Evidence mapPaperPMID 41858896Full record

ReviewiScience2026

Skeletal muscle metabolism in health and disease: Mechanisms, interventions, and clinical perspectives.

Donghai Lin, Linglin Zhang, Caihua Huang, Wei Shao

Abstract readReview
In one paragraph

Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

4 authors.

Donghai LinKey Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
Linglin ZhangKey Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
Caihua HuangXiamen Humanity Hospital, Fujian Medical University, Fujian, China.
Wei ShaoResearch and Communication Center of Exercise and Health, Xiamen University of Technology, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skeletal muscle is a vital metabolic organ that regulates systemic energy homeostasis by coordinating glucose uptake, fatty acid oxidation, and amino acid metabolism. Its remarkable capacity for dynamic adaptation, termed metabolic flexibility, underpins physical performance and protects against metabolic diseases such as obesity, type 2 diabetes, and sarcopenia. This review provides an integrative synthesis of the molecular and signaling networks that orchestrate skeletal muscle metabolism, focusing on key regulators including insulin, AMPK, mTOR, and PGC-1α. We also examine how disruptions in these pathways lead to mitochondrial dysfunction, lipid dysregulation, and muscle wasting. We explore the therapeutic landscape across pharmacological, exercise-based, and nutritional interventions, emphasizing mitochondrial-targeted strategies and myokine-mediated communication as emerging modalities for restoring metabolic resilience. Additionally, we emphasize the growing importance of multi-omics technologies and inter-tissue communication in improving mechanistic understanding and advancing precision medicine. This review integrates mechanistic, translational, and clinical perspectives to underscore the importance of a systems-level approach to skeletal muscle metabolism. This approach is essential for developing targeted, multidimensional therapies aimed at enhancing metabolic health and extending healthspan.

Indexed as

endocrinologyhealth sciencesmedical specialtymedicine

Identifiers

PMID41858896
PMCPMC12996713

What Socratic 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 Socratic graph.