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Liu D, Nagana Gowda GA, Jiang Z, Alemdjrodo K, Zhang M, Zhang D, Raftery D. Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts. Proc Natl Acad Sci U S A 2024; 121:e2307430121. [PMID: 38359289 PMCID: PMC10895372 DOI: 10.1073/pnas.2307430121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024] Open
Abstract
Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.
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Affiliation(s)
- Danni Liu
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
| | - Zhongli Jiang
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Kangni Alemdjrodo
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Min Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Dabao Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
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Choi JJ, Koscik RL, Jonaitis EM, Panyard DJ, Morrow AR, Johnson SC, Engelman CD, Schmitz LL. Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics. Metabolites 2023; 13:1154. [PMID: 37999250 PMCID: PMC10673384 DOI: 10.3390/metabo13111154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023] Open
Abstract
(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.
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Affiliation(s)
- Jerome J. Choi
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Rebecca L. Koscik
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Daniel J. Panyard
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA 94305, USA;
| | - Autumn R. Morrow
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (R.L.K.); (E.M.J.)
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI 53792, USA
- William S. Middleton Memorial Veterans Hospital, Middleton, WI 53705, USA
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Corinne D. Engelman
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA; (J.J.C.); (A.R.M.)
| | - Lauren L. Schmitz
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706, USA;
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Hukku A, Sampson MG, Luca F, Pique-Regi R, Wen X. Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility. Am J Hum Genet 2022; 109:825-837. [PMID: 35523146 PMCID: PMC9118134 DOI: 10.1016/j.ajhg.2022.04.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/04/2022] [Indexed: 11/29/2022] Open
Abstract
Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.
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Affiliation(s)
- Abhay Hukku
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Matthew G Sampson
- Division of Nephrology, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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Abstract
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.
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Brial F, Le Lay A, Dumas ME, Gauguier D. Implication of gut microbiota metabolites in cardiovascular and metabolic diseases. Cell Mol Life Sci 2018; 75:3977-3990. [PMID: 30101405 PMCID: PMC6182343 DOI: 10.1007/s00018-018-2901-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 07/31/2018] [Accepted: 08/08/2018] [Indexed: 12/18/2022]
Abstract
Evidence from the literature keeps highlighting the impact of mutualistic bacterial communities of the gut microbiota on human health. The gut microbita is a complex ecosystem of symbiotic bacteria which contributes to mammalian host biology by processing, otherwise, indigestible nutrients, supplying essential metabolites, and contributing to modulate its immune system. Advances in sequencing technologies have enabled structural analysis of the human gut microbiota and allowed detection of changes in gut bacterial composition in several common diseases, including cardiometabolic disorders. Biological signals sent by the gut microbiota to the host, including microbial metabolites and pro-inflammatory molecules, mediate microbiome-host genome cross-talk. This rapidly expanding line of research can identify disease-causing and disease-predictive microbial metabolite biomarkers, which can be translated into novel biodiagnostic tests, dietary supplements, and nutritional interventions for personalized therapeutic developments in common diseases. Here, we review results from the most significant studies dealing with the association of products from the gut microbial metabolism with cardiometabolic disorders. We underline the importance of these postbiotic biomarkers in the diagnosis and treatment of human disorders.
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Affiliation(s)
- Francois Brial
- Sorbonne University, University Paris Descartes, INSERM UMR_S1138, Cordeliers Research Centre, 15 rue de l'Ecole de Médecine, 75006, Paris, France
| | - Aurélie Le Lay
- Sorbonne University, University Paris Descartes, INSERM UMR_S1138, Cordeliers Research Centre, 15 rue de l'Ecole de Médecine, 75006, Paris, France
| | - Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, London, UK
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC, H3A 0G1, Canada
| | - Dominique Gauguier
- Sorbonne University, University Paris Descartes, INSERM UMR_S1138, Cordeliers Research Centre, 15 rue de l'Ecole de Médecine, 75006, Paris, France.
