Effects of walking on epigenetic age acceleration: a Mendelian randomization study

Effects of walking on epigenetic age acceleration: a 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 Effects of walking on epigenetic age acceleration: a Mendelian randomization study Guanyi Chen, Chao Liu, Yu Xia, Pingxiao Wang, Ziyue Zhao, Ao-yu Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4085508/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Clinical Epigenetics → Version 1 posted 8 You are reading this latest preprint version Abstract Introduction : Walking stands as the most prevalent physical activity in the daily lives of individuals and is closely associated with physical functioning and the aging process. Nonetheless, the precise cause-and-effect connection between walking and aging remains unexplored. The epigenetic clock emerges as the most promising biological indicator of aging, capable of mirroring the biological age of the human body and facilitating an investigation into the association between walking and aging. Our primary objective is to investigate the causal impact of walking with epigenetic age acceleration (EAA). Methods We conducted a two-sample two-way Mendelian randomization (MR) study to investigate the causal relationship between walking and EAA. Walking and Leisure sedentary behaviour data were sourced from UK Biobank, while EAA data were gathered from a total of 28 cohorts. The MR analysis was carried out using several methods, including the inverse variance weighted (IVW), weighted median, MR-Egger, and Robust Adjusted Profile Score (RAPS). To ensure the robustness of our findings, we conducted sensitivity analyses, which involved the MR-Egger intercept test, Cochran’s Q test, and MR-PRESSO, to account for and mitigate potential pleiotropy. Results The IVW MR results indicate a significant impact of usual walking pace on GrimAge (BETA = -1.84, 95% CI (-2.94, -0.75)), PhenoAge (BETA = -1.57, 95% CI (-3.05, -0.08)), Horvath (BETA = -1.09 (-2.14, -0.04)), and Hannum (BETA = -1.63, 95% CI (-2.70, -0.56)). Usual walking pace is significantly associated with a delay in Epigenetic Aging Acceleration (EAA) (P < 0.05). Moreover, the direction of effect predicted by the gene remained consistent across RAPs outcomes and sensitivity MR Analyses. There is a lack of robust causal relationships between other walking conditions, such as walking duration and walking frequency, on EAA (P > 0.05). Conclusion Our evidence demonstrates that a higher usual walking pace is associated with a deceleration of the acceleration of all four classical epigenetic clocks acceleration. Aging Walking Epigenetic Clock Mendelian Randomization Study Epigenetic Age Acceleration Usual Walking Pace GrimAge Hannum PhenoAge Horvath Figures Figure 1 Figure 2 Figure 3 Key Message In recent years, observational studies have found that there is a correlation between walking and aging, but the specific relationship is not clear We used a Mendelian randomization approach to test the causal effect of walking and sedentarism on epigenetic clock acceleration Mendelian randomization studies have found that faster walking speed may delay physiological aging through causal analysis at the genetic level 1. Introduction The aging process is a complex phenomenon characterized by cumulative changes in life activities, resulting in diseases and eventual mortality. 1 While life expectancy has increased, the prevalence of chronic diseases remains high. 2 Traditional metrics like life span may not fully capture the intricacies of aging, prompting a contemporary shift in research focus towards interventions that address aging itself rather than isolated aspects like life span or individual diseases. Unlike chronological aging, biological aging is a modifiable process, and recent advancements in aging research have identified biomarkers, particularly methylation time clocks, as promising predictors of aging. 3, 4 In recent years, its plausibility as a predictor of biological age has been strongly supported. 5 DNA methylation, a crucial epigenetic process involving the addition of a methyl group to the cytosine ring, plays a pivotal role in growth, development, and aging. 6 It is a form of epigenetic modification in which DNA is directly methylated through covalent linkage of the methyl group to the fifth position of the cytosine ring to generate 5-methylcytosine (5mC). 7 Dynamic changes in DNA methylation patterns, especially in CpG dinucleotide-rich regions, occur with age, leading to the development of epigenetic clocks that measure biological age. 8 Typically, CpGs in promoter regions are hypermethylated during aging, while other CpGs are hypomethylated. 9 DNA methylation patterns have been used as a measure of biological age, currently known as the epigenetic clock. These clocks, particularly those based on methylation, have demonstrated correlations with age-related morbidity, mortality, and various other factors. This underscores their potential to predict and identify risks associated with aging. As our understanding of aging advances, the focus on epigenetic measures provides insights into the intricate processes underlying aging, offering avenues for targeted interventions to promote healthier aging. This shift towards a more holistic view of aging, encompassing biological and epigenetic aspects, represents a promising approach in the quest for interventions to enhance the quality of life in aging populations. The discovery and study of the epigenetic clock offers great opportunities to explore interventions in aging. Acceleration of aging, also known as epigenetic age acceleration (EAA), can be generated by comparing biological age with chronological age. Exploring the effects of different interventions on aging through changes in apparent age can provide means to delay and reverse aging. The daily activity of walking is an essential daily activity for everyone. For individuals, walking is good for both physical and mental health. 10, 11 For society, walking can effectively reduce healthcare costs. 12 However, most of the previous studies were small-scale clinical intervention studies or observational studies, and the evidence had certain limitations, such as limited samples, lack of specificity, and lack of clear causality. In this study, we employed a Mendelian randomization (MR) approach to investigate the causal relationship between various walking conditions, including walking speed, walking duration, 4-week walking frequency, and Epigenetic Aging Acceleration (EAA) at the genetic level. These walking conditions were compared against sedentary leisure behavior. Our findings reveal a causal relationship, indicating that faster walking is associated with slower epigenetic aging. 2. Method 2.1 Study design In this study, a two-sample bidirectional MR was employed to investigate the causal relationship between walking and EAA. MR studies are grounded on three fundamental hypotheses: ( 1 ) Instrumental variables (IVs) are strongly associated with the exposure of interest. ( 2 ) IVs are not influenced by confounding factors. ( 3 ) IVs represent an outcome resulting from the exposure, rather than directly affecting the outcome (Fig. 1 ). IVs: Instrumental variables. SNPs: Single nucleotide polymorphisms. Assumption 1: The selected genetic IVs are robustly associated with the exposure. Assumption 2: The chosen IVs are not associated with potential confounders. Assumption 3: The IVs can only influence the risk of the outcome dependently through exposure. 2.2 Data sources Data on usual walking pace (n = 459,915), walking duration (n = 288,266), frequency of pleasant walking in the past 4 weeks (n = 328,320), and sedentary leisure behavior (n = 408,815) used in this study were obtained from European ancestry data from the largest UK Biobank study to date. The estimates for genetic associations with EAA measurements, including Hannum 13 , Horvath 14 , DNA methylation(DNAm) PhenoAge 5 , and DNAm GrimAge 15 , were obtained from a substantial meta-study in the realm of genome-wide association studies (GWAS) involving 34,710 participants. This meta-study served as the primary source for the EAA data. To ensure the mitigation of potential confounding factors, we exclusively selected samples of European descent. For more comprehensive details regarding the samples and methodology, we refer readers to the original GWAS meta-study. 16 2.3 MR analysis In our study, we carefully chose independent single nucleotide polymorphisms (SNPs) that exhibited significant associations with the exposure at the genome-wide level (p < 5×10^-8) to serve as IVs. To prevent issues related to linkage disequilibrium, we excluded SNPs with an r^2 greater than 0.001 within a 10,000-kilobase (10,000KB) range. 17 We applied various Mendelian randomization (MR) statistical methods, including Inverse Variance Weighting (IVW), Weighted Median, Weighted Model, and MR-Egger. Among these methods, IVW is considered the most crucial. IVW is generally acknowledged as the most accurate and stable method for estimating causality. 18 However, it assumes that SNPs are not pleiotropic, which may introduce significant bias if pleiotropy is present. To address this concern, we conducted a horizontal pleiotropy test using the MR-Egger intercept. 19 Additionally, we utilized MR-Pleiotropy Residual and Outlier (MR-PRESSO) to identify and remove SNPs with pleiotropic effects. 20 Following this adjustment, we repeated the IVW calculation. Furthermore, we assessed SNP heterogeneity through Cochran's Q statistic. Based on the results, we employed fixed or random effects models (fixed-effect model for p > 0.05, random-effect model in cases of heterogeneity). To handle bias and assess causality, including situations with substantial variation arising from weak SNPs, we used Robust Adjusted Profile Scores (RAPS). 