Epigenetic age acceleration and prevalent age- related neurological disorders: a two-sample Mendelian randomization study

Epigenetic age acceleration and prevalent age- related neurological disorders: a two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epigenetic age acceleration and prevalent age- related neurological disorders: a two-sample Mendelian randomization study Ahmed Mohammed Fawaz, Amro Elsayed Mokhtar, Momen Ahmed Hassan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7000076/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Age-related neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and stroke, pose an increasing global health burden. Epigenetic age acceleration (EAA), measured through DNA methylation-based epigenetic clocks, has emerged as a promising biomarker that links biological aging to disease susceptibility. This study employs a two-sample Mendelian randomization (MR) approach to investigate the causal impact of EAA on neurological outcomes, utilizing genetic instruments derived from large-scale genome-wide association studies (GWAS) for epigenetic clocks, including HannumAge, GrimAge, and plasminogen activator inhibitor-1 (PAI-1). MR analyses identified significant associations between specific epigenetic clocks and neurological diseases. HannumAge was positively associated with an increased risk of multiple sclerosis (OR = 1.068, 95% CI 1.005–1.173, p = 0.047), while elevated PAI-1 levels were linked to a higher risk of Alzheimer’s disease (OR = 1.00008, 95% CI 1.000006–1.000163, p = 0.035) and a reduced risk of stroke (OR = 0.999998, 95% CI 0.999997–1.0, p = 0.024). Additionally, GrimAge was inversely associated with Parkinson’s disease risk (OR = 0.903, 95% CI 0.819–0.995, p = 0.04). These findings provide evidence for a potential causal relationship between EAA and neurological disorders, highlighting the utility of epigenetic clocks in elucidating aging mechanisms and informing diagnostic, prognostic, and therapeutic strategies. Further research is warranted to assess the clinical implications of EAA in personalized medicine and neurodegenerative disease prevention. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Neurological diseases, encompassing a staggering array of several conditions affecting the brain, spine, and nerves, pose a significant global health concern. These disorders’ prevalence is particularly high among the elderly, making them a major contributor to global healthcare burdens. For example, dementia, stroke, or parkinsonism will likely affect one in two women and one in three men during their lifetime (1) . The Global Burden of Disease Study exposes the substantial burden of neurological disorders, which were identified as the primary cause of disability (measured in disability-adjusted life-years) and the second leading cause of death globally in 2016 (2) . In that year, the study revealed a significant number of individuals living with these conditions. Migraine and tension-type headaches emerged as the most prevalent, affecting an estimated three billion individuals worldwide (3) . Stroke remained a leading concern, with over 80 million survivors identified worldwide (4) . and dementia (43.8 million diagnosed cases) (5) followed in prevalence. Parkinson's disease impacted an estimated 6.1 million individuals (6) . The study additionally identified 2.2 million prevalent cases of Multiple Sclerosis globally (7) . Increasing age is undeniably the strongest risk factor for most neurological diseases (2) . This is underscored by the Global Burden of Disease Study, which revealed a two- to threefold increase in the global prevalence of dementia, Stroke, and Parkinsonism between 1990 and 2016 (2) . As the population ages, the burden of these debilitating conditions is expected to rise further. Epigenetic age has emerged as a promising metric for gauging biological aging, surpassing traditional markers like telomere length and even newer omics-based approaches (8, 9) . Epigenetic clocks, which analyze DNA methylation patterns (DNAm) across the genome, have demonstrated superior accuracy in predicting both chronological age and mortality risk (8) . Numerous epigenetic age measures have been developed, each capturing distinct aspects of aging. These clocks offer valuable insights into an individual's biological aging profile. "First generation" clocks like HannumAge rely heavily on cytosine-phosphate-guanine base pairs (CpGs) sites closely linked to chronological age (10) . Recent advancements include "second generation" clocks like PhenoAge and GrimAge, which demonstrate a remarkable ability to predict age-related morbidity and mortality, including an increased risk of neurological diseases (11, 12) . One key metric derived from epigenetic clocks is, Intrinsic epigenetic age acceleration (IEAA). IEAA reflects the body's inherent aging process, independent of external factors, by calculating the difference between epigenetic age and chronological age (8) . This measure helps identify individuals with faster biological aging, a condition associated with an increased risk of neurological diseases (11, 12, 13) . Several studies have utilized epigenetic clocks to explore the connections between developmental processes, biological aging, and neurological diseases (13) . These studies investigated the potential clinical utility of epigenetic clocks in assessing various aspects of neurological diseases, including risk factors, age of onset, diagnosis, and disease progression (13) . However, limitations exist. The temporal nature of the observed relationships between epigenetic clocks and neurological diseases remains unclear, and the inherent confounding factors associated with observational study designs require further investigation. Mendelian randomization (MR) offers a powerful approach to establish causality, particularly for complex traits like EAA (14) . In this study, we leverage a two-sample MR design to explore the potential causal association between EAA and a spectrum of age-related neurological disorders, including Headache, Alzheimer's disease, Stroke, Parkinson's disease, and Multiple Sclerosis. MR utilizes genetic variants as instrumental variables (IVs), mimicking the random allocation of treatment in randomized controlled trials (RCTs) (14) . This analysis employs genetic instruments identified through genome-wide association studies (GWAS) with genome-wide significance (15) . We investigate EAA estimated by epigenetic clocks (HannumAge, Intrinsic HorvathAge, PhenoAge, plasminogen activator inhibitor-1 (PAI-1), and GrimAge) as potential causal exposures on these neurological outcomes. OBJECTIVE Ascertain the causal relationship between epigenetic age acceleration and prevalent age-related neurological disorders utilizing two-sample Mendelian Randomization. METHODS 5.1 Study design : Our study employs a two-sample MR design to explore potential causal relationships between EAA and various neurological disorders. MR utilizes genetic variants as IVs, mimicking randomized exposure assignment and minimizing confounding factors prevalent in observational studies (14) . 5.2 MR assumptions : Three key assumptions underpin MR validity. ( 1 ) relevance assumption: the chosen IVs must be strongly associated with EAA, effectively capturing individual variations in EAA levels. ( 2 ) independence assumption: the IVs should be independent of any confounding factors that might influence the EAA-neurological outcome association. By using genetic variants, MR mitigates confounding as these variants are randomly assigned at conception. ( 3 ) exclusion-restriction assumption: the IVs solely influence neurological outcomes through their effect on EAA, avoiding pleiotropic effects that could bias estimates. ( Fig. 1 ) visually depicts the MR principles, design, and analysis employed. 5.3 Selection of genetic instruments : Our study utilizes EAA as the exposure variable. To investigate potential causal relationships between EAA and neurological disorders, we leverage genetic instruments derived from a recent, large-scale GWAS meta-analysis focused on epigenetic aging (15) . This meta-analysis, the largest for EAA to date, included data from 34,710 participants of European ancestry across 28 cohorts. To ensure robust and informative genetic instruments, we implemented a multi-step quality control process. First, highly significant single nucleotide polymorphisms (SNPs) associated with EAA (p-value < 5 x 10^-8) were identified from GWAS. Subsequently, linkage disequilibrium (LD) analysis using the 1000 Genomes Project (European ancestry) data ensured minimal LD between selected SNPs (R² < 0.001, clumping distance = 10,000 kb). This minimized potential confounding arising from collinearity. Additionally, palindromic SNPs and those with low minor allele frequency (MAF 0.80) were employed to maintain comprehensive coverage. To assess instrument strength, F-statistics were calculated for each SNP based on the explained variance in EAA (R²). A threshold of F > 10 indicated a low risk of weak instrument bias. These stringent selection criteria ensured the chosen instruments were highly informative for the MR analysis, minimizing confounding and strengthening the interpretability of the results (16) . For age-related neurological disorders. Summary-level data for each disease were obtained from publicly available resources within the IEU OpenGWAS catalog (17) . For Alzheimer's Disease (AD), data were sourced from the International Genomics of Alzheimer's Project (IGAP) meta-analysis of stage 1. This meta-analysis included a substantial sample size of 63,926 individuals (21,982 diagnosed with AD and 41,944 controls) drawn from four different consortiums (18) . Parkinson's Disease (PD) data originated from the International Parkinson's Disease Genomics Consortium (IPDGC) GWAS meta-analysis. This consortium combined data from three previously published GWAS studies, encompassing a total of 33,674 PD cases and 449,056 control subjects (19) . Multiple Sclerosis (MS) data were obtained from the most recent publication of the International Multiple Sclerosis Genetics Consortium (IMSGC) shared dataset. This dataset included a significant number of participants, with 47,429 individuals diagnosed with MS and 68,374 controls (20) . Data for stroke originated from a study published on the UK Biobank dataset. This study identified four distinct disease clusters based on age-of-onset profiles. The researchers used data from 6,925 stroke cases and 477,673 controls (21) . It's important to note that this data source deviates from the GWAS framework employed for other diseases in this study. Finally, data for headache were retrieved from the MRC-IEU consortium. This consortium's dataset included 4,293 cases of headache and 458,717 controls (22) . 