Research article Special Issues

Moderating effect of coping strategies on the association between perceived discrimination and blood pressure outcomes among young Black mothers in the InterGEN study

  • Research suggests experiences of racial discrimination influence blood pressure outcomes among Black women, but little is known about how coping strategies may influence this relationship. Our study aimed to assess the moderating effects of coping strategies on perceived racial discrimination and blood pressure among young Black mothers. We conducted a secondary analysis on data from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure study. Eligible participants were African American or Black women aged 21 and older, who did not present with any cognitive disorder that may obscure reporting data, and who had a biological child who was 3–5 years old at the time of study enrollment. In our analysis, systolic and diastolic blood pressure were the primary outcomes, and experiences of discrimination situations and frequency subscales were the primary predictors. We considered the three subscales of the Coping Strategy Indicator (problem-solving, seeking social support, and avoidance) as moderators. Linear regression models were used. Of the 246 female participants (mean age: 31.3 years; SD = 5.8), the mean systolic and diastolic blood pressures were 114 mmHg (SD = 13.8) and 73 mmHg (SD = 10.9), respectively. The frequency of experiences of perceived racial discrimination was significantly associated with higher systolic blood pressure, but this relationship was moderated among participants with greater seeking social support scores (p = 0.01). There were no significant moderation effects in models with diastolic blood pressure as the outcome. Future studies should examine this relationship longitudinally and further investigate specific coping strategies Black women use to manage perceived racial discrimination.

    Citation: Alexandria Nyembwe, Yihong Zhao, Billy A. Caceres, Kelli Hall, Laura Prescott, Stephanie Potts-Thompson, Morgan T. Morrison, Cindy Crusto, Jacquelyn Y. Taylor. Moderating effect of coping strategies on the association between perceived discrimination and blood pressure outcomes among young Black mothers in the InterGEN study[J]. AIMS Public Health, 2025, 12(1): 217-232. doi: 10.3934/publichealth.2025014

    Related Papers:

    [1] Li Cai, Yu Hao, Pengfei Ma, Guangyu Zhu, Xiaoyu Luo, Hao Gao . Fluid-structure interaction simulation of calcified aortic valve stenosis. Mathematical Biosciences and Engineering, 2022, 19(12): 13172-13192. doi: 10.3934/mbe.2022616
    [2] Rongxing Qin, Lijuan Huang, Wei Xu, Qingchun Qin, Xiaojun Liang, Xinyu Lai, Xiaoying Huang, Minshan Xie, Li Chen . Identification of disulfidptosis-related genes and analysis of immune infiltration characteristics in ischemic strokes. Mathematical Biosciences and Engineering, 2023, 20(10): 18939-18959. doi: 10.3934/mbe.2023838
    [3] Yuedan Wang, Jinke Huang, Jiaqi Zhang, Fengyun Wang, Xudong Tang . Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning. Mathematical Biosciences and Engineering, 2023, 20(6): 10741-10756. doi: 10.3934/mbe.2023476
    [4] Xuesi Chen, Qijun Zhang, Qin Zhang . Predicting potential biomarkers and immune infiltration characteristics in heart failure. Mathematical Biosciences and Engineering, 2022, 19(9): 8671-8688. doi: 10.3934/mbe.2022402
    [5] Jun Wang, Mingzhi Gong, Zhenggang Xiong, Yangyang Zhao, Deguo Xing . Immune-related prognostic genes signatures in the tumor microenvironment of sarcoma. Mathematical Biosciences and Engineering, 2021, 18(3): 2243-2257. doi: 10.3934/mbe.2021113
    [6] Shengjue Xiao, Yufei Zhou, Ailin Liu, Qi Wu, Yue Hu, Jie Liu, Hong Zhu, Ting Yin, Defeng Pan . Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis. Mathematical Biosciences and Engineering, 2021, 18(4): 4696-4712. doi: 10.3934/mbe.2021238
    [7] Jie Wang, Md. Nazim Uddin, Rehana Akter, Yun Wu . Contribution of endothelial cell-derived transcriptomes to the colon cancer based on bioinformatics analysis. Mathematical Biosciences and Engineering, 2021, 18(6): 7280-7300. doi: 10.3934/mbe.2021360
    [8] Xiangyue Zhang, Wen Hu, Zixian Lei, Hongjuan Wang, Xiaojing Kang . Identification of key genes and evaluation of immune cell infiltration in vitiligo. Mathematical Biosciences and Engineering, 2021, 18(2): 1051-1062. doi: 10.3934/mbe.2021057
    [9] Gaozhong Sun, Tongwei Zhao . Lung adenocarcinoma pathology stages related gene identification. Mathematical Biosciences and Engineering, 2020, 17(1): 737-746. doi: 10.3934/mbe.2020038
    [10] Sufang Wu, Hua He, Jingjing Huang, Shiyao Jiang, Xiyun Deng, Jun Huang, Yuanbing Chen, Yiqun Jiang . FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP. Mathematical Biosciences and Engineering, 2022, 19(9): 9295-9320. doi: 10.3934/mbe.2022432
  • Research suggests experiences of racial discrimination influence blood pressure outcomes among Black women, but little is known about how coping strategies may influence this relationship. Our study aimed to assess the moderating effects of coping strategies on perceived racial discrimination and blood pressure among young Black mothers. We conducted a secondary analysis on data from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure study. Eligible participants were African American or Black women aged 21 and older, who did not present with any cognitive disorder that may obscure reporting data, and who had a biological child who was 3–5 years old at the time of study enrollment. In our analysis, systolic and diastolic blood pressure were the primary outcomes, and experiences of discrimination situations and frequency subscales were the primary predictors. We considered the three subscales of the Coping Strategy Indicator (problem-solving, seeking social support, and avoidance) as moderators. Linear regression models were used. Of the 246 female participants (mean age: 31.3 years; SD = 5.8), the mean systolic and diastolic blood pressures were 114 mmHg (SD = 13.8) and 73 mmHg (SD = 10.9), respectively. The frequency of experiences of perceived racial discrimination was significantly associated with higher systolic blood pressure, but this relationship was moderated among participants with greater seeking social support scores (p = 0.01). There were no significant moderation effects in models with diastolic blood pressure as the outcome. Future studies should examine this relationship longitudinally and further investigate specific coping strategies Black women use to manage perceived racial discrimination.



    Calcific aortic valve stenosis (CAVS) is a continuous global progressive disease that causes stenosis and contraction of the left ventricular outflow tract in the later stage of the disease, causing destructive damage to the heart that affects hemodynamics [1]. Several epidemiological studies have shown that 2.8% of the elderly (over 75 years old) have varying degrees of CAVS, and as many as 25% of the community population over 65 years old have risk factors for valve sclerosis. Older men, high triglyceride levels, smoking time limit, and early aortic valve replacement have been determined to be associated with the progression of CAVS [2]. A calcified aortic valve often leads to aortic valve stenosis. Inflammatory cell infiltration, lipid accumulation, and tissue fibrosis play a leading role in the initial stage of the mechanism of CAVS [3]. Therefore, exploring the pathophysiological process of CAVS is essential for the diagnosis and treatment of this complex disease with a poor prognosis.

    The immune and inflammatory response is a key link in the pathological process of CAVS [4]. A variety of inflammatory markers such as Toll-like receptor (TLR), interleukin-37, interleukin-6, transforming growth factor-β1 are closely related to aortic valve stenosis caused by calcified aortic valve Related [5,6]. Amyloid P Component Serum (APCS), Heat Shock Protein 90 (HSP90), Protein Disulfide Isomerase Family A Member 3 (PDIA3), Annexin A2 (ANXA2), Toll Like Receptor 7 (TLR7) and other immune-related genes (IRGs) also suggest that they have therapeutic effects in the process of CAVS fibrosis [7,8]. Immune cell infiltration is also closely related to CAVS. CD8 T lymphocytes, macrophages, and regulatory T lymphocytes (Tregs) appear in the pathophysiological process of CAVS [9,10,11]. In addition, statin anti-inflammatory therapy affects CAVS and maybe a drug target for the prevention of related diseases [12,13].

    This study conducted bioinformatics and machine learning analysis of IRGs in CAVS and explored potential regulatory methods and functional differences related to IRGs. Compared with traditional models, machine learning models show superior performance in disease classification and prediction [14]. The use of machine learning models is a novel method for disease diagnosis and prediction [15,16,17]. We use Single-sample GSEA (ssGSEA) and CIBERSORT to explore the relationship between differential immunity-related genes (DEIRGs) and the level of immune infiltration of cell subsets. In addition, to better understand the immune mechanism of CAVS, the potential connection between key immune signals and immune cell subsets was studied. The flow chart of this research analysis is shown in Supplementary Figure 1.

    Figure 1.  GSVA analysis results and differential genes. (A) GSVA analysis of gene sets. (B) DEGs and DEIRGs expression volcano graphs of CAVS and normal controls.

    Download the micro data set from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). The following are the screening criteria: 1) Select tissue samples including CAVS patients and normal controls; 2) Exclude samples from mitral and tricuspid valves; 3) No other organic diseases. The data set GSE12644 and GSE51472 of the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array) were selected as the training data set, and the data set GSE83453 of the GPL10558 platform (Illumina HumanHT-12 V4.0 expression beadchip) was selected as the verification. GSE12644 includes 10 calcified aortic valve samples and 10 normal controls (10 CAVS vs 10 NC). GSE51472 includes 5 calcified aortic valve tissues and 5 normal controls (5 CAVS vs 5 NC). GSE83453 includes 9 aortic valve tissues with stenosis and calcification and 8 normal controls (9 CAVS vs 8 NC). All tissue sample data undergoes background correction and de-batch effect.

    Use GSVA to analyze the data set to find pathways with significant differences between samples, analyzing genes more biologically meaningful [18]. The Benjamini & Hochberg method was used for multiple testing calibrations. The score value > 0.5 and adjust P-value < 0.05 is the cutoff value of pathway enrichment. The Limma package [19] (Version 3.44.3) in the R software (Version 4.0.2; https://www.r-project.org/) is used to screen differentially expressed genes (DEGs). The screening criteria for significant differences are P-value < 0.05, | log2 (Fold Change) | ≥ 1. A total of 2483 IRGs were obtained from the ImmPort (https://www.immport.org/) database, and DEIRGs were identified by matching IRGs and DEGs. The ggplot2 package [20] (Version 3.3.5) draws bar graphs of GSVA and volcano graphs of DEIRGs.

