Evidence map›Paper›PMID 32013105›Full record

ReviewMetabolites2020

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

Nguyen Phuoc Long, Tran Diem Nghi, Yun Pyo Kang, Nguyen Hoang Anh, Hyung Min Kim, Sang Ki Park, Sung Won Kwon

Open access · goldAbstract readReview
In one paragraph

Review in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Editorial: Challenges and opportunities in tumor metabolomics.Frontiers in molecular biosciences · 2026
    Article
  10. Review
  11. Review
  12. Observational
  13. Review
  14. Article
  15. Review
  16. Article
  17. Applications of chromatographic methods in metabolomics: A review.Journal of chromatography. B, Analytical technologies in the biomedical and life sciences · 2024
    Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Nguyen Phuoc LongCollege of Pharmacy, Seoul National University, Seoul 08826, Korea.
Tran Diem NghiDepartment of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
Yun Pyo KangDepartment of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Nguyen Hoang AnhCollege of Pharmacy, Seoul National University, Seoul 08826, Korea.
Hyung Min KimCollege of Pharmacy, Seoul National University, Seoul 08826, Korea.
Sang Ki ParkDepartment of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
Sung Won KwonCollege of Pharmacy, Seoul National University, Seoul 08826, Korea.
Seoul National University · KRPohang University of Science and Technology · KRMoffitt Cancer Center · US

Funding

National Research Foundation of Korea NRF-2018R1A5A2024425/2012M3A9C4048796
6 · The paper itself

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.

Indexed as

adaptive metabolomicslipidomicsmachine learningmulti-omicsprecision medicinesystems biology

Identifiers

PMID32013105
PMCPMC7074059
OpenAlexW3003671594

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.