The Mediating Role of IDL Particles in the Relationship between Primary Hypertension and Cardiovascular Diseases: Insights from Mendelian Randomization and Multi-Omics Analysis

doi:10.21203/rs.3.rs-4588496/v1

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The Mediating Role of IDL Particles in the Relationship between Primary Hypertension and Cardiovascular Diseases: Insights from Mendelian Randomization and Multi-Omics Analysis

https://doi.org/10.21203/rs.3.rs-4588496/v1

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Background

Primary hypertension is a major risk factor for various cardiovascular diseases. Although low-density lipoprotein and high-density lipoprotein are known to influence cardiovascular diseases, the role of intermediate-density lipoprotein remains unclear.

Objective

This study investigated the mediating role of intermediate-density lipoprotein particle concentration in primary hypertension in four major cardiovascular diseases (coronary heart disease, peripheral atherosclerosis, stroke, and deep vein thrombosis) using multiomics data and Mendelian randomization methods.

Methods

This study was conducted in three phases: confirming the relationship between hypertension and cardiovascular outcomes via two-step Mendelian randomization, identifying common risk genes through colocalization analysis, and analyzing key genes in lipid metabolism pathways via differential expression and GSEA, with further validation via PheWas analysis.

Results

Primary hypertension significantly increased the risk of four cardiovascular diseases. Intermediate-density lipoprotein particle concentration mediates the risk of hypertension on coronary heart disease and peripheral atherosclerosis, reducing the risk by 22% and 13%, respectively. Colocalization analysis identified 119 common risk genes, with CYP21A2 and HSD17B8 involved in lipid metabolism pathways. GSEA highlighted the importance of the glycerophospholipid metabolism pathway.

Conclusion

This study revealed the mediating role of intermediate-density lipoprotein particle concentration in hypertension and cardiovascular diseases, emphasizing the critical role of lipid metabolism pathways. These findings offer new perspectives and potential therapeutic targets for hypertension-related cardiovascular diseases.

Primary hypertension

Cardiovascular diseases

Intermediate-density lipoprotein

Primary hypertension is defined as elevated blood pressure of unknown cause and is a prevalent risk factor for cardiovascular diseases (CVDs) ^1,2. CVDs encompass a spectrum of complex diseases affecting cardiac function, vascular structure, and the circulatory system. CVDs account for approximately one-third of global deaths and are a leading cause of mortality worldwide, with hypertension being implicated in various CVDs, such as coronary artery disease, emphasizing the importance of CVD management³. Moreover, hypertension is a risk factor for stroke and venous thromboembolism in patients with atherosclerosis^4,5.

Dyslipidemia accompanying primary hypertension includes elevated plasma triglycerides (TG), decreased high-density lipoprotein (HDL), and increased levels of low-density lipoprotein (LDL) particles⁶. Lipoprotein particles, such as very low-density lipoprotein (VLDL) and intermediate-density lipoprotein (IDL), which are elevated in hypertension⁷, play a significant role in atherosclerosis, contributing to various cardiovascular diseases, such as stroke and peripheral arterial disease⁶. In the lipid metabolism pathway, the transition of VLDL, which is composed of cholesterol and TG to LDL and subsequently to HDL, plays a crucial role in preventing atherosclerosis⁸, highlighting the importance of IDL as an intermediate metabolite. Furthermore, earlier studies have shown that participants with higher concentrations of TG in the IDL are less likely to have an increased risk of stroke⁹.

Although safe and effective antihypertensive medications have been developed and strategies such as PCSK9 inhibitors, in combination with high-dose statins, have significantly reduced cardiovascular events and all-cause mortality in clinical ASCVD patients⁴, there are differences in the efficacy of different drugs in reducing target organ damage and preventing major cardiovascular events, with many hypertensive patients still inadequately controlling their hypertension and accompanying risk factors1. It has been reported that changes in other lipoprotein cholesterol levels, such as IDL, may predict the progression of cardiovascular diseases such as atherosclerosis more effectively than changes in LDL¹⁰, suggesting a potential protective role of lowering IDL levels against CVD⁹. The mechanistic role of IDL in hypertensive patients with concurrent cardiovascular diseases is poorly understood and warrants further investigation.

With advancements in sequencing technologies and bioinformatics tools, bulk RNA-Seq has been widely employed across various fields, including healthcare¹¹, microbial diversity¹², and evolutionary biology¹³. This transcriptomic analysis provides insights into the functional characteristics of coding and noncoding regions, aiding in understanding various cellular states or disease phenotypes¹⁴. Genome-wide association studies (GWASs) have revealed strong statistical associations between common genetic variants at thousands of loci and more than 250 human traits¹⁵. By providing data and resources on expression quantitative trait loci (eQTLs) across diverse tissues relevant to many diseases, the GTEx project helps interpret GWAS findings for translational research, shedding light on complex genetic variations and gene regulatory patterns across different human tissue types¹⁶. However, omics experiments themselves are subject to biases and overinterpretation, necessitating cross-validation across different technical platforms. Thus, multiomics analyses, which integrate diverse biological data to provide comprehensive biological insights, hold significant advantages in the field of cardiovascular diseases¹⁷.

Our study not only contributes to identifying novel therapeutic targets for stroke, coronary artery disease, deep vein thrombosis, and peripheral arterial atherosclerosis but also provides a theoretical basis for understanding the impact of primary hypertension on these four diseases. By employing various omics techniques, we bridged primary hypertension with four cardiovascular diseases (stroke, coronary artery disease, deep vein thrombosis, and peripheral arterial atherosclerosis) through IDL and investigated the potential mechanistic effects of IDL in hypertensive patients with concurrent cardiovascular diseases such as stroke and coronary artery disease. The findings of this study have the potential to be applied in clinical prediction, prevention, and the development of personalized treatment strategies, thereby positively impacting public health.

Study design

The data used in this analysis were obtained from publicly available databases; thus, ethical approval and informed consent were not needed. Our study comprised three phases: 1. Initially, suitable intermediate variables (IDL concentration) were selected through bidirectional Mendelian randomization, and the overall impact of hypertension on cardiovascular outcomes and the mediating effect of IDL concentration were determined using univariable MR and multivariable Mendelian randomization (MVMR) (as shown in Fig. 1). 2. Subsequently, colocalization analysis was conducted to validate the shared risk locus target genes between primary hypertension and CVD patients. 3. Differential expression analysis and gene set enrichment analysis (GSEA) were employed to identify risk genes associated with hypertension and CVDs, along with their expression patterns in lipid metabolism pathways. Further validation of the impact of these genes on CVDs was conducted through PheWas analysis.

Data source

Data on the exposure variable primary hypertension (Hyp) and the mediator variable IDL concentration were obtained from the IEU GWAS database. Data on two cardiovascular outcome variables, deep vein thrombosis (DVT) and coronary heart disease (CHD), were also obtained from the IEU GWAS database, while data on stroke and peripheral artery disease (PA) were obtained from the FINNGEN database. All variables except the IDL concentration are binary variables. RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Specifically, for primary hypertension, we utilized the GSE234085 dataset¹⁸, comprising peripheral blood lymphocyte expression data from 6 healthy controls and 6 patients with primary hypertension. For coronary artery disease (CAD), we utilized two datasets: GSE202625¹⁹, consisting of whole blood expression data from 25 healthy controls and 27 CAD patients, and GSE242046²⁰, comprising feces and blood expression data from 20 healthy controls and 30 CAD patients. For peripheral arterial disease (PA), we utilized the GSE100927 dataset²¹, which includes carotid artery expression data from 12 healthy controls and 29 PA patients. For stroke, we utilized the GSE16561 dataset^22–24, comprising whole blood expression data from 24 healthy controls and 39 ischemic stroke patients. During data preprocessing, to conform to the data usage standards of the limma package, FPKM and count data, except for those from GSE16561 (Stroke) and GSE100927 (PA), were subjected to log2 transformation. For detailed information on the data, please refer to Table 1.

Table 1

The GWAS Data Used in the MR Analyses
Category	Trait	Year	GwasID	Number of case	Number of control	Sample size	nSNP	Population	Consortium
Exposure	I10 Essential (primary) hypertension(EHYP)	2018	ukb-b-12493	54,358	408,652	463,010	9,851,867	European	MRC-IEU
Mediator	Concentration of IDL particles	2020	met-d-IDL_P	NA	NA	115,078	12,321,875	European	NA
Outcome	Coronary heart disease (CHD)	2011	ieu-a-6	15,420	15,062	30,482	540,233	Mixed	C4D
	Peripheral atherosclerosis(PA)	2021	finn-b-DM_PERIPHATHERO	6,631	162,201	168,832	16,380,247	European	FINN
	Stroke, including SAH(STK)	2021	finn-b-I9_STR_SAH	12,632	200,315	212,947	16,380,445	European	FINN
	Deep venous thrombosis(DVT)	2017	ukb-a-65	6,767	330,392	337,159	10,894,596	European	Neale Lab

Selection of mediating variables

Initially, primary hypertension and four CVDs were selected as the exposure and outcome variables, respectively. The selection criteria for intermediate variables using univariable MR were as follows: 1. A significant outcome was primary hypertension. 2. There was no bidirectional MR association with primary hypertension²⁵. 3. A significant exposure to at least two cardiovascular disease outcomes. Ultimately, the IDL particle concentration was confirmed to be an intermediate variable. Subsequently, two-step Mendelian randomization (two-step MR) was employed to evaluate the mediating role of IDL particle concentration in the association between primary hypertension and the four cardiovascular outcomes.

Colocalization analysis

To identify risk genes shared between hypertension patients and CVD patients, colocalization analysis was performed using the R package coloc²⁶. Colocalization identifies GWAS loci that may affect gene phenotypes by matching GWAS data with eQTL data from multiple tissues. We analyzed eQTL data from Genotype-Tissue Expression (GTEx) for whole blood, atrial appendage, left ventricle, and tibial artery, corresponding to GWAS data for target traits. Colocalization analysis provided posterior probabilities for five outcomes: H₀: no association with either trait; H₁: associated with trait 1, not trait 2; H₂: associated with trait 2, not trait 1; H₃: associated with both traits (two independent SNPs); H₄: associated with both traits, with one shared SNP. Following convention, sites with PP.H₃ + PP.H₄ > 0.8²⁷ were considered to have shared variations between GWAS loci and tissues.

Differential expression analysis

Analysis Differential expression analysis comparing healthy controls with disease groups was conducted using the R package limma²⁸. Genes with a |fold change (FC)| > 1.25 and a P value < 0.05 were considered significant. All genes were ranked based on the differential expression-derived logFC. Subsequently, the clusterProfiler package²⁹ in R was used for gene set enrichment analysis (GSEA), with visualization of enrichment analysis results achieved using the GseaVis package¹². Enriched genes with a P value < 0.05 were considered significant.

Lipid metabolism pathway analysis

The lipid metabolism pathways were selected from the KEGG database (https://www.genome.jp/kegg/pathway.html#lipid) and accessed using the R package KEGGREST³⁰ to retrieve 506 genes from 16 lipid metabolism pathways. The differentially expressed genes (DEGs) from the transcriptomic analysis were compared with the genes involved in lipid metabolism pathways (LIGs) and matched with the corresponding pathways. Subsequently, enriched results of lipid metabolism pathways identified from GSEA were selected, and the ggplot2 package³¹ was used for visualization.

PheWAS analysis

We conducted a comprehensive phenome-wide association study (PheWas) aimed at determining the correlation between known drug targets and phenotypic data to investigate their potential pleiotropy and side effects, thereby providing better guidance for future drug development. The phenotypic genome-wide association study (GWAS) data we used originated from version 1.3.15 of the PheWeb website, specifically the UK Biobank TOPMed dataset (https://pheweb.org/UKB-TOPMed/), which encompasses 1419 phenotypes. The specific construction methods and detailed information can be found in the original study³². To further validate the results obtained from two-step Mendelian randomization (MR), we included 10 common differentially expressed genes (DEGs) in lipid metabolism pathways associated with primary hypertension and CVD for PheWas analysis. We performed PheWas analysis using six methods: inverse variance weighted, MR‒Egger, simple mode, Wald ratio, weighted median, and weighted mode, with a significance threshold set at a P value < 0.05.

Statistical analysis

The default settings of the R package TwoSampleMR³³ were used to identify significant SNPs as instrumental variables, with SNPs having a P value less than 5×10^− 8 extracted. An R² threshold of 0.001 was set to exclude SNPs in linkage disequilibrium within a 10 Mb range, and SNPs were adjusted based on the results of the sensitivity analysis. The inverse variance weighting (IVW) method was used to calculate the Wald ratio of each SNP's effect on the outcome. In the analysis, β₁ and SE₁, representing the effect of exposure on mediation, were obtained through univariable MR, while β₂ and SE₂, representing the effect of mediation on outcome after adjusting for exposure, were obtained through MVMR. The β and SE of the estimated mediating effects were calculated using the coefficient product method and the delta method³⁴. For univariable MR, Cochran’s Q test, leave-one-out analysis, and the MR‒Egger intercept test were used to determine the presence of pleiotropy and heterogeneity and the reliability of the results, while the F value obtained through the MVMR package³⁵ was used to estimate instrument strength, with F > 10 as the screening criterion.

Mendelian randomization

When selecting the mediator variables, we excluded those with bidirectional associations with Hyp and filtered out 51 variables associated with CVD outcomes. Ultimately, based on the integrated MR analysis results and literature verification, we selected the IDL concentration as the mediator variable. The MR results indicate that patients with primary hypertension exhibit decreased IDL particle concentrations. (See Supplement Table 1 for details.)

Hyp was considered as the exposure, IDL concentration as the mediator, and four CVDs (CHD, PA, stroke, DVT) as outcomes in the two-step inverse variance-weighted (IVW) MR analysis, as shown in Fig. 2.

Hyp increased the risk of developing four CVDs, CHD (OR range: 48.31, 95% CI 10.66–218.9), PA (OR range: 103.3, 95% CI 20.59–518.7), stroke (OR range: 32.52, 95% CI 13.98–75.54), and DVT (OR range: 1.03, 95% CI 1.01–1.05).

Moreover, when the IDL concentration served as a mediator, it could reduce the risk of CHD and PA associated with Hyp. The mediation ratio for CHD was 22%, with an OR of 0.43 (95% CI 0.34–0.54), and that for PA was 13%, with an OR of 0.54 (95% CI 0.41–0.71). Further investigation is needed to understand the mediating effect of IDL concentration on Hyp and stroke as well as DVT. The mediation ratio for stroke was 4%, with an OR of 0.88 (95% CI 0.78-1.000), and that for DVT was 13%, with an OR of 0.996 (95% CI 0.991–1.002).

As shown in Supplement Table 2–3, all univariable MR analyses were not affected by pleiotropy (MR‒Egger, all P values > 0.05), and no heterogeneity was detected among IVs for most associations (Q p value > 0.05). Additionally, the results of leave-one-out analysis indicated that these associations were not driven by a single IV (see Fig. S1). For multivariable MR, the instrument strength of the instrumental variables was well validated (28.9 < F < 74.82).

The Involvement of Lipid Metabolism Pathways in the Regulation of Hypertension and Various Cardiovascular Diseases

DEGs related to Hyp, CHD, PA, and stroke were screened based on thresholds of P value < 0.05 and |FC| > 1.25 (see Fig. S2). Furthermore, to explore the expression of these genes in lipid metabolism pathways, we intersected DEGs between Hyp and cardiovascular outcomes with genes in lipid metabolism pathways (LiGs), resulting in 228 genes (white numbers in Fig. 4A), which were subsequently displayed in corresponding lipid metabolism pathways (Fig. 4B). Among them, 5 genes in the Hyp group were involved in steroid hormone biosynthesis, 7 genes in the CHD group were involved in glycerophospholipid metabolism, 21 genes in the PA group were involved in glycerolipid metabolism, and 6 genes in the stroke group were involved in sphingolipid metabolism, indicating the relevance between these traits and lipid metabolism. To further elucidate the specific roles and mechanisms of these genes in corresponding traits, we used gene set enrichment analysis (GSEA) to calculate enrichment scores for Hyp and CVDs in these pathways (ALL P value < 0.05). The results showed the involvement of Hyp and CVDs in lipid metabolism-related processes, especially in the glycerophospholipid metabolism pathway (Hyp, CHD, PA), explaining the rationality of the IDL concentration as a mediating variable.

Colocalization analysis revealed 119 common risk genes between Hyp and CVDs. The CYP21A2 and HSD17B8 genes were identified to be involved in lipid metabolism pathways, suggesting shared causal variants in these gene regions between hypertension and cardiovascular outcomes. Both CYP21A2 and HSD17B8 were colocalized at Hyp, PA, CHD, and tissue eQTL sites (PPH3 + PPH4 > 0.8)³⁶, indicating their potential involvement in the lipid metabolism-mediated pathway for hypertension and CVDs. (For full results, see Supplement Table 4.)

PheWAS analysis

To further validate the association between Hyp and CVDs, as well as the mediating role of IDL concentration in this process, we conducted PheWas analysis on common DEGs in lipid metabolism pathways between Hyp and other CVDs (bold italicized white numbers in Fig. 4A). PheWas analysis confirmed the influence of 10 genes (AKR1C1, COMT, CPT1B, ELOVL6, ELOVL7, GLB1,LYPLA2, MGLL, PAFAH2, PLD2) on 19 lipid metabolism and cardiovascular disease traits (Fig. 5). Our research included variables from previous two-step MR studies, such as Hyp, and other findings included features directly related to Hyp, such as coronary artery disease, peripheral vascular disease, chronic ischemic heart disease, dyslipidemia, hyperlipidemia, other CVDs (10 traits) and lipid metabolism disorders (3 traits).

Our study employed a comprehensive approach to analyze the causal relationship between hypertension and cardiovascular outcomes while also revealing the potential modulatory effect of intermediate-density lipoprotein (IDL) particle concentration. Through Mendelian randomization, we demonstrated that hypertension significantly increases the risk of four major CVDs, coronary heart disease (CHD), peripheral artery disease (PA), stroke, and deep vein thrombosis (DVT). Furthermore, we found that hypertension could reduce the risk of adverse cardiovascular outcomes by decreasing the IDL concentration, particularly by reducing the risk of CHD and PA. The ranking of mediation percentages from high to low was as follows: CHD (22%), peripheral arterial atherosclerosis (13%), DVT (13%), and stroke (4%). This finding has important implications for understanding the role of IDL particles in hypertension and CVDs.

The modulation of IDL concentration as an intermediate factor between hypertension and cardiovascular outcomes is intriguing. The levels of lipids, which are carriers of lipids in the blood, affect lipid metabolism pathways. The colocalization analysis results indicated that hypertension and CVD were associated with genes related to lipid metabolism pathways, suggesting the possible involvement of lipid metabolism pathways in the relationship between hypertension and CVD. Through differential gene expression and lipid metabolism pathway analysis, we further discovered a close connection between genes that are differentially expressed between hypertension and CVDs, especially those involved in the glycerophospholipid pathway. Triglycerides are the main components of IDL particles and have been considered risk factors for CVDs in some studies. Genes closely associated with CVDs and the glycerophospholipid pathway, such as LYPLA2, MGLL, and PLD2, are likely directly related to the association between CVDs and IDL particles, which is crucial for enhancing our molecular understanding of their mechanisms. These results further emphasize the importance of lipid metabolism pathways in the relationship between hypertension and CVD. Additionally, the results of the PheWas analysis further validated the effects of several genes in the lipid metabolism pathway on hypertension and CVDs. This series of findings not only deepens our understanding of the complex relationship between hypertension and CVDs but also provides important clues for further research on the role of lipid metabolism in the pathogenesis of these diseases.

LYPLA2 is closely associated with lysophospholipids³⁷, and MGLL is an important fatty acid metabolism enzyme that converts monoglycerides into free fatty acids (FFAs) and glycerol. Previous studies have shown that MGLL is associated with tumor signaling pathways³⁸. PLD2 plays a key role in cell membrane lipid remodeling³⁹, and earlier studies have confirmed its association with cardiac metabolic diseases⁴⁰. In our study, we detected slightly increased expression of LYPLA2, MGLL, and PLD2 in hypertension and PA samples. Considering that MR analysis revealed the potential mediating role of IDL particles in the relationship between hypertension and cardiovascular outcomes, the relationships between LYPLA2, MGLL, PLD2, and hypertension and CVDs deserve further attention. We speculate that the upregulation of LYPLA2, MGLL, and PLD2 in hypertension and PA may be associated with the onset and progression of CVDs, especially those related to lipid metabolism.

Hypertension is a major risk factor for CVDs^41,42, while dyslipidemia has also been shown to increase the risk of CVDs such as atherosclerosis and stroke⁷. Previous studies may have used single methods or focused only on certain aspects, such as using epidemiological surveys or genetic association studies to explore the relationship between hypertension or dyslipidemia and CVDs without integrating the associations between them. This may limit the credibility and interpretability of the results. Björnson et al. demonstrated the association between lipoprotein remnants and CHD¹², and our study further narrowed the focus to the IDL concentration, proving the significant association between IDL particles and CVDs. Chen et al. reported associations between adipokines and hypertension and CVDs, but due to the cross-sectional nature of their study, specific causal relationships could not be determined⁴³. Wang et al. identified the predictive value of hypertension for CVDs⁴⁴, focusing on epidemiological surveys without molecular biology methods to validate their results. In contrast, our study employed a comprehensive approach using multiomics analysis methods, emphasizing the role of the IDL concentration as a mediator and providing a more comprehensive and in-depth understanding of the relationship between hypertension and CVDs and their potential underlying mechanisms, with greater credibility and persuasiveness.

The main advantage of our study is the use of two-step MR, which minimizes the influence of confounding factors. The use of bidirectional MR when including intermediate variables excludes variables associated with hypertension bidirectionally, ensuring that the intermediate variables used are influenced by hypertension in one direction only. To our knowledge, we are also the only study to date to analyze the association between IDL concentration/lipid metabolism, an intermediate variable, and hypertension and other CVDs. The results of the MR analysis were further validated via colocalization analysis. In addition, we used GEO data to further molecularly study the CVDs investigated, revealing the intermediate relationship network between IDL concentration and hypertension and other CVDs. However, our study has several limitations. First, due to the lack of DVT-related expression data in the GEO database, we were unable to further investigate the biological mechanisms underlying DVT. Additionally, due to the lack of data related to CHD, we used CAD instead of CHD for differential gene expression analysis. Second, the samples we used mainly came from European populations, which may limit the applicability of the results of this study to other populations. Finally, to identify as many associations as possible during the differential expression analysis, we set the FC threshold to 1.25, which may have led to the presence of false positives.

MR Mendelian randomization

MVMR Multivariable Mendelian randomization

GSEA Gene Set Enrichment Analysis

Hyp Primary hypertension

CVDs Cardiovascular diseases

LDL Low-density lipoprotein

HDL High-density lipoprotein

IDL Intermediate-density lipoprotein

DVT Deep vein thrombosis

CHD Coronary heart disease

PA Peripheral artery disease

IVW Inverse Variance Weighting

GEO Gene Expression Omnibus

FC FoldChange

DEGs Differentially expressed genes

LiG lipid metabolism pathway genes

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

Publicly available datasets (GSE234085, GSE16561, GSE100927, GSE202625, and GSE242046) were obtained from the GEO (http://www.ncbi.nlm.nih.gov/geo). The Eqtl data are available through the GTEx V8 (https://www.gtexportal.org/home/) database. The GWAS data are available through the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/) and FINNGEN (https://www.finngen.fi/en). Lipid metabolism pathways were selected from the KEGG database (https://www.genome.jp/kegg/pathway.html#lipid).

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable

Authors' contributions

Y.T.Z. and Y.M.L. proposed the concept and study design. H.Y.W. performed the initial data download and data cleaning. H.Y.W. and Y.T.Z. conducted the data analysis and interpreted the results. Y.M.L. and H.L. completed the manuscript revisions. All authors have read and approved the final manuscript.

Acknowledgments

We acknowledge the GEO, FINNGEN, GTEx, KEGG and IEU Open GWAS project databases for providing their platforms as well as their contributors for uploading meaningful datasets.

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The Mediating Role of IDL Particles in the Relationship between Primary Hypertension and Cardiovascular Diseases: Insights from Mendelian Randomization and Multi-Omics Analysis

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Abstract

Background

Objective

Methods

Results

Conclusion

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Background

Materials and methods

Study design

Data source

Selection of mediating variables

Colocalization analysis

Differential expression analysis

Lipid metabolism pathway analysis

PheWAS analysis

Statistical analysis

Results

Mendelian randomization

The Involvement of Lipid Metabolism Pathways in the Regulation of Hypertension and Various Cardiovascular Diseases

PheWAS analysis

Discussion

Abbreviations

Declarations

References

Supplementary Tables and Figures

Additional Declarations

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