    Enrichment analysis of pathways and functions of DEIRGs to discover possible immune pathways and functions. Gene Ontology (GO) and Disease Ontology (DO) enrichment analysis are sorted by adjust P-value < 0.05 and Count value. We use the ggplot2 package (Version 3.3.5) of R software to visualize the plot. The Molecular Signatures Database (MSigDB) library is a collection of annotated gene sets. We can perform a series of analyses on the predefined gene set in the MSigDB library. Gene Set Enrichment Analysis (GSEA) uses "c2.cp.kegg.v7.2.symbols.gmt" under MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/) [21] as the reference gene set, and the cutoff value is set to adjust P-value < 0.05.

    The obtained DEIRGs were further screened using machine learning methods to obtain key immune signals. Two machine learning methods—Least Absolute Shrinkage Selection Operator (LASSO) [22] and Support Vector Machine Recursive Feature Elimination (SVM-RFE) [23] perform feature screening for differences in gene expression values to obtain more accurate screening predictions. LASSO is an analysis method that can perform feature selection on research models. After screening, it aims to enhance the prediction accuracy and reliability of the model. SVM-RFE adopts the risk minimization principle and the experience error minimization principle. It can be used to improve learning performance to filter models. We use the glmnet package [24] (Version 4.1) and e1071 package [25] (Version 1.7) of the R software to execute the LASSO and SVM-RFE algorithms. Afterward, the external verification data set GSE83453 was verified against the selected key immune signals.

    ssGSEA [26] and CIBERSORT [27] are two tools for analyzing immune infiltrating cell subtypes. CIBERSORT analyzes the infiltration of immune cells between CAVS and normal controls. Subsequently, the relationship between the DEIRGs of CAVS and the subtypes of immune infiltrating cells was established. At the same time, the key immune signals obtained by machine learning screening are correlated with the immune infiltrating cells obtained by two analysis methods. Finally, in the MSigDB library "c5.all.v7.4.symbols.gmt" uses "IMMUNITY" as the keyword to find the immune pathway of interest, calculate the pathway enrichment score and analyze the key immune signals found and their correlation. Spearman correlation analysis was used for the correlation. We used the corrplot package [28] (Version 0.9) of R software to make a related heat map. The pheatmap [29] package (Version 1.0.12) constructs a heat map of immune cells. The vioplot package [30] (Version 0.3.7) is used to compare the levels of immune cells between the two groups.

    The Connectivity Map (CMap) (https://www.broadinstitute.org/connectivity-map-cmap) [31] is a database that analyzes the relationship between genes and their possible targeted drugs. Use CLUE (https://clue.io/) to predict the targeted drugs for CAVS immunotherapy on the 150 most significant up-and down-regulated immune genes in the data set. At the same time, use the Touchstone module to analyze the mechanism of actions (MoA) of the drug of interest and explore potential modes of action.

    All statistical analysis uses R software (Version 4.0.2). Use Student's t test for normally distributed variables and Mann-Whitney U test for abnormally distributed variables to compare the differences between the two groups.

    Use non-parametric unsupervised GSVA for gene sets to find the difference between the gene set in CAVS and normal control in the enrichment pathway, suggesting that it has a greater correlation with multiple immune pathways (Figure 1A). After the integration of GSE12644 and GSE51472 microarray matrix standardization and removal of batch effects, 266 DEGs (164 up-regulated genes, 102 down-regulated genes) were obtained, and 71 significant DEIRGs were obtained through integration with immune genes (60 up-regulated genes and 11 down-regulated genes). The volcano map shows the differences in genes. The DEIRGs related to immunity are marked by black circles. The names of some DEIRGs of interest are also marked (Figure 1B).

    GO enrichment found that the biological processes (BP) mainly focused on reactive oxygen species, oxidative stress, cellular response to oxidative stress, and decreased oxygen levels. Cell component (CC) is mainly related to various components of the synaptic membrane. Molecular function (MF) is mainly related to receptor activity and enzyme binding (Figure 2A). DO found that cardiovascular diseases such as arteriosclerotic cardiovascular disease, arteriosclerosis, atherosclerosis, coronary artery disease, and myocardial infarction were significantly enriched. In addition, it includes lung disease, obstructive lung disease, kidney disease, and so on (Figure 2B). GSEA found that two immune-related pathways, Cytokine-cytokine receptor interaction and Chemokine signaling pathway were significantly enriched. It may be related to the immune-related pathways of CAVS (Figure 2C).

    Figure 2.  Pathway and function enrichment analysis results. (A) Enrichment results of BP, CC, and MF. (B) Results of DO analysis. (C) Two immune-related pathways of CAVS.

