Evidence mapPaperPMID 41803464Full record

ReviewNature reviews. Clinical oncology2026

Glucagon-like peptide 1 receptor agonists and cancer risk: the good, the bad and the unknown.

Edoardo Mannucci, Ilaria Dicembrini

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Edoardo MannucciExperimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy. edoardo.mannucci@unifi.it.ORCID http://orcid.org/0000-0001-9759-9408
Ilaria DicembriniExperimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glucagon-like peptide 1 receptor agonists (GLP1RAs) are widely used for the treatment of type 2 diabetes mellitus (T2DM) and, at higher doses, obesity. Both T2DM and obesity are associated with a higher risk of cancer, which can be reduced by intentional weight loss, whereas effects of a reduction in hyperglycaemia are uncertain. GLP1RAs might have further direct effects, either beneficial or detrimental, on the development of specific malignancies. Evidence from preclinical and clinical studies suggests heterogeneous effects of GLP1RAs on cancer risk: the incidence of hepatocellular, oesophageal, endometrial, ovarian and prostate cancers might be reduced, whereas safety concerns persist with respect to thyroid (both medullary and non-medullary) carcinomas. Conversely, initial concerns on the risk of pancreatic cancer have not been confirmed. Nonetheless, the interpretation of current data is limited by detection and prescription biases in observational studies as well as insufficient follow-up and number of events in randomized trials. In this Review, we summarize current preclinical and clinical evidence, showing that the risk-benefit profile of GLP1RAs remains favourable in individuals with T2DM and obesity, although caution is warranted in those with a low cardiometabolic risk, for whom the potential risks of cancer might outweigh any expected benefits; conversely, the potential use of GLP1RAs as adjuvant therapies for certain forms of cancer needs to be further investigated.

Indexed as

Diabetes Mellitus, Type 2Glucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsNeoplasmsObesityAnimalsHumansRisk FactorsGlucagon-Like Peptide-1 Receptor AgonistsHypoglycemic Agents

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.