Evidence map›Paper›PMID 39011161›Full record

ArticleMetabolism open2024

Clinical metabolomics: Useful insights, perspectives and challenges.

Maria Dalamaga

Abstract readEditorial
In one paragraph

Article in Metabolism open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
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

18 citing papers in PubMed.

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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

1 author.

Maria DalamagaDepartment of Biological Chemistry, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.

Indexed as

Mass spectrometryMetaboliteMetabolomicsMulti-omicsObesityOmicsPrecision medicine

Identifiers

PMID39011161
PMCPMC11247213

What OpenQuestion holds

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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 OpenQuestion graph.