Effects of probiotics on bone mineral density and bone turnover: A systematic review
Bibliographic record
Abstract
Probiotic supplements have been shown to improve bone health in animal models, although it remains uncertain whether these beneficial effects extend to humans. We undertook a systematic review of the literature to determine the effects of probiotic interventions on skeletal outcomes in postmenopausal women. MEDLINE, EMBASE, CENTRAL, and the Cochrane Database of Systematic Reviews were searched from inception to October 2020 for controlled trials comparing the effects of probiotic-containing supplements with placebo on bone mineral density (BMD) or bone turnover markers. Risk of bias was assessed using the Cochrane Risk of Bias 2 Tool. Of 338 records identified, six randomized, placebo-controlled trials (n = 632) were eligible for inclusion. All studies assessed postmenopausal women for durations of 6–12 months; three were considered to be at high risk of bias. Four studies examined Lactobacillus-containing probiotics, one assessed a proprietary blend of lactic acid bacteria, and one evaluated Bacillus subtilis. Effects of probiotic interventions on BMD were inconsistent, with the majority of studies demonstrating no benefit at the spine or hip. Probiotic effects on bone turnover markers were similarly heterogeneous. High quality studies are needed to determine whether probiotic interventions have a role in maintaining bone health in humans.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".