21 We also tested the strength of genetic SNPs with the F-statistic, considering F < 10 as indicative of low power for the SNP-exposure association, which could introduce bias when weak instrumental variables are present. All MR analyses were conducted using the "MungeSumstats", "TwosampleMR", "MR-PRESSO", R packages in R statistical software (version 4.2.2). 3. Result 3.1 a bi-directional two-sample MR analysis We first calculated the F-statistics for the SNPS for usual walking speed, walking time, and walking frequency in the last four weeks, each of which had an F-statistic above 10 (Supplementary file 1). It can be considered that the selected SNPS are not weak instrumental variables. As shown in Table 1 , after removal of linkage disequilibrium and deletion of repetitive SNPS, usual walking pace was inversely associated with the acceleration of the four classical aging clocks, GrimAge (-1.842(-2.937, -0.747), P < 0.001). PhenoAge (1.567 (3.052, 0.082), P = 0.039) Horvath (1.089 (2.142, 0.035), P = 0.043), Hannum (-1.626(-2.695, -0.557), P = 0.003) (Fig. 2 ). There is often a genetic causal relationship between walking speed and epigenetic age acceleration. The results obtained using RAPs method also proved this point, GrimAge (-1.721(-2.731, -0.711) P < 0.001), PhenoAge (-1.261(-2.722, -0.178), P = 0.025), Horvath (-1.105079(-2.131, -0.079) P = 0.035), Hannum (-1.359455(-2.353, -0.365) P = 0.007) (Fig. 3 ). However, walking time and the frequency of walking in the last four weeks did not have a causal effect on the four epigenetic age accelerations (P > 0.05). We observed a significant positive association between leisure sedentary behavior and GrimAge EAA. However, there was a lack of reliable causality with the other three classical epigenetic clocks acceleration. (Table 1 ) We then performed inverse MR Tests, which showed no causal effects of acceleration of the four epigenetic clocks on walking speed, time, near-four-week frequency or leisure sedentary behavior (P > 0.05). (Table S1 ) TABLE 1 Primary mendelian randomization estimates of Walking and Sedentarism on EAA. CI, Confdent interval; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted. 3.2 Stage II: sensitivity analysis We calculated the statistical values of SNPs for the acceleration of genetic time in the four methylation time clocks, and the F values were also all above 10. In the subsequent sensitivity analysis, the heterogeneity results revealed significant heterogeneity between walking time and PhenoAge EAA SNPs (P = 0.042), as well as between leisure-time sedentary behavior and Hannum EAA SNPs (P = 0.023). The newly calculated results using the random effects model indicated that there may still be no causal relationship between walking time and PhenoAge EAA (P = 0.956) or between leisure-time sedentary behavior and Hannum EAA (P = 0.559). All other results showed no heterogeneity (P > 0.05). Among the horizontal pleiotropy results, MR-Egger results showed that there may be horizontal pleiotropy between SNPs with accelerated PhenoAge and those with usual walking pace (P < 0.019). MR-PRESSO was employed to identify potential pleiotropic SNPs, revealing the presence of pleiotropic SNPs between leisure sedentary behavior and Hannum EAA (P = 0.030). However, the results remained unchanged even after the removal of outliers. The results of IVW method are reliable. There was no horizontal pleiotropy between the other SNPs for walking speed, time, frequency of walking in the last four weeks, or leisure sedentary behavior on the four epigenetic age accelerations. (Table 2 ) TABLE 2 Association Walking with Epigenetic age acceleration using heterogeneity test, pleiotropy test, MR-PRESSO and MR Egger. MR-PRESSO, pleiotropy Residual and Outlier; RAPS, Robust Adjusted Profile Scores 4. Discussion This represents the inaugural large-scale two-sample Mendelian randomization (MR) study uncovering the causal link between walking and epigenetic aging. Our results illuminate a consistent and significant causal association, indicating that increased walking speed correlates with the deceleration of epigenetic aging. Essentially, brisk walking appears to exert a beneficial influence on slowing down the aging process. Notably, this causal relationship persists uniformly across all four classical epigenetic clocks. Furthermore, a thorough sensitivity analysis was conducted, underscoring the robustness of our findings. The results maintained stability even after rigorous testing for horizontal pleiotropy and adjustments for heterogeneity. In contrast, alternate facets of walking, including walking duration and frequency over the past four weeks, did not exhibit resilient causality concerning accelerated epigenetic aging. Additionally, a comparative analysis