Metabolic phenotyping in personalized and public healthcare

Metabolic Phenotyping in Personalized and Public Healthcare Metabolic Phenotyping in Personalized and Public Healthcare Edited by Elaine Holmes Jeremy K Nicholson Ara W Darzi John C Lindon AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125, London Wall, EC2Y 5AS iv  B  Street, Suite 1800, San Diego, CA 92101-4495, USA 525 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Copyright © 2016 Elsevier Inc All rights reserved Professor Dame Sally Davies retains the copyright to the Foreword No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers may always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-800344-2 For information on all Academic Press publications visit our website at http://store.elsevier.com Publisher: Mica Haley Acquisition Editor: Catherine Van Der Laan Editorial Project Manager: Lisa Eppich Production Project Manager: Melissa Read Designer: Maria Ines Cruz Printed and bound in the United States of America Foreword Clinical medicine has always been driven forward by advances in technology, whether it be new approaches to drug design and discovery, new imaging modalities for improved diagnosis, or, more recently, the advent of the so-called “-omics” sciences Huge amounts of data are now acquired from subjects, and these are interrogated in relation to disease or population risk factors to reveal biomarkers that can be used for exposure measures, susceptibility factors, improved diagnosis, and better prognosis The human genome project proved to be the first of these new paradigms, and the improved understanding of genetic mutations has already yielded novel diagnostic and therapeutic combinations, particularly in the field of oncology It is clear that human beings are much more than the combination of their genes The complex interactions between these genes and their environment and a person’s lifestyle lead to the realization that the study of human metabolism might offer better clues to the malfunctions underlying diseases Many diseases and conditions could have both genetic and environmental components, and there exists a wide spectrum, from the purely genetic (eg, Huntington’s disease) to the predominantly environmental (eg, smoking-induced lung cancer) The complex nature of the microbial colonies that also share the human body, especially the gut bacteria, also play a vital part as they affect and impact the human metabolism The measurement of large amounts of data that describe a human being (the phenotype) is now known as phenomics, and the human metabolic phenotype, in particular, captures information on both human biochemistry and the effects of the microbiome, and this approach offers new insights into the perturbations caused by diseases or by exposure to external agents—be they air pollution, trace chemicals in the environment, lifestyle choices, or diet Metabolic phenotyping is most often, but not exclusively, conducted by performing analysis of thousands of metabolites simultaneously, using advanced analytical chemistry technology on biofluids, such as urine or blood serum, or on tissue biopsy specimens Metabolic phenotyping is now developing to have an impact on both clinical medicine and on epidemiologic studies of disease risk, and this text provides a comprehensive survey and assessment of the field in both areas of application It discusses unmet medical needs, provides an overview of the technologies used, and shows how the methodology can be used It also offers a critical evaluation of the problems still to be overcome xiii xiv  Foreword As Chief Medical Officer for England and Chair of the UK National Institute of Health Research (NIHR), which is part of the UK National Health Service, I am pleased to say that the NIHR, in conjunction with the UK Medical Research Council, awarded a grant to Imperial College London to set up the world’s first phenome center This is now fully functional and has carried out a number of successful studies in a wide range of disease risk areas, including cardiovascular disease, dementia, and diabetes The template used has now been applied to setting up other phenome centers around the world The approach promises to address major areas of risk in the populations of today The topics that I have highlighted recently include the urgent need to tackle microbial resistance to currently available antibiotics, diabetes, and the associated rise in obesity, especially in females The editors of this book have brought together an impressive array of international experts who have provided a unified and comprehensive account of the history, development, and practice of metabolic phenotyping This text should not only provide valuable information to the reader but also stimulate thinking about possible areas of application I believe that this knowledge will be useful to research scientists, clinicians, epidemiologists, and other health service providers, as well as those involved in health economics The state of the art is now such that real progress can be made quickly Professor Dame Sally Davies FRS FMedSci Chief Medical Officer for England Preface Biomolecular research studies in the areas of clinical medicine and epidemiology are increasingly being directed by analyses of “big data” under the umbrella known as phenotyping One major new and burgeoning aspect of phenotyping is the broad multi-analyte determination of a subject’s metabolic phenotype This can be based on analytes in biofluids or tissue samples, for example This field has been previously known as metabonomics or metabolomics The cohesion between metabolic phenotyping and clinical and population profiling studies has recently been shown, through the large published literature, to be highly fruitful in the search for biomarkers of diseases or population disease risks factors, and hence this leads to a better understanding of the underlying biochemical mechanisms of diseases and disease risks This book begins with a view on unmet medical needs, and this is followed by a chapter that reviews the history of metabolic phenotyping Other chapters are devoted to the basis of the technological aspects of metabolic phenotyping, whether they are based on sample collection needs, the analytical chemistry of assays, or the subsequent comprehensive statistical analysis of data and the downstream biochemical interpretations One chapter covers the principles and applications of predictive metabolic phenotyping, which can be used for prognostic studies, a process that is linked to the subject of another chapter on phenotyping the “patient journey” through diagnosis, therapy, and outcome We have been fortunate to commission chapters from world experts describing various areas of application, from early life through to old age, including the effects of the microbiome, and another chapter covers the use of real-time metabolic phenotyping during surgery with the “intelligent knife.” We have taken care to include the paradigm for studying the effects of exposure to environmental factors such as air pollution, exogenous chemicals, and so on Finally, we have provided