5.4 Statistical analysis : The inverse variance weighted (IVW) method served as the primary approach for the core MR analysis. This method offers high statistical power by incorporating all available data under the assumption that all instrumental variables (SNPs) are valid (23) . To assess for potential heterogeneity, a statistical test known as Cochran's Q statistic was employed. If statistically significant heterogeneity was detected, indicating inconsistencies across the SNPs, a more robust approach utilizing the IVW method with a multiplicative random-effects model was implemented. Conversely, in the absence of significant heterogeneity, suggesting consistent SNP effects, the IVW method with a fixed-effects model was employed. This model offers the greatest statistical power under the assumption of all valid instrumental variables (24) . However, to account for the potential influence of horizontal pleiotropy, if the MR-Egger intercept deviated significantly from zero (p < 0.05), indicating the presence of horizontal pleiotropy, we would prioritize the MR-Egger findings as the primary method. This approach provides more robust estimates when pleiotropic effects are a concern (25) . All summary-level MR analyses were performed using R software 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria) with the "TwoSampleMR" package. We also employed several sensitivity analyses to strengthen the robustness of our findings. These analyses explored alternative MR methods, including the weighted median and weighted mode approaches. While some of these methods might not have yielded statistically significant results on their own, we considered the overall evidence supportive if the primary inverse variance weighted (IVW) method produced significant results (p < 0.05) and the direction of the beta coefficients (estimates of effect size) remained consistent across all methods. Additionally, we implemented the Steiger test for directionality to formally assess the validity of the observed directional effects between EAA and neurological diseases (24) . To quantify the potential impact of EAA on the risk of age-related neurological disorders, we calculated odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). A significance threshold of p < 0.05 was used to determine statistically significant associations. Furthermore, to evaluate the stability of our findings and the influence of individual SNPs on the overall results, we conducted a systematic "leave-one-out" analysis. This analysis involved sequentially excluding each SNP from the MR analysis and observing its effect on the overall estimates. Stringent analytical approaches were adopted to guarantee the reliability and validity of the results ( Fig. 2 ) . RESULTS 6.1 Selection of genetic instruments Stringent criteria were implemented to ensure the validity of the instruments used in the MR analysis. Selected genetic instruments exhibited highly significant associations with epigenetic age (p-value < 5 x 10^-8) to establish Independence. Additionally, palindromic single nucleotide polymorphisms (SNPs) were excluded due to alignment uncertainties. F-statistics for all instruments surpassed 10, indicating a low risk of weak instrument bias. 6.2 Causal effect of epigenetic age acceleration on neurological diseases The IVW approach in our MR analysis revealed a significant positive association between HannumAge and MS (OR = 1.068, 95% CI 1.005–1.173, p = 0.047) ( Table 1 ) . Importantly, both weighted median and weighted mode analyses yielded consistent directional effects for these association. As for estimated PAI-1 levels, we observed an increased risk for AD (OR = 1.00008, 95% CI 1.000006-1.000163, p = 0.035) and a negative association with stroke risk (OR = 0.999998, 95% CI 0.999997-1.0, p = 0.024) ( Table 1 ) . Notably, the weighted median and weighted mode analyses mirrored these directional associations. For PD, GrimAge exhibited a negative association (OR = 0.903, 95% CI 0.819–0.995, p = 0.04) ( Table 1 ) . Consistent with the previous findings, the weighted median and weighted mode methods again indicated directional effects aligned with the IVW approach. Notably, our analysis did not detect any other statistically significant causal associations between the remaining epigenetic age acceleration measures and neurological diseases (Supplementary Table 2) . Table 1 Causal effect of epigenetic age acceleration on neurological diseases Exposure Outcome nSNP Method OR (95% CI) p-value Steiger test for directionality Correct causal direction p-value DNA Methylation Hannum Age Acceleration Multiple Sclerosis 3 IVW-FE 1.068 (1.005–1.173) 0.047 True 1.11E-44 Weighted median 1.053 (0.94–1.178) 0.373 Weighted mode 1.043 (0.91–1.196) 0.603 DNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels Stroke 5 IVW-FE 0.999998 (0.999997–1) 0.024 True 6.61E-63 Weighted median 0.99999857 (0.999997–1) 0.075 Weighted mode 0.99999857 (0.999996–1.000001) 0.27 DNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels Alzheimer's Disease 5 IVW-FE 1.00008 (1.000006–1.000163) 0.035 True 1.63E-39 Weighted median 1.00007273 (0.999958–1.000187) 0.212 Weighted mode 1.00004492 (0.999825–1.000265) 0.709 DNA Methylation GrimAge Acceleration Parkinson's Disease 4 IVW-FE 0.903 (0.819–0.995) 0.04 True 1.64E-28 Weighted median 0.9129 (0.8115–1.0271) 0.1299 Weighted mode 0.9216 (0.7918–1.0726) 0.3689 IVW-FE: Inverse variance weighted fixed effect model, nSNP: Number of single nucleotide polymorphism, OR: Odds ratio, CI: Confidence interval 6.3 Sensitivity analyses To ensure the robustness of our causal inferences, we employed several methods to assess potential biases in the MR analysis. MR-Egger regression intercept yielded p-values greater than 0.05, suggesting minimal influence of pleiotropy (p > 0.05). Additionally, Cochrane's Q statistics did not reveal significant heterogeneity among the estimates (p > 0.05), indicating consistency in the effects of the instrumental variables ( Table 2 , Supplementary Table 3) . Furthermore, a leave-one-out sensitivity analysis demonstrated no significant changes in effect estimates with removal of individual SNPs, suggesting minimal influence from any single variant ( Figs. 3 (A-D)) . Finally, the MR-Streiger directionality test yielded significant results, supporting a true causal direction for all identified associations ( Table 1 ) . Collectively, these robustness checks strengthen the validity of the observed causal relationships between epigenetic age and the investigated neurological diseases. Table 2 Sensitivity analyses of causal association between epigenetic age acceleration and neurological diseases Exposure Outcome Cochran's Q Heterogeneity statistic MR-Egger for Horizontal Pleiotropy Q Q_df Q_pval Intercept SE P-value DNA Methylation Hannum Age Acceleration Multiple Sclerosis 0.424 1 0.514 -0.087 0.117 0.594 DNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels Stroke 5.207 3 0.157 0.0004 0.002 0.863 DNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels Alzheimer's Disease 5.12 3 0.163 -0.222 0.15 0.236 DNA Methylation GrimAge Acceleration Parkinson's Disease 2.66 2 0.264 -0.156 0.196 0.509 Q_df: Q statistic Degrees of freedom, Q_pval: Q statistic p-value DISCUSSION Our study advances the growing body of research on DNA methylation (DNAm) by examining its utility as a biomarker for biological aging and its potential causal role in neurological disorders through Mendelian Randomization (MR) analysis. While previous research has established associations between epigenetic aging and neurological diseases, relatively few studies have focused on determining causality ( 9 , 11 , 26 – 28 ). Establishing a causal relationship between DNA methylation and neurological diseases is crucial, as it may provide insights into novel therapeutic targets and prevention strategies ( 29 ). Although our MR analysis supports a causal link between accelerated epigenetic aging and the risk of common age-related neurological, interpreting the effect size of this association requires caution. MR relies on common genetic variants as instrumental variables, and these variants typically explain only a small fraction of the variance in epigenetic age acceleration​. Consequently, the effect estimates derived from MR reflect the impact of a relatively modest genetic predisposition to faster epigenetic aging, rather than the effect of a large change in biological age. This means that while MR can provide evidence of causality, the magnitude of the observed effect is expectedly small and should not be overstated ​ ( 30 ). In general, MR is more powerful for detecting the existence of a causal relationship than for precisely quantifying its strength, especially when using instruments of such small effect size. This underscores the need for careful interpretation of our findings and suggests that any potential clinical impact of modifying epigenetic age might be modest, pending further validation with complementary approaches​ ( 30 ). The development of DNAm clocks has revolutionized the study of biological aging. These clocks are constructed using collections of DNA methylation sites whose cumulative methylation patterns estimate biological age ( 10 , 26 ). Hannum’s blood-based algorithm ( 10 ) and Horvath’s multi-tissue algorithm ( 26 ) are among the most commonly used epigenetic clocks. Both clocks produce DNAm age estimates that exhibit a strong correlation with chronological age (CA), with correlation coefficients of approximately 0.94. Notably, discrepancies between DNAm age and CA are hypothesized to reflect biological age (BA), offering insights into an individual’s overall health and aging trajectory. Second-generation or "composite" epigenetic clocks build upon these foundational methods by incorporating a larger number of DNA methylation sites and integrating proxies for aging-related biomarkers ( 31 , 32 ). For instance, the DNAmPhenoAge clock was developed by regressing PhenoAge—a physiological