRC risk. METHODS We measured plasma metabolites among 5269 participants from the Nurses' Health Study (NHS), NHSII, and Health Professionals Follow-Up study (HPFS). We calculated partial Spearman correlations between each metabolite and self-reported intake of seven red meat, poultry, and fish groups. Metabolite profile scores correlated to self-reported dietary intakes were developed using elastic net regression. Associations between self-reported intakes, metabolite profile scores, and subsequent CRC risk were further evaluated using conditional logistic regression among 559 matched (1:1) case-control pairs in NHS/HPFS and replicated among 266 pairs in Women's Health Study. RESULTS Plasma metabolites, especially highly unsaturated lipids, were differentially associated with red meat and fish groups. Metabolite profile scores for each food group were significantly correlated with the corresponding self-reported dietary intake. A higher dietary intake of processed red meat was associated with a higher risk of CRC (pooled OR per 1 SD, 1.15; 95% CI: 1.03, 1.29). In contrast, higher metabolite profile scores for all fish groups, not dietary intakes, were consistently associated with a lower CRC risk: the pooled OR per 1 SD was 0.86 (95% CI: 0.78, 0.96) for total fish, 0.86 (95% CI: 0.77, 0.96) for dark meat fish, and 0.87 (95% CI: 0.78, 0.97) for canned tuna fish. No significant associations were found for other food groups. CONCLUSIONS Red meat and fish intake exhibited systematically different plasma metabolite profiles. Plasma metabolite profile of fish intake was inversely associated with CRC risk.
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Amino Acids and Lipids Associated with Long-Term and Short-Term Red Meat Consumption in the Chinese Population: An Untargeted Metabolomics Study. Nutrients 2021; 13:nu13124567. [PMID: 34960119 PMCID: PMC8709332 DOI: 10.3390/nu13124567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/12/2021] [Accepted: 12/16/2021] [Indexed: 12/30/2022] Open
Abstract
Red meat (RM) consumption is correlated with multiple health outcomes. This study aims to identify potential biomarkers of RM consumption in the Chinese population and evaluate their predictive ability. We selected 500 adults who participated in the 2015 China Health and Nutrition Survey and examined their overall metabolome differences by RM consumption by using elastic-net regression, then evaluate the predictivity of a combination of filtered metabolites; 1108 metabolites were detected. In the long-term RM consumption analysis 12,13-DiHOME, androstenediol (3α, 17α) monosulfate 2, and gamma-Glutamyl-2-aminobutyrate were positively associated, 2-naphthol sulfate and S-methylcysteine were negatively associated with long-term high RM consumption, the combination of metabolites prediction model evaluated by area under the receiver operating characteristic curve (AUC) was 70.4% (95% CI: 59.9–80.9%). In the short-term RM consumption analysis, asparagine, 4-hydroxyproline, and 3-hydroxyisobutyrate were positively associated, behenoyl sphingomyelin (d18:1/22:0) was negatively associated with short-term high RM consumption. Combination prediction model AUC was 75.6% (95% CI: 65.5–85.6%). We identified 10 and 11 serum metabolites that differed according to LT and ST RM consumption which mainly involved branch-chained amino acids, arginine and proline, urea cycle and polyunsaturated fatty acid metabolism. These metabolites may become a mediator of some chronic diseases among high RM consumers and provide new evidence for RM biomarkers.
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Gao P, Huang X, Fang XY, Zheng H, Cai SL, Sun AJ, Zhao L, Zhang Y. Application of metabolomics in clinical and laboratory gastrointestinal oncology. World J Gastrointest Oncol 2021; 13:536-549. [PMID: 34163571 PMCID: PMC8204353 DOI: 10.4251/wjgo.v13.i6.536] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/09/2021] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.
