Jiacai Feng
Xingyu Huang1
Ming Liu
Renli Deng- 1Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
- 2International Human Phenome Institutes, Shanghai, China
- 3Peking University Health Centre-Macao Polytechnic University, Macau, Macao SAR, China
Background: Although traditional vascular risk factors, such as hypertension and diabetes, are incorporated into stroke risk prediction models, a significant proportion of stroke events remain unexplained by these models. Increasing evidence suggests that accelerated biological aging, as measured by DNA methylation clocks, may reflect reduced organ function and heightened susceptibility to disease. However, the relationship between epigenetic age acceleration (EAA) and stroke risk remains poorly understood, with limited comprehensive synthesis of the available evidence.
Methods: We conducted a systematic search of PubMed, Embase, Web of Science, and Cochrane Library databases (up to January 10, 2025) for observational studies examining the relationship between DNA methylation-derived EAA and stroke risk. The study protocol was registered with PROSPERO (CRD420251010621).
Results: Thirteen studies met the inclusion criteria. Random-effects meta-analysis revealed a significant positive association between accelerated biological aging and stroke risk (OR = 1.16, 95% CI 1.13–1.19, I2 = 98.9%, p < 0.001). Stratified analysis by stroke event demonstrated a stronger association with incident stroke (OR = 1.28, 95% CI 1.25–1.35, I2 = 92.6%, p = 0.001) compared to stroke recurrence (OR = 1.11, 95% CI 1.06–1.16, I2 = 63.6%, p = 0.041). Sensitivity analyses confirmed the robustness of these findings.
Conclusion: DNA methylation-derived measures of accelerated biological aging are robust predictors of stroke. These findings provide new insights into stroke risk assessment and emphasize potential biomarkers for early detection and prevention. Further large-scale prospective studies are needed to validate these associations and examine the role of additional modifying factors.
1 Introduction
Stroke, ranked as the second leading cause of death globally and the primary etiology of adult disability, has seen its pathogenesis and precise risk factor identification remain a central focus in neuroscience research (1, 2). Epidemiologically, one in four individuals will experience a stroke during their lifetime, with one-third of survivors developing long-term disabilities (3). Although improved secondary prevention strategies have reduced stroke incidence in recent years, its prevalence continues to rise due to increasing life expectancy.
Current clinical risk assessment models incorporate traditional factors such as hypertension, diabetes, and smoking. However, a proportion of stroke events remain unexplained by existing models, suggesting undiscovered biological mechanisms driving stroke event (4, 5). In this context, epigenetic regulation—a critical bridge between genotype and phenotype—has demonstrated groundbreaking progress in aging-related disease research. Notably, deoxyribonucleic acid (DNA) methylation-based epigenetic clocks quantify methylation variations at specific cytosine-guanine dinucleotides (CpG) sites, providing an innovative tool to assess biological aging rates (6). Studies indicate that epigenetic age acceleration (the deviation between DNA methylation age and chronological age) may reflect pathophysiological processes like organ functional decline and inflammatory activation, showing significant associations with cardiovascular and neurodegenerative diseases (7). Multiple independent teams have developed disease-specific methylation clocks, including Horvath’s multi-tissue clock (8), Hannum’s blood clock (9), and PhenoAge (10), with predictive efficacy validated across chronic disease cohorts. Emerging prospective studies suggest a potential link between epigenetic age acceleration and stroke risk. Nevertheless, current evidence exhibits substantial heterogeneity: (1) divergent predictive power among methylation clock models; (2) potential confounding from variations in study design, ethnic characteristics, and epigenetic data adjustment strategies. Crucially, no systematic study has clarified whether epigenetic aging influences stroke occurrence independently of traditional vascular risk factors.
To address these gaps, this study employs systematic review and meta-analysis methodologies to synthesize existing evidence, elucidating the association between biological aging and stroke risk. Our findings aim to provide evidence-based insights for early clinical identification and stroke prevention.
2 Methods
2.1 Search strategy
This meta-analysis and systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (11) and was prospectively registered in the PROSPERO database (CRD420251010621). A comprehensive literature search was conducted across multiple databases, including EMBASE, PubMed, Web of Science, and the Cochrane Library, with the search period extending through January 10, 2025. No limitations were imposed regarding participants’ age, gender, racial background, or country of origin. To ensure thorough coverage, we additionally examined the reference lists of selected articles and pertinent review papers to identify other potentially relevant studies that might have been missed by our initial search. The complete search methodology is provided in Additional file 1: Supplementary Table S1. Briefly, main search terms included the following terms: (“DNA methylation” OR “methylation” OR “epigenetic” OR biological age) and (“Stroke” OR “Cerebrovascular Accident” OR “Cerebral Stroke” OR “Cerebrovascular Apoplexy” OR “Brain Vascular Accident” OR “Apoplexy”). Two independent investigators (JF and RW), who received standardized training, performed the initial screening of relevant studies. Any discrepancies in their assessments were resolved through discussion until a consensus was reached on the final selection of records.
2.2 Inclusion and exclusion criteria
In accordance with the Population, Intervention, Comparison, Outcomes, and Study (PICOS) framework, the inclusion criteria were defined as follows:
(P) The study population was adult participants of all genders and ethnic backgrounds; (I) (Intervention/Exposure) was accelerated biological aging, which was assessed by DNA methylation clocks (e.g., Horvath’s multi-tissue clock, Hannum’s blood clock, PhenoAge) and defined as the deviation between DNA methylation age and chronological age (epigenetic age acceleration, EAA); (C) Controls were participants with non-accelerated biological aging, i.e., those whose DNA methylation age was close to or less than their chronological age; (O) Study outcomes were stroke occurrence, including incident stroke (first-ever stroke) and recurrent stroke, with outcomes quantified by reporting odds ratio (OR), relative risk (RR), or hazard ratio (HR) along with 95% confidence intervals (CIs); (S) Study design included observational studies, specifically prospective cohort studies, retrospective cohort studies, and case–control studies.
The following studies were excluded from the analysis:
(1) Reviews, letters, personal opinions, book chapters, case reports, conference abstracts, and meeting proceedings; (2) Duplicate publications; (3) Incomplete data; (4) In vitro or in vivo animal experiments.
2.3 Data extraction
Data extraction was performed independently by two authors (JF and XH), who collected study characteristics and relevant outcomes from the selected articles. Initial disagreements in extracted data were resolved through mutual discussion, with any unresolved discrepancies adjudicated by a third reviewer to achieve final consensus. Extracted information included first author (year of publishment), country, participants (including sample size, number of stroke cases, age, and sex distribution), study design, biological sample type used for DNA methylation analysis, epigenetic clock models measured, adjustment for confounders in the statistical analysis, and correlation index of DNA methylation clocks with stroke risk. Studies were permitted to be included multiple times if they reported data from different epigenetic clocks or examined distinct tissue types. For publications containing multiple analytical results, we preferentially selected estimates that accounted for the greatest number of confounding variables to ensure the most robust data extraction.
2.4 Assessment of study quality
We referenced Newcastle-Ottawa Scale (NOS) to evaluate the quality of included studies (12). The NOS consisted of 8 items with 9 scores totally, among which assessed the selection (maximum 4 stars), comparability (maximum 2 stars), and outcomes (maximum 3 stars) for cohort study or exposure (maximum 3 stars) for case control study. The Newcastle-Ottawa Scale (NOS) awards a maximum of 9 points, and studies scoring≥ 7 points were deemed high-quality (13) (Additional file 1: Supplementary Table S2).
2.5 Statistical analysis
This meta-analysis evaluated the relationship between DNA methylation-based accelerated biological aging and stroke risk using both unadjusted and adjusted OR with corresponding 95% confidence intervals. For studies reporting RR or HR, these measures were analytically treated as equivalent to OR (14). All such effect size estimates were collectively categorized as original data for the purposes of our quantitative synthesis. When OR >1 indicated a closer correlation between DNA methylation-based biological age acceleration and stroke occurrence. The software of Stata 15.0 was applied in this meta-analysis and the heterogeneity among studies was evaluated by Cochran’s Q test and I2 statistics (15). When significant heterogeneity was detected (I2 ≥ 50% or p ≤ 0.05), the random-effects model was employed. Conversely, the fixed-effects model was applied to calculate the observed OR (16). Subsequently, subgroup analyses and meta-regression were conducted to investigate the sources of heterogeneity. Sensitivity analysis was performed using the leave-one-out method, which involves sequentially excluding one study at a time, to assess the robustness of the overall estimates (17). Potential publication bias was evaluated using Begg’s test (18) and Egger’s test (19), funnel plot was also performed to examine publication bias. If publication bias was identified, the trim-and-fill method was utilized to assess the stability of the pooled results (20).
3 Results
3.1 Characteristics of the included studies
After searching the PubMed, Embase, Cochrane Library, and Web of Science databases, we initially identified 1,150 articles. After removing duplicates (n = 194), we screened the remaining records by title and abstract, excluding 924 ineligible studies. The remaining articles (n = 32) underwent full-text evaluation. After thorough screening of the study titles, abstracts, and full-texts, a total of 13 articles were included in the final analysis. A flow diagram of the study selection process was shown in Figure 1. Among these 13 articles, 9 were prospective cohort studies (21–29), 3 were retrospective cohort studies (30–32), and 1 was a case–control study (33). Of these studies, 4 were conducted in Asia [2 from Korea (28, 31) and 2 from China (21, 30)], 8 in Europe [3 from the United Kingdom (22, 24, 26), 2 from Spain (32, 33), and 1 each from the Netherlands (23), Germany (25), and Sweden (27)], with 1 study from North America (29). The primary characteristics of included studies were shown in Additional file 1: Supplementary Table S3.
Figure 1. The PRISMA flow diagram showing process of study selection for inclusion in our meta-analyses.
3.2 Predictive value of DNA methylation clocks
A total of 29 effect sizes were extracted from the 13 included studies. Figure 2 presents the effect sizes of the association between EAA (measured by DNA methylation clocks) and stroke risk across the included studies, as well as the pooled result. In the forest plot, each row represents an individual study, and the middle section uses squares and horizontal lines to indicate the effect size of each study and its 95% confidence interval (CI), respectively. The pooled analysis revealed a significant association between biologically predicted age acceleration and stroke incidence, with a combined odds ratio of 1.16 (95%CI 1.13–1.19, p < 0.001). A random-effects model was employed due to substantial heterogeneity among the studies (I2 = 98.9%).
Figure 2. Forest plot of the association between biological aging predicted by DNA methylation clock and stroke occurrence risk (OR, odds ratio; CI, confidential interval).
3.3 Subgroup analyses and meta-regression
To elucidate the sources of heterogeneity, subgroup analyses and univariate meta-regression were conducted on the included effect sizes that were derived from more than three studies. The details could be seen in the Table 1. The results of meta-regression analysis demonstrated that study population (p = 0.074), sample size (p = 0.866), age (p = 0.236), stroke subtype (p = 0.279), sex distribution (p = 0.264), epigenetic clock type (p = 0.773), tissue type (p = 0.949), and follow-up duration (p = 0.877) were not significant sources of heterogeneity. In contrast, study design (p = 0.027) and stroke event classification (p = 0.002) were identified as statistically significant sources of heterogeneity for the association between epigenetic age acceleration and stroke risk. However, subsequent subgroup analyses stratified by study design and event classification revealed persistent high heterogeneity within certain subgroups (I2 > 50%), indicating that these identified factors alone could not fully account for the observed variation. These findings suggest that while the meta-regression detected statistically significant moderators, they likely represent partial rather than exhaustive explanations for heterogeneity. We posit that this residual heterogeneity may stem from both the limited number of included studies (n = 13) and unmeasured confounding variables, particularly baseline population characteristics that were inconsistently reported across studies. Notably, although not reaching statistical significance in our analysis, population demographics (age distribution and ethnic composition) and choice of epigenetic clock methodology emerged as clinically plausible sources of heterogeneity that warrant consideration in future research.
Table 1. Subgroup analysis and meta-regression on DNA methylation clock and stroke.
3.4 Publication bias and sensitivity analysis
Publication bias was assessed through visual inspection of funnel plot symmetry and formal statistical testing using Begg’s and Egger’s tests. Visual inspection of the funnel plot suggested asymmetry, indicating potential publication bias (Additional file 2: Supplementary Figure S1). In our meta-analysis, Begg’s test indicated no significant publication bias (p = 0.053) (Additional file 2: Supplementary Figure S2), whereas Egger’s test revealed evidence of potential publication bias in the estimates of the biological aging-stroke association (p = 0.006) (Additional file 2: Supplementary Figure S3). Following adjustment for funnel plot asymmetry using the trim-and-fill method, the significant association between DNA methylation-based age acceleration and stroke risk remained robust (Additional file 2: Supplementary Figure S4). Sensitivity analysis demonstrated the stability of our findings, as shown in Figure 3, where the sequential exclusion of individual studies did not substantially alter the overall effect estimate, confirming the reliability of our meta-analytic results.
Figure 3. Sensitivity analysis of the association between biological age predicted by DNA methylation clock and stroke occurrence risk.
4 Discussion
This study, through a systematic review and meta-analysis, elucidates the association between DNA methylation-derived accelerated biological aging and stroke risk, addressing prior inconclusive evidence and the lack of comprehensive synthesis in this area. The results confirm a significant positive association between EAA and stroke incidence (OR = 1.16, 95% CI 1.13–1.19), with a stronger correlation observed in incident stroke (OR = 1.28, 95% CI 1.25–1.35) compared to recurrent stroke (OR = 1.11, 95% CI 1.06–1.16). This finding suggests that EAA could serve as a novel biomarker for stroke risk assessment, particularly for identifying individuals at risk of a first-ever stroke.
Increasing evidence suggests that epigenetic age, as measured through DNA methylation clocks, is a more reliable biomarker of biological aging and disease susceptibility than chronological age (34, 35), thus supporting our findings on EAA and stroke. As previously noted, advanced epigenetic clocks (e.g., DNAm PhenoAge, DNAm GrimAge) integrate clinical biomarkers and methylation signatures linked to lifestyle factors, thus enhancing predictive accuracy for various conditions, including cardiovascular diseases, cancer, and neurodegenerative disorders (10). This predictive capacity also extends to stroke-related outcomes: a 10-year longitudinal study found that accelerated biological aging is associated with a higher mortality risk among stroke patients (HR = 1.33, 95% CI 1.26–1.41) (36), emphasizing the relevance of epigenetic aging not only to stroke incidence but also to post-stroke prognosis.
Notably, our subgroup analysis revealed that study design (p = 0.027) and stroke event classification (p = 0.002) were significant sources of heterogeneity, while clock type (p = 0.773) was not, despite the biological differences between clocks. For example, PhenoAge, which integrates clinical biomarkers (e.g., albumin, C-reactive protein) (10), might be more closely tied to vascular physiological aging than Horvath’s multi-tissue clock, yet our analysis did not detect a significant difference in effect size. This may be due to the small number of studies using each clock, which limits statistical power to detect clock-specific effects—a gap that future studies with larger clock-stratified samples should address.
To explain the association between EAA and stroke risk, we draw on plausible biological mechanisms supported by existing evidence and our findings. First, vascular aging, a key driver of stroke, is tightly regulated by DNA methylation. Genes such as ELOVL2 (fatty acid elongase) and KLF14 (transcription factor) (37), whose methylation status defines EAA, are essential for vascular elasticity and endothelial function. Accelerated methylation of these genes suppresses their expression, promoting arteriosclerosis and endothelial dysfunction. These processes are more evident in first-ever stroke (OR = 1.28), which reflects the greater impact of cumulative vascular damage from biological aging. Second, chronic inflammation—a hallmark of both aging and stroke (38)—is amplified by EAA. Hypomethylation of pro-inflammatory cytokines (IL-6, TNF-α) (39, 40) sustains inflammation, damages the vascular endothelium, and accelerates atherosclerosis. In recurrent stroke, where vascular damage is already established, the additional contribution of EAA and its related inflammation may be smaller, consistent with the lower OR of 1.11. Third, impairment of the neurovascular unit (NVU)—a central pathological feature of stroke—can be exacerbated by EAA. Increased methylation of SIRT1 (a neuroprotective gene) (41) suppresses its expression, weakens the blood–brain barrier (BBB), and heightens brain vulnerability to ischemic injury. Collectively, these mechanisms indicate that EAA functions as a “biological integrator” of vascular, inflammatory, and neural damage, thereby contributing to stroke risk in a subtype-specific manner.
Despite support from the aforementioned mechanisms, the clinical application of EAA warrants cautious consideration, particularly because of its modest effect size (OR = 1.16). Compared with established stroke risk factors (e.g., hypertension; diabetes mellitu), the independent contribution of EAA is substantially lower. Therefore, EAA cannot replace traditional risk assessment tools such as the CHA₂DS₂-VASc score. Nevertheless, EAA holds potential as a complementary biomarker. It can provide biological information not captured by traditional factors, aid in optimizing risk stratification, and offer additional support for clinical decision-making. The core challenges in translating EAA into clinical practice currently include two aspects. First, there is no international consensus on defining the quantitative threshold for “high-risk EAA” (i.e., the number of years of accelerated biological aging that can be deemed as an elevation in stroke risk). Second, existing studies have only confirmed the association between EAA and stroke risk, but have not verified whether intervening in EAA (e.g., regulating relevant methylation sites) can reduce the incidence of stroke.
To enhance the translational value of DNA methylation–based EAA in stroke risk assessment, future research should prioritize standardized methodological frameworks. These include adopting validated, vascular health–specific epigenetic clocks and using uniform stroke outcome definitions—explicitly excluding transient ischemic attack and standardizing subtype classification—to minimize heterogeneity. At the same time, cohort diversity should be expanded to include underrepresented populations from Africa, South America, and Oceania, enabling race-stratified analyses that clarify ethnic specificity in the EAA–stroke association. In addition, future studies should aim to establish causality through Mendelian randomization, using EAA-related genetic variants as instrumental variables. They should also evaluate interventional efficacy in trials targeting EAA (e.g., lifestyle modification or epigenetic regulators), and integrate EAA into traditional risk models to assess incremental predictive value using metrics such as AUC and net reclassification improvement. Finally, defining quantifiable “high-risk EAA” thresholds through dose–response meta-analyses or machine learning, and validating these across diverse populations, will be essential to advancing EAA from a biological marker to a clinically actionable tool in stroke prevention.
One of the strengths of this study lies in its pioneering nature: to our knowledge, this is the first study to conduct a systematic review and meta-analysis of the association between DNA methylation clocks and stroke risk, representing a comprehensive approach that, even if not exhaustive, can better elucidate the relationship between this potentially promising biomarker and stroke. Additionally, all studies included in this analysis were of high quality, with scores above 7 on the NOS, thereby ensuring reliability. All articles included in this analysis were published within the past decade, reflecting the latest research in this field.
Despite these strengths, several limitations of this study must be acknowledged. First, substantial heterogeneity remained across studies even after subgroup analysis (I2 = 98.9%). This may be explained by unmeasured factors such as inconsistent confounder adjustment, variation in tissue types used for methylation detection, and unreported baseline characteristics (e.g., socioeconomic status, dietary patterns). Second, Egger’s test indicated publication bias (p = 0.006), which may reduce the reliability of the pooled effect size. Although we applied trim-and-fill adjustment to reduce this bias, residual distortion cannot be excluded, especially given the small number of included studies (n = 13), which limits the effectiveness of correction methods. Third, small-sample bias may have influenced the results. Six of the 13 studies included fewer than 5,000 participants, and these smaller studies tended to report more variable effect sizes, potentially amplifying the observed heterogeneity. Fourth, the lack of individual-level data prevented adjustment for detailed confounders (e.g., baseline BMI, insulin resistance, smoking intensity). These factors may correlate with both EAA and stroke risk, introducing residual confounding that could bias the observed association. Fifth, insufficient data prevented evaluation of differences in the predictive ability of specific clocks for stroke risk.
5 Conclusion
This systematic review and meta-analysis demonstrates that DNA methylation clocks represent a potentially promising tool for stroke risk prediction. However, further research is needed to better identify stratification algorithms and develop specific epigenetic clocks and measurements, which must be validated across diverse populations. The growing body of literature on epigenetic measures calls for systematic organization and classification to reduce heterogeneity between studies and facilitate the translation of these tools into clinical practice and preventive strategies. In the future, personalized aging assessment systems based on DNA methylation clocks may emerge as a critical breakthrough in precision medicine for cerebrovascular diseases.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
JF: Methodology, Writing – original draft, Data curation, Conceptualization. XH: Conceptualization, Data curation, Writing – original draft. RW: Methodology, Software, Writing – original draft. GD: Writing – review & editing, Supervision, Project administration. ML: Writing – review & editing, Supervision, Validation. RD: Supervision, Funding acquisition, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. The study was supported by Science and Technology Project of Guizhou Province (Project Number: Guizhou Science and Technology Support [2021] General 4).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2025.1640853/full#supplementary-material
References
1. Virani, SS, Alonso, A, Benjamin, EJ, Bittencourt, MS, Callaway, CW, Carson, AP, et al. Heart disease and stroke statistics-2020 update: a report from the American Heart Association. Circulation. (2020) 141:e139–596. doi: 10.1161/CIR.0000000000000757
2. Feigin, VL, Brainin, M, Norrving, B, Martins, S, Sacco, RL, Hacke, W, et al. World stroke organization (Wso): global stroke fact sheet 2022. Int J Stroke. (2022) 17:18–29. doi: 10.1177/17474930211065917
3. Mozaffarian, D, Benjamin, EJ, Go, AS, Arnett, DK, Blaha, MJ, Cushman, M, et al. Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation. (2015) 131:e29–e322. doi: 10.1161/CIR.0000000000000152
4. Ciarambino, T, Crispino, P, Mastrolorenzo, E, Viceconti, A, and Giordano, M. Stroke and etiopathogenesis: what is known? Genes. (2022) 13:978. doi: 10.3390/genes13060978
5. Dichgans, M, Pulit, SL, and Rosand, J. Stroke genetics: discovery, biology, and clinical applications. Lancet Neurol. (2019) 18:587–99. doi: 10.1016/S1474-4422(19)30043-2
6. Bell, CG, Lowe, R, Adams, PD, Baccarelli, AA, Beck, S, Bell, JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. (2019) 20:249. doi: 10.1186/s13059-019-1824-y
7. de Lima Camillo, LP, Lapierre, LR, and Singh, R. A pan-tissue DNA-methylation epigenetic clock based on deep learning. NPJ Aging. (2022) 8:4. doi: 10.1038/s41514-022-00085-y
8. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. (2013) 14:R115. doi: 10.1186/gb-2013-14-10-r115
9. Hannum, G, Guinney, J, Zhao, L, Zhang, L, Hughes, G, Sadda, S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. (2013) 49:359–67. doi: 10.1016/j.molcel.2012.10.016
10. Levine, ME, Lu, AT, Quach, A, Chen, BH, Assimes, TL, Bandinelli, S, et al. An epigenetic biomarker of aging for lifespan and Healthspan. Aging (Albany NY). (2018) 10:573–91. doi: 10.18632/aging.101414
11. Moher, D, Shamseer, L, Clarke, M, Ghersi, D, Liberati, A, Petticrew, M, et al. Preferred reporting items for systematic review and meta-analysis protocols (Prisma-P) 2015 statement. Syst Rev. (2015) 4:1. doi: 10.1186/2046-4053-4-1
12. Stang, A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in Meta-analyses. Eur J Epidemiol. (2010) 25:603–5. doi: 10.1007/s10654-010-9491-z
13. Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. Rev Esp Cardiol (Engl Ed). (2021) 74:790–9. doi: 10.1016/j.rec.2021.07.010
14. Greenland, S. Quantitative methods in the review of epidemiologic literature. Epidemiol Rev. (1987) 9:1–30. doi: 10.1093/oxfordjournals.epirev.a036298
15. Higgins, JP, Thompson, SG, Deeks, JJ, and Altman, DG. Measuring inconsistency in Meta-analyses. BMJ. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557
16. DerSimonian, R. Meta-analysis in the design and monitoring of clinical trials. Stat Med. (1996) 15:1237–48. doi: 10.1002/(SICI)1097-0258(19960630)15:12<1237::AID-SIM301>3.0.CO;2-N
17. Xi, B, Huang, Y, Reilly, KH, Li, S, Zheng, R, Barrio-Lopez, MT, et al. Sugar-sweetened beverages and risk of hypertension and Cvd: a dose-response Meta-analysis. Br J Nutr. (2015) 113:709–17. doi: 10.1017/S0007114514004383, 709, 717
18. Begg, CB, and Mazumdar, M. Operating characteristics of a rank correlation test for publication bias. Biometrics. (1994) 50:1088–101. doi: 10.2307/2533446
19. Egger, M, Davey Smith, G, Schneider, M, and Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ. (1997) 315:629–34. doi: 10.1136/bmj.315.7109.629
20. Duval, S, and Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication Bias in Meta-analysis. Biometrics. (2000) 56:455–63. doi: 10.1111/j.0006-341x.2000.00455.x
21. Wang, M, Yan, H, Zhang, Y, Zhou, Q, Meng, X, Lin, J, et al. Accelerated biological aging increases the risk of short- and long-term stroke prognosis in patients with ischemic stroke or Tia. EBioMedicine. (2025) 111:105494. doi: 10.1016/j.ebiom.2024.105494
22. Zhang, X, Zhao, H, Li, Z, Liu, X, Zhang, Y, Yang, N, et al. Accelerated biological aging, healthy Behaviors, and genetic susceptibility with incidence of stroke and its subtypes: a prospective cohort study. Aging Cell. (2025) 24:e14427. doi: 10.1111/acel.14427
23. Waziry, R, Hofman, A, Ghanbari, M, Tiemeier, H, Ikram, MA, Viswanathan, A, et al. Biological aging for risk prediction of first-ever intracerebral Hemorrhage and cerebral infarction in advanced age. J Stroke Cerebrovasc Dis. (2022) 31:106568. doi: 10.1016/j.jstrokecerebrovasdis.2022.106568
24. Mak, JKL, McMurran, CE, and Hagg, S. Clinical biomarker-based biological ageing and future risk of neurological disorders in the Uk biobank. J Neurol Neurosurg Psychiatry. (2024) 95:481–4. doi: 10.1136/jnnp-2023-331917
25. Wang, C, Ni, W, Yao, Y, Just, A, Heiss, J, Wei, Y, et al. DNA methylation-based biomarkers of age acceleration and all-cause death, myocardial infarction, stroke, and Cancer in two cohorts: the Nas, and Kora F4. EBioMedicine. (2021) 63:103151. doi: 10.1016/j.ebiom.2020.103151
26. He, Y, Jia, Y, Li, Y, Wan, Z, Lei, Y, Liao, X, et al. Accelerated biological aging: unveiling the path to cardiometabolic multimorbidity, dementia, and mortality. Front Public Health. (2024) 12:1423016. doi: 10.3389/fpubh.2024.1423016
27. McMurran, CE, Wang, Y, Mak, JKL, Karlsson, IK, Tang, B, Ploner, A, et al. Advanced biological ageing predicts future risk for neurological diagnoses and clinical examination findings. Brain. (2023) 146:4891–902. doi: 10.1093/brain/awad252
28. Bae, CY, Kim, IH, Kim, BS, Kim, JH, and Kim, JH. Predicting the incidence of age-related diseases based on biological age: the 11-year National Health Examination Data Follow-up. Arch Gerontol Geriatr. (2022) 103:104788. doi: 10.1016/j.archger.2022.104788
29. Rivier, C, Szejko, N, Renedo, D, Clocchiatti-Tuozzo, S, Huo, S, de Havenon, A, et al. Bidirectional relationship between epigenetic age and brain health events. Res Sq. (2024):rs.3.rs-4378855. doi: 10.21203/rs.3.rs-4378855/v1
30. Liu, Y, Wang, J, Wei, Z, Wang, Y, Wu, M, Wang, J, et al. Association of phenotypic age and accelerated aging with severity and disability in patients with acute ischemic stroke. J Nutr Health Aging. (2024) 28:100405. doi: 10.1016/j.jnha.2024.100405
31. Kang, YG, Suh, E, Lee, JW, Kim, DW, Cho, KH, and Bae, CY. Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service—health screening 11-year follow-up study. Clin Interv Aging. (2018) 13:429–36. doi: 10.2147/CIA.S157014
32. Soriano-Tarraga, C, Lazcano, U, Jimenez-Conde, J, Ois, A, Cuadrado-Godia, E, Giralt-Steinhauer, E, et al. Biological age is a novel biomarker to predict stroke recurrence. J Neurol. (2021) 268:285–92. doi: 10.1007/s00415-020-10148-3
33. Soriano-Tarraga, C, Giralt-Steinhauer, E, Mola-Caminal, M, Vivanco-Hidalgo, RM, Ois, A, Rodriguez-Campello, A, et al. Ischemic stroke patients are biologically older than their chronological age. Aging (Albany NY). (2016) 8:2655–66. doi: 10.18632/aging.101028
34. Chen, BH, Marioni, RE, Colicino, E, Peters, MJ, Ward-Caviness, CK, Tsai, PC, et al. DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging (Albany NY). (2016) 8:1844–65. doi: 10.18632/aging.101020
35. Declerck, K, and Vanden Berghe, W. Back to the future: epigenetic clock plasticity towards healthy aging. Mech Ageing Dev. (2018) 174:18–29. doi: 10.1016/j.mad.2018.01.002
36. Chen, L, Zhang, Y, Yu, C, Guo, Y, Sun, D, Pang, Y, et al. Modeling biological age using blood biomarkers and physical measurements in Chinese adults. EBioMedicine. (2023) 89:104458. doi: 10.1016/j.ebiom.2023.104458
37. Gensous, N, Bacalini, MG, Pirazzini, C, Marasco, E, Giuliani, C, Ravaioli, F, et al. The epigenetic landscape of age-related diseases: the Geroscience perspective. Biogerontology. (2017) 18:549–59. doi: 10.1007/s10522-017-9695-7
38. Stevenson, AJ, McCartney, DL, Harris, SE, Taylor, AM, Redmond, P, Starr, JM, et al. Trajectories of inflammatory biomarkers over the eighth decade and their associations with immune cell profiles and epigenetic ageing. Clin Epigenetics. (2018) 10:159. doi: 10.1186/s13148-018-0585-x
39. Lossi, L, Castagna, C, and Merighi, A. An overview of the epigenetic modifications in the brain under Normal and pathological conditions. Int J Mol Sci. (2024) 25:3881. doi: 10.3390/ijms25073881
40. Nevalainen, T, Kananen, L, Marttila, S, Jylha, M, Hervonen, A, Hurme, M, et al. Transcriptomic and epigenetic analyses reveal a gender difference in aging-associated inflammation: the vitality 90+ study. Age (Dordr). (2015) 37:9814. doi: 10.1007/s11357-015-9814-9
Keywords: DNA methylation, stroke, biological age, epigenetic age acceleration, meta-analysis
Citation: Feng J, Huang X, Wu R, Ding G, Liu M and Deng R (2025) Accelerated biological aging based on DNA methylation clocks is a predictor of stroke occurrence: a systematic review and meta-analysis. Front. Neurol. 16:1640853. doi: 10.3389/fneur.2025.1640853
Edited by:
Dorin Dragoş, Carol Davila University of Medicine and Pharmacy, RomaniaReviewed by:
Jose Laffita Mesa, Karolinska Institutet (KI), SwedenCristina Gallego Fabrega, Sant Pau Institute for Biomedical Research, Spain
Copyright © 2025 Feng, Huang, Wu, Ding, Liu and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Renli Deng, NjkwODkxMTkyQHFxLmNvbQ==