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Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our...

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Detalles Bibliográficos
Autores principales: Long, Nguyen Phuoc, Nghi, Tran Diem, Kang, Yun Pyo, Anh, Nguyen Hoang, Kim, Hyung Min, Park, Sang Ki, Kwon, Sung Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074059/
https://www.ncbi.nlm.nih.gov/pubmed/32013105
http://dx.doi.org/10.3390/metabo10020051
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author Long, Nguyen Phuoc
Nghi, Tran Diem
Kang, Yun Pyo
Anh, Nguyen Hoang
Kim, Hyung Min
Park, Sang Ki
Kwon, Sung Won
author_facet Long, Nguyen Phuoc
Nghi, Tran Diem
Kang, Yun Pyo
Anh, Nguyen Hoang
Kim, Hyung Min
Park, Sang Ki
Kwon, Sung Won
author_sort Long, Nguyen Phuoc
collection PubMed
description Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.
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spelling pubmed-70740592020-03-19 Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine Long, Nguyen Phuoc Nghi, Tran Diem Kang, Yun Pyo Anh, Nguyen Hoang Kim, Hyung Min Park, Sang Ki Kwon, Sung Won Metabolites Review Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system. MDPI 2020-01-29 /pmc/articles/PMC7074059/ /pubmed/32013105 http://dx.doi.org/10.3390/metabo10020051 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Long, Nguyen Phuoc
Nghi, Tran Diem
Kang, Yun Pyo
Anh, Nguyen Hoang
Kim, Hyung Min
Park, Sang Ki
Kwon, Sung Won
Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title_full Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title_fullStr Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title_full_unstemmed Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title_short Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine
title_sort toward a standardized strategy of clinical metabolomics for the advancement of precision medicine
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074059/
https://www.ncbi.nlm.nih.gov/pubmed/32013105
http://dx.doi.org/10.3390/metabo10020051
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