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Affiliation(s)
- Peng Gao
- Department ofClinical Laboratory, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Xin Huang
- Department of Internal Medicine, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Xue-Yan Fang
- Department of Nursing, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Hui Zheng
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Shu-Ling Cai
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Ai-Jun Sun
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Liang Zhao
- Department of Internal Medicine, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Yong Zhang
- Department of Surgery, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
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Schulz CA, Oluwagbemigun K, Nöthlings U. Advances in dietary pattern analysis in nutritional epidemiology. Eur J Nutr 2021; 60:4115-4130. [PMID: 33899149 PMCID: PMC8572214 DOI: 10.1007/s00394-021-02545-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Background and Purpose It used to be a common practice in the field of nutritional epidemiology to analyze separate nutrients, foods, or food groups. However, in reality, nutrients and foods are consumed in combination. The introduction of dietary patterns (DP) and their analysis has revolutionized this field, making it possible to take into account the synergistic effects of foods and to account for the complex interaction among nutrients and foods. Three approaches of DP analysis exist: (1) the hypothesis-based approach (based on prior knowledge regarding the current understanding of dietary components and their health relation), (2) the exploratory approach (solely relying on dietary intake data), and (3) the hybrid approach (a combination of both approaches). During the recent past, complementary approaches for DP analysis have emerged both conceptually and methodologically. Method We have summarized the recent developments that include incorporating the Treelet transformation method as a complementary exploratory approach in a narrative review. Results Uses, peculiarities, strengths, limitations, and scope of recent developments in DP analysis are outlined. Next, the narrative review gives an overview of the literature that takes into account potential relevant dietary-related factors, specifically the metabolome and the gut microbiome in DP analysis. Then the review deals with the aspect of data processing that is needed prior to DP analysis, particularly when dietary data arise from assessment methods other than the long-established food frequency questionnaire. Lastly, potential opportunities for upcoming DP analysis are summarized in the outlook.
Conclusion Biological factors like the metabolome and the microbiome are crucial to understand diet-disease relationships. Therefore, the inclusion of these factors in DP analysis might provide deeper insights.
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Affiliation(s)
- Christina-Alexandra Schulz
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Endenicher Allee 19b, 53115, Bonn, Germany
| | - Kolade Oluwagbemigun
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Endenicher Allee 19b, 53115, Bonn, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Endenicher Allee 19b, 53115, Bonn, Germany.
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Shibutami E, Ishii R, Harada S, Kurihara A, Kuwabara K, Kato S, Iida M, Akiyama M, Sugiyama D, Hirayama A, Sato A, Amano K, Sugimoto M, Soga T, Tomita M, Takebayashi T. Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan. PLoS One 2021; 16:e0246456. [PMID: 33566801 PMCID: PMC7875413 DOI: 10.1371/journal.pone.0246456] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/19/2021] [Indexed: 11/18/2022] Open
Abstract
Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.
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Affiliation(s)
- Eriko Shibutami
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
| | - Ryota Ishii
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Miki Akiyama
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Daisuke Sugiyama
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Faculty of Nursing and Medical Care, Keio University, Fujisawa, Kanagawa, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kaori Amano
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Toru Takebayashi
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- * E-mail:
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Xu L, Cai J, Gao T, Ma A. Shellfish consumption and health: A comprehensive review of human studies and recommendations for enhanced public policy. Crit Rev Food Sci Nutr 2021; 62:4656-4668. [PMID: 33527847 DOI: 10.1080/10408398.2021.1878098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Shellfish, including various species of mollusks (e.g., clams, oysters, and mussels) and crustaceans (e.g., shrimp and crab), have been a cornerstone of healthy dietary recommendations. However, beyond providing basic nutrition needs, their health-promoting effects have been suggested to include inflammation reduction and prevention of various chronic non-communicable diseases. Currently, studies on the association between shellfish consumption and health outcomes have reported conflicting results. The present comprehensive review summarized the latest studies on shellfish consumption and synthesized the available evidence on the potential health benefits or risks of shellfish consumption. The findings demonstrated that shellfish consumption may increase the risk of hyperuricemia and gout but may not increase the risk of type 2 diabetes, cardiovascular diseases, and thyroid cancer. Adequate evidence is lacking on the association between shellfish consumption and the risk of colorectal cancer, pancreatic cancer, oral cancer, endometriosis, hip fracture, cognitive function, wheeze, eczema and food allergy. Raw shellfish consumption may cause gastroenteritis and other diseases infected by bacteria or viruses. This review thus provides consumers and other relevant stakeholders with the latest evidence-based information on the potential benefits and risks of shellfish consumption.
