基于孟德尔随机化及生物信息学分析探讨糖尿病与外周动脉粥样硬化的关联性及关键基因、通路研究

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

摘要/Abstract

摘要：

目的利用孟德尔随机化(Mendelian randomization,MR)和生物信息学方法探讨糖尿病(diabetes mellitus, DM)与外周动脉粥样硬化的遗传因果关联及共病分子机制,为疾病早期筛查、干预提供理论基础。方法采用MR策略,基于GWAS数据集,经工具变量筛选、多模型MR分析(IVW等)及敏感性分析验证DM和外周动脉粥样硬化的因果关系;同时利用GEO数据库相关数据集,筛选两种疾病的差异表达基因(differentially expressed genes, DEGs),开展GO、KEGG富集分析及蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络构建以识别核心基因。结果 DM与外周动脉粥样硬化呈显著正向因果关联(OR=1.413,95%CI:1.316~1.516,P<0.01);生物信息学分析获得交集DEGs 156个,富集于免疫炎症相关功能与通路,PPI网络筛选的枢纽基因为TLR2、CSF1R、CYBB、IL1B、SPI1。结论 DM与外周动脉粥样硬化存在显著正向遗传因果关联,其共病机制可能涉及上述关键基因及免疫炎症通路激活,研究结果为DM合并外周动脉粥样硬化的早期风险预测、靶向干预提供理论框架与潜在试验靶点。

关键词: 糖尿病, 动脉粥样硬化, 孟德尔随机化, 生物信息学, 信号通路

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

中图分类号:

R587.1

孙萌萌, 张志功. 基于孟德尔随机化及生物信息学分析探讨糖尿病与外周动脉粥样硬化的关联性及关键基因、通路研究[J]. 临床荟萃, 2026, 41(1): 24-32.

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.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2026.01.004

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

图/表 9

表1 两样本MR分析结果

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)

图1 两样本MR分析结果散点图

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

图2 两样本MR分析结果漏斗图

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

图3 DM对外周动脉粥样硬化影响的单个SNP森林图

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

图4 “留一法”对SNP进行敏感性分析

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

图5 GSE95849与 GSE100927数据集DEGs的筛选与交集分析 a.热图呈现GSE95849 DM患者与对照组样本间差异最显著的top100基因;b.热图呈现GSE100927外周动脉粥样硬化与对照组样本间差异最显著的top100基因;c.火山图展示GSE95849数据集DEGs;d.火山图展示GSE100927数据集DEGs;e.GSE95849与GSE100927差异表达基因Venn图

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

图6 GO富集分析结果

Fig.6 GO enrichment analysis

图7 KEGG富集分析结果

Fig.7 KEGG pathway enrichment analysis

图8 DEGs 蛋白质相互作用网络与多算法核心靶点网络构建 a.DEGs PPI网络图;b.MCC法构建核心靶点网络;c.Degree法构建核心靶点网络;d.MNC法构建核心靶点网络

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

参考文献 25

[1]	Zhou YC, Liu JM, Zhao ZP, et al. The national and provincial prevalence and non-fatal burdens of diabetes in China from 2005 to 2023 with projections of prevalence to 2050[J]. Mil Med Res, 2025, 12(1): 28.doi:10.1186/s40779-025-00615-1.
[2]	Rosana M, Yunir E, Saragih N, et al. Risk factors for peripheral arterial disease in type 2 diabetes mellitus patients: A systematic review and meta-analysis[J]. Diabetes Res Clin Pract, 2025, 224: 112170.doi:10.1016/j.diabres.2025.112170.
[3]	Kaneko M, Fujihara K, Harada MY, et al. Rates and risk factors for amputation in people with diabetes in Japan: A historical cohort study using a nationwide claims database[J]. J Foot Ankle Res, 2021, 14(1): 29.doi:10.1186/s13047-021-00474-8.
[4]	Luan J, Xu J, Zhong W, et al. Adverse prognosis of peripheral artery disease treatments associated with diabetes: A comprehensive meta-analysis[J]. Angiology, 2022, 73(4): 318-330.doi:10.1177/00033197211042494.
[5]	李盾, 王耀刚. 孟德尔随机化因果推断应用与进展[J]. 中国慢性病预防与控制, 2024, 32(10): 778-784.doi:10.16386/j.cjpccd.issn.1004-6194.2024.10.010.
[6]	潘凌, 周秋莲. 生物信息学:前沿进展、核心方法与应用领域[J]. 科技通报, 2025, 41(10): 37-49.doi:10.13774/j.cnki.kjtb.2025.10.004.
[7]	VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, et al. Methodological challenges in mendelian randomization[J]. Epidemiology, 2014, 25(3): 427-435.doi:10.1097/ede.0000000000000081.
[8]	Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors[J]. Stat Methods Med Res, 2012, 21(3): 223-242.doi:10.1177/0962280210394459.
[9]	于天琦, 徐文涛, 苏雅娜, 等. 孟德尔随机化研究基本原理、方法和局限性[J]. 中国循证医学杂志, 2021, 21(10): 1227-1234.
[10]	Szklarczyk D, Kirsch R, Koutrouli M, et al. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest[J]. Nucleic Acids Res, 2023, 51(D1): D638-d646.doi:10.1093/nar/gkac1000.
[11]	Wang T, Dong Y, Yao L, et al. Adoptive transfer of metabolically reprogrammed macrophages for atherosclerosis treatment in diabetic ApoE (-/-) mice[J]. Bioact Mater, 2022, 16: 82-94.doi:10.1016/j.bioactmat.2022.02.002.
[12]	Singh P, Sun J, Cavalera M, et al. Dysregulation of MMP2-dependent TGF-β2 activation impairs fibrous cap formation in type 2 diabetes-associated atherosclerosis[J]. Nat Commun, 2024, 15(1): 10464.doi:10.1038/s41467-024-50753-8.
[13]	Shao L, Zhou HJ, Zhang H, et al. SENP1-mediated NEMO deSUMOylation in adipocytes limits inflammatory responses and type-1 diabetes progression[J]. Nat Commun, 2015, 6: 8917.doi:10.1038/ncomms9917.
[14]	Ma J, Wu H, Zhao CY, et al. Requirement for TLR2 in the development of albuminuria, inflammation and fibrosis in experimental diabetic nephropathy[J]. Int J Clin Exp Pathol, 2014, 7(2): 481-495.
[15]	Frᶏk W, Wojtasińska A, Lisińska W, et al. Pathophysiology of cardiovascular diseases: New insights into molecular mechanisms of atherosclerosis, arterial hypertension, and coronary artery disease[J]. Biomedicines, 2022, 10(8):1938.doi:10.3390/biomedicines10081938.
[16]	Wei Y, Zhu M, Corbalán-Campos J, et al. Regulation of Csf1r and Bcl6 in macrophages mediates the stage-specific effects of microRNA-155 on atherosclerosis[J]. Arterioscler Thromb Vasc Biol, 2015, 35(4): 796-803.doi:10.1161/atvbaha.114.304723.
[17]	Castelo-Soccio L, Kim H, Gadina M, et al. Protein kinases: Drug targets for immunological disorders[J]. Nat Rev Immunol, 2023, 23(12): 787-806.doi:10.1038/s41577-023-00877-7.
[18]	Gawargi FI, Mishra PK. Regulation of cardiac ferroptosis in diabetic human heart failure: Uncovering molecular pathways and key targets[J]. Cell Death Discov, 2024, 10(1): 268.doi:10.1038/s41420-024-02044-w.
[19]	Gerrick KY, Gerrick ER, Gupta A, et al. Transcriptional profiling identifies novel regulators of macrophage polarization[J]. PLoS One, 2018, 13(12): e0208602.doi:10.1371/journal.pone.0208602.
[20]	Ayash R, Kabalan Y, Chamaa S. Exploring the predictive potentials of IL-1β and TNFR1 in atherogenic risk in prediabetes[J]. Sci Rep, 2025, 15(1): 37369.doi:10.1038/s41598-025-12852-4.
[21]	王艳, 刘馨桐, 陈伟, 等. 基于TXNIP/NLRP3/IL-1β信号通路探讨生脉注射液对糖氧剥夺/复糖复氧微血管内皮细胞的保护作用[J]. 中国中药杂志, 2025, 50(22): 6450-6460.
[22]	Zhou Y, Song W, Wang C, et al. Integrated metabolomics and transcriptomics reveal the anti-aging effect of melanin from Sepiella maindroni ink (MSMI) on D-galactose-induced aging mice[J]. Aging (Albany NY), 2021, 13(8): 11889-11906.doi:10.18632/aging.202890.
[23]	Huo S, Wang Q, Shi W, et al. ATF3/SPI1/SLC31A1 signaling promotes cuproptosis induced by advanced glycosylation end products in diabetic myocardial injury[J]. Int J Mol Sci, 2023, 24(2): 1667.doi:10.3390/ijms24021667.
[24]	Liu Z, Huang S. Upregulation of SPI1 during myocardial infarction aggravates cardiac tissue injury and disease progression through activation of the TLR4/NFκB axis[J]. Am J Transl Res, 2022, 14(4): 2709-2727.
[25]	Park SH. Regulation of macrophage activation and differentiation in atherosclerosis[J]. J Lipid Atheroscler, 2021, 10(3): 251-267.doi:10.12997/jla.2021.10.3.251.