Evidence map›Paper›PMID 38561446›Full record

ArticleScientific reports2024

Mechanism of muscle atrophy in a normal-weight rat model of type 2 diabetes established by using a soft-pellet diet.

Sayaka Akieda-Asai, Hao Ma, Wanxin Han, Junko Nagata, Fumitake Yamaguchi, Yukari Date

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Sayaka Akieda-AsaiFrontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan. akieda@med.miyazaki-u.ac.jp.
Hao MaFrontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan.
Wanxin HanFrontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan.
Junko NagataDepartment of Sensory and Motor Organs, Faculty of Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan.
Fumitake YamaguchiFrontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan.
Yukari DateFrontier Science Research Center, University of Miyazaki, Miyazaki, 889-1692, Japan. dateyuka@med.miyazaki-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dietary factors such as food texture affect feeding behavior and energy metabolism, potentially causing obesity and type 2 diabetes. We previously found that rats fed soft pellets (SPs) were neither hyperphagic nor overweight but demonstrated glucose intolerance, insulin resistance, and hyperplasia of pancreatic β-cells. In the present study, we investigated the mechanism of muscle atrophy in rats that had been fed SPs on a 3-h time-restricted feeding schedule for 24 weeks. As expected, the SP rats were normal weight; however, they developed insulin resistance, glucose intolerance, and fat accumulation. In addition, skeletal muscles of SP rats were histologically atrophic and demonstrated disrupted insulin signaling. Furthermore, we learned that the muscle atrophy of the SP rats developed via the IL-6-STAT3-SOCS3 and ubiquitin-proteasome pathways. Our data show that the dietary habit of consuming soft foods can lead to not only glucose intolerance or insulin resistance but also muscle atrophy.

Indexed as

Diabetes Mellitus, Type 2Glucose IntoleranceInsulin ResistanceAnimalsDietDiet, High-FatInsulinMuscle, SkeletalMuscular AtrophyRatsInsulinDiabetesEating habitsEnergy metabolismGlucose metabolismMuscle atrophySoft food

Identifiers

PMID38561446
PMCPMC10984920

What OpenQuestion 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 OpenQuestion graph.