The Role of Sex Hormones in Cartilaginous Tissues: A Scoping Review
Bibliographic record
Abstract
Background: The use of sex hormones in the clinic for the management of musculoskeletal conditions is increasingly common. Despite this, the role of sex hormones in various joint tissues such as the intervertebral disc (IVD), temporomandibular joint (TMJ), and articular cartilage remains poorly understood. Here, we employ a database search strategy to critically examine the available literature in this field through a scoping review. Methods: Using a 4-step protocol, primary research articles pertaining to sex hormones and the IVD, TMJ, or articular cartilage were identified and reviewed by two independent reviewers. ~3900 articles were identified in our initial search, and after review, ~140 were identified to be relevant to our tissues of interest and the effects of sex hormones. Results: Within all joint tissues investigated here, there were limited investigations on the effects of testosterone. Studies reported here for these tissues indicate that sex hormones are likely beneficial in the context of age-associated joint diseases, but there are important limitations to how this translates to the clinic given that various animal models can display distinct responses to sex hormone exposure. Direct comparisons of sex hormone therapies are limited between biological sexes, but evidence indicates that the molecular responses are likely similar. Current evidence indicates that sex hormone exposure likely has anti-inflammatory effects within joint tissues at the level of gene and protein expression, but the mechanism is unknown. Conclusion: Sex hormones such as testosterone and estrogen play an important role in inflammatory signaling within joint tissues, which could lead to novel interventions within the clinic for joint degeneration. However, understanding the biological mechanisms of hormones in these distinct tissues, between sexes, and with age is imperative for their proper implementation.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".