Zhang Huihe, Li Wei, Wang Juwei, Wu Zhimin, Zhao Na, Jiang Yue
Department of Neurology, Wenzhou TCM Hospital of Zhejiang Chinese Medical University, Wenzhou, Zhejiang, China.
Graduate College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Mol Neurobiol. 2025 May 21. doi: 10.1007/s12035-025-05063-5.
Decreased glucose tolerance is recognized as a factor associated with Parkinson's disease (PD) progression, yet the relationship between HbA1c and PD prognosis remains insufficiently explored. Using data from the Integrated Epidemiological Unit (IEU) open Genome-Wide Association Study (GWAS), PD's IEU-b-7 and HbA1c's IEU-b-104 were extracted. RNA-seq data from GSE20292 and single-cell RNA-seq data from GSE157783 were retrieved from Gene Expression Omnibus (GEO). Mendelian Randomization (MR) analysis, with HbA1c as the exposure and PD as the outcome, was performed using the inverse variance weighted (IVW) method. Differentially expressed genes (DEGs) between PD and controls in GSE20292 were identified, and overlapping instrumental variables (IVs) and DEGs pinpointed a set of candidate genes. Machine learning refinement selected biomarkers, leading to the development of a PD biomarker-based nomogram. Key cell lineages in GSE140231 were characterized, and communication and pseudotime analyses explored cell crosstalk and evolution. Using 223 independent single nucleotide polymorphisms (SNPs)as IVs, HbA1c was found causally [IVW: Odds Ratio (OR) = 1.438, P = 0.026, 95% Confidence Interval (CI) = 1.043-1.981].. Among 625 genes associated with these SNPs, 842 DEGs were identified by comparing PD vs. controls, intersecting with 27 candidate genes. Notably, five biomarkers-FASN, MICAL3, TCIRG1, CDK10, and MFSD1-emerged as potential diagnostic targets for PD. The receiver operating characteristic (ROC) curve demonstrated the high diagnostic accuracy of these biomarkers. Analysis of key cell lineages revealed strong interactions between excitatory and inhibitory cells and oligodendrocyte precursor cells and Astrocytes cells. In conclusion, HbA1c is identified as a risk factor for PD, with FASN, MICAL3, TCIRG1, CDK10, and MFSD1 representing promising targets for PD diagnosis and treatment.
葡萄糖耐量降低被认为是与帕金森病(PD)进展相关的一个因素,然而糖化血红蛋白(HbA1c)与PD预后之间的关系仍未得到充分探索。利用综合流行病学单位(IEU)开放的全基因组关联研究(GWAS)数据,提取了PD的IEU-b-7和HbA1c的IEU-b-104。从基因表达综合数据库(GEO)中检索了来自GSE20292的RNA测序数据和来自GSE157783的单细胞RNA测序数据。采用逆方差加权(IVW)方法进行以HbA1c为暴露因素、PD为结局的孟德尔随机化(MR)分析。确定了GSE20292中PD与对照之间的差异表达基因(DEG),重叠的工具变量(IV)和DEG确定了一组候选基因。机器学习优化选择了生物标志物,从而开发了基于PD生物标志物的列线图。对GSE140231中的关键细胞谱系进行了表征,并通过通讯和伪时间分析探索了细胞间的串扰和进化。以223个独立单核苷酸多态性(SNP)作为IV,发现HbA1c具有因果关系[IVW:比值比(OR)=1.438,P=0.026,95%置信区间(CI)=1.043-1.981]。在与这些SNP相关的625个基因中,通过比较PD与对照确定了842个DEG,与27个候选基因相交。值得注意的是,五个生物标志物——脂肪酸合酶(FASN)、微管相关单加氧酶3(MICAL3)、质子转运V型ATP酶1亚基(TCIRG1)、细胞周期蛋白依赖性激酶10(CDK10)和主要促进因子超家族结构域包含蛋白1(MFSD1)——成为PD的潜在诊断靶点。受试者工作特征(ROC)曲线证明了这些生物标志物具有较高的诊断准确性。对关键细胞谱系的分析揭示了兴奋性和抑制性细胞与少突胶质前体细胞和星形胶质细胞之间的强烈相互作用。总之,HbA1c被确定为PD的一个危险因素,FASN、MICAL3、TCIRG1、CDK10和MFSD1是PD诊断和治疗的有前景的靶点。