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Affiliation(s)
- Lei Xu
- Institute of Nutrition and Health, School of Public Health, Qingdao University, Qngdao, China
| | - Jing Cai
- Institute of Nutrition and Health, School of Public Health, Qingdao University, Qngdao, China
| | - Tianlin Gao
- Institute of Nutrition and Health, School of Public Health, Qingdao University, Qngdao, China
| | - Aiguo Ma
- Institute of Nutrition and Health, School of Public Health, Qingdao University, Qngdao, China
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Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116161] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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16
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Gunther SH, Khoo CM, Sim X, Tai ES, van Dam RM. Diet, Physical Activity and Adiposity as Determinants of Circulating Amino Acid Levels in a Multiethnic Asian Population. Nutrients 2020; 12:nu12092603. [PMID: 32867058 PMCID: PMC7551953 DOI: 10.3390/nu12092603] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/23/2020] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
Abstract
Profiles of circulating amino acids have been associated with cardiometabolic diseases. We investigated the associations between dietary protein intake, physical activity and adiposity and serum amino acid profiles in an Asian population. We used data from 3009 male and female participants from the Singapore Prospective Study Program cohort. Dietary and physical activity data were obtained from validated questionnaires; anthropometric measurements were collected during a health examination; and fasting concentrations of 16 amino acids were measured using targeted LC-MS. The association between lifestyle factors and amino acid levels was modeled using multiple linear regression with adjustment for other sociodemographic and lifestyle factors and correction for multiple testing. We observed significant associations between seafood intake (β-coefficient 0.132, 95% CI 0.006, 0.257 for a 100% increment), physical activity (β-coefficient −0.096, 95% CI −0.183, −0.008 in the highest versus lowest quartile) and adiposity (BMI β-coefficient 0.062, 95% CI 0.054, 0.070 per kg/m2; waist circumference β-coefficient 0.034, 95% CI 0.031, 0.037 per cm) and branched-chain amino acid levels (expressed per-SD). We also observed significant interactions with sex for the association between meat and seafood and total intakes and BCAA levels (P for interaction 0.007), which were stronger in females than in males. Our findings suggest novel associations between modifiable lifestyle factors and amino acid levels in Asian populations.
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Affiliation(s)
- Samuel H. Gunther
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; (X.S.); (ES.T.); (R.M.v.D.)
- Correspondence: ; Tel.: +65-8661-0319
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University Health System, Singapore 119228, Singapore;
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; (X.S.); (ES.T.); (R.M.v.D.)
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; (X.S.); (ES.T.); (R.M.v.D.)
- Department of Medicine, Yong Loo Lin School of Medicine, National University Health System, Singapore 119228, Singapore;
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; (X.S.); (ES.T.); (R.M.v.D.)
