From Bone Health to Gut Shield: A Reanalysis & Meta‑Analysis of Long‑Term Oral Bisphosphonates in Colorectal Cancer Prevention
Bibliographic record
Abstract
Background: Oral bisphosphonates (BP) are standard therapy for osteoporosis and skeletal metastases and exhibit anti tumor properties in preclinical models. Observa-tional studies assessing their impact on colorectal cancer (CRC) risk have yielded in-consistent results. We aimed to systematically review and meta analyze the association between oral bisphosphonate use and CRC risk, applying a unified exposure definition. Methods: We searched PubMed, Embase and Scopus (January 1966–April 2025) for cohort, nested or population based case control studies reporting adjusted relative risks, odds ratios (ORs) or hazard ratios (HRs) for CRC among oral bisphosphonate users. Two reviewers independently screened studies, extracted data and assessed quality using the Newcastle–Ottawa Scale. Random effects meta analyses pooled risk estimates for “any use” of bisphosphonates, with subgroup analyses by duration of use (< 1, 1–3, >3 years). Publication bias was evaluated via Egger’s test and trim and fill. Results: Eight studies (29169 CRC cases) met inclusion criteria. Any bisphosphonate use was not significantly associated with CRC risk (pooled OR 0.97; 95% C.I., 0.90–1.03). However, 1–3 years of use conferred a protective effect (OR 0.86; 95% C.I., 0.73–0.99), as did >3 years (OR 0.91; 95% C.I., 0.85–0.97). Heterogeneity was moderate and no significant publication bias was detected. Conclusions: While overall oral bisphos-phonate exposure is not significantly linked to CRC risk, prolonged use (≥1 year) ap-pears to reduce risk. Prospective studies and randomized trials are needed to confirm these chemo preventive effects and guide clinical recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.094 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".