using sedentary behavior revealed that leisurely sedentary behavior induced GrimAge EAA. While no conclusive causal link was identified in the analysis of sedentary behavior on the remaining three epigenetic clocks, the heightened correlation of GrimAge with behavioral lifestyle suggests a potential association between sedentary behavior and accelerated aging. MR results offer valuable insights into the precise relationship between walking and the aging process. In previous observational studies, walking has consistently been recognized as closely linked to aging, with walking speed serving as a significant indicator of the aging process. As individuals age, their walking speed tends to slow down significantly, emphasizing the role of walking in assessing age-related changes. 22, 23 Simultaneously, several studies have identified a strong correlation between gait speed and the onset of various age-related physical conditions and adverse events. For instance, a Chinese study involving 3,009 individuals with an average age of 66.4 years found that slower walking speed was associated with a more pronounced future cognitive decline. This underscores the significance of gait speed as a comprehensive marker for assessing not only the aging process but also a range of related health outcomes and cognitive changes. 24 These additional studies have consistently demonstrated that increased gait speed is associated with a reduced likelihood of cognitive impairment and enhanced cardiovascular and cerebrovascular function. These findings underscore the potential advantages of preserving or improving gait speed in promoting cognitive well-being and overall cardiovascular health. 25, 26 Furthermore, a lower incidence of movement disorders and reduced mortality rates have been closely linked to gait speed. 27, 28 A new observational study has found that walking is associated not only with aging in older adults, but also with accelerated apparent age and older faces in younger adulthood. 29 This suggests that walking may be a potential intervention for senescence rather than just an aging feature. The above observational studies have provided many new suggestions for the relationship between walking and aging, but they still have some observational limitations because they cannot provide clear causality due to the lack of powerful interventions. Our MR results can provide further valuable insight into the precise relationship between walking and the aging process in the current study, suggesting that walking acceleration may be a potential daily measure to slow the rate of aging. MR studies stand out as a powerful approach for establishing causal relationships, particularly rooted in genetic factors. This potency is exemplified by the observed larger effects of associations between risk factors and health outcomes in MR studies compared to what is suggested by observational or interventional studies, as seen in contexts such as blood pressure and lipid levels. 30–32 This difference is attributed to MR studies measuring lifetime exposure, while observational studies capture exposure at a single time point, and interventional studies track changes over relatively short time frames. 33 The unique strengths of MR studies, including lifetime exposure measurement and extensive sample sizes, prove invaluable when investigating complex phenomena like gait speed. Routine intervention studies often struggle to achieve comparable sample sizes and comprehensive lifetime exposure data. Furthermore, the chronic effects of walking speed on the aging process pose challenges due to the lack of reliable markers based on age-related characteristics. MR studies, characterized by their genetic foundation and large-scale scope, bridge this research gap and provide a more thorough understanding of the causal relationship between walking speed and aging. Horvath's epigenetic clock, among the earliest methylation clocks, demonstrates a remarkable correlation between DNAm age and chronological age (0.96). 14, 34 Renowned for its accuracy and versatility across diverse tissues and cell types, this model has been validated using hundreds of datasets. 35 Its applicability spans various tissues and organs, including whole blood, cerebellum, colon, kidney, liver, and lung. As a result, its acceleration serves as a reflective indicator of aging across different tissues within the body. 14 The Hannum Epigenetic clock, based on 450K methylation data from whole blood samples, complements Horvath's epigenetic clock by exhibiting increased accuracy in predicting adult blood samples. 13 PhenoAge, constructed from 513 age-related CpG sites across three methylation chip platforms, distinguishes itself with cross-platform applicability, mortality risk differentiation among individuals of the same chronological age, and a stronger correlation with behavioral lifestyle compared to Horvath's epigenetic clock. 