Weight-lowering Effects of Glucagon-like Peptide-1 Receptor Agonists and Detection of Breast Cancer Among Obese Women with Diabetes
Bibliographic record
Abstract
BACKGROUND: It has been proposed that the weight loss associated with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may improve detection of breast cancer in patients undergoing this treatment. We aimed to determine whether the weight-lowering effects of GLP-1 RAs are associated with an increased detection of breast cancer among obese women with type 2 diabetes. METHODS: Using the UK Clinical Practice Research Datalink, we conducted a propensity score-matched cohort study among female obese patients with type 2 diabetes newly treated with antidiabetic drugs between 1 January 2007 and 31 January 2018. New users of GLP-1 RAs (n = 5,510) were matched to new users of second- to third-line noninsulin antidiabetic drugs (n = 5,510). We used time-dependent Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of breast cancer associated with different GLP-1 RA maximal weight loss categories (<5%, 5%-10%, >10%). RESULTS: Breast cancer incidence gradually increased with GLP-1 RA maximal weight loss categories, with the highest HR observed for patients achieving at least 10% weight loss (HR = 1.8, 95% CI = 1.1, 2.8). In secondary analyses, the HR for >10% weight loss was highest in the 2-3 years since treatment initiation (HR = 2.9, 95% CI = 1.2, 6.9). CONCLUSIONS: In this population-based study, the detection of breast cancer gradually increased with GLP-1 RA weight loss categories, particularly among those achieving >10% weight loss. These results are consistent with the hypothesis that substantial weight loss with GLP-1 RAs may improve detection of breast cancer among obese patients with type 2 diabetes.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".