Revealing the molecular links between coronary heart disease and cognitive impairment: the role of aging-related genes and therapeutic potential of stellate ganglion block

Abstract

Coronary heart disease (CHD) and cognitive impairment frequently co-occur in aging populations, yet the molecular mechanisms linking these conditions remain unclear. This study aims to elucidate the roles of key aging-related genes (ARGs), specifically FKBP5 and DDIT3, in the pathophysiology of CHD and cognitive impairment, and to evaluate the therapeutic potential of stellate ganglion block (SGB). Using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data, we identified FKBP5 and DDIT3 as pivotal genes upregulated in both conditions. Experimental findings show that SGB effectively modulates these ARG-related pathways through autonomic regulation, specifically suppressing estrogen and NF-κB signaling pathways, thereby reducing the expression of pro-inflammatory cytokines such as SRC, MMP2, FKBP5, IRAK1, and MYD88, while upregulating the vasodilation-related gene NOS3. This modulation improved endothelial and cardiac function and enhanced cerebral blood flow (CBF), leading to cognitive improvement. Behavioral assessments, including novel object recognition (NOR) and Morris water maze (MWM) tests, demonstrated that SGB-treated rats outperformed untreated MI rats, with significant cognitive recovery over time. Further support from laser Doppler flowmetry (LDF) and electroencephalogram (EEG) analyses revealed increased left frontal blood flow and stabilized neural activity, indicating a favorable neurophysiological environment for cognitive rehabilitation. Our findings suggest that left stellate ganglion block (LSGB) provides both cardiac and cognitive benefits through targeted gene modulation, establishing its therapeutic potential for addressing the intersecting pathologies of CHD and cognitive impairment.

Keywords: Aging-related genes; Autonomic nervous system; Cognitive impairment; Coronary heart disease; DDIT3; FKBP5; Stellate ganglion block.

Fig. 1

Cellular heterogeneity and differential expression analysis. a PCA dimensionality reduction analysis. b Sample UMAP cell group clustering. c SingleR cell type annotation. d Volcano plot of gene distribution between CHD and normal cells. e Heatmap of differentially expressed genes between CHD and normal cells

Fig. 2

WGCNA uncovers aging-related gene modules and cellular differentiation in CHD. a Selection of soft threshold. b Identification of co-expressed modules. c Heatmap of module and phenotype correlation. d Cell differentiation time differences, cell differentiation stages, differentiation of CHD and normal cells. e Box plot of cell aging-related gene scores between CHD and control. f Hierarchical clustering of samples. g Hierarchical clustering of samples after introducing traits

Fig. 3

Differential expression of candidate genes, machine learning model validation, and hub gene expression differences. a Volcano plot of gene distribution between CHD and control. b Heatmap of differentially expressed genes between CHD and control. c Candidate genes GO enrichment bar chart. d Candidate genes GO enrichment chord diagram. e Volcano plot of candidate gene distribution. f Heatmap of candidate gene differences. g Lasso coefficient spectrum. h Tenfold cross-validation in LASSO analysis. i RF model decision trees. j RF model gene importance. k XGBoost model gene importance. l Venn diagram of candidate hub genes. m Box plot of candidate hub genes between CHD and control in the training set. n Box plot of candidate hub genes between CHD and control in the validation set

Fig. 4

Enrichment analysis and immune dynamics. a DDIT3 enrichment signal pathway. b FKBP5 enrichment signal pathway. c Histogram of 22 types of immune infiltration abundance. d Heatmap of immune infiltration cell correlation. e Box plot of immune infiltration cell abundance between CHD and control samples. f Lollipop graph of correlation between differential immune cells and hub genes

Fig. 5

Clinical application prediction model and molecular docking, potential therapeutic targets. a Nomogram model constructed with hub genes. b Calibration curve. c Decision curve. d Clinical impact curve. e Overall view of FKBP5 and NEFAZODONE molecular docking (− 8.0 kcal/mol). f Detailed view of FKBP5 and NEFAZODONE molecular docking (− 8.0 kcal/mol). g Small molecule drugs corresponding to hub genes

Fig. 6

Expression profiles of FKBP5 and correlation with CHD and various diseases. a Expression of FKBP5 in CHD patients versus non-CHD individuals in clinical blood samples. b Heatmap of correlation between biomarkers and circadian rhythm-related genes. c expression differences of biomarkers between insomnia patients and healthy control samples. d Expression differences of biomarkers between mild cognitive impairment, Alzheimer's disease, and healthy control samples. e The KEGG pathway enrichment results for FKBP5. f The GO enrichment results for FKBP5

Fig. 7

Cardiac function and structural assessment tests. a Comparison of electrocardiographic parameters and heart rate variability (HRV) indices before and after LSGB treatment. b Comparison of echocardiographic changes among the three groups 1 month later. c HE staining results of the infarcted area 1 month after myocardial infarction. d Masson's trichrome staining results of the infarcted area 1 month after myocardial infarction. e Detection of cardiac enzyme and troponin levels 1 month later. *P < 0.05,**P < 0.01,***P < 0.001,****P < 0.0001

Fig. 8

Results of the NORTest and the Water Maze Experiment at 1 month and 10 months following MI. a Exploration time for the new object and old object 1 months post-MI. EA is the exploration time for the old object, and EB is the exploration time for the new object. b DI index of new object recognition in the Sham, MI, and LSGB groups. c Distance traveled in NOR (T2) phase. d Exploration time in familiarization (T1) phase. e Exploration time for the new object and old object 10 months post-MI. EA is the exploration time for the old object, and EB is the exploration time for the new object. f DI index of new object recognition in the Sham, MI, and LSGB groups 10 months post-MI. g Distance traveled in NOR (T2) phase 10 months post-MI. h Exploration time in familiarization (T1) phase 10 months post-MI. i Escape latency at different time points in the water maze test 1 months post-MI. j Traveled distance in the water maze test 1 months post-MI. k Time spent by the rat in the target quadrant 1 months post-MI. l Exploration results for the old platform 10 months post-myocardial infarction. m Escape latency at different time points in the water maze test 10 months post-MI. n Traveled distance in the water maze test 10 months post-MI. o Time spent by the rat in the target quadrant 10 months post-MI

Fig. 9

LDF and EEG related results. a LDF measurement images and statistical graphs. b EEG measurement images and statistical graphs. c Line graphs showing time-course changes for each EEG band

Fig. 10

Changes in FKBP5 gene-associated KEGG pathways in the transcriptome. a Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in infarct heart zone in the estrogen signaling pathway. b Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in normal heart zone in the estrogen signaling pathway. c Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in peripheral blood in the estrogen signaling pathway. d Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in LSG in the estrogen signaling pathway. e Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in RSG in the estrogen signaling pathway. f Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in infarct heart zone in the NF-κB signaling pathway. g Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in normal heart zone in the NF-κB signaling pathway. h Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in peripheral blood in the NF-κB signaling pathway. i Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in LSG in the NF-κB signaling pathway. j Differences in KEGG pathway components between MI versus SHAM and LSGB versus MI in RSG in the NF-κB signaling pathway