- Department of Medicine, Yong Loo Lin School of Medicine, National University Health System, Singapore 119228, Singapore;
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
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17
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Li J, Guasch-Ferré M, Chung W, Ruiz-Canela M, Toledo E, Corella D, Bhupathiraju SN, Tobias DK, Tabung FK, Hu J, Zhao T, Turman C, Feng YCA, Clish CB, Mucci L, Eliassen AH, Costenbader KH, Karlson EW, Wolpin BM, Ascherio A, Rimm EB, Manson JE, Qi L, Martínez-González MÁ, Salas-Salvadó J, Hu FB, Liang L. The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur Heart J 2020; 41:2645-2656. [PMID: 32406924 PMCID: PMC7377580 DOI: 10.1093/eurheartj/ehaa209] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/10/2020] [Accepted: 03/18/2020] [Indexed: 12/16/2022] Open
Abstract
AIMS To investigate whether metabolic signature composed of multiple plasma metabolites can be used to characterize adherence and metabolic response to the Mediterranean diet and whether such a metabolic signature is associated with cardiovascular disease (CVD) risk. METHODS AND RESULTS Our primary study cohort included 1859 participants from the Spanish PREDIMED trial, and validation cohorts included 6868 participants from the US Nurses' Health Studies I and II, and Health Professionals Follow-up Study (NHS/HPFS). Adherence to the Mediterranean diet was assessed using a validated Mediterranean Diet Adherence Screener (MEDAS), and plasma metabolome was profiled by liquid chromatography-tandem mass spectrometry. We observed substantial metabolomic variation with respect to Mediterranean diet adherence, with nearly one-third of the assayed metabolites significantly associated with MEDAS (false discovery rate < 0.05). Using elastic net regularized regressions, we identified a metabolic signature, comprised of 67 metabolites, robustly correlated with Mediterranean diet adherence in both PREDIMED and NHS/HPFS (r = 0.28-0.37 between the signature and MEDAS; P = 3 × 10-35 to 4 × 10-118). In multivariable Cox regressions, the metabolic signature showed a significant inverse association with CVD incidence after adjusting for known risk factors (PREDIMED: hazard ratio [HR] per standard deviation increment in the signature = 0.71, P < 0.001; NHS/HPFS: HR = 0.85, P = 0.001), and the association persisted after further adjustment for MEDAS scores (PREDIMED: HR = 0.73, P = 0.004; NHS/HPFS: HR = 0.85, P = 0.004). Further genome-wide association analysis revealed that the metabolic signature was significantly associated with genetic loci involved in fatty acids and amino acids metabolism. Mendelian randomization analyses showed that the genetically inferred metabolic signature was significantly associated with risk of coronary heart disease (CHD) and stroke (odds ratios per SD increment in the genetically inferred metabolic signature = 0.92 for CHD and 0.91 for stroke; P < 0.001). CONCLUSIONS We identified a metabolic signature that robustly reflects adherence and metabolic response to a Mediterranean diet, and predicts future CVD risk independent of traditional risk factors, in Spanish and US cohorts.
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Affiliation(s)
- Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Wonil Chung
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 4th Floor, Boston, MA 02115, USA
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Irunlarrea 1, Pamplona 31008, Spain
- IdiSNA (Instituto de Investigación Sanitaria de Navarra), Edificio LUNA-Navarrabiomed, Irunlarrea 3, Pamplona 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, Pabellón 11, Madrid 28029, Spain
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Irunlarrea 1, Pamplona 31008, Spain
- IdiSNA (Instituto de Investigación Sanitaria de Navarra), Edificio LUNA-Navarrabiomed, Irunlarrea 3, Pamplona 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, Pabellón 11, Madrid 28029, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, Pabellón 11, Madrid 28029, Spain
- Department of Preventive Medicine, University of Valencia, Valencia 46010, Spain
| | - Shilpa N Bhupathiraju
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 900 Commonwealth Avenue, Boston, MA 02115, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine and Comprehensive Cancer Center – James Cancer Hospital and Solove Research Institute, 410 W 12th Ave Columbus, OH 43210, USA
| | - Jie Hu
- Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 1620 Tremont St, 3rd floor, Boston, MA 02120, USA
| | - Tong Zhao
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 4th Floor, Boston, MA 02115, USA
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
| | - Yen-Chen Anne Feng
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Metabolomics Platform,Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA
| | - Clary B Clish
- Metabolomics Platform,Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
| | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 900 Commonwealth Avenue, Boston, MA 02115, USA
- Mary Horrigan Connors Center for Women’s Health and Gender Biology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Lu Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, New Orleans, LA 70112, USA
| | - Miguel Ángel Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Department of Preventive Medicine and Public Health, University of Navarra, Irunlarrea 1, Pamplona 31008, Spain
- IdiSNA (Instituto de Investigación Sanitaria de Navarra), Edificio LUNA-Navarrabiomed, Irunlarrea 3, Pamplona 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, Pabellón 11, Madrid 28029, Spain
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, Pabellón 11, Madrid 28029, Spain
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C/Sant Llorenç 21, Reus 43201, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 3rd Floor, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 2nd Floor, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building II 4th Floor, Boston, MA 02115, USA
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18
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McGee EE, Kiblawi R, Playdon MC, Eliassen AH. Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions. Curr Nutr Rep 2020; 8:187-201. [PMID: 31129888 DOI: 10.1007/s13668-019-00279-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer. RECENT FINDINGS Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.
