The Use of Metformin and the Incidence of Lung Cancer in Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Observational studies have associated metformin use with a decreased risk of lung cancer incidence in patients with type 2 diabetes, but the studies had important methodological shortcomings. The objective of this study was to determine whether metformin use is associated with a decreased risk of lung cancer in patients with type 2 diabetes, while avoiding previous biases. RESEARCH DESIGN AND METHODS: Using the U.K. General Practice Research Database, we assembled a cohort of patients newly treated with oral hypoglycemic agents (OHAs) between 1988 and 2009. A nested case-control analysis was conducted, where case subjects with lung cancer occurring during follow-up were matched with up to 10 control subjects for age, sex, calendar time, and duration of follow-up. Conditional logistic regression was used to estimate adjusted rate ratios of lung cancer associated with ever use of metformin, along with measures of duration and cumulative dose. Models were adjusted for potential confounders, which included smoking. RESULTS: The cohort included 115,923 new users of OHAs, with 1,061 patients diagnosed with lung cancer during follow-up (rate 2.0/1,000 person-years). Metformin use was not associated with a decreased rate of lung cancer (rate ratio 0.94 [95% CI 0.76-1.17]). No dose-response was observed by number of prescriptions received, cumulative duration of use, and dose. CONCLUSIONS: Metformin use is not associated with a decreased risk of lung cancer in patients with type 2 diabetes. The decreased risk reported in other observational studies is likely due to bias from methodological shortcomings. Nonetheless, greater consideration should be given to clarify inconsistencies between experimental models and population studies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".