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Yan S, Li L, Horner D, Ebrahimi P, Chawes B, Dragsted LO, Rasmussen MA, Smilde AK, Acar E. Characterizing human postprandial metabolic response using multiway data analysis. Metabolomics 2024; 20:50. [PMID: 38722393 PMCID: PMC11082008 DOI: 10.1007/s11306-024-02109-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: 10/03/2023] [Accepted: 03/19/2024] [Indexed: 05/12/2024]
Abstract
INTRODUCTION Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.
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Affiliation(s)
- Shi Yan
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Lu Li
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - David Horner
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Parvaneh Ebrahimi
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Food Science, University of Copenhagen, Copenhagen, Denmark
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Lars O Dragsted
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Morten A Rasmussen
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Food Science, University of Copenhagen, Copenhagen, Denmark
| | - Age K Smilde
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Evrim Acar
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
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Makhnovskii PA, Lednev EM, Gavrilova AO, Kurochkina NS, Vepkhvadze TF, Shestakova MV, Popov DV. Dysregulation of early gene response to a mixed meal in skeletal muscle in obesity and type 2 diabetes. Physiol Genomics 2023; 55:468-477. [PMID: 37545425 DOI: 10.1152/physiolgenomics.00046.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/10/2023] [Accepted: 07/30/2023] [Indexed: 08/08/2023] Open
Abstract
Obesity- and type 2 diabetes mellitus-induced changes in the expression of protein-coding genes in human skeletal muscle were extensively examined at baseline (after an overnight fast). We aimed to compare the early transcriptomic response to a typical single meal in skeletal muscle of metabolically healthy subjects and obese individuals without and with type 2 diabetes. Transcriptomic response (RNA-seq) to a mixed meal (nutritional drink, ∼25 kJ/kg of body mass) was examined in the vastus lateralis muscle (1 h after a meal) in 7 healthy subjects and 14 obese individuals without or with type 2 diabetes. In all obese individuals, the transcriptome response to a meal was dysregulated (suppressed and altered) and associated with different biological processes compared with healthy control. To search for potential transcription factors regulating transcriptomic response to a meal, the enrichment of transcription factor-binding sites in individual promoters of the human skeletal muscle was examined. In obese individuals, the transcriptomic response is associated with a different set of transcription factors than that in healthy subjects. In conclusion, metabolic disorders are associated with a defect in the regulation of mixed meal/insulin-mediated gene expression-insulin resistance in terms of gene expression. Importantly, this dysregulation occurs in obese individuals without type 2 diabetes, i.e., at the first stage of the development of metabolic disorders.NEW & NOTEWORTHY In skeletal muscle of metabolically healthy subjects, a typical single meal normalized to body mass induces activation of various transcription factors, expression of numerous receptor tyrosine kinases associated with the insulin signaling cascade, and transcription regulators. In skeletal muscle of obese individuals without and with type 2 diabetes, this signaling network is poorly regulated at the transcriptional level, indicating dysregulation of the early gene response to a mixed meal.
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Affiliation(s)
- Pavel A Makhnovskii
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | - Egor M Lednev
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
- Diabetes Institute, National Medical Research Centre for Endocrinology, Moscow, Russia
| | - Alina O Gavrilova
- Diabetes Institute, National Medical Research Centre for Endocrinology, Moscow, Russia
| | - Nadia S Kurochkina
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | - Tatiana F Vepkhvadze
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
- Faculty of Fundamental Medicine, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Marina V Shestakova
- Diabetes Institute, National Medical Research Centre for Endocrinology, Moscow, Russia
| | - Daniil V Popov
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
- Faculty of Fundamental Medicine, M.V. Lomonosov Moscow State University, Moscow, Russia
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Weinisch P, Fiamoncini J, Schranner D, Raffler J, Skurk T, Rist MJ, Römisch-Margl W, Prehn C, Adamski J, Hauner H, Daniel H, Suhre K, Kastenmüller G. Dynamic patterns of postprandial metabolic responses to three dietary challenges. Front Nutr 2022; 9:933526. [PMID: 36211489 PMCID: PMC9540193 DOI: 10.3389/fnut.2022.933526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jarlei Fiamoncini
- Food Research Center – FoRC, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Daniela Schranner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Thomas Skurk
- Core Facility Human Studies, ZIEL Institute for Food and Health, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Hans Hauner
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- *Correspondence: Gabi Kastenmüller
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Lépine G, Tremblay-Franco M, Bouder S, Dimina L, Fouillet H, Mariotti F, Polakof S. Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases. Nutrients 2022; 14:nu14030472. [PMID: 35276829 PMCID: PMC8840206 DOI: 10.3390/nu14030472] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 12/16/2022] Open
Abstract
This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.
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Affiliation(s)
- Gaïa Lépine
- Université Clermont Auvergne, INRAE, UMR 1019, Unité Nutrition Humaine, 63000 Clermont-Ferrand, France; (G.L.); (S.B.); (L.D.)
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 75005 Paris, France; (H.F.); (F.M.)
| | - Marie Tremblay-Franco
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, 31300 Toulouse, France;
- Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, 31300 Toulouse, France
| | - Sabrine Bouder
- Université Clermont Auvergne, INRAE, UMR 1019, Unité Nutrition Humaine, 63000 Clermont-Ferrand, France; (G.L.); (S.B.); (L.D.)
| | - Laurianne Dimina
- Université Clermont Auvergne, INRAE, UMR 1019, Unité Nutrition Humaine, 63000 Clermont-Ferrand, France; (G.L.); (S.B.); (L.D.)
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 75005 Paris, France; (H.F.); (F.M.)
| | - Hélène Fouillet
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 75005 Paris, France; (H.F.); (F.M.)
| | - François Mariotti
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 75005 Paris, France; (H.F.); (F.M.)
| | - Sergio Polakof
- Université Clermont Auvergne, INRAE, UMR 1019, Unité Nutrition Humaine, 63000 Clermont-Ferrand, France; (G.L.); (S.B.); (L.D.)
- Correspondence:
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5
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Gallegos-Cabriales EC, Rodriguez-Ayala E, Laviada-Molina HA, Nava-Gonzalez EJ, Salinas-Osornio RA, Orozco L, Leal-Berumen I, Castillo-Pineda JC, Gonzalez-Lopez L, Escudero-Lourdes C, Cornejo-Barrera J, Escalante-Araiza F, Huerta-Avila EE, Buenfil-Rello FA, Peschard VG, Silva E, Veloz-Garza RA, Martinez-Hernandez A, Barajas-Olmos FM, Molina-Segui F, Gonzalez-Ramirez L, Arjona-Villicaña RD, Hernandez-Escalante VM, Gaytan-Saucedo JF, Vaquera Z, Acebo-Martinez M, Murillo-Ramirez A, Diaz-Tena SP, Figueroa-Nuñez B, Valencia-Rendon ME, Garzon-Zamora R, Viveros-Paredes JM, Valdovinos-Chavez SB, Comuzzie AG, Haack K, Thorsell AA, Han X, Cole SA, Bastarrachea RA. Replication of Integrative Data Analysis for Adipose Tissue Dysfunction, Low-Grade Inflammation, Postprandial Responses and OMICs Signatures in Symptom-Free Adults. BIOLOGY 2021; 10:1342. [PMID: 34943258 PMCID: PMC8698545 DOI: 10.3390/biology10121342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/11/2021] [Accepted: 12/12/2021] [Indexed: 11/16/2022]
Abstract
We previously reported preliminary characterization of adipose tissue (AT) dysfunction through the adiponectin/leptin ratio (ALR) and fasting/postprandial (F/P) gene expression in subcutaneous (SQ) adipose tissue (AT) biopsies obtained from participants in the GEMM study, a precision medicine research project. Here we present integrative data replication of previous findings from an increased number of GEMM symptom-free (SF) adults (N = 124) to improve characterization of early biomarkers for cardiovascular (CV)/immunometabolic risk in SF adults with AT dysfunction. We achieved this goal by taking advantage of the rich set of GEMM F/P 5 h time course data and three tissue samples collected at the same time and frequency on each adult participant (F/P blood, biopsies of SQAT and skeletal muscle (SKM)). We classified them with the presence/absence of AT dysfunction: low (<1) or high (>1) ALR. We also examined the presence of metabolically healthy (MH)/unhealthy (MUH) individuals through low-grade chronic subclinical inflammation (high sensitivity C-reactive protein (hsCRP)), whole body insulin sensitivity (Matsuda Index) and Metabolic Syndrome criteria in people with/without AT dysfunction. Molecular data directly measured from three tissues in a subset of participants allowed fine-scale multi-OMIC profiling of individual postprandial responses (RNA-seq in SKM and SQAT, miRNA from plasma exosomes and shotgun lipidomics in blood). Dynamic postprandial immunometabolic molecular endophenotypes were obtained to move towards a personalized, patient-defined medicine. This study offers an example of integrative translational research, which applies bench-to-bedside research to clinical medicine. Our F/P study design has the potential to characterize CV/immunometabolic early risk detection in support of precision medicine and discovery in SF individuals.