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Affiliation(s)
- Emma E McGee
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Rama Kiblawi
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Mary C Playdon
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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19
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Oluwagbemigun K, Foerster J, Watkins C, Fouhy F, Stanton C, Bergmann MM, Boeing H, Nöthlings U. Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults. J Nutr 2020; 150:149-158. [PMID: 31504715 PMCID: PMC6946898 DOI: 10.1093/jn/nxz194] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 04/18/2019] [Accepted: 07/24/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored. OBJECTIVES We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs). METHODS This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R. RESULTS We identified 5 DPs and 7 BCPs. The "bread, margarine, and processed meat" and "fruiting vegetables and vegetable oils" DPs were positively associated with the "amino acids" (β = 0.35; 95% CI: 0.02, 0.69; P = 0.03) and "fatty acids" MPs (β = 0.45; 95% CI: 0.16, 0.74; P = 0.01), respectively. The "tea and miscellaneous" was inversely associated with the "amino acids" (β = -0.28; 95% CI: -0.52, -0.05; P = 0.02) and "amino acid derivatives" MPs (β = -0.21; 95% CI: -0.39, -0.02; P = 0.03). One BCP negatively modified the association between the "bread, margarine, and processed meat" DP and the "amino acids" MP (P-interaction = 0.01). CONCLUSIONS In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP-MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.
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Affiliation(s)
- Kolade Oluwagbemigun
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany
| | - Jana Foerster
- Center for Population and Health eV, Wiesbaden, Germany
| | - Claire Watkins
- APC Microbiome Ireland, Teagasc/University College Cork, Cork, Ireland
- Teagasc Moorepark Food Research Centre, Fermoy, Ireland
| | - Fiona Fouhy
- Teagasc Moorepark Food Research Centre, Fermoy, Ireland
| | - Catherine Stanton
- APC Microbiome Ireland, Teagasc/University College Cork, Cork, Ireland
- Teagasc Moorepark Food Research Centre, Fermoy, Ireland
| | - Manuela M Bergmann
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam–Rehbrüke, Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam–Rehbrüke, Nuthetal, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany
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20
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Cuparencu C, Praticó G, Hemeryck LY, Sri Harsha PSC, Noerman S, Rombouts C, Xi M, Vanhaecke L, Hanhineva K, Brennan L, Dragsted LO. Biomarkers of meat and seafood intake: an extensive literature review. GENES & NUTRITION 2019; 14:35. [PMID: 31908682 PMCID: PMC6937850 DOI: 10.1186/s12263-019-0656-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/12/2019] [Indexed: 01/16/2023]
Abstract
Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases. The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.