    According to the LASSO method, the optimal lambda.min is set as 0.007915132 based on the amount of gene expression. The 6 immune signals screened out are Angiotensin II Receptor Type 1(AGTR1), C-X-C Motif Chemokine Ligand 16 (CXCL16), Leptin Receptor (LEPR), Phospholipid Transfer Protein (PLTP), Secretogranin II (SCG2), Secretory Leukocyte Peptidase Inhibitor (SLPI) (Figure 3A). After screening using SVM-RFE, the first 50 variables were screened by 5x cross-check, and the first 4 immune signals were Angiotensin II Receptor Type 1(AGTR1), Phospholipid Transfer Protein (PLTP), Secretogranin II (SCG2), and Tenascin C (TNC) (Figure 3B). Integrating the two results, AGTR1, PLTP, and SCG2 are considered by us to be the key immune signals of CAVS (Figure 3C). The external validation data set GSE83453 verified it and found that the three key immune signals distinguished well between CAVS and normal control (Figure 3D). The areas under the ROC curve were AGTR1 (AUC = 0.917), PLTP (AUC = 0.875), and SCG2 (AUC = 0.917), with high diagnostic value (Figure 3E).

    Figure 3.  Key immune signals and verification. (A) LASSO. (B) SVM-RFE. (C) 3 key immune signals integrated. (D) Verify the expression of key immune signals in the data set. (E) ROC curve.

    Two methods of CIBERSORT and ssGSEA were used to analyze the immune infiltration of CAVS and normal controls. The relative percentages of the 22 immune cells evaluated by CIBERSORT are displayed in a bar graph (Figure 4A). Correlation analysis between immune cells found that NK cells resting and T cells regulatory (Tregs) were positively correlated, and the correlation reached 0.81. The highest negative correlation between Mast cells resting and Mast cells activated, T cells gamma delta and T cells regulatory (Tregs) reached 0.77(Figure 4B). The violin chart shows that B cells naïve (P < 0.01), Macrophages M0 (P < 0.01), Macrophages M2 (P < 0.01) have a higher degree of discrimination between CAVS and normal controls. B cells naïve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS (Figure 4C). The PCA chart also shows that CAVS is well distinguished from normal controls (Figure 4D). The correlation results between immune-related differential genes (DEIRGs) and immune infiltrating cells produced by the two methods are represented by heat maps (Figure 5A, B). ssGSEA found that most DEIRGs are positively correlated with more immune cells, while CIBERSORT shows that B cells memory, Macrophages.M2, and NK cells activated are negatively correlated with DEIRGs.

    Figure 4.  CIBERSORT immune infiltration analysis. (A) Percentage of immune cells. (B) Correlation between immune cells. (C) Immune cell difference between CAVS and normal control. (D) PCA chart of CAVS and normal control.
    Figure 5.  Correlation heat map of DEIRGs with CIBERSORT and ssGSEA immune cell subtypes. (A) Correlation of immune cells was obtained by DEIRGs and CIBERSORT analysis. (B) Correlation of immune cells obtained by DEIRGs and ssGSEA analysis.

    Among the three key immune signals of CAVS, AGTR1 is negatively correlated with the immune pathway of interest in MSigDB, while PLTP and SCG2 are positively correlated (Figure 6AC). There is a high degree of positive correlation between most immune pathways. Among the 28 immune cells analyzed by ssGSEA, AGTR1 was negatively correlated with most, while PLTP and SCG2 were positively correlated with most (Figure 6DF). The correlation among 28 kinds of immune cells is shown in the correlation heat map. Among the 22 immune cells analyzed by CIBERSORT, the most significant is that AGTR1 is positively correlated with NK cells activated (r = 0.83, P = 1.32E-08), and NK cells resting is negatively correlated (r = -0.63, P = 0.000194) (Figure 6G). PLTP had the most significant positive correlation with Neutrophils (r = 0.69, P = 3.89E-05), and the most significant negative correlation with B cells naive (r = -0.36, P = 0.045151) (Figure 6H). SCG2 had the most significant positive correlation with Macrophages.M0 (r = 0.68, P = 2.63E-05), and the most significant negative correlation with Macrophages.M2 (r = -0.56, P = 0.001529) (Figure 6I).

    Figure 6.  Key immune signals and specific immune pathways, immune infiltration subcellular correlation. (A) The correlation between AGTR1 and immune pathways. (B) The correlation between PLTP and immune pathways. (C) The correlation between SCG2 and immune pathways. (D) The correlation between AGTR1 and immune cell infiltration of ssGSEA. (E) The correlation between PLTP and immune cell infiltration of ssGSEA. (F) The correlation between SCG2 and immune cell infiltration of ssGSEA. (G) The correlation between AGTR1 and CIBERSORT immune cell infiltration. (H) The correlation between PLTP and CIBERSORT immune cell infiltration. (I) The correlation between SCG2 and CIBERSORT immune cell infiltration.

    CMap analysis found that isoliquiritigenin, parthenolide, pyrrolidine-dithiocarbamate, radicicol, RITA, roscovitine, securinine, midazolam, mitomycin-c, and colforsin are the top ten targeted drugs related to CAVS immunity (Figure 7A). The scores of isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate were 92.40, 88.23, and 84.24. The first three drugs isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate were analyzed by the drug MoA and found to be related to the NFkB pathway inhibitor, Immunostimulant, etc (Figure 7BD).

