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Record W4407932394 · doi:10.2196/64456

Systemic Inflammation and Disruption of the Local Microenvironment Compromise Muscle Regeneration: Critical Pathogenesis of Autoimmune-Associated Sarcopenia

2025· article· en· W4407932394 on OpenAlexvenueno aff
Yingjuan Zhang, Qingqian Wu, Shuang Han, Qingwen Zhao, Qianzhuo Wang, Yue Gao

Bibliographic record

VenueInteractive Journal of Medical Research · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaRegeneration (biology)CompromiseInflammationPathogenesisImmunologyMedicineBiologyCell biologyAnatomyPolitical science

Abstract

fetched live from OpenAlex

Unlabelled: Sarcopenia is defined by age-related reductions in muscle mass, strength, and physiological function, and it is especially prevalent among individuals with autoimmune diseases. Autoimmune disorders, characterized by immune dysregulation, cause systemic inflammation and damage to multiple tissues through unregulated immune activity. Research indicates that autoimmune diseases negatively impact skeletal muscle functions and may worsen the progression of sarcopenia. This viewpoint comprehensively discusses the pathogenesis and potential mechanism of sarcopenia in 3 autoimmune diseases: inflammatory bowel disease, rheumatoid arthritis, and type 1 diabetes mellitus. Mechanistically, chronic immune microenvironment alterations induce compartment-specific redistribution of leukocyte subsets and cytokine networks. These perturbations disrupt critical signaling pathways governing muscle protein synthesis, satellite cell activation, and mitochondrial bioenergetics, leading to impaired regeneration and accelerated sarcopenia progression. By delineating shared and distinct pathomechanisms across these models, this analysis reframes our understanding of immune-mediated muscle wasting. Beyond mechanistic insights, it establishes a translational framework for targeted therapies and highlights emerging research directions bridging immunology and age-related musculoskeletal decline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.430
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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