Plant-based diet and risk of osteoporosis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Plant-based diet is growing in popularity throughout the world for various reasons, yet its effect on bone health, especially osteoporosis, remains controversial. This systematic review and meta-analysis aim to investigate the association between plant-based diet and risk of osteoporosis. METHODS: A systematic literature search of observational studies examining the relationship between plant-based diets and osteoporosis risk was performed across PubMed, Embase, Web of Science, Scopus, and ProQuest from inception to June 1, 2024. Two reviewers independently extracted data and assessed study quality using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and the Newcastle-Ottawa Scale. To synthesize effect estimates, a random-effects meta-analysis with inverse variance weighting was applied to pool odds ratios (ORs) and 95 % confidence intervals (CIs). Subgroup analysis and meta-regression were used to explore sources of heterogeneity. RESULTS: = 94.9 %). Subgroup analysis revealed vegans (FN: OR = 1.79, 95%CI = 0.94-3.54, P = 0.10; LS: OR = 1.45, 95%CI = 1.00-2.12, P = 0.05) and those who followed a plant-based diet for ≥10 y (FN: OR = 1.79, 95%CI = 1.29-2.49, P < 0.01; LS: OR = 1.35, 95%CI = 0.97-1.87, P = 0.07) to exhibit a more pronounced risk of osteoporosis. Heterogeneity was primarily driven by study design. CONCLUSIONS: This systematic review and meta-analysis indicate that adherence to plant-based diet may be associated with an elevated risk of osteoporosis, particularly at the lumbar spine, among individuals following a vegan diet or following a plant-based diet for ≥10 y. However, the heterogeneity observed across studies highlights the need for well-designed prospective studies in future, to clarify this relationship.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| 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".