measure of mortality risk—on DNA methylation data ( 33 ). Elevated DNAmPhenoAge values have been associated with increased activation of pro-inflammatory and interferon pathways and reduced activity in pathways related to transcription, DNA repair, and mitochondrial function, emphasizing their role in aging mechanisms ( 11 ). Similarly, DNAmGrimAge integrates DNA methylation markers for seven age-related plasma proteins alongside markers of smoking exposure, further enhancing its relevance as a biomarker for biological aging ( 12 ). By investigating the causal relationship between epigenetic aging and neurological disorders, our study contributes to the understanding of the underlying mechanisms linking biological aging to age-related diseases and offers potential directions for targeted interventions. Multiple sclerosis (MS) is a chronic and complex neuroinflammatory disease that leads to demyelinating and neurodegenerative lesions within the central nervous system (CNS). It is widely recognized as an autoimmune condition with progressive pathology, shaped by both genetic predispositions and environmental influences. While CD4 + T cells have traditionally been considered central to MS pathogenesis, recent evidence increasingly implicates CD8 + T cells as significant contributors to CNS damage ( 34 , 35 ). Investigations into the interplay between B and T cells as drivers of MS pathogenesis have suggested that dysregulated peripheral B cells may escape immune tolerance checkpoints due to impaired regulation by either chronically exhausted or genetically altered regulatory T cells. In this model, B cells interact with IFN-γ-producing effector T-helper (Th) cells in the germinal centers of lymphoid organs, establishing a feedforward loop that intensifies the disease. These pathogenic B cell subsets can breach the blood-CNS barriers and, together with infiltrating CD8 + cytotoxic T lymphocytes (CTLs), undergo local reactivation within the CNS, thus contributing to the neuroinflammatory lesions characteristic of MS ( 34 ). Persistent infections, particularly Epstein-Barr virus (EBV), and specific genetic variants have also been proposed as risk factors in MS. Although direct causal evidence remains to be established, these factors are thought to facilitate pathogenic processes by altering the selection, differentiation, and functionality of B and T cell subsets ( 34 ). Our findings introduce HannumAge as a potential specific risk marker for MS. Derived from DNA methylation patterns at 71 CpG sites, HannumAge reflects age-related changes in immune system composition, showing positive correlation with exhausted plasmablast cells and negative correlation with naive CD8 + T cells. These correlations underscore its potential as an indicator of immune aging in MS, a disease where both genetic susceptibility and environmental exposures accelerate pathology ( 36 ). HannumAge’s responsiveness to environmental and lifestyle factors further underscores its relevance as a marker of immune aging in MS ( 36 ). Epigenetic aging, particularly assessed through HannumAge, has shown notable correlations with inflammatory pathways implicated in MS. Multi omics study demonstrate that pathway enrichment associated with HannumAge is positively correlated with activation of pro-inflammatory pathways, especially nuclear factor-κB (NF-κB), while inversely correlated with the activity of karyopherin alpha (Kpna) proteins ( 38 ). NF-κB is a transcription factor central to inflammation and cell viability regulation, making it particularly significant in chronic inflammatory conditions like MS. Within the CNS of MS patients, NF-κB activation occurs in multiple cell types, including T cells, microglia/macrophages, astrocytes, oligodendrocytes, and neurons. Data from animal models, particularly experimental autoimmune encephalomyelitis, indicate that NF-κB activation in each of these cell types has distinct effects on MS progression, underscoring the complexity of its involvement in disease pathophysiology ( 39 ). A coordinated transcriptional program is essential for oligodendrocyte (OL) differentiation, which is critical for CNS myelin formation and repair. Nuclear import, regulated in part by Kpna proteins, enables transcription factors to access the genome, a crucial step for OL differentiation. This study identifies specific Kpna family members as regulators of OL differentiation, with distinct Kpna proteins responding variably to pro-myelinating signals ( 40 ). Ischemic stroke (IS) results from the obstruction of a cerebral artery by an embolus or thrombus, which significantly reduces blood flow and oxygen supply to the brain, leading to neuronal cell death when prolonged ( 41 ). This deprivation initiates a cascade that can cause irreversible brain damage. Both inflammation and oxidative stress are also elevated in IS; they can act as contributing factors or result from the ischemic injury itself ( 42 ). IS is multifactorial and heterogeneous, arising from a complex interplay of genetic and environmental risk factors ( 43 , 44 ). In our study, we found a statistically significant inverse relationship between estimated levels of plasminogen activator inhibitor-1 (PAI-1) and stroke risk (Odds Ratio [OR] = 0.999998, 95% Confidence Interval [CI] 0.999997-1.0, p = 0.024), suggesting that higher PAI-1 levels may be associated with reduced stroke susceptibility. A meta-analysis of genetic polymorphisms linked to IS, identified using the candidate gene approach, suggests that the high-expression PAI-1 4G allele may confer protection against IS while simultaneously increasing myocardial infarction risk ( 45 ). Cerebral injury progression following focal ischemia involves vascular thrombosis, ischemic damage, inflammation, tissue recovery, and remodeling processes, all of which can be modulated by PAI-1 activity across various cell types. In transgenic mice overexpressing PAI-1, infarct volumes following mechanically induced focal ischemia were smaller than those in wild-type controls, indicating potential neuroprotective effects of PAI-1 under these conditions. Conversely, in thrombosis-induced ischemia models, PAI-1 overexpression resulted in larger infarct volumes compared to wild-type mice, highlighting how PAI-1's effects on infarct size may vary with the mechanism of ischemia induction ( 46 ). Alzheimer’s disease (AD) is a progressive, age-related neurodegenerative disorder that causes cognitive decline, marking it as a common form of dementia ( 47 ). AD pathology is characterized by the extracellular build-up of amyloid-beta (Aβ) peptides and intracellular tau protein aggregates known as neurofibrillary tangles. Soluble oligomers of Aβ are especially neurotoxic, as they disrupt synaptic function and are central to the cognitive impairments seen in AD. Our research identified a causal link between AD and plasminogen activator inhibitor-1 (PAI-1), finding that elevated PAI-1 levels correlate with an increased AD risk. Higher PAI-1 expression interferes with plasmin-mediated degradation of Aβ, preventing the clearance of neurotoxic Aβ plaques in the brain ( 48 – 50 ). Studies also indicate that inhibiting PAI-1 activity can decrease Aβ accumulation and improve synaptic function, thereby enhancing memory in AD model mice ( 51 ). Brain-derived neurotrophic factor (BDNF), the most abundant neurotrophin in the brain, supports neuronal survival and differentiation across many brain regions, including the cortex, hippocampus, and cerebellum ( 52 ). BDNF, synthesized initially as pro-BDNF, is converted into its active, mature form by plasmin. This extracellular conversion is crucial for regulating neuronal activity and memory formation. PAI-1, the primary inhibitor of tissue plasminogen activator (tPA), modulates this BDNF maturation process by controlling the tPA/plasmin pathway. Thus, the tPA/PAI-1 system is a key regulator of the balance between BDNF and pro-BDNF levels, influencing synaptic function and cognitive processes ( 53 ). Recent research supports the therapeutic potential of PAI-1 inhibition in AD. A six-week regimen of a PAI-1 inhibitor in an AD mouse model reduced Aβ deposits in the hippocampus and cortex, leading to cognitive improvements, with these effects linked to increased tPA and plasmin activities ( 51 ). In a multiple sclerosis model, oral administration of the blood-brain barrier-permeable PAI-1 inhibitor TM5484 increased BDNF and choline acetyltransferase expression, markers associated with reduced neurodegeneration and improved neuronal health ( 54 ). Further, in vitro studies show that PAI-1 inhibitors lower pro-BDNF levels in hippocampal slices from epileptic mice, suggesting that targeting the tPA/PAI-1 pathway may modulate BDNF levels beneficially in AD and other neurodegenerative diseases ( 55 ). Suggesting that PAI-1 inhibitors, by modulating the tPA/PAI-1 axis, could be promising tools in regulating BDNF expression and neuroprotection in AD and related neurodegenerative conditions. As for PD, we observed a potential causal relationship between GrimAA and Parkinson’s disease (PD) risk. GrimAge, a second-generation epigenetic clock, integrates multiple biomarkers such as DNA methylation patterns, telomere length, and blood biochemistry to provide a robust predictive measure of mortality and health outcomes, surpassing chronological age in accuracy ( 29 ). Although PD is a neurodegenerative disorder, the annual mortality rate among patients has been steadily increasing ( 56 ). The etiology of mortality in PD remains a contentious issue, with substantial variability observed among individuals. Recent research suggests that factors such as advanced age at onset, dementia, cardiac irregularities, and autonomic dysfunction may contribute significantly to elevated mortality rates ( 57 ). While the interpretation of these findings requires caution due to the limited genetic variants incorporated in the GrimAA instrument, prior studies have demonstrated a strong link between DNAm-age acceleration and the age of onset in idiopathic and LRRK2-associated PD. Notably, every 5-year increment in DNAm-age acceleration correlates with an earlier disease onset by up to six years ( 58 ). Advances in multi-omics approaches, including transcriptomic imputation, fine-mapping, and conditional analysis, have identified high-confidence associations with GrimAge. GrimAge has been inversely associated with the ubiquitin-proteasomal pathway (UPP), a critical mechanism for degrading damaged and toxic proteins through ubiquitin-mediated proteolysis ( 37 ). The dysfunction of the UPP, observed in several neurodegenerative diseases, leads to the accumulation of toxic proteins, formation of inclusions, and ultimately neuronal impairment and cell death ( 59 ). CONCLUSION This study highlights the potential of epigenetic clocks, such as HannumAge and GrimAge, as biomarkers for biological aging and contributors to the pathogenesis of age-related neurological disorders. By linking DNA methylation patterns to diseases like multiple sclerosis, Parkinson’s disease, ischemic stroke, and Alzheimer’s disease, our findings underscore their utility in understanding immune aging, inflammation, and protein homeostasis. Future research should focus on refining these tools to predict disease outcomes and exploring their role in personalized interventions to mitigate the progression of neurological disorders. Declarations Ethical Considerations This study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR). Ethical approval from an Institutional Review Board (IRB) was not required, as the analyses were conducted using publicly available summary statistics from studies that had previously obtained IRB approval and participants' informed consent. DECLARATION COMPETING INTERESTS we declare that we have no financial or non-financial competing interests related to this research study. No conflicts of interest exist that could influence the interpretation of the results or the presentation of the findings. Funding declarations there is no funding for this paper Author Contribution A.M.F. (Ahmed Mohammed Fawaz) conceived the study, conducted the primary data analysis, and drafted the manuscript. N.S.F. (Nesrine Saad Farrag) and N.F.I. (Nesreen Farouk Ibrahim) supervised the study, provided critical revisions, and reviewed the manuscript. A.E.M. (Amro Elsayed Mokhtar) contributed to data acquisition and statistical analysis. M.A.H. (Momen Ahmed Hassan) assisted in data interpretation and manuscript editing. O.E.E.D. (Omar Elsayed Elaraby Dora) was responsible for data visualization, figure preparation, and formatting. All authors reviewed and approved the final version of the manuscript. References Licher S, Darweesh SKL, Wolters FJ, Fani L, Heshmatollah A, Mutlu U, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry. 2019;90(2):148–56. Feigin VL, Nichols E, Alam T, Bannick MS, Beghi E, Blake N, Global Burden of Disease Study 2016. 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Neural Regeneration Res. 2018;13(9):1507. Laitman BM, Mariani JN, Zhang C, Sawai S, John GR. Karyopherin Alpha Proteins Regulate Oligodendrocyte Differentiation. PLoS ONE. 2017;12(1):e0170477. Raichle ME. The pathophysiology of brain ischemia. Ann Neurol. 1983;13(1):2–10. Chehaibi K, Trabelsi I, Mahdouani K, Slimane MN. Correlation of Oxidative Stress Parameters and Inflammatory Markers in Ischemic Stroke Patients. J Stroke Cerebrovasc Dis. 2016;25(11):2585–93. Rubattu S, Giliberti R, Volpe M. Etiology and pathophysiology of stroke as a complex trait. Am J Hypertens. 2000;13(10):1139–48. Hassan A. Genetics and ischaemic stroke. Brain. 2000;123(9):1784–812. Bentley P, Peck G, Smeeth L, Whittaker J, Sharma P. Causal Relationship of Susceptibility Genes to Ischemic Stroke: Comparison to Ischemic Heart Disease and Biochemical Determinants. PLoS ONE. 2010;5(2):e9136. NAGAI N, SUZUKI Y, van HOEF B, LIJNEN HR. 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Akhter H, Huang WT, van Groen T, Kuo HC, Miyata T, Liu RM. A Small Molecule Inhibitor of Plasminogen Activator Inhibitor-1 Reduces Brain Amyloid-β Load and Improves Memory in an Animal Model of Alzheimer’s Disease. J Alzheimer’s Disease. 2018;64(2):447–57. Huang EJ, Reichardt LF. Neurotrophins: Roles in Neuronal Development and Function. Annu Rev Neurosci. 2001;24(1):677–736. Angelucci F, Čechová K, Průša R, Hort J. Amyloid beta soluble forms and plasminogen activation system in Alzheimer’s disease: Consequences on extracellular maturation of brain-derived neurotrophic factor and therapeutic implications. CNS Neurosci Ther. 2019;25(3):303–13. Pelisch N, Dan T, Ichimura A, Sekiguchi H, Vaughan DE, van Ypersele de Strihou C, et al. Plasminogen Activator Inhibitor-1 Antagonist TM5484 Attenuates Demyelination and Axonal Degeneration in a Mice Model of Multiple Sclerosis. PLoS ONE. 2015;10(4):e0124510. Thomas AX, Cruz Del Angel Y, Gonzalez MI, Carrel AJ, Carlsen J, Lam PM et al. Rapid Increases in proBDNF after Pilocarpine-Induced Status Epilepticus in Mice Are Associated with Reduced proBDNF Cleavage Machinery. eneuro. 2016;3(1):ENEURO.0020-15.2016. Scorza FA, de Almeida AG, Scorza CA, Finsterer J. Parkinson’s Disease, Premature Mortality, and Amygdala. Mov Disord. 2022;37(5):1110–1. Diederich NJ, Goldman JG, Stebbins GT, Goetz CG. Failing as doorman and disc jockey at the same time: Amygdalar dysfunction in Parkinson’s disease. Mov Disord. 2016;31(1):11–22. Tang X, Gonzalez-Latapi P, Marras C, Visanji NP, Yang W, Sato C, et al. Epigenetic Clock Acceleration Is Linked to Age at Onset of Parkinson’s Disease. Mov Disord. 2022;37(9):1831–40. Chung KKK, Dawson VL, Dawson TM. The role of the ubiquitin-proteasomal pathway in Parkinson’s disease and other neurodegenerative disorders. Trends Neurosci. 2001;24(11):S7–14. Additional Declarations No competing interests reported. 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Fawaz","email":"data:image/png;base64,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","orcid":"","institution":"Port Said University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"Mohammed","lastName":"Fawaz","suffix":""},{"id":478888019,"identity":"41179472-bb4e-4f1f-ad8f-beaf8a2ec0d3","order_by":1,"name":"Amro Elsayed Mokhtar","email":"","orcid":"","institution":"Port Said 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University","correspondingAuthor":false,"prefix":"","firstName":"Nesreen","middleName":"Farouk","lastName":"Ibrahim","suffix":""},{"id":478888023,"identity":"c174cbe4-d0d1-432f-a5ed-2d1df91863d1","order_by":5,"name":"Nesrine Saad Farrag","email":"","orcid":"","institution":"Port Said University","correspondingAuthor":false,"prefix":"","firstName":"Nesrine","middleName":"Saad","lastName":"Farrag","suffix":""}],"badges":[],"createdAt":"2025-06-29 00:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7000076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7000076/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85968050,"identity":"fbb6d9e9-21e3-49b7-ba90-940ae83df5a7","added_by":"auto","created_at":"2025-07-03 17:31:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design of causal association of Epigenetic Age Acceleration and Age-Related Neurological diseases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: EAA (Epigenetic Age Acceleration), SNPs (Single Nucleotide Polymorphisms)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7000076/v1/6c6c41b8f49c4f599b43bfb8.jpeg"},{"id":85967224,"identity":"18e76538-104c-4a75-b0e8-7cd57eb1075f","added_by":"auto","created_at":"2025-07-03 17:23:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of causal association of Epigenetic Age Acceleration and Age-Related Neurological diseases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7000076/v1/f86ed5bf55c12eb3f8a17487.png"},{"id":85968051,"identity":"cf90ed9f-75b3-4ac7-acd8-9b0dad00dcb2","added_by":"auto","created_at":"2025-07-03 17:31:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3A: Leave-one-out analysis about risk of Multiple Sclerosis with HannumAge.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3B: Leave-one-out analysis about risk of Stroke with plasminogen activator inhibitor-1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3C: Leave-one-out analysis about risk of Alzheimer’s disease with plasminogen activator inhibitor-1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3D: Leave-one-out analysis about risk of Parkinson’s disease with GrimAge\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7000076/v1/c71c739d923a2e661ba1d9f1.png"},{"id":87211083,"identity":"f1c492d5-b5f4-4cf6-b5ae-85053b3a1274","added_by":"auto","created_at":"2025-07-21 14:53:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1220430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7000076/v1/ea5a367f-93e3-4a77-a416-6b9eb8c60796.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epigenetic age acceleration and prevalent age- related neurological disorders: a two-sample Mendelian randomization study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNeurological diseases, encompassing a staggering array of several conditions affecting the brain, spine, and nerves, pose a significant global health concern. These disorders\u0026rsquo; prevalence is particularly high among the elderly, making them a major contributor to global healthcare burdens. For example, dementia, stroke, or parkinsonism will likely affect one in two women and one in three men during their lifetime \u003csup\u003e(1)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease Study exposes the substantial burden of neurological disorders, which were identified as the primary cause of disability (measured in disability-adjusted life-years) and the second leading cause of death globally in 2016 \u003csup\u003e(2)\u003c/sup\u003e. In that year, the study revealed a significant number of individuals living with these conditions. Migraine and tension-type headaches emerged as the most prevalent, affecting an estimated three billion individuals worldwide \u003csup\u003e(3)\u003c/sup\u003e. Stroke remained a leading concern, with over 80\u0026nbsp;million survivors identified worldwide \u003csup\u003e(4)\u003c/sup\u003e. and dementia (43.8\u0026nbsp;million diagnosed cases) \u003csup\u003e(5)\u003c/sup\u003e followed in prevalence. Parkinson's disease impacted an estimated 6.1\u0026nbsp;million individuals \u003csup\u003e(6)\u003c/sup\u003e. The study additionally identified 2.2\u0026nbsp;million prevalent cases of Multiple Sclerosis globally \u003csup\u003e(7)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIncreasing age is undeniably the strongest risk factor for most neurological diseases \u003csup\u003e(2)\u003c/sup\u003e. This is underscored by the Global Burden of Disease Study, which revealed a two- to threefold increase in the global prevalence of dementia, Stroke, and Parkinsonism between 1990 and 2016 \u003csup\u003e(2)\u003c/sup\u003e. As the population ages, the burden of these debilitating conditions is expected to rise further.\u003c/p\u003e \u003cp\u003eEpigenetic age has emerged as a promising metric for gauging biological aging, surpassing traditional markers like telomere length and even newer omics-based approaches \u003csup\u003e(8, 9)\u003c/sup\u003e. Epigenetic clocks, which analyze DNA methylation patterns (DNAm) across the genome, have demonstrated superior accuracy in predicting both chronological age and mortality risk \u003csup\u003e(8)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous epigenetic age measures have been developed, each capturing distinct aspects of aging. These clocks offer valuable insights into an individual's biological aging profile. \"First generation\" clocks like HannumAge rely heavily on cytosine-phosphate-guanine base pairs (CpGs) sites closely linked to chronological age \u003csup\u003e(10)\u003c/sup\u003e. Recent advancements include \"second generation\" clocks like PhenoAge and GrimAge, which demonstrate a remarkable ability to predict age-related morbidity and mortality, including an increased risk of neurological diseases \u003csup\u003e(11, 12)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne key metric derived from epigenetic clocks is, Intrinsic epigenetic age acceleration (IEAA). IEAA reflects the body's inherent aging process, independent of external factors, by calculating the difference between epigenetic age and chronological age \u003csup\u003e(8)\u003c/sup\u003e. This measure helps identify individuals with faster biological aging, a condition associated with an increased risk of neurological diseases \u003csup\u003e(11, 12, 13)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have utilized epigenetic clocks to explore the connections between developmental processes, biological aging, and neurological diseases \u003csup\u003e(13)\u003c/sup\u003e. These studies investigated the potential clinical utility of epigenetic clocks in assessing various aspects of neurological diseases, including risk factors, age of onset, diagnosis, and disease progression \u003csup\u003e(13)\u003c/sup\u003e. However, limitations exist. The temporal nature of the observed relationships between epigenetic clocks and neurological diseases remains unclear, and the inherent confounding factors associated with observational study designs require further investigation.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) offers a powerful approach to establish causality, particularly for complex traits like EAA \u003csup\u003e(14)\u003c/sup\u003e. In this study, we leverage a two-sample MR design to explore the potential causal association between EAA and a spectrum of age-related neurological disorders, including Headache, Alzheimer's disease, Stroke, Parkinson's disease, and Multiple Sclerosis.\u003c/p\u003e \u003cp\u003eMR utilizes genetic variants as instrumental variables (IVs), mimicking the random allocation of treatment in randomized controlled trials (RCTs) \u003csup\u003e(14)\u003c/sup\u003e. This analysis employs genetic instruments identified through genome-wide association studies (GWAS) with genome-wide significance \u003csup\u003e(15)\u003c/sup\u003e. We investigate EAA estimated by epigenetic clocks (HannumAge, Intrinsic HorvathAge, PhenoAge, plasminogen activator inhibitor-1\u003c/p\u003e \u003cp\u003e(PAI-1), and GrimAge) as potential causal exposures on these neurological outcomes.\u003c/p\u003e\n\u003ch3\u003eOBJECTIVE\u003c/h3\u003e\n\u003cp\u003eAscertain the causal relationship between epigenetic age acceleration and prevalent age-related neurological disorders utilizing two-sample Mendelian Randomization.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e5.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStudy design\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eOur study employs a two-sample MR design to explore potential causal relationships between EAA and various neurological disorders. MR utilizes genetic variants as IVs, mimicking randomized exposure assignment and minimizing confounding factors prevalent in observational studies \u003csup\u003e(14)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e5.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMR assumptions\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eThree key assumptions underpin MR validity. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) relevance assumption: the chosen IVs must be strongly associated with EAA, effectively capturing individual variations in EAA levels. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) independence assumption: the IVs should be independent of any confounding factors that might influence the EAA-neurological outcome association. By using genetic variants, MR mitigates confounding as these variants are randomly assigned at conception. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) exclusion-restriction assumption: the IVs solely influence neurological outcomes through their effect on EAA, avoiding pleiotropic effects that could bias estimates.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e visually depicts the MR principles, design, and analysis employed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e5.3 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSelection of genetic instruments\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eOur study utilizes EAA as the exposure variable. To investigate potential causal relationships between EAA and neurological disorders, we leverage genetic instruments derived from a recent, large-scale GWAS meta-analysis focused on epigenetic aging \u003csup\u003e(15)\u003c/sup\u003e. This meta-analysis, the largest for EAA to date, included data from 34,710 participants of European ancestry across 28 cohorts.\u003c/p\u003e \u003cp\u003eTo ensure robust and informative genetic instruments, we implemented a multi-step quality control process. First, highly significant single nucleotide polymorphisms (SNPs) associated with EAA (p-value\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10^-8) were identified from GWAS. Subsequently, linkage disequilibrium (LD) analysis using the 1000 Genomes Project (European ancestry) data ensured minimal LD between selected SNPs (R\u0026sup2; \u0026lt; 0.001, clumping distance\u0026thinsp;=\u0026thinsp;10,000 kb). This minimized potential confounding arising from collinearity. Additionally, palindromic SNPs and those with low minor allele frequency (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were excluded due to alignment uncertainties and potential for unreliable effect size estimates, respectively. Finally, for missing SNPs in outcome GWAS datasets, proxy SNPs with high LD (r\u0026sup2; \u0026gt; 0.80) were employed to maintain comprehensive coverage. To assess instrument strength, F-statistics were calculated for each SNP based on the explained variance in EAA (R\u0026sup2;). A threshold of F\u0026thinsp;\u0026gt;\u0026thinsp;10 indicated a low risk of weak instrument bias. These stringent selection criteria ensured the chosen instruments were highly informative for the MR analysis, minimizing confounding and strengthening the interpretability of the results \u003csup\u003e(16)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor age-related neurological disorders. Summary-level data for each disease were obtained from publicly available resources within the IEU OpenGWAS catalog \u003csup\u003e(17)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor Alzheimer's Disease (AD), data were sourced from the International Genomics of Alzheimer's Project (IGAP) meta-analysis of stage 1. This meta-analysis included a substantial sample size of 63,926 individuals (21,982 diagnosed with AD and 41,944 controls) drawn from four different consortiums \u003csup\u003e(18)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eParkinson's Disease (PD) data originated from the International Parkinson's Disease Genomics Consortium (IPDGC) GWAS meta-analysis. This consortium combined data from three previously published GWAS studies, encompassing a total of 33,674 PD cases and 449,056 control subjects \u003csup\u003e(19)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMultiple Sclerosis (MS) data were obtained from the most recent publication of the International Multiple Sclerosis Genetics Consortium (IMSGC) shared dataset. This dataset included a significant number of participants, with 47,429 individuals diagnosed with MS and 68,374 controls \u003csup\u003e(20)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eData for stroke originated from a study published on the UK Biobank dataset. This study identified four distinct disease clusters based on age-of-onset profiles. The researchers used data from 6,925 stroke cases and 477,673 controls \u003csup\u003e(21)\u003c/sup\u003e. It's important to note that this data source deviates from the GWAS framework employed for other diseases in this study.\u003c/p\u003e \u003cp\u003eFinally, data for headache were retrieved from the MRC-IEU consortium. This consortium's dataset included 4,293 cases of headache and 458,717 controls \u003csup\u003e(22)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.4 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStatistical analysis\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003eThe inverse variance weighted (IVW) method served as the primary approach for the core MR analysis. This method offers high statistical power by incorporating all available data under the assumption that all instrumental variables (SNPs) are valid \u003csup\u003e(23)\u003c/sup\u003e. To assess for potential heterogeneity, a statistical test known as Cochran's Q statistic was employed. If statistically significant heterogeneity was detected, indicating inconsistencies across the SNPs, a more robust approach utilizing the IVW method with a multiplicative random-effects model was implemented. Conversely, in the absence of significant heterogeneity, suggesting consistent SNP effects, the IVW method with a fixed-effects model was employed. This model offers the greatest statistical power under the assumption of all valid instrumental variables \u003csup\u003e(24)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, to account for the potential influence of horizontal pleiotropy, if the MR-Egger intercept deviated significantly from zero (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating the presence of horizontal pleiotropy, we would prioritize the MR-Egger findings as the primary method. This approach provides more robust estimates when pleiotropic effects are a concern \u003csup\u003e(25)\u003c/sup\u003e. All summary-level MR analyses were performed using R software 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria) with the \"TwoSampleMR\" package.\u003c/p\u003e \u003cp\u003eWe also employed several sensitivity analyses to strengthen the robustness of our findings. These analyses explored alternative MR methods, including the weighted median and weighted mode approaches. While some of these methods might not have yielded statistically significant results on their own, we considered the overall evidence supportive if the primary inverse variance weighted (IVW) method produced significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the direction of the beta coefficients (estimates of effect size) remained consistent across all methods. Additionally, we implemented the Steiger test for directionality to formally assess the validity of the observed directional effects between EAA and neurological diseases \u003csup\u003e(24)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo quantify the potential impact of EAA on the risk of age-related neurological disorders, we calculated odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to determine statistically significant associations. Furthermore, to evaluate the stability of our findings and the influence of individual SNPs on the overall results, we conducted a systematic \"leave-one-out\" analysis. This analysis involved sequentially excluding each SNP from the MR analysis and observing its effect on the overall estimates. Stringent analytical approaches were adopted to guarantee the reliability and validity of the results \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e6.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSelection of genetic instruments\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eStringent criteria were implemented to ensure the validity of the instruments used in the MR analysis. Selected genetic instruments exhibited highly significant associations with epigenetic age (p-value\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10^-8) to establish Independence. Additionally, palindromic single nucleotide polymorphisms (SNPs) were excluded due to alignment uncertainties.\u003c/p\u003e \u003cp\u003eF-statistics for all instruments surpassed 10, indicating a low risk of weak instrument bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e6.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCausal effect of epigenetic age acceleration on neurological diseases\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe IVW approach in our MR analysis revealed a significant positive association between HannumAge and MS (OR\u0026thinsp;=\u0026thinsp;1.068, 95% CI 1.005\u0026ndash;1.173, p\u0026thinsp;=\u0026thinsp;0.047) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Importantly, both weighted median and weighted mode analyses yielded consistent directional effects for these association.\u003c/p\u003e \u003cp\u003eAs for estimated PAI-1 levels, we observed an increased risk for AD (OR\u0026thinsp;=\u0026thinsp;1.00008, 95% CI 1.000006-1.000163, p\u0026thinsp;=\u0026thinsp;0.035) and a negative association with stroke risk (OR\u0026thinsp;=\u0026thinsp;0.999998, 95% CI 0.999997-1.0, p\u0026thinsp;=\u0026thinsp;0.024) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Notably, the weighted median and weighted mode analyses mirrored these directional associations.\u003c/p\u003e \u003cp\u003eFor PD, GrimAge exhibited a negative association (OR\u0026thinsp;=\u0026thinsp;0.903, 95% CI 0.819\u0026ndash;0.995, p\u0026thinsp;=\u0026thinsp;0.04) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Consistent with the previous findings, the weighted median and weighted mode methods again indicated directional effects aligned with the IVW approach.\u003c/p\u003e \u003cp\u003eNotably, our analysis did not detect any other statistically significant causal associations between the remaining epigenetic age acceleration measures and neurological diseases \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCausal effect of epigenetic age acceleration on neurological diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSteiger test for directionality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCorrect causal direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDNA Methylation Hannum Age Acceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMultiple Sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW-FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.068 (1.005\u0026ndash;1.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.11E-44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.053 (0.94\u0026ndash;1.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.043 (0.91\u0026ndash;1.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW-FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999998 (0.999997\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.61E-63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99999857 (0.999997\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99999857 (0.999996\u0026ndash;1.000001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAlzheimer's Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW-FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00008 (1.000006\u0026ndash;1.000163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.63E-39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00007273 (0.999958\u0026ndash;1.000187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00004492 (0.999825\u0026ndash;1.000265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDNA Methylation GrimAge Acceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParkinson's Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW-FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.903 (0.819\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.64E-28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9129 (0.8115\u0026ndash;1.0271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9216 (0.7918\u0026ndash;1.0726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eIVW-FE: Inverse variance weighted fixed effect model, nSNP: Number of single nucleotide polymorphism, \u003c/p\u003e \u003cp\u003eOR: Odds ratio, CI: Confidence interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e6.3 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSensitivity analyses\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eTo ensure the robustness of our causal inferences, we employed several methods to assess potential biases in the MR analysis. MR-Egger regression intercept yielded p-values greater than 0.05, suggesting minimal influence of pleiotropy (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, Cochrane's Q statistics did not reveal significant heterogeneity among the estimates\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating consistency in the effects of the instrumental variables\u003c/p\u003e \u003cp\u003e \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;3)\u003c/b\u003e. Furthermore, a leave-one-out sensitivity analysis demonstrated no significant changes in effect estimates with removal of individual SNPs, suggesting minimal influence from any single variant \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(A-D))\u003c/b\u003e. Finally, the MR-Streiger directionality test yielded significant results, supporting a true causal direction for all identified associations \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Collectively, these robustness checks strengthen the validity of the observed causal relationships between epigenetic age and the investigated neurological diseases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analyses of causal association between epigenetic age acceleration and neurological diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCochran's Q Heterogeneity statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMR-Egger for Horizontal Pleiotropy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA Methylation Hannum Age Acceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiple Sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA Methylation-estimated Plasminogen Activator Inhibitor-1 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlzheimer's Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA Methylation GrimAge Acceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParkinson's Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eQ_df: Q statistic Degrees of freedom, Q_pval: Q statistic p-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study advances the growing body of research on DNA methylation (DNAm) by examining its utility as a biomarker for biological aging and its potential causal role in neurological disorders through Mendelian Randomization (MR) analysis. While previous research has established associations between epigenetic aging and neurological diseases, relatively few studies have focused on determining causality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Establishing a causal relationship between DNA methylation and neurological diseases is crucial, as it may provide insights into novel therapeutic targets and prevention strategies (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough our MR analysis supports a causal link between accelerated epigenetic aging and the risk of common age-related neurological, interpreting the effect size of this association requires caution. MR relies on common genetic variants as instrumental variables, and these variants typically explain only a small fraction of the variance in epigenetic age acceleration​. Consequently, the effect estimates derived from MR reflect the impact of a relatively modest genetic predisposition to faster epigenetic aging, rather than the effect of a large change in biological age. This means that while MR can provide evidence of causality, the magnitude of the observed effect is expectedly small and should not be overstated ​ (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In general, MR is more powerful for detecting the existence of a causal relationship than for precisely quantifying its strength, especially when using instruments of such small effect size. This underscores the need for careful interpretation of our findings and suggests that any potential clinical impact of modifying epigenetic age might be modest, pending further validation with complementary approaches​ (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe development of DNAm clocks has revolutionized the study of biological aging. These clocks are constructed using collections of DNA methylation sites whose cumulative methylation patterns estimate biological age (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Hannum\u0026rsquo;s blood-based algorithm (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and Horvath\u0026rsquo;s multi-tissue algorithm (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) are among the most commonly used epigenetic clocks. Both clocks produce DNAm age estimates that exhibit a strong correlation with chronological age (CA), with correlation coefficients of approximately 0.94. Notably, discrepancies between DNAm age and CA are hypothesized to reflect biological age (BA), offering insights into an individual\u0026rsquo;s overall health and aging trajectory.