Association of glucagon-like peptide-1 receptor agonists with risk of cancers-evidence from a drug target Mendelian randomization and clinical trials
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor (GLP1R) agonists have been approved by Food and Drug Administration for management of obesity. However, the causal relationship of GLP1R agonists (GLP1RA) with cancers still unclear. METHODS: The available cis-eQTLs for drugs target genes (GLP1R) were used as proxies for exposure to GLP1RA. Mendelian randomizations (MR) were performed to reveal the association of genetically-proxied GLP1RA with 14 common types cancer from large-scale consortia. Type 2 diabetes was used as positive control, and the GWASs data including 80 154 cases and 853 816 controls. Replicating the findings in the FinnGen study and then pooled with meta-analysis. Finally, all the related randomized controlled trails (RCTs) on GLP1RA were systematically searched from PubMed, Embase, and the Cochrane Library to comprehensively synthesize the evidence to validate any possible association with cancers. RESULT: A total of 22 significant cis-eQTL single-nucleotide polymorphisms were included as genetic instrument. The association of genetically-proxied GLP1RA with significantly decreased type 2 diabetes risk [OR (95%)=0.82 (0.79-0.86), P <0.001], which ensuring the effectiveness of identified genetic instruments. The authors found favorable evidence to support the association of GLP1RA with reduced breast cancer and basal cell carcinoma risk [0.92 (0.88-0.96), P <0.001, 0.92 (0.85-0.99), P =0.029, respectively], and with increased colorectal cancer risk [1.12 (1.07-1.18), P <0.001]. In addition, there was no suggestive evidence to support the association of GLP1RA with ovarian cancer [0.99 (0.90-1.09), P =0.827], lung cancer [1.01 (0.93-1.10), P =0760], and thyroid cancer [0.83 (0.63-1.10), P =0.187]. Our findings were consistent with the meta-analysis. Finally, 80 RCTs were included in the systematic review, with a low incidence of different kinds of cancer. CONCLUSIONS: Our study suggests that GLP1RA may decrease the risk of breast cancer and basal cell carcinoma, but increase the risk of colorectal cancer. However, according to the systematic review of RCTs, the incidence of cancer in patients treated with GLP1RA is low. Larger sample sizes of RCTs with long-term follow-up are necessary to establish the incidence of cancers and evaluate the risk-benefit ratios.
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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 | 0.080 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".