Evidence map›Paper›PMID 42064762›Full record

SynthesisFrontiers in endocrinology2026

Type 2 diabetes mellitus and cancer: A systematic review and meta-analysis of Mendelian randomization studies.

Wei Wu, Guo-Liang Huang, Jia Cui

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Wei WuDepartment of Geriatrics, Chun'an Country First People's Hospital, Chun'an Branch of Zhejiang Provincial People's Hospital, Hangzhou, China.
Guo-Liang HuangDepartment of Nephrology, The Second People's Hospital of Yuhang District, Hangzhou, China.
Jia CuiDepartment of Endocrinology, Chun'an Country First People's Hospital, Chun'an Branch of Zhejiang Provincial People's Hospital, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Type 2 diabetes mellitus (T2DM) and cancer are both major global public health concerns; however, their causal relationship remains unclear. This study aims to quantitatively investigate the potential causal associations between T2DM and 17 site-specific cancers through a systematic review and meta-analysis of Mendelian randomization (MR) studies. Methods: We systematically searched Scopus, PubMed, the Cochrane Library, Web of Science, Embase, and Ovid MEDLINE to identify MR studies investigating the association between T2DM and cancer published up to June 2025. A meta-analysis was performed on extracted data, accompanied by heterogeneity testing, sensitivity analysis, and publication bias assessment. Results: The initial search yielded 1,143 articles. After multi-level screening, 44 articles were ultimately included, with 42 articles (comprising 131 MR studies) eligible for meta-analysis. The pooled results demonstrated that T2DM was significantly associated with an increased risk of pancreatic cancer (OR = 1.09, 95% CI: 1.04-1.15, Conclusion: The causal relationship between T2DM and cancer appears to be tissue-specific. T2DM significantly increases the risk of pancreatic and endometrial cancers while demonstrating a negative association with gastric cancer, melanoma, and esophageal cancer. Clinical trial registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251066404.

Indexed as

Diabetes Mellitus, Type 2Mendelian Randomization AnalysisNeoplasmsHumansRisk FactorscancerMendelian randomizationmeta-analysissystematic reviewtype 2 diabetes mellitus

Identifiers

PMID42064762
PMCPMC13124634

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Registered trials

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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 OpenQuestion graph.