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Affiliation(s)
- Esther C. Gallegos-Cabriales
- Facultad de Enfermería, Universidad Autónoma de Nuevo León (UANL), Monterrey 64460, Mexico; (E.C.G.-C.); (R.A.V.-G.); (S.B.V.-C.)
| | - Ernesto Rodriguez-Ayala
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, Lomas Anahuac 52786, Mexico; (E.R.-A.); (F.E.-A.); (V.-G.P.); (E.S.)
| | - Hugo A. Laviada-Molina
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Mérida 97300, Mexico; (H.A.L.-M.); (F.M.-S.); (L.G.-R.); (R.D.A.-V.); (V.M.H.-E.)
| | | | - Rocío A. Salinas-Osornio
- Departamento de Nutrición, Universidad del Valle de Atemajac (UNIVA), Zapopan 45050, Mexico; (R.A.S.-O.); (L.G.-L.); (M.E.V.-R.); (R.G.-Z.); (J.M.V.-P.)
| | - Lorena Orozco
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, SS, Ciudad de México 14610, Mexico; (L.O.); (E.E.H.-A.); (A.M.-H.); (F.M.B.-O.)
| | - Irene Leal-Berumen
- Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua 31125, Mexico;
| | - Juan Carlos Castillo-Pineda
- Departamento de Nutrición Humana, Universidad Latina de América, Morelia 58170, Mexico; (J.C.C.-P.); (A.M.-R.); (S.P.D.-T.)
| | - Laura Gonzalez-Lopez
- Departamento de Nutrición, Universidad del Valle de Atemajac (UNIVA), Zapopan 45050, Mexico; (R.A.S.-O.); (L.G.-L.); (M.E.V.-R.); (R.G.-Z.); (J.M.V.-P.)
| | - Claudia Escudero-Lourdes
- Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi 78240, Mexico; (C.E.-L.); (M.A.-M.)
| | - Judith Cornejo-Barrera
- Departamento de Enseñanza, Postgrado e Investigación, Hospital Infantil de Tamaulipas, Ciudad Victoria 87150, Mexico;
| | - Fabiola Escalante-Araiza
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, Lomas Anahuac 52786, Mexico; (E.R.-A.); (F.E.-A.); (V.-G.P.); (E.S.)
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, SS, Ciudad de México 14610, Mexico; (L.O.); (E.E.H.-A.); (A.M.-H.); (F.M.B.-O.)
| | - Eira E. Huerta-Avila
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, SS, Ciudad de México 14610, Mexico; (L.O.); (E.E.H.-A.); (A.M.-H.); (F.M.B.-O.)
| | - Fatima A. Buenfil-Rello
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
| | - Vanessa-Giselle Peschard
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, Lomas Anahuac 52786, Mexico; (E.R.-A.); (F.E.-A.); (V.-G.P.); (E.S.)
| | - Eliud Silva
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, Lomas Anahuac 52786, Mexico; (E.R.-A.); (F.E.-A.); (V.-G.P.); (E.S.)
| | - Rosa A. Veloz-Garza
- Facultad de Enfermería, Universidad Autónoma de Nuevo León (UANL), Monterrey 64460, Mexico; (E.C.G.-C.); (R.A.V.-G.); (S.B.V.-C.)
| | - Angelica Martinez-Hernandez
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, SS, Ciudad de México 14610, Mexico; (L.O.); (E.E.H.-A.); (A.M.-H.); (F.M.B.-O.)
| | - Francisco M. Barajas-Olmos
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, SS, Ciudad de México 14610, Mexico; (L.O.); (E.E.H.-A.); (A.M.-H.); (F.M.B.-O.)
| | - Fernanda Molina-Segui
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Mérida 97300, Mexico; (H.A.L.-M.); (F.M.-S.); (L.G.-R.); (R.D.A.-V.); (V.M.H.-E.)