5 GrimAge, featuring DNA methylation-based plasma protein markers and smoking pack-years, prioritizes lifestyle and age-related diseases, resulting in more accurate lifespan predictions. 15 Its acceleration mitigation by walking speed suggests an association between higher gait speed and lower mortality. The investigation into the causal effects of walking on four classical epigenetic clocks provides a nuanced understanding of how walking influences accelerated aging. The emergence of the aging clock not only serves as a crucial assessment tool for aging research but also presents an avenue for interventions aimed at delaying or potentially reversing the aging process. Our findings indicate that increasing daily walking speed may serve as a viable lifestyle strategy to slow down the aging process. However, the effects of walking duration and frequency on aging deceleration lack convincing evidence. The study boasts technical and conceptual strengths, being the first to explore the bidirectional relationship between epigenetic aging acceleration and walking speed. Leveraging multiple large datasets, including summary statistics from the UK Biobank GWAS meta-analysis and apparent time-clock acceleration data from a substantial GWAS meta-analysis, enhances the robustness of our findings. However, limitations include the exclusive use of data from individuals of European ancestry, impacting population diversity, and the unknown mechanism through which walking affects aging, necessitating further exploration of potential mediators. Additionally, future studies are crucial to determining the specific relationship between different walking speeds and the degree of aging acceleration, paving the way for the development of more scientific anti-aging walking protocols. Conclusion Our study demonstrates that a faster usual walking speed is significantly associated with a delay in epigenetic age acceleration. This finding highlights the potential benefits of maintaining a brisk walking pace for slowing down the aging process, as supported by our analysis of the four classical epigenetic ages. Declarations Availability of data and materials The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Funding This work was supported by Research Project of Human HealthCommission (grant number 202204073071) and Fundamental Research Funds for Central Universities of the Central South University (grant number 2024ZZTS0937). Acknowledgments All data used in this study were obtained from openly available databases and consortiums. The authors thank all investigators for providing the data publicly. Supplementary material The Supplementary Material for this article can be found online. References Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell 2023; 186: 3758–75. Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature 2018; 561: 45–56. Jylhava J, Pedersen NL, Hagg S. Biological Age Predictors. EBioMedicine 2017; 21: 29–36. Lee HY, Lee SD, Shin KJ. 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Supplementary Files sup.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Clinical Epigenetics → Version 1 posted Editorial decision: Revision requested 03 Jun, 2024 Reviews received at journal 29 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers invited by journal 21 Mar, 2024 Editor assigned by journal 19 Mar, 2024 Submission checks completed at journal 19 Mar, 2024 First submitted to journal 12 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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16:16:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4085508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4085508/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13148-024-01707-w","type":"published","date":"2024-07-18T16:13:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53277105,"identity":"9fb259e3-2a10-46ab-b323-38aaec0f0c4e","added_by":"auto","created_at":"2024-03-22 18:19:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25907,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the three assumptions and study design.\u003c/p\u003e\n\u003cp\u003eIVs: Instrumental variables. SNPs: Single nucleotide polymorphisms. Assumption 1: The selected genetic IVs are robustly associated with the exposure. Assumption 2: The chosen IVs are not associated with potential confounders. Assumption 3: The IVs can only influence the risk of the outcome dependently through exposure.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4085508/v1/08c2525a53c1c2744c279c76.jpg"},{"id":53277106,"identity":"14ec9a99-7912-4dd5-acbb-687090c3cc85","added_by":"auto","created_at":"2024-03-22 18:19:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60567,"visible":true,"origin":"","legend":"\u003cp\u003eCausal estimates from genetically predicted usual walking speed to four epigenetic age acceleration (GrimAge, PhenoAge, Horvath, Hannum) were obtained using IVW methods.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4085508/v1/be32bade1b6043849733c668.jpg"},{"id":53277108,"identity":"abab1a8a-9829-4e8d-b187-2cc763ac00e8","added_by":"auto","created_at":"2024-03-22 18:19:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61841,"visible":true,"origin":"","legend":"\u003cp\u003eCausal estimates from genetically predicted usual walking speed to epigenetic clock acceleration (GrimAge, PhenoAge, Horvath, Hannum) were obtained using RAPs methods.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4085508/v1/6c175e54edfc316eeb9ab64d.jpg"},{"id":61595275,"identity":"b5f60d10-53c1-4a37-9d42-319b5eb17074","added_by":"auto","created_at":"2024-08-01 17:21:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":587871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4085508/v1/2706fd7a-93b8-4d38-81fc-40e87f2b7e7b.pdf"},{"id":53277107,"identity":"1a13ef5e-ae63-4ef2-8033-465c41eb46c5","added_by":"auto","created_at":"2024-03-22 18:19:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13167,"visible":true,"origin":"","legend":"","description":"","filename":"sup.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4085508/v1/5be42a3b9531e9511124d140.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of walking on epigenetic age acceleration: a Mendelian randomization study","fulltext":[{"header":"Key Message","content":"\u003cul start=\"12\"\u003e\n \u003cli\u003eIn recent years, observational studies have found that there is a correlation between walking and aging, but the specific relationship is not clear\u003c/li\u003e\n \u003cli\u003eWe used a Mendelian randomization approach to test the causal effect of walking and sedentarism on epigenetic clock acceleration\u003c/li\u003e\n \u003cli\u003eMendelian randomization studies have found that faster walking speed may delay physiological aging through causal analysis at the genetic level\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe aging process is a complex phenomenon characterized by cumulative changes in life activities, resulting in diseases and eventual mortality.\u003csup\u003e1\u003c/sup\u003e While life expectancy has increased, the prevalence of chronic diseases remains high.\u003csup\u003e2\u003c/sup\u003e Traditional metrics like life span may not fully capture the intricacies of aging, prompting a contemporary shift in research focus towards interventions that address aging itself rather than isolated aspects like life span or individual diseases. Unlike chronological aging, biological aging is a modifiable process, and recent advancements in aging research have identified biomarkers, particularly methylation time clocks, as promising predictors of aging.\u003csup\u003e3, 4\u003c/sup\u003e In recent years, its plausibility as a predictor of biological age has been strongly supported.\u003csup\u003e5\u003c/sup\u003e DNA methylation, a crucial epigenetic process involving the addition of a methyl group to the cytosine ring, plays a pivotal role in growth, development, and aging. \u003csup\u003e6\u003c/sup\u003e It is a form of epigenetic modification in which DNA is directly methylated through covalent linkage of the methyl group to the fifth position of the cytosine ring to generate 5-methylcytosine (5mC).\u003csup\u003e7\u003c/sup\u003e Dynamic changes in DNA methylation patterns, especially in CpG dinucleotide-rich regions, occur with age, leading to the development of epigenetic clocks that measure biological age.\u003csup\u003e8\u003c/sup\u003e Typically, CpGs in promoter regions are hypermethylated during aging, while other CpGs are hypomethylated.\u003csup\u003e9\u003c/sup\u003e DNA methylation patterns have been used as a measure of biological age, currently known as the epigenetic clock. These clocks, particularly those based on methylation, have demonstrated correlations with age-related morbidity, mortality, and various other factors. This underscores their potential to predict and identify risks associated with aging. As our understanding of aging advances, the focus on epigenetic measures provides insights into the intricate processes underlying aging, offering avenues for targeted interventions to promote healthier aging. This shift towards a more holistic view of aging, encompassing biological and epigenetic aspects, represents a promising approach in the quest for interventions to enhance the quality of life in aging populations.\u003c/p\u003e \u003cp\u003eThe discovery and study of the epigenetic clock offers great opportunities to explore interventions in aging. Acceleration of aging, also known as epigenetic age acceleration (EAA), can be generated by comparing biological age with chronological age. Exploring the effects of different interventions on aging through changes in apparent age can provide means to delay and reverse aging.\u003c/p\u003e \u003cp\u003eThe daily activity of walking is an essential daily activity for everyone. For individuals, walking is good for both physical and mental health.\u003csup\u003e10, 11\u003c/sup\u003e For society, walking can effectively reduce healthcare costs.\u003csup\u003e12\u003c/sup\u003e However, most of the previous studies were small-scale clinical intervention studies or observational studies, and the evidence had certain limitations, such as limited samples, lack of specificity, and lack of clear causality. In this study, we employed a Mendelian randomization (MR) approach to investigate the causal relationship between various walking conditions, including walking speed, walking duration, 4-week walking frequency, and Epigenetic Aging Acceleration (EAA) at the genetic level. These walking conditions were compared against sedentary leisure behavior. Our findings reveal a causal relationship, indicating that faster walking is associated with slower epigenetic aging.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eIn this study, a two-sample bidirectional MR was employed to investigate the causal relationship between walking and EAA. MR studies are grounded on three fundamental hypotheses: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Instrumental variables (IVs) are strongly associated with the exposure of interest. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) IVs are not influenced by confounding factors. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) IVs represent an outcome resulting from the exposure, rather than directly affecting the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIVs: Instrumental variables. SNPs: Single nucleotide polymorphisms. Assumption 1: The selected genetic IVs are robustly associated with the exposure. Assumption 2: The chosen IVs are not associated with potential confounders. Assumption 3: The IVs can only influence the risk of the outcome dependently through exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eData on usual walking pace (n\u0026thinsp;=\u0026thinsp;459,915), walking duration (n\u0026thinsp;=\u0026thinsp;288,266), frequency of pleasant walking in the past 4 weeks (n\u0026thinsp;=\u0026thinsp;328,320), and sedentary leisure behavior (n\u0026thinsp;=\u0026thinsp;408,815) used in this study were obtained from European ancestry data from the largest UK Biobank study to date. The estimates for genetic associations with EAA measurements, including Hannum\u003csup\u003e13\u003c/sup\u003e, Horvath\u003csup\u003e14\u003c/sup\u003e, DNA methylation(DNAm) PhenoAge\u003csup\u003e5\u003c/sup\u003e, and DNAm GrimAge\u003csup\u003e15\u003c/sup\u003e, were obtained from a substantial meta-study in the realm of genome-wide association studies (GWAS) involving 34,710 participants. This meta-study served as the primary source for the EAA data. To ensure the mitigation of potential confounding factors, we exclusively selected samples of European descent. For more comprehensive details regarding the samples and methodology, we refer readers to the original GWAS meta-study.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MR analysis\u003c/h2\u003e \u003cp\u003eIn our study, we carefully chose independent single nucleotide polymorphisms (SNPs) that exhibited significant associations with the exposure at the genome-wide level (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^-8) to serve as IVs. To prevent issues related to linkage disequilibrium, we excluded SNPs with an r^2 greater than 0.001 within a 10,000-kilobase (10,000KB) range.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe applied various Mendelian randomization (MR) statistical methods, including Inverse Variance Weighting (IVW), Weighted Median, Weighted Model, and MR-Egger. Among these methods, IVW is considered the most crucial. IVW is generally acknowledged as the most accurate and stable method for estimating causality.\u003csup\u003e18\u003c/sup\u003e However, it assumes that SNPs are not pleiotropic, which may introduce significant bias if pleiotropy is present. To address this concern, we conducted a horizontal pleiotropy test using the MR-Egger intercept.\u003csup\u003e19\u003c/sup\u003e Additionally, we utilized MR-Pleiotropy Residual and Outlier (MR-PRESSO) to identify and remove SNPs with pleiotropic effects.\u003csup\u003e20\u003c/sup\u003e Following this adjustment, we repeated the IVW calculation.\u003c/p\u003e \u003cp\u003eFurthermore, we assessed SNP heterogeneity through Cochran's Q statistic. Based on the results, we employed fixed or random effects models (fixed-effect model for p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, random-effect model in cases of heterogeneity).\u003c/p\u003e \u003cp\u003eTo handle bias and assess causality, including situations with substantial variation arising from weak SNPs, we used Robust Adjusted