a chapter that describes the concept of specialized phenome centers for metabolic phenotyping, with the first now operational at Imperial College London and others coming on stream worldwide and a chapter on the problems and solutions associated with studies on “big data,” and we conclude with a brief summary, a look at the state of the art, what problems still remain to be solved, and the exciting prospects for the future We hope that is text will become essential reading for academic, industrial, and clinical scientists who wish to gain a better understanding of the field and of the prospects of the metabolic phenotyping approach xv xvi  Preface We are indebted to all of the very busy authors who agreed to write chapters for this book and thank them for their efforts We would also like to thank the team at Elsevier and its associates (especially Lisa Eppich, who handled the difficult commissioning stage with great efficiency and Melissa Read who was most helpful during the production stage), who all contributed to bringing this book to publication We hope that this text will contribute to better understanding of how metabolic phenotyping can fit into clinical medicine and population screening, along with the other many advances that are paving the way to precision medicine and hence patient benefit Elaine Holmes Jeremy K Nicholson Ara W Darzi John C Lindon List of Contributors Hutan Ashrafian Department of Surgery and Cancer, Imperial College London, London, UK Thanos Athanasiou Department of Surgery and Cancer, Imperial College London, London, UK Seth Chitayat Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada; Department of Surgery, Queen’s University, Kingston, ON, Canada Ara W Darzi Department of Surgery and Cancer, Imperial College London, London, UK; Institute of Global Health Innovation, Imperial College London, London, UK Anthony C Dona MRC-NIHR National Phenome Centre, Department of Surgery & Cancer, Imperial College London, London, UK; Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, St Leonards, NSW, Australia Jeremy R Everett Medway Metabonomics Research Group, University of Greenwich, Kent, UK Young-Mi Go Division of Pulmonary, Allergy and Critical Care Medicine, Emory University School of Medicine, Atlanta, GA, USA Elaine Holmes Department of Surgery and Cancer, Imperial College London, London, UK Dean P Jones Division of Pulmonary, Allergy and Critical Care Medicine, Emory University School of Medicine, Atlanta, GA, USA Sarah Kenderdine Expanded Perception and Interaction Centre, University of New South Wales, Sydney, New South Wales, Australia James Kinross Section of Biosurgery and Surgical Technology, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK Nadine Levin Institute for Society and Genetics, University of California, Los Angeles, CA, USA Jia Li Department of Surgery and Cancer, Imperial College London, London, UK John C Lindon Department of Surgery and Cancer, Imperial College London, London, UK Ken Liu Division of Pulmonary, Allergy and Critical Care Medicine, Emory University School of Medicine, Atlanta, GA, USA xvii xviii  List of Contributors David MacIntyre Institute of Reproductive and Developmental Biology, Department of Surgery and Cancer, Imperial College London, The Hammersmith Hospital, London, UK Julian R Marchesi Institute of Reproductive and Developmental Biology, Department of Surgery and Cancer, Imperial College London, The Hammersmith Hospital, London, UK; Centre for Digestive and Gut Health, Imperial College London, London, UK; School of Biosciences, Cardiff University, Cardiff, Wales, UK Ingrid Mason Intersect Australia Pty Ltd, Sydney, New South Wales, Australia Neena Modi Department of Medicine, Imperial College London, London, UK Laura Muirhead Section of Biosurgery and Surgical Technology, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK Jeremy K Nicholson Department of Surgery and Cancer, Imperial College London, London, UK Kurt D Pennell Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA John F Rudan Department of Surgery, Queen’s University, Kingston, ON, Canada; Human Mobility Research Centre, Queen’s University and Kingston General Hospital, Kingston, ON, Canada Reza M Salek The European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK Christoph Steinbeck The European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK Zoltan Takats Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK Douglas I Walker Division of Pulmonary, Allergy and Critical Care Medicine, Emory University School of Medicine, Atlanta, GA, USA; Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA Ian D Wilson Department of Surgery and Cancer, Imperial College London, London, UK Chapter Unmet Medical Needs Hutan Ashrafian1, Thanos Athanasiou1, Jeremy K Nicholson1 and Ara W Darzi1,2 Department of Surgery and Cancer, Imperial College London, London, UK 2Institute of Global Health Innovation, Imperial College London, London, UK Chapter Outline 1.1 1.2 1.3 1.4 A Historical Perspective Unmet Medical Needs Addressing the Problems Personalized Medicine 1.5 Personalized Medicine: The Role of Metabolic Phenotyping 12 References 13 1.1  A HISTORICAL PERSPECTIVE The 21st century has heralded dramatic changes in the global health care ecology There have been significant fluxes in population dynamics, occupational shifts, environmental changes, drivers of health care economics, political forces, and a technologic explosion that can be considered as dramatic as the advances of both the agricultural and industrial revolutions Despite an overall increase in the awareness of disease and a more unified approach to its management, health care remains a global problem that challenges society with a voluminous corpus of unmet medical needs As early as the 5th century BC, Hippocrates had clarified that disease pathology originated from both inherent patient factors and those of the patient’s environment [1] In subsequent eras, the increased scrutiny and understanding of disease mechanisms has offered a tentative breakdown of the relative contribution of disease from these two factors These led to many of the very foundations of medicine as we know it today, including Edward Jenner’s demonstration of controlled immunity, Louis Pasteur’s germ theory of disease [2], and hybridization of Greek anatomy and eastern proto-pharmacotherapy described in Avicenna’s Canon of Medicine [3] E Holmes, J.K Nicholson, A.W Darzi & J.C Lindon (Eds): Metabolic Phenotyping in Personalized and Public Healthcare DOI: http://dx.doi.org/10.1016/B978-0-12-800344-2.00001-X © 2016 2013 Elsevier Inc All rights reserved 396  Index Geographic metabolic phenotype, 186–187 See also Age-related metabolic phenotype; Dietary metabolic phenotype; Disease metabolic phenotype; Diurnal metabolic phenotype; Gender metabolic phenotype; Nutritional metabolic phenotype Geographical information systems (GIS), 186–187 Germ-free (GF) animals, 267 Gestational diabetes, 232 metabonomic technology, 232–233 microbiome, 233 postpartum glucose dysregulation, 233 Gestational diabetes mellitus (GDM), 232 GF animals See Germ-free (GF) animals GI tract