| | - Lucia Gonzalez-Ramirez
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Mérida 97300, Mexico; (H.A.L.-M.); (F.M.-S.); (L.G.-R.); (R.D.A.-V.); (V.M.H.-E.)
| | - Ruy D. Arjona-Villicaña
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Mérida 97300, Mexico; (H.A.L.-M.); (F.M.-S.); (L.G.-R.); (R.D.A.-V.); (V.M.H.-E.)
| | - Victor M. Hernandez-Escalante
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Mérida 97300, Mexico; (H.A.L.-M.); (F.M.-S.); (L.G.-R.); (R.D.A.-V.); (V.M.H.-E.)
| | - Janeth F. Gaytan-Saucedo
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
| | - Zoila Vaquera
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
| | - Monica Acebo-Martinez
- Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi 78240, Mexico; (C.E.-L.); (M.A.-M.)
| | - Areli Murillo-Ramirez
- Departamento de Nutrición Humana, Universidad Latina de América, Morelia 58170, Mexico; (J.C.C.-P.); (A.M.-R.); (S.P.D.-T.)
| | - Sara P. Diaz-Tena
- Departamento de Nutrición Humana, Universidad Latina de América, Morelia 58170, Mexico; (J.C.C.-P.); (A.M.-R.); (S.P.D.-T.)
| | - Benigno Figueroa-Nuñez
- Clínica de Enfermedades Crónicas y Procedimientos Especiales (CECYPE), Morelia 58249, Mexico;
| | - Melesio E. Valencia-Rendon
- Departamento de Nutrición, Universidad del Valle de Atemajac (UNIVA), Zapopan 45050, Mexico; (R.A.S.-O.); (L.G.-L.); (M.E.V.-R.); (R.G.-Z.); (J.M.V.-P.)
| | - Rafael Garzon-Zamora
- Departamento de Nutrición, Universidad del Valle de Atemajac (UNIVA), Zapopan 45050, Mexico; (R.A.S.-O.); (L.G.-L.); (M.E.V.-R.); (R.G.-Z.); (J.M.V.-P.)
| | - Juan Manuel Viveros-Paredes
- Departamento de Nutrición, Universidad del Valle de Atemajac (UNIVA), Zapopan 45050, Mexico; (R.A.S.-O.); (L.G.-L.); (M.E.V.-R.); (R.G.-Z.); (J.M.V.-P.)
| | - Salvador B. Valdovinos-Chavez
- Facultad de Enfermería, Universidad Autónoma de Nuevo León (UANL), Monterrey 64460, Mexico; (E.C.G.-C.); (R.A.V.-G.); (S.B.V.-C.)
| | | | - Karin Haack
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
| | | | - Xianlin Han
- Department of Medicine, Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health San Antonio, San Antonio, TX 78229, USA;
| | - Shelley A. Cole
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
| | - Raul A. Bastarrachea
- Population Health Program, Southwest National Primate Research Center (SNPRC), Texas Biomedical Research Institute, San Antonio, TX 78227-0549, USA; (F.A.B.-R.); (J.F.G.-S.); (Z.V.); (K.H.); (S.A.C.)
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105, USA;
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LaBarre JL, Singer K, Burant CF. Advantages of Studying the Metabolome in Response to Mixed-Macronutrient Challenges and Suggestions for Future Research Designs. J Nutr 2021; 151:2868-2881. [PMID: 34255076 PMCID: PMC8681069 DOI: 10.1093/jn/nxab223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/22/2022] Open
Abstract
Evaluating the postprandial response to a dietary challenge containing all macronutrients-carbohydrates, lipids, and protein-may provide stronger insights of metabolic health than a fasted measurement. Metabolomic profiling deepens the understanding of the homeostatic and adaptive response to a dietary challenge by classifying multiple metabolic pathways and biomarkers. A total of 26 articles were identified that measure the human blood metabolome or lipidome response to a mixed-macronutrient challenge. Most studies were cross-sectional, exploring the baseline and postprandial response to the dietary challenge. Large variations in study designs were reported, including the macronutrient and caloric composition of the challenge and the delivery of the challenge as a liquid shake or a solid meal. Most studies utilized a targeted metabolomics platform, assessing only a particular metabolic pathway, however, several studies utilized global metabolomics and lipidomics assays demonstrating the expansive postprandial response of the metabolome. The postprandial response of individual amino acids was largely dependent on the amino acid composition of the test meal, with the exception of alanine and proline, 2 nonessential amino acids. Long-chain fatty acids and unsaturated long-chain acylcarnitines rapidly decreased in response to the dietary challenges, representing the switch from fat to carbohydrate oxidation. Studies were reviewed that assessed the metabolome response in the context of obesity and metabolic diseases, providing insight on how weight status and disease influence the ability to cope with a nutrient load and return to homeostasis. Results demonstrate that the flexibility to respond to a substrate load is influenced by obesity and metabolic disease and flexibility alterations will be evident in downstream metabolites of fat, carbohydrate, and protein metabolism. In response, we propose suggestions for standardization between studies with the potential of creating a study exploring the postprandial response to a multitude of challenges with a variety of macronutrients.