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Affiliation(s)
- Cătălina Cuparencu
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
| | - Giulia Praticó
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
| | - Lieselot Y. Hemeryck
- Department of Veterinary Public Health & Food Safety, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Pedapati S. C. Sri Harsha
- School of Agriculture and Food Science, Institute of Food & Health, University College Dublin, Belfield 4, Dublin, Ireland
| | - Stefania Noerman
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland
| | - Caroline Rombouts
- Department of Veterinary Public Health & Food Safety, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Muyao Xi
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
| | - Lynn Vanhaecke
- Department of Veterinary Public Health & Food Safety, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food & Health, University College Dublin, Belfield 4, Dublin, Ireland
| | - Lars O. Dragsted
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
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21
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Shi L, Brunius C, Bergdahl IA, Johansson I, Rolandsson O, Donat Vargas C, Kiviranta H, Hanhineva K, Åkesson A, Landberg R. Joint Analysis of Metabolite Markers of Fish Intake and Persistent Organic Pollutants in Relation to Type 2 Diabetes Risk in Swedish Adults. J Nutr 2019; 149:1413-1423. [PMID: 31209490 DOI: 10.1093/jn/nxz068] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 02/13/2019] [Accepted: 03/20/2019] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND There is conflicting evidence regarding the association between fish intake and type 2 diabetes (T2D) incidence, possibly owing to measurement errors in self-reported intake and coexposure to persistent organic pollutants (POPs) present in fish. OBJECTIVE The aim of this study was to identify plasma metabolites associated with fish intake and to assess their association with T2D risk, independently of POPs, in Swedish adults. METHODS In a case-control study nested in the Swedish Västerbotten Intervention Programme, fasting plasma samples from 421 matched T2D case-control pairs of men and women aged 30-60 y at baseline and 10-y follow-up samples from a subset of 149 pairs were analyzed using untargeted metabolomics. Moreover, 16 plasma POPs were analyzed for the 149 pairs who had repeated samples available. Fish-related plasma metabolites were identified using multivariate modelling and partial correlation analysis. Reproducibility of metabolites and metabolite patterns, derived via principal component analysis (PCA), was assessed by intraclass correlation. A unique component of metabolites unrelated to POPs was dissected by integrating metabolites and POPs using 2-way orthogonal partial least squares regression. ORs of T2D were estimated using conditional logistic regression. RESULTS We identified 31 metabolites associated with fish intake that had poor to good reproducibility. A PCA-derived metabolite pattern strongly correlated with fish intake (ρ = 0.37, P < 0.001) but showed no association with T2D risk. Integrating fish-related metabolites and POPs led to a unique metabolite component independent of POPs, which tended to be inversely associated with T2D risk (OR: 0.75; 95% CI: 0.54, 1.02, P = 0.07). This component mainly consisted of metabolites reflecting fatty fish intake. CONCLUSIONS Our results suggest that fatty fish intake may be beneficial for T2D prevention, after removing the counteractive effects of coexposure to POPs in Swedish adults. Integrating metabolite markers and POP exposures appears a promising approach to advance the understanding of associations between fish intake and T2D incidence.
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Affiliation(s)
- Lin Shi
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Carl Brunius
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ingvar A Bergdahl
- Department of Biobank Research.,Department of Public Health and Clinical Medicine
| | | | | | | | - Hannu Kiviranta
- Environmental Health Unit, National Institute for Health and Welfare, Kuopio, Finland
| | - Kati Hanhineva
- LC-MS Metabolomics Center, Kuopio, Finland.,Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Agneta Åkesson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Department of Public Health and Clinical Medicine
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22
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Lim SL, Ng CT, Zou L, Lu Y, Chen J, Bay BH, Shen HM, Ong CN. Targeted metabolomics reveals differential biological effects of nanoplastics and nanoZnO in human lung cells. Nanotoxicology 2019; 13:1117-1132. [DOI: 10.1080/17435390.2019.1640913] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Swee Ling Lim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Cheng Teng Ng
- NUS Environmental Research Institute, National University of Singapore, Singapore, Singapore
| | - Li Zou
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Yonghai Lu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jiaqing Chen
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Boon Huat Bay
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han-Ming Shen
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Choon Nam Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- NUS Environmental Research Institute, National University of Singapore, Singapore, Singapore
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