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, London, UK.
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC, H3A 0G1, Canada.
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Abstract
PURPOSE OF REVIEW Disruption of metabolic homeostasis is universal in the critically ill. Macronutrients and micronutrients are major environmental regulators of metabolite production through their gene regulation effects. The study of large numbers of circulating metabolites is beginning to emerge through the comprehensive profiling of the critically ill. In the critically ill, metabolomic studies consistently show that changes in fatty acids, lipids and tryptophan metabolite pathways are common and are associated with disease state and outcomes. RECENT FINDINGS Metabolomics is now being applied in research studies to determine the critical illness response to nutrient deficiency and delivery. Nutritional metabolomics approaches in nutrient deficiency, malnutrition and nutrient delivery have included single time point studies and dynamic studies of critically ill patients over time. Integration of metabolomics and clinical outcome data may create a more complete understanding of the control of metabolism in critical illness. SUMMARY The integration of metabolomic profiling with transcription and genomic data may allow for a unique window into the mechanism of how nutrient deficiency and delivery alters cellular homeostasis during critical illness and modulates the regain of cellular homeostasis during recovery. The progress and the challenges of the study of nutritional metabolomics are reviewed here.
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Affiliation(s)
- Kenneth B Christopher
- Division of Renal Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, Massachusetts, USA
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Ebbels TMD, Rodriguez-Martinez A, Dumas ME, Keun HC. Advances in Computational Analysis of Metabolomic NMR Data. NMR-BASED METABOLOMICS 2018. [DOI: 10.1039/9781782627937-00310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
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Affiliation(s)
- Timothy M. D. Ebbels
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Andrea Rodriguez-Martinez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Marc-Emmanuel Dumas
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London SW7 2AZ UK
| | - Hector C. Keun
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London London W12 0NN UK
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8
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Kaput J, Perozzi G, Radonjic M, Virgili F. Propelling the paradigm shift from reductionism to systems nutrition. GENES & NUTRITION 2017; 12:3. [PMID: 28138347 PMCID: PMC5264346 DOI: 10.1186/s12263-016-0549-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 12/13/2016] [Indexed: 12/14/2022]
Abstract
The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.
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Affiliation(s)
- Jim Kaput
- Nestle Institute of Health Sciences, Lausanne, Switzerland
| | | | | | - Fabio Virgili
- CREA-NUT, Food & Nutrition Research Centre, Rome, Italy
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Jobard E, Trédan O, Postoly D, André F, Martin AL, Elena-Herrmann B, Boyault S. A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies. Int J Mol Sci 2016; 17:ijms17122035. [PMID: 27929400 PMCID: PMC5187835 DOI: 10.3390/ijms17122035] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/14/2016] [Accepted: 11/29/2016] [Indexed: 12/11/2022] Open
Abstract
The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies.
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Affiliation(s)
- Elodie Jobard
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques UMR 5280, 5 rue de la Doua, F-69100 Villeurbanne, France.
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Olivier Trédan
- Centre Léon Bérard, Département d'oncologie Médicale, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Déborah Postoly
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, Génomique des Cancers, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
| | - Fabrice André
- Department of Medical Oncology, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France.
| | | | - Bénédicte Elena-Herrmann
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques UMR 5280, 5 rue de la Doua, F-69100 Villeurbanne, France.
| | - Sandrine Boyault
- Centre Léon Bérard, Département de Recherche Translationnelle et de l'Innovation, Génomique des Cancers, 28 rue Laënnec, 69373 Lyon, CEDEX 08, France.
