Abstract
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.
Highlights
Metabolomics offers a pragmatic and robust framework for the comprehensive measurement and identification of the endogenous and exogenous low-molecular-weight metabolites in biological systems [1]
Authors are urged to report whether data appear reasonably matched to the hypothesis, whether there are alternative approaches to explore data, whether the analysis addresses any other premises or questions, whether data pertain directly to the investigation or just contribute to relevant background, whether there are means such as data visualization or data summary included in the analysis that highlights the connection between data and results, and whether the code and data are made available in open sources [145]
Throughout the paper, we have made considerable attempts to explore the remaining challenges, evaluate current opportunities, and suggest several future directions that should be considered by the metabolomics and lipidomics community to facilitate data acquisition, analysis, and sharing at the clinical and epidemiological scale
Summary
Metabolomics offers a pragmatic and robust framework for the comprehensive measurement and identification of the endogenous and exogenous low-molecular-weight metabolites in biological systems [1]. It is understood that the causation in systems biology should determine how and why a process occurs in a certain way rather than identify separate molecular components [3]. In this regard, measuring and modeling the Metabolites 2020, 10, 51; doi:10.3390/metabo10020051 www.mdpi.com/journal/metabolites. In clinical medicine and epidemiology, together with the development of machine learning and artificial intelligence, omics analyses empower systems biology-based methods for precision health monitoring and treatment [4]. Up to the present time, a significant part of the metabolomics-based epidemiological and clinical studies is cross-sectional or non-prospective case-control, which compares the metabolic phenotypes of participants by disease or exposure status [11,12]. Provide the scientific and clinical background (including the diagnostic or prognostic purpose) and explain the rationale for developing and/or validating the multivariable prediction model, in regard to existing models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
