Slotting metabolomics into routine precision medicine

ABSTRACT

Introduction

Despite an impressive amount of metabolomics studies in animal models and humans, most findings have not yet translated into the clinical setting, and the road ahead remains still long.

Areas covered

This review provides the most challenging applications of clinical metabolomics testing in human health and disease. Personalized clinical metabolomics testing is incorporated within the test panel to diagnose inborn errors of metabolism, optimize dietary regimens, and discover and develop new drugs. The potential routine utilization of metabolomics in precision medicine has been revised in cancer and nutrition. The association between metabolomics with artificial intelligence and machine learning may open emerging perspectives for more effective utilization and timely introduction of clinical metabolomics testing in the care of patients with acute and chronic diseases.

Expert opinion

In conclusion, slotting metabolomics into routine precision medicine implies the direct relationship between metabolomic results and clinical decision-making, similarly to any other clinical test result, as well as it requires the application of clinical laboratory standards, protocols, training, the oversight to a global biochemical profiling technology, and the availability of metabolic profiles from reference populations, defining cutoff values and decision levels.

KEYWORDS:

• Clinical testing metabolomics
• system biology
• precision medicine
• oncometabolites
• nutrimetabolomics

Article highlights

• Metabolomics is strategic for the application of individualized patient’s care and tailored therapeutic treatments

• With the primary contribution of mathematical modeling combined with experimental biology, system metabolomics provides an overarching vision on biological systems as a whole

• The individual metabotype provides a molecular snapshot in health and disease

• Translating metabolomics from clinical research to clinical testing means that results must induce clinical decision-making, similarly to any other clinical test result

• Personalized clinical metabolomics testing represents the next challenge in nutrition and wellness

• Clinical metabolomics testing plays a prominent role for the evaluation of drug effectiveness and toxicity, especially in the follow-up of patients with cancer

• Artificial Intelligence and Machine Learning play a crucial role in managing clinical and metabolicdata’s imprecision and uncertainty

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties.

Reviewer disclosure

Peer reviewers in this manuscript have no relevant financial relationships or otherwise to disclose.

Author contributions

MM, AN, CP, LA, and VF developed the concept, performed the literature research, manuscript writing, revision, and supervision

Additional information

Funding

This paper was not funded.