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. 2025 Sep 1;31:e27. doi: 10.5646/ch.2025.31.e27

Causal associations between inflammatory cytokines and hypertensive disorders

Xiaosong Li 1,†, Zhaoting Gong 1,†, Yuejin Yang 2,✉, Haiyan Qian 1,✉
PMCID: PMC12411069  PMID: 40918221

Abstract

Background

Several inflammatory cytokines (ICs) have been implicated in the development of hypertensive disorders. This study aimed to establish a causal relationship between 91 ICs and hypertensive disorders using Mendelian randomization (MR).

Methods

Single nucleotide polymorphisms associated with 91 ICs, hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were obtained from publicly available genome-wide association studies. MR analyses were conducted using inverse variance weighting as the primary method, complemented by MR-Egger and weighted median approaches. Significant ICs were further analyzed through Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analyses.

Results

A total of 18 ICs exhibited significant associations with at least 1 hypertensive disorder, with 8, 7, 7, and 5 ICs associated with hypertension, SBP, DBP, and MAP, respectively. Among these, fibroblast growth factor 5 (FGF5) was uniquely associated with all 4 hypertensive conditions. Additionally, FGF5 was identified as a central hub in the PPI network. KEGG pathway analysis highlighted the involvement of the mitogen-activated protein kinase (MAPK) signaling pathway.

Conclusions

This study underscores the pivotal role of FGF5 and MAPK signaling pathway in the pathogenesis of hypertensive disorders. Targeting inflammatory pathways may offer therapeutic strategies for hypertension management.

Keywords: Inflammatory cytokines, Hypertension, Mendelian randomization, FGF5, MAPK signaling pathway

BACKGROUND

Hypertension is a major contributor to the global burden of disease, affecting approximately 35–40% of the adult population worldwide [1,2]. As a leading risk factor for morbidity and premature mortality, hypertension surpasses many occupational, environmental, and lifestyle risk factors in its contribution to adverse health outcomes. It significantly increases the risk of stroke, myocardial infarction, heart failure, chronic kidney disease, and vascular dementia [3]. With the global population aging, hypertension has become even more prevalent, affecting nearly 70% of adults aged 70 years and older [1,2]. Moreover, the prevalence of hypertension among adolescents is rising, raising concerns about early cardiovascular damage. Prolonged exposure to elevated systolic blood pressure (SBP) and diastolic blood pressure (DBP) during adolescence has been linked to cardiac injury [4]. Given these concerns, a deeper understanding of hypertension pathogenesis is crucial.

Hypertension arises from a multifaceted interplay of inflammation, immune dysregulation, and oxidative stress, contributing to endothelial dysfunction, vascular hyperresponsiveness, vascular injury, arterial remodeling, impaired renal function, sympathetic nervous system activation, and immune cell activation [5,6,7,8]. Growing evidence suggests a strong link between inflammatory cytokines (ICs) and hypertension. Plasma levels of C-reactive protein (CRP) and cytokines such as interleukin (IL)-6 and tumor necrosis factor (TNF)-α are elevated in patients with essential hypertension [9,10,11]. Moreover, increased circulating levels of IL-6 and CRP have been associated with a higher likelihood of developing hypertension [12]. Additional studies have reported elevated levels of IL-1β, IL-17, IL-18, and CC motif chemokine 2 (CCL2) in hypertensive individuals [13,14,15,16]. However, confounding factors and reverse causation inherent in observational studies make it challenging to establish a causal link between specific ICs and hypertension [17]. For example, elevated blood pressure may itself promote systemic inflammation, complicating causal inference [18]. Confounding factors in traditional observational studies, such as age, obesity, smoking, and socioeconomic status, are often linked to both inflammation and blood pressure regulation [19,20].

