Evidence map›Paper›PMID 41705230›Full record

ReviewFrontiers in endocrinology2025

Branched-chain amino acids and insulin resistance in type 2 diabetes: from metabolic dysregulation to therapeutic targets.

Jie Mei, Fu-Yuan Yang, Quan Gong

Abstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
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

3 authors.

Jie Mei *Department of Anesthesiology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Fu-Yuan YangDepartment of Anesthesiology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Quan GongSchool of Basic Medicine, Yangtze University Health Science Center, Jingzhou, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Branched-chain amino acids (BCAAs) are a class of amino acids characterized by a branched aliphatic side chain, and they play critical physiological roles in humans, including protein synthesis, metabolic regulation, and immune system maintenance. Beyond serving as fundamental building blocks for protein biosynthesis, BCAAs and their metabolites also function as signaling molecules that regulate a variety of physiological processes, notably insulin secretion. Accumulating evidence indicates that plasma BCAAs levels are markedly elevated in patients with type 2 diabetes (T2DM), a phenomenon that may result from impaired activity of key enzymes in the BCAAs catabolic pathway, leading to metabolic dysregulation. It is widely recognized that BCAAs can activate the mTOR signaling cascade, thereby affecting insulin receptor sensitivity. In addition, aberrant BCAAs metabolism has been closely linked to alterations in the gut microbiota, which may further aggravate insulin resistance (IR). Taken together, dysregulated BCAAs metabolism may represent a critical mechanism underlying IR in T2DM. Therefore, this review summarizes current knowledge on BCAAs metabolism, explores its potential roles in the pathogenesis of IR in T2DM, and highlights emerging therapeutic strategies to reduce IR by targeting BCAAs metabolism.

Indexed as

Amino Acids, Branched-ChainDiabetes Mellitus, Type 2Insulin ResistanceAnimalsHumansSignal TransductionTOR Serine-Threonine KinasesAmino Acids, Branched-ChainTOR Serine-Threonine KinasesBCAAsBCAAs metabolisminsulin resistancemTOR signaling pathwayT2DM

Identifiers

PMID41705230
PMCPMC12907165

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.