Effects of Lycopene Supplementation on Bone Tissue: A Systematic Review of Clinical and Preclinical Evidence
Bibliographic record
Abstract
Background: Bone tissue undergoes continuous remodeling, and imbalances in this process can lead to osteometabolic disorders, such as osteoporosis. Thus, bioactive compounds, like lycopene (LYC), have been investigated for their potential protective effects in bone health. This systematic review (SR) aimed to evaluate the effects of LYC supplementation on bone tissue. Methods: The SR was registered in PROSPERO (CRD42023417346) and followed the PRISMA guidelines. A comprehensive search was conducted in electronic databases up to May 2025. Two independent reviewers selected clinical trials and animal studies evaluating the effects of LYC supplementation in bone tissue. Methodological quality and risk of bias were assessed using the Cochrane Risk of Bias tool for randomized controlled trials, the Newcastle–Ottawa Scale for non-randomized clinical studies, and the SYRCLE tool for animal studies. Results: A total of 21 studies met the eligibility criteria, consisting of 6 clinical trials and 15 studies in animal models. LYC supplementation promotes an increase in bone mineral density, preserves trabecular microarchitecture, stimulates osteoblastic activity, and inhibits osteoblast apoptosis. Conclusions: LYC supplementation promotes beneficial effects on both the formation and preservation of bone tissue, suggesting that this carotenoid may represent a potential adjuvant strategy in the management of osteometabolic disorders.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".