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Luo P, Yin P, Zhang W, Zhou L, Lu X, Lin X, Xu G. Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography-mass spectrometry. J Chromatogr A 2016; 1437:127-136. [PMID: 26877181 DOI: 10.1016/j.chroma.2016.01.078] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 12/29/2015] [Accepted: 01/30/2016] [Indexed: 12/30/2022]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) is now a main stream technique for large-scale metabolic phenotyping to obtain a better understanding of genomic functions. However, repeatability is still an essential issue for the LC-MS based methods, and convincing strategies for long time analysis are urgently required. Our former reported pseudotargeted method which combines nontargeted and targeted analyses, is proved to be a practical approach with high-quality and information-rich data. In this study, we developed a comprehensive strategy based on the pseudotargeted analysis by integrating blank-wash, pooled quality control (QC) sample, and post-calibration for the large-scale metabolomics study. The performance of strategy was optimized from both pre- and post-acquisition sections including the selection of QC samples, insertion frequency of QC samples, and post-calibration methods. These results imply that the pseudotargeted method is rather stable and suitable for large-scale study of metabolic profiling. As a proof of concept, the proposed strategy was applied to the combination of 3 independent batches within a time span of 5 weeks, and generated about 54% of the features with coefficient of variations (CV) below 15%. Moreover, the stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110h) while still providing excellent stability, the CV of 63% metabolic features was less than 15%. Taken together, the improved repeatability of our strategy provides a reliable protocol for large-scale metabolomics studies.
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Affiliation(s)
- Ping Luo
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Peiyuan Yin
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Weijian Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Lina Zhou
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xin Lu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Guowang Xu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
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Abstract
The human metabolome-the complement of small molecule metabolites present in biofluids and tissues-represents a significant part of the internal chemical milieu and is therefore an important aspect of the human exposome. Metabolic profiling approaches, commonly referred to as metabonomics or metabolomics, permit detailed and efficient characterisation of human biospecimens; application to population studies holds great promise for uncovering new associations and causal relationships between environmental factors and chronic disease. In addition to the insight metabolic information can provide, metabolic phenotypes anchor other molecular readouts and help formulate a systems-level interpretation of biological phenomena. In this commentary, we discuss the general approach for applying metabolic profiling in exposome studies, alongside recent exemplars. We also comment on the complexity and dynamism of the metabolome and highlight both the limitations such properties impart and the requirements for dealing with such issues in real-world phenotyping studies. Given that several large-scale exposome studies are now underway, we offer a perspective on current and future challenges that will need to be addressed to maximise their utility in environmental health research.
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Affiliation(s)
- Toby J Athersuch
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College Norfolk Place, London, London W2 1PG, UK
| | - Hector C Keun
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College Norfolk Place, London, London W2 1PG, UK
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12
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Probert F, Rice P, Scudamore CL, Wells S, Williams R, Hough TA, Cox IJ. 1H NMR Metabolic Profiling of Plasma Reveals Additional Phenotypes in Knockout Mouse Models. J Proteome Res 2015; 14:2036-45. [DOI: 10.1021/pr501039k] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Fay Probert
- Mary
Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, United Kingdom
- Institute of Hepatology, Foundation for Liver Research, 69-75 Chenies Mews, London WC1E 6HX, United Kingdom
| | - Paul Rice
- Mary
Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, United Kingdom
| | | | - Sara Wells
- Mary
Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, United Kingdom
| | - Roger Williams
- Institute of Hepatology, Foundation for Liver Research, 69-75 Chenies Mews, London WC1E 6HX, United Kingdom
| | - Tertius A. Hough
- Mary
Lyon Centre, MRC Harwell, Oxfordshire OX11 0RD, United Kingdom
| | - I. Jane Cox
- Institute of Hepatology, Foundation for Liver Research, 69-75 Chenies Mews, London WC1E 6HX, United Kingdom
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13
