Association between GLP-1 receptor agonists as a class and colorectal cancer risk: a meta-analysis of retrospective cohort studies
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are extensively used in the management of type 2 diabetes mellitus (T2DM) and obesity. While these medications offer glycemic control and cardiovascular benefits, the risks have increased because of their potential impact on cancer risk, particularly colorectal cancer (CRC). This meta-analysis aimed to evaluate the association between GLP-1 RAs and CRC risk in patients receiving GLP-1 RAs. METHODS: This study was conducted the PRISMA guidelines. Electronic databases (PubMed, Embase, Cochrane Library Web of Science, and ClinicalTrials.gov) were searched from inception to December 2024. The inclusion criteria encompassed Studies analyzing the effects of GLP-1 RA on CRC risk in patients with T2DM. The Newcastle-Ottawa Scale was used for the quality assessment of the included cohort studies. Random-effects models were employed for the pooled analysis, and heterogeneity was evaluated using the I2 statistic. RESULTS: Seven retrospective cohort studies involving 5,066,681 patients were included. The pooled analysis revealed a significantly increased risk of CRC among patients receiving GLP-1 RAs (RR, 2.31; 95% CI, 1.82-2.93; I2 = 36%; p < 0.0001). However, the incidence of CRC was not significantly associated with GLP-1 RA use compared with other drugs (OR, 1.73; 95% CI: 0.21-14.18, p = 0.61; I2 = 100%). Quality assessment indicated a low-to-moderate risk of bias across the included studies. CONCLUSION: Overall, this study suggests a significantly increased risk of colorectal cancer associated with GLP-1 RA use in patients receiving GLP-1 RAs. However, the incidence of CRC is not considerably high. These findings highlight the need for further long-term, large-scale clinical trials to elucidate the relationship between GLP-1 RAs and cancer risk. Clinicians should consider these results when prescribing GLP-1 RAs, particularly in patients with CRC risk factors.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.060 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".