See Gastrointestinal (GI) tract GIS See Geographical information systems (GIS) GLCA See Glycolithocholic acid (GLCA) Gluconeogenesis, 216 Glycerophosphocholine (GPC), 83 Glycochenodeoxycholic acid, (GCDCA), 152–153 Glycocholic acid (GCA), 152–153 Glycolithocholic acid (GLCA), 152–153 Glycotic switch, 90–91 Glycoursodeoxycholic acid (GUDCA), 152–153 GMD See Golm Metabolome Database (GMD) Golm Metabolome Database (GMD), 320, 322–323 GOSs See Galacto-oligosaccharides (GOSs) GP See General practitioner (GP) GPC See Glycerophosphocholine (GPC) Graft dysfunction, 61–64 Gross domestic product (GDP), 281–283 GUDCA See Glycoursodeoxycholic acid (GUDCA) Gut microbiome, 283, 377–379, 382 genes, 31 in pregnancy, 223 Gut-associated lymphoid tissue (GALT), 278–279 Gut–brain axis, 243 in adults, 243–245 autism risk, 248 body weight, 246 clostridia, 247–248 Fleming, 243 metabolic consequences, 245–246 metabolic phenotype of children, 248 microbiota and ASD, 247 GWASs See Genome-wide association studies (GWASs) H H NMR spectroscopy, 20–21, 152, 157 H&E staining See Hematoxylin and eosin (H&E) staining Haemophilus influenza type b (Hib), HAMD17 See 17-Seventeen-item Hamilton Rating Scale for Depression (HAMD17) HAPI See Heredity and Phenotype Intervention Heart Study (HAPI) Hazard identification, population screening for, 189–190 chemical exposure survey data in humans, 190–191 concentration of selected environmental chemicals, 192t reference standardization, 191 untargeted metabolomic approaches, 190 HCI See Human–computer interaction (HCI) Head-mounted displays (HMDs), 353 Health care, 11 phenomics in, 304–305 chronic disease management, 307–309 phenome center, 306–307 phenome centers, 307–309 population-level analysis, 305 systems, 49–50, 54 Heat shock factor (HSF-1), 278 Helicobacter pylori infection, 274 Hematoxylin and eosin (H&E) staining, 88 Hemoglobin A1C, 181 Heredity and Phenotype Intervention Heart Study (HAPI), 156–157 Heteronuclear multiple-bond correlation spectroscopy (HMBC), 113–114 Heteronuclear single quantum correlation spectroscopy (HSQC), 113–114 HFD intake See High-fat diet (HFD) intake 5-HIAA See 5-Hydroxyindoleactic acid (5-HIAA) Hib See Haemophilus influenza type b (Hib) High resolution (HR) environments, 334 High-fat diet (HFD) intake, 271–272 High-performance liquid chromatography (HPLC), 24, 122–123, 370–371 High-resolution metabolomics (HRM), 171– 172, 172f, 198 High-throughput metabolic screening, 111–112 Index  397 analytical platforms differences between urine and plasma or serum metabolic profiles, 126 gas chromatography, 123 liquid chromatography, 122–123 mass spectrometry, 124–126 NMR spectroscopy, 119–122, 121f bioinformatics processes, 126 data analysis, 130–131 data archiving, 131–132 data visualization, 132–133 feature extraction, 128–130 LIMS, 126 QA/QC workflows, 127–128 sample procedures collection protocols, 115–117 sample preparation, 117–119 High-throughput toxicity screening (HTS), 189–190 HILIC See Hydrophilic interaction liquid chromatography (HILIC) Histopathologic assessment, 77–78 HIV See Human immunodeficiency virus (HIV) HMB See β-hydroxy-β-methylbutyrate (HMB) HMBC See Heteronuclear multiple-bond correlation spectroscopy (HMBC) HMDB See Human Metabolome Database (HMDB) HMDs See Head-mounted displays (HMDs) HMO See Human milk oligosaccharides (HMO) 4-HPLA See 4-Hydroxyphenyllactic acid (4-HPLA) HPLC See High-performance liquid chromatography (HPLC) HR environments See High resolution (HR) environments HRM See High-resolution metabolomics (HRM) HSF-1 See Heat shock factor (HSF-1) HSQC See Heteronuclear single quantum correlation spectroscopy (HSQC) 5-HT See Serotonin (5-HT) HTS See High-throughput toxicity screening (HTS) Human being as supra-organism, 377–378 Human cells, 91 Human exposome, 170–171 Human immunodeficiency virus (HIV), 180–181 Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/ AIDS), 4, Human metabolism, microbiome and, 218–219 epidemiologic evidence, 219–220 metabolic profiling, 220–221 urine composition, 220 Human metabolome, 168, 169f Human Metabolome Database (HMDB), 30, 320, 323 Human milk oligosaccharides (HMO), 236–237 blueprint and structures, 236f Human phenome, 301 Human plasma collection, 116–117 Human Serum Metabolome, 168–169 Human urine collection and storage, 115–116 Human–computer interaction (HCI), 334–335 HUSERMET consortium, 25, 29 Hydrogen peroxide (H2O2), 221–222 Hydrophilic interaction liquid chromatography (HILIC), 119, 123 2-Hydroxyglutarate, 89–90 5-Hydroxyindoleactic acid (5-HIAA), 155–156 3-Hydroxykynurenine (3-OHKY), 155–156 4-Hydroxyphenyllactic acid (4-HPLA), 155–156 3-Hydroxyquinine (3-OHQ), 151 I I kappa B kinase (IKKβ), 232 i-Knife See intelligent knife (i-Knife) IBD See Inflammatory bowel disease (IBD) ICP See Intrahepatic cholestasis of pregnancy (ICP) ICU See Intensive care unit (ICU) IGF-1 See Insulin-like growth factor (IGF-1) IIS See Insulin-like growth factor signaling (IIS) IKKβ See I kappa B kinase (IKKβ) IL-1β See Interleukin-1β (IL-1β) IL-6 See Interleukin-6 (IL-6) Immersive interactive virtual environments, 345 Allobrain project, 348–349, 349f AlloSphere, 348, 348f AVIE, 355–357 BactoGeNIE, 359–361 CAVE, 350–353 CAVE2, 353–354 DOMELAB, 355–357 multi-tile displays, 357 Nanomed/MRI, 349–350 398  Index Immersive interactive virtual environments (Continued) OmegaDesk, 361 Omniform, 355–357 tunnel vision vs omnidirectional vision, 346–347 Virtual Microscope, 357–358 Immersive Visualisation project, 356–357 Imperial College Clinical Phenome Centre, 305 In utero toxicity, 189–190 In vivo tissue diagnostics, 97 Individual’s surgical phenome, 76–77 Infant health, metabonomic framework for monitoring, 249 challenges of infant biosamples metabolic profiling, 249–251 600 MHz 1H NMR spectrum of water flush of diaper, 250f Infant microbiome, 273–274 See also Aging— microbiome Infant microbiota and mother’s milk interaction, 235 artificial milk, 237–238 changes in maternal milk composition, 237f GOSs, 237–238 HMO blueprint and structures, 236f oligosaccharides, 236–237 Infant obesity, 239–240 Infection, 241–242 Inflamm-aging, 278–281 Inflammatory bowel disease (IBD), 273–274 Informatics, 304 Infra-red spectroscopy, 20 iNKT cells See invariant natural killer T (iNKT) cells Insulin-like growth factor (IGF-1), 266–267 Insulin-like growth factor signaling (IIS), 266–267 intelligent knife (i-Knife), 38–39, 60, 305, 308–309, 341 technology, 12–13, 90 “Intelligent” surgical devices, 92 alternative spectroscopic techniques, 94–97 intraoperative frozen section, 92 limitations, 92 MarginProbe, 94 requirements for real-time diagnostic instruments, 93t traditional pathologic methods, 94 in vivo tissue diagnostics, 97 Intensive care unit (ICU), 66 Interactomics, 376–377 Interleukin-1β (IL-1β), 222–223 Interleukin-6 (IL-6), 281 INTERMAP project, 25, 32 International Phenome Centre Network, 375 International Union of Pure and Applied Chemistry (IUPAC), 324 Intestinal stem cells (ISCs), 277–278 Intrahepatic cholestasis of pregnancy (ICP), 229–230 Intraoperative frozen section, 92 Intraoperative translational application, 89–90 Intrauterine growth restriction (IUGR), 217–218 invariant natural killer T (iNKT) cells, 281 Ion suppression, 370–371 ISCs See Intestinal stem cells (ISCs) IUGR See Intrauterine growth restriction (IUGR) IUPAC See International Union of Pure and Applied Chemistry (IUPAC) J J-resolved sequence (JRES), 113–114 JNK See c-Jun NH2-terminal kinase (JNK) K KEGG See Kyoto Encyclopedia of Genes and Genomes (KEGG) KEGG database See Kyoto Encyclopedia of Genes and Genomes (KEGG) database KORA See Cooperative Health Research in Region of Augsburg (KORA) Kupffer vesicle, 350–351 Kyoto Encyclopedia of Genes and Genomes (KEGG), 320–321 databases, 198, 202t, 321t L Laboratory Information Management System (LIMS), 120, 126 Laboratory Information System (LIS) See Laboratory Information Management Systems (LIMS) Laboratory Management System (LMS) See Laboratory Information Management Systems (LIMS) Lactobacillus plantarum (L plantarum), 278 Lactobacillus spp., 218–219, 221–222 Lactobacillus species–dominated vaginal microbiome, 224–225 Index  399 Laser desorption ionization–mass spectrometry, 97–99 LC See Liquid chromatography (LC) LC-ECA See Liquid chromatography electrochemical array (LC-ECA) LC-MS–based metabolic profiling, 36 LCA See Lithocholic acid (LCA) LCD See Liquid crystal display (LCD) LC–MS See Liquid chromatography–mass spectrometry (LC–MS) LDL-C See Low-density lipoprotein cholesterol (LDL-C) Leeds Virtual Microscope (LVM), 357–358, 359f Length of stay in hospitals, 52–53 LIMS See Laboratory Information Management System (LIMS) Line broadening, 122 Linear projection methods, 340 Linked open data (LOD), 343–344 Lipid MAPS See Lipid Metabolites and Pathways Strategy (Lipid MAPS) Lipid metabolism in cancer, 90 alteration, 92 human cells, 91 Warburg effect, 90–91 Lipid Metabolites and Pathways Strategy (Lipid MAPS), 25 Structure Database, 323 Lipidomic/metabonomic MS, 99–100 Lipoproteins, 116 Liquid chromatography (LC), 19, 111–112, 122–123, 198, 339–340 Liquid chromatography electrochemical array (LC-ECA), 155–156 Liquid chromatography–mass spectrometry (LC–MS), 60–61, 113–114, 124–125 See also Gas chromatography–mass spectrometry (GC–MS) feature extraction, 128–129 sample preparation for, 119 Liquid crystal display (LCD), 351 Listeria spp., 215 Lithocholic acid (LCA), 152–153 Liver toxin, 28–29 LOD See Linked open data (LOD) Low-density lipoprotein cholesterol (LDL-C), 152–153 LPC See Lysophosphatidylcholine (LPC) LVM See Leeds Virtual Microscope (LVM) Lysophosphatidylcholine (LPC), 159 M m-aconitase (ACON), 79–83 m/z ratio See mass-to-charge ratio (m/z ratio) MACEs See Major adverse cardiac events (MACEs) Madison Metabolomic Consortium (MMC), 322 Magic angle spinning (MAS), 57–59 Magic angle spinning NMR (MAS NMR), 79–83 metabolites in breast cancer tissue, 83 off-tumor chemistry, 83–86 Magic-angle spinning, 12 Magnesium (Mg2+), 116–117 Magnetic resonance imaging (MRI), 319, 335 Magnetic resonance spectroscopy (MRS), 79–83 Major adverse cardiac events (MACEs), 159 Major depressive disorder (MDD), 155 MALDI See Matrix-assisted laser desorption ionization (MALDI) Malnourishment, 241 Malnutrition, 240–241 Mammalian target of rapamycin (mTOR), 269 Mann–Whitney U test, 146 “Map of the spots”, 18–19 MarginProbe, 94 MAS See Magic angle spinning (MAS) MAS NMR See Magic angle spinning NMR (MAS NMR) Mass spectrometry (MS), 19, 22, 55, 77, 111–112, 124, 138, 139t, 171, 339b–341b, 369–370 See also Gas chromatography (GC) DIMS, 124 GC–MS, 125–126 LC–MS, 124–125 QC-RSC, 124 Mass spectrometry imaging in surgery, 86 DESI-MS, 88, 89f ambient ionization of tissue samples, 88–89 numerous tumors analysis, 89–90 sampling probe, 90 spectral interconversion algorithm, 90 imaging techniques in biological research, 86 MALDI-MS, 86–87 application, 88 candidate biomarkers, 87–88 for chemical mapping of intact tissue sections, 87f time demand of analysis, 88 400  Index Mass-to-charge ratio (m/z ratio), 171–172 MassBank, 323 Maternal health, strategy for metabonomic framework for monitoring, 249 challenges of infant biosamples metabolic profiling, 249–251 600 MHz 1H NMR spectrum of water flush of diaper, 250f Maternal microbiota, 223 Maternal obesity, 230–231 Matrix-assisted laser desorption ionization (MALDI), 324 Matrix-assisted laser desorption ionization– time of flight mass (MALDI-TOF) spectrometric method, 228–229 Matrix-associated laser desorption ionization– mass spectrometry (MALDI-MS), 86–87 See also Desorption electrospray ionization and mass spectrometry (DESI-MS) application, 88 candidate biomarkers, 87–88 for chemical mapping of intact tissue sections, 87f time demand of analysis, 88 MDD See Major depressive disorder (MDD) Measurement to Understand Reclassification of Disease of Cabarrus and Kannapolis Cardiovascular Study (MURDOCK CV), 159 Medical imaging, 334–335 accelerating data acquisition, 336–337 multifield MRI data, 336 time-varying medical volume data visualization, 336 Medical Research Council (MRC), 20 Medical tourism proliferation, Medical visualization, modalities and methods in, 337 metabolic profiling, 339–341 population models, 338–339 predictive models and visualization in systems biology, 339 topologic methods, 337 VPH, 338 Meet-in-the-middle (MITM) approach, 193–194, 193f Meta-flammation, 279–281 “MetaboCard”, 323 Metabolic inflammation See Meta-flammation Metabolic markers, 181 Metabolic pathway advanced metabolic pathway visualization, 356–357 enrichment, 203–204, 204f Metabolic phenotyping, 7–8, 12, 23–24, 138, 168, 220, 232–233 biofluid composition and standardized procedures, 29–30 case study, 196–197 chlorophenylacetic acid MWAS, 198–199 correlation-based interaction network, 203f DDT, 197–198 environmental epidemiology studies, 197 high-resolution metabolomics, 198 KEGG database, 202t metabolic association with CPAA, 199–200 metabolic feature association, 201f metabolic pathway enrichment, 203–204, 204f metabolomics results, 199 network analysis, 200–203 plasma chlorophenylacetic acid levels distribution, 200f study population, 198 in clinical medicine and epidemiology, 378–379 gene–environment interactions, 379 pharmacometabonomics, 379 population risk studies, 380 COMET project classes of chemicals used and types of toxicity, 26 COMET-2, 28–29 for drug toxicity, 25–26 FDA, 27 high degree of biochemical consistency, 28f new metabolites of bromoethanamine, 29 NMR/pattern recognition approach, 26–27 objectives, 26 operational flow chart for, 27f sponsoring companies, 28 useful supplementary result, 27–28 current status analytical approaches, 373–374 real-time metabolic profiling, 373 small-scale exploratory clinical studies, 372 data, 133 development, 369–370 Index  401 bioinformatics, 371–372 modern, 370 MS-based metabolic profiling, 370–371 Personalized Medicine, 372 for environmental exposures, 187–188 categorization of metabolic phenotyping data, 189f chemical exposure measurement in personalized medicine, 196 endogenous and exogenous sources, 188 population screening for hazard identification, 189–191 in EWAS of disease, 193–194 detected PAH biomarkers, 195 PBDE, 195–196 proof-of-concept implementation of MITM approach, 194 series of steroid-related