Exploring the association between diabetes mellitus and peripheral arterial atherosclerosis using Mendelian randomization and bioinformatics: Identification of key genes and pathways

doi:10.3969/j.issn.1004-583X.2026.01.004

Abstract

Abstract:

Objective To investigate the genetic causal relationship between diabetes mellitus (DM) and peripheral arterial atherosclerosis (PAA), and to elucidate shared molecular mechanisms, using Mendelian randomization (MR) and bioinformatics approaches. The aim is to provide a theoretical basis for early screening and targeted interventions. Methods We employed an MR framework based on genome-wide association study (GWAS) data. Instrumental variables were selected and multiple MR models (including inverse-variance weighted, IVW) and sensitivity analyses were conducted to assess and validate the causal effect of DM on PAA. Concurrently, gene expression datasets from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) for each condition. Shared DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was constructed to identify hub genes. Results MR analysis demonstrated a significant positive causal effect of DM on PAA (OR=1.413; 95%CI: 1.316-1.516; P<0.01). Bioinformatics analysis identified 156 overlapping DEGs between DM and PAA, which were enriched in immune- and inflammation-related functions and pathways. PPI network analysis highlighted hub genes including Toll-like receptor 2 (TLR2), colony stimulating factor-1 receptor (CSF1R), cytochrome b558 heavy chain gene (CYBB), interleukin-1-beta (IL1B), and salmonella pathogenicity island 1 (SPI1). Conclusion There is a significant positive genetic causal association between DM and peripheral arterial atherosclerosis. Shared pathogenic mechanisms likely involve activation of immune-inflammatory pathways and the identified hub genes (TLR2, CSF1R, CYBB, IL1B, SPI1). These findings offer a theoretical framework and potential experimental targets for early risk prediction and targeted interventions in patients with DM at risk for PAA.

Key words: diabetes mellitus, atherosclerosis, Mendelian randomization, bioinformatics, signaling pathways

CLC Number:

R587.1

Sun Mengmeng, Zhang Zhigong. Exploring the association between diabetes mellitus and peripheral arterial atherosclerosis using Mendelian randomization and bioinformatics: Identification of key genes and pathways[J]. Clinical Focus, 2026, 41(1): 24-32.

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URL: https://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2026.01.004

https://www.lchc.cn/EN/Y2026/V41/I1/24

Figures/Tables 9

Tab.1 Two-sample MR analysis results

方法	SNP数量	β值	P值	OR (95%CI)
MR Egger	55	0.324210	2.35E-05	1.383(1.206~1.586)
WME	55	0.377333	2.06E-14	1.458(1.324~1.606)
IVW	55	0.345530	8.56E-22	1.413(1.316~1.516)
Simple mode	55	0.408145	2.39E-04	1.504(1.227~1.843)
Weighted mode	55	0.388335	3.68E-08	1.475(1.309~1.660)

Fig.1 Scatter plot of two-sample MR analysis results

Fig.2 Funnel plot of two-sample MR analysis results

Fig.3 Forest plot of the effect of individual SNPs on the association between DM and peripheral atherosclerosis

Fig.4 Leave-one-out sensitivity analysis for SNPs

Fig.5 Screening and intersection analysis of DEGs in GSE95849 and GSE100927 datasets a. Heatmap showing the top 100 most significantly DEGs between DM patients and control samples in the GSE95849 dataset; b. Heatmap showing the top 100 most significantly DEGs between peripheral atherosclerosis patients and control samples in the GSE100927 dataset; c. Volcano plot displaying DEGs in the GSE95849 dataset; d. Volcano plot displaying DEGs in the GSE100927 dataset; e. Venn diagram of DEGs overlapping between the GSE95849 and GSE100927 datasets

Fig.6 GO enrichment analysis

Fig.7 KEGG pathway enrichment analysis

Fig.8 Construction of the DEGs protein-protein interaction network and core-target networks identified by multiple algorithms a. PPI network of DEGs; b. Core-target network constructed using the MCC (maximum clique centrality) algorithm; c. Core-target network constructed using the Degree algorithm; d. Core-target network constructed using the MNC (maximum neighborhood component) algorithm

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