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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".