\u003c/p\u003e \u003cp\u003eSecond-generation or \"composite\" epigenetic clocks build upon these foundational methods by incorporating a larger number of DNA methylation sites and integrating proxies for aging-related biomarkers (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). For instance, the DNAmPhenoAge clock was developed by regressing PhenoAge\u0026mdash;a physiological measure of mortality risk\u0026mdash;on DNA methylation data (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Elevated DNAmPhenoAge values have been associated with increased activation of pro-inflammatory and interferon pathways and reduced activity in pathways related to transcription, DNA repair, and mitochondrial function, emphasizing their role in aging mechanisms (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, DNAmGrimAge integrates DNA methylation markers for seven age-related plasma proteins alongside markers of smoking exposure, further enhancing its relevance as a biomarker for biological aging (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy investigating the causal relationship between epigenetic aging and neurological disorders, our study contributes to the understanding of the underlying mechanisms linking biological aging to age-related diseases and offers potential directions for targeted interventions.\u003c/p\u003e \u003cp\u003eMultiple sclerosis (MS) is a chronic and complex neuroinflammatory disease that leads to demyelinating and neurodegenerative lesions within the central nervous system (CNS). It is widely recognized as an autoimmune condition with progressive pathology, shaped by both genetic predispositions and environmental influences. While CD4\u0026thinsp;+\u0026thinsp;T cells have traditionally been considered central to MS pathogenesis, recent evidence increasingly implicates CD8\u0026thinsp;+\u0026thinsp;T cells as significant contributors to CNS damage (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInvestigations into the interplay between B and T cells as drivers of MS pathogenesis have suggested that dysregulated peripheral B cells may escape immune tolerance checkpoints due to impaired regulation by either chronically exhausted or genetically altered regulatory T cells. In this model, B cells interact with IFN-γ-producing effector T-helper (Th) cells in the germinal centers of lymphoid organs, establishing a feedforward loop that intensifies the disease. These pathogenic B cell subsets can breach the blood-CNS barriers and, together with infiltrating CD8\u0026thinsp;+\u0026thinsp;cytotoxic T lymphocytes (CTLs), undergo local reactivation within the CNS, thus contributing to the neuroinflammatory lesions characteristic of MS (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePersistent infections, particularly Epstein-Barr virus (EBV), and specific genetic variants have also been proposed as risk factors in MS. Although direct causal evidence remains to be established, these factors are thought to facilitate pathogenic processes by altering the selection, differentiation, and functionality of B and T cell subsets (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings introduce HannumAge as a potential specific risk marker for MS. Derived from DNA methylation patterns at 71 CpG sites, HannumAge reflects age-related changes in immune system composition, showing positive correlation with exhausted plasmablast cells and negative correlation with naive CD8\u0026thinsp;+\u0026thinsp;T cells. These correlations underscore its potential as an indicator of immune aging in MS, a disease where both genetic susceptibility and environmental exposures accelerate pathology (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). HannumAge\u0026rsquo;s responsiveness to environmental and lifestyle factors further underscores its relevance as a marker of immune aging in MS (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpigenetic aging, particularly assessed through HannumAge, has shown notable correlations with inflammatory pathways implicated in MS. Multi omics study demonstrate that pathway enrichment associated with HannumAge is positively correlated with activation of pro-inflammatory pathways, especially nuclear factor-κB (NF-κB), while inversely correlated with the activity of karyopherin alpha (Kpna) proteins (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNF-κB is a transcription factor central to inflammation and cell viability regulation, making it particularly significant in chronic inflammatory conditions like MS. Within the CNS of MS patients, NF-κB activation occurs in multiple cell types, including T cells, microglia/macrophages, astrocytes, oligodendrocytes, and neurons. Data from animal models, particularly experimental autoimmune encephalomyelitis, indicate that NF-κB activation in each of these cell types has distinct effects on MS progression, underscoring the complexity of its involvement in disease pathophysiology (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA coordinated transcriptional program is essential for oligodendrocyte (OL) differentiation, which is critical for CNS myelin formation and repair. Nuclear import, regulated in part by Kpna proteins, enables transcription factors to access the genome, a crucial step for OL differentiation. This study identifies specific Kpna family members as regulators of OL differentiation, with distinct Kpna proteins responding variably to pro-myelinating signals (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIschemic stroke (IS) results from the obstruction of a cerebral artery by an embolus or thrombus, which significantly reduces blood flow and oxygen supply to the brain, leading to neuronal cell death when prolonged (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This deprivation initiates a cascade that can cause irreversible brain damage. Both inflammation and oxidative stress are also elevated in IS; they can act as contributing factors or result from the ischemic injury itself (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). IS is multifactorial and heterogeneous, arising from a complex interplay of genetic and environmental risk factors (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, we found a statistically significant inverse relationship between estimated levels of plasminogen activator inhibitor-1 (PAI-1) and stroke risk (Odds Ratio [OR]\u0026thinsp;=\u0026thinsp;0.999998, 95% Confidence Interval [CI] 0.999997-1.0, p\u0026thinsp;=\u0026thinsp;0.024), suggesting that higher PAI-1 levels may be associated with reduced stroke susceptibility. A meta-analysis of genetic polymorphisms linked to IS, identified using the candidate gene approach, suggests that the high-expression PAI-1 4G allele may confer protection against IS while simultaneously increasing myocardial infarction risk (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCerebral injury progression following focal ischemia involves vascular thrombosis, ischemic damage, inflammation, tissue recovery, and remodeling processes, all of which can be modulated by PAI-1 activity across various cell types. In transgenic mice overexpressing PAI-1, infarct volumes following mechanically induced focal ischemia were smaller than those in wild-type controls, indicating potential neuroprotective effects of PAI-1 under these conditions. Conversely, in thrombosis-induced ischemia models, PAI-1 overexpression resulted in larger infarct volumes compared to wild-type mice, highlighting how PAI-1's effects on infarct size may vary with the mechanism of ischemia induction (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive, age-related neurodegenerative disorder that causes cognitive decline, marking it as a common form of dementia (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). AD pathology is characterized by the extracellular build-up of amyloid-beta (Aβ) peptides and intracellular tau protein aggregates known as neurofibrillary tangles. Soluble oligomers of Aβ are especially neurotoxic, as they disrupt synaptic function and are central to the cognitive impairments seen in AD.\u003c/p\u003e \u003cp\u003eOur research identified a causal link between AD and plasminogen activator inhibitor-1 (PAI-1), finding that elevated PAI-1 levels correlate with an increased AD risk. Higher PAI-1 expression interferes with plasmin-mediated degradation of Aβ, preventing the clearance of neurotoxic Aβ plaques in the brain (\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Studies also indicate that inhibiting PAI-1 activity can decrease Aβ accumulation and improve synaptic function, thereby enhancing memory in AD model mice (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBrain-derived neurotrophic factor (BDNF), the most abundant neurotrophin in the brain, supports neuronal survival and differentiation across many brain regions, including the cortex, hippocampus, and cerebellum (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). BDNF, synthesized initially as pro-BDNF, is converted into its active, mature form by plasmin. This extracellular conversion is crucial for regulating neuronal activity and memory formation. PAI-1, the primary inhibitor of tissue plasminogen activator (tPA), modulates this BDNF maturation process by controlling the tPA/plasmin pathway. Thus, the tPA/PAI-1 system is a key regulator of the balance between BDNF and pro-BDNF levels, influencing synaptic function and cognitive processes (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research supports the therapeutic potential of PAI-1 inhibition in AD. A six-week regimen of a PAI-1 inhibitor in an AD mouse model reduced Aβ deposits in the hippocampus and cortex, leading to cognitive improvements, with these effects linked to increased tPA and plasmin activities (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In a multiple sclerosis model, oral administration of the blood-brain barrier-permeable PAI-1 inhibitor TM5484 increased BDNF and choline acetyltransferase expression, markers associated with reduced neurodegeneration and improved neuronal health (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Further, in vitro studies show that PAI-1 inhibitors lower pro-BDNF levels in hippocampal slices from epileptic mice, suggesting that targeting the tPA/PAI-1 pathway may modulate BDNF levels beneficially in AD and other neurodegenerative diseases (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Suggesting that PAI-1 inhibitors, by modulating the tPA/PAI-1 axis, could be promising tools in regulating BDNF expression and neuroprotection in AD and related neurodegenerative conditions.\u003c/p\u003e \u003cp\u003eAs for PD, we observed a potential causal relationship between GrimAA and Parkinson\u0026rsquo;s disease (PD) risk. GrimAge, a second-generation epigenetic clock, integrates multiple biomarkers such as DNA methylation patterns, telomere length, and blood biochemistry to provide a robust predictive measure of mortality and health outcomes, surpassing chronological age in accuracy (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although PD is a neurodegenerative disorder, the annual mortality rate among patients has been steadily increasing (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). The etiology of mortality in PD remains a contentious issue, with substantial variability observed among individuals. Recent research suggests that factors such as advanced age at onset, dementia, cardiac irregularities, and autonomic dysfunction may contribute significantly to elevated mortality rates (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the interpretation of these findings requires caution due to the limited genetic variants incorporated in the GrimAA instrument, prior studies have demonstrated a strong link between DNAm-age acceleration and the age of onset in idiopathic and LRRK2-associated PD. Notably, every 5-year increment in DNAm-age acceleration correlates with an earlier disease onset by up to six years (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvances in multi-omics approaches, including transcriptomic imputation, fine-mapping, and conditional analysis, have identified high-confidence associations with GrimAge. GrimAge has been inversely associated with the ubiquitin-proteasomal pathway (UPP), a critical mechanism for degrading damaged and toxic proteins through ubiquitin-mediated proteolysis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The dysfunction of the UPP, observed in several neurodegenerative diseases, leads to the accumulation of toxic proteins, formation of inclusions, and ultimately neuronal impairment and cell death (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study highlights the potential of epigenetic clocks, such as HannumAge and GrimAge, as biomarkers for biological aging and contributors to the pathogenesis of age-related neurological disorders. By linking DNA methylation patterns to diseases like multiple sclerosis, Parkinson\u0026rsquo;s disease, ischemic stroke, and Alzheimer\u0026rsquo;s disease, our findings underscore their utility in understanding immune aging, inflammation, and protein homeostasis.\u003c/p\u003e \u003cp\u003eFuture research should focus on refining these tools to predict disease outcomes and exploring their role in personalized interventions to mitigate the progression of neurological disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR). Ethical approval from an Institutional Review Board (IRB) was not required, as the analyses were conducted using publicly available summary statistics from studies that had previously obtained IRB approval and participants\u0026apos; informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION COMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewe declare that we have no financial or non-financial competing interests related to this research study. No conflicts of interest exist that could influence the interpretation of the results or the presentation of the findings.\u003c/p\u003e\n\u003ch3\u003eFunding declarations\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;there is no funding for this paper\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.M.F. (Ahmed Mohammed Fawaz) conceived the study, conducted the primary data analysis, and drafted the manuscript. N.S.F. (Nesrine Saad Farrag) and N.F.I. (Nesreen Farouk Ibrahim) supervised the study, provided critical revisions, and reviewed the manuscript. A.E.M. (Amro Elsayed Mokhtar) contributed to data acquisition and statistical analysis. M.A.H. (Momen Ahmed Hassan) assisted in data interpretation and manuscript editing. O.E.E.D. (Omar Elsayed Elaraby Dora) was responsible for data visualization, figure preparation, and formatting. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLicher S, Darweesh SKL, Wolters FJ, Fani L, Heshmatollah A, Mutlu U, et al. Lifetime risk of common neurological diseases in the elderly population. J Neurol Neurosurg Psychiatry. 2019;90(2):148\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeigin VL, Nichols E, Alam T, Bannick MS, Beghi E, Blake N, Global Burden of Disease Study 2016. 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CNS Neurosci Ther. 2019;25(3):303\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelisch N, Dan T, Ichimura A, Sekiguchi H, Vaughan DE, van Ypersele de Strihou C, et al. Plasminogen Activator Inhibitor-1 Antagonist TM5484 Attenuates Demyelination and Axonal Degeneration in a Mice Model of Multiple Sclerosis. PLoS ONE. 2015;10(4):e0124510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas AX, Cruz Del Angel Y, Gonzalez MI, Carrel AJ, Carlsen J, Lam PM et al. Rapid Increases in proBDNF after Pilocarpine-Induced Status Epilepticus in Mice Are Associated with Reduced proBDNF Cleavage Machinery. eneuro. 2016;3(1):ENEURO.0020-15.2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScorza FA, de Almeida AG, Scorza CA, Finsterer J. Parkinson\u0026rsquo;s Disease, Premature Mortality, and Amygdala. Mov Disord. 2022;37(5):1110\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiederich NJ, Goldman JG, Stebbins GT, Goetz CG. Failing as doorman and disc jockey at the same time: Amygdalar dysfunction in Parkinson\u0026rsquo;s disease. Mov Disord. 2016;31(1):11\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang X, Gonzalez-Latapi P, Marras C, Visanji NP, Yang W, Sato C, et al. Epigenetic Clock Acceleration Is Linked to Age at Onset of Parkinson\u0026rsquo;s Disease. Mov Disord. 2022;37(9):1831\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung KKK, Dawson VL, Dawson TM. The role of the ubiquitin-proteasomal pathway in Parkinson\u0026rsquo;s disease and other neurodegenerative disorders. Trends Neurosci. 2001;24(11):S7\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7000076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7000076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAge-related neurological disorders, including Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, multiple sclerosis, and stroke, pose an increasing global health burden. Epigenetic age acceleration (EAA), measured through DNA methylation-based epigenetic clocks, has emerged as a promising biomarker that links biological aging to disease susceptibility. This study employs a two-sample Mendelian randomization (MR) approach to investigate the causal impact of EAA on neurological outcomes, utilizing genetic instruments derived from large-scale genome-wide association studies (GWAS) for epigenetic clocks, including HannumAge, GrimAge, and plasminogen activator inhibitor-1 (PAI-1).\u003c/p\u003e \u003cp\u003eMR analyses identified significant associations between specific epigenetic clocks and neurological diseases. HannumAge was positively associated with an increased risk of multiple sclerosis (OR\u0026thinsp;=\u0026thinsp;1.068, 95% CI 1.005\u0026ndash;1.173, p\u0026thinsp;=\u0026thinsp;0.047), while elevated PAI-1 levels were linked to a higher risk of Alzheimer\u0026rsquo;s disease (OR\u0026thinsp;=\u0026thinsp;1.00008, 95% CI 1.000006\u0026ndash;1.000163, p\u0026thinsp;=\u0026thinsp;0.035) and a reduced risk of stroke (OR\u0026thinsp;=\u0026thinsp;0.999998, 95% CI 0.999997\u0026ndash;1.0, p\u0026thinsp;=\u0026thinsp;0.024). Additionally, GrimAge was inversely associated with Parkinson\u0026rsquo;s disease risk (OR\u0026thinsp;=\u0026thinsp;0.903, 95% CI 0.819\u0026ndash;0.995, p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003eThese findings provide evidence for a potential causal relationship between EAA and neurological disorders, highlighting the utility of epigenetic clocks in elucidating aging mechanisms and informing diagnostic, prognostic, and therapeutic strategies. Further research is warranted to assess the clinical implications of EAA in personalized medicine and neurodegenerative disease prevention.\u003c/p\u003e","manuscriptTitle":"Epigenetic age acceleration and prevalent age- related neurological disorders: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 17:22:58","doi":"10.21203/rs.3.rs-7000076/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26518769-974d-408f-b0c4-a1d9c5a90179","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T14:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 17:22:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7000076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7000076","identity":"rs-7000076","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}