Sex differences in molecular pathways underlying cardiovascular health in Black Americans

Sex differences in molecular pathways underlying cardiovascular health in Black Americans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sex differences in molecular pathways underlying cardiovascular health in Black Americans Harriet NA Blankson, Cecilia Delmer, Rashi Verma, Emine Guven, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8391154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Black Americans face a high burden of cardiovascular disease (CVD), with more than 60% of Black adult women affected. However, sex-specific molecular mechanisms underlying poor cardiovascular health (CVH) in this population remain largely unknown. In this study, we examined sex-specific transcriptomics signatures associated with CVH among Black adult men and women. Methods Whole blood RNA-sequencing was performed on 373 Black adults. CVH was assessed using the American Heart Association Life’s Simple 7 (LS7) score. Differential gene expression (DGE) analysis comparing participants with poor-to-intermediate CVH (LS7 < 10) versus ideal CVH (LS7 scores ≥ 10) was conducted using LIMMA. Sex-stratified functional enrichment analysis was conducted using FGSEA and ClueGo. Shared differentially expressed genes (DEGs) were evaluated using fixed-effects meta-analysis. Upstream transcription factor, cytokine, and kinase activities were inferred using DoRothEA and OmniPath to assess sex-specific gene expression regulation at the transcriptional, and post-transcription level. Results Among females, 430 DEGs were identified and indicated activation of RUNX2, PBX3, TFAP4 and enrichment of actin cytoskeletal pathways, consistent with vascular remodeling. In males with poor-to-intermediate CVH, 344 DEGs were detected and indicated inferred activation of GATA4, MAZ, and SOX10 and enrichment of pathways related to cardiac conduction and cellular metabolism. Thirteen DEGs were shared across sexes, including upregulation of DNAJC6, KANK2, SPTB , and MSTRG.22508, reflecting conserved stress response programs involving cytoskeletal remodeling and membrane stabilization. Although both sexes with poor-to-intermediate CVH exhibited suppression of adaptive immune effectors, in females the downregulation of KIR2DL4, KLRF1 , and SH2D1B occurred alongside inhibition of RFX1/5, transcription factors essential for MHC class II expression and antigen presentation. In males, immune suppression was instead associated with inhibition of STAT1, indicating a shift away from cytokine-driven signaling. Conclusions We identified distinct sex-specific molecular differences underlying CVH in a cohort of Black adults. Females with poor-to-intermediate CVH activate cytoskeletal and vascular remodeling pathways, consistent with structural reshaping. In contrast, males activate cardiac conduction and metabolic signaling programs, reflecting functional and bioenergetic compensation. Although both sexes exhibit immune repression in poor-to-intermediate CVH compared to ideal CVH, the mechanisms diverge, underscoring distinct sex-specific biological trajectories that may contribute to differential CVD risk and therapeutic effectiveness. Sex Differences Cardiovascular Health Transcriptomics Cytoskeletal Immune Regulation Black adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiovascular disease (CVD) remains the leading cause of death worldwide, accounting for over 19 million deaths in 2021,( 1 – 3 ) and presents significant health challenges across different demographic groups( 4 ). In the United States, Black Americans are disproportionately affected, experiencing higher rates of hypertension, stroke, heart failure, and coronary artery disease than other demographic groups( 4 – 6 ). The burden of CVD is especially pronounced among Black women, who exhibit higher cardiometabolic risk but also face poorer clinical outcomes compared to Black men and non-Black women( 7 , 8 ). Between 2000 and 2018, CVD mortality among Black women was two to three times higher than among white women( 9 , 10 ). Despite these differences in prevalence and prognosis, the underlying mechanisms, which are driven by a complex interplay of genetic, environmental, and socio-economic factors, remain incompletely understood. High-throughput transcriptomic technologies provide power tools to investigate the molecular pathways underlying disease heterogeneity. RNA expression reflects the dynamic state of gene regulation influenced by both genetic background and environmental exposures, thereby integrating inherited and acquired risk factors( 11 , 12 ). Whole blood RNA sequencing (RNA-seq) enables comprehensive profiling of gene expression signatures associated with clinical phenotypes and therapeutic responses( 13 , 14 ). This approach has been informative in cardiovascular research, including heart failure( 15 ) and coronary artery disease( 16 ). Transcriptomic analyses have also revealed sex-specific differences in immune signaling, hormonal regulation and metabolic pathways( 17 , 18 ), underscoring relevance to precision medicine. Whole blood also captures circulating signaling molecules, including cytokines and miRNAs, that influence gene expression across tissues. ( 19 ) Since blood transcriptomic profiles often mirror tissue-specific patterns, these profiles may provide insights into broader physiological processes ( 20 ). This whole blood transcriptome analysis expands on the Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study, a community-based investigation of CVD risk and resilience to CVD among Black Americans( 21 ). In the present study, cardiovascular health (CVH) was assessed using American Heart Association’s Life’s Simple 7 (AHA LS7) score, which incorporates seven modifiable lifestyle and clinical factors: smoking, physical activity, diet, body mass index (BMI), blood pressure, cholesterol levels, and blood glucose( 22 , 23 ). While LS7 provides a practical framework for clinical risk assessment and intervention, integrating it with transcriptomic profiling enables a unique opportunity to uncover the molecular mechanisms linking these risk factors to cardiovascular outcomes. In this study, our objective was to identify differentially expressed genes and regulatory networks associated with CVH in Black adults, with a specific focus on identifying sex-specific transcriptomic profiles. By characterizing sex-specific molecular differences, our work provides a foundation for developing precision-targeted strategies to improve cardiovascular outcomes in Black adults. Methods Data and Sample collection Participants of the MECA study completed study visits at either Emory University or Morehouse School of Medicine where they underwent a physical examination, blood draws, and standardized questionnaires. Vital signs and anthropometric measures were recorded. All blood samples were collected after > 6h of fasting, and fasting cholesterol and glucose levels were measured. Hypertension was defined as current use of anti-hypertensive medications, systolic blood pressure ≥ 130 mmHg, or diastolic blood pressure ≥ 80 mmHg. Diabetes mellitus was defined as current use of diabetes medications or fasting glucose ≥ 126 mg/dL. Hyperlipidemia was defined as current use of lipid-lowering medications or fasting total cholesterol ≥ 240 mg/dL. The study protocol was approved by the Institutional Review Boards at Morehouse School of Medicine (RB-FY2026-44) and Emory University (IRB00083584) and all participants provided written informed consent. Participant selection The MECA study recruited adults ages 30 to 70 years who identified as Black and residents of the Atlanta metropolitan area for more than six years. The details of the study design have been previously described( 21 ). Briefly, individuals with known CVD (e.g., myocardial infarction, congestive heart failure, cerebrovascular accident, coronary artery disease, peripheral arterial disease, atrial fibrillation, and cardiomyopathies), concomitant chronic illness (e.g., cancer, lupus, or HIV), substance abuse, psychiatric illness, pregnant or lactating females, and immobility such that physical activity could not be increased were excluded( 21 ). Life’s Simple 7 metrics The LS7 score, developed by the American Heart Association, is a validated metric for assessing CVH that incorporates both health behaviors (diet, exercise and smoking) and measurable health factors (BMI, cholesterol, fasting blood glucose and blood pressure)( 24 ). Each LS7 component was scored as 0 (poor), 1 (intermediate) or 2 (ideal) based on established criteria,( 21 ) and the total score was calculated by summing all seven subdomains, with a maximum score of 14 representing ideal CVH( 22 ). None of the MECA study participants whose blood was studied had a LS7 score at the extremes (14 or < 3). Initially, the cohort was divided into tertiles based on natural breaks in total LS7 distribution: low ( 3 – 6 ), intermediate ( 7 – 9 ) and high LS7 scores ( 10 – 13 ). However, preliminary DGE analyses showed substantial overlap in the DEGs identified in males when comparing the low and intermediate versus high groups (Supplementary Fig. S1 ). Therefore, the low and intermediate LS7 groups were combined into a single category (poor-to-intermediate CVH, LS7 < 10) for comparison with the high LS7 group (ideal CVH, LS7 ≥ 10) (Figs. 1 and 2 a). This data-driven approach minimized redundancy and strengthened group contrasts. RNA extraction Blood was collected into PAXgene Blood RNA tubes (PreAnalytiX/Qiagen/BD Biosciences), and RNA was extracted using the PAXgene Blood RNA Kit (PreAnalytiX/Qiagen/BD Biosciences). RNA quality was assessed using a Fragment Analyzer (Agilent). One microgram of total RNA was subjected to ribosomal RNA (rRNA) and globin transcript depletion using the GLOBINclear Kit, human (ThermoFisher Scientific). Ten nanograms of the globin-depleted RNA was used as input for cDNA synthesis using the Clontech SMART-Seq v4 Ultra Low Input RNA kit (Takara Bio) according to the manufacturer’s instructions. Amplified cDNA was fragmented and appended with dual-indexed bar codes using the Nextera XT DNA library preparation kit (Illumina). Libraries were validated by capillary electrophoresis on a TapeStation 4200 (Agilent), pooled at equimolar concentrations, and sequenced with PE100 reads on an Illumina NovaSeq 6000, yielding ~ 30 million reads per sample on average. Data alignment and Differential Gene Expression Analysis Raw RNA sequencing reads were initially pre-processed to remove rRNA that may still be present. Adapter sequences and low bases were trimmed using Trim Galore (0.6.4)( 25 ). Cleaned reads were aligned to the human reference genome (Homo_sapiens.GRCh38.dna.primary_assembly.fa) using STAR (v2.7.3a)( 26 ) and Bowtie2 (v2.3.5.1)( 27 ), with the corresponding Ensembl annotation file (Homo_sapiens.GRCh38.109.gtf). Reads were aligned in two-pass using STAR( 26 ). The aligned reads were then sorted, indexed, and filtered using SAMtools (v1.1.0)( 28 ). Transcript assembly and quantitation were performed using StringTie (v2.2.1). Then prepDE.py was used to generate a unified count matrix for downstream analysis using R (v4.4.1). Fastq files were submitted to dbGAP (waiting on number). Count-level RNA-seq data and phenotype data were analyzed using LIMMA in R (V4.4.1)( 29 ). Samples were matched to the phenotype data, and duplicated samples were removed. Raw gene-level counts were imported into edgeR( 30 ) using DGEList object. To remove extreme expression outliers, we calculated the maximum counts-per-million (CPM) value per gene and excluded genes above the 99th percentile. Low-expressed genes were filtered by retaining only those with CPM > 1 in at least the minimum group sample size. Counts were normalized using the trimmed mean of m-values (TMM) method to account for library size differences. The resulting filtered and normalized dataset was used as input for LIMMA-VOOM modeling. The design matrix included LS7 groups and age as covariates. Differential gene expression (DGE) analysis was performed separately for females and then males, using empirical Bayes moderation( 31 ). Genes were considered significantly differentially expressed if at log 2 FC > 1.2 and FDR-adjusted P < 0.05 (Benjamini Hochberg correction( 32 )). Resulting differentially expressed genes (DEGs) lists were used in downstream analyses and visualization, including volcano plots. Data tables were used for subsequent analysis and plots. Fixed Effects Meta-Analysis of Differentially Expressed Genes Shared by Sexes For the each shared DEG, the combined effect size across sexes was estimated using fixed-effects meta-analysis implemented in the metafor R package( 33 ). The fixed-effects model was applied given the small number of groups (2 sexes) and low group heterogeneity. Genes with p < 0.05 and consistent directionality (same sign of log 2 FC across sexes) were interpreted as having sex-independent differential regulation. Forest plots were generated to visualize the direction and precision of effect estimates (Supplementary Fig. S2 ). Gene ontology and regulatory pathway analysis Gene ontology (GO) and regulatory pathway analysis were conducted using fast gene set enrichment analysis (FGSEA)( 34 ). Gene set used for enrichment was the GO terms (c5.all.v2023.2), KEGG (c2.cp.kegg_legacy.v2023.2) and Reactome (c3.all.v2023.2) from the Molecular Signatures Database( 35 ). DGE analysis results were ranked by t-statistics. FGSEA( 34 ) was applied to the entire dataset, utilizing 1000 permutations for gene sets to assess statistical significance( 36 ). Pathways with adjust p-value ≤ 0.05 were considered significant, and top 10 upregulated and 10 downregulated pathways were visualized using dot plots. To investigate the regulatory mechanisms underlying sex-specific transcriptional profiles, transcription factor (TF), cytokine, and kinase activity were inferred using decoupleR and DorothEA( 37 – 39 ) framework and visualized in heatmap format. DEGs were used to build a comprehensive regulatory network with ClueGo( 40 ) in Cytoscape ( 41 ). Statistical analyses Statistical analyses (LIMMA, Spearman’s correlation, Student’s t-test, and Fisher’s exact test) were performed in R (version 4.4.1) Results Study Population Characteristics and CVH Assessment Whole blood transcriptomic profiles were assessed in 373 self-identified Black adults living in the Atlanta metropolitan area. The mean age of participants was 53 years, and 60% (n = 225) were female (Table 1 ). Other demographic and clinical characteristics of the cohort have been described previously,( 42 ) including prevalence of hypertension (53%), hyperlipidemia (31%), diabetes mellitus (21%), and current smoking (24%). The mean BMI was 33 kg/m 2 . Table 1 Distribution and comparison of Clinical measures among study Participants BMI = body mass index, SBP = systolic blood pressure, DBP = diastolic blood pressure, HDL = high density lipoprotein, LDL = low density lipoprotein, LS7 = life simple 7, N = number, SD = standard deviation. P-values were calculated using the two-sample t-tests, and statically significant values were < 0.05 and marked *. Total number of participants 148 225 Age (years) 52.53 ± 10.51 53.62 ± 10.25 0.319 Clinical Measures BMI (kg/m 2 ) 30.10 ± 6.59 34.60 ± 8.62 1.2x10 -7 * Glucose (mg/dL) 102.82 ± 32.10 102.28 ± 39 0.888 SBP (mmHg) 132.26 ± 19.61 129.56 ± 19.47 0.192 DBP (mmHg) 79.68 ± 11.59 80.90 ± 11.77 0.326 Years Smoked (years) 9.28 ± 12.40 3.92 ± 9.07 2.29x10 -6 * HDL (mg/dL) 55.78 ± 19.28 58.48 ± 15.60 0.137 LDL (mg/dL) 108.05 ± 38.44 117.48 ± 32.48 1.15x10 -2 * Triglycerides (mg/dL) 107.30 ± 75.47 99.56 ± 45.58 0.218 Cholesterol (mg/dL) 185.46 ± 41.93 196.07 ± 37.75 1.15x10 -2 * LS7 Total Score 8.17 ± 2.29 7.91 ± 2.09 0.255 Total LS7 scores ranged from 3 to 13 in the cohort, with a median of 8 for both sexes (Fig. 2 a-b). There was no significant difference in total LS7 scores between females and males (7.9 ± 2.1 vs 8.2 ± 2.3) (Table 1 ). However, females had higher average BMI and total cholesterol, while smoking prevalence was lower compared to males (Table 1 , Fig. 2 c-d). Regarding LS7 subdomains, 67.6% (n = 152) of females and 43.2% (n = 64) of males had poor BMI (Fig. 2 c-d). Poor blood pressure was also common, affecting 49.3% of females (n = 111) and 43.3% of males (n = 65). Ideal diet scores were rare in both sexes, 5.3% of females (n = 12) and 6.1% of males (n = 9). However, most participants had ideal fasting blood glucose (females: 66.2%, n = 149; males: 62.8%, n = 93) and ideal physical activity (females: 50.7%, n = 114; males: 71.6% n = 106). Ideal total cholesterol was achieved by 45% of females (n = 102) and 52% of males (n = 77). Spearman correlation analysis revealed that fasting blood glucose and blood pressure were the strongest correlates of total LS7 scores in both sexes, while total cholesterol and BMI showed a stronger correlation with total LS7 score in males (Fig. 2 e-f). Differential Gene Expression The DGE analysis of low and intermediate (< 10 ) versus high (≥ 10) LS7 scores identified 430 DEGs in females and 344 DEGs in males (adj. P < 0.05, Fig. 3 a-b, Supplementary Table 1&2). The number of upregulated DEGs in females (n = 180) was approximately 30% higher than in males (n = 130), while the number of downregulated DEGS in females (n = 250) was about 16% higher than in males (n = 214). Among the significant DEGs (adj. p < 0.05), fold change ranged from + 2.6 to -2.4 in females and + 2.6 to -1.9 in males. A greater proportion of DEGs in females were novel genes annotated only by StringTie (15%, n = 66) compared to males (12%, n = 42). Shared Differentially Expressed Genes A total of 13 DEGs were shared between males and females with poor-to-intermediate CVH (LS7 scores < 10), of which DNAJC6, KANK2, SPTB and MSTRG.22508 were upregulated in both sexes, indicating conserved cellular stress response involving cytoskeletal remodeling, vascular development and membrane stabilization( 43 – 45 ) (Fig. 3 c-d, Table 2 ). A fixed effects meta-analysis confirmed consistent directionality for these genes (pooled log 2 FC ≈ 0.65–0.8, meta p < 0.001), indicating a sex-independent association with poor-to-intermediate CVH. Notably, SPATC1L displayed sex divergent regulation, upregulated (log 2 FC = 1.26, adj.P = 7.4E-05) in males, but downregulated in females (log2FC = -0.9, adj.P = 0.001), with heterogeneity observed in the meta-test, though not statistically significant (Supplementary Table S2 &3, Table 2 ). All other shared DEGs ( ADGRA3, AKR1C3, B4GAT1, KIR2DL4, KLRF1, SH2D1B, WAPL-DT and ZNT595 ) were consistently downregulated across sexes, reflecting coordinated suppression of immune-related and NK-cell activation pathways in poor-to-intermediate CVH individuals compared to ideal CVH (Fig. 3 d). Table 2 Heterogeneity analysis of common differentially expressed genes in males and females logFC_= Log2 fold change se = standard error, p = p-value Gene logFC Female logFC Male Meta logFC Meta SE Meta p-value Consistency ADGRA3 -1.2638462 -1.2880897 -1.2744465 0.25128566 3.94E-07 TRUE AKR1C3 -0.5784595 -0.4145407 -0.5060374 0.08262534 9.10E-10 TRUE B4GAT1 -0.2846451 -0.3448068 -0.3108695 0.06914057 6.92E-06 TRUE DNAJC6 0.59268818 0.73121454 0.65305189 0.14276674 4.78E-06 TRUE KANK2 0.71891867 0.80813988 0.75785486 0.16620402 5.12E-06 TRUE KIR2DL4 -1.1521196 -1.2369734 -1.189208 0.1933893 7.78E-10 TRUE KLRF1 -0.3972859 -0.4642975 -0.4265114 0.09178079 3.37E-06 TRUE MSTRG.22508 0.47361841 0.59287208 0.52556376 0.109439 1.57E-06 TRUE SH2D1B -0.4707316 -0.5202137 -0.4923344 0.08802647 2.23E-08 TRUE SPATC1L -0.8981771 1.26176526 0.04044289 0.20989822 0.85E-07 FALSE SPTB 0.78834466 0.75932866 0.77564282 0.15112251 2.86E-07 TRUE WAPL-DT -0.5422633 -0.6436078 -0.5864529 0.1301706 6.63E-06 TRUE ZNF595 -0.6536046 -0.5042423 -0.5877143 0.09389103 3.86E-10 TRUE Functional enrichment analysis of Differentially Expression Genes – Gene Ontology Terms When the gene expression profiles for females with poor-to-intermediate CVH were assessed for Gene Ontology (GO) enrichment analyses, we observed significant upregulation of GO terms related to cytoskeletal organization and actin filament dynamics, including negative regulation of actin filament polymerization (NES = 2.12, p.adj = 0.004), actin polymerization or depolymerization, exocytotic processes and hormone-responsive cytoskeletal remodeling (Table 3 ). In contrast, DEGs for males with poor-to-intermediate CVH showed upregulation of GO Terms such as cardiac-specific processes, such as regulation of heart rate by cardiac conduction (NES = 2.1, p.adj = 0.03), cardiac conduction and extracellular matrix organization (external encapsulating structure) (Table 4 ). Table 3 Top 10 Upregulated and Downregulated Gene Ontology Terms in Females based on all genes pval = p-value, padj = p- adjusted value, ES = enrichment score, NES = normalized enrichment score. Table is ordered by normalized