The role of osteoprotegerin (OPG) in exercise-induced skeletal muscle adaptation
Bibliographic record
Abstract
Abstract Objectives The purpose of this narrative review is to offer an updated perspective on the current research on the glycoprotein Osteoprotegerin (OPG), including its potential therapeutic impact and mechanisms of action, and interaction with bone and muscle tissues. Content As health and social care advances people are living longer, with projections suggesting that in 2050 there will be 2 billion people who are aged over 60 years. Yet musculoskeletal health still declines into older age and as a result there is an increase in the proportion of older populations that spend more time with persistent disabilities. Although physical exercise is repeatedly demonstrated to minimise detrimental effects of ageing, it is not always a feasible intervention, and other directions must be considered. Summary and outlook OPG, a glycoprotein decoy receptor for the receptor activator of nuclear factor kappa-β ligand (RANKL) is a key regulator of bone formation yet emerging evidence has presented its potential to offer positive outcomes in regard to the preservation of skeletal muscle mass and function. Animal models have shown that OPG levels increase during exercise, and independently acts to restore losses of muscle strength and reduce bone resorption. Interventions to increase circulating OPG alongside exercise may act as a therapeutic target to combat the decline in quality of life in older age in humans. Further research is needed on the mechanisms of its action and interaction in humans in combination with exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".