Relationship between glucosamine use and the risk of lung cancer: data from a nationwide prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Research on glucosamine shows anti-inflammatory and anti-cancer benefits with minimal adverse effects. We aimed to explore the relationship between use of glucosamine and risk of lung cancer and lung cancer mortality based on data from the large-scale nationwide prospective UK Biobank cohort study. METHODS: Participants were enrolled between 2006 and 2010 and followed-up to 2020. The Cox proportional hazards model was used to assess the relationship between glucosamine use and risk of lung cancer and lung cancer mortality. Subgroup analyses and sensitivity analyses were performed to explore the potential effect modifications and the robustness of the main findings. RESULTS: 439 393 participants (mean age 56 years; 53% females) with a mean follow-up of 11 years were included for analyses. 82 603 (18.80%) participants reported regular use of glucosamine at baseline. During follow-up, 1971 (0.45%) lung cancer events were documented. Glucosamine use was significantly associated with a decreased risk of lung cancer (hazard ratio (HR) 0.84, 95% CI 0.75-0.92; p<0.001) and lung cancer mortality (HR 0.88, 95% CI 0.81-0.96; p=0.002) in fully adjusted models. A stronger association between glucosamine use and decreased lung cancer risk was observed in participants with a family history of lung cancer when compared with those without a family history. CONCLUSION: Regular use of glucosamine was significantly related with decreased risk of lung cancer and lung cancer mortality, based on data from this nationwide prospective cohort study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".