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

1
College of Pharmacy, Seoul National University, Seoul 08826, Korea
2
Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea
3
Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2020, 10(2), 51; https://doi.org/10.3390/metabo10020051
Received: 4 December 2019 / Revised: 17 January 2020 / Accepted: 21 January 2020 / Published: 29 January 2020
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.
Keywords: adaptive metabolomics; lipidomics; multi-omics; precision medicine; systems biology; machine learning adaptive metabolomics; lipidomics; multi-omics; precision medicine; systems biology; machine learning
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MDPI and ACS Style

Long, N.P.; Nghi, T.D.; Kang, Y.P.; Anh, N.H.; Kim, H.M.; Park, S.K.; Kwon, S.W. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020, 10, 51.

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