Literature DB >> 30242936

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing.

Adam D Kennedy1, Bryan M Wittmann1, Anne M Evans1, Luke A D Miller1, Douglas R Toal1, Shaun Lonergan1, Sarah H Elsea2, Kirk L Pappan1.   

Abstract

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Clinical Laboratory Improvement Amendments (CLIA); College of American Pathologists (CAP); assay development; biochemical phenotype; biochemistry; biomarker; chromatography; clinical; laboratory developed test (LDT); longitudinal analysis; mass spectrometry; metabolomics; quality control; validation

Mesh:

Year:  2018        PMID: 30242936     DOI: 10.1002/jms.4292

Source DB:  PubMed          Journal:  J Mass Spectrom        ISSN: 1076-5174            Impact factor:   1.982


  21 in total

1.  Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics.

Authors:  Kelli Goodman; Matthew Mitchell; Anne M Evans; Luke A D Miller; Lisa Ford; Bryan Wittmann; Adam D Kennedy; Douglas Toal
Journal:  Metabolomics       Date:  2021-03-11       Impact factor: 4.290

2.  Metabolic network-based identification of plasma markers for non-small cell lung cancer.

Authors:  Linling Guo; Linrui Li; Zhiyun Xu; Fanchen Meng; Huimin Guo; Peijia Liu; Peifang Liu; Yuan Tian; Fengguo Xu; Zunjian Zhang; Shuai Zhang; Yin Huang
Journal:  Anal Bioanal Chem       Date:  2021-10-07       Impact factor: 4.142

3.  A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients.

Authors:  Oiana Telleria; Oihane E Alboniga; Marc Clos-Garcia; Beatriz Nafría-Jimenez; Joaquin Cubiella; Luis Bujanda; Juan Manuel Falcón-Pérez
Journal:  Metabolites       Date:  2022-06-15

4.  Experiences with offering pro bono medical genetics services in the West Indies: Benefits to patients, physicians, and the community.

Authors:  Andrew K Sobering; Dong Li; Jennifer S Beighley; John C Carey; Tyhiesia Donald; Sarah H Elsea; Karla P Figueroa; Jennifer Gerdts; Andre Hamlet; Ghayda M Mirzaa; Beverly Nelson; Stefan M Pulst; Janice L Smith; Flora Tassone; Helga V Toriello; Ruth H Walker; Katherine R Yearwood; Elizabeth J Bhoj
Journal:  Am J Med Genet C Semin Med Genet       Date:  2020-12-04       Impact factor: 3.908

5.  Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data.

Authors:  Lillian R Thistlethwaite; Xiqi Li; Lindsay C Burrage; Kevin Riehle; Joseph G Hacia; Nancy Braverman; Michael F Wangler; Marcus J Miller; Sarah H Elsea; Aleksandar Milosavljevic
Journal:  Sci Rep       Date:  2022-04-21       Impact factor: 4.996

6.  Untargeted Metabolomics-Based Screening Method for Inborn Errors of Metabolism using Semi-Automatic Sample Preparation with an UHPLC- Orbitrap-MS Platform.

Authors:  Ramon Bonte; Michiel Bongaerts; Serwet Demirdas; Janneke G Langendonk; Hidde H Huidekoper; Monique Williams; Willem Onkenhout; Edwin H Jacobs; Henk J Blom; George J G Ruijter
Journal:  Metabolites       Date:  2019-11-26

Review 7.  Breast Cancer: Targeting of Steroid Hormones in Cancerogenesis and Diagnostics.

Authors:  Marcela Valko-Rokytovská; Peter Očenáš; Aneta Salayová; Zuzana Kostecká
Journal:  Int J Mol Sci       Date:  2021-05-30       Impact factor: 5.923

8.  Mass Spectrometry-Based Metabolomics Analysis of Obese Patients' Blood Plasma.

Authors:  Petr G Lokhov; Elena E Balashova; Oxana P Trifonova; Dmitry L Maslov; Elena A Ponomarenko; Alexander I Archakov
Journal:  Int J Mol Sci       Date:  2020-01-15       Impact factor: 5.923

9.  Method development and validation for the quantification of organic acids in microbial samples using anionic exchange solid-phase extraction and gas chromatography-mass spectrometry.

Authors:  Simone Heyen; Barbara M Scholz-Böttcher; Ralf Rabus; Heinz Wilkes
Journal:  Anal Bioanal Chem       Date:  2020-09-24       Impact factor: 4.142

10.  Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism.

Authors:  Ning Liu; Jing Xiao; Charul Gijavanekar; Kirk L Pappan; Kevin E Glinton; Brian J Shayota; Adam D Kennedy; Qin Sun; V Reid Sutton; Sarah H Elsea
Journal:  JAMA Netw Open       Date:  2021-07-01
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