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Larive CK, Barding GA, Dinges MM. NMR spectroscopy for metabolomics and metabolic profiling. Anal Chem 2014; 87:133-46. [PMID: 25375201 DOI: 10.1021/ac504075g] [Citation(s) in RCA: 170] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Cynthia K Larive
- Department of Chemistry, University of California-Riverside , Riverside, California 92521, United States
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14
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Monteiro JP, Wise C, Morine MJ, Teitel C, Pence L, Williams A, McCabe-Sellers B, Champagne C, Turner J, Shelby B, Ning B, Oguntimein J, Taylor L, Toennessen T, Priami C, Beger RD, Bogle M, Kaput J. Methylation potential associated with diet, genotype, protein, and metabolite levels in the Delta Obesity Vitamin Study. GENES & NUTRITION 2014; 9:403. [PMID: 24760553 PMCID: PMC4026438 DOI: 10.1007/s12263-014-0403-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 04/06/2014] [Indexed: 12/28/2022]
Abstract
Micronutrient research typically focuses on analyzing the effects of single or a few nutrients on health by analyzing a limited number of biomarkers. The observational study described here analyzed micronutrients, plasma proteins, dietary intakes, and genotype using a systems approach. Participants attended a community-based summer day program for 6-14 year old in 2 years. Genetic makeup, blood metabolite and protein levels, and dietary differences were measured in each individual. Twenty-four-hour dietary intakes, eight micronutrients (vitamins A, D, E, thiamin, folic acid, riboflavin, pyridoxal, and pyridoxine) and 3 one-carbon metabolites [homocysteine (Hcy), S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH)], and 1,129 plasma proteins were analyzed as a function of diet at metabolite level, plasma protein level, age, and sex. Cluster analysis identified two groups differing in SAM/SAH and differing in dietary intake patterns indicating that SAM/SAH was a potential marker of nutritional status. The approach used to analyze genetic association with the SAM/SAH metabolites is called middle-out: SNPs in 275 genes involved in the one-carbon pathway (folate, pyridoxal/pyridoxine, thiamin) or were correlated with SAM/SAH (vitamin A, E, Hcy) were analyzed instead of the entire 1M SNP data set. This procedure identified 46 SNPs in 25 genes associated with SAM/SAH demonstrating a genetic contribution to the methylation potential. Individual plasma metabolites correlated with 99 plasma proteins. Fourteen proteins correlated with body mass index, 49 with group age, and 30 with sex. The analytical strategy described here identified subgroups for targeted nutritional interventions.
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Affiliation(s)
- Jacqueline Pontes Monteiro
- />Department of Pediatrics, Faculty of Medicine, Faculty of Nutrition and Metabolism, University of São Paulo, Ribeirão Prêto, SP Brazil
| | - Carolyn Wise
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Melissa J. Morine
- />Department of Mathematics, University of Trento, Trento, Italy
- />The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Candee Teitel
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Lisa Pence
- />Division of Systems Biology, NCTR/FDA, Jefferson, AR USA
| | - Anna Williams
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Beverly McCabe-Sellers
- />Delta Obesity Prevention Research Unit, United States Department of Agriculture, Agricultural Research Service, Little Rock, AR USA
| | - Catherine Champagne
- />Dietary Assessment and Nutrition Counseling, Pennington Biomedical Research Center, Baton Rouge, LA USA
| | - Jerome Turner
- />Boys, Girls, Adults Community Development Center & The Phillips County Community Partners, Marvell, AR USA
| | - Beatrice Shelby
- />Boys, Girls, Adults Community Development Center & The Phillips County Community Partners, Marvell, AR USA
| | - Baitang Ning
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Joan Oguntimein
- />Shepherd Program for the Interdisciplinary Study of Poverty and Human Capability, Washington and Lee University, Lexington, VA USA
- />Medical School, Drexel University, Philadelphia, PA USA
| | - Lauren Taylor
- />Shepherd Program for the Interdisciplinary Study of Poverty and Human Capability, Washington and Lee University, Lexington, VA USA
- />Emory School of Public Health, Atlanta, GA USA
| | - Terri Toennessen
- />Division of Personalized Nutrition and Medicine, National Center for Toxicological Research (NCTR), Food and Drug Administration (FDA), Jefferson, AR USA
| | - Corrado Priami
- />Department of Mathematics, University of Trento, Trento, Italy
- />The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Margaret Bogle
- />Delta Obesity Prevention Research Unit, United States Department of Agriculture, Agricultural Research Service, Little Rock, AR USA
| | - Jim Kaput
- />Systems Nutrition and Health Unit, Nestle Institute of Health Sciences, Innovation Square, EPFL Campus, 1015 Lausanne, Switzerland
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15
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Issa NT, Byers SW, Dakshanamurthy S. Big data: the next frontier for innovation in therapeutics and healthcare. Expert Rev Clin Pharmacol 2014; 7:293-8. [PMID: 24702684 DOI: 10.1586/17512433.2014.905201] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.
