Evidence map›Paper›PMID 39960897›Full record

SynthesisMedicine2025

TCF7L2 as a target of peripheral artery disease in patients with type 2 diabetes: A 2-sample Mendelian randomization and bioinformatics study.

Jie Liu, XingDe Liu, Rui Rao, Wen Li

Abstract readMeta-Analysis
In one paragraph

Synthesis in Medicine, 2025. 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

4 authors.

Jie LiuDepartment of Basic Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.ORCID 0009-0005-9084-5625
XingDe LiuDepartment of Cardiology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
Rui RaoDepartment of Endocrinology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
Wen LiDepartment of Basic Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.ORCID 0000-0002-3975-3312

Funding

Science and Technology Fund of Guizhou Provincial Department of Education 2022XMSB00035072
6 · The paper itself

Abstract

This study examines the causal relationship between type 2 diabetes (T2D) and peripheral artery disease (PAD) and their potential mechanisms based on the analysis of the Gene Expression Omnibus database and 2-sample Mendelian randomization (MR). The first part involved a 2-sample MR study and a comprehensive meta-analysis. Differences in the results were assessed using inverse-variance weighting. Heterogeneity was examined using the Cochrane Q statistical test. The leave-one-out method was applied for sensitivity analysis. The potential horizontal pleiotropic effect was assessed using the MR-Egger intercept technique. The second part involved differential gene analysis and weighted gene coexpression network analysis. Subsequently, we overlapped and consolidated the results from the 2 parts to identify the key genes between them. MR analysis results suggested a statistically significant correlation between the incidence of PAD and T2D (odds ratio: 1.22, 95% confidence interval: 1.13-1.32, P = 3.74e-07). We anticipated a pivotal role for TCF7L2 in PAD and T2D. T2D was significantly associated with PAD risk. Simultaneously, the study deepened our understanding of the underlying mechanisms of both diseases, proposing TCF7L2 as a promising target.

Indexed as

Diabetes Mellitus, Type 2Peripheral Arterial DiseaseTranscription Factor 7-Like 2 ProteinComputational BiologyHumansMendelian Randomization AnalysisTCF7L2 protein, humanTranscription Factor 7-Like 2 Protein

Identifiers

PMID39960897
PMCPMC11835089

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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.