A systematic review and meta-analysis of the effects of probiotics on bone outcomes in rodent models
Bibliographic record
Abstract
Emerging evidence demonstrates an opportunity for using probiotics to support bone health, but findings in humans are limited. This systematic review investigated if probiotic supplementation improves bone mineral density (BMD) and bone structure in rodent models compared to no supplementation. Studies (n = 71) examining the effect of oral consumption of any probiotic strain on BMD or bone structure in rodents were included. Meta-analyses were conducted separately by study model (intact, ovariectomized) and bone site (femur, tibia, spine) to determine the probiotic effect (standardized mean difference, SMD) on volumetric BMD (vBMD), bone volume fraction (BV/TV), and cortical thickness (Ct.Th). Reasons for heterogeneity were explored (probiotic genus, sex, type of rodent). In intact rodents, probiotics resulted in greater vBMD (SMD = 0.43, 95% CI [0.13, 0.74], I2 = 3%, p < 0.05) and higher BV/TV (SMD = 0.63, 95% CI [0.25, 1.02], I2 = 57%, p < 0.05) at the femur without changes in cortical bone structure. In ovariectomized models, probiotic supplementation resulted in greater vBMD (femur: SMD = 1.28, 95% CI [1.01, 1.55], I2 = 3%, p < 0.05; tibia: SMD = 1.29, 95% CI [0.52, 2.05], I2 = 67%, p < 0.05; and spine: SMD = 1.47, 95% CI [0.97, 1.97], I2 = 26%, p < 0.05) as well as higher BV/TV (femur: SMD = 1.16, 95% CI [0.80, 1.52], I2 = 56%, p < 0.05; tibia: SMD = 2.13, 95% CI [1.09, 3.17], I2 = 79%, p < 0.05; spine: SMD = 2.04, 95% CI [1.17, 2.90], I2 = 76%, p < 0.05) and Ct.Th at the tibia (SMD = 2.35; 95% CI [0.72, 3.97], I2 = 82%, p < .0.05) but not at the femur versus control. The syntheses support probiotics as a strategy to improve bone outcomes in rodent models.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".