Mendelian randomization (MR) approach utilizes genetic variants as instrumental variables (IVs), which are randomly assigned at conception and are less likely to be confounded by environmental factors or affected by reverse causation. This allows for a more robust estimation of the causal impact of ICs on blood pressure outcomes [21]. In this study, we employed MR to investigate the causal relationship between 91 ICs and hypertensive disorders, including hypertension, SBP, DBP, and mean arterial pressure (MAP), using genetic data from publicly available genome-wide association studies (GWAS).

METHODS

This MR analysis was conducted following the STROBE-MR checklist [22]. MR analysis requires three core assumptions to be met: relevance, independence, and exclusion restriction. Specifically, the selected genetic variants must be associated with the exposure (relevance), not associated with any confounders in the exposure-outcome relationship (independence), and must influence the outcome solely through the exposure of interest and not via alternative pathways (exclusion restriction, Fig. 1).

Fig. 1. Schematic overview of the study design.

Fig. 1

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SNP, single nucleotide polymorphisms; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; ICBP, International Consortium for Blood Pressure; IV, instrumental variable; IVW, inverse variance weighting; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction.

IV selection

Initially, single nucleotide polymorphisms (SNPs) were selected based on a genome-wide significance threshold of P < 5 × 10−8. However, due to the limited number of SNPs meeting this criterion, a more lenient threshold of P < 1 × 10−5 was adopted. To minimize linkage disequilibrium, SNPs were clumped using a window size of 10,000 kb and an r2 threshold of 0.001. Palindromic SNPs were removed to ensure consistent alignment of alleles across exposure and outcome datasets. The instrument strength was assessed using the F-statistic, with SNPs having F > 10 retained to mitigate weak instrument bias.

Data source

GWAS data for 91 ICs were obtained from the Olink Target Inflammation panel, which included 14,824 participants of European ancestry across 11 cohorts [23]. GWAS data for hypertension were derived from the UK Biobank, consisting of 129,909 cases and 354,689 controls [24]. Participants were considered hypertensive if they met any of the following criteria: 1) SBP ≥ 140 mmHg or DBP ≥ 90 mmHg measured at baseline; 2) self-reported physician diagnosis of hypertension; or 3) current use of antihypertensive medications [25]. GWAS data for SBP and DBP were sourced from the International Consortium for Blood Pressure (ICBP), comprising 757,601 participants. GWAS data for MAP were obtained from the UK Biobank and FinnGen, encompassing 360,863 participants [26]. MAP is a continuous hemodynamic parameter that reflects the average arterial pressure during a single cardiac cycle and is calculated as: MAP = DBP + 1/3(SBP − DBP). It serves as a surrogate marker of perfusion pressure to vital organs but is not itself used as a diagnostic criterion for hypertension. The characteristics of included GWAS can be found in Supplementary Table 1. Since all included GWAS summary statistics were previously published, written informed consent was obtained during the original studies by the respective Institutional Review Boards, and no additional ethical approval or informed consent was required for this analysis.

Statistical analysis

To infer causal relationships, the inverse variance weighting (IVW), MR-Egger, and weighted median (WM) methods were employed. The IVW method, which assumes all genetic variants are valid IVs, was designated as the primary analysis method. It can provide an unbiased causal estimate in the absence of horizontal pleiotropy [17]. The WM method offers robustness to invalid instruments, yielding reliable estimates if at least 50% of the weight originates from valid instruments [27]. The MR-Egger method detects and accounts for horizontal pleiotropy and provides a valid causal estimate when pleiotropy is present [28]. The MR-Egger regression intercept was examined to assess directional pleiotropy, with a non-zero intercept indicating its presence [28]. Cochran’s Q test was performed to evaluate the heterogeneity among the IVs. ICs identified as having a significant causal effect (P < 0.05) were subjected to further analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. A schematic overview of the study design is presented in Fig. 1. All MR analyses were conducted in R (version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria) using the “TwoSampleMR” package.

