Evidence map›Paper›PMID 37510368›Full record

ReviewGenes2023

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review.

Mina Shahisavandi, Kan Wang, Mohsen Ghanbari, Fariba Ahmadizar

Abstract readReview
In one paragraph

Review in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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  5. Article
  6. Review
  7. Article
  8. Metabolomics: Uncovering Insights into Obesity and Diabetes.International journal of molecular sciences · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
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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

4 authors.

Mina ShahisavandiDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands.
Kan WangDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands.ORCID 0000-0002-0294-7594
Mohsen GhanbariDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands.ORCID 0000-0002-9476-7143
Fariba AhmadizarDepartment of Data Science & Biostatistics, Julius Global Health, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands.ORCID 0000-0002-6261-9445

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Indexed as

Diabetes Mellitus, Type 2BiomarkersGlucoseHumansMass SpectrometryMetabolomicsBiomarkersGlucoseglucose-lowering medicationsmedicationsmetabolitesmetabolomicstype 2 diabetes mellitus

Identifiers

PMID37510368
PMCPMC10379356

What OpenQuestion holds

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Registered trials

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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.