MétaCan
Menu
← Back to cohort
Record W4415559860 · doi:10.3390/ijms262110370

Redox Homeostasis in Metabolic Syndrome and Type II Diabetes: Role of Skeletal Muscle and Impact of Gold-Standard Treatments

2025· review· en· W4415559860 on OpenAlexafffund
Mia Wilkinson, Thomas A. Rollin, Michelle Kuriakose, Roan A. L. Haggerty-Goede, D.A. Miller, Kimberly J. Dunham‐Snary

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsSkeletal muscleMetabolic syndromeHomeostasisGlucose homeostasisEndogenyRedoxType 2 diabetesDiseaseMetformin

Abstract

fetched live from OpenAlex

Metabolic syndrome and type II diabetes pose a significant international health burden, with the latter characterized by insulin resistance. Patients must rely on therapies that maintain glucose homeostasis when endogenous systems become dysfunctional. Skeletal muscle, as the largest insulin-sensitive tissue in the body, plays a critical role in maintaining glucose homeostasis. During disease progression, chronic nutrient overload shifts redox balance to a pro-oxidant state, further exacerbating metabolic dysfunction. First-line treatments, such as metformin and insulin, along with newly adopted incretin-based therapies, modulate the redox state of skeletal muscle. This review explores how the redox state of healthy skeletal muscle is altered throughout metabolic disease progression and how these changes contribute to a worsening phenotype. We also highlight how each class of regularly prescribed medications targets redox-sensitive systems in skeletal muscle, identifying literature gaps and areas for future investigation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.324
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Molecular Sciences→Same topicMetabolism, Diabetes, and Cancer→French-language works237,207→