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Affiliation(s)
| | - Kanakadurga Singer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
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Rodriguez-Ayala E, Gallegos-Cabrales EC, Gonzalez-Lopez L, Laviada-Molina HA, Salinas-Osornio RA, Nava-Gonzalez EJ, Leal-Berumen I, Escudero-Lourdes C, Escalante-Araiza F, Buenfil-Rello FA, Peschard VG, Laviada-Nagel A, Silva E, Veloz-Garza RA, Martinez-Hernandez A, Barajas-Olmos FM, Molina-Segui F, Gonzalez-Ramirez L, Espadas-Olivera R, Lopez-Muñoz R, Arjona-Villicaña RD, Hernandez-Escalante VM, Rodriguez-Arellano ME, Gaytan-Saucedo JF, Vaquera Z, Acebo-Martinez M, Cornejo-Barrera J, Jancy Andrea HQ, Castillo-Pineda JC, Murillo-Ramirez A, Diaz-Tena SP, Figueroa-Nuñez B, Valencia-Rendon ME, Garzon-Zamora R, Viveros-Paredes JM, Ángeles-Chimal J, Santa-Olalla Tapia J, Remes-Troche JM, Valdovinos-Chavez SB, Huerta-Avila EE, Lopez-Alvarenga JC, Comuzzie AG, Haack K, Han X, Orozco L, Weintraub S, Kent JW, Cole SA, Bastarrachea RA. Towards precision medicine: defining and characterizing adipose tissue dysfunction to identify early immunometabolic risk in symptom-free adults from the GEMM family study. Adipocyte 2020; 9:153-169. [PMID: 32272872 PMCID: PMC7153654 DOI: 10.1080/21623945.2020.1743116] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Interactions between macrophages and adipocytes are early molecular factors influencing adipose tissue (AT) dysfunction, resulting in high leptin, low adiponectin circulating levels and low-grade metaflammation, leading to insulin resistance (IR) with increased cardiovascular risk. We report the characterization of AT dysfunction through measurements of the adiponectin/leptin ratio (ALR), the adipo-insulin resistance index (Adipo-IRi), fasting/postprandial (F/P) immunometabolic phenotyping and direct F/P differential gene expression in AT biopsies obtained from symptom-free adults from the GEMM family study. AT dysfunction was evaluated through associations of the ALR with F/P insulin-glucose axis, lipid-lipoprotein metabolism, and inflammatory markers. A relevant pattern of negative associations between decreased ALR and markers of systemic low-grade metaflammation, HOMA, and postprandial cardiovascular risk hyperinsulinemic, triglyceride and GLP-1 curves was found. We also analysed their plasma non-coding microRNAs and shotgun lipidomics profiles finding trends that may reflect a pattern of adipose tissue dysfunction in the fed and fasted state. Direct gene differential expression data showed initial patterns of AT molecular signatures of key immunometabolic genes involved in AT expansion, angiogenic remodelling and immune cell migration. These data reinforce the central, early role of AT dysfunction at the molecular and systemic level in the pathogenesis of IR and immunometabolic disorders.
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Affiliation(s)
- Ernesto Rodriguez-Ayala
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, México City, México
| | | | - Laura Gonzalez-Lopez
- Dirección de Postgrado e Investigación, Universidad del Valle de Atemajac (UNIVA), Zapopan, México
| | | | - Rocio A. Salinas-Osornio
- Dirección de Postgrado e Investigación, Universidad del Valle de Atemajac (UNIVA), Zapopan, México
| | | | - Irene Leal-Berumen
- Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, México
| | | | - Fabiola Escalante-Araiza
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, México City, México
| | - Fatima A. Buenfil-Rello
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Vanessa-Giselle Peschard
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, México City, México
| | - Antonio Laviada-Nagel
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Eliud Silva
- Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, México City, México
| | - Rosa A. Veloz-Garza
- Facultad de Enfermería, Universidad Autónoma de Nuevo León (UANL), Monterrey, México
| | - Angelica Martinez-Hernandez
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, México City, México
| | - Francisco M. Barajas-Olmos
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, México City, México
| | | | | | | | - Ricardo Lopez-Muñoz
- Escuela de Ciencias de la Salud, Universidad Marista de Mérida, Yucatán, Mexico
| | | | - Victor M. Hernandez-Escalante
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | | | - Janeth F. Gaytan-Saucedo
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Zoila Vaquera
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | | | - Judith Cornejo-Barrera
- Departamento de Enseñanza, Postgrado e Investigación, Hospital Infantil de Tamaulipas, Ciudad, México
| | - Huertas-Quintero Jancy Andrea
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | | | | | - Sara P. Diaz-Tena
- Departamento de Nutrición Humana, Universidad Latina de América, Morelia, México
| | | | | | - Rafael Garzon-Zamora
- Dirección de Postgrado e Investigación, Universidad del Valle de Atemajac (UNIVA), Zapopan, México
| | | | - José Ángeles-Chimal
- Facultad de Medicina, Universidad Autónoma Estado de Morelos, Cuernavaca, México
| | | | - José M. Remes-Troche
- Instituto de Investigaciones Médico-Biológicas, Universidad Veracruzana, Veracruz, México
| | | | - Eira E. Huerta-Avila
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, México City, México
| | - Juan Carlos Lopez-Alvarenga
- School of Medicine & South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | | | - Karin Haack
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Xianlin Han
- Department of Medicine, Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Lorena Orozco
- Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, México City, México
| | - Susan Weintraub
- Department of Biochemistry, University of Texas Health Science Center, San Antonio, TX, USA
| | - Jack W. Kent
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Shelley A. Cole
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
| | - Raul A. Bastarrachea
- Population Health Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA
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The COVID-19 Pandemic during the Time of the Diabetes Pandemic: Likely Fraternal Twins? Pathogens 2020; 9:pathogens9050389. [PMID: 32438687 PMCID: PMC7281197 DOI: 10.3390/pathogens9050389] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/13/2020] [Accepted: 05/16/2020] [Indexed: 02/06/2023] Open
Abstract
An altered immune response to pathogens has been suggested to explain increased susceptibility to infectious diseases in patients with diabetes. Recent evidence has documented several immunometabolic pathways in patients with diabetes directly related to the COVID-19 infection. This also seems to be the case for prediabetic subjects with proinflammatory insulin resistance syndrome accompanied with prothrombotic hyperinsulinemic and dysglycemic states. Patients with frank hyperglycemia, dysglycemia and/or hyperinsulinemia develop systemic immunometabolic inflammation with higher levels of circulating cytokines. This deleterious scenario has been proposed as the underlying mechanism enhancing a cytokine storm-like hyperinflammatory state in diabetics infected with severe COVID-19 triggering multi-organ failure. Compared with moderately affected COVID-19 patients, diabetes was found to be highly prevalent among severely affected patients suggesting that this non-communicable disease should be considered as a risk factor for adverse outcomes. The COVID-19 pandemic mirrors with the diabetes pandemic in many pathobiological aspects. Our interest is to emphasize the ties between the immunoinflammatory mechanisms that underlie the morbidity and lethality when COVID-19 meets diabetes. This review brings attention to two pathologies of highly complex, multifactorial, developmental and environmentally dependent manifestations of critical importance to human survival. Extreme caution should be taken with diabetics with suspected symptoms of COVID-19 infection.
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Teclemariam ET, Pergande MR, Cologna SM. Considerations for mass spectrometry-based multi-omic analysis of clinical samples. Expert Rev Proteomics 2020; 17:99-107. [PMID: 31996049 DOI: 10.1080/14789450.2020.1724540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.
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Affiliation(s)
- Esei T Teclemariam
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa R Pergande
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL, USA.,Laboratory of Integrated Neuroscience, University of Illinois at Chicago, Chicago, IL, USA
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