    Figure 7.  Drug CMap and MoA analysis. (A) Top ten drugs and scores. (B) MoA analysis of isoliquiritigenin. (C) MoA analysis of parthenolide. (D) MoA analysis of pyrrolidine-dithiocarbamate.

    Calcific aortic valve stenosis is the most prevalent valve disease in the world. It exists in large numbers in the elderly, and the disease has caused severe damage to the patients [32]. Once the clinical symptoms of severe CAVS appear, the prognosis is poor without intervention. Although there is still a lack of precise molecular insights into the pathophysiological process of CAVS, early intervention of the disease has become more realistic. More evidence shows that aortic valve calcification is closely related to immune inflammation [33,34]. Therefore, we tried to find DEIRGs and explore the possible role of immune cell infiltration in CAVS. 71 DEIRGs (60 up-regulated genes, 11 down-regulated genes) were identified as biomarkers of CAVS, and the potential enrichment function of DEIRGs was further studied. GO enrichment revealed that DEIRGs are associated with the immune inflammatory response. DO enrichment analysis found that DEIRGs are mainly closely related to cardiovascular diseases. GSEA enrichment revealed that two immune-related pathways, Cytokine-cytokine receptor interaction and Chemokine signaling pathway were significantly enriched. Two machine learning methods, LASSO and SVM-RFE, are used to confirm that AGTR1, PLTP, and SCG2 are the key immune signals of CAVS.

    The protein encoded by AGTR1 is part of the renin-angiotensin system and is used to regulate the balance of blood and body fluids. This gene may play a role in the production of arrhythmia after reperfusion after ischemia or infarcted myocardial blood flow is restored. A study showed that the AGTR1 gene has a moderate level of evidence that may be related to the risk of CAVS [35]. At the same time, the pathogenesis of some cardiovascular diseases is also related to AGTR1 [36,37]. PLTP is one of the lipid transfer proteins, which binds to Apolipoprotein A1(ApoA) [38]. Several studies have shown that ApoA is related to calcification and stenosis of the aortic valve. It is mostly located in its fibrous tissue and co-localizes with the calcified area [39,40,41]. In addition, studies in mice have shown that PLTP deficiency can reduce plasma total cholesterol and triglycerides and prevent the progression of arterial calcification [42]. SCG2 is a member of the granulin family. Studies have found that SCG2 is present in mouse myocardium [43]. In addition, a study also found that SCG2 plays a key role in the development of aortic valve calcification [44]. However, the specific mechanism of SCG2 and CAVS remains to be studied.

    CIBERSORT and ssGSEA analyzed the subtypes of CAVS immune infiltrating cells and found that B cells naïve (P < 0.01) and Macrophages M2 (P < 0.01) were less in CAVS, while Macrophages M0 (P < 0.01) were more in CAVS. ssGSEA and CIBERSORT found that most DEIRGs are positively correlated with immune cells, and DEIRGs are more negatively correlated with B cells memory, Macrophages.M2, and NK cells activated. Among the three key immune signals of CAVS, AGTR1 is negatively correlated with the immune pathway of interest in MSigDB, while PLTP and SCG2 are positively correlated. Analysis of the key immune signals and immune cell subtypes of the three CAVS showed that among the 28 immune cells obtained from ssGSEA, AGTR1 was negatively correlated with the majority, while PLTP and SCG2 were positively correlated with the majority. Among the 22 immune cells analyzed by CIBERSORT, the most significant is that AGTR1 is positively correlated with NK cells activated, and NK cells resting is negatively correlated. PLTP is positively correlated with Neutrophils and negatively correlated with B cells naive. SCG2 is positively correlated with Macrophages.M0 and negatively correlated with Macrophages.M2.

    Finally, we performed CMap analysis on immune genes in the dataset to find possible related drugs. Isoliquiritigenin, parthenolide, pyrrolidine-dithiocarbamate, radicicol, RITA, roscovitine, securinine, midazolam, mitomycin-c, and colforsin are the top ten targeted drugs related to the immune mechanism of CAVS. At the same time, it was found that the top three drugs isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are related to NFkB pathway inhibitor and Immunostimulant. Based on these results, AGTR1, PLTP, and SCG2 seem to play a key role in CSVA by regulating immune infiltration.

    In this study, we obtained 266 DEGs in CAVS and normal controls, and 71 DEIRGs were obtained through integration with immune genes. Enrichment analysis found that DEIRGs are related to oxidative stress, synaptic membrane components, receptor activity, and a variety of cardiovascular diseases and immune pathways. Two machine learning algorithms identified AGTR1, PLTP, and SCG2 as the key immune signals of CAVS. Immune infiltration found that B cells naïve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS. At the same time, AGTR1, PLTP, SCG2 are highly correlated with a variety of immune cell subtypes. CMap analysis found that isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are the top three targeted drugs related to CAVS immunity. Our findings will help improve the understanding of CAVS disease and explain new molecular mechanisms and potential targets.

    We acknowledge GEO database for providing their platforms and contributors for uploading their meaningful datasets.

    The authors declare that they have no conflict of interest.


    Acknowledgments



    The research leading to these results was supported by the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure study, grant number 1R01NR013520–01A1, the T32 Arteriosclerosis, grant number 2T32HL007343–46, and the Mentored Research Scientist Development Award from NHLBI (K01HL146965–01). We would like to thank the participants of the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure study and the research team members who assisted in the data collection for this study.

    Authors' contribution



    Conceptualization: Alexandria Nyembwe, Yihong Zhao, and Jacquelyn Y. Taylor; Data Curation: Alexandria Nyembwe, Yihong Zhao, and Jacquelyn Y. Taylor; Formal Analysis: Yihong Zhao; Funding Acquisition: Jacquelyn Y. Taylor, and Cindy Crusto; Investigation Methodology: Alexandria Nyembwe, Yihong Zhao, and Jacquelyn Y. Taylor; Project Administration Resources: Jacquelyn Y. Taylor and Cindy Crusto; Software: NA; Supervision: Alexandria Nyembwe and Jacquelyn Y. Taylor; Validation: Alexandria Nyembwe, Yihong Zhao, and Jacquelyn Y. Taylor; Visualization: Yihong Zhao; Writing – original draft: Alexandria Nyembwe, Yihong Zhao, Billy A. Caceres, Kelli Hall, Laura Prescott, Stephanie Potts-Thompson, Morgan T. Morrison, and Jacquelyn Y. Taylor; Writing – review & editing: Alexandria Nyembwe, Yihong Zhao, Billy A. Caceres, Kelli Hall, Laura Prescott, Stephanie Potts-Thompson, Morgan T. Morrison, Cindy Crusto, and Jacquelyn Y. Taylor. The final manuscript was approved by all authors.

    Conflict of interest



    All authors declare no conflicts of interest in this paper.

    [1] Carson A, Howard G, Burke G, et al. (2011) Ethnic differences in hypertension incidence among middle-aged and older adults: the multi-ethnic study of atherosclerosis. Hypertension 57: 1101-1107. https://doi.org/10.1161/HYPERTENSIONAHA.110.168005
    [2] Selassie A, Wagner C, Laken M, et al. (2011) Progression is accelerated from prehypertension to hypertension in blacks. Hypertension 58: 579-587. https://doi.org/10.1161/HYPERTENSIONAHA.111.177410
    [3] Paradies Y, Ben J, Denson N, et al. (2015) Racism as a determinant of health: a systematic review and meta-analysis. PLoS One 10: e0138511. https://doi.org/10.1371/journal.pone.0138511
    [4] Williams D, Lawrence J, Davis B, et al. (2019) Understanding how discrimination can affect health. Health Serv Res 54: 1374-1388. https://doi.org/10.1111/1475-6773.13222
    [5] Sheehy S, Brock M, Palmer J, et al. (2023) Abstract P406: Association Between Perceived Interpersonal Racism and Incident Coronary Heart Disease Among Black Women. Circulation 147: AP406. https://doi.org/10.1161/circ.147.suppl_1.P406
    [6] Beatty Moody D, Waldstein S, Tobin J, et al. (2016) Lifetime racial/ethnic discrimination and ambulatory blood pressure: The moderating effect of age. Health Psychol 35: 333. https://dx.doi.org/10.1037/hea0000270
    [7] Forde A, Sims M, Muntner P, et al. (2020) Discrimination and hypertension risk among African Americans in the Jackson Heart Study. Hypertension 76: 715-723. https://doi.org/10.1161/HYPERTENSIONAHA.119.14492
    [8] Lewis T, Barnes L, Bienias J, et al. (2009) Perceived discrimination and blood pressure in older African American and white adults. J Gerontol A Biol Sci Med Sci 64: 1002-1008. https://doi.org/10.1093/gerona/glp062
    [9] Sims M, Diez-Roux A, Dudley A, et al. (2012) Perceived discrimination and hypertension among African Americans in the Jackson Heart Study. Am J Public Health 102: S258-265. https://doi.org/10.2105/AJPH.2011.300523
    [10] Mensah G, Wei G, Sorlie P, et al. (2017) Decline in cardiovascular mortality: possible causes and implications. Circ Res 120: 366-380. https://doi.org/10.1161/CIRCRESAHA.116.309115
    [11] Kyalwazi A, Loccoh E, Brewer L, et al. (2022) Disparities in cardiovascular mortality between Black and White adults in the United States, 1999 to 2019. Circulation 146: 211-228. https://doi.org/10.1161/CIRCULATIONAHA.122.060199
    [12] Shah N, Lloyd-Jones D, O'Flaherty M, et al. (2019) Trends in cardiometabolic mortality in the United States, 1999–2017. JAMA 322: 780-782.
    [13] Brown K, Hui Q, Huang Y, et al. (2019) Association between stress and coping with DNA methylation of blood pressure-related genes among African American women. Chronic Stress 3: 2470547019879088. https://doi.org/10.1177/2470547019879088
    [14] Leibbrand C, Massey C, Alexander JT, et al. (2020) The Great Migration and Residential Segregation in American Cities during the Twentieth Century. Soc Sci Hist 44: 19-55. https://doi.org/10.1017/ssh.2019.46
    [15] Nuru-Jeter A, Dominguez T, Hammond W, et al. (2009) “It's the skin you're in”: African-American women talk about their experiences of racism. An exploratory study to develop measures of racism for birth outcome studies. Matern Child Health J 13: 29-39. https://doi.org/10.1007/s10995-008-0357-x
    [16] Millender E, Harris R, Bagneris J, et al. (2022) The cumulative influence of perceived discrimination, stress, and coping responses on symptoms of depression among young African American mothers. J Am Psychiatr Nurses Assoc 14: 10783903221105281. https://doi.org/10.1177/10783903221105281
    [17] Ford C, Sims M, Higginbotham J, et al. (2016) Psychosocial factors are associated with blood pressure progression among African Americans in the Jackson Heart Study. Am J Hypertens 29: 913-924. https://doi.org/10.1093/ajh/hpw013
    [18] Knox S, Hausdorff J, Markovitz JH (2002) Reactivity as a predictor of subsequent blood pressure: racial differences in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Hypertension 40: 914-919. https://doi.org/10.1161/01.HYP.0000041417.94797.57
    [19] Lazarus RS (1984) Stress, appraisal and coping. Springer.
    [20] Cooper D, Thayer J, Waldstein S (2014) Coping with racism: The impact of prayer on cardiovascular reactivity and post-stress recovery in African American women. Ann Behav Med 47: 218-230. https://doi.org/10.1007/s12160-013-9540-4
    [21] Cozier Y, Yu J, Wise L, et al. (2018) Religious and spiritual coping and risk of incident hypertension in the Black Women's Health Study. Ann Behav Med 52: 989-998. https://doi.org/10.1093/abm/kay001
    [22] Sullivan J, Harman M, Sullivan S (2021) Gender differences in African Americans' reactions to and coping with discrimination: Results from The National Study of American Life. J Community Psychol 49: 2424-2440. https://doi.org/10.1002/jcop.22677
    [23] Jarrett R, Jefferson S, Kelly J (2010) Finding community in family: Neighborhood effects and African American kin networks. J Comp Fam Stud 41: 299-328. https://doi.org/10.3138/jcfs.41.3.299
    [24] Beauboeuf-Lafontant T (2008) Listening past the lies that make us sick: A voice-centered analysis of strength and depression among Black women. Qual Sociol 31: 391-406. https://doi.org/10.1007/s11133-008-9113-1
    [25] Crusto C, De Mendoza V, Connell C, et al. (2016) The intergenerational impact of genetic and psychological factors on blood pressure study (InterGEN): design and methods for recruitment and psychological measures. Nurs Res 65: 331-338. https://doi.org/10.1097/NNR.0000000000000163
    [26] Taylor J, Wright M, Crusto C, et al. (2016) The intergenerational impact of genetic and psychological factors on blood pressure (InterGEN) study: Design and methods for complex DNA analysis. Biol Res Nurs 18: 521-530. https://doi.org/10.1177/1099800416645399
    [27] Chobanian A, Bakris G, Black H, et al. (2003) The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 289: 2560-2572. https://doi.org/10.1001/jama.289.19.2560
    [28] Krieger N, Smith K, Naishadham D, et al. (2005) Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med 61: 1576-1596. https://doi.org/10.1016/j.socscimed.2005.03.006
    [29] Amirkhan J (1990) A factor analytically derived measure of coping: The Coping Strategy Indicator. J Pers Soc Psychol 59: 1066. https://doi.org/10.1037/0022-3514.59.5.1066
    [30] Utsey S, Ponterotto J, Reynolds A, et al. (2000) Racial discrimination, coping, life satisfaction, and self-esteem among African Americans. J Couns Dev 78: 72-80. https://doi.org/10.1002/j.1556-6676.2000.tb02562.x
    [31] Glover L, Cain-Shields L, Wyatt S, et al. (2020) Life course socioeconomic status and hypertension in African American adults: the Jackson Heart Study. Am J Hypertens 33: 84-91. https://doi.org/10.1093/ajh/hpz133
    [32] Lackland D (2014) Racial differences in hypertension: implications for high blood pressure management. Am J Med Sci 348: 135-138. https://doi.org/10.1097/MAJ.0000000000000308
    [33] Neter J, Stam B, Kok F, et al. (2003) Influence of weight reduction on blood pressure: a meta-analysis of randomized controlled trials. Hypertension 42: 878-884. https://doi.org/10.1161/01.HYP.0000094221.86888.AE
    [34] Pickett S, McCoy T, Odetola L (2020) The influence of chronic stress and emotions on eating behavior patterns and weight among young African American women. Western J Nurs Res 42: 894-902. https://doi.org/10.1177/0193945919897541
    [35] Schwandt H, Coresh J, Hindin M (2010) Marital Status, Hypertension, Coronary Heart Disease, Diabetes, and Death Among African American Women and Men: Incidence and Prevalence in the Atherosclerosis Risk in Communities (ARIC) Study Participants. J Fam Issues 31: 1211-1229. https://doi.org/10.1177/0192513X10365487
    [36] The R Project for Statistical Computing [cited 2025 February 07]. Available from: https://www.R-project.org/
    [37] Gabriel A, Bell C, Bowie J, et al. (2020) The role of social support in Moderating the relationship between race and hypertension in a low-income, urban, racially integrated community. J Urban Health 97: 250-259. https://doi.org/10.1007/s11524-020-00421-1
    [38] Vila J (2021) Social Support and Longevity-Meta-Analysis-Based Evidence and Psychobiological Mechanisms. Front Psychol 12: 717164. https://doi.org/10.3389/fpsyg.2021.717164
    [39] Shorter-Gooden K (2004) Multiple resistance strategies: How African American women cope with racism and sexism. J Black Psychol 30: 406-425. https://doi.org/10.1177/0095798404266050
    [40] Seawell AH, Cutrona CE, Russell DW (2014) The effects of general social support and social support for racial discrimination on African American women's well-being. J Black Psychol 40: 3-26. https://doi.org/10.1177/0095798412469227
    [41] Spates K, Evans N, Watts B, et al. (2020) Keeping ourselves sane: A qualitative exploration of Black women's coping strategies for gendered racism. Sex Roles 82: 513-524. https://doi.org/10.1007/s11199-019-01077-1
    [42] Harding BN, Hawley CN, Kalinowski J, et al. (2022) Relationship between social support and incident hypertension in the Jackson Heart Study: a cohort study. BMJ open 12: e054812. https://doi.org/10.1136/bmjopen-2021-054812
    [43] Makarem N, Alcántara C, Williams N, et al. (2021) Effect of sleep disturbances on blood pressure. Hypertension 77: 1036-1046. https://doi.org/10.1161/HYPERTENSIONAHA.120.14479
    [44] Barone Gibbs B, Hivert MF, Jerome GJ, et al. (2021) Physical activity as a critical component of first-line treatment for elevated blood pressure or cholesterol: who, what, and how?: a scientific statement from the American Heart Association. Hypertension 78: e26-e37. https://doi.org/10.1161/HYP.0000000000000196
    [45] Liu F, Liu Y, Sun X, et al. (2020) Race-and sex-specific association between alcohol consumption and hypertension in 22 cohort studies: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis 30: 1249-1259. https://doi.org/10.1016/j.numecd.2020.03.018
    [46] Bleich S, Findling M, Casey L, et al. (2019) Discrimination in the United States: experiences of black Americans. Health Serv Res 54: 1399-1408. https://doi.org/10.1111/1475-6773.13220
    [47] Alhusen J, Bower K, Epstein E, et al. (2016) Racial Discrimination and Adverse Birth Outcomes: An Integrative Review. J Midwifery Womens Health 61: 707-720. https://doi.org/10.1111/jmwh.12490
    [48] Mehra R, Boyd L, Magriples U, et al. (2020) Black Pregnant Women “Get the Most Judgment”: A Qualitative Study of the Experiences of Black Women at the Intersection of Race, Gender, and Pregnancy. Womens Health Issues 30: 484-492. https://doi.org/10.1016/j.whi.2020.08.001
    [49] Attanasio L, Ranchoff B, Geissler K (2021) Perceived discrimination during the childbirth hospitalization and postpartum visit attendance and content: Evidence from the Listening to Mothers in California survey. PLoS One 16: e0253055. https://doi.org/10.1371/journal.pone.0253055
    [50] Williams D, Cooper L (2019) Reducing racial inequities in health: using what we already know to take action. Int J Environ Res Public Health 16: 606. https://doi.org/10.3390/ijerph16040606
    [51] SteelFisher GK, Findling MG, Bleich SN, et al. (2019) Gender discrimination in the United States: Experiences of women. Health Serv Res 54: 1442-1453. https://doi.org/10.1111/1475-6773.13217
  • This article has been cited by:

    1. Yanli Du, Kun Wang, Xiannian Zi, Xiao Wang, Meiquan Li, Bo Zhang, Jinshan Ran, Wei Huang, Jing Wang, Cuilian Dong, Hanyi Xiang, Li Lei, Changrong Ge, Yong Liu, Combined Transcriptome and Metabolome Analysis of Stable Knockdown and Overexpression of the CD8A Gene in Chicken T Lymphocytes, 2024, 00325791, 104686, 10.1016/j.psj.2024.104686
    2. Ursula Houessou, Pardis Zamani, Hasanga D. Manikpurage, Zhonglin Li, Nathalie Gaudreault, Manel Dahmene, François Dagenais, Christian Couture, Marie-Annick Clavel, Philippe Pibarot, Patrick Mathieu, Yohan Bossé, Sébastien Thériault, Transcriptomic Signatures of Calcific Aortic Valve Stenosis Severity in Human Tricuspid and Bicuspid Aortic Valves, 2025, 2452302X, 10.1016/j.jacbts.2025.01.018
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(751) PDF downloads(48) Cited by(1)

Figures and Tables

Figures(1)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog