Abstract
Chronological age is a well-established risk factor for Hypertension (HTN), yet while biological ageing markers such as epigenetic age acceleration (EAA), have been associated with HTN, findings are inconsistent. This study aimed to conduct a systematic review and meta-analysis to evaluate the association between EAA, HTN and blood pressure (BP) to provide an understanding of the role of EAA in HTN development and progression. Six databases were searched, and studies which reported associations between DNA and HTN, and/or BP were included. Functional enrichment analysis was conducted using DAVID and STRING to elucidate underlying molecular pathways. From 4334 studies, 165 met the inclusion criteria. Qualitative analysis indicated that 17.0% of studies reporting global methylation and 49.1% of studies reporting gene-specific methylation demonstrated significant associations with HTN and/or BP. A random effects meta-analysis of 16,136 participants from 8 studies using three epigenetic clock algorithms demonstrated that HTN was associated with increased EAA (β: 0.29, 95%Cl: 0.15–0.43; P < 0.0001). All three individual epigenetic clocks demonstrated a positive association between clinically measured HTN and EAA (Horvath β: 0.33, 95%Cl: 0.08–0.58, P = 0.010; Hannum β: 0.64, 95%Cl: 0.09–1.20; PhenoAge β: 1.21, 95%Cl: 0.56–1.86), whereas this relationship was not clear when using self-reported HTN. This study is the first to systematically demonstrate that HTN is associated with EAA. We recommend the use of clinically measured over self-reported HTN in appropriately powered studies of epigenetic age to obtain an accurate understanding of BP regulation/HTN on the epigenome, supporting pathways to translation and development of novel therapeutic targets for HTN.
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Introduction
Hypertension (HTN) is the leading modifiable risk factor for cardiovascular disease (CVD), a highly prevalent worldwide contributor to mortality, responsible for 8.5 million deaths annually [1]. While advancements in diagnosis and treatment have been observed in high- and middle-income countries, HTN remains highly prevalent, affecting 31% of adults worldwide. From 1990 to 2019, the global prevalence of HTN has doubled and more than 50% of HTN cases remain undiagnosed [2, 3]. Despite the availability of medical and lifestyle interventions to improve cardiovascular outcomes, HTN control rates remain poor, with only 24% of women and 20% of men worldwide achieving target BP [3, 4].
Hundreds of common single-nucleotide polymorphisms (SNPs) have been identified for HTN, a heritable complex polygenic trait. Genetic variants, however, do not fully explain HTN heritability suggesting the involvement of additional mechanisms [5,6,7]. Epigenetic modifications, such as DNA methylation, have been implicated in HTN, BP and stroke [8,9,10]. DNA methylation is responsive to environmental factors, such as diet and nutritional status, and may mediate the interaction between genetic predisposition and development and progression of HTN [11,12,13,14].
Specific subsets of CpG sites undergo programmed methylation changes which have informed the development of biological clock algorithms to provide estimates of epigenetic age [15]. The difference between chronological and epigenetic age provides a measure of EAA where higher epigenetic age than chronological age indicates accelerated epigenetic age, and vice versa. Various epigenetic clocks, including the Horvath and Hannum algorithms, have been developed to predict epigenetic age and its association with disease states [16,17,18]. Second-generation clock algorithms, such as DNAmGrimAge and DNAmPhenoAge, incorporate age-related biomarkers to improve accuracy in assessing biological ageing and estimating all-cause mortality risk [18,19,20]. Recently, epigenetic clocks such as Horvath, Hannum and PhenoAge have demonstrated clinical utility, in patients with chronic kidney disease who exhibited increased EAA, an effect mitigated by transplantation but not dialysis [21].
Despite growing interest in this area, current evidence linking epigenetic age and BP or HTN remains conflicting. Several studies have reported significant associations between epigenetic clock algorithms, such as Horvath and HTN as well as systolic and diastolic BP [22, 23]. In contrast, other studies have reported no such associations [24, 25]. This inconsistency extends to studies investigating HTN and/or BP using more targeted DNA methylation assessment methods, including global, gene-specific and epigenome-wide approaches [26,27,28,29]. Consequently, despite increasing evidence supporting the involvement of DNA methylation in HTN [8, 10], there is are a lack of systematic analysis and evaluation of the existing evidence linking various measures of DNA methylation (global, gene-specific, epigenome-wide), epigenetic age and HTN.
The aim of this study was to conduct a systematic review to investigate the association between DNA methylation and HTN and/or BP in adults. Furthermore, we performed a comprehensive meta-analysis of studies in adults reporting epigenetic age performed using various clock algorithms to determine the association between HTN and EAA. Finally, functional enrichment analysis was conducted to explore the relationship between epigenome-wide DNA methylation and biological pathways in HTN.
Methods
This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Screening of eligible studies, full-text assessment, data extraction, and quality assessment of studies were independently carried out by two authors. Any discrepancies were discussed and resolved by consensus. Studies were selected in line with the PICOS (Population, Intervention, Comparison, Outcomes, Study Type) criteria outlined in Table S1.
Search strategy and study selection
Systematic searches were performed in 6 bibliographic databases (Medline (Ovid), Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science and Scopus). The search covered the period from January 1st 2000 to 14th October 2024 (date of last search). The search strategy combined terms related to DNA methylation (e.g., global, gene-specific, epigenome-wide) and BP (e.g., systolic, diastolic, HTN, high BP). Medical subject headings and keyword searches were conducted in databases where a thesaurus was available (Embase, Medline, CINAHL, Cochrane Library), while keyword searches were performed in other databases (Web of Science, Scopus). Only full text studies involving humans, published in English were included. Detailed search terms and search strategies are provided in Table S2. Retrieved records from databases were exported to the systematic review manager Rayyan [30] for the removal of duplicates. Full-text studies selected for inclusion were further imported into an additional systematic review manager, Covidence (www.covidence.org), where studies were further assessed for full-text eligibility and data extraction.
Inclusion and exclusion criteria
Studies were deemed eligible for inclusion in this review if they were original, peer-reviewed, full-text articles published in English and met all defined inclusion criteria. These criteria included studies that: (1) assessed DNA methylation, specifically 5-methylcytosine (5mC) (global, gene-specific and epigenome-wide); (2) measured or recorded data on HTN and/or BP; (3) were conducted in adult humans aged >18 years; and (4) investigated an association between DNA methylation and HTN and/or BP. HTN status was defined to the European Society of Hypertension/European Society of Cardiology guidelines (HTN defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg and/or anti-HTN usage) [31] and the American College of Cardiology/American Heart Association Task Force guidelines (HTN defined as SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg and/or anti-HTN usage [32]. Additionally, we included studies that defined HTN as self-reported and studies which provided a diagnosis of HTN from medical history. No restrictions were imposed regarding the tissue in which DNA methylation was assessed allowing for a comprehensive analysis of the current landscape of studies. Animal studies, studies involving pregnant women and children, or in vitro studies using human or animal cell lines were excluded from this review (Table S1).
Risk of bias assessment
Bias within each included study was assessed using the Newcastle-Ottawa Scale [33] a semi-quantitative scale designed to evaluate the quality of non-randomised studies. Study quality was assessed based on the selection criteria of participants, comparability and exposure and outcome assessment. Studies that received a score of 9 stars were considered to have a low risk of bias, and those that scored 7–8 stars were considered to have a medium risk of bias; and those that scored less than 7 were considered to have a high risk of bias.
Data extraction and analysis
A predesigned data collection form was created within Covidence to extract the relevant information from the included studies, including the first author, study design, percentage of male participants, age range, sample size and location where possible. Additionally, a description of the cohorts, such as the names of large prospective cohorts or the diseased population (e.g., patients with HTN), was included. Furthermore, information regarding the type of tissue, molecular technique and outcomes related to DNA methylation was extracted.
A qualitative analysis was presented for associations between DNA methylation (global methylation, gene-specific, and epigenome-wide) and HTN and/or BP, however, due to considerable heterogeneity in study aims, meta-analysis was not appropriate for these measures. Instead, a meta-analysis was conducted to examine the association between HTN and EAA which employed 3 different epigenetic clock algorithms; Horvath, Hannum and PhenoAge.
Meta-analysis
We conducted a random-effects meta-analysis to examine the effect of HTN on EAA. The meta-analysis included studies that investigated the association between HTN and EAA, where EAA was reported as an outcome. Studies eligible for inclusion in the meta-analysis measured at least one of the definitions of EAA. Only studies reporting HTN were included rather than other measures of BP due to the low number of studies reporting statistically comparable outcomes for BP. All eligible studies for meta-analysis, defined HTN as SBP > 140 or DBP > 90 and/or use of antihypertensive medication or separately as self-reported HTN.
Subgroup analysis was performed to determine the effect of HTN on each epigenetic clock algorithm, both independently and as a combined effect of the 3 algorithms. While first-generation epigenetic clocks are derived by a linear regression algorithm that trains chronological age against a select set of CpGs [16,17,18] more recent clocks have included additional parameters, such as the inclusion of 9 biomarkers in PhenoAge [18]. Studies using GrimAge [19] were ineligible for inclusion in meta-analysis due to incompatible statistical reporting, such as reporting EAA as a predictor variable, which limited comparability with other studies included for meta-analysis. Furthermore to account for variability in HTN definitions, subgroup analysis was utilised to examine the association between HTN and EAA based on whether HTN was defined by European clinical guidelines [31] or by self-report.
Statistical analysis
Review Manager Version 5.3 software was used to perform a random-effects meta-analysis [34]. We employed a random-effects model to account for expected heterogeneity in effect sizes across clocks and studies. The random-effects model estimates between-study variance, allowing for the assignment of weight to individual studies when calculating an overall pooled effect that reflects this variability. Beta effect estimations and standard errors were extracted from included studies that investigated epigenetic age as an outcome. In studies where standard errors were absent, the standard error was estimated from 95% confidence intervals using Cochrane formulas. Results were expressed as beta effect estimates and 95% confidence intervals, in addition to the overall effect Z value. Pre-specified subgroup analysis, grouped by epigenetic clock and by clinically measured vs self-reported HTN was performed.
Publication bias for studies included in the meta-analysis was assessed through visual inspection of funnel plots, Egger’s regression test and the trim-and-fill procedure using the metafor package within the statistical software platform R (Version 4.1.2) [35, 36].
Heterogeneity was assessed using chi-squared testing (χ value), heterogeneity index (I2) statistics and corresponding P value. Heterogeneity thresholds were predefined according to Cochrane guidelines, which stated that an I2 value between 0% and 40% indicates low heterogeneity, between 30% and 60% represents moderate heterogeneity, between 50% and 90% represents substantial heterogeneity, and between 75% and 100% indicates considerable heterogeneity. Sensitivity analysis was performed by conducting the meta-analysis excluding one study at a time to determine stability of the overall pooled effect across the 3 clock algorithms.
Functional analysis
Functional analysis was conducted separately for previously identified CpG sites associated with BP and/or HTN within epigenome-wide association studies using the DAVID Bioinformatics Resource and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), which allowed for the formation of protein-protein interaction networks [37, 38]. Clustering within STRING analysis was performed using the Markov Clustering Algorithm. Overlapping differentially methylated regions (DMR) were determined using the GenomicRanges package (Version 1.46.1) with R (Version 4.1.2) to identify DMRs with the same genomic region prior to visualisation. CpG sites and DMRs associated with BP traits were visualised individually using the InteractiVenn software [39].
Results
Overall, 4334 potentially relevant records were identified through a systematic search of 6 databases. Based on the title and abstract, 584 articles were identified for detailed evaluation. After full-text assessment, 165 studies met the predefined eligibility criteria and were included in this review. Detailed screening, eligibility and selection processes are outlined in Fig. 1.
Flow diagram of study selection for systematic review and meta-analysis. aSeven publications reported both global and gene-specific methylation. bFive publications reported both gene-specific and epigenome-wide methylation. cOne publication reported epigenome-wide methylation and epigenetic age
Characteristics of included studies
Overall, 165 studies were included that involved adults aged 18–99 years, ranging in size from 6 to 17,010 participants. Individuals were recruited from 34 different countries, with the most prevalent being China (29.1%), the USA (24.2%) and Spain (6.1%). DNA methylation was assessed in a gene-specific manner in 45.8% of studies, while 20.9% investigated epigenetic age, 19.8% of studies investigated epigenome-wide methylation, and 14.1% investigated global methylation (Fig. 2A). DNA methylation was predominantly examined in blood (91.1% of studies), followed by tissue (7.1%), saliva (1.8%), plasma (1.2%), and serum (0.6%). DNA methylation was examined in multiple tissues within 2.4% of studies. A total of 24 different techniques were used for DNA methylation analysis, with the majority employing Infinium HumanMethylation 450 K BeadChip microarray (Illumina, San Diego, CA) (23.6%), pyrosequencing (22.4%), Infinium MethylationEPIC BeadChip microarray (Illumina, San Diego, CA) (18.8%) and methylation-specific PCR (10.9%). Seven studies reported the use of more than one method. Detailed participant characteristics for each of the 165 studies within this review were stratified by global methylation (Table 1), gene-specific methylation (Table 2), epigenome-wide methylation (Table 3) and epigenetic age (Table 4).
A Study characteristics stratified by methylation assessment method, tissue and technique (B) Studies reporting significant associations between BP traits and global DNA methylation stratified by global DNA methylation marker; (C) Assessment of studies reporting significant associations between BP traits and gene-specific DNA methylation
Outcome assessment
Studies were included if they investigated an association between DNA methylation and either HTN or BP, resulting in outcomes being reported separately as either a diagnosis of HTN (38.8%), a measure of BP (34.8%), or both (26.4%) (Tables 1–4). HTN was defined by European guidelines in 64.7% of 116 studies reporting HTN, while 9.5% of studies defined HTN according to American guidelines. In addition, 8.6% of studies reported HTN based on medical history, a further 9.5% based on self-reported HTN, and 0.9% defined HTN as SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg. Furthermore, 0.9% of studies reported HTN as a binary yes/no answer without further definition, while another 0.9% of studies did not indicate HTN definition and 1.7% of studies reported HTN as solely antihypertensive medication usage. Additionally, 3.4% of studies reported multiple definitions of HTN. Regarding BP measurement, 61.5% of 109 studies reporting BP measured BP in a standardised manner (after a period of rest, seated with multiple measurements taken), 6.4% of studies reported BP levels from medical history, 0.9% measured BP over a 24 h period, 2.8% report multiple BP measurement methods and 28.4% provided an indication of BP measurement without further definition.
Risk of Bias assessment
A case-control design was used in 32.1% of studies, while the majority of studies (55.8%) used a cross-sectional approach. Most of the included studies (54.5%) achieved a moderate rating of 5-6 stars, suggesting moderate methodological quality. A substantial number of studies (43.0%) attained a higher rating of 7-9 stars, indicating good methodological quality, while only 2.4% of studies were rated below 4 stars, indicating lower quality (Table S3).
Global methylation
Global DNA methylation was investigated in 25 publications, including 4 studies within large prospective cohorts and 21 original study cohorts (Table 1). Global DNA methylation analysis was performed in healthy individuals in 16.0% of studies and was investigated in several disease state cohorts, such as HTN (20.0% of studies) and diabetes (16.0% of studies). The majority of studies investigating global methylation, focused on repeat sequences and transposable elements as a proxy, which have been shown to correlate with total genomic content [40, 41]. The remaining studies investigating global methylation assessed DNA methylation as a level of 5 mC or as a percentage of total cytosine (MC/C ratio).
Several studies report conflicting findings in regard to the association between SBP and LINE-1 methylation, with varying directions of association (Fig. 2B) [42, 43]. Three studies report a negative association between DBP and LINE-1 methylation [26, 43, 44]. Furthermore, a single study also reports a negative association between HTN and LINE-1 methylation [45].
Alu methylation was associated with both SBP and DBP with negative and positive associations [26, 46, 47]. Alu methylation was negatively associated with HTN [47] and positively associated with pre-HTN [48]. Additionally, 5mC was negatively associated with HTN [49, 50]. No significant associations were reported between BP traits and MC/C ratio.
Gene-specific DNA methylation
Gene-specific DNA methylation of 138 candidate genes was examined utilising data from 81 distinct studies (Table 2). Significant associations with either BP or HTN were reported for a total of 88 candidate genes. Most studies were conducted in original cohorts comprising individuals with diverse characteristics, including healthy individuals (9.9% of studies), those with HTN (38.3%), and obese individuals (6.2%), in addition to other disease states.
Twenty-five studies reported associations between SBP and DNA methylation examining a total of 45 genes. The most commonly reported genes included Tumour Necrosis Factor Alpha (TNF-a), Glucocorticoid receptor gene (GR) and Interleukin-6 (IL-6), all of which demonstrated conflicting directions of association between methylation and SBP (Fig. 2C) [51,52,53,54]. Similarly, among the 26 studies reporting an association between DNA methylation and DBP, the most commonly reported genes also included TNF-a, GR and IL-6, along with Toll-like receptor 2 (TLR2) methylation, which exhibited both positive and negative associations with DBP (Fig. 2b) [54, 55].
Angiotensin II Receptor Type 1 (AGTR1) methylation was the most frequently studied gene in relation to HTN, with studies again reporting both positive and negative associations (Fig. 2C) [56,57,58]. Cystathionine Beta Synthase (CBS) methylation was positively associated with HTN [59, 60], while ADD1 methylation was negatively associated with HTN [61, 62]. MTHFR methylation was positively associated with HTN, in addition to other genes involved in folate and one-carbon metabolism, such as dihydrofolate reductase (DHFR) and methylenetetrahydrofolate dehydrogenase (MTHFD1) [29, 63, 64].
Epigenome-wide methylation
Epigenome-wide methylation was investigated in 35 studies, resulting in the identification of 1003 CpG sites associated with various BP traits (Fig. 3A). The most commonly reported CpG sites included cg19693031 and cg18120259 which were reported in 5 publications (Table S4). The top genes annotated to these CpGs included Thioredoxin Interaction Protein (TXNIP) and a long intergenic non-protein coding RNA (LOC100132354). In total, CpG sites were annotated to 569 genes, with 61 genes reported in >1 publication. 1391 DMRs were associated with various BP traits (Fig. 3B). Eight DMRs were reported in >1 publication (Table S5).
A CpG sites and (B) Differentially methylated regions (DMRs) associated with blood pressure (BP) traits. Systolic blood pressure (SBP), Diastolic blood pressure (DBP), Hypertension (HTN), Pulse Pressure (PP), Mean arterial pressure (MAP)
Functional enrichment analysis of genes annotated from CpG sites significantly associated with any BP trait indicated several highly enriched disease terms, including systemic lupus erythematosus (PFDR = 1.84E-17), schizophrenia (PFDR = 0.001), HTN (PFDR = 0.002), and SBP (PFDR = 0.005) (Table S6). Functional enrichment of the genes annotated from DMRs significantly associated with any BP trait identified biological processes such as regulation of transcription by RNA polymerase II (PFDR = 0.0003), cartilage development (PFDR = 0.0004) and intracellular signal transduction (PFDR = 0.009) (Table S7). Regarding cellular components, significantly associated terms included chromatin (PFDR = 1.59E-07), nucleoplasm (PFDR = 0.0002), post synaptic density (PFDR = 0.003), cell surface (PFDR = 0.032), focal adhesion (PFDR = 0.032), synapse (PFDR = 0.049), and axon (PFDR = 0.049). (Table S7). In terms of molecular function, protein binding was found to be statistically significant (PFDR = 0.0001), in addition to RNA polymerase II-specific DNA-binding transcription factor activity (PFDR = 0.0003), metal ion binding (PFDR = 0.0003), zinc ion binding (PFDR = 0.011), sequence-specific double-stranded DNA binding (PFDR = 0.013), and identical protein binding (PFDR = 0.034) (Table S7). STRING analysis with the confidence setting ‘high’ revealed significant evidence for protein-protein interactions between the products of genes annotated to CpG sites associated with BP (P = 1.46E-07) (Fig. 4). The average node degree was 0.83, while the average local clustering coefficient was 0.27.
Network analysis of genes annotated to CpG sites associated with any BP outcome using STRING-db; genes without connections are hidden
Epigenetic age
Fourteen DNA methylation algorithms were implemented across 37 studies investigating epigenetic age (Table 4). Most studies calculated epigenetic age using more than one algorithm, with the Horvath (Pan-tissue) clock being the most frequently used (73.0% of studies). Epigenetic age was most commonly reported as EAA; however, 6 studies additionally reported Delta Age (ΔAge), DiffAge, PCAge, changing rate of age acceleration (ΔAA), and ageing rate [20, 65,66,67,68,69]. The majority of studies within this review demonstrate a very high correlation between epigenetic age and chronological age (r = 0.7–0.9), which is representative of the function of these algorithms, however, some individual studies did report lower correlations between epigenetic and chronological age [70].
Six studies reported positive associations between AgeAccelHorvath and SBP [22, 69,70,71]. Four studies reported positive associations between AgeAccelHannum and SBP [22, 70, 72, 73]. Similarly, 4 studies reported a positive association between PhenoAgeAccel and SBP [18, 22, 72, 73]. GrimAgeAccel was positively associated with SBP in 4 separate studies, while a further study reported a negative association [19, 20, 72, 74, 75].
Four studies reported a positive association between AgeAccelHorvath and DBP [20, 70, 75, 76]. AgeAccelHannum was associated with DBP in 1 study [70], while a positive association between GrimAgeAccel and DBP was reported in 2 studies [72, 74]. No significant associations were reported between DBP and PhenoAgeAccel.
Meta-analysis
A meta-analysis was conducted to examine the association between HTN and EAA. Data from a total of 16,136 individuals across 8 studies were included in the analysis. Beta coefficients and standard errors (or calculated standard errors from published 95% confidence intervals) were obtained from each study. The majority of studies assessed DNA methylation in blood, while 1 study used saliva.
The meta-analysis, using a random-effects model (Fig. 5), demonstrated a significant positive association between HTN and EAA across the three epigenetic clock algorithms (β = 0.29, P < 0.001; 95% Cl: 0.15–0.43, P < 0.0001). Subgroup analysis further revealed that clinically measured HTN as determined by European guidelines [31], was associated with each epigenetic clock individually (Horvath: β = 0.33, 95% Cl: 0.08–0.58, P = 0.010; Hannum: β = 0.64, 95% Cl: 0.09–1.20, P = 0.02; PhenoAge: β = 1.21, 95% Cl: 0.56–1.86, P = 0.0003). Further subgroup analysis based on self-reported HTN status demonstrated no significant association with AgeAccelHorvath (β = 0.09, P = 0.09, 95% Cl: −0.01 to 0.20); however, a significant association with AgeAccelHannum (β = 0.17, P = 0.003, 95% Cl: 0.06–0.28) was observed. Heterogeneity was observed in the overall meta-analysis across the three epigenetic clock algorithms (I2 = 64%, P = 0.001) and for AgeAccelHannum in individuals who reported HTN according to European guidelines (I2 = 78%, P = 0.001). No significant heterogeneity was observed in either the AgeAccelHorvath subgroup, when HTN was either clinically measured or self-reported (I2 = 0%, P = 0.87; I2 = 32%, P = 0.22), or the AgeAccelHannum subgroup when HTN was self-reported (I2 = 0%, P = 0.48), or PhenoAgeAccel (I2 = 0%, P = 0.61) when HTN was clinically measured.
Random effects meta-analysis of the association of HTN with epigenetic age acceleration subgrouped by epigenetic clock algorithm and clinically-defined v self-report of hypertension. Horizontal lines represent the 95% confidence interval (CI) for each study. Diamonds indicate pooled effect and 95%CI for each subgroup and overall effect (Z). χ 2 chi-squared test assesses whether observed difference in results are compatible with chance alone; I2 heterogeneity index (0%–100%); SD, standard deviation; IV, Random, a random effects meta-analysis is applied with weights based on inverse variances
Publication bias and Sensitivity analysis
Visual assessment of publication bias by funnel plot (Figure S1) determined significant asymmetry which was further confirmed by a significant Egger’s regression test (P = 0.0002). The small number of studies included in the meta-analysis or heterogeneity among these studies may have influenced the results of Egger’s regression test and observed asymmetry, introducing potential variability and making limiting the ability to draw definitive conclusions regarding publication bias. Trim-and-fill analysis estimated three missing studies (Figure S2), with the overall association measure based on this analysis remaining significant (β = 0.260, P = 0.0005; I2 = 65.18%, P < 0.0001). Given the observed heterogeneity, we performed a sensitivity analysis of the eight included studies. The overall effect remained consistent overall effect after sequential exclusion of each individual study, (Figure S3) indicating that the findings are stable and not driven by one particular study.
Discussion
This study is the first to demonstrate that HTN is significantly associated with accelerated epigenetic age by systematically evaluating current evidence, highlighting an important role for DNA methylation in the development and pathophysiology of HTN in adults. Furthermore, each epigenetic clock algorithm individually demonstrated that clinically measured HTN was significantly associated with increased EAA.
In our meta-analysis of 16,136 individuals, we demonstrated a significant association between HTN and increased EAA combining three main epigenetic clock algorithms. Accelerated epigenetic ageing was observed in individuals with clinically measured HTN in subgroup analysis using the Horvath clock, despite none of the included studies individually reaching statistical significance [17, 18, 68, 77, 78], perhaps due to a lack of statistical power within individual studies. Similarly, a small meta-analysis using the Horvath clock previously reported accelerated biological ageing in >5600 participants with clinically defined HTN across three pooled studies in which no significant effect was observed in each individual cohort [72]. While results are generally consistent for clinically measured hypertension, there appears to be a disparity in using self-reported HTN which may prevent identification of positive associations between epigenetic age and HTN, introducing potential misclassification bias. Self-reporting is considerably reliable for ruling out HTN, however, the probability of correctly identifying patients with HTN through self-reporting is only mildly sensitive, correctly identifying individuals with HTN in approximately 37% of cases, indicating that a large number of hypertensive individuals remain undiagnosed [79]. In accordance, one well-powered study within our meta-analysis reported no significant association between Horvath EAA and self-reported HTN in >5100 participants; perhaps attributable to the use of self-reported HTN rather than more discriminatory clinical measurement [80]. Subgroup analysis, employing Hannum and PhenoAge clocks, also demonstrated accelerated epigenetic ageing in individuals with both clinically and self-reported HTN despite a lack of consistent evidence across individual studies. Increased EAA was also consistently associated with BP traits, such as SBP and DBP, within studies that were ineligible for meta-analysis (Table 4).
The overall association between HTN and EAA was observed despite each clock using different CpG sites and parameters in the algorithms used to calculate epigenetic age, therefore, it is not surprising that heterogeneity was observed in the meta-analysis. The heterogeneity observed in the overall meta-analysis may reflect differences in study design across the limited number of studies, including variation in study design, epigenetic clock algorithm, participant characteristics and levels of covariate adjustment. The observed heterogeneity likely reflects the biological and methodological diversity inherent to epigenetic ageing measures. The pooled estimate is therefore interpreted with caution, as an overall summary of the association between EAA and HTN rather than a precise effect size. Although the meta-analysis was limited by the small number of studies, the overall positive effect of the meta-analysis remained robust following sensitivity analysis indicating that the pooled result is not driven by one study (Figure S3). Lack of overlap in genomic locations used in epigenetic clock algorithms suggests that each clock investigates a separate measure of biological age, drawing methylation markers from entirely different regions of the genome [81]. Notably GrimAgeAccel was not represented among eligible studies for meta-analysis, however, the overall association of HTN and EAA using three first- and second- generation clocks, suggests wide-ranging perturbations across the epigenome.
The causal relationship between EAA and hypertension remains an area of active investigation. Longitudinal studies have suggested that individuals with higher baseline EAA are at increased risk of developing incident hypertension [82], supporting the hypothesis that accelerated epigenetic aging may predispose to hypertension. Conversely, other evidence indicates that established hypertension may itself contribute to EAA through mechanisms such as vascular remodelling, chronic low-grade inflammation, oxidative stress, and endothelial injury, which are known to influence DNA methylation patterns [68, 78]. These mechanisms highlight the potential for a feedback loop where increasing EAA and HTN mutually reinforce each other, and it currently remains unclear whether epigenetic alterations precede adverse cardiovascular remodelling events in HTN [83]. It is plausible that epigenetic age acceleration is correlated with HTN but may not lie along the causal pathway of incident HTN. Future research directed towards dissecting molecular mechanisms connecting EAA and HTN may help elucidate the mechanism underlying hypertension physiology. Furthermore, studies investigating whether epigenetic aging influences gene expression at the transcriptomic or proteomic level and development of models to identify key physiological pathways in HTN will be valuable in answering these important questions.
There is currently immense interest in epigenome-wide methylation and epigenetic age investigations. In the systematic review a diverse range of experimental design was observed in included studies (Fig. 2A). Large cohorts such as the Women’s Health Initiative (WHI) have employed the 450 K array which has proved very useful and has been extensively used over the past decade [18, 19, 23, 70, 84, 85]. Moving forward studies should take advantage of assays providing more epigenomic coverage e.g., the Generation Scotland Family Health Study utilises the IlluminaMethylationEPIC array which covers over 850 CpG sites [80, 86, 87]. DNA repetitive element methylation, however, is not directly analysed in commonly used methylation arrays and are therefore reported separately. Repeat elements LINE-1 and Alu were the most commonly reported markers of global methylation within this review (Table 1). Global methylation is inversely associated with BP traits in the majority of studies reporting significant outcomes (Fig. 2B) consistent with previous findings [8, 10].
The most frequently reported candidate gene was AGTR1 (Fig. 2C), methylation of which has been inversely associated with HTN [88], while SNPs in AGTR1 result in decreased expression and increased HTN risk [89]. MTHFR methylation was also reported to be associated with HTN (Fig. 2C). Common polymorphisms in MTHFR, resulting in the 677TT genotype yields an increased risk of HTN [90]. Hypermethylation of MTHFR is observed in individuals with the TT genotype, blood pressure is also higher compared to CC counterparts. Significant reduction in MTHFR methylation was observed in TT adults following intervention with the enzyme co-factor riboflavin [11].
We identified 246 differentially methylated CpG sites which were associated with both SBP and DBP (Fig. 3A). Only three CpG sites were identified to be associated with SBP, DBP and HTN, which may be due to the paucity of epigenome-wide methylation studies investigating HTN as an outcome. Three additional CpG sites were reported in five publications (Table S4), including cg19693031 annotated to the gene TXNIP [23, 28, 91,92,93]. Overexpression of TXNIP has been associated with increased oxidative stress and excessive ROS [94]. Seven studies report 1,391 DMRs to be significantly associated with various BP traits, with 56 of these common to both SBP and DBP (Fig. 3B). Eight DMRs were reported in more than one publication (Table S5).
Network analysis of genes annotated from CpG sites highlighted a protein cluster significantly enriched in biological processes associated with circadian rhythm (Fig. 4). Variations in BP occur naturally with circadian rhythm, and polymorphisms in genes associated with these proteins have been associated with both circadian phenotype and myocardial infarction [95,96,97]. Interestingly, functional analysis of all CpGs associated with BP traits highlights chromatin as the most significantly enriched cellular component (PFDR = 1.59E-07) (Table S6) strengthening the evidence for the role of epigenetic modifications in HTN [97, 98].
Taken together, our results demonstrate that HTN is associated with accelerated epigenetic ageing indicating that EAA is higher in hypertensive individuals compared to normotensive individuals. The identification of robust biomarkers for accelerated epigenetic age in HTN patients may help cardiologists identify patients at greatest risk of complications and encourage lifestyle modifications such as exercise and targeted nutritional interventions. Further research is required to determine if EAA may be predictive of HTN outcomes or if measures to reduce EAA are useful in management of HTN.
Strengths and Limitations
This review critically investigates the association between epigenetic age and HTN and importantly, is the first to employ a systematic approach to identify the studies subjected to meta-analysis to ensure a robust overview of current evidence. The analysis was conducted in line with PRISMA guidelines with defined inclusion and exclusion criteria. We have outlined a comprehensive summary using a combination of quantitative and qualitative evidence for the role of DNA methylation in HTN and BP. Limitations of this study include any fixed exposures that may influence epigenetic age, such as age, sex or race, which were not considered due to the small number of studies available for meta-analysis, however, all included studies bar one, cofounded for age and sex as appropriate [83, 99]. Studies included within the meta-analysis were considered to be sufficiently homogenous for analysis, however due to the exploratory nature of combining multiple epigenetic clocks studies, heterogeneity was introduced in the combined estimate limiting the generalisability of findings and we suggest caution in the interpretation of the pooled estimate broad summary of the overall association between EAA and HTN rather than a definitive result. A further limitation is that most studies included within the meta-analysis were conducted in the USA, while the remaining studies were conducted in other predominantly white Caucasian populations such as the UK and Australia. Although, 5/8 of included studies cofounded for ethnicity as a covariate, only one cohort included only African American participants (n = 227), introducing potential population bias and limiting generalisation of findings to non-European populations. The majority of studies assessed DNA methylation in blood due to sample accessibility with lack of readily available cardiac tissue studies remaining a constraint. DNA methylation exhibits tissue-specificity, influenced by leukocyte composition [100,101,102,103,104,105]. A recently defined cardiac-specific epigenetic clock indicated, however, that both blood and cardiac tissue reflect chronological age, suggesting that blood is a suitable proxy for cardiac epigenetic age studies [106, 107]. Moreover, the epigenetic clock algorithms applied in included studies were trained on multiple tissue types, including blood, and have been demonstrated robust performance across different tissues [16,17,18,19].
Conclusion
This study is the first to demonstrate that HTN is associated with accelerated epigenetic ageing, by systematically evaluating the current evidence. There is need for further epigenome-wide approaches, and we recommend the use of clinically measured HTN over self-reported HTN in appropriately powered studies of epigenetic age to provide clarity on the relationship between environment, epigenome and HTN. The identification of robust biomarkers for accelerated epigenetic age in HTN patients may help clinicians identify patients at greatest risk of complications and encourage lifestyle modifications such as exercise and targeted nutritional interventions. In conclusion, accelerated epigenetic ageing as an underlying mechanism for hypertension holds much promise through the potential to impact development of novel therapeutic targets for HTN.
Data availability
All data used within this study were obtained from previously published articles and publicly available sources. Full details of data sources, including citations are available in the main text tables and supplementary materials. Readers may access the original datasets by referring to the cited publications.
Change history
20 January 2026
Author’s affiliation and Grant number have been updated.
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Acknowledgements
We thank the authors of the original publications included within this review, particularly those who were approached for additional information. We additionally thank Joan Atkinson, Ulster University Library, for help with the systematic search strategy.
Funding
This work was supported by the Northern Ireland Chest, Heart & Stroke Association (H09) (MW, DLM), the Higher Education Authority North South Research Programme (EpiHyper) (MM, DLM) and the Department for Economy postgraduate studentship scheme (CD). The funders played no role in the conception, design, performance, and approval of the work.
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DJL-M and MW designed the research. CD, MYCS, EK-K and LC conducted the research. CD performed the statistical and functional analysis. CD, DJL-M and MW wrote the article. HM, FB and MM critically revised the manuscript for important intellectual content. DJL-M has primary responsibility for the final content. All authors read and approved the final version of the manuscript.
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Dollin, C., Ward, M., Stafford, M.Y.C. et al. Accelerated epigenetic age in hypertension: a systematic review and meta-analysis. Hypertens Res (2026). https://doi.org/10.1038/s41440-025-02470-y
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DOI: https://doi.org/10.1038/s41440-025-02470-y