metabolites, 194–195 human being as supra-organism, 377–378 human exposome, 170–171 Human Serum Metabolome, 168–169 impacts in medicine, 382–384 950 MHz 1H NMR spectrum of human urine, 34f new outputs, 380–382 with omics data, 30–31 patient journey monitoring, 36, 37f clinical phenotyping process, 36–37 multivariate statistical analysis of malignant tumor data, 39f pharmacometabonomics, 37–38 rapid evaporative ionization mass spectrometry, 38–39 robust predictive models, 38 spectra, 39 patient’s metabolic trajectory, 12–13 phenome center concept, 40 MRC-NIHR Phenome Centre, 40 NIH, 41 patient journey phenotyping, 40 pisse prophets, 17–18 population scale studies and biomarkers of disease risk, 31–35 population screening, 12, 171 age-related metabolic phenotype, 178–180 comparison of untargeted and targeted chemical profiling techniques, 173f conceptual framework for use of metabolic phenotyping, 175f data-driven approach, 172–174 dietary and nutritional metabolic phenotype, 182–186 disease metabolic phenotype, 180–182 diurnal metabolic phenotype, 176–177 gender metabolic phenotype, 177–178 geographic metabolic phenotype, 186–187 HRM, 171–172, 172f metabolomics-based, 174 MWASs, 174–175 Personalized Medicine framework, 175 translational applications, 174 predictive, 35–36 stratified medicine, application to, 35–36 techniques, 12 platforms and analytical timescales, 380–382 technologic challenges for integration into medicine, 374 data analysis, integration, and big data, 375–377 standardization protocols, 374–375 validation of protocols, 374–375 20th century, 18–19 H NMR spectra, 20–23 biofluid analysis, 21 biomarkers, 24–25 capillary electrophoresis, 24 chemometrics, 22 COMET project, 25 Dendral program, 22 400 MHz 1H NMR spectrum of human urine, 21f functional genomics, 24 HPLC, 24 metabolic composition of biofluids, 25 metabonomics, 23–24 MS, 22 natural mixtures, 22 nonlinear maps and plots of first two PCs, 23f NuGO, 25 packed column GC, 19 Pauling’s study, 19–20 pulse-Fourier transform proton NMR spectroscopy, 20 spectroscopic profiling of inborn errors of metabolism, 20 unmet medical needs, 13 untargeted metabolic profiling in human populations, 169–170 urine wheels, 18f 402  Index Metabolic profiling, 51–52, 111–113, 120, 220–221, 247–248 role in patient journey phenotyping, 56–57 analysis of samples, 57 analysis of three stages of Dengue fever, 62f combination of liquid chromatography, 60–61 iKnife, 60 metabolic characterization of patient journey, 64f metabolic trajectories, 60 pharmacometabonomic studies, 59–60 principal component scores plots, 61f properties, 64–65 series of 1H NMR spectra of urine, 63f spectroscopic profiles, 66 urine and plasma, 57 Metabolic switch, 229–230 Metabolic syndrome, 230–231 Metabolic trajectories, 60 Metabolic Workbench, 376 Metabolic–proteomic correlations, 376–377 MetaboLights, 325–326 database, 30, 374–375 system, 376 Metabolism development in neonates, 216–218 Metabolites, 113, 132, 323 profile changes, 144–145 profiling, 138–140 Metabolome-wide association studies (MWASs), 174–175 Metabolomes, 25 MetabolomeXchange, 132, 327 Metabolomics, 23–24, 111–112, 138, 374 phenomics vs., 300 results, 199 Metabolomics Standards Initiative (MSI), 30, 328 Metabolomics-based experiments, databases containing, 325 experimental databases MetaboLights, 325–326 Metabolomics Workbench, 326–327 warehouses, 327 MetabolomicsWorkbench, 30, 132, 326–327 Metabonomic framework for monitoring maternal and infant health, 249 challenges of infant biosamples metabolic profiling, 249–251 600 MHz 1H NMR spectrum of water flush of diaper, 250f Metabonomic strategy, 78 Metabonomics, 23–24, 78, 111–112, 131, 138–140, 270–271, 372 Metformin biguanide drug, 284–285 5-Methoxytryptamine (5-MTPM), 155–156 5-Methoxytryptophol (5-MTPOL), 155–156 N-Methylnicotinamide, 217–218 METLIN, 323–324 Metabolomics Database, 323–324 Mexico, Indonesia, Nigeria, and Turkey (MINT), MIAME See Minimum Information About Microarray Experiment (MIAME) Microbiome, 31, 233 and human metabolism, 218–219 epidemiologic evidence, 219–220 metabolic profiling, 220–221 urine composition, 220 immune modulation, 278–281 Microbiota, 272 Microbiota–host relationship, 279–281 Mid-Life in the US study (MIDUS), 270–271 Milk, 216, 233 interaction between infant microbiota and mother’s milk, 235 artificial milk, 237–238 changes in maternal milk composition, 237f GOSs, 237–238 HMO blueprint and structures, 236f oligosaccharides, 236–237 Minimum Information About Microarray Experiment (MIAME), 30 MINT See Mexico, Indonesia, Nigeria, and Turkey (MINT) MITM approach See Meet-in-the-middle approach (MITM approach) mitochondrial DNA (mtDNA), 269 MMC See Madison Metabolomic Consortium (MMC) Mosaic of aging, 278–279 MRC See Medical Research Council (MRC) MRC-NIHR Phenome Centre, 40 MRI See Magnetic resonance imaging (MRI) MRS See Magnetic resonance spectroscopy (MRS) MS See Mass spectrometry (MS) MS-based imaging (MSI), 57–59 MS-based study, 242 MSI See Metabolomics Standards Initiative (MSI); MS-based imaging (MSI) mtDNA See mitochondrial DNA (mtDNA) mTOR See mammalian target of rapamycin (mTOR) Index  403 5-MTPM See 5-Methoxytryptamine (5-MTPM) 5-MTPOL See 5-Methoxytryptophol (5-MTPOL) Multi-tile displays, 357 Multiorgan dysfunction syndrome, 78 MURDOCK CV See Measurement to Understand Reclassification of Disease of Cabarrus and Kannapolis Cardiovascular Study (MURDOCK CV) MWASs See Metabolome-wide association studies (MWASs) N Nanomed/MRI, 349–350 National Center for Biotechnology Information (NCBI), 319 National Health and Nutrition Examination Survey, 190 National Health Services (NHS), 54–55 National Institute for Health Research (NIHR), 34 National Institute of Standards and Technology (NIST), 320, 324 National Phenome Centre (NPC), 302 Natural mixtures, 22 NCBI See National Center for Biotechnology Information (NCBI) Near-infrared-capable laparoscopic tool, 94–97 Neonates, metabolism development in, 216–218 Neovascular age-related macular degeneration (NVAMD), 195–196 Network analysis, 200–203 Newton’s Third Law of Motion, 291–292 NexCAVE, 352 NFκB See Nuclear factor kappa B (NFκB) NGOs See Nongovernmental organizations (NGOs) NHS See National Health Services (NHS) NIH See US National Institutes of Health (NIH) NIHR See National Institute for Health Research (NIHR) NIST See National Institute of Standards and Technology (NIST) Nitric oxide (NO), 278 NMR spectroscopy See Nuclear magnetic resonance (NMR) spectroscopy NMR-MS See Nuclear magnetic resonance with mass spectrometry (NMR-MS) NO See Nitric oxide (NO) NOESY See Nuclear Overhauser effect spectroscopy (NOESY) Nongovernmental organizations (NGOs), Nonsteroidal anti-inflammatory drug (NSAID), 274–276 Nontargeted metabolomics approaches, 179–180 Normal-phase liquid chromatography (NPLC), 123 Novel methods, 94 NP-LC See Normal-phase liquid chromatography (NP-LC) NPC See National Phenome Centre (NPC) NSAID See Nonsteroidal anti-inflammatory drug (NSAID) Nuclear factor kappa B (NFκB), 224, 279 Nuclear magnetic resonance (NMR) spectroscopy, 12, 20, 55, 77, 111–114, 128, 138, 139t, 171, 217–218, 270– 271, 293–294, 303, 320, 339b–341b, 369–370 analytical platforms, 119–122, 121f sample preparation for, 118–119 Nuclear magnetic resonance with mass spectrometry (NMR-MS), 293–294 Nuclear Overhauser effect spectroscopy (NOESY), 120–121 Nucleocytosolic AcCoA See Nucleocytosolic acetyl-coenzyme A (Nucleocytosolic AcCoA) Nucleocytosolic acetyl-coenzyme A (Nucleocytosolic AcCoA), 272–273 NuGO See Nutrigenomic Organization (NuGO) Nutrigenomic Organization (NuGO), 25 Nutritional metabolic phenotype, 182–186 See also Age-related metabolic phenotype; Dietary metabolic phenotype; Disease metabolic phenotype; Diurnal metabolic phenotype; Gender metabolic phenotype; Geographic metabolic phenotype NVAMD See Neovascular age-related macular degeneration (NVAMD) O Obesity, 3-OHKY See 3-Hydroxykynurenine (3-OHKY) 3-OHQ See 3-Hydroxyquinine (3-OHQ) Oligosaccharides, 236–237 OmegaDesk, 361 Omegalib, 361 404  Index “-omics”, 319 analysis of aging, 268–269 epigenetics, 269–270 genomics, 269 metabonomics, 270–271 molecular mechanisms of aging, 268f transcriptomics, 269–270 data, 334 technologies, 7–8, 55, 382–384 Omnidirectional vision, tunnel vision vs., 346–347 Omniform, 355 operating theater of future, 356–357 visualization of complex MRI data sets, 357 “One size fits all” approach, 239 One-dimensional 1H CPMG spin-echo with presaturation, 121–122 One-dimensional 1H diffusion-edited sequence, 122 One-dimensional 1H NOESY, 120–121 OPLS-DA See Orthogonal partial least squares discriminant analysis (OPLS-DA) Optical spectroscopy, 94–97 Optimized patient journey, 53 Orosomucoid, 87–88 Orthogonal partial least squares discriminant analysis (OPLS-DA), 132 Oviductin, 87–88 Oxidoreductase enzyme, 199–200 P p-HPA See para-hydroxyphenylacetate (p-HPA) Packed column gas chromatography (Packed column GC), 19 PAG See Phenylacetylglutamine (PAG) PAH See Polycyclic aromatic hydrocarbon (PAH) Pan-colonic chromoendoscopy, 100–101 Paper spray–mass spectrometry (PS-MS), 101 PAPS See 3′-Phosphoadenosine 5′-phosphosulfate (PAPS) Para-hydroxyphenylacetate (p-HPA), 225–227 Paracetamol metabolism prediction, 145 endogenous urine metabolites, 146 in humans, 146 Mann–Whitney U test, 146 para-cresol, 150 para–cresol sulfate, 150 predose 1H NMR spectra, 148, 148f ratio of paracetamol sulfate, 149f molecular structures, 145f Para–cresol molecular structures, 150f sulfate, 150 Parkinson’s disease (PD), 187–188 Partial least square-discriminant analysis (PLSDA), 78, 155–156 Partial least squares (PLS), 132, 340 Pathologic effects, 25–26 Pathway-centric databases See also Compound-centric databases BioCyc, 320 KEGG, 320–321 Reactome, 321–322 Wikipathways, 322 Pathway/Genome Database (PGDB), 320 Patient journey monitoring, 36, 37f clinical phenotyping process, 36–37 multivariate statistical analysis of malignant tumor data, 39f pharmacometabonomics, 37–38 rapid evaporative ionization mass spectrometry, 38–39 robust predictive models, 38 spectra, 39 Patient journey phenotyping, 49–53 advanced metabolic phenotyping methods, 55–56 bottlenecks in, 52–53 clear planning, 55 communication with patient and family, 69 CPC concept, 69–70 data visualization for physician, 68–69 diagnostic assays, 55 features, 54 GPs and highly specialized clinical experts, 50–51 health care systems, 54 internet search results for, 54–55 metabolic profiling role in, 56–57 analysis of samples, 57 analysis of three stages of Dengue fever, 62f combination of liquid chromatography, 60–61 iKnife, 60 metabolic characterization of patient journey, 64f metabolic trajectories, 60 pharmacometabonomic studies, 59–60 principal component scores plots, 61f properties, 64–65 series of 1H NMR spectra of urine, 63f spectroscopic profiles, 66 urine and plasma, 57 Index  405 stratified and personalized health care, 66 data capture, 66–67 data processing and modeling, 67–68 data storage and query, 68 sample collection and processing, 67 spectroscopic profiling of biofluids, cells, and tissues, 67 Patient’s metabolic trajectory, 12–13 Pauling’s study, 19–20 PBDE See Polybrominated diphenyl ether (PBDE) PC See Phosphatidylcholine (PC) PCA See Principal component analysis (PCA) PCB See Polychlorinated biphenyls (PCB) PCho See Phosphocholine (PCho) PD See Parkinson’s disease (PD) PDB See Program database (PDB); Protein Data Bank (PDB) PE See Phosphatidylethanolamine (PE) Pediatric intensive care unit (PICU), 60 Peptide YY (PYY), 242 Peptidoglycan recognition protein SC2 (PGRP-SC2), 277–278 Personalized medicine, 7–8, 61–64, 140–142, 372, 379 chemical exposure measurement in, 196 components, 11f definitions, 9t–10t Entscheidungs problem, framework, 175 global shift in characters and trends of diseases, 12 strength, 11 ultimate goal of, 11–12 PESI-MS See Probe electrospray ionization– mass spectrometry (PESI-MS) PET See Positron emission tomography (PET) PGDB See Pathway/Genome Database (PGDB) PGRN See Pharmacogenetics Research Network (PGRN) PGRP-SC2 See Peptidoglycan recognition protein SC2 (PGRP-SC2) Pharmaceuticals, 29, 380–382 Pharmacometabonomics, 35, 37–38, 78, 144–145, 379 See also Phenomics for drug efficacy, 152–153 GC–MS approach, 155 H NMR spectroscopy, 157 human pharmacometabonomics experiments, 154t LC-ECA, 155–156 3-OHKY, 156 pharmacometabonomics, 158 for drug safety, 152 preclinical, 142–144 for prediction of pharmacokinetics and drug metabolism, 151–152 in humans, 145–151 paracetamol metabolism, 145–151 Pharmacoproteomics, 144–145 Pharmacogenetics Research Network (PGRN), 155 Pharmacogenomics, 35 Phenome, 291–292, 296 augmented analysis, 76–77 center, 40, 307–309, 373–374, 380–382 in health research, 309–311 informatics, 304 MRC-NIHR Phenome Centre, 40 NIH, 41 operational view, 302–303 patient journey phenotyping, 40 in 21st century health research and medicine, 301 Phenomics, 291–292 centers in 21st century health research and medicine, 301 in health research, 309–311 informatics, 304 operational view, 302–303 disease, 293 gene–protein axis, 297–299 in health care, 304–309 health care policy, 294–295 and human condition, 291–294 limitations and challenges, 311 macro-and micro-environmental contributions, 292 metabolic outputs, 297 metabolic profiling, 297 metabolomics vs., 300 phenomes, 296 center, 295–296 phenomics-based approaches, 294 value proposition of research, 301 Phenylacetylglutamine (PAG), 270–271 Phosphatidylethanolamine (PE), 152–153 Phosphatidylcholine (PC), 152–153 3′-Phosphoadenosine 5′-phosphosulfate (PAPS), 150 Phosphocholine (PCho), 83 Photosensitizers, 100–101 PICU See Pediatric intensive care unit (PICU) Pisse prophets, 17–18 406  Index Plasma, 57 metabolic profiles, 126 PLS See Partial least squares (PLS) PLS model See Projection to latent structure (PLS) model PLS-DA See Partial least square-discriminant analysis (PLS-DA) Point measurement techniques, 100–101 Polybrominated diphenyl ether (PBDE), 195–196 Polychlorinated biphenyls (PCB), 187–188 Polycyclic aromatic hydrocarbon (PAH), 195 Pooled reference, 190–191 Population models, 338–339 Population scale studies, 31–35 Population screening, 171 age-related metabolic phenotype, 178 chronologic and physiologic aging, 180 metabolites, 178–179 nontargeted metabolomics approaches, 179–180 prevalence of multimorbidities, 179f comparison of untargeted and targeted chemical profiling techniques, 173f conceptual framework for use of metabolic phenotyping, 175f data-driven approach, 172–174 dietary metabolic phenotype, 182–186, 184t–185t disease metabolic phenotype, 180–181 biological fluids, 182 heterogeneity in disease processes, 181–182 metabolic markers, 181 number of articles listed in PubMed, 181f diurnal metabolic phenotype, 176–177 gender metabolic phenotype, 177–178 geographic metabolic phenotype, 186–187 for hazard identification, 189–190 chemical exposure survey data in humans, 190–191 concentration of selected environmental chemicals, 192t reference standardization, 191 untargeted metabolomic approaches, 190 HRM, 171–172, 172f metabolomics-based, 174 MWASs, 174–175 nutritional metabolic phenotype, 182–186 Personalized Medicine framework, 175 translational applications, 174 Population-level analysis, 305 Porphyria, 17–18 Positron emission tomography (PET), 335 Post-translational modification (PTM), 298 Postgenomic technologies, 25–26 PPROM See Preterm prelabor rupture of fetal membranes (PPROM) Pre-eclampsia, 234–235 Prebiotics, 239 Precision medicine, 8, 379, 382–384 Precision surgery and surgical spectroscopy chemical biopsy and chemistry in clinic magic angle spinning NMR, 79–86 mass spectrometry imaging in surgery, 86–90 current challenges in surgical cancer biomarker discovery, 78–79 demands on personalized surgery, 75–76 individual’s surgical phenome, 76–77 “intelligent” surgical devices, 92 alternative spectroscopic techniques, 94–97 intraoperative frozen section, 92 limitations, 92 MarginProbe, 94 requirements for real-time diagnostic instruments, 93t traditional pathologic methods, 94 in vivo tissue diagnostics, 97 lipid metabolism in cancer, 90 alteration, 92 human cells, 91 Warburg effect, 90–91 metabotypes of individuals, 76 molecular phenotyping and sample phenotyping, 77–78 REIMS, 97–99, 98f, 100f ambient MS, 101, 102t applications, 100–101 lipidomic/metabonomic MS, 99–100 surgical environment, 99 thermal evaporation approaches, 99 visualization of spectral data during surgery, 100f surgery as model for studying metabolism, 77 unique genetic fingerprint, 76 Preclinical pharmacometabonomics, 142–144 Preclinical testing, 11 Predictive metabolic phenotyping, 35–36 Predictive metabonomics, 158–160 See also Pharmacometabonomics Predose urine, 143–144, 143f Index  407 Pregnancy affecting offspring, conditions in asymptomatic bacteriuria, 228–229 gestational diabetes, 232–233 gut microbiome in pregnancy, 223 intrahepatic cholestasis, 229–230 maternal obesity, 230–231 PPROM, 225–227 pre-eclampsia, 234–235 premature birth, 224–225 vaginal microbiome in pregnancy, 221–223 Premature birth, 224–225 Preterm infants, 217–218 Preterm labor (PTL), 224 Preterm prelabor rupture of fetal membranes (PPROM), 225 comparison of vaginal microbiome, 226f p-HPA, 225–227 urinary metabolome reflects system response, 228f Principal component analysis (PCA), 132, 133f, 142–143, 340 Probe electrospray ionization–mass spectrometry (PESI-MS), 101 Probiotics, 239 Program database (PDB), 351–352 Projection to latent structure (PLS) model, 143–144 Protein Data Bank (PDB), 298–299 Protein expression, 299 Protein Viewer application, 351–352 Proteobacteria, 273–274 Proteolytic functions, 277 Proteomics Standards Initiative (PSI), 328 PS-MS See Paper spray–mass spectrometry (PS-MS) PsA See Psoriatic arthritis (PsA) Pseudomonas, 283 PSI See Proteomics Standards Initiative (PSI) Psoriatic arthritis (PsA), 157 PTL See Preterm labor (PTL) PTM See Post-translational modification (PTM) PubChem, 325 Public health, 11 Publicly available metabolic phenotype data repositories, 132 Pulse-Fourier transform proton NMR spectroscopy, 20 PYY See Peptide YY (PYY) Q QA program See Quality assurance (QA) program QBP See Quality-based procedure (QBP) QC See Quality control (QC) QC-RSC See Quality control–robust spline correction (QC-RSC) QIDS-C See Quick Inventory of Depressive Symptomatology–Clinician Rated (QIDS-C) Quality assurance (QA) program, 127–128 Quality control (QC), 117–118, 127–128 Quality control–robust spline correction (QC-RSC), 124 Quality-based procedure (QBP), 305–306 Quantitative NMR-based metabolic phenotyping, 32–33 Quick Inventory of Depressive Symptomatology–Clinician Rated (QIDS-C), 155 Quinine (Q), 151 R R&D See Research and Development (R&D) RA See Rheumatoid arthritis (RA) RACHS See Risk adjusted congenital heart surgery (RACHS) Rapid evaporative ionization–mass spectrometry (REIMS), 38–39, 97–99, 228–229, 308–309, 378 RCMRC See Regional Comprehensive Metabolomics Research Core (RCMRC) Reactive oxygen species (ROS), 272 Reactome, 321–322 Real-time electrospray ionization–mass spectrometry (REIMS), 97–99, 98f, 100f ambient MS, 101, 102t applications, 100–101 lipidomic/metabonomic MS, 99–100 surgical environment, 99 thermal evaporation approaches, 99 visualization of spectral data during surgery, 100f Reference chemical databases, 320 compound-centric databases, 322–325 pathway-centric databases BioCyc, 320 KEGG, 320–321 Reactome, 321–322 Wikipathways, 322 Reference standardization, 172–174, 191 Reg-Alpha, 87–88 408  Index Regional Comprehensive Metabolomics Research Core (RCMRC), 326–327 REIMS See Rapid evaporative ionization– mass spectrometry (REIMS); Realtime electrospray ionization–mass spectrometry (REIMS) Relative standard deviation (RSD), 199 Renal toxin, 28–29 Research and Development (R&D), 138 Reversed phase chromatography (RPC), 119 Reversed phase liquid chromatography (RP-LC), 123 Rheumatoid arthritis (RA), 157 Risk adjusted congenital heart surgery (RACHS), 60 Robotic preparation workflows, 118–119 Robust predictive models, 38 ROS See Reactive oxygen species (ROS) RP-LC See Reversed phase liquid chromatography (RP-LC) RPC See Reversed phase chromatography (RPC) RSD See Relative standard deviation (RSD) S SAGE See Scalable Adaptive Graphics Environment (SAGE) Sample preparation, 117 for LC–MS, 119 for NMR spectroscopy, 118–119 QC, 117–118 San Diego Supercomputer Center (SDSC), 326–327 Scalable Adaptive Graphics Environment (SAGE), 354 SCFA See Short-chain fatty acid (SCFA) SCoTMI analysis See Sparse Conditional Trajectory Mutual Information (SCoTMI) analysis) Scripps Research Institute, 323–324 SDSC See San Diego Supercomputer Center (SDSC) SEBAS See Social Environment and Biomarkers of Aging Study (SEBAS) Secondary ion mass spectrometry (SIMS), 86 Selective serotonin reuptake inhibitor (SSRI), 155 Senescence-associated secretory phenotype, 281 Senescent cells, 281 Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, 155 Sequential Organ Failure Assessment (SOFA), 157–158 Serotonin (5-HT), 155–156 Serum, 112 collection, 116–117 metabolic profiles, 126 17-Seventeen-item Hamilton Rating Scale for Depression (HAMD17), 155–156 SFC See Supercritical fluid chromatography (SFC) Shared and unique structure plot (SUS-plot), 133 SHIP See Study of Health in Pomerania (SHIP) Short-chain fatty acid (SCFA), 220–221, 274, 370–371 SIMS See Secondary ion mass spectrometry (SIMS) Simulation and data fusion design, 356 Single nucleotide polymorphism (SNP), 141– 142, 172–174 Skin cancer, 54–55 SMRS See Standard Metabolic Reporting Structure (SMRS) SNP See Single nucleotide polymorphism (SNP) Social Environment and Biomarkers of Aging Study (SEBAS), 270–271 SOFA See Sequential Organ Failure Assessment (SOFA) “Soft ionization” technique, 88 SOP See Standard operating procedure (SOP) Sparse Conditional Trajectory Mutual Information (SCoTMI) analysis, 358f Spectral interconversion algorithm, 90 Spectroscopic profiling of biofluids, cells, and tissues, 67 Spectroscopic techniques, 94 Spiking, 113–114 Spin-echo spectra, 21 SR See Study reference (SR) SSRI See Selective serotonin reuptake inhibitor (SSRI) Standard Metabolic Reporting Structure (SMRS), 374–375 Standard operating procedure (SOP), 127 Standardization protocols, 374–375 STAR*D study See Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study StarCAVE, 351 blood flow data in visualization facility, 351f diagnostic tools, 353 Index  409 NexCAVE, 352 Protein Viewer application, 351–352 RNA protein rendering, 352f Statistical correlation spectroscopy (STOCSY), 128, 129f Statistical heterospectroscopy, 340 Statistical spectroscopy, 340 STOCSY See Statistical correlation spectroscopy (STOCSY) Stratified and personalized health care, 66 data capture, 66–67 data processing and modeling, 67–68 data storage and query, 68 sample collection and processing, 67 spectroscopic profiling of biofluids, cells, and tissues, 67 Stratified medicine, 8, 356, 379 application to, 35–36 Streptococcus pneumoniae, Study of Health in Pomerania (SHIP), 338–339 Study reference (SR), 117–118 Supercritical fluid chromatography (SFC), 370–371 Supervised methods, 67–68, 340 Supra-organism, human being as, 377–378 Surgical intervention, 308 Surgical metabonomics, 380 SUS-plot See Shared and unique structure plot (SUS-plot) Systemic inflammatory response syndrome, 78 Systems biology, 7–8 approaches, 285–286 predictive models and visualization in, 339 T T2D See Type diabetes (T2D) Target of rapamycin (TOR), 266–267 Taurochenodeoxycholic acid (TCDCA), 152–153 Taurocholic acid (TCA), 152–153 Taurodeoxycholic acid (TDCA), 152–153 Taurolithocholic acid (TLCA), 152–153 TCA See Taurocholic acid (TCA) TCA cycle See Tricarboxylic acid (TCA) cycle TCDCA See Taurochenodeoxycholic acid (TCDCA) TDCA See Taurodeoxycholic acid (TDCA) Technical incompetence, 142–143 Thin layer chromatography (TLC), 152–153 TIA See Transient ischemic attack (TIA) Tiled LCD screen systems, 357 Time demand of analysis, 88 Time series analysis, 68 Tipping elements, 276–277 Tissue biopsy, 77–78 TLC See Thin layer chromatography (TLC) TLCA See Taurolithocholic acid (TLCA) TLRs See Toll-like receptors (TLRs) TMAO See Trimethylamine-N-oxide (TMAO) TNF-α See Tumor necrosis factor alpha (TNF-α) TOCSY See Total correlation spectroscopy (TOCSY) Toll-like receptors (TLRs), 224, 278–279 TLR2, 231 TLR4, 231 Topologic methods, 337 TOR See Target of rapamycin (TOR) Total correlation spectroscopy (TOCSY), 113–114 Toxic Substance Control Act, 169–170 Toxicity, 20–23, 26, 28–29 Toxicology, 113 Tracker, 350 “Training” set, 340 Transcriptomics, 269–270 Transient ischemic attack (TIA), 160 Translational medicine, 49–50 Tricarboxylic acid (TCA) cycle, 217–218, 270 Trimethoprim antibiotic, 284–285 Trimethylamine-N-oxide (TMAO), 142–143, 183–185 Tumor margin with clearances, 92 Tumor necrosis factor alpha (TNF-α), 231 Tunnel vision, omnidirectional vision vs., 346–347 Tweets, 341–342 Two-dimensional 1H JRES, 122 Type diabetes (T2D), 232, 292–293 U UC See University of California (UC) UCSB See University of California, Santa Barbara (UCSB) UCSD See University of California San Diego (UCSD) UIC See University of Illinois at Chicago (UIC) Ultra-high performance liquid chromatography (UPLC), 122–123, 302–303, 339–340, 370–371 Ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS), 55–56, 125f Ultraviolet (UV), 122–123 410  Index University of California (UC), 24 University of California, Santa Barbara (UCSB), 347 University of California San Diego (UCSD), 29, 347 University of Illinois at Chicago (UIC), 347 University of New South Wales (UNSW), 347 Unmet medical needs, 1–3 addressing problems, 6–7 analysis of global data, causes of global mortality, 2f current rates of global mortality, of disease management, 4–5 disease trends, 3, 3f epidemiologic shifts in disease, 3–4 Epstein–Barr virus, 5–6 historical perspective, 1–2 HIV/AIDS, metabolic phenotyping, 12 patient’s metabolic trajectory, 12–13 population screening, 12 techniques, 12 unmet medical needs, 13 obesity, personalized medicine, 7–8 components, 11f definitions, 9t–10t Entscheidungs problem, global shift in characters and trends of diseases, 12 strength, 11 ultimate goal of, 11–12 unmet needs in health care, 4f worsening threat of antimicrobial resistance, Unsupervised methods, 67–68 UNSW See University of New South Wales (UNSW) Untargeted metabolomic approaches, 190 Untargeted profiles, 374 UPLC See Ultra-high performance liquid chromatography (UPLC) UPLC-MS See Ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) Ureaplasma species, 224–225 Urinary tract infection (UTI), 228–229 Urine, 57, 112 profiles, 126 Ursodeoxycholic acid, 229–230 U.S Environmental Protection Agency (EPA), 169–170 US Food and Drug Administration (FDA), 27, 94 US National Institutes of Health (NIH), 30, 326, 344, 371–372 UTI See Urinary tract infection (UTI) UV See Ultraviolet (UV) V Vaginal microbiome in pregnancy, 221–223 Validation of protocols, 374–375 set, 340 VC See Virtual colonoscopy (VC) VE-HuNT system See Virtual Environment Human Navigation Task (VE-HuNT) system Venturi easy ambient sonic-spray ionization, 97–99 Verrucomicrobia, 273–274 Very low-density lipoprotein particle size, 32 VH data See Virtual human (VH) data Virtual colonoscopy (VC), 335 Virtual Environment Human Navigation Task (VE-HuNT) system, 352 Virtual human (VH) data, 337 Virtual Microscope, 357–358 Virtual Physiologic Human (VPH), 337–338 Virtual Reality Modeling Language (VRML), 351–352 Virtual reality (VR) systems, 347 Visible Human Project, 337 Visualization paradigms, 334 Volume, variety, and velocity (3Vs), 318 VPH See Virtual Physiologic Human (VPH) VR systems See Virtual reality (VR) systems VRML See Virtual Reality Modeling Language (VRML) 3Vs See Volume, variety, and velocity (3Vs) W Warburg effect, 90–91, 272 WavSTAT system, 94–97 Weaning food, 238–239 Wikipathways, 322 World Health Organization (WHO), 2–3, 235 X XML See Extensible Markup Language (XML) ... samples, noting the lipoprotein profiles in serum and plasma, observing altered metabolic profiles in certain diseases, and picking out drug metabolite peaks in urine and then identifying them Spectroscopic... blood plasma and serum, where it has been widely used to attenuate the interfering broad peaks from proteins and lipoproteins 22  Metabolic Phenotyping in Personalized and Public Healthcare At... how metabolic phenotyping can fit into clinical medicine and population screening, along with the other many advances that are paving the way to precision medicine and hence patient benefit Elaine