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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7074059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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