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Affiliation(s)
- Naiem T Issa
- Department of Oncology, Lombardi Cancer Center, Georgetown University Medical Center, Washington, DC USA
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16
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Rueedi R, Ledda M, Nicholls AW, Salek RM, Marques-Vidal P, Morya E, Sameshima K, Montoliu I, Da Silva L, Collino S, Martin FP, Rezzi S, Steinbeck C, Waterworth DM, Waeber G, Vollenweider P, Beckmann JS, Le Coutre J, Mooser V, Bergmann S, Genick UK, Kutalik Z. Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS Genet 2014; 10:e1004132. [PMID: 24586186 PMCID: PMC3930510 DOI: 10.1371/journal.pgen.1004132] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 12/10/2013] [Indexed: 12/15/2022] Open
Abstract
Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on (1)H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10(-8)) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10(-44)) and lysine (rs8101881, P = 1.2×10(-33)), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.
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Affiliation(s)
- Rico Rueedi
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mirko Ledda
- Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland
| | - Andrew W. Nicholls
- Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Ware, Herts, United Kingdom
| | - Reza M. Salek
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom
| | - Pedro Marques-Vidal
- Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Lausanne, Switzerland
| | - Edgard Morya
- Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio, Libanes Hospital, São Paulo, Brazil
- Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil
| | - Koichi Sameshima
- Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Ivan Montoliu
- Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland
| | - Laeticia Da Silva
- Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland
| | - Sebastiano Collino
- Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland
| | | | - Serge Rezzi
- Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland
| | - Christoph Steinbeck
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Dawn M. Waterworth
- Medical Genetics, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America
| | - Gérard Waeber
- Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Jacques S. Beckmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Johannes Le Coutre
- Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland
- Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan
| | - Vincent Mooser
- Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sven Bergmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ulrich K. Genick
- Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Lausanne, Switzerland
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17
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Current practice of liquid chromatography–mass spectrometry in metabolomics and metabonomics. J Pharm Biomed Anal 2014; 87:12-25. [DOI: 10.1016/j.jpba.2013.06.032] [Citation(s) in RCA: 280] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Revised: 06/26/2013] [Accepted: 06/29/2013] [Indexed: 02/06/2023]
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18
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Dumas ME, Kinross J, Nicholson JK. Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. Gastroenterology 2014; 146:46-62. [PMID: 24211299 DOI: 10.1053/j.gastro.2013.11.001] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 11/01/2013] [Accepted: 11/05/2013] [Indexed: 12/17/2022]
Abstract
Metabolic syndrome, a cluster of risk factors for type 2 diabetes mellitus and cardiovascular disease, is becoming an increasing global health concern. Insulin resistance is often associated with metabolic syndrome and also typical hepatic manifestations such as nonalcoholic fatty liver disease. Profiling of metabolic products (metabolic phenotyping or metabotyping) has provided new insights into metabolic syndrome and nonalcoholic fatty liver disease. Data from nuclear magnetic resonance spectroscopy and mass spectrometry combined with statistical modeling and top-down systems biology have allowed us to analyze and interpret metabolic signatures in terms of metabolic pathways and protein interaction networks and to identify the genomic and metagenomic determinants of metabolism. For example, metabolic phenotyping has shown that relationships between host cells and the microbiome affect development of the metabolic syndrome and fatty liver disease. We review recent developments in metabolic phenotyping and systems biology technologies and how these methodologies have provided insights into the mechanisms of metabolic syndrome and nonalcoholic fatty liver disease. We discuss emerging areas of research in this field and outline our vision for how metabolic phenotyping could be used to study metabolic syndrome and fatty liver disease.
