Evidence map›Paper›PMID 33974145›Full record

ReviewCurrent diabetes reports2021

Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease.

Sho Hasegawa, Reiko Inagi

Open access · hybridAbstract readReview
In one paragraph

Review in Current diabetes reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

20 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  15. The AKI-to-CKD Transition: The Role of Uremic Toxins.International journal of molecular sciences · 2023
    Review
  16. Article
  17. Article
  18. Review
  19. Frontiers in physiology · 2021
    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

2 authors at 1 institution in 1 country.

Sho HasegawaDivision of Chronic Kidney Disease Pathophysiology, The University of Tokyo Graduate School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.ORCID https://orcid.org/0000-0002-0798-9400
Reiko InagiDivision of Chronic Kidney Disease Pathophysiology, The University of Tokyo Graduate School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. inagi-r@m.u-tokyo.ac.jp.
The University of Tokyo · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewDiabetic kidney disease (DKD), a leading cause of end-stage kidney disease, is the result of metabolic network alterations in the kidney. Therefore, metabolomics is an effective tool for understanding its pathophysiology, finding key biomarkers, and developing a new treatment strategy. In this review, we summarize the application of metabolomics to DKD research. RECENT

findingsAlterations in renal energy metabolism including the accumulation of tricarboxylic acid cycle and glucose metabolites are observed in the early stage of DKD, and they finally lead to mitochondrial dysfunction in advanced DKD. Mitochondrial fission-fusion imbalance and dysregulated organelle crosstalk might contribute to this process. Moreover, metabolomics has identified several uremic toxins including phenyl sulfate and tryptophan derivatives as promising biomarkers that mediate DKD progression. Recent advances in metabolomics have clarified the role of dysregulated energy metabolism and uremic toxins in DKD pathophysiology. Integration of multi-omics data will provide additional information for identifying critical drivers of DKD.

Indexed as

Diabetes MellitusDiabetic NephropathiesKidney Failure, ChronicBiomarkersDisease ProgressionHumansKidneyMetabolomicsBiomarkersBiomarkerDiabetic kidney diseaseMetabolomicsMitochondriaOrganelle crosstalkUremic toxin

Identifiers

PMID33974145
PMCPMC8113300
OpenAlexW3163933252

What OpenQuestion holds

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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 OpenQuestion graph.