<i>Lactobacillus reuteri</i> reduces bone loss in older women with low bone mineral density: a randomized, placebo‐controlled, double‐blind, clinical trial
Bibliographic record
Abstract
BACKGROUND: The importance of the gut microbiome for bone metabolism in mice has recently been demonstrated, but no studies are available in humans. Lactobacillus reuteri ATCCPTA 6475 (L. reuteri 6475) has been reported to increase bone mineral density (BMD) in mice but its effect on the human skeleton is unknown. The objective of this trial was to investigate if L. reuteri 6475 affects bone loss in older women with low BMD. METHODS: colony-forming units of L. reuteri 6475 daily or placebo. The predefined primary end-point was relative change after 12 months in tibia total volumetric BMD (vBMD). RESULTS: Ninety women were included and 70 completed the study. L. reuteri 6475 reduced loss of total vBMD compared to placebo both in the intention-to-treat (ITT) analysis [-0.83% (95% confidence interval [CI], -1.47 to -0.19%) vs. -1.85% (95% CI, -2.64 to -1.07%); mean difference 1.02% (95% CI, 0.02-2.03)] and per protocol analysis [-0.93% (95% CI, -1.45 to -0.40) vs. -1.86% (95% CI, -2.35 to -1.36); mean difference 0.93% (95% CI, 0.21-1.65)]. In general, similar but smaller effects were observed in the secondary bone variable outcomes, but these differences did not reach statistical significance in the ITT population. Adverse events did not differ between groups. CONCLUSIONS: Supplementation with L. reuteri 6475 should be further explored as a novel approach to prevent age-associated bone loss and osteoporosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".