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Affiliation(s)
- Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington, London, England.
| | - James Kinross
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington, London, England; Section of Biosurgery and Surgical Technology, Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, St. Mary's Hospital, Imperial College London, London, England
| | - Jeremy K Nicholson
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington, London, England
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19
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Cornelis MC, Hu FB. Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research. Curr Nutr Rep 2013; 2. [PMID: 24278790 DOI: 10.1007/s13668-013-0052-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Systems epidemiology applied to the field of nutrition has potential to provide new insight into underlying mechanisms and ways to study the health effects of specific foods more comprehensively. Human intervention and population-based studies have identified i) common genetic factors associated with several nutrition-related traits and ii) dietary factors altering the expression of genes and levels of proteins and metabolites related to inflammation, lipid metabolism and/or gut microbial metabolism, results of high relevance to metabolic disease. System-level tools applied type 2 diabetes and related conditions have revealed new pathways that are potentially modified by diet and thus offer additional opportunities for nutritional investigations. Moving forward, harnessing the resources of existing large prospective studies within which biological samples have been archived and diet and lifestyle have been measured repeatedly within individual will enable systems-level data to be integrated, the outcome of which will be improved personalized optimal nutrition for prevention and treatment of disease.
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Affiliation(s)
- Marilyn C Cornelis
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
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20
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Sun J, Beger RD, Schnackenberg LK. Metabolomics as a tool for personalizing medicine: 2012 update. Per Med 2013; 10:149-161. [DOI: 10.2217/pme.13.8] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.
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Affiliation(s)
- Jinchun Sun
- Division of Systems Biology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Richard D Beger
- Division of Systems Biology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Laura K Schnackenberg
- Division of Systems Biology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA
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21
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Martin FPJ, Montoliu I, Nagy K, Moco S, Collino S, Guy P, Redeuil K, Scherer M, Rezzi S, Kochhar S. Specific dietary preferences are linked to differing gut microbial metabolic activity in response to dark chocolate intake. J Proteome Res 2012; 11:6252-63. [PMID: 23163751 DOI: 10.1021/pr300915z] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Systems biology approaches are providing novel insights into the role of nutrition for the management of health and disease. In the present study, we investigated if dietary preference for dark chocolate in healthy subjects may lead to different metabolic response to daily chocolate consumption. Using NMR- and MS-based metabolic profiling of blood plasma and urine, we monitored the metabolic response of 10 participants stratified as chocolate desiring and eating regularly dark chocolate (CD) and 10 participants stratified as chocolate indifferent and eating rarely dark chocolate (CI) to a daily consumption of 50 g of dark chocolate as part of a standardized diet over a one week period. We demonstrated that preference for chocolate leads to different metabolic response to chocolate consumption. Daily intake of dark chocolate significantly increased HDL cholesterol by 6% and decreased polyunsaturated acyl ether phospholipids. Dark chocolate intake could also induce an improvement in the metabolism of long chain fatty acid, as noted by a compositional change in plasma fatty acyl carnitines. Moreover, a relationship between regular long-term dietary exposure to a small amount of dark chocolate, gut microbiota, and phenolics was highlighted, providing novel insights into biological processes associated with cocoa bioactives.
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22
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Dumas ME. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes. MOLECULAR BIOSYSTEMS 2012; 8:2494-502. [DOI: 10.1039/c2mb25167a] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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