enrichment scores. Pathway pval padj ES NES NEGATIVE REGULATION OF ACTIN FILAMENT POLYMERIZATION 2.25E-05 0.004 0.53 2.12 NEGATIVE REGULATION OF SIGNAL TRANSDUCTION BY P53 CLASS MEDIATOR 3.81E-4 0.02 0.60 2.05 CELLULAR RESPONSE TO GROWTH HORMONE STIMULUS 1.38E-3 0.048 0.62 2 NEGATIVE REGULATION OF ACTIN FILAMENT DEPOLYMERIZATION 7.81E-4 0.03 0.52 1.93 EXOCYTIC PROCESS 1.88E-4 0.02 0.47 1.92 ACTIN POLYMERIZATION OR DEPOLYMERIZATION 4.23E-05 0.006 0.37 1.8 CELLULAR RESPONSE TO PEPTIDE HORMONE STIMULUS 1.08E-05 0.002 0.35 1.79 CYTOPLASMIC SIDE OF PLASMA MEMBRANE 1.53E-4 0.01 0.38 1.77 ACTIN FILAMENT 8.49E-4 0.03 0.41 1.76 REGULATION OF ACTIN FILAMENT LENGTH 3.83E-4 0.02 0.37 1.71 PROTEASOME REGULATORY PARTICLE 1.21E-4 0.01 -0.67 -2.07 DNA UNWINDING INVOLVED IN DNA REPLICATION 1.83E-4 0.02 -0.65 -2.08 REGULATION OF MITOTIC CELL CYCLE SPINDLE ASSEMBLY CHECKPOINT 6.75E-05 0.008 -0.68 -2.11 TRNA METABOLIC PROCESS 5.66E-09 1.01E-05 -0.42 -2.11 REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 3.18E-05 0.0047 -0.56 -2.12 ENDOPEPTIDASE COMPLEX 1.87E-06 6.0E-4 -0.50 -2.12 ANTIGEN BINDING 1.81E-07 1.6E-4 -0.46 -2.13 NATURAL KILLER CELL MEDIATED IMMUNITY 7.24E-06 0.002 0.53 -2.17 ANTIGEN PROCESSING AND PRESENTATION OF EXOGENOUS ANTIGEN 2.68E-05 0.004 -0.63 -2.23 IMMUNOGLOBULIN COMPLEX 5.70E-10 2.04E-06 -0.52 -2.36 Table 4 Top 10 Upregulated and Downregulated Gene Ontology Terms in Males based on all genes Pathway pval padj ES NES REGULATION OF HEART RATE BY CARDIAC CONDUCTION 2.16E-04 0.03 0.64 2.10 CARDIAC CONDUCTION 2.76E-04 0.03 0.47 1.91 EXTERNAL ENCAPSULATING STRUCTURE 3.68E-04 0.03 0.28 1.52 POST TRANSCRIPTIONAL REGULATION OF GENE EXPRESSION 1.49E-04 0.02 -0.29 -1.49 HP INTRAUTERINE GROWTH RETARDATION 1.23E-04 0.02 -0.28 -1.49 TRANSPORT VESICLE 5.52E-04 0.05 -0.30 -1.50 SMALL MOLECULE CATABOLIC PROCESS 5.35E-04 0.05 -0.30 -1.51 PURINE CONTAINING COMPOUND METABOLIC PROCESS 2.00E-04 0.02 -0.30 -1.52 HP AGE OF DEATH 1.34E-04 0.02 -0.30 -1.52 GOLGI VESICLE TRANSPORT 3.23E-04 0.03 -0.31 -1.52 COPII VESICLE COAT 6.25E-04 0.05 -0.67 -1.94 RNA METHYLATION 2.52E-05 0.01 -0.45 -1.95 COPII COATED VESICLE BUDDING 1.75E-04 0.02 -0.54 -1.96 NUCLEOSIDE MONOPHOSPHATE METABOLIC PROCESS 5.31E-05 0.01 -0.49 -1.97 NUCLEOSIDE DIPHOSPHATE METABOLIC PROCESS 1.15E-04 0.02 -0.58 -1.98 ER TO GOLGI TRANSPORT VESICLE MEMBRANE 7.28E-05 0.01 -0.53 -1.98 HP ACCESSORY ORAL FRENULUM 3.52E-04 0.03 -0.69 -1.98 TRICARBOXYLIC ACID CYCLE 8.38E-05 0.02 -0.62 -2.07 TRANSLATIONAL INITIATION 7.71E-07 6.89E-04 -0.47 -2.08 IMMUNOGLOBULIN COMPLEX 4.76E-09 3.40E-05 -0.50 -2.24 pval = p-value, padj = p- adjusted value, ES = enrichment score, NES = normalized enrichment score. Table is ordered by normalized enrichment scores. The highest downregulated GO terms in females with poor-to-intermediate CVH included immune and proteostasis pathways, particularly those involving immunoglobulin complex (NES = -2.36, p.adj = 2.04 x 10 − 6 ), antigen processing and presentation of exogenous antigen and antigen binding (Table 3 ). In males with poor-to-intermediate CVH, the highest downregulated pathways were predominantly associated with mitochondrial and metabolic processes, such as translational initiation, tricarboxylic acid cycle, nucleoside phosphate metabolism, and reduced ER-Golgi trafficking (Table 4 ). Consistent with females, the immunoglobulin complex (NES = -2.24, p.adj = 3.5 x 10 − 5 ) was downregulated in males indicating suppression of adaptive immune function in both sexes with poor-to-intermediate CVH. A ClueGO( 40 ) network analysis, which informs how GO terms are linked, was performed to visualize functional interrelationships among up- and downregulated biological processes in males and females (Fig. 4 ). In females with poor-to-intermediate CVH, upregulation networks were mainly cytoskeletal and vascular remodeling, including negative regulation of endothelial cell migration, complement activation, negative regulation of protein polymerization and cytoplasmic microtubule organization, whereas males showed enrichment of neuromuscular junction development, negative regulation of actin filament polymerization and membrane repolarization pathways (Fig. 4 a&c). Conversely, immune and extracellular biosynthetic processes were downregulated in females, while males exhibited suppression of metabolic and biosynthetic functions such as tRNA medication and long-chain fatty acid transport (Fig. 4 b&d). KEGG Pathway Enrichment Reveal Sex-Specific Metabolic Signaling Differences To extend the functional insights from GO analysis, KEGG pathway enrichment was performed to identify broader signaling and metabolic networks associated with poor-to-intermediate CVH in males and females. In females, upregulated pathways were predominantly linked to cellular signaling and vascular function, including dorsoventral axis formation, ERBB signaling, JAK-STAT signaling, long-term potentiation and vascular smooth muscle contraction (Fig. 5 a). No KEGG pathways were significantly upregulated in males, suggesting that poor-to-intermediate CVH does not trigger strong coordinated increases in major biological pathways in men. On the other hand, downregulated KEGG pathways in females included antigen processing and presentation, proteosome, graft-versus-host disease, biosynthesis and DNA replication (Fig. 5 a), reflecting suppression of immune, proteolytic and biosynthesis functions, in line with the GO terms analysis. Both males and females showed downregulation of valine, leucine and isoleucine degradation -branched-chain amino acids (BCAA) (Fig. 5 a&c) ( 46 ). Although BCAAs support muscle protein synthesis and cellular energy, elevated circulating BCAA levels are associated with cardiometabolic dysfunction and increased CVD risk( 46 – 54 ), suggesting impaired BCAA utilization in poor-to-intermediate CVH males and females. In males with poor-to-intermediate CVH, downregulated pathways were primarily metabolic, encompassing valines, leucine, and isoleucine degradation, and the citrate cycle, suggesting reduced mitochondrial energy metabolism in poor-to-intermediate CVH males also consistent with the GO terms analysis (Fig. 5 c). Reactome Pathways Enrichment Highlights Distinct Signaling and Contractile Pathways by Sex Reactome pathway analysis was performed to complement KEGG by showing in more detail how specific biological pathways are altered. Females with poor-to-intermediate CVH had upregulated Reactome pathways enriched for cytoskeletal and growth factor mediated signaling, including signaling by cytosolic FGFR1 fusion mutants, striated muscle contraction, growth hormone receptor signaling, RAC1 GTPase cycle, RHO GTPase activating WASPs and WAVES, which is consistent with observed GO enrichment for cytoskeletal organization (Fig. 5 b). These pathways were consistent with the GO enrichment for cytoskeletal organization and actin filament dynamics and the KEGG enrichment for vascular and smooth muscle contraction, highlighting a coordinated activation of Rho-GTPase dependent cytoskeletal remodeling and hormone stress signaling in females with poor-to-intermediate CVH. Additional enrichment of striated muscle contraction and smooth muscle contraction indicates increased vascular and muscular contractile activity in females with poor-to-intermediate CVH. In males, upregulated Reactome pathways were limited to cardiac-specific contractile processes, specifically, cardiac contraction and muscle contraction, which was also consistent with the GO analysis cardiac conduction and extracellular matrix, suggesting increased myocardial electrical and mechanical signaling, which could be a male specific adaptive response to poor-to-intermediate CVH (Fig. 5 d). Downregulated Reactome pathways in females with poor-to-intermediate CVH were enriched for proteolytic and immune regulatory processes including VIF mediated degradation of APOBEC3G, proteosome assembly, immunoregulatory interactions between a lymphoid and a non-lymphoid cell (Fig. 5 b). In males, downregulated Reactome pathways were primarily associated with mitochondrial, translational, and immune signaling functions, including SNRNP assembly, FCGR activation, translation, mitochondrial protein degradation, and role of LAT2 NTAL LAB on calcium mobilization (Fig. 5 d). Upstream Regulator Analysis To identify upstream molecular drivers of the observed transcriptional patterns, we performed regulator enrichment analyses, including transcription factor (TF), cytokine, and kinase activity inference, to determine key signaling regulators associated with poor-to-intermediate CVH in males and females. In females, TF activity analysis inferred activation of RUNX2, PBX3, and TFAP4 (Fig. 6 a), regulators that control cytoskeletal organization, vascular remodeling, and stress-responsive gene expression ( 55 – 57 ). On the other hand, CEBPA, CEBPD, E2F1-4, FOXM1, TFAP2C, TP63, and RFX1/5 were inhibited, which is consistent with the observed suppression of immune and cell-cycle pathways ( 58 – 62 ). Cytokine activity inference showed activation of TGFB3, IFNA1/2, IFNB1, IFNG, and TNFRSF10A/B/12A, while canonical inflammatory (TNF) and SMD-dependent TGF-β signaling components (TGFB1/2, TGFBR2) were inhibited( 63 – 68 ), suggesting bias toward non-canonical, focal-adhesion linked remodeling. Correspondingly, kinase analysis identified activation of STK4/38, PTPRK, CDK5-CDK5R1, PRKACA/B, and CAMKK1, highlighting MAPK, AKT, and PKA/aPKC signaling cascades that likely drive vascular contractile and actin reorganization programs( 69 – 71 ). In males, TF activity analysis inferred activation of GATA6, MAZ, and SOX10, regulators associated with cardiac and vascular differentiation as well as neuromuscular conduction ( 72 – 77 ), while HNF4A, NFYB, E2F1/4/6, LEF1, SNAI2, and STAT1 were inhibited, indicating broad suppression of transcriptional programs governing metabolism, cell-cycle regulation, and immune signaling( 78 – 81 ) (Fig. 6 b). Cytokine analysis revealed limited activation of interferon and TNF receptor pathways (IFNG, IFNE, TNFRSF12A, TGFB1|1) but inhibition of TNF, TGFBR1, IFNAR1, and IFNGR2, reflecting reduced inflammatory tone( 64 , 65 , 68 , 81 ). Kinase activity in males was nominated by activation of MAPK1(ERK2), AMPK, PKA (PRKACA/B), aPKC (PRKCI/Z), PRKCD, and PRKC1, consistent with enhanced cardiac conduction and contractile signaling, whereas PIK3CA, MAPK4, BTK, and several CDKs were inhibited, indicating suppression of proliferative and immune signaling. While some of the regulators did not meet FDR thresholds, it is expected in enrichment-based inference. Also, the directional activity patterns were coherent with the observed sex-specific DEGs and pathway pathways enrichments. Discussion In this study of 373 Black adults, we compared sex-specific whole blood transcriptomic profiles in participants with poor-to-intermediate CVH to those with ideal CVH. Although the distribution of total LS7 scores was similar between males and female participants, we identified distinct sex-specific gene expression patterns. Both males and females with poor-to-intermediate CVH had suppression of immune related and proteostatic pathways, but the upstream regulatory profiles and biological emphasis differed between sexes. Females had a structural and cytoskeletal remodeling phenotype, whereas males displayed a cardiac conduction and metabolic adaptation phenotype. These findings highlight sex-specific transcriptional, signaling, and kinase activities that may underlie different cardiovascular responses to lifestyle and cardiometabolic risk factors. Males and female with poor-to-intermediate CVH had shared core cellular stress responses. This was reflected in the 13 DEGs common to both sexes. Two of these DEGs, KANK2 and SPTB - involved in cytoskeletal remodeling, vascular development, and membrane stabilization were upregulated in both sexes( 43 , 44 , 82 ). Another of the common DEGs, SPATC1 , was upregulated in males and downregulated in females, which is appropriate given this gene’s role in spermatogenesis. Among the other genes downregulated in both sexes, ADGRA3 has been shown to be involved in fat burning, and downregulation of this gene has been associated with obesity( 83 ), an important risk factor of poor CVH. The downregulation of immune related genes ( SH2D1B and KIR2DL4 ) links poor-to-intermediate CVH with reduced immunity in both males and females( 84 – 87 ). This is also explicitly shown in high expression of immunoglobulin heavy and light chain genes in both males and females with high LS7 (Ideal CVH) scores and relatively decreased expression of these genes in males and females in lower LS7 scores (poor-to-intermediate CVH). While we had expected to see transcriptomic profiles predominantly associated with inflammation, our data indicate predominance of transcriptomic profiles associated with structural adaptation in females with poor-to-intermediate CVH. Females showed activation of TFs (RUNX2, PBX3 and TFAP4) involved in osteogenic and vascular remodeling( 55 – 57 ), accompanied by inhibition of TFs (E2F, CEBP, and RFX families) that coordinate immune and cell-cycle programs( 58 – 62 ). Notably, suppression of RFX1 and RFX5 - key regulators of MHC class II expression - aligns with the decreased expression of antigen presentation and immunoglobulin complex genes, supporting relative immune suppression in females with poor-intermediate CVH( 60 – 62 ). Males, in contrast, exhibited activation of GATA6, MAZ, and SOX10 -a regulatory network promoting cardiac contractility and neuromuscular signaling( 72 – 77 ). In parallel, inhibition of STAT1 and HNF4A reflected suppression of metabolic and immune function( 78 – 81 ). Interestingly, the transcriptomics profiles of males with poor-to-intermediate CVH, similar to that of females, indicated inhibition of E2F family of TFs, which have been shown to have a protective role in preventing cardiomyocyte hypertrophy( 88 ). At the post transcriptional level, that is changes that occur after mRNA is produced, both sexes displayed changes in immunoglobulin complex and antigen presentation pathways, but the regulation of these pathways appeared to differ. Females with poor-to-intermediate CVH had downregulation of KIR2DL4, KLRF1 , and SH2D1B along suppression of RFX1/5 signaling, implying reduced activation of MHC class II and B-cell genes( 86 , 87 , 89 , 90 ). This shows post-transcriptional repression of adaptive immune effectors to maintain endothelial integrity during stress. In males, decreased expression of B4GAT1 and ADGRA3 and inhibition of STAT1 signaling suggested dampening of cytokine dependent transcription and interferon signaling( 83 , 91 ). Overall, immune gene repression appears to be conserved between males and females, but immune gene repression is mediated through different processing and transcriptional silencing in males versus post-transcriptional dampening via proteostatic control in females. Conclusion In summary this work reveals that poor-to-intermediate CVH elicits sex-specific transcriptomic programs involving vascular remodeling, kinase and TF activity and cytokine networks. Females engage cytoskeletal and vascular remodeling programs and TGFB-RUNX2 signaling, while males emphasize cardiac conduction and metabolic compensatory pathways through ERK2-AMPK-GATA6 signaling, both coupled to suppress immune transcriptional activity. These differences highlight the potential importance of sex-specific precision medicine strategies, integrating molecular and behavioral data to tailor interventions that mitigate cardiovascular risk in diverse populations. Limitations While this study provides valuable insights into sex-specific molecular correlates of CVH in Black adults, several limitations should be acknowledged. First, the FGSEA and enrichment analyses relies on existing gene annotations and pathway databases, which may not fully capture novel or context-specific interactions. Additionally, further validation of these pathways in independent cohorts is necessary to confirm their relevance. Future research should focus on integrating these findings with proteomic and metabolomic data. Another limitation is the lack of longitudinal data, which prevents us from assessing how transcriptomic changes overtime correlate with the development or prevention of CVDs in individuals with high or poor-to-intermediate LS7 scores. Since this is a cross-sectional study, all clinical and transcriptomic measurements represent a single point in time, and we lack information about the duration or consistency of the participants’ lifestyles. Additionally, the lifestyle factors were self-reported and may not accurately reflect participants’ actual behaviors, potentially introducing reporting bias. The absence of FDR-significant regulators likely reflects the conservative multiple-testing correction inherent to enrichment-based upstream analysis rather than a lack of true biological signal. Notably, the predicted activation patterns showed strong internal consistency with DEGs and pathway enrichments, supporting biological relevance of these regulatory networks. Clinical and Biological Relevance Together, these findings indicate that poor-to-intermediate CVH in females is associated with a gene expression profile consistent with stress-driven vascular remodeling, whereas males engage contractile and metabolic compensatory pathways within cardiac tissue. In both sexes, these adaptations are coupled with suppressed immune transcriptional activity. These results suggest that sex differences arise not only from hormonal influences, but also from distinct transcriptional and signaling networks. Overall, poor-to-intermediate CVH is characterized by increased structural repair processes and reduced immunological activity. Abbreviations Abbreviation Meaning AHA American Heart Association BCAA Branched-chain amino acids BMI Body mass index CPM Counts-per-million CVD Cardiovascular disease CVH Cardiovascular health DEGs Differentially expressed genes DGE Differential gene expression FC Fold change FGSEA Fast gene set enrichment analysis GO Gene ontology LS7 Life’s Simple 7 MECA Morehouse-Emory Cardiovascular Center for Health Equity NES Normalized Enrichment Scores TF Transcription factor TMM Trimmed mean of m-values Declarations Ethics approval and consent to participate All participants provided written informed consent. The research work was approved by the Institutional review board of Emory University school of Medicine (IRB00083584) and Morehouse School of Medicine (RB-FY2026-44). Consent for publication Not applicable Availability of data and materials The datasets generated during the current study are under submission at dBgap. Other meta-data analyzed during the current study are available from the author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by 1RM1HG012334 (CDS & RM), 15SFCRN23910003 (CDS) , R01NS112422 (RM), and U54HG013595-01 (NIH/NIGMS). Author’s contributions Conceptualization (CDS, RM & HT), Design and interpretation of results (CDS, RM, NHB), Data curation (PB, AAQ, PP), Bioinformatics and statistical analysis (HNB, CD, RV, EG, IKJ), laboratory work (KR), manuscript drafting (HNB), critical revision of manuscript (CDS, RM). All authors reviews and approved the final manuscript. Acknowledgements Next generation sequencing services were provided by the Emory NPRC Genomics Core which is supported in part by NIH P51 OD011132. Sequencing data was acquired on an Illumina NovaSeq6000 funded by NIH S10 OD026799.” We are grateful for all the study participants and volunteers. References Lopez EO, Ballard BD, Jan A. Cardiovascular Disease. Nursing (Brux) [Internet]. 2023 Aug 22 [cited 2024 Aug 20]; Available from: https://www.ncbi.nlm.nih.gov/books/NBK535419/ Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol [Internet]. 2020 Dec 12 [cited 2024 Aug 20];76(25):2982. Available from: /pmc/articles/PMC7755038/ Cardiovascular diseases [Internet]. [cited 2024 Aug 20]. Available from: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 Mital R, Bayne J, Rodriguez F, Ovbiagele B, Bhatt DL, Albert MA. Race and Ethnicity Considerations in Patients With Coronary Artery Disease and Stroke: JACC Focus Seminar 3/9. J Am Coll Cardiol [Internet]. 2021 Dec 14 [cited 2024 Aug 20];78(24):2483–92. Available from: https://www.jacc.org/doi/ 10.1016/j.jacc.2021.05.051 Mensah GA. Cardiovascular Diseases in African Americans: Fostering Community Partnerships to Stem the Tide. Am J Kidney Dis [Internet]. 2018 Nov 1 [cited 2024 Aug 20];72(5 Suppl 1):S37. Available from: /pmc/articles/PMC6200348/ Graham G. Disparities in Cardiovascular Disease Risk in the United States. Curr Cardiol Rev [Internet]. 2015 May 18 [cited 2024 Aug 20];11(3):238. Available from: /pmc/articles/PMC4558355/ Ogunniyi MO, Mahmoud Z, Commodore-Mensah Y, Fleg JL, Fatade YA, Quesada O, et al. Eliminating Disparities in Cardiovascular Disease for Black Women: JACC Review Topic of the Week HHS Public Access. J Am Coll Cardiol. 2022;80(18):1762–71. Ebong I, Breathett K. The Cardiovascular Disease Epidemic in African American Women: Recognizing and Tackling a Persistent Problem. Post WS, Watson KE, Hansen S, Folsom AR, Szklo M, Shea S et al. Racial/Ethnic Differences in All-Cause and Cardiovascular Disease Mortality: The Multi-Ethnic Study of Atherosclerosis (MESA). Circulation [Internet]. 2022 Jul 19 [cited 2025 Nov 30];146(3):229. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9937428/ Kyalwazi AN, Loccoh EC, Brewer LC, Ofili EO, Xu J, Song Y et al. Disparities in Cardiovascular Mortality Between Black and White Adults in the United States, 1999 to 2019. Circulation [Internet]. 2022 Jul 19 [cited 2025 Nov 30];146(3):211. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9310198/ Richards AL, Watza D, Findley A, Alazizi A, Wen X, Pai AA et al. Environmental perturbations lead to extensive directional shifts in RNA processing. PLoS Genet [Internet]. 2017 Oct 1 [cited 2025 Nov 9];13(10):e1006995. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5667937/ Favé MJ, Lamaze FC, Soave D, Hodgkinson A, Gauvin H, Bruat V et al. Gene-by-environment interactions in urban populations modulate risk phenotypes. Nature Communications 2018 9:1 [Internet]. 2018 Mar 6 [cited 2025 Nov 9];9(1):1–12. Available from: https://www.nature.com/articles/s41467-018-03202-2 Barrett TJ, Lee AH, Smilowitz NR, Hausvater A, Fishman GI, Hochman JS et al. Whole-Blood Transcriptome Profiling Identifies Women With Myocardial Infarction With Nonobstructive Coronary Artery Disease. Circ Genom Precis Med [Internet]. 2018 Dec 1 [cited 2025 May 21];11(12):e002387. Available from: /doi/pdf/10.1161/CIRCGEN.118.002387?download = true Andreini D, Melotti E, Vavassori C, Chiesa M, Piacentini L, Conte E et al. Whole-Blood Transcriptional Profiles Enable Early Prediction of the Presence of Coronary Atherosclerosis and High-Risk Plaque Features at Coronary CT Angiography. Biomedicines [Internet]. 2022 Jun 1 [cited 2025 May 21];10(6):1309. Available from: https://www.mdpi.com/2227-9059/10/6/1309/htm Nath M, Romaine SPR, Koekemoer A, Hamby S, Webb TR, Nelson CP et al. Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure. Eur J Heart Fail [Internet]. 2022 Jun 1 [cited 2025 Mar 11];24(6):1009. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9546237/ Wingrove JA, Daniels SE, Sehnert AJ, Tingley W, Elashoff MR, Rosenberg S, et al. Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ Cardiovasc Genet. 2008;1(1):31–8. Shahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW et al. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med [Internet]. 2025 Nov [cited 2025 Nov 9];102449. Available from: https://pubmed.ncbi.nlm.nih.gov/41205594/ Bongen E, Lucian H, Khatri A, Fragiadakis GK, Bjornson ZB, Nolan GP et al. Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection. Cell Rep [Internet]. 2019 Nov 12 [cited 2025 Nov 9];29(7):1961. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6856718/ Martinez-Usatorre A, Ciarloni L, Angelino P, Wosika V, Conforte AJ, Costa SSF et al. Human blood cell transcriptomics unveils dynamic systemic immune modulation along colorectal cancer progression. J Immunother Cancer [Internet]. 2024 Nov 1 [cited 2025 Jan 30];12(11):e009888. Available from: https://jitc.bmj.com/content/12/11/e009888 Basu M, Wang K, Ruppin E, Hannenhalli S. Predicting tissue-specific gene expression from whole blood transcriptome. Sci Adv [Internet]. 2021 Mar 1 [cited 2025 Mar 11];7(14). Available from: https://www.science.org/doi/ 10.1126/sciadv.abd6991 Islam SJ, Kim JH, Topel M, Liu C, Ko YA, Mujahid MS et al. Cardiovascular Risk and Resilience Among Black Adults: Rationale and Design of the MECA Study. J Am Heart Assoc [Internet]. 2020 [cited 2024 Aug 21];9(9). Available from: https://pubmed.ncbi.nlm.nih.gov/32340530/ Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: The american heart association’s strategic impact goal through 2020 and beyond. Circulation [Internet]. 2010 Feb 2 [cited 2024 Aug 20];121(4):586–613. Available from: https://www.ahajournals.org/doi/ 10.1161/CIRCULATIONAHA.109.192703 Wang Q, Schmidt AF, Lennon LT, Papacosta O, Whincup PH, Wannamethee G. Association of Life’s Simple 7 lifestyle metric with cardiometabolic disease-free life expectancy in older British men. Communications Medicine 2024 4:1 [Internet]. 2024 Jun 4 [cited 2024 Aug 20];4(1):1–8. Available from: https://www.nature.com/articles/s43856-024-00534-7 Fang N, Jiang M, Fan Y. Ideal cardiovascular health metrics and risk of cardiovascular disease or mortality: A meta-analysis. Int J Cardiol [Internet]. 2016 Jul 1 [cited 2025 Mar 24];214:279–83. Available from: https://pubmed.ncbi.nlm.nih.gov/27085116/ FelixKrueger/TrimGalore. v0.6.7 - DOI via Zenodo. [cited 2024 Nov 18]; Available from: https://zenodo.org/records/5127899 Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics [Internet]. 2013 Jan [cited 2024 Sep 2];29(1):15–21. Available from: https://pubmed.ncbi.nlm.nih.gov/23104886/ Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods [Internet]. 2012 Apr [cited 2024 Sep 2];9(4):357. Available from: /pmc/articles/PMC3322381/ Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics [Internet]. 2009 Aug [cited 2024 Nov 18];25(16):2078–9. Available from: https://pubmed.ncbi.nlm.nih.gov/19505943/ Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res [Internet]. 2015 Apr 4 [cited 2024 Sep 2];43(7):e47. Available from: /pmc/articles/PMC4402510/ Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics [Internet]. 2010 Jan 1 [cited 2025 Nov 20];26(1):139–40. Available from: https://dx.doi.org/10.1093/bioinformatics/btp616 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res [Internet]. 2015 Apr 4 [cited 2024 Sep 2];43(7):e47. Available from: /pmc/articles/PMC4402510/ Haynes W. Benjamini–Hochberg Method. Encyclopedia of Systems Biology [Internet]. 2013 [cited 2024 Sep 2];78–78. Available from: https://link.springer.com/referenceworkentry/ 10.1007/978-1-4419-9863-7_1215 Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package [Internet]. Vol. 36, JSS Journal of Statistical Software. 2010. Available from: http://www.jstatsoft.org/ Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv [Internet]. 2021 Feb 1 [cited 2024 Sep 2];060012. Available from: https://www.biorxiv.org/content/ 10.1101/060012v3 Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics [Internet]. 2011 Jun [cited 2025 Nov 9];27(12):1739. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3106198/ Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A [Internet]. 2005 Oct 25 [cited 2025 Jan 23];102(43):15545–50. Available from: https://www.pnas.org/doi/abs/ 10.1073/pnas.0506580102 Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res [Internet]. 2019 Aug 1 [cited 2025 Aug 13];29(8):1363–75. Available from: http://genome.cshlp.org/content/29/8/1363.full Badia-I-Mompel P, Vélez Santiago J, Braunger J, Geiss C, Dimitrov D, Müller-Dott S et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances [Internet]. 2022 Jan 10 [cited 2025 Aug 13];2(1). Available from: https://dx.doi.org/10.1093/bioadv/vbac016 Müller-Dott S, Tsirvouli E, Vázquez M, Flores ROR, Badia-i-Mompel P, Fallegger R et al. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. bioRxiv [Internet]. 2023 Apr 1 [cited 2025 Aug 13];2023.03.30.534849. Available from: https://www.biorxiv.org/content/ 10.1101/2023.03.30.534849v1 Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A et al. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics [Internet]. 2009 [cited 2025 May 12];25(8):1091–3. Available from: https://pubmed.ncbi.nlm.nih.gov/19237447/ Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res [Internet]. 2003 Nov [cited 2025 May 12];13(11):2498. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC403769/ Islam SJ, Liu C, Mohandas AN, Rooney K, Nayak A, Mehta A et al. Metabolomic signatures of ideal cardiovascular health in black adults. Sci Rep [Internet]. 2024 Dec 1 [cited 2025 Jan 11];14(1). Available from: https://pubmed.ncbi.nlm.nih.gov/38245568/ KANK2 KN motif. and ankyrin repeat domains 2 [Homo sapiens (human)] - Gene - NCBI [Internet]. [cited 2025 Oct 19]. Available from: https://www.ncbi.nlm.nih.gov/gene/25959 Hensley MR, Cui Z, Chua RFM, Simpson S, Shammas NL, Yang JY et al. Evolutionary and developmental analysis reveals KANK genes were co-opted for vertebrate vascular development. Sci Rep [Internet]. 2016 Jun 13 [cited 2025 Oct 19];6(1):1–10. Available from: https://www.nature.com/articles/srep27816 Gee HY, Zhang F, Ashraf S, Kohl S, Sadowski CE, Vega-Warner V, et al. KANK deficiency leads to podocyte dysfunction and nephrotic syndrome. J Clin Invest. 2015;125(6):2375–84. Platell C, Kong SE, McCauley R, Hall JC. Branched-chain amino acids. J Gastroenterol Hepatol [Internet]. 2000 [cited 2025 Dec 9];15(7):706–17. Available from: https://pubmed.ncbi.nlm.nih.gov/10937674/ Sun W, Lin R, Li Y, Yao Y, Lu B, Yu Y. Circulating branched-chain amino acids and the risk of major adverse cardiovascular events in the UK biobank. Front Endocrinol (Lausanne). 2025;16:1510910. White PJ, Newgard CB. Branched-chain amino acids and cardiometabolic diseases: biomarker, causal agent, or both? Science [Internet]. 2019 Feb 8 [cited 2025 Dec 9];363(6427):582. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9940269/ White PJ, McGarrah RW, Herman MA, Bain JR, Shah SH, Newgard CB. Insulin action, type 2 diabetes, and branched-chain amino acids: A two-way street. Mol Metab [Internet]. 2021 Oct 1 [cited 2025 Dec 9];52. Available from: https://pubmed.ncbi.nlm.nih.gov/34044180/ Hunter WG, Kelly JP, Mcgarrah RW, Khouri MG, Craig D, Haynes C et al. Metabolomic Profiling Identifies Novel Circulating Biomarkers of Mitochondrial Dysfunction Differentially Elevated in Heart Failure With Preserved Versus Reduced Ejection Fraction: Evidence for Shared Metabolic Impairments in Clinical Heart Failure. J Am Heart Assoc [Internet]. 2016 Aug 1 [cited 2025 Dec 9];5(8). Available from: https://pubmed.ncbi.nlm.nih.gov/27473038/ Shah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet [Internet]. 2010 Apr [cited 2025 Dec 9];3(2):207–14. Available from: https://pubmed.ncbi.nlm.nih.gov/20173117/ Bhattacharya S, Granger CB, Craig D, Haynes C, Bain J, Stevens RD et al. Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization. Atherosclerosis [Internet]. 2014 [cited 2025 Dec 9];232(1):191–6. Available from: https://pubmed.ncbi.nlm.nih.gov/24401236/ Flores-Guerrero JL, Groothof D, Connelly MA, Otvos JD, Bakker SJL, Dullaart RPF. Concentration of Branched-Chain Amino Acids Is a Strong Risk Marker for Incident Hypertension. Hypertension [Internet]. 2019 Dec 1 [cited 2025 Dec 9];74(6):1428–35. Available from: https://pubmed.ncbi.nlm.nih.gov/31587574/ Portero V, Nicol T, Podliesna S, Marchal GA, Baartscheer A, Casini S et al. Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc Res [Internet]. 2022 May 1 [cited 2025 Dec 9];118(7):1742–57. Available from: https://pubmed.ncbi.nlm.nih.gov/34142125/ Potts MA, Mizutani S, Garnham AL, Suen CSNLW, Kueh AJ, Tai L et al. Deletion of the transcriptional regulator TFAP4 accelerates c-MYC-driven lymphomagenesis. bioRxiv [Internet]. 2022 Dec 20 [cited 2025 Oct 21];2022.12.19.520971. Available from: https://www.biorxiv.org/content/ 10.1101/2022.12.19.520971v1 Tonc E, Takeuchi Y, Chou C, Xia Y, Holmgren M, Fujii C et al. Unexpected suppression of tumorigenesis by c-MYC via TFAP4-dependent restriction of stemness in B lymphocytes. Blood [Internet]. 2021 Dec 16 [cited 2025 Oct 21];138(24):2526. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8678995/ Chen Y, Zhao X, Wu H. Transcriptional Programming in Arteriosclerotic Disease: A Multifaceted Function of the Runt-related Transcription Factor 2 (Runx2). Arterioscler Thromb Vasc Biol [Internet]. 2020 Jan 1 [cited 2025 Oct 20];41(1):20. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7770073/ Liao R, Xie B, Cui J, Qi Z, Xue S, Wang Y. E2F transcription factor 1 (E2F1) promotes the transforming growth factor TGF-β1 induced human cardiac fibroblasts differentiation through promoting the transcription of CCNE2 gene. Bioengineered [Internet]. 2021 [cited 2025 Oct 21];12(1):6869. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8806588/ Lee BK, Bhinge AA, Iyer VR. Wide-ranging functions of E2F4 in transcriptional activation and repression revealed by genome-wide analysis. Nucleic Acids Res [Internet]. 2011 May [cited 2025 Oct 21];39(9):3558. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3089461/ Du P, Gao K, Cao Y, Yang S, Wang Y, Guo R et al. RFX1 downregulation contributes to TLR4 overexpression in CD14 + monocytes via epigenetic mechanisms in coronary artery disease. Clin Epigenetics [Internet]. 2019 Mar 11 [cited 2025 Oct 21];11(1):44. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6413463/ Steimle V, Durand B, Barras E, Zufferey M, Hadam MR, Mach B, et al. A novel DNA-binding regulatory factor is mutated in primary MHC class II deficiency (bare lymphocyte syndrome). Genes Dev. 1995;9(9):1021–32. Chen S, Xu Y, Qian Y, Li Z, Dong M. Case Report: Novel splicing mutations in RFX5 causing MHC class II deficiency. Front Genet [Internet]. 2022 Oct 7 [cited 2025 Oct 21];13:978688. Available from: http://decipher.sanger.ac Principe DR, Doll JA, Bauer J, Jung B, Munshi HG, Bartholin L et al. TGF-β: Duality of Function Between Tumor Prevention and Carcinogenesis. JNCI Journal of the National Cancer Institute [Internet]. 2014 Feb [cited 2025 Oct 21];106(2):djt369. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3952197/ Rojas A, Padidam M, Cress D, Grady WM. TGF-β receptor levels regulate the specificity of signaling pathway activation and biological effects of TGF-β. Biochim Biophys Acta [Internet]. 2009 Jul [cited 2025 Oct 21];1793(7):1165. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC2700179/ Massagué J, Sheppard D. TGF-β signaling in health and disease. Cell [Internet]. 2023 Sep 14 [cited 2025 Oct 21];186(19):4007–37. Available from: https://www.sciencedirect.com/science/article/pii/S0092867423008516 Puimège L, Libert C, Van Hauwermeiren F. Regulation and dysregulation of tumor necrosis factor receptor-1. Cytokine Growth Factor Rev [Internet]. 2014 Jun 1 [cited 2025 Oct 21];25(3):285–300. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1359610114000240 Dick AD, Forrester JV, Liversidge J, Cope AP. The role of tumour necrosis factor (TNF-α) in experimental autoimmune uveoretinitis (EAU). Prog Retin Eye Res [Internet]. 2004 Nov 1 [cited 2025 Oct 21];23(6):617–37. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1350946204000552 Galeone A, Grano M, Brunetti G. Tumor Necrosis Factor Family Members and Myocardial Ischemia-Reperfusion Injury: State of the Art and Therapeutic Implications. Int J Mol Sci [Internet]. 2023 Mar 1 [cited 2025 Oct 21];24(5):4606. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10003149/ Schulman H, Hanson PI. Multifunctional Ca2+/calmodulin-dependent protein kinase. Neurochem Res [Internet]. 1993 Jan [cited 2025 Oct 21];18(1):65–77. Available from: https://pubmed.ncbi.nlm.nih.gov/8385278/ Stabile E, Zhou YF, Saji M, Castagna M, Shou M, Kinnaird TD et al. Akt controls vascular smooth muscle cell proliferation in vitro and in vivo by delaying G1/S exit. Circ Res [Internet]. 2003 Nov 28 [cited 2025 Oct 21];93(11):1059–65. Available from: https://pubmed.ncbi.nlm.nih.gov/14605018/ Kyriakis JM, Avruch J, Mammalian MAPK. Signal Transduction Pathways Activated by Stress and Inflammation: A 10-Year Update. https://doi.org/101152 /physrev000282011 [Internet]. 2012 Apr 1 [cited 2025 Oct 21];92(2):689–737. Available from: /doi/pdf/10.1152/physrev.00028.2011. Britsch S, Goerich DE, Riethmacher D, Peirano RI, Rossner M, Nave KA et al. The transcription factor Sox10 is a key regulator of peripheral glial development. Genes Dev [Internet]. 2001 Jan 1 [cited 2025 Oct 21];15(1):66–78. Available from: https://pubmed.ncbi.nlm.nih.gov/11156606/ Himeda CL, Ranish JA, Hauschka SD. Quantitative Proteomic Identification of MAZ as a Transcriptional Regulator of Muscle-Specific Genes in Skeletal and Cardiac Myocytes. Mol Cell Biol [Internet]. 2008 Oct 1 [cited 2025 Oct 21];28(20):6521. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC2577440/ Maity G, Haque I, Ghosh A, Dhar G, Gupta V, Sarkar S et al. The MAZ transcription factor is a downstream target of the oncoprotein Cyr61/CCN1 and promotes pancreatic cancer cell invasion via CRAF–ERK signaling. J Biol Chem [Internet]. 2018 Mar 23 [cited 2025 Oct 21];293(12):4334. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5868262/ Van Berlo JH, Elrod JW, Van Den Hoogenhof MMG, York AJ, Aronow BJ, Duncan SA et al. The transcription factor GATA-6 regulates pathological cardiac hypertrophy. Circ Res [Internet]. 2010 Oct 15 [cited 2025 Oct 21];107(8):1032–40. Available from: https://pubmed.ncbi.nlm.nih.gov/20705924/ Lepore JJ, Cappola TP, Mericko PA, Morrisey EE, Parmacek MS. GATA-6 regulates genes promoting synthetic functions in vascular smooth muscle cells. Arterioscler Thromb Vasc Biol [Internet]. 2005 Feb [cited 2025 Oct 21];25(2):309–14. Available from: https://pubmed.ncbi.nlm.nih.gov/15591218/ Yin F, Herring BP. GATA-6 can act as a positive or negative regulator of smooth muscle-specific gene expression. Journal of Biological Chemistry [Internet]. 2005 Feb 11 [cited 2025 Oct 21];280(6):4745–52. Available from: https://pubmed.ncbi.nlm.nih.gov/15550397/ Odom DT, Zizlsperger H, Gordon DB, Bell GW, Rinaldi NJ, Murray HL et al. Control of pancreas and liver gene expression by HNF transcription factors. Science [Internet]. 2004 Feb 27 [cited 2025 Oct 21];303(5662):1378–81. Available from: https://pubmed.ncbi.nlm.nih.gov/14988562/ Martinez-Jimenez CP, Kyrmizi I, Cardot P, Gonzalez FJ, Talianidis I. Hepatocyte nuclear factor 4alpha coordinates a transcription factor network regulating hepatic fatty acid metabolism. Mol Cell Biol [Internet]. 2010 Feb 1 [cited 2025 Oct 21];30(3):565–77. Available from: https://pubmed.ncbi.nlm.nih.gov/19933841/ Dolfini D, Mantovani R. Targeting the Y/CCAAT box in cancer: YB-1 (YBX1) or NF-Y? Cell Death Differ [Internet]. 2013 May [cited 2025 Oct 21];20(5):676. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3619239/ Lv Y, Qi J, Babon JJ, Cao L, Fan G, Lang J et al. The JAK-STAT pathway: from structural biology to cytokine engineering. Signal Transduct Target Ther [Internet]. 2024 Dec 1 [cited 2025 Oct 21];9(1):1–37. Available from: https://www.nature.com/articles/s41392-024-01934-w Panarach C, Netsawang C, Nuchprayoon I, Leecharoenkiat K. Identification and functional analysis of novel SPTB and ANK1 mutations in hereditary spherocytosis patients. Sci Rep [Internet]. 2024 Dec 1 [cited 2025 Oct 22];14(1):1–10. Available from: https://www.nature.com/articles/s41598-024-78622-w Zhao Z, Hu L, Song B, Jiang T, Wu Q, Lin J et al. Constitutively active receptor ADGRA3 signaling induces adipose thermogenesis. bioRxiv [Internet]. 2024 Nov 19 [cited 2025 Oct 22];2024.06.08.598066. Available from: https://www.biorxiv.org/content/ 10.1101/2024.06.08.598066v3 SH2D1B protein expression summary -. The Human Protein Atlas [Internet]. [cited 2025 Oct 22]. Available from: https://www.proteinatlas.org/ENSG00000198574-SH2D1B Rajagopalan S, Long EO. KIR2DL4 (CD158d): An activation receptor for HLA-G. Front Immunol [Internet]. 2012 [cited 2025 Oct 22];3(AUG):258. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3422731/ Rajagopalan S. Endosomal signaling and a novel pathway defined by the natural killer receptor KIR2DL4 (CD158d). Traffic [Internet]. 2010 Nov [cited 2025 Oct 22];11(11):1381–90. Available from: https://pubmed.ncbi.nlm.nih.gov/20854369/ Zheng G, Jia L, Yang AG. Roles of HLA-G/KIR2DL4 in Breast Cancer Immune Microenvironment. Front Immunol [Internet]. 2022 Feb 3 [cited 2025 Oct 22];13:791975. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8850630/ Wolfram JA, Liner A, Richardson SL, Zhu X, Smith MA, Hoit BD et al. The role of E2F1 in the development of hypertrophic cardiomyopathy. Int J Clin Exp Pathol [Internet]. 2011 [cited 2025 Oct 22];4(5):521. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3127073/ Bassani B, Baci D, Gallazzi M, Poggi A, Bruno A, Mortara L. Natural Killer Cells as Key Players of Tumor Progression and Angiogenesis: Old and Novel Tools to Divert Their Pro-Tumor Activities into Potent Anti-Tumor Effects. Cancers 2019, Vol 11, Page 461 [Internet]. 2019 Apr 1 [cited 2025 Oct 22];11(4):461. Available from: https://www.mdpi.com/ 2072-6694/11/4/461/htm. Zhao Y, Xie X, Liao W, Zhang H, Cao H, Fei R et al. The transcription factor RFX5 is a transcriptional activator of the TPP1 gene in hepatocellular carcinoma. Oncol Rep [Internet]. 2017 Jan 1 [cited 2025 Oct 22];37(1):289–96. Available from: https://pubmed.ncbi.nlm.nih.gov/27840983/ Praissman JL, Live DH, Wang S, Ramiah A, Chinoy ZS, Boons GJ et al. B4GAT1 is the priming enzyme for the LARGE-dependent functional glycosylation of α-dystroglycan. Elife. 2014;3. Additional Declarations No competing interests reported. Supplementary Files supplementaryfig.pdf Supplementarytable.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8391154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572632451,"identity":"eaa1dcc1-7e90-4391-a1e4-2dcbe0dbd0cc","order_by":0,"name":"Harriet NA Blankson","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Harriet","middleName":"NA","lastName":"Blankson","suffix":""},{"id":572632458,"identity":"8a93af50-0807-4433-8804-2e5a13f69e48","order_by":1,"name":"Cecilia Delmer","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Cecilia","middleName":"","lastName":"Delmer","suffix":""},{"id":572632461,"identity":"fdb52420-417b-4b36-8db7-2370a20392df","order_by":2,"name":"Rashi Verma","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rashi","middleName":"","lastName":"Verma","suffix":""},{"id":572632462,"identity":"a228725b-33b9-4d29-9624-9b1f40fe1fd1","order_by":3,"name":"Emine Guven","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Emine","middleName":"","lastName":"Guven","suffix":""},{"id":572632463,"identity":"8e167941-67a7-4d81-b250-76a98a5094ad","order_by":4,"name":"Kimberly Rooney","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Kimberly","middleName":"","lastName":"Rooney","suffix":""},{"id":572632464,"identity":"371b9874-3ecd-490e-8a34-a95ab8fc930c","order_by":5,"name":"Andrea Pearson","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Pearson","suffix":""},{"id":572632465,"identity":"3b4169ed-892a-4ed3-ba85-3ccd92dec128","order_by":6,"name":"Peter Baltrus","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Baltrus","suffix":""},{"id":572632466,"identity":"7a59caac-779d-45e4-bfec-0de134cabcee","order_by":7,"name":"Arshed A Quyumi","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Arshed","middleName":"A","lastName":"Quyumi","suffix":""},{"id":572632467,"identity":"c2491d84-6cd9-4555-9f0c-2a93ae70e73b","order_by":8,"name":"Priscilla Pemu","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"","lastName":"Pemu","suffix":""},{"id":572632468,"identity":"f3b4a58b-402b-4b39-ac84-30d10fdf317f","order_by":9,"name":"I.King Jordan","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"I.King","middleName":"","lastName":"Jordan","suffix":""},{"id":572632471,"identity":"5b1871f6-35f5-4f7e-bae3-53f7bac95c3a","order_by":10,"name":"Taylor Herman","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Herman","suffix":""},{"id":572632474,"identity":"8444ebce-6562-490c-b98c-2cea87611b49","order_by":11,"name":"Robert Meller","email":"","orcid":"","institution":"Morehouse School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Meller","suffix":""},{"id":572632475,"identity":"f37b0f59-b5d4-4fbe-aa70-10eaf6379025","order_by":12,"name":"Charles D Searles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYNCCAgY5GJOxgTgtBgzGpGtJhKkkrIW///DBzxUGNun9YoePfeZhsJHdcICAFokbacmSZwzScmfOTkuezcOQZkxQi4EEj4Fkg8Hh3A23c4yZeRgOJxLWwn/+888Gg//p9hAt/4nQwpDDBrTlQIKBNFjLAcJagH4xs2wwSDaccTstmXGOQbLxTEJagCH2+GZDhZ08/+zkwwxvKuxk+whpQQFMPAakKAcBxh+k6hgFo2AUjIIRAQAbQD64+JR7AgAAAABJRU5ErkJggg==","orcid":"","institution":"Emory University","correspondingAuthor":true,"prefix":"","firstName":"Charles","middleName":"D","lastName":"Searles","suffix":""}],"badges":[],"createdAt":"2025-12-18 05:08:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8391154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8391154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100594856,"identity":"22d83461-6c5f-404f-a147-9704a2b0e15f","added_by":"auto","created_at":"2026-01-19 13:45:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":894265,"visible":true,"origin":"","legend":"","description":"","filename":"final.docx","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/d2f2d738f5fba93037a442b1.docx"},{"id":100567950,"identity":"39432c62-9da8-43ef-be9f-7a9d3d843e8f","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16194,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/d6f62b0898c992344a74a96b.docx"},{"id":100567956,"identity":"67d2db75-f0ff-438d-928d-983e83e17e60","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16439,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/9b28f187fc91a9b85a8a6d59.docx"},{"id":100594944,"identity":"c0f20658-8a75-45dc-bc1b-1666ad82fc40","added_by":"auto","created_at":"2026-01-19 13:46:37","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16930,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/3737a5afda504f9eef9af996.docx"},{"id":100567953,"identity":"815616de-a793-4671-96ef-99df16993858","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16915,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/8ff1b4cc4acdc04c7136b349.docx"},{"id":100595565,"identity":"e98affe1-0ab2-4823-b238-e304d111b923","added_by":"auto","created_at":"2026-01-19 13:48:48","extension":"json","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14387,"visible":true,"origin":"","legend":"","description":"","filename":"ce957fc9425246b3bf2de266cd3951a0.json","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/c98941266d046b839a431df5.json"},{"id":100594940,"identity":"ea0d833b-e6e9-425d-8e3f-67f3248b07fa","added_by":"auto","created_at":"2026-01-19 13:46:36","extension":"xls","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147456,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xls","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/22f166a6545958c3ae84f8fd.xls"},{"id":100595605,"identity":"c5d9b694-d762-4c0d-87ce-838a40b1d71a","added_by":"auto","created_at":"2026-01-19 13:48:54","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":948296,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfig.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/8293d697b5b5eb8a4250b063.pdf"},{"id":100567959,"identity":"888b2868-38b7-4b89-91d0-43e22c76de15","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":223837,"visible":true,"origin":"","legend":"","description":"","filename":"ce957fc9425246b3bf2de266cd3951a01enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/2df886e0dcddca9c52a2ea9a.xml"},{"id":100567962,"identity":"1fd99a12-9c8a-4ebf-a13f-2019daad3b93","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":789887,"visible":true,"origin":"","legend":"","description":"","filename":"fig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/6df3506a8fc41f8652a51645.pdf"},{"id":100595351,"identity":"9a8ff514-d798-4a40-83c2-5086e159d720","added_by":"auto","created_at":"2026-01-19 13:48:17","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1597103,"visible":true,"origin":"","legend":"","description":"","filename":"fig2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/f49720c8da0182718ba7563f.pdf"},{"id":100595572,"identity":"c0b7759b-45c8-432d-a86a-3560277d87ad","added_by":"auto","created_at":"2026-01-19 13:48:49","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1482086,"visible":true,"origin":"","legend":"","description":"","filename":"fig3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/7fb895030d8a49b99ff6bb4e.pdf"},{"id":100595950,"identity":"d42c418a-7861-460a-b735-d166598b5720","added_by":"auto","created_at":"2026-01-19 13:49:50","extension":"pdf","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2134384,"visible":true,"origin":"","legend":"","description":"","filename":"fig4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/2f82156f62da11759ffb9b4d.pdf"},{"id":100595616,"identity":"0c53289d-3045-4317-935b-d6d0d41f5284","added_by":"auto","created_at":"2026-01-19 13:48:56","extension":"pdf","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2493039,"visible":true,"origin":"","legend":"","description":"","filename":"fig5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/3a5f754016a0ee0fe07206b7.pdf"},{"id":100567965,"identity":"bbb264b5-bdea-4ecd-bed7-aea1c6fa2beb","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"pdf","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2070993,"visible":true,"origin":"","legend":"","description":"","filename":"fig6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/aad0233a5cf4afe94411cd56.pdf"},{"id":100567967,"identity":"32300b7d-a924-456d-8534-aeb6df88ca00","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":218076,"visible":true,"origin":"","legend":"","description":"","filename":"ce957fc9425246b3bf2de266cd3951a01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/8dd3c1aaf4c65234d6c19da9.xml"},{"id":100595592,"identity":"bc4e866e-a5d7-4b04-a1f8-f067aaafb0fc","added_by":"auto","created_at":"2026-01-19 13:48:52","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":245493,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/a9abf56060c47bb5c79d6223.html"},{"id":100594925,"identity":"e0982634-d759-4149-910d-db6974dff33c","added_by":"auto","created_at":"2026-01-19 13:46:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-seq preprocessing and analysis pipeline.\u003c/strong\u003eOverview of RNA-sequenced data processing, quality control, and differential expression workflow. CVH = cardiovascular health; LS7 = Life’s Simple 7, DEG = differentially expressed gene; FDR = false discovery rate; FC = fold change, n = number.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/4017eb2077c3bba6d69142e4.jpg"},{"id":100567946,"identity":"7a550893-adcd-4d0c-a90a-e72f68acdc5d","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution and Correlation of Life’s Simple 7 (LS7) Cardiovascular Health Metrics. \u003c/strong\u003ePanel a and b show the distribution of LS7 total scores in females and male respectively. Panels c (females) and d (males) display the proportion of participants with ideal (green), intermediate (cream), and poor (black) LS7 subcomponent scores by sex. Panels e, and f illustrate Spearman correlation matrices of LS7 subcomponents for females, and males, respectively. Positive correlations are shown in blue and negative correlations in orange, with color intensity proportional to correlation strength. Together, these plots depict the overall CVH distribution and interrelationships among LS7 components by sex. BMI = body mass index; BP = blood pressure; CVH = cardiovascular health; LS7 = Life’s Simple 7.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/8b31bf8b832f828171201093.jpg"},{"id":100595117,"identity":"e0baa86a-3aa2-4516-a976-1442deeb47ae","added_by":"auto","created_at":"2026-01-19 13:47:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":309566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-Stratified Differential Gene Expression and Functional Enrichment\u003c/strong\u003e. Volcano plots illustrate differential gene expression between poor-to-intermediate and ideal cardiovascular health (CVH) in (a) females (b) and males. Genes with an adjusted p value \u0026lt; 0.05 were considered statistically significant; the horizontal dashed line marks the adjusted p value threshold (0.05). Red points represent significant differentially expressed genes (DEGs), whereas gray points denote nonsignificant genes. The Venn diagram (c) shows overlap of DEGs between females and males, identifying 13 shared genes. Network of Gene Ontology enrichment of these shared DEGs (d). CVH = cardiovascular health; DEG = differentially expressed gene; GO = Gene Ontology; LS7 = Life’s Simple 7.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/977bf20c7c2c67deb6341168.jpg"},{"id":100595980,"identity":"5ce1f752-18a3-4e39-9a79-c952f59f4340","added_by":"auto","created_at":"2026-01-19 13:50:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":379593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-Specific Functional Enrichment Networks of Differentially Expressed Genes\u003c/strong\u003e. ClueGO functional enrichment analysis showing biological processes enriched among up- and downregulated genes in females (a, b) and males (b, d) with poor-to-intermediate versus ideal cardiovascular health (CVH). Node size represents the number of genes within each pathway, and color denotes functional grouping or biological theme. CVH = cardiovascular health.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/00a6682e4b461813d12b4c3c.jpg"},{"id":100595597,"identity":"d15f9d16-8b6f-4888-9951-ede523c6e792","added_by":"auto","created_at":"2026-01-19 13:48:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":434713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG and Reactome Pathway Enrichment by Sex.\u003c/strong\u003eGene set enrichment analysis (GSEA) of KEGG and Reactome pathways in females (a–b) and males (c–d) with poor-to-intermediate versus ideal cardiovascular health (CVH). Node size indicates gene count per pathway; color scale reflects statistical significance (–log₁₀ adjusted p value), and position reflects normalized enrichment score (NES). CVH = cardiovascular health; GSEA = gene set enrichment analysis; KEGG = Kyoto Encyclopedia of Genes and Genomes; NES = normalized enrichment score.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/7e1beed3e81717eb4d3685bb.jpg"},{"id":100595345,"identity":"d13bc426-297d-4be3-bb10-3ff42703205f","added_by":"auto","created_at":"2026-01-19 13:48:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":378572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex-Specific Transcription Factor, Cytokine, and Kinase Activity Profiles. \u003c/strong\u003eHeatmaps of inferred transcription factor (TF), cytokine, and kinase activity in females (a) and males (b) with poor-to-intermediate versus ideal cardiovascular health (CVH). Regulator activity was estimated using decoupleR with the DoRothEA and OmniPath knowledge bases. Red indicates activation and blue indicates inhibition (z-score scale). Right-side color bars indicate –log₁₀(FDR) values from upstream regulator analysis, with darker shades denoting higher significance. CVH = cardiovascular health; TF = transcription factor.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/cb01836231b12eb4b2d39bb9.jpg"},{"id":100690698,"identity":"f50ba790-b130-4757-97f9-66fbd470c454","added_by":"auto","created_at":"2026-01-20 13:56:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3562479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/ff5b3237-f187-453d-9f98-4f30a3ec4377.pdf"},{"id":100567948,"identity":"8b983704-3ddf-4b0d-86a8-26b6b981f9dd","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":948296,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfig.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/ce11bbceb45a346285ecee9c.pdf"},{"id":100567957,"identity":"558350ac-2f85-41f9-984c-bbd63e7aa053","added_by":"auto","created_at":"2026-01-19 09:14:48","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147456,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xls","url":"https://assets-eu.researchsquare.com/files/rs-8391154/v1/9b8164fd4120e9008eb7c6d6.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex differences in molecular pathways underlying cardiovascular health in Black Americans","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains the leading cause of death worldwide, accounting for over 19\u0026nbsp;million deaths in 2021,(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and presents significant health challenges across different demographic groups(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the United States, Black Americans are disproportionately affected, experiencing higher rates of hypertension, stroke, heart failure, and coronary artery disease than other demographic groups(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The burden of CVD is especially pronounced among Black women, who exhibit higher cardiometabolic risk but also face poorer clinical outcomes compared to Black men and non-Black women(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Between 2000 and 2018, CVD mortality among Black women was two to three times higher than among white women(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Despite these differences in prevalence and prognosis, the underlying mechanisms, which are driven by a complex interplay of genetic, environmental, and socio-economic factors, remain incompletely understood.\u003c/p\u003e \u003cp\u003eHigh-throughput transcriptomic technologies provide power tools to investigate the molecular pathways underlying disease heterogeneity. RNA expression reflects the dynamic state of gene regulation influenced by both genetic background and environmental exposures, thereby integrating inherited and acquired risk factors(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Whole blood RNA sequencing (RNA-seq) enables comprehensive profiling of gene expression signatures associated with clinical phenotypes and therapeutic responses(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This approach has been informative in cardiovascular research, including heart failure(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and coronary artery disease(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Transcriptomic analyses have also revealed sex-specific differences in immune signaling, hormonal regulation and metabolic pathways(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), underscoring relevance to precision medicine. Whole blood also captures circulating signaling molecules, including cytokines and miRNAs, that influence gene expression across tissues. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Since blood transcriptomic profiles often mirror tissue-specific patterns, these profiles may provide insights into broader physiological processes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis whole blood transcriptome analysis expands on the Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study, a community-based investigation of CVD risk and resilience to CVD among Black Americans(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In the present study, cardiovascular health (CVH) was assessed using American Heart Association\u0026rsquo;s Life\u0026rsquo;s Simple 7 (AHA LS7) score, which incorporates seven modifiable lifestyle and clinical factors: smoking, physical activity, diet, body mass index (BMI), blood pressure, cholesterol levels, and blood glucose(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While LS7 provides a practical framework for clinical risk assessment and intervention, integrating it with transcriptomic profiling enables a unique opportunity to uncover the molecular mechanisms linking these risk factors to cardiovascular outcomes.\u003c/p\u003e \u003cp\u003eIn this study, our objective was to identify differentially expressed genes and regulatory networks associated with CVH in Black adults, with a specific focus on identifying sex-specific transcriptomic profiles. By characterizing sex-specific molecular differences, our work provides a foundation for developing precision-targeted strategies to improve cardiovascular outcomes in Black adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and Sample collection\u003c/h2\u003e \u003cp\u003eParticipants of the MECA study completed study visits at either Emory University or Morehouse School of Medicine where they underwent a physical examination, blood draws, and standardized questionnaires. Vital signs and anthropometric measures were recorded. All blood samples were collected after \u0026gt;\u0026thinsp;6h of fasting, and fasting cholesterol and glucose levels were measured. Hypertension was defined as current use of anti-hypertensive medications, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg, or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg. Diabetes mellitus was defined as current use of diabetes medications or fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL. Hyperlipidemia was defined as current use of lipid-lowering medications or fasting total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;240 mg/dL. The study protocol was approved by the Institutional Review Boards at Morehouse School of Medicine (RB-FY2026-44) and Emory University (IRB00083584) and all participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant selection\u003c/h3\u003e\n\u003cp\u003eThe MECA study recruited adults ages 30 to 70 years who identified as Black and residents of the Atlanta metropolitan area for more than six years. The details of the study design have been previously described(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Briefly, individuals with known CVD (e.g., myocardial infarction, congestive heart failure, cerebrovascular accident, coronary artery disease, peripheral arterial disease, atrial fibrillation, and cardiomyopathies), concomitant chronic illness (e.g., cancer, lupus, or HIV), substance abuse, psychiatric illness, pregnant or lactating females, and immobility such that physical activity could not be increased were excluded(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLife’s Simple 7 metrics\u003c/h3\u003e\n\u003cp\u003eThe LS7 score, developed by the American Heart Association, is a validated metric for assessing CVH that incorporates both health behaviors (diet, exercise and smoking) and measurable health factors (BMI, cholesterol, fasting blood glucose and blood pressure)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Each LS7 component was scored as 0 (poor), 1 (intermediate) or 2 (ideal) based on established criteria,(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and the total score was calculated by summing all seven subdomains, with a maximum score of 14 representing ideal CVH(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). None of the MECA study participants whose blood was studied had a LS7 score at the extremes (14 or \u0026lt;\u0026thinsp;3). Initially, the cohort was divided into tertiles based on natural breaks in total LS7 distribution: low (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), intermediate (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and high LS7 scores (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, preliminary DGE analyses showed substantial overlap in the DEGs identified in males when comparing the low and intermediate versus high groups (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Therefore, the low and intermediate LS7 groups were combined into a single category (poor-to-intermediate CVH, LS7\u0026thinsp;\u0026lt;\u0026thinsp;10) for comparison with the high LS7 group (ideal CVH, LS7 \u0026ge; 10) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This data-driven approach minimized redundancy and strengthened group contrasts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRNA extraction\u003c/h3\u003e\n\u003cp\u003eBlood was collected into PAXgene Blood RNA tubes (PreAnalytiX/Qiagen/BD Biosciences), and RNA was extracted using the PAXgene Blood RNA Kit (PreAnalytiX/Qiagen/BD Biosciences). RNA quality was assessed using a Fragment Analyzer (Agilent). One microgram of total RNA was subjected to ribosomal RNA (rRNA) and globin transcript depletion using the GLOBINclear Kit, human (ThermoFisher Scientific). Ten nanograms of the globin-depleted RNA was used as input for cDNA synthesis using the Clontech SMART-Seq v4 Ultra Low Input RNA kit (Takara Bio) according to the manufacturer\u0026rsquo;s instructions. Amplified cDNA was fragmented and appended with dual-indexed bar codes using the Nextera XT DNA library preparation kit (Illumina). Libraries were validated by capillary electrophoresis on a TapeStation 4200 (Agilent), pooled at equimolar concentrations, and sequenced with PE100 reads on an Illumina NovaSeq 6000, yielding\u0026thinsp;~\u0026thinsp;30\u0026nbsp;million reads per sample on average.\u003c/p\u003e\n\u003ch3\u003eData alignment and Differential Gene Expression Analysis\u003c/h3\u003e\n\u003cp\u003eRaw RNA sequencing reads were initially pre-processed to remove rRNA that may still be present. Adapter sequences and low bases were trimmed using Trim Galore (0.6.4)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Cleaned reads were aligned to the human reference genome (Homo_sapiens.GRCh38.dna.primary_assembly.fa) using STAR (v2.7.3a)(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and Bowtie2 (v2.3.5.1)(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), with the corresponding Ensembl annotation file (Homo_sapiens.GRCh38.109.gtf). Reads were aligned in two-pass using STAR(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The aligned reads were then sorted, indexed, and filtered using SAMtools (v1.1.0)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Transcript assembly and quantitation were performed using StringTie (v2.2.1). Then prepDE.py was used to generate a unified count matrix for downstream analysis using R (v4.4.1). Fastq files were submitted to dbGAP (waiting on number).\u003c/p\u003e \u003cp\u003eCount-level RNA-seq data and phenotype data were analyzed using LIMMA in R (V4.4.1)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Samples were matched to the phenotype data, and duplicated samples were removed. Raw gene-level counts were imported into edgeR(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) using DGEList object. To remove extreme expression outliers, we calculated the maximum counts-per-million (CPM) value per gene and excluded genes above the 99th percentile. Low-expressed genes were filtered by retaining only those with CPM\u0026thinsp;\u0026gt;\u0026thinsp;1 in at least the minimum group sample size. Counts were normalized using the trimmed mean of m-values (TMM) method to account for library size differences. The resulting filtered and normalized dataset was used as input for LIMMA-VOOM modeling. The design matrix included LS7 groups and age as covariates. Differential gene expression (DGE) analysis was performed separately for females and then males, using empirical Bayes moderation(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Genes were considered significantly differentially expressed if at log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;1.2 and FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Benjamini Hochberg correction(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)). Resulting differentially expressed genes (DEGs) lists were used in downstream analyses and visualization, including volcano plots. Data tables were used for subsequent analysis and plots.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFixed Effects Meta-Analysis of Differentially Expressed Genes Shared by Sexes\u003c/h2\u003e \u003cp\u003eFor the each shared DEG, the combined effect size across sexes was estimated using fixed-effects meta-analysis implemented in the metafor R package(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The fixed-effects model was applied given the small number of groups (2 sexes) and low group heterogeneity. Genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and consistent directionality (same sign of log\u003csub\u003e2\u003c/sub\u003eFC across sexes) were interpreted as having sex-independent differential regulation. Forest plots were generated to visualize the direction and precision of effect estimates (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene ontology and regulatory pathway analysis\u003c/h3\u003e\n\u003cp\u003eGene ontology (GO) and regulatory pathway analysis were conducted using fast gene set enrichment analysis (FGSEA)(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Gene set used for enrichment was the GO terms (c5.all.v2023.2), KEGG (c2.cp.kegg_legacy.v2023.2) and Reactome (c3.all.v2023.2) from the Molecular Signatures Database(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). DGE analysis results were ranked by t-statistics. FGSEA(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) was applied to the entire dataset, utilizing 1000 permutations for gene sets to assess statistical significance(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Pathways with adjust p-value \u0026le; 0.05 were considered significant, and top 10 upregulated and 10 downregulated pathways were visualized using dot plots.\u003c/p\u003e \u003cp\u003eTo investigate the regulatory mechanisms underlying sex-specific transcriptional profiles, transcription factor (TF), cytokine, and kinase activity were inferred using decoupleR and DorothEA(\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) framework and visualized in heatmap format. DEGs were used to build a comprehensive regulatory network with ClueGo(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) in Cytoscape (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses (LIMMA, Spearman\u0026rsquo;s correlation, Student\u0026rsquo;s t-test, and Fisher\u0026rsquo;s exact test) were performed in R (version 4.4.1)\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population Characteristics and CVH Assessment\u003c/h2\u003e \u003cp\u003eWhole blood transcriptomic profiles were assessed in 373 self-identified Black adults living in the Atlanta metropolitan area. The mean age of participants was 53 years, and 60% (n\u0026thinsp;=\u0026thinsp;225) were female (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Other demographic and clinical characteristics of the cohort have been described previously,(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) including prevalence of hypertension (53%), hyperlipidemia (31%), diabetes mellitus (21%), and current smoking (24%). The mean BMI was 33 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution and comparison of Clinical measures among study Participants BMI\u0026thinsp;=\u0026thinsp;body mass index, SBP\u0026thinsp;=\u0026thinsp;systolic blood pressure, DBP\u0026thinsp;=\u0026thinsp;diastolic blood pressure, HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein, LDL\u0026thinsp;=\u0026thinsp;low density lipoprotein, LS7\u0026thinsp;=\u0026thinsp;life simple 7, N\u0026thinsp;=\u0026thinsp;number, SD\u0026thinsp;=\u0026thinsp;standard deviation. P-values were calculated using the two-sample t-tests, and statically significant values were \u0026lt;\u0026thinsp;0.05 and marked *.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e52.53 \u0026plusmn; 10.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e53.62 \u0026plusmn; 10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e30.10 \u0026plusmn; 6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e34.60 \u0026plusmn; 8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2x10\u003csup\u003e-7\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e102.82 \u0026plusmn; 32.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e102.28 \u0026plusmn; 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e132.26 \u0026plusmn; 19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e129.56 \u0026plusmn; 19.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e79.68 \u0026plusmn; 11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e80.90 \u0026plusmn; 11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears Smoked (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.28 \u0026plusmn; 12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.92 \u0026plusmn; 9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.29x10\u003csup\u003e-6\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.78 \u0026plusmn; 19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e58.48 \u0026plusmn; 15.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e108.05 \u0026plusmn; 38.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e117.48 \u0026plusmn; 32.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15x10\u003csup\u003e-2\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e107.30 \u0026plusmn; 75.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e99.56 \u0026plusmn; 45.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e185.46 \u0026plusmn; 41.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e196.07 \u0026plusmn; 37.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15x10\u003csup\u003e-2\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLS7 Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.17 \u0026plusmn; 2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.91 \u0026plusmn; 2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal LS7 scores ranged from 3 to 13 in the cohort, with a median of 8 for both sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). There was no significant difference in total LS7 scores between females and males (7.9 \u0026plusmn; 2.1 vs 8.2 \u0026plusmn; 2.3) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, females had higher average BMI and total cholesterol, while smoking prevalence was lower compared to males (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d).\u003c/p\u003e \u003cp\u003eRegarding LS7 subdomains, 67.6% (n\u0026thinsp;=\u0026thinsp;152) of females and 43.2% (n\u0026thinsp;=\u0026thinsp;64) of males had poor BMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). Poor blood pressure was also common, affecting 49.3% of females (n\u0026thinsp;=\u0026thinsp;111) and 43.3% of males (n\u0026thinsp;=\u0026thinsp;65). Ideal diet scores were rare in both sexes, 5.3% of females (n\u0026thinsp;=\u0026thinsp;12) and 6.1% of males (n\u0026thinsp;=\u0026thinsp;9). However, most participants had ideal fasting blood glucose (females: 66.2%, n\u0026thinsp;=\u0026thinsp;149; males: 62.8%, n\u0026thinsp;=\u0026thinsp;93) and ideal physical activity (females: 50.7%, n\u0026thinsp;=\u0026thinsp;114; males: 71.6% n\u0026thinsp;=\u0026thinsp;106). Ideal total cholesterol was achieved by 45% of females (n\u0026thinsp;=\u0026thinsp;102) and 52% of males (n\u0026thinsp;=\u0026thinsp;77).\u003c/p\u003e \u003cp\u003eSpearman correlation analysis revealed that fasting blood glucose and blood pressure were the strongest correlates of total LS7 scores in both sexes, while total cholesterol and BMI showed a stronger correlation with total LS7 score in males (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Expression\u003c/h2\u003e \u003cp\u003eThe DGE analysis of low and intermediate (\u0026lt;\u0026thinsp;10 ) versus high (\u0026ge; 10) LS7 scores identified 430 DEGs in females and 344 DEGs in males (adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b, Supplementary Table\u0026nbsp;1\u0026amp;2). The number of upregulated DEGs in females (n\u0026thinsp;=\u0026thinsp;180) was approximately 30% higher than in males (n\u0026thinsp;=\u0026thinsp;130), while the number of downregulated DEGS in females (n\u0026thinsp;=\u0026thinsp;250) was about 16% higher than in males (n\u0026thinsp;=\u0026thinsp;214). Among the significant DEGs (adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), fold change ranged from +\u0026thinsp;2.6 to -2.4 in females and +\u0026thinsp;2.6 to -1.9 in males. A greater proportion of DEGs in females were novel genes annotated only by StringTie (15%, n\u0026thinsp;=\u0026thinsp;66) compared to males (12%, n\u0026thinsp;=\u0026thinsp;42).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eShared Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eA total of 13 DEGs were shared between males and females with poor-to-intermediate CVH (LS7 scores\u0026thinsp;\u0026lt;\u0026thinsp;10), of which \u003cem\u003eDNAJC6, KANK2, SPTB\u003c/em\u003e and MSTRG.22508 were upregulated in both sexes, indicating conserved cellular stress response involving cytoskeletal remodeling, vascular development and membrane stabilization(\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A fixed effects meta-analysis confirmed consistent directionality for these genes (pooled log\u003csub\u003e2\u003c/sub\u003eFC \u0026asymp; 0.65\u0026ndash;0.8, meta p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a sex-independent association with poor-to-intermediate CVH. Notably, \u003cem\u003eSPATC1L\u003c/em\u003e displayed sex divergent regulation, upregulated (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.26, adj.P\u0026thinsp;=\u0026thinsp;7.4E-05) in males, but downregulated in females (log2FC = -0.9, adj.P\u0026thinsp;=\u0026thinsp;0.001), with heterogeneity observed in the meta-test, though not statistically significant (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u0026amp;3, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All other shared DEGs (\u003cem\u003eADGRA3, AKR1C3, B4GAT1, KIR2DL4, KLRF1, SH2D1B, WAPL-DT\u003c/em\u003e and \u003cem\u003eZNT595\u003c/em\u003e) were consistently downregulated across sexes, reflecting coordinated suppression of immune-related and NK-cell activation pathways in poor-to-intermediate CVH individuals compared to ideal CVH (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis of common differentially expressed genes in males and females logFC_= Log2 fold change se\u0026thinsp;=\u0026thinsp;standard error, p\u0026thinsp;=\u0026thinsp;p-value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elogFC Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elogFC Male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeta logFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeta SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeta p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADGRA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.2638462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.2880897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.2744465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25128566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.94E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKR1C3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.5784595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4145407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5060374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08262534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.10E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4GAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2846451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3448068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3108695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06914057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.92E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAJC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59268818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73121454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65305189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14276674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.78E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKANK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71891867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80813988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75785486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16620402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.12E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIR2DL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.1521196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.2369734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.189208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1933893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.78E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3972859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4642975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.4265114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09178079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.37E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTRG.22508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47361841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59287208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52556376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSH2D1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.4707316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5202137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.4923344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08802647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.23E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPATC1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.8981771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26176526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04044289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20989822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFALSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78834466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75932866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77564282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15112251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWAPL-DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.5422633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6436078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5864529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1301706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.63E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.6536046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5042423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5877143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09389103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.86E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of Differentially Expression Genes \u0026ndash; Gene Ontology Terms\u003c/h2\u003e \u003cp\u003eWhen the gene expression profiles for females with poor-to-intermediate CVH were assessed for Gene Ontology (GO) enrichment analyses, we observed significant upregulation of GO terms related to cytoskeletal organization and actin filament dynamics, including negative regulation of actin filament polymerization (NES\u0026thinsp;=\u0026thinsp;2.12, p.adj\u0026thinsp;=\u0026thinsp;0.004), actin polymerization or depolymerization, exocytotic processes and hormone-responsive cytoskeletal remodeling (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, DEGs for males with poor-to-intermediate CVH showed upregulation of GO Terms such as cardiac-specific processes, such as regulation of heart rate by cardiac conduction (NES\u0026thinsp;=\u0026thinsp;2.1, p.adj\u0026thinsp;=\u0026thinsp;0.03), cardiac conduction and extracellular matrix organization (external encapsulating structure) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Upregulated and Downregulated Gene Ontology Terms in Females based on all genes pval\u0026thinsp;=\u0026thinsp;p-value, padj\u0026thinsp;=\u0026thinsp;p- adjusted value, ES\u0026thinsp;=\u0026thinsp;enrichment score, NES\u0026thinsp;=\u0026thinsp;normalized enrichment score. Table is ordered by normalized enrichment scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epadj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGATIVE REGULATION OF ACTIN FILAMENT POLYMERIZATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGATIVE REGULATION OF SIGNAL TRANSDUCTION BY P53 CLASS MEDIATOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCELLULAR RESPONSE TO GROWTH HORMONE STIMULUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38E-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGATIVE REGULATION OF ACTIN FILAMENT DEPOLYMERIZATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.81E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXOCYTIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTIN POLYMERIZATION OR DEPOLYMERIZATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCELLULAR RESPONSE TO PEPTIDE HORMONE STIMULUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYTOPLASMIC SIDE OF PLASMA MEMBRANE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTIN FILAMENT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.49E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGULATION OF ACTIN FILAMENT LENGTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.83E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROTEASOME REGULATORY PARTICLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA UNWINDING INVOLVED IN DNA REPLICATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGULATION OF MITOTIC CELL CYCLE SPINDLE ASSEMBLY CHECKPOINT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRNA METABOLIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.66E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.18E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENDOPEPTIDASE COMPLEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTIGEN BINDING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNATURAL KILLER CELL MEDIATED IMMUNITY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.24E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANTIGEN PROCESSING AND PRESENTATION OF EXOGENOUS ANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMUNOGLOBULIN COMPLEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.70E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 Upregulated and Downregulated Gene Ontology Terms in Males based on all genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epadj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGULATION OF HEART RATE BY CARDIAC CONDUCTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCARDIAC CONDUCTION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXTERNAL ENCAPSULATING STRUCTURE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.68E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOST TRANSCRIPTIONAL REGULATION OF GENE EXPRESSION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP INTRAUTERINE GROWTH RETARDATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRANSPORT VESICLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.52E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMALL MOLECULE CATABOLIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePURINE CONTAINING COMPOUND METABOLIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP AGE OF DEATH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOLGI VESICLE TRANSPORT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPII VESICLE COAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNA METHYLATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPII COATED VESICLE BUDDING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUCLEOSIDE MONOPHOSPHATE METABOLIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUCLEOSIDE DIPHOSPHATE METABOLIC PROCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER TO GOLGI TRANSPORT VESICLE MEMBRANE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.28E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP ACCESSORY ORAL FRENULUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRICARBOXYLIC ACID CYCLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRANSLATIONAL INITIATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.71E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.89E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMMUNOGLOBULIN COMPLEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.76E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003epval\u0026thinsp;=\u0026thinsp;p-value, padj\u0026thinsp;=\u0026thinsp;p- adjusted value, ES\u0026thinsp;=\u0026thinsp;enrichment score, NES\u0026thinsp;=\u0026thinsp;normalized enrichment score. Table is ordered by normalized enrichment scores.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe highest downregulated GO terms in females with poor-to-intermediate CVH included immune and proteostasis pathways, particularly those involving immunoglobulin complex (NES = -2.36, p.adj\u0026thinsp;=\u0026thinsp;2.04 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), antigen processing and presentation of exogenous antigen and antigen binding (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In males with poor-to-intermediate CVH, the highest downregulated pathways were predominantly associated with mitochondrial and metabolic processes, such as translational initiation, tricarboxylic acid cycle, nucleoside phosphate metabolism, and reduced ER-Golgi trafficking (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Consistent with females, the immunoglobulin complex (NES = -2.24, p.adj\u0026thinsp;=\u0026thinsp;3.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) was downregulated in males indicating suppression of adaptive immune function in both sexes with poor-to-intermediate CVH.\u003c/p\u003e \u003cp\u003eA ClueGO(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) network analysis, which informs how GO terms are linked, was performed to visualize functional interrelationships among up- and downregulated biological processes in males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In females with poor-to-intermediate CVH, upregulation networks were mainly cytoskeletal and vascular remodeling, including negative regulation of endothelial cell migration, complement activation, negative regulation of protein polymerization and cytoplasmic microtubule organization, whereas males showed enrichment of neuromuscular junction development, negative regulation of actin filament polymerization and membrane repolarization pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026amp;c). Conversely, immune and extracellular biosynthetic processes were downregulated in females, while males exhibited suppression of metabolic and biosynthetic functions such as tRNA medication and long-chain fatty acid transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u0026amp;d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Pathway Enrichment Reveal Sex-Specific Metabolic Signaling Differences\u003c/h2\u003e \u003cp\u003eTo extend the functional insights from GO analysis, KEGG pathway enrichment was performed to identify broader signaling and metabolic networks associated with poor-to-intermediate CVH in males and females. In females, upregulated pathways were predominantly linked to cellular signaling and vascular function, including dorsoventral axis formation, ERBB signaling, JAK-STAT signaling, long-term potentiation and vascular smooth muscle contraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). No KEGG pathways were significantly upregulated in males, suggesting that poor-to-intermediate CVH does not trigger strong coordinated increases in major biological pathways in men. On the other hand, downregulated KEGG pathways in females included antigen processing and presentation, proteosome, graft-versus-host disease, biosynthesis and DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), reflecting suppression of immune, proteolytic and biosynthesis functions, in line with the GO terms analysis. Both males and females showed downregulation of valine, leucine and isoleucine degradation -branched-chain amino acids (BCAA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026amp;c) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Although BCAAs support muscle protein synthesis and cellular energy, elevated circulating BCAA levels are associated with cardiometabolic dysfunction and increased CVD risk(\u003cspan additionalcitationids=\"CR47 CR48 CR49 CR50 CR51 CR52 CR53\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), suggesting impaired BCAA utilization in poor-to-intermediate CVH males and females. In males with poor-to-intermediate CVH, downregulated pathways were primarily metabolic, encompassing valines, leucine, and isoleucine degradation, and the citrate cycle, suggesting reduced mitochondrial energy metabolism in poor-to-intermediate CVH males also consistent with the GO terms analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eReactome Pathways Enrichment Highlights Distinct Signaling and Contractile Pathways by Sex\u003c/h2\u003e \u003cp\u003eReactome pathway analysis was performed to complement KEGG by showing in more detail how specific biological pathways are altered. Females with poor-to-intermediate CVH had upregulated Reactome pathways enriched for cytoskeletal and growth factor mediated signaling, including signaling by cytosolic FGFR1 fusion mutants, striated muscle contraction, growth hormone receptor signaling, RAC1 GTPase cycle, RHO GTPase activating WASPs and WAVES, which is consistent with observed GO enrichment for cytoskeletal organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). These pathways were consistent with the GO enrichment for cytoskeletal organization and actin filament dynamics and the KEGG enrichment for vascular and smooth muscle contraction, highlighting a coordinated activation of Rho-GTPase dependent cytoskeletal remodeling and hormone stress signaling in females with poor-to-intermediate CVH. Additional enrichment of striated muscle contraction and smooth muscle contraction indicates increased vascular and muscular contractile activity in females with poor-to-intermediate CVH. In males, upregulated Reactome pathways were limited to cardiac-specific contractile processes, specifically, cardiac contraction and muscle contraction, which was also consistent with the GO analysis cardiac conduction and extracellular matrix, suggesting increased myocardial electrical and mechanical signaling, which could be a male specific adaptive response to poor-to-intermediate CVH (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eDownregulated Reactome pathways in females with poor-to-intermediate CVH were enriched for proteolytic and immune regulatory processes including VIF mediated degradation of APOBEC3G, proteosome assembly, immunoregulatory interactions between a lymphoid and a non-lymphoid cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In males, downregulated Reactome pathways were primarily associated with mitochondrial, translational, and immune signaling functions, including SNRNP assembly, FCGR activation, translation, mitochondrial protein degradation, and role of LAT2 NTAL LAB on calcium mobilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eUpstream Regulator Analysis\u003c/h2\u003e \u003cp\u003eTo identify upstream molecular drivers of the observed transcriptional patterns, we performed regulator enrichment analyses, including transcription factor (TF), cytokine, and kinase activity inference, to determine key signaling regulators associated with poor-to-intermediate CVH in males and females.\u003c/p\u003e \u003cp\u003eIn females, TF activity analysis inferred activation of RUNX2, PBX3, and TFAP4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), regulators that control cytoskeletal organization, vascular remodeling, and stress-responsive gene expression (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). On the other hand, CEBPA, CEBPD, E2F1-4, FOXM1, TFAP2C, TP63, and RFX1/5 were inhibited, which is consistent with the observed suppression of immune and cell-cycle pathways (\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Cytokine activity inference showed activation of TGFB3, IFNA1/2, IFNB1, IFNG, and TNFRSF10A/B/12A, while canonical inflammatory (TNF) and SMD-dependent TGF-β signaling components (TGFB1/2, TGFBR2) were inhibited(\u003cspan additionalcitationids=\"CR64 CR65 CR66 CR67\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), suggesting bias toward non-canonical, focal-adhesion linked remodeling. Correspondingly, kinase analysis identified activation of STK4/38, PTPRK, CDK5-CDK5R1, PRKACA/B, and CAMKK1, highlighting MAPK, AKT, and PKA/aPKC signaling cascades that likely drive vascular contractile and actin reorganization programs(\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn males, TF activity analysis inferred activation of GATA6, MAZ, and SOX10, regulators associated with cardiac and vascular differentiation as well as neuromuscular conduction (\u003cspan additionalcitationids=\"CR73 CR74 CR75 CR76\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), while HNF4A, NFYB, E2F1/4/6, LEF1, SNAI2, and STAT1 were inhibited, indicating broad suppression of transcriptional programs governing metabolism, cell-cycle regulation, and immune signaling(\u003cspan additionalcitationids=\"CR79 CR80\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Cytokine analysis revealed limited activation of interferon and TNF receptor pathways (IFNG, IFNE, TNFRSF12A, TGFB1|1) but inhibition of TNF, TGFBR1, IFNAR1, and IFNGR2, reflecting reduced inflammatory tone(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Kinase activity in males was nominated by activation of MAPK1(ERK2), AMPK, PKA (PRKACA/B), aPKC (PRKCI/Z), PRKCD, and PRKC1, consistent with enhanced cardiac conduction and contractile signaling, whereas PIK3CA, MAPK4, BTK, and several CDKs were inhibited, indicating suppression of proliferative and immune signaling. While some of the regulators did not meet FDR thresholds, it is expected in enrichment-based inference. Also, the directional activity patterns were coherent with the observed sex-specific DEGs and pathway pathways enrichments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study of 373 Black adults, we compared sex-specific whole blood transcriptomic profiles in participants with poor-to-intermediate CVH to those with ideal CVH. Although the distribution of total LS7 scores was similar between males and female participants, we identified distinct sex-specific gene expression patterns. Both males and females with poor-to-intermediate CVH had suppression of immune related and proteostatic pathways, but the upstream regulatory profiles and biological emphasis differed between sexes. Females had a structural and cytoskeletal remodeling phenotype, whereas males displayed a cardiac conduction and metabolic adaptation phenotype. These findings highlight sex-specific transcriptional, signaling, and kinase activities that may underlie different cardiovascular responses to lifestyle and cardiometabolic risk factors.\u003c/p\u003e \u003cp\u003eMales and female with poor-to-intermediate CVH had shared core cellular stress responses. This was reflected in the 13 DEGs common to both sexes. Two of these DEGs, \u003cem\u003eKANK2\u003c/em\u003e and \u003cem\u003eSPTB\u003c/em\u003e - involved in cytoskeletal remodeling, vascular development, and membrane stabilization were upregulated in both sexes(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Another of the common DEGs, \u003cem\u003eSPATC1\u003c/em\u003e, was upregulated in males and downregulated in females, which is appropriate given this gene\u0026rsquo;s role in spermatogenesis. Among the other genes downregulated in both sexes, \u003cem\u003eADGRA3\u003c/em\u003e has been shown to be involved in fat burning, and downregulation of this gene has been associated with obesity(\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e), an important risk factor of poor CVH. The downregulation of immune related genes (\u003cem\u003eSH2D1B\u003c/em\u003e and \u003cem\u003eKIR2DL4\u003c/em\u003e) links poor-to-intermediate CVH with reduced immunity in both males and females(\u003cspan additionalcitationids=\"CR85 CR86\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). This is also explicitly shown in high expression of immunoglobulin heavy and light chain genes in both males and females with high LS7 (Ideal CVH) scores and relatively decreased expression of these genes in males and females in lower LS7 scores (poor-to-intermediate CVH).\u003c/p\u003e \u003cp\u003eWhile we had expected to see transcriptomic profiles predominantly associated with inflammation, our data indicate predominance of transcriptomic profiles associated with structural adaptation in females with poor-to-intermediate CVH. Females showed activation of TFs (RUNX2, PBX3 and TFAP4) involved in osteogenic and vascular remodeling(\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), accompanied by inhibition of TFs (E2F, CEBP, and RFX families) that coordinate immune and cell-cycle programs(\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Notably, suppression of RFX1 and RFX5 - key regulators of MHC class II expression - aligns with the decreased expression of antigen presentation and immunoglobulin complex genes, supporting relative immune suppression in females with poor-intermediate CVH(\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Males, in contrast, exhibited activation of GATA6, MAZ, and SOX10 -a regulatory network promoting cardiac contractility and neuromuscular signaling(\u003cspan additionalcitationids=\"CR73 CR74 CR75 CR76\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In parallel, inhibition of STAT1 and HNF4A reflected suppression of metabolic and immune function(\u003cspan additionalcitationids=\"CR79 CR80\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Interestingly, the transcriptomics profiles of males with poor-to-intermediate CVH, similar to that of females, indicated inhibition of E2F family of TFs, which have been shown to have a protective role in preventing cardiomyocyte hypertrophy(\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the post transcriptional level, that is changes that occur after mRNA is produced, both sexes displayed changes in immunoglobulin complex and antigen presentation pathways, but the regulation of these pathways appeared to differ. Females with poor-to-intermediate CVH had downregulation of \u003cem\u003eKIR2DL4, KLRF1\u003c/em\u003e, and \u003cem\u003eSH2D1B\u003c/em\u003e along suppression of RFX1/5 signaling, implying reduced activation of MHC class II and B-cell genes(\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). This shows post-transcriptional repression of adaptive immune effectors to maintain endothelial integrity during stress. In males, decreased expression of \u003cem\u003eB4GAT1\u003c/em\u003e and \u003cem\u003eADGRA3\u003c/em\u003e and inhibition of STAT1 signaling suggested dampening of cytokine dependent transcription and interferon signaling(\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). Overall, immune gene repression appears to be conserved between males and females, but immune gene repression is mediated through different processing and transcriptional silencing in males versus post-transcriptional dampening via proteostatic control in females.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary this work reveals that poor-to-intermediate CVH elicits sex-specific transcriptomic programs involving vascular remodeling, kinase and TF activity and cytokine networks. Females engage cytoskeletal and vascular remodeling programs and TGFB-RUNX2 signaling, while males emphasize cardiac conduction and metabolic compensatory pathways through ERK2-AMPK-GATA6 signaling, both coupled to suppress immune transcriptional activity. These differences highlight the potential importance of sex-specific precision medicine strategies, integrating molecular and behavioral data to tailor interventions that mitigate cardiovascular risk in diverse populations.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights into sex-specific molecular correlates of CVH in Black adults, several limitations should be acknowledged. First, the FGSEA and enrichment analyses relies on existing gene annotations and pathway databases, which may not fully capture novel or context-specific interactions. Additionally, further validation of these pathways in independent cohorts is necessary to confirm their relevance. Future research should focus on integrating these findings with proteomic and metabolomic data.\u003c/p\u003e \u003cp\u003eAnother limitation is the lack of longitudinal data, which prevents us from assessing how transcriptomic changes overtime correlate with the development or prevention of CVDs in individuals with high or poor-to-intermediate LS7 scores. Since this is a cross-sectional study, all clinical and transcriptomic measurements represent a single point in time, and we lack information about the duration or consistency of the participants\u0026rsquo; lifestyles. Additionally, the lifestyle factors were self-reported and may not accurately reflect participants\u0026rsquo; actual behaviors, potentially introducing reporting bias.\u003c/p\u003e \u003cp\u003eThe absence of FDR-significant regulators likely reflects the conservative multiple-testing correction inherent to enrichment-based upstream analysis rather than a lack of true biological signal. Notably, the predicted activation patterns showed strong internal consistency with DEGs and pathway enrichments, supporting biological relevance of these regulatory networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Biological Relevance\u003c/h2\u003e \u003cp\u003eTogether, these findings indicate that poor-to-intermediate CVH in females is associated with a gene expression profile consistent with stress-driven vascular remodeling, whereas males engage contractile and metabolic compensatory pathways within cardiac tissue. In both sexes, these adaptations are coupled with suppressed immune transcriptional activity. These results suggest that sex differences arise not only from hormonal influences, but also from distinct transcriptional and signaling networks. Overall, poor-to-intermediate CVH is characterized by increased structural repair processes and reduced immunological activity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAbbreviation\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Meaning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAHA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;American Heart Association\u003c/p\u003e\n\u003cp\u003eBCAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Branched-chain amino acids\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Counts-per-million\u003c/p\u003e\n\u003cp\u003eCVD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiovascular health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDGE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differential gene expression\u003c/p\u003e\n\u003cp\u003eFC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fold change\u003c/p\u003e\n\u003cp\u003eFGSEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fast gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLS7\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Life\u0026rsquo;s Simple 7\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMECA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Morehouse-Emory Cardiovascular \u0026nbsp;Center for Health Equity\u003c/p\u003e\n\u003cp\u003eNES\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Normalized Enrichment Scores\u003c/p\u003e\n\u003cp\u003eTF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcription factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTMM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Trimmed mean of m-values\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent. The research work was approved by the Institutional review board of Emory University school of Medicine (IRB00083584) and Morehouse School of Medicine (RB-FY2026-44).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are under submission at dBgap. Other meta-data analyzed during the current study are available from the author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by 1RM1HG012334 (CDS \u0026amp; RM), 15SFCRN23910003 (CDS) , R01NS112422 (RM), and U54HG013595-01 (NIH/NIGMS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization (CDS, RM \u0026amp; HT), Design and interpretation of results (CDS, RM, NHB), Data curation (PB, AAQ, PP), Bioinformatics and statistical analysis (HNB, CD, RV, EG, IKJ), laboratory work (KR), manuscript drafting (HNB), critical revision of manuscript (CDS, RM). All authors reviews and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext generation sequencing services were provided by the Emory NPRC Genomics Core which is supported in part by NIH P51 OD011132. Sequencing data was acquired on an Illumina NovaSeq6000 funded by NIH S10 OD026799.\u0026rdquo; We are grateful for all the study participants and volunteers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLopez EO, Ballard BD, Jan A. Cardiovascular Disease. Nursing (Brux) [Internet]. 2023 Aug 22 [cited 2024 Aug 20]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK535419/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK535419/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol [Internet]. 2020 Dec 12 [cited 2024 Aug 20];76(25):2982. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC7755038/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC7755038/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardiovascular diseases [Internet]. [cited 2024 Aug 20]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMital R, Bayne J, Rodriguez F, Ovbiagele B, Bhatt DL, Albert MA. Race and Ethnicity Considerations in Patients With Coronary Artery Disease and Stroke: JACC Focus Seminar 3/9. J Am Coll Cardiol [Internet]. 2021 Dec 14 [cited 2024 Aug 20];78(24):2483\u0026ndash;92. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jacc.org/doi/\u003c/span\u003e\u003cspan address=\"https://www.jacc.org/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2021.05.051\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2021.05.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMensah GA. Cardiovascular Diseases in African Americans: Fostering Community Partnerships to Stem the Tide. Am J Kidney Dis [Internet]. 2018 Nov 1 [cited 2024 Aug 20];72(5 Suppl 1):S37. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC6200348/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC6200348/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham G. Disparities in Cardiovascular Disease Risk in the United States. Curr Cardiol Rev [Internet]. 2015 May 18 [cited 2024 Aug 20];11(3):238. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC4558355/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC4558355/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunniyi MO, Mahmoud Z, Commodore-Mensah Y, Fleg JL, Fatade YA, Quesada O, et al. Eliminating Disparities in Cardiovascular Disease for Black Women: JACC Review Topic of the Week HHS Public Access. J Am Coll Cardiol. 2022;80(18):1762\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbong I, Breathett K. The Cardiovascular Disease Epidemic in African American Women: Recognizing and Tackling a Persistent Problem.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePost WS, Watson KE, Hansen S, Folsom AR, Szklo M, Shea S et al. Racial/Ethnic Differences in All-Cause and Cardiovascular Disease Mortality: The Multi-Ethnic Study of Atherosclerosis (MESA). Circulation [Internet]. 2022 Jul 19 [cited 2025 Nov 30];146(3):229. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC9937428/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC9937428/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyalwazi AN, Loccoh EC, Brewer LC, Ofili EO, Xu J, Song Y et al. Disparities in Cardiovascular Mortality Between Black and White Adults in the United States, 1999 to 2019. Circulation [Internet]. 2022 Jul 19 [cited 2025 Nov 30];146(3):211. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC9310198/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC9310198/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards AL, Watza D, Findley A, Alazizi A, Wen X, Pai AA et al. Environmental perturbations lead to extensive directional shifts in RNA processing. PLoS Genet [Internet]. 2017 Oct 1 [cited 2025 Nov 9];13(10):e1006995. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC5667937/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC5667937/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFav\u0026eacute; MJ, Lamaze FC, Soave D, Hodgkinson A, Gauvin H, Bruat V et al. Gene-by-environment interactions in urban populations modulate risk phenotypes. Nature Communications 2018 9:1 [Internet]. 2018 Mar 6 [cited 2025 Nov 9];9(1):1\u0026ndash;12. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41467-018-03202-2\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41467-018-03202-2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett TJ, Lee AH, Smilowitz NR, Hausvater A, Fishman GI, Hochman JS et al. Whole-Blood Transcriptome Profiling Identifies Women With Myocardial Infarction With Nonobstructive Coronary Artery Disease. Circ Genom Precis Med [Internet]. 2018 Dec 1 [cited 2025 May 21];11(12):e002387. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/doi/pdf/10.1161/CIRCGEN.118.002387?download\u0026thinsp;=\u0026thinsp;true\u003c/span\u003e\u003cspan address=\"/doi/pdf/10.1161/CIRCGEN.118.002387?download\u0026thinsp;=\u0026thinsp;true\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreini D, Melotti E, Vavassori C, Chiesa M, Piacentini L, Conte E et al. Whole-Blood Transcriptional Profiles Enable Early Prediction of the Presence of Coronary Atherosclerosis and High-Risk Plaque Features at Coronary CT Angiography. Biomedicines [Internet]. 2022 Jun 1 [cited 2025 May 21];10(6):1309. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/2227-9059/10/6/1309/htm\u003c/span\u003e\u003cspan address=\"https://www.mdpi.com/2227-9059/10/6/1309/htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNath M, Romaine SPR, Koekemoer A, Hamby S, Webb TR, Nelson CP et al. Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure. Eur J Heart Fail [Internet]. 2022 Jun 1 [cited 2025 Mar 11];24(6):1009. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC9546237/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC9546237/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingrove JA, Daniels SE, Sehnert AJ, Tingley W, Elashoff MR, Rosenberg S, et al. Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ Cardiovasc Genet. 2008;1(1):31\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW et al. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med [Internet]. 2025 Nov [cited 2025 Nov 9];102449. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/41205594/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/41205594/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBongen E, Lucian H, Khatri A, Fragiadakis GK, Bjornson ZB, Nolan GP et al. Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection. Cell Rep [Internet]. 2019 Nov 12 [cited 2025 Nov 9];29(7):1961. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC6856718/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC6856718/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Usatorre A, Ciarloni L, Angelino P, Wosika V, Conforte AJ, Costa SSF et al. Human blood cell transcriptomics unveils dynamic systemic immune modulation along colorectal cancer progression. J Immunother Cancer [Internet]. 2024 Nov 1 [cited 2025 Jan 30];12(11):e009888. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jitc.bmj.com/content/12/11/e009888\u003c/span\u003e\u003cspan address=\"https://jitc.bmj.com/content/12/11/e009888\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu M, Wang K, Ruppin E, Hannenhalli S. Predicting tissue-specific gene expression from whole blood transcriptome. Sci Adv [Internet]. 2021 Mar 1 [cited 2025 Mar 11];7(14). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.science.org/doi/\u003c/span\u003e\u003cspan address=\"https://www.science.org/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.abd6991\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abd6991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam SJ, Kim JH, Topel M, Liu C, Ko YA, Mujahid MS et al. Cardiovascular Risk and Resilience Among Black Adults: Rationale and Design of the MECA Study. J Am Heart Assoc [Internet]. 2020 [cited 2024 Aug 21];9(9). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/32340530/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/32340530/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: The american heart association\u0026rsquo;s strategic impact goal through 2020 and beyond. Circulation [Internet]. 2010 Feb 2 [cited 2024 Aug 20];121(4):586\u0026ndash;613. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ahajournals.org/doi/\u003c/span\u003e\u003cspan address=\"https://www.ahajournals.org/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.109.192703\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.109.192703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Schmidt AF, Lennon LT, Papacosta O, Whincup PH, Wannamethee G. Association of Life\u0026rsquo;s Simple 7 lifestyle metric with cardiometabolic disease-free life expectancy in older British men. Communications Medicine 2024 4:1 [Internet]. 2024 Jun 4 [cited 2024 Aug 20];4(1):1\u0026ndash;8. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s43856-024-00534-7\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s43856-024-00534-7\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang N, Jiang M, Fan Y. Ideal cardiovascular health metrics and risk of cardiovascular disease or mortality: A meta-analysis. Int J Cardiol [Internet]. 2016 Jul 1 [cited 2025 Mar 24];214:279\u0026ndash;83. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27085116/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27085116/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelixKrueger/TrimGalore. v0.6.7 - DOI via Zenodo. [cited 2024 Nov 18]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/5127899\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/5127899\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics [Internet]. 2013 Jan [cited 2024 Sep 2];29(1):15\u0026ndash;21. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/23104886/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/23104886/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods [Internet]. 2012 Apr [cited 2024 Sep 2];9(4):357. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC3322381/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC3322381/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics [Internet]. 2009 Aug [cited 2024 Nov 18];25(16):2078\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/19505943/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/19505943/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res [Internet]. 2015 Apr 4 [cited 2024 Sep 2];43(7):e47. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC4402510/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC4402510/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics [Internet]. 2010 Jan 1 [cited 2025 Nov 20];26(1):139\u0026ndash;40. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/bioinformatics/btp616\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btp616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res [Internet]. 2015 Apr 4 [cited 2024 Sep 2];43(7):e47. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC4402510/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC4402510/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaynes W. Benjamini\u0026ndash;Hochberg Method. Encyclopedia of Systems Biology [Internet]. 2013 [cited 2024 Sep 2];78\u0026ndash;78. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/referenceworkentry/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/referenceworkentry/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4419-9863-7_1215\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4419-9863-7_1215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViechtbauer W. Conducting Meta-Analyses in R with the metafor Package [Internet]. Vol. 36, JSS Journal of Statistical Software. 2010. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstatsoft.org/\u003c/span\u003e\u003cspan address=\"http://www.jstatsoft.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv [Internet]. 2021 Feb 1 [cited 2024 Sep 2];060012. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biorxiv.org/content/\u003c/span\u003e\u003cspan address=\"https://www.biorxiv.org/content/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/060012v3\u003c/span\u003e\u003cspan address=\"10.1101/060012v3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsd\u0026oacute;ttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics [Internet]. 2011 Jun [cited 2025 Nov 9];27(12):1739. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3106198/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3106198/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A [Internet]. 2005 Oct 25 [cited 2025 Jan 23];102(43):15545\u0026ndash;50. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pnas.org/doi/abs/\u003c/span\u003e\u003cspan address=\"https://www.pnas.org/doi/abs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res [Internet]. 2019 Aug 1 [cited 2025 Aug 13];29(8):1363\u0026ndash;75. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.cshlp.org/content/29/8/1363.full\u003c/span\u003e\u003cspan address=\"http://genome.cshlp.org/content/29/8/1363.full\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadia-I-Mompel P, V\u0026eacute;lez Santiago J, Braunger J, Geiss C, Dimitrov D, M\u0026uuml;ller-Dott S et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances [Internet]. 2022 Jan 10 [cited 2025 Aug 13];2(1). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/bioadv/vbac016\u003c/span\u003e\u003cspan address=\"10.1093/bioadv/vbac016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;ller-Dott S, Tsirvouli E, V\u0026aacute;zquez M, Flores ROR, Badia-i-Mompel P, Fallegger R et al. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. bioRxiv [Internet]. 2023 Apr 1 [cited 2025 Aug 13];2023.03.30.534849. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biorxiv.org/content/\u003c/span\u003e\u003cspan address=\"https://www.biorxiv.org/content/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2023.03.30.534849v1\u003c/span\u003e\u003cspan address=\"10.1101/2023.03.30.534849v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A et al. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics [Internet]. 2009 [cited 2025 May 12];25(8):1091\u0026ndash;3. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/19237447/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/19237447/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res [Internet]. 2003 Nov [cited 2025 May 12];13(11):2498. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC403769/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC403769/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam SJ, Liu C, Mohandas AN, Rooney K, Nayak A, Mehta A et al. Metabolomic signatures of ideal cardiovascular health in black adults. Sci Rep [Internet]. 2024 Dec 1 [cited 2025 Jan 11];14(1). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38245568/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38245568/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKANK2 KN motif. and ankyrin repeat domains 2 [Homo sapiens (human)] - Gene - NCBI [Internet]. [cited 2025 Oct 19]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/25959\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/25959\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHensley MR, Cui Z, Chua RFM, Simpson S, Shammas NL, Yang JY et al. Evolutionary and developmental analysis reveals KANK genes were co-opted for vertebrate vascular development. Sci Rep [Internet]. 2016 Jun 13 [cited 2025 Oct 19];6(1):1\u0026ndash;10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/srep27816\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/srep27816\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGee HY, Zhang F, Ashraf S, Kohl S, Sadowski CE, Vega-Warner V, et al. KANK deficiency leads to podocyte dysfunction and nephrotic syndrome. J Clin Invest. 2015;125(6):2375\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlatell C, Kong SE, McCauley R, Hall JC. Branched-chain amino acids. J Gastroenterol Hepatol [Internet]. 2000 [cited 2025 Dec 9];15(7):706\u0026ndash;17. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/10937674/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/10937674/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Lin R, Li Y, Yao Y, Lu B, Yu Y. Circulating branched-chain amino acids and the risk of major adverse cardiovascular events in the UK biobank. Front Endocrinol (Lausanne). 2025;16:1510910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite PJ, Newgard CB. Branched-chain amino acids and cardiometabolic diseases: biomarker, causal agent, or both? Science [Internet]. 2019 Feb 8 [cited 2025 Dec 9];363(6427):582. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC9940269/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC9940269/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite PJ, McGarrah RW, Herman MA, Bain JR, Shah SH, Newgard CB. Insulin action, type 2 diabetes, and branched-chain amino acids: A two-way street. Mol Metab [Internet]. 2021 Oct 1 [cited 2025 Dec 9];52. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/34044180/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/34044180/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter WG, Kelly JP, Mcgarrah RW, Khouri MG, Craig D, Haynes C et al. Metabolomic Profiling Identifies Novel Circulating Biomarkers of Mitochondrial Dysfunction Differentially Elevated in Heart Failure With Preserved Versus Reduced Ejection Fraction: Evidence for Shared Metabolic Impairments in Clinical Heart Failure. J Am Heart Assoc [Internet]. 2016 Aug 1 [cited 2025 Dec 9];5(8). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27473038/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27473038/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet [Internet]. 2010 Apr [cited 2025 Dec 9];3(2):207\u0026ndash;14. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20173117/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20173117/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharya S, Granger CB, Craig D, Haynes C, Bain J, Stevens RD et al. Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization. Atherosclerosis [Internet]. 2014 [cited 2025 Dec 9];232(1):191\u0026ndash;6. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24401236/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/24401236/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-Guerrero JL, Groothof D, Connelly MA, Otvos JD, Bakker SJL, Dullaart RPF. Concentration of Branched-Chain Amino Acids Is a Strong Risk Marker for Incident Hypertension. Hypertension [Internet]. 2019 Dec 1 [cited 2025 Dec 9];74(6):1428\u0026ndash;35. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/31587574/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/31587574/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePortero V, Nicol T, Podliesna S, Marchal GA, Baartscheer A, Casini S et al. Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc Res [Internet]. 2022 May 1 [cited 2025 Dec 9];118(7):1742\u0026ndash;57. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/34142125/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/34142125/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotts MA, Mizutani S, Garnham AL, Suen CSNLW, Kueh AJ, Tai L et al. Deletion of the transcriptional regulator TFAP4 accelerates c-MYC-driven lymphomagenesis. bioRxiv [Internet]. 2022 Dec 20 [cited 2025 Oct 21];2022.12.19.520971. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biorxiv.org/content/\u003c/span\u003e\u003cspan address=\"https://www.biorxiv.org/content/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2022.12.19.520971v1\u003c/span\u003e\u003cspan address=\"10.1101/2022.12.19.520971v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonc E, Takeuchi Y, Chou C, Xia Y, Holmgren M, Fujii C et al. Unexpected suppression of tumorigenesis by c-MYC via TFAP4-dependent restriction of stemness in B lymphocytes. Blood [Internet]. 2021 Dec 16 [cited 2025 Oct 21];138(24):2526. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC8678995/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8678995/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Zhao X, Wu H. Transcriptional Programming in Arteriosclerotic Disease: A Multifaceted Function of the Runt-related Transcription Factor 2 (Runx2). Arterioscler Thromb Vasc Biol [Internet]. 2020 Jan 1 [cited 2025 Oct 20];41(1):20. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC7770073/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC7770073/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao R, Xie B, Cui J, Qi Z, Xue S, Wang Y. E2F transcription factor 1 (E2F1) promotes the transforming growth factor TGF-β1 induced human cardiac fibroblasts differentiation through promoting the transcription of CCNE2 gene. Bioengineered [Internet]. 2021 [cited 2025 Oct 21];12(1):6869. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC8806588/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8806588/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee BK, Bhinge AA, Iyer VR. Wide-ranging functions of E2F4 in transcriptional activation and repression revealed by genome-wide analysis. Nucleic Acids Res [Internet]. 2011 May [cited 2025 Oct 21];39(9):3558. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3089461/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3089461/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu P, Gao K, Cao Y, Yang S, Wang Y, Guo R et al. RFX1 downregulation contributes to TLR4 overexpression in CD14\u0026thinsp;+\u0026thinsp;monocytes via epigenetic mechanisms in coronary artery disease. Clin Epigenetics [Internet]. 2019 Mar 11 [cited 2025 Oct 21];11(1):44. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC6413463/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC6413463/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteimle V, Durand B, Barras E, Zufferey M, Hadam MR, Mach B, et al. A novel DNA-binding regulatory factor is mutated in primary MHC class II deficiency (bare lymphocyte syndrome). Genes Dev. 1995;9(9):1021\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Xu Y, Qian Y, Li Z, Dong M. Case Report: Novel splicing mutations in RFX5 causing MHC class II deficiency. Front Genet [Internet]. 2022 Oct 7 [cited 2025 Oct 21];13:978688. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://decipher.sanger.ac\u003c/span\u003e\u003cspan address=\"http://decipher.sanger.ac\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrincipe DR, Doll JA, Bauer J, Jung B, Munshi HG, Bartholin L et al. TGF-β: Duality of Function Between Tumor Prevention and Carcinogenesis. JNCI Journal of the National Cancer Institute [Internet]. 2014 Feb [cited 2025 Oct 21];106(2):djt369. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3952197/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3952197/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas A, Padidam M, Cress D, Grady WM. TGF-β receptor levels regulate the specificity of signaling pathway activation and biological effects of TGF-β. Biochim Biophys Acta [Internet]. 2009 Jul [cited 2025 Oct 21];1793(7):1165. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC2700179/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC2700179/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassagu\u0026eacute; J, Sheppard D. TGF-β signaling in health and disease. Cell [Internet]. 2023 Sep 14 [cited 2025 Oct 21];186(19):4007\u0026ndash;37. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S0092867423008516\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S0092867423008516\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuim\u0026egrave;ge L, Libert C, Van Hauwermeiren F. Regulation and dysregulation of tumor necrosis factor receptor-1. Cytokine Growth Factor Rev [Internet]. 2014 Jun 1 [cited 2025 Oct 21];25(3):285\u0026ndash;300. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S1359610114000240\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/abs/pii/S1359610114000240\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDick AD, Forrester JV, Liversidge J, Cope AP. The role of tumour necrosis factor (TNF-α) in experimental autoimmune uveoretinitis (EAU). Prog Retin Eye Res [Internet]. 2004 Nov 1 [cited 2025 Oct 21];23(6):617\u0026ndash;37. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S1350946204000552\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/abs/pii/S1350946204000552\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaleone A, Grano M, Brunetti G. Tumor Necrosis Factor Family Members and Myocardial Ischemia-Reperfusion Injury: State of the Art and Therapeutic Implications. Int J Mol Sci [Internet]. 2023 Mar 1 [cited 2025 Oct 21];24(5):4606. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC10003149/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC10003149/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulman H, Hanson PI. Multifunctional Ca2+/calmodulin-dependent protein kinase. Neurochem Res [Internet]. 1993 Jan [cited 2025 Oct 21];18(1):65\u0026ndash;77. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/8385278/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/8385278/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStabile E, Zhou YF, Saji M, Castagna M, Shou M, Kinnaird TD et al. Akt controls vascular smooth muscle cell proliferation in vitro and in vivo by delaying G1/S exit. Circ Res [Internet]. 2003 Nov 28 [cited 2025 Oct 21];93(11):1059\u0026ndash;65. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/14605018/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/14605018/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyriakis JM, Avruch J, Mammalian MAPK. Signal Transduction Pathways Activated by Stress and Inflammation: A 10-Year Update. \u003cdiv class=\"ExternalRefDOI\"\u003ehttps://doi.org/101152\u003c/div\u003e/physrev000282011 [Internet]. 2012 Apr 1 [cited 2025 Oct 21];92(2):689\u0026ndash;737. Available from: /doi/pdf/10.1152/physrev.00028.2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBritsch S, Goerich DE, Riethmacher D, Peirano RI, Rossner M, Nave KA et al. The transcription factor Sox10 is a key regulator of peripheral glial development. Genes Dev [Internet]. 2001 Jan 1 [cited 2025 Oct 21];15(1):66\u0026ndash;78. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/11156606/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/11156606/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHimeda CL, Ranish JA, Hauschka SD. Quantitative Proteomic Identification of MAZ as a Transcriptional Regulator of Muscle-Specific Genes in Skeletal and Cardiac Myocytes. Mol Cell Biol [Internet]. 2008 Oct 1 [cited 2025 Oct 21];28(20):6521. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC2577440/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC2577440/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaity G, Haque I, Ghosh A, Dhar G, Gupta V, Sarkar S et al. The MAZ transcription factor is a downstream target of the oncoprotein Cyr61/CCN1 and promotes pancreatic cancer cell invasion via CRAF\u0026ndash;ERK signaling. J Biol Chem [Internet]. 2018 Mar 23 [cited 2025 Oct 21];293(12):4334. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC5868262/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC5868262/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Berlo JH, Elrod JW, Van Den Hoogenhof MMG, York AJ, Aronow BJ, Duncan SA et al. The transcription factor GATA-6 regulates pathological cardiac hypertrophy. Circ Res [Internet]. 2010 Oct 15 [cited 2025 Oct 21];107(8):1032\u0026ndash;40. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20705924/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20705924/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLepore JJ, Cappola TP, Mericko PA, Morrisey EE, Parmacek MS. GATA-6 regulates genes promoting synthetic functions in vascular smooth muscle cells. Arterioscler Thromb Vasc Biol [Internet]. 2005 Feb [cited 2025 Oct 21];25(2):309\u0026ndash;14. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/15591218/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/15591218/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin F, Herring BP. GATA-6 can act as a positive or negative regulator of smooth muscle-specific gene expression. Journal of Biological Chemistry [Internet]. 2005 Feb 11 [cited 2025 Oct 21];280(6):4745\u0026ndash;52. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/15550397/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/15550397/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdom DT, Zizlsperger H, Gordon DB, Bell GW, Rinaldi NJ, Murray HL et al. Control of pancreas and liver gene expression by HNF transcription factors. Science [Internet]. 2004 Feb 27 [cited 2025 Oct 21];303(5662):1378\u0026ndash;81. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/14988562/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/14988562/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Jimenez CP, Kyrmizi I, Cardot P, Gonzalez FJ, Talianidis I. Hepatocyte nuclear factor 4alpha coordinates a transcription factor network regulating hepatic fatty acid metabolism. Mol Cell Biol [Internet]. 2010 Feb 1 [cited 2025 Oct 21];30(3):565\u0026ndash;77. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/19933841/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/19933841/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolfini D, Mantovani R. Targeting the Y/CCAAT box in cancer: YB-1 (YBX1) or NF-Y? Cell Death Differ [Internet]. 2013 May [cited 2025 Oct 21];20(5):676. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3619239/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3619239/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv Y, Qi J, Babon JJ, Cao L, Fan G, Lang J et al. The JAK-STAT pathway: from structural biology to cytokine engineering. Signal Transduct Target Ther [Internet]. 2024 Dec 1 [cited 2025 Oct 21];9(1):1\u0026ndash;37. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41392-024-01934-w\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41392-024-01934-w\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanarach C, Netsawang C, Nuchprayoon I, Leecharoenkiat K. Identification and functional analysis of novel SPTB and ANK1 mutations in hereditary spherocytosis patients. Sci Rep [Internet]. 2024 Dec 1 [cited 2025 Oct 22];14(1):1\u0026ndash;10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41598-024-78622-w\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41598-024-78622-w\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Z, Hu L, Song B, Jiang T, Wu Q, Lin J et al. Constitutively active receptor ADGRA3 signaling induces adipose thermogenesis. bioRxiv [Internet]. 2024 Nov 19 [cited 2025 Oct 22];2024.06.08.598066. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biorxiv.org/content/\u003c/span\u003e\u003cspan address=\"https://www.biorxiv.org/content/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2024.06.08.598066v3\u003c/span\u003e\u003cspan address=\"10.1101/2024.06.08.598066v3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSH2D1B protein expression summary -. The Human Protein Atlas [Internet]. [cited 2025 Oct 22]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/ENSG00000198574-SH2D1B\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/ENSG00000198574-SH2D1B\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajagopalan S, Long EO. KIR2DL4 (CD158d): An activation receptor for HLA-G. Front Immunol [Internet]. 2012 [cited 2025 Oct 22];3(AUG):258. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3422731/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3422731/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajagopalan S. Endosomal signaling and a novel pathway defined by the natural killer receptor KIR2DL4 (CD158d). Traffic [Internet]. 2010 Nov [cited 2025 Oct 22];11(11):1381\u0026ndash;90. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20854369/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20854369/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng G, Jia L, Yang AG. Roles of HLA-G/KIR2DL4 in Breast Cancer Immune Microenvironment. Front Immunol [Internet]. 2022 Feb 3 [cited 2025 Oct 22];13:791975. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC8850630/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8850630/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfram JA, Liner A, Richardson SL, Zhu X, Smith MA, Hoit BD et al. The role of E2F1 in the development of hypertrophic cardiomyopathy. Int J Clin Exp Pathol [Internet]. 2011 [cited 2025 Oct 22];4(5):521. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC3127073/\u003c/span\u003e\u003cspan address=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC3127073/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassani B, Baci D, Gallazzi M, Poggi A, Bruno A, Mortara L. Natural Killer Cells as Key Players of Tumor Progression and Angiogenesis: Old and Novel Tools to Divert Their Pro-Tumor Activities into Potent Anti-Tumor Effects. Cancers 2019, Vol 11, Page 461 [Internet]. 2019 Apr 1 [cited 2025 Oct 22];11(4):461. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/\u003c/span\u003e\u003cspan address=\"https://www.mdpi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2072-6694/11/4/461/htm.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Xie X, Liao W, Zhang H, Cao H, Fei R et al. The transcription factor RFX5 is a transcriptional activator of the TPP1 gene in hepatocellular carcinoma. Oncol Rep [Internet]. 2017 Jan 1 [cited 2025 Oct 22];37(1):289\u0026ndash;96. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27840983/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27840983/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePraissman JL, Live DH, Wang S, Ramiah A, Chinoy ZS, Boons GJ et al. B4GAT1 is the priming enzyme for the LARGE-dependent functional glycosylation of α-dystroglycan. Elife. 2014;3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sex Differences, Cardiovascular Health, Transcriptomics, Cytoskeletal, Immune Regulation, Black adults","lastPublishedDoi":"10.21203/rs.3.rs-8391154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8391154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Black Americans face a high burden of cardiovascular disease (CVD), with more than 60% of Black adult women affected. However, sex-specific molecular mechanisms underlying poor cardiovascular health (CVH) in this population remain largely unknown. In this study, we examined sex-specific transcriptomics signatures associated with CVH among Black adult men and women.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e Whole blood RNA-sequencing was performed on 373 Black adults. CVH was assessed using the American Heart Association Life\u0026rsquo;s Simple 7 (LS7) score. Differential gene expression (DGE) analysis comparing participants with poor-to-intermediate CVH (LS7\u0026thinsp;\u0026lt;\u0026thinsp;10) versus ideal CVH (LS7 scores \u0026ge; 10) was conducted using LIMMA. Sex-stratified functional enrichment analysis was conducted using FGSEA and ClueGo. Shared differentially expressed genes (DEGs) were evaluated using fixed-effects meta-analysis. Upstream transcription factor, cytokine, and kinase activities were inferred using DoRothEA and OmniPath to assess sex-specific gene expression regulation at the transcriptional, and post-transcription level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Among females, 430 DEGs were identified and indicated activation of RUNX2, PBX3, TFAP4 and enrichment of actin cytoskeletal pathways, consistent with vascular remodeling. In males with poor-to-intermediate CVH, 344 DEGs were detected and indicated inferred activation of GATA4, MAZ, and SOX10 and enrichment of pathways related to cardiac conduction and cellular metabolism. Thirteen DEGs were shared across sexes, including upregulation of \u003cem\u003eDNAJC6, KANK2, SPTB\u003c/em\u003e, and MSTRG.22508, reflecting conserved stress response programs involving cytoskeletal remodeling and membrane stabilization. Although both sexes with poor-to-intermediate CVH exhibited suppression of adaptive immune effectors, in females the downregulation of \u003cem\u003eKIR2DL4, KLRF1\u003c/em\u003e, and \u003cem\u003eSH2D1B\u003c/em\u003e occurred alongside inhibition of RFX1/5, transcription factors essential for MHC class II expression and antigen presentation. In males, immune suppression was instead associated with inhibition of STAT1, indicating a shift away from cytokine-driven signaling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe identified distinct sex-specific molecular differences underlying CVH in a cohort of Black adults. Females with poor-to-intermediate CVH activate cytoskeletal and vascular remodeling pathways, consistent with structural reshaping. In contrast, males activate cardiac conduction and metabolic signaling programs, reflecting functional and bioenergetic compensation. Although both sexes exhibit immune repression in poor-to-intermediate CVH compared to ideal CVH, the mechanisms diverge, underscoring distinct sex-specific biological trajectories that may contribute to differential CVD risk and therapeutic effectiveness.\u003c/p\u003e","manuscriptTitle":"Sex differences in molecular pathways underlying cardiovascular health in Black Americans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:14:43","doi":"10.21203/rs.3.rs-8391154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6a6e0ee-a7ec-478d-9a55-51d28307a55a","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T11:37:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:14:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8391154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8391154","identity":"rs-8391154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}