RESULTS

Influence of 91 ICs on hypertension

The primary results of the associations between 91 ICs and hypertension are illustrated in Fig. 2A. According to the IVW method in Fig. 3A, genetically elevated levels of CD5, fibroblast growth factor (FGF) 5, FGF21, and leukemia inhibitory factor receptor (LIFR; per standard deviation increase) were suggestively associated with higher odds of hypertension. Conversely, higher genetically determined levels of FGF19, IL18R1, IL1A, and TNF-β were associated with lower odds of hypertension. Significant heterogeneity was observed for CD5, FGF19, FGF21, FGF5, and LIFR, while pleiotropy was detected for FGF5 and LIFR. These findings were consistent with those obtained from the WM and MR-Egger methods (Fig. 4A).

Fig. 2. Annular heat map of 91 inflammatory cytokines on hypertensive disorders. (A) Hypertension, (B) SBP, (C) DBP, and (D) MAP.

Fig. 2

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SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure.

Fig. 3. Forest plot of significant inflammatory cytokines on hypertensive disorders using IVW method. (A) Hypertension, (B) SBP, (C) DBP, and (D) MAP.

Fig. 3

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IVW, inverse variance weighting; SBP, systolic blood pressure; MAP, mean arterial pressure; DBP, diastolic blood pressure; SNP, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; FGF, fibroblast growth factor; IL, interleukin; LIFR, leukemia inhibitory factor receptor; TNF, tumor necrosis factor; ADA, adenosine deaminase; HGF, hepatocyte growth factor; TRAIL, TNF-related apoptosis-inducing ligand; CST5, cystatin D; CXCL1, C-X-C motif chemokine ligand 1; TRANCE, TNF-related activation-induced cytokine; SIRT, sirtuin.

Fig. 4. Forest plot of significant inflammatory cytokines on hypertensive disorders using sensitivity analysis. (A) Hypertension, (B) SBP, (C) DBP, and (D) MAP.

Fig. 4

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SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SNP, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighting; MR, Mendelian randomization; WM, weighted median; FGF, fibroblast growth factor; IL, interleukin; LIFR, leukemia inhibitory factor receptor; TNF, tumor necrosis factor; ADA, adenosine deaminase; HGF, hepatocyte growth factor; TRAIL, TNF-related apoptosis-inducing ligand; CST5, cystatin D; CXCL1, C-X-C motif chemokine ligand 1; TRANCE, TNF-related activation-induced cytokine; SIRT, sirtuin.

Influence of 91 ICs on SBP

The primary results for SBP are presented in Fig. 2B. According to the IVW method (Fig. 3B), genetically predicted higher levels of adenosine deaminase (ADA), CD5, FGF23, FGF5, and hepatocyte growth factor were associated with an increase in SBP, whereas higher levels of FGF19 and TNF-related apoptosis-inducing ligand were linked to a reduction in SBP. However, significant heterogeneity was observed for most ICs, except for ADA. Pleiotropy was detected for CD5 and FGF5. These results were consistent with findings from the WM and MR-Egger methods (Fig. 4B).

Influence of 91 ICs on DBP

The primary results for DBP are depicted in Fig. 2C. According to the IVW method (Fig. 3C), cystatin D, C-X-C motif chemokine ligand 1, and FGF19 were associated with a decrease in DBP, while CD5, FGF5, LIFR, and TNF-related activation-induced cytokine (TRANCE) were associated with an increase in DBP. Significant heterogeneity was observed for these ICs, and pleiotropy was detected for CD5, FGF5, and LIFR. The results from the WM and MR-Egger methods (Fig. 4C) were consistent with these findings.

Influence of 91 ICs on MAP

The primary results for MAP are illustrated in Fig. 2D. According to the IVW method (Fig. 3D), genetically elevated levels of CD244, FGF5, IL22RA1, sirtuin (SIRT) 2, and TRANCE were associated with increased MAP. Significant heterogeneity was observed for CD244 and FGF5, while pleiotropy was detected for FGF5. The findings from the WM and MR-Egger methods (Fig. 4D) were consistent with these results.

Functional and pathway enrichment analysis

For hypertension, SBP, DBP, and MAP, the number of ICs with significant associations was 8, 7, 7, and 5, respectively. The Venn diagram (Fig. 5A) revealed that only one IC, FGF5, was associated with all 4 conditions. A union of ICs associated with hypertension, SBP, DBP, and MAP identified 18 ICs with at least one significant association with hypertensive disorders. PPI analysis of these ICs highlighted FGF5 as the hub gene (Fig. 5B). GO analysis indicated that these ICs were primarily involved in the positive regulation of mitogen-activated protein kinase (MAPK) cascade (Fig. 6A), with the main cellular component being the external side of the plasma membrane (Fig. 6B). The primary molecular function identified was growth factor activity (Fig. 6C). KEGG pathway analysis further underscored the MAPK signaling pathway as a key mechanism underlying the development of hypertension (Fig. 6D).

Fig. 5. Protein-protein interaction of significant inflammatory cytokines. (A) Venn diagram showing FGF5 as the only IC associated with all 4 conditions. (B) PPI analysis highlighting FGF5 as the hub gene.

Fig. 5

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FGF, fibroblast growth factor; IC, inflammatory cytokine; PPI, protein-protein interaction; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; ADA, adenosine deaminase; HGF, hepatocyte growth factor; TNF, tumor necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand; CST5, cystatin D; CXCL1, C-X-C motif chemokine ligand 1; IL, interleukin; SIRT, sirtuin; TRANCE, TNF-related activation-induced cytokine; LIFR, leukemia inhibitory factor receptor; LTA, lymphotoxin alpha.

Fig. 6. GO and KEGG analysis of significant inflammatory cytokines. (A) Biological process, (B) cellular component, (C) molecular function, and (D) top 10 enriched KEGG pathways.

Fig. 6

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GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase; ERK, extracellular signal-regulated kinase; PI3K, phosphatidylinositol 3-kinase.

DISCUSSION

To our knowledge, this is the first study to comprehensively assess the causal relationship between 91 ICs and hypertension. The MR analysis identified 18 ICs associated with 1 or more hypertension disorders. Specifically, ICs were linked to hypertension, SBP, DBP, and MAP in 8, 7, 7, and 5 cases, respectively. Notably, FGF5 was the only IC implicated in all four conditions. Moreover, FGF5 was identified as a key node in the PPI network, and KEGG pathway analysis highlighted its involvement in the MAPK signaling pathway. These findings provide new insights into the mechanisms underlying the complex interplay between ICs and hypertension.

FGF is a cell signaling protein that regulates various important biological processes, including tissue growth, morphogenesis, development, and repair. FGF5 is one of the 18 secreted classical FGF in this ligand family [29,30,31]. Previous studies have identified FGF5 as a gene associated with susceptibility to hypertension [32,33,34,35], particularly in salt-sensitive and obese populations [36,37]. In addition, elevated FGF5 levels have been observed in the peripheral blood of patients with hypertension, with significant correlations to SBP and DBP [38]. Consistent with our findings, previous studies have demonstrated that FGF5 induced cardiomyocyte hypertrophy and proliferation [39,40], potentially contributing to myocardial wall thickening and increased blood pressure. Interestingly, a recent MR study reported that elevated SBP or DBP leads to elevated plasma FGF5 levels and suggested that FGF5 mediates the causal relationship between high SBP or DBP and increased susceptibility to heart failure [41]. Despite these insights, most research has predominantly focused on FGF2 [42] and FGF21 [43], leaving FGF5 relatively unexplored. Future studies are needed to further elucidate the functional role of FGF5 in hypertension.

The MAPK signaling pathway is a crucial mediator in various cellular processes, including proliferation, differentiation, development, transformation, inflammation, and apoptosis. In mammalian cells, MAPK signaling pathway mainly includes extracellular signal-regulated kinase (ERK), p38 MAPK, c-Jun N-terminal kinase, and ERK5 [44]. This pathway plays a significant role in cardiovascular diseases, including heart failure and hypertension [45]. Activation of ERK1/2 has been implicated in vascular smooth muscle hypertrophy and hyperplasia, resulting in increased peripheral resistance and elevated blood pressure [46]. Recent studies have further highlighted the involvement of MAPK signaling in hypertension. Li et al. [47] found that resveratrol restored cyclosporine A-induced upregulation of thromboxane a 2 receptor and hypertension in rats via AMP-activated protein kinase/SIRT1 and MAPK/nuclear factor-κB pathways. Another study indicated that Resolvin D1 alleviated hypertension in mice by inhibiting the proliferation, migration, and phenotypic transformation of vascular smooth muscle cells by blocking the RhoA/MAPK pathway [48]. In addition, apoptosis signal-regulated kinase 1 was found to modulate the p38-MAPK pathway, which in turn orchestrates cardiac remodeling in hypertension [49]. A recent study also demonstrated that the MAPK inhibitor BIRB796 reversed hypertension, enhanced physical activity, and ameliorated myocardial hypertrophy and fibrosis in patients with salt-sensitive hypertensive heart failure with preserved ejection fraction [50]. Collectively, these findings emphasize the pivotal role of MAPK signaling in hypertension, suggesting that targeting this pathway could offer promising therapeutic strategies. Future translational studies are warranted to validate these findings in cellular and animal models, assess the feasibility of targeting FGF5 and MAPK signaling in clinical settings, and explore potential drug repurposing strategies. Identifying small molecules or biologics that can modulate these pathways may lead to more precise and personalized treatment options for hypertension patients.

Beyond FGF5, other ICs have also been implicated in hypertension. IL-1A, a central regulator of leukocyte-endothelial cell adhesion in myocardial infarction and chronic kidney disease [51], has been identified as a critical gene in salt-sensitive hypertension through RNA sequencing-based transcriptional analysis [52]. In addition, cardiotrophin-1 (CT-1), which produces longitudinal elongation in newborn cardiomyocytes, may promote left ventricular hypertrophy in patients with hereditary hypertension via interactions with the renin-angiotensin system [53]. The LIFR/gp130 survival pathway has also been shown to modulate CT-1 activity in spontaneously hypertensive rat models [54]. FGF19, a well-known regulator of glucose, lipid, and energy homeostasis, is involved in various metabolic diseases, including obesity, type 2 diabetes, hepatic steatosis, biliary diseases, and cancer [55,56]. However, its direct association with hypertension remains unreported. In contrast, FGF21 has been shown to mitigate angiotensinogen II-induced hypertension and vascular dysfunction in mice via activation of the angiotensin-converting enzyme 2 axis and to ameliorate salt-sensitive hypertension-induced nephropathy through anti-inflammatory and antioxidant pathways [57,58]. Consistent with our study, another study identified FGF21 as an independent marker of hypertension in young individuals [59]. These findings underscore the diverse roles of ICs in hypertension, warranting further investigation into their underlying mechanisms.

The use of MR in this study provides several advantages. First, MR employs genetic variants as IVs to minimize confounding and reverse causation, allowing for robust causal inferences regarding the role of ICs in hypertension. Second, MR helps prioritize specific ICs for further research by distinguishing correlation from causation. Third, extensive sensitivity analyses were conducted to enhance the reliability of our MR results. However, some limitations must be acknowledged. First, MR relies on the availability of valid IVs, which may not always be present for every IC of interest. Second, MR assumes that the genetic variants used as instruments are not pleiotropic, meaning they do not influence the outcome through pathways unrelated to the exposure. Although sensitivity analyses, including MR-Egger and WM approaches, yielded consistent results, some ICs exhibited significant heterogeneity and pleiotropy. Third, the present MR analysis relies on summary-level GWAS data, which may obscure individual-level variations and interactions. The numerical thresholds or cut-off values of SBP, DBP, or MAP cannot be evaluated as well. Fourth, the study population was predominantly of European ancestry, potentially limiting the generalizability of our findings to other ethnic groups. Future studies should aim to replicate these findings in diverse populations to ensure broader clinical applicability. Fifth, although the MR analysis suggested a statistically significant association between FGF5 and hypertension, the observed effect size was small (odds ratio, 1.022). Given the polygenic and multifactorial nature of hypertension, the clinical utility of FGF5 as a therapeutic target may be limited. Meanwhile, as FGF5 is involved in multiple physiological and pathological processes, including inflammation, tissue repair, and carcinogenesis, the clinical feasibility and safety of targeting FGF5 for hypertension prevention or treatment remain uncertain.

CONCLUSIONS

In summary, this MR study examined the causal association between 91 ICs and four hypertensive disorders, identifying FGF5 as a key node and the MAPK signaling pathway as a critical mechanism in hypertension development. These findings suggest that targeting FGF5 and the MAPK signaling pathway may offer new strategies for hypertension prevention and treatment, warranting further clinical and translational research.

Abbreviations

ADA

adenosine deaminase

CCL2

CC motif chemokine 2

CI

confidence interval

CRP

C-reactive protein

CST5

cystatin D

CT-1

cardiotrophin-1

CXCL1

C-X-C motif chemokine ligand 1

DBP

diastolic blood pressure

ERK

extracellular signal-regulated kinase

FGF

fibroblast growth factor

GO

Gene Ontology

GWAS

genome-wide association studies

HGF

hepatocyte growth factor

IC

inflammatory cytokine

ICBP

International Consortium for Blood Pressure

IL

interleukin

IV

instrumental variable

IVW

inverse variance weighting

KEGG

Kyoto Encyclopedia of Genes and Genomes

LIFR

leukemia inhibitory factor receptor

MAP

mean arterial pressure

MAPK

mitogen-activated protein kinase

MR

Mendelian randomization

OR

odds ratio

PPI

protein-protein interaction

SBP

systolic blood pressure

SIRT

sirtuin

SNP

single nucleotide polymorphisms

TNF

tumor necrosis factor

TRAIL

TNF-related apoptosis-inducing ligand

TRANCE

TNF-related activation-induced cytokine

WM

weighted median

Footnotes

Funding: This work was supported the Twelfth Five-Year Planning Project of the Scientific and Technological Department of China (2011BAI11B02).

Competing interest: The authors declare that they have no competing interests.

Availability of data and materials: Our data and materials will be shared upon reasonable request by contacting the corresponding author.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Yes.

Authors' contributions:
  • Conceptualization: Li X, Gong Z, Yang Y, Qian H.
  • Data curation: Li X, Gong Z, Qian H.
  • Formal analysis: Li X, Gong Z, Yang Y.
  • Funding acquisition: Yang Y, Qian H.
  • Investigation: Li X, Gong Z.
  • Methodology: Li X, Gong Z, Qian H.
  • Project administration: Yang Y, Qian H.
  • Resources: Yang Y, Qian H.
  • Software: Li X, Gong Z.
  • Supervision: Li X, Gong Z, Yang Y, Qian H.
  • Validation: Li X, Gong Z.
  • Visualization: Li X, Gong Z, Yang Y.
  • Writing - original draft: Li X, Gong Z.
  • Writing - review & editing: Li X, Gong Z, Yang Y, Qian H.

SUPPLEMENTARY MATERIAL

Supplementary Table 1

Baseline characteristics of included genome-wide association studies

ch-31-e27-s001.xls (27.5KB, xls)

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Associated Data

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

Supplementary Materials

Supplementary Table 1

Baseline characteristics of included genome-wide association studies

ch-31-e27-s001.xls (27.5KB, xls)

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