Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2026 May 22;105(21):e48842. doi: 10.1097/MD.0000000000048842

Inflammatory factors and hypertensive nephropathy: A bidirectional 2-sample Mendelian randomization study

Yu Yang a, Jianfeng Xue b, Lei Wang c, Lei Tao d, Lu Tian e,*
PMCID: PMC13201012  PMID: 42175402

Abstract

Hypertensive nephropathy (HTN) is a major public health concern worldwide. This study aims to investigate the potential causal relationships between inflammatory factors and HTN, as well as to identify possible underlying biological pathways. Using genome-wide association study data from large publicly available population cohorts, we applied a bidirectional 2-sample Mendelian randomization approach to investigate whether 91 inflammatory factors influence the risk of HTN. Additionally, we examined whether HTN has a potential causal effect on the levels of these inflammatory factors. Finally, we performed pathway enrichment analysis to explore the underlying biological pathways involved in these associations. Mendelian randomization results indicated that interleukin-20 receptor subunit alpha, caspase 8, CUB domain-containing protein 1, and stem cell factor were associated with a reduced HTN, while interleukin-18 receptor 1, leukemia inhibitory factor, oncostatin-M, and interferon-gamma were associated with an increased HTN. Pathway enrichment analysis further identified several biological pathways potentially involved in the development of HTN. This study provides evidence for causal relationships between multiple inflammatory factors and HTN, offering valuable insights that may inform disease management strategies and policy development related to HTN.

Keywords: hypertensive nephropathy, inflammation, Mendelian randomization

1. Introduction

Hypertensive nephropathy (HTN) refers to kidney damage caused by chronic hypertension, characterized by renal tissue fibrosis and thickening.[1] Patients may present with signs and symptoms of chronic kidney disease, including anorexia, nausea, vomiting, pruritus, lethargy or confusion, weight loss, and a foul taste in the mouth.[1] Since 2000, the incidence of hypertensive kidney disease has increased by 8.7%, and this figure is likely an underestimation.[2] It has been reported that HTN accounts for approximately 20% of patients undergoing dialysis for end-stage renal disease, posing a substantial public health burden.[3] Therefore, identifying risk factors related to HTN and implementing early control measures are critical.

An increasing body of evidence suggests that a heightened inflammatory response plays an indispensable role in the development and progression of hypertension and is closely associated with hypertension-related complications, such as renal injury and cardiovascular disease.[4] In patients with hypertension, chronically elevated levels of inflammatory factors may silently contribute to increased blood pressure by affecting vascular function, neural regulation, and even the gut microbiota, ultimately leading to kidney damage.[5–14] For example, inflammatory cytokines such as TNF-α and IL-6 can activate the NF-κB signaling pathway, promoting the expression of adhesion molecules on vascular endothelial cells and inducing the recruitment of inflammatory cells. This process exacerbates vascular inflammation and endothelial dysfunction, ultimately leading to elevated blood pressure and glomerular injury.[15–18] In addition, IL-1β and the NLRP3 inflammasome contribute to the epithelial-to-mesenchymal transition of renal tubular epithelial cells, resulting in renal interstitial fibrosis and vascular remodeling, which further aggravates structural kidney damage.[19–22] Moreover, inflammatory factors can activate the renin-angiotensin system; in particular, TNF-α may enhance vascular contractility and sympathetic activity by downregulating nitric oxide production or activating soluble epoxide hydrolase (sEH)–related pathways, thereby promoting sustained elevation of blood pressure.[23–25] IFN-γ regulates hypertension at the neurohormonal level by promoting intrarenal angiotensin II (Ang II) production, enhancing sodium transport, and activating tubular sodium reabsorption, ultimately contributing to renal injury.[26,27] Based on these findings, we hypothesize that there may be a causal relationship between inflammation and HTN.[28]

Mendelian randomization (MR) is a genetic epidemiological approach that uses specific genetic variants as proxies for exposures of interest, enabling causal inference at the genetic level while substantially reducing confounding bias.[29,30] In this study, we employed a 2-sample MR approach to investigate the potential causal relationships between 91 inflammatory factors and HTN, aiming to provide genetic evidence for the role of inflammation in the development and progression of HTN.

2. Materials and methods

2.1. Data source

The genome-wide association study (GWAS) data for inflammatory cytokines were obtained from the GWAS Catalog (https://www.ebi.ac.uk/), with study identifiers ranging from GCST90274758 to GCST90274848. This study was based on a total of 14,824 individuals of European ancestry from 11 cohorts and performed a genome-wide association analysis of 91 circulating inflammation-related plasma proteins using the Olink Target Inflammation panel.[28] Summary-level GWAS data for HTN were obtained from the R12 release of the FinnGen database (https://r12.finngen.fi/), which included 1367 cases of HTN and 345,634 controls. All participants were of European ancestry and provided informed consent.[31]

2.2. Selection of instrumental variables

To ensure that the selection of instrumental variables (IVs) satisfied the 3 core assumptions of MR: the genetic variation must exhibit a direct association with the exposure of interest; it should not be linked with potential confounders that might influence both the exposure and outcome; and the genetic variation impacts the outcome exclusively through its effect on the exposure. we implemented a series of quality control steps to identify the most suitable IVs. First, due to the limited number of IVs available under the conventional significance threshold (P < 5 × 10−8), in order to comprehensively investigate the potential causal relationship between inflammatory factors and hypertensive nephropathy and obtain a sufficient number of single nucleotide polymorphisms (SNPs) significantly associated with inflammatory factors, we set the significance threshold at P < 1 × 10−5.[32] Second, to minimize potential false positives due to linkage disequilibrium, we conducted linkage disequilibrium clumping using a threshold of r2 < 0.001 and a clumping window of 10,000 kb.[33] In addition, palindromic SNPs and weak instruments (defined as those with an F-statistic < 10) were excluded. The F-statistic was calculated using the formula: F = [R2 × (N − k − 1)]/ [k × (1–R2)], where N is the sample size of the exposure GWAS, k is the number of IVs, and R2 represents the proportion of variance in the exposure explained by each SNP.[34]

2.3. Mendelian randomization analysis

After a rigorous selection process for IVs, we conducted bidirectional 2-sample MR analyses, using 91 inflammatory cytokines as exposures and HTN as the outcome. For exposures with 2 or more associated SNPs, we applied a comprehensive set of MR methods – including MR-Egger regression, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode – to ensure the robustness of the causal inferences.[35] Among these, the IVW method was designated as the primary approach for estimating causal effects. For exposures with fewer than 2 SNPs, the Wald ratio method was employed. A P-value of less than .05 was considered statistically significant.[36]

Additionally, to ensure the stability and reliability of the findings, we conducted a series of sensitivity analyses. Cochran Q statistic was used to assess heterogeneity among the IVs. A statistically significant result (P < .05) indicated the presence of heterogeneity, suggesting that the effects of the SNPs were not consistent. In such cases, a random-effects model was employed for the analysis.[30,37]

Horizontal pleiotropy was assessed using the intercept term from the MR-Egger regression, with a P-value < .05 indicating the presence of directional pleiotropy.[38] Subsequently, leave-one-out sensitivity analysis was performed by iteratively removing each SNP to evaluate whether any single instrumental variable had a disproportionate influence on the overall MR estimate, thereby identifying potential outlier effects.[39] For inflammatory cytokines showing significant causal associations, we conducted reverse MR to rule out the possibility of reverse causation. In the reverse MR analysis, the SNP selection criteria and causal effect estimation methods were kept consistent with those used in the forward analysis. All analyses were conducted using the “TwoSampleMR” R package (version 0.6.8).

2.4. Function annotation

For inflammatory cytokines showing significant results, the g:Profiler platform (https://biit.cs.ut.ee/gprofiler/gost) was used to map SNPs to their corresponding genes, facilitating the interpretation of the relationships between genetic variants and their target genes.[40] Functional annotation and pathway enrichment analyses were then performed on the identified genes using the “clusterProfiler” R package to explore their potential biological functions and underlying mechanisms.[41]

2.5. Ethics statement

The data from this study are all publicly available, so no additional ethical approval is required.

3. Result

3.1. The causal effect of inflammatory factors on hypertensive nephropathy

After a series of rigorous instrumental variable selection procedures, 91 SNPs related to inflammatory factors (Supplementary Table 1) were identified and used as exposures in a 2-sample MR analysis, with HTN as the outcome. The IVW method revealed causal associations between 9 inflammatory factors and the risk of HTN (Figs. 1 and 2; Supplementary Table 2). Specifically, higher levels of tumor necrosis factor (P = .002, OR = 0.711, 95% CI: 0.574–0.882), interleukin-20 receptor subunit alpha (P = .047, OR = 0.775, 95% CI: 0.603–0.996), caspase 8 (P = .042, OR = 0.777, 95% CI: 0.609–0.991), CUB domain-containing protein 1 (P = .021, OR = 0.822, 95% CI: 0.696–0.971), and stem cell factor (P = .028, OR = 0.834, 95% CI: 0.709–0.980) were significantly associated with a reduced risk of HTN. In contrast, elevated levels of interleukin-18 (IL-18) receptor 1 (P = .028, OR = 1.074, 95% CI: 1.008–1.144), leukemia inhibitory factor (P = .042, OR = 1.300, 95% CI: 1.010–1.675), oncostatin-M (P = .002, OR = 1.449, 95% CI: 1.142–1.838), and interferon-gamma (IFN-γ) (P = .004, OR = 1.480, 95% CI: 1.135–1.929) were significantly associated with an increased risk of HTN.

Figure 1.

Figure 1.

Open in a new tab

Forest diagram of significant causal association of inflammatory factors on hypertensive nephropathy. CI = confidence interval, MR analysis = Mendelian Randomization analysis, OR = odds ratio, SNP = single nucleotide polymorphism.

Figure 2.

Figure 2.

Open in a new tab

This scatter plot illustrates the genetically predicted association of 9 significant inflammatory cytokines (labeled A–I on the subpanels) for hypertensive nephropathy. IVW = inverse-variance weighting, MR analysis = Mendelian randomization analysis.

Sensitivity analyses (Supplementary Tables 3 and 4) revealed no evidence of heterogeneity among the 9 inflammatory factors (P > .05). Leave-one-out analyses indicated that no single SNP disproportionately influenced the causal estimates, with all point estimates remaining on the same side of the null (Supplementary Figs. S1–9). Except for tumor necrosis factor, no evidence of horizontal pleiotropy was detected for the remaining factors (P >.05). Therefore, tumor necrosis factor was excluded from subsequent analyses.

3.2. The causal effect of hypertensive nephropathy on significant inflammatory factors

To further investigate the directionality of the observed associations, we conducted a reverse MR analysis, using HTN as the exposure and the previously identified significant and robust inflammatory factors as outcomes. The results showed no evidence of causal effects of HTN on any of the 8 significant inflammatory factors (Fig. 3; Supplementary Table 5).

Figure 3.

Figure 3.

Open in a new tab

Forest diagram of inverse causal association of hypertensive nephropathy with 8 significant inflammatory factors. CI = confidence interval, MR analysis = Mendelian Randomization analysis, OR = odds ratio, SNP = single nucleotide polymorphism.

3.3. Biological function

We annotated the SNPs of the 8 significant inflammatory factors that passed stringent quality control, with the detailed results presented in Supplementary Tables 6–13. The genes identified from SNP annotation were subsequently subjected to enrichment analyses to explore the potential mechanisms by which these inflammatory factors influence HTN. Both gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant enrichment of the 8 inflammatory factors in various biological functions and pathways. gene ontology enrichment analysis showed that interleukin-20 receptor subunit alpha levels were significantly enriched across multiple functional categories (Fig. 4A), including calcium-dependent cell-cell adhesion mediated by plasma membrane cell adhesion molecules, the catenin complex, and cadherin binding. KEGG analysis indicated that its biological pathways were primarily involved in taste transduction and axon guidance (Fig. 4B). For caspase 8 levels, biological functions were mainly enriched in arginine catabolic processes, the CCR4–NOT complex, and scaffold protein binding (Fig. 4C), while its biological pathways were predominantly enriched in Salmonella infection (Fig. 4D). CUB domain-containing protein 1 levels showed enrichment in nuclear-transcribed mRNA poly(A) tail shortening dependent deadenylation, primary cilium tip, and immune receptor activity (Fig. 4E), with corresponding pathways mainly enriched in nuclear-transcribed mRNA poly(A) tail shortening dependent deadenylation (Fig. 4F). The biological functions associated with stem cell factor levels were mainly enriched in negative regulation of cholesterol storage, Golgi apparatus pool, and hydrolase activity acting on carbon-nitrogen (but not peptide) bonds in cyclic amides (Fig. 4G). KEGG pathways were predominantly enriched in mucin-type O-glycan biosynthesis (Fig. 4H). For IL-18 receptor 1 levels, enriched biological functions included pulmonary valve morphogenesis, myofibril membrane, and cofilin binding (Fig. 4I), with pathways enriched in the Rap1 signaling pathway (Fig. 4J). Leukemia inhibitory factor levels were mainly enriched in immune response–regulating cell surface receptor signaling pathways involved in phagocytosis, SNARE complex, and phosphate ion transmembrane transporter activity (Fig. 4K), while pathways were enriched in Fc γ R-mediated phagocytosis (Fig. 4L). The biological functions of oncostatin-M levels were enriched in regulation of developmental growth, plasma membrane cytoplasmic side, and G protein-coupled receptor binding (Fig. 4M), with pathways enriched in amyotrophic lateral sclerosis (Fig. 4N). Finally, IFN-γ levels showed enrichment in regeneration, Golgi apparatus, and transferrin receptor binding (Fig. 4O), with pathways enriched in nicotinate and nicotinamide metabolism (Fig. 4P). Collectively, the functional and pathway enrichment analyses of these 8 inflammatory factors provide deeper insights into the potential mechanisms by which they influence the risk of HTN.

Figure 4.

Figure 4.

Open in a new tab

The results of gene enrichment analysis of 8 significant inflammatory factors were displayed. The left column shows the GO enrichment analysis results (A–P). The right column shows the results of the KEGG enrichment analysis. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.

4. Discussion

Through MR analysis, this study identified that elevated levels of interleukin-20 receptor subunit alpha, caspase 8, CUB domain-containing protein 1, and stem cell factor are associated with a reduced risk of HTN. In contrast, higher levels of IL-18 receptor 1, leukemia inhibitory factor, Oncostatin-M, and IFN-γ are associated with an increased risk of HTN.[28,42] Reverse MR analysis did not reveal genetic evidence supporting a causal effect of HTN on the levels of these 9 inflammatory factors. Additionally, pathway enrichment analysis uncovered several potential biological pathways involved in these associations.[43]

This study has found that IL-18 receptor 1, leukemia inhibitory factor, Oncostatin-M, and IFN-γ are risk factors for HTN. IFN-γ, the sole member of the type II interferon family, is primarily secreted by Th1 cells. It plays a critical role not only in adaptive immunity but also in host defense against infections. Studies have shown that in hypertensive mice, IFN-γ levels produced by CD4+ and CD8+ T cells remain persistently elevated.[15] Although some evidence suggests that subcutaneous administration of IFN-γ can lower blood pressure, reduce proteinuria, and alleviate glomerular injury in Dahl salt-sensitive rats, the same intervention failed to reduce blood pressure in mice with primary hypertension.[44] This inconsistency suggests that the role of endogenous IFN-γ in hypertension may have been underestimated in previous research. Moreover, deletion of the IFN-γ receptor significantly attenuates ventricular hypertrophy and electrical remodeling.[26] Even under high-dose angiotensin II infusion, mice lacking IFN-γ receptor 1 exhibit milder renal fibrosis and maintain normal glomerular filtration rates.[26] One of the key mechanisms by which IFN-γ contributes to hypertension is through upregulation of angiotensinogen expression in renal proximal tubular cells via the JAK2/STAT3 signaling pathway, leading to increased blood volume.[45] Collectively, these findings suggest that IFN-γ–producing T cells may promote hypertension and renal injury by regulating angiotensinogen production and interacting with the renin-angiotensin-aldosterone system. IL-18 was initially thought to be primarilyreleased by monocytes and macrophages; however, studies have demonstrated that in the 1K/DOCA/salt model, renal proximal tubular epithelial cells are the main source of IL-18. Notably, deficiency of IL-18 in non–bone marrow–derived cells alleviates hypertension and renal injury, whereas its deficiency in bone marrow–derived cells does not confer protection.[46–56] Although IL-18 mRNA levels are not significantly elevated, the expression of its receptor IL-18R1, IL-18 receptor accessory protein (IL-18RAP), and the binding protein IL-18BP is markedly upregulated, suggesting that IL-18 activation may be regulated at the translational or posttranslational level.[57,58] Activation of the inflammasome and caspase-1 is considered a rate-limiting step in IL-18 maturation, and this process may be triggered by early albuminuria.[59–62] Additionally, IL-18 enhances renal inflammation by stimulating T cells in the kidney to produce interferon-gamma (IFN-γ).[63–68] Other studies have shown that IL-18R1 can interact with the Na-Cl co-transporter (NCC), suggesting a potential mechanism by which IL-18 may promote hypertension through regulation of sodium retention.[69,70] However, regrettably, our study demonstrated a protective effect of TNF against hypertensive nephropathy, which contradicts both traditional research findings and established biological understanding. This discrepancy may stem from survival bias or residual pleiotropy, and we therefore exclude it from discussion.[71]

Compared to traditional observational studies, MR analysis offers distinct advantages in elucidating the relationship between inflammatory cytokines and hypertensive nephropathy (HTN). First, this study leveraged large-scale GWAS data and employed rigorous sensitivity analyses, including corrections for sample overlap, to enhance result robustness. Second, restricting the analysis to individuals of European ancestry minimized the potential for population stratification bias.[72] Finally, the application of MR methodology helps mitigate the confounding effects of reverse causality and unmeasured variables, thereby providing clearer insights into the potential causal pathways linking inflammatory cytokines to the development of HTN. By integrating MR findings with biological functional annotations and observational data, this research not only validates core results but also deepens our mechanistic understanding of the implicated biological processes. This integrated approach holds significant potential for advancing kidney-protective strategies in HTN prevention and management, refines our comprehension of the role of inflammatory cytokines in HTN pathogenesis, and paves the way for novel nephrology-focused clinical applications.

However, this study is not without limitations. For instance, the selected IVs explain only a fraction of the variance in the exposure phenotype and may not perfectly represent the exposure. Second, despite employing multiple methods for robust causal inference, residual confounding cannot be entirely ruled out. Third, the IVs reflect the effects of lifelong exposure levels, making the assessment of short-term exposure effects challenging. Finally, since all data used in this study were derived from European populations, further research is needed to determine whether the findings apply to other ethnic groups.[34,72,73]

Despite these constraints, this study provides the first MR evidence supporting a causal relationship between inflammatory cytokines and hypertensive nephropathy (HTN). We acknowledge these limitations and will address them while continuing to investigate and advance research on the association between inflammatory cytokines and HTN.

5. Conclusion

In conclusion, this study employed MR analysis based on GWAS data from large population cohorts to elucidate the causal relationships between inflammatory factors and HTN. Our findings provide theoretical evidence that may inform future strategies for the treatment and management of HTN.

Acknowledgments

The authors would like to thank the researchers who provided the aggregated statistics of GWAS used in this study.

Author contributions

Conceptualization: Yu Yang, Jianfeng Xue, Lei Tao.

Data curation: Yu Yang, Jianfeng Xue, Lei Wang, Lei Tao.

Formal analysis: Yu Yang, Jianfeng Xue, Lei Wang, Lei Tao.

Funding acquisition: Yu Yang, Jianfeng Xue.

Investigation: Yu Yang, Jianfeng Xue, Lei Wang, Lei Tao.

Methodology: Yu Yang, Jianfeng Xue, Lei Wang, Lei Tao.

Project administration: Yu Yang, Jianfeng Xue, Lei Wang.

Resources: Lu Tian.

Software: Yu Yang, Jianfeng Xue.

Supervision: Yu Yang, Jianfeng Xue, Lei Tao.

Validation: Yu Yang, Jianfeng Xue.

Visualization: Yu Yang, Jianfeng Xue.

Writing – original draft: Yu Yang, Jianfeng Xue, Lei Wang, Lei Tao.

Writing – review & editing: Lu Tian.

medi-105-e48842-s001.xlsx (2.3MB, xlsx)
medi-105-e48842-s002.xlsx (78.6KB, xlsx)
medi-105-e48842-s003.xlsx (22.4KB, xlsx)
medi-105-e48842-s004.pdf (7.4KB, pdf)
medi-105-e48842-s005.xlsx (16.2KB, xlsx)
medi-105-e48842-s006.xlsx (10.8KB, xlsx)
medi-105-e48842-s007.xlsx (16.8KB, xlsx)

Abbreviations:

CI
confidence interval
GO
gene ontology
GWAS
genome-wide association study
HTN
hypertensive nephropathy
IFN-γ
interferon-gamma
IL-18
levels of interleukin-18
IVs
instrumental variables
IVW
inverse-variance weighted
KEGG
Kyoto Encyclopedia of Genes
LD
linkage disequilibrium
MR
Mendelian randomization
NCC
Na-Cl co-transporter
OR
odds ratio
SNP
single nucleotide polymorphisms
TECs
tubular epithelial cells

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000048842).

How to cite this article: Yang Y, Xue J, Wang L, Tao L, Tian L. Inflammatory factors and hypertensive nephropathy: A bidirectional 2-sample Mendelian randomization study. Medicine 2026;105:21(e48842).

References

  • [1].Toto RB. Hypertensive nephrosclerosis in African Americans. Kidney Int. 2003;64:2331–41. [DOI] [PubMed] [Google Scholar]
  • [2].Yarnell J, Dermot OR. Epidemiology and Disease Prevention: A Global Approach. Oxford University Press; 2013. [Google Scholar]
  • [3].Fervenza FC. Nephrosclerosis. 2021. Available at: https://emedicine.medscape.com/article/244342-overview#a6. Accessed June 9, 2021.
  • [4].Barrows IR, Ramezani A, Raj DS. Inflammation, immunity, and oxidative stress in hypertension-partners in crime? Adv Chronic Kidney Dis. 2019;26:122–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Lu X, Crowley SD. Inflammation in salt-sensitive hypertension and renal damage. Curr Hypertens Rep. 2018;20:103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Caillon A, Schiffrin EL. Role of inflammation and immunity in hypertension: recent epidemiological, laboratory, and clinical evidence. Curr Hypertens Rep. 2016;18:21. [DOI] [PubMed] [Google Scholar]
  • [7].Muller DN, Shagdarsuren E, Park J-K, et al. Immunosuppressive treatment protects against angiotensin II-induced renal damage. Am J Pathol. 2002;161:1679–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [8].Rodríguez-Iturbe B, Pons H, Quiroz Y, et al. Mycophenolate mofetil prevents salt-sensitive hypertension resulting from angiotensin II exposure. Kidney Int. 2001;59:2222–32. [DOI] [PubMed] [Google Scholar]
  • [9].Ko EA, Amiri F, Pandey NR, et al. Resistance artery remodeling in deoxycorticosterone acetate-salt hypertension is dependent on vascular inflammation: evidence from m-CSF-deficient mice. Am J Physiol Heart Circ Physiol. 2007;292:H1789–95. [DOI] [PubMed] [Google Scholar]
  • [10].Kossmann S, Hu H, Steven S, et al. Inflammatory monocytes determine endothelial nitric-oxide synthase uncoupling and nitro-oxidative stress induced by angiotensin II. J Biol Chem. 2014;289:27540–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Wiig H, Schröder A, Neuhofer W, et al. Immune cells control skin lymphatic electrolyte homeostasis and blood pressure. J Clin Invest. 2013;123:2803–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Xiao L, Kirabo A, Wu J, et al. Renal denervation prevents immune cell activation and renal inflammation in angiotensin II-Induced hypertension. Circ Res. 2015;117:547–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Norlander AE, Madhur MS, Harrison DG. The immunology of hypertension. J Exp Med. 2018;215:21–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Zaldivia MT, Rivera J, Hering D, et al. Renal denervation reduces monocyte activation and monocyte-platelet aggregate formation: an anti-inflammatory effect relevant for cardiovascular risk. Hypertension. 2017;69:323–31. [DOI] [PubMed] [Google Scholar]
  • [15].McMaster WG, Kirabo A, Madhur MS, Harrison DG. Inflammation, immunity, and hypertensive end-organ damage. Circ Res. 2015;116:1022–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Wenzel UO, Ehmke H, Bode M. Immune mechanisms in arterial hypertension. Recent advances. Cell Tissue Res. 2021;385:393–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Hirohama D, Fujita T. Evaluation of the pathophysiological mechanisms of salt-sensitive hypertension. Hypertens Res. 2019;42:1848–57. [DOI] [PubMed] [Google Scholar]
  • [18].Yan Y, Zhou XE, Xu HE, Melcher K. Structure and physiological regulation of AMPK. Int J Mol Sci. 2018;19:e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Duewell P, Kono H, Rayner KJ, et al. NLRP3 inflammasomes are required for atherogenesis and activated by cholesterol crystals. Nature. 2010;464:1357–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Folco EJ, Sukhova GK, Quillard T, Libby P. Moderate hypoxia potentiates interleukin-1β production in activated human macrophages. Circ Res. 2014;115:875–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Rajamäki K, Lappalainen J, Oörni K, et al. Cholesterol crystals activate the NLRP3 inflammasome in human macrophages: a novel link between cholesterol metabolism and inflammation. PLoS One. 2010;5:e11765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Chen D, Xiong X-Q, Zang Y-H, et al. BCL6 attenuates renal inflammation via negative regulation of NLRP3 transcription. Cell Death Dis. 2017;8:e3156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Mattson DL. Immune mechanisms of salt-sensitive hypertension and renal end-organ damage. Nat Rev Nephrol. 2019;15:290–300. [DOI] [PubMed] [Google Scholar]
  • [24].Venegas-Pont M, Manigrasso MB, Grifoni SC, et al. Tumor necrosis factor-alpha antagonist etanercept decreases blood pressure and protects the kidney in a mouse model of systemic lupus erythematosus. Hypertension. 2010;56:643–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Elmarakby AA, Quigley JE, Imig JD, Pollock JS, Pollock DM. TNF-alpha inhibition reduces renal injury in DOCA-salt hypertensive rats. Am J Physiol Regul Integr Comp Physiol. 2008;294:R76–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Markó L, Kvakan H, Park J-K, et al. Interferon-γ signaling inhibition ameliorates angiotensin II-induced cardiac damage. Hypertension. 2012;60:1430–6. [DOI] [PubMed] [Google Scholar]
  • [27].Trott DW, Thabet SR, Kirabo A, et al. Oligoclonal CD8+ T cells play a critical role in the development of hypertension. Hypertension. 2014;64:1108–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Zhao JH, Stacey D, Eriksson N, et al. ; Estonian Biobank Research Team. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol. 2023;24:1540–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Sanderson E, Glymour MM, Holmes MV, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2:6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Kurki MI, Karjalainen J, Palta P, et al. ; FinnGen. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Xie S, Zhang RF, Tang YR, Dai QQ. Exploring causal correlations between inflammatory cytokines and Ménière’s disease: a Mendelian randomization. Front Immunol. 2024;15:1373723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Ye Y, Wang C-E, Zhong R, Xiong X-M. Associations of the circulating levels of cytokines with risk of ankylosing spondylitis: a Mendelian randomization study. Front Immunol. 2023;14:1291206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Burgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64. [DOI] [PubMed] [Google Scholar]
  • [35].Wei D, Jiang Y, Cheng J, Wang H, Sha K, Zhao J. Assessing the association of leukocyte telomere length with ankylosing spondylitis and rheumatoid arthritis: a bidirectional Mendelian randomization study. Front Immunol. 2023;14:1023991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Long J, Dang H, Su W, Moneruzzaman M, Zhang H. Interactions between circulating inflammatory factors and autism spectrum disorder: a bidirectional Mendelian randomization study in European population. Front Immunol. 2024;15:1370276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019;48:713–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Chen S, Zhou G, Han H, Jin J, Li Z. Causal effects of specific gut microbiota on bone mineral density: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne). 2023;14:1178831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Kolberg L, Raudvere U, Kuzmin I, Adler P, Vilo J, Peterson H. g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 2023;51:W207–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2:100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Zheng J, Haberland V, Baird D, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Lin S, Tian X, Kong S, et al. Causal effects of inflammatory cytokines and immune cell phenotypes on spontaneous abortion: evidence from mendelian randomization. Int J Womens Health. 2025;17:793–806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Ishimitsu T, Uehara Y, Numabe A, et al. Interferon gamma attenuates hypertensive renal injury in salt-sensitive Dahl rats. Hypertension. 1992;19:804–8. [DOI] [PubMed] [Google Scholar]
  • [45].Satou R, Miyata K, Gonzalez-Villalobos RA, Ingelfinger JR, Navar LG, Kobori H. Interferon-γ biphasically regulates angiotensinogen expression via a JAK-STAT pathway and suppressor of cytokine signaling 1 (SOCS1) in renal proximal tubular cells. FASEB J. 2012;26:1821–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Tanino A, Okura T, Nagao T, et al. Interleukin-18 deficiency protects against renal interstitial fibrosis in aldosterone/salt-treated mice. Clin Sci (Lond). 2016;130:1727–39. [DOI] [PubMed] [Google Scholar]
  • [47].Stokman G, Kers J, Yapici U, et al. Predominant tubular interleukin-18 expression in polyomavirus-associated nephropathy. Transplantation. 2016;100:e88–95. [DOI] [PubMed] [Google Scholar]
  • [48].Liang D, Liu H-F, Yao C-W, et al. Effects of interleukin 18 on injury and activation of human proximal tubular epithelial cells. Nephrology (Carlton). 2007;12:53–61. [DOI] [PubMed] [Google Scholar]
  • [49].Faust J, Menke J, Kriegsmann J, et al. Correlation of renal tubular epithelial cell-derived interleukin-18 up-regulation with disease activity in MRL-Faslpr mice with autoimmune lupus nephritis. Arthritis Rheum. 2002;46:3083–95. [DOI] [PubMed] [Google Scholar]
  • [50].Franke EI, Vanderbrink BA, Hile KL, et al. Renal IL-18 production is macrophage independent during obstructive injury. PLoS One. 2012;7:e47417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [51].Cameron LA, Taha RA, Tsicopoulos A, et al. Airway epithelium expresses interleukin-18. Eur Respir J. 1999;14:553–9. [DOI] [PubMed] [Google Scholar]
  • [52].Muñoz M, Eidenschenk C, Ota N, et al. Interleukin-22 induces interleukin-18 expression from epithelial cells during intestinal infection. Immunity. 2015;42:321–31. [DOI] [PubMed] [Google Scholar]
  • [53].Sugawara S, Uehara A, Nochi T, et al. Neutrophil proteinase 3-mediated induction of bioactive IL-18 secretion by human oral epithelial cells. J Immunol. 2001;167:6568–75. [DOI] [PubMed] [Google Scholar]
  • [54].Bellora F, Castriconi R, Doni A, et al. M-CSF induces the expression of a membrane-bound form of IL-18 in a subset of human monocytes differentiating in vitro toward macrophages. Eur J Immunol. 2012;42:1618–26. [DOI] [PubMed] [Google Scholar]
  • [55].Wyburn K, Wu H, Chen G, Yin J, Eris J, Chadban S. Interleukin-18 affects local cytokine expression but does not impact on the development of kidney allograft rejection. Am J Transplant. 2006;6:2612–21. [DOI] [PubMed] [Google Scholar]
  • [56].Pirhonen J, Sareneva T, Kurimoto M, Julkunen I, Matikainen S. Virus infection activates IL-1 beta and IL-18 production in human macrophages by a caspase-1-dependent pathway. J Immunol. 1999;162:7322–9. [PubMed] [Google Scholar]
  • [57].Puren AJ, Fantuzzi G, Dinarello CA. Gene expression, synthesis, and secretion of interleukin 18 and interleukin 1beta are differentially regulated in human blood mononuclear cells and mouse spleen cells. Proc Natl Acad Sci U S A. 1999;96:2256–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [58].Pizarro TT, Michie MH, Bentz M, et al. IL-18, a novel immunoregulatory cytokine, is up-regulated in Crohn’s disease: expression and localization in intestinal mucosal cells. J Immunol. 1999;162:6829–35. [PubMed] [Google Scholar]
  • [59].Zhuang Y, Ding G, Zhao M, et al. NLRP3 inflammasome mediates albumin-induced renal tubular injury through impaired mitochondrial function. J Biol Chem. 2014;289:25101–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [60].Zhuang Y, Hu C, Ding G, et al. Albumin impairs renal tubular tight junctions via targeting the NLRP3 inflammasome. Am J Physiol Renal Physiol. 2015;308:F1012–9. [DOI] [PubMed] [Google Scholar]
  • [61].Fang L, Xie D, Wu X, Cao H, Su W, Yang J. Involvement of endoplasmic reticulum stress in albuminuria induced inflammasome activation in renal proximal tubular cells. PLoS One. 2013;8:e72344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [62].Krishnan SM, Dowling JK, Ling YH, et al. Inflammasome activity is essential for one kidney/deoxycorticosterone acetate/salt-induced hypertension in mice. Br J Pharmacol. 2016;173:752–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [63].Sun XN, Li C, Liu Y, et al. T-cell mineralocorticoid receptor controls blood pressure by regulating interferon-gamma. Circ Res. 2017;120:1584–97. [DOI] [PubMed] [Google Scholar]
  • [64].Saleh MA, McMaster WG, Wu J, et al. Lymphocyte adaptor protein LNK deficiency exacerbates hypertension and end-organ inflammation. J Clin Invest. 2015;125:1189–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [65].Huan T, Meng Q, Saleh MA, et al. ; International Consortium for Blood Pressure GWAS (ICBP). Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol Syst Biol. 2015;11:799. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [66].Levy D, Ehret GB, Rice K, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [67].Kirabo A, Fontana V, de Faria APC, et al. DC isoketal-modified proteins activate T cells and promote hypertension. J Clin Invest. 2014;124:4642–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [68].Krishnan SM, Ling YH, Huuskes BM, et al. Pharmacological inhibition of the NLRP3 inflammasome reduces blood pressure, renal damage, and dysfunction in salt-sensitive hypertension. Cardiovasc Res. 2019;115:776–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [69].Liu CL, Ren J, Wang Y, et al. Adipocytes promote interleukin-18 binding to its receptors during abdominal aortic aneurysm formation in mice. Eur Heart J. 2020;41:2456–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [70].Wang J, Sun C, Gerdes N, et al. Interleukin 18 function in atherosclerosis is mediated by the interleukin 18 receptor and the Na-Cl co-transporter. Nat Med. 2015;21:820–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [71].Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [72].Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [73].Davies J, Read J, Hengartner MP, et al. Clinical guidelines on antidepressant withdrawal urgently need updating. BMJ. 2019;365:l2238. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-105-e48842-s001.xlsx (2.3MB, xlsx)
medi-105-e48842-s002.xlsx (78.6KB, xlsx)
medi-105-e48842-s003.xlsx (22.4KB, xlsx)
medi-105-e48842-s004.pdf (7.4KB, pdf)
medi-105-e48842-s005.xlsx (16.2KB, xlsx)
medi-105-e48842-s006.xlsx (10.8KB, xlsx)
medi-105-e48842-s007.xlsx (16.8KB, xlsx)

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES