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Record W4210332185 · doi:10.3390/nu14030544

Anti-Inflammatory and Anti-Catabolic Effects of Creatine Supplementation: A Brief Review

2022· review· en· W4210332185 on OpenAlexaff
Dean M. Cordingley, Stephen M. Cornish, Darren G. Candow

Bibliographic record

VenueNutrients · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of ReginaUniversity of ManitobaPan Am Clinic
Fundersnot available
KeywordsCreatineAnabolismCatabolismCreatine MonohydrateBone resorptionEndocrinologyBone remodelingInternal medicineInflammationResistance trainingMedicineMetabolismPathology

Abstract

fetched live from OpenAlex

It is well established that creatine supplementation, primarily when combined with resistance training, significantly increases measures of muscle mass and performance (primarily strength). Emerging research also indicates that creatine supplementation may have favorable effects on measures of bone biology. These anabolic adaptations may be related to creatine influencing cellular hydration status, high-energy phosphate metabolism, growth factors, muscle protein kinetics, and the bone remodeling process. Accumulating research also suggests that creatine supplementation has anti-inflammatory and anti-catabolic properties, which may help create a favorable environment for muscle and bone accretion and recovery from exercise. Creatine supplementation has the ability to decrease markers of inflammation and possibly attenuate cancerous tumor growth progression. From a musculoskeletal perspective, there is some evidence to show that creatine supplementation reduces measures of muscle protein catabolism (primarily in males) and bone resorption when combined with resistance training. The purpose of this brief review is to summarize the current body of literature examining the potential anti-inflammatory and anti-catabolic effects of creatine supplementation across various research populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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.

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

Citations44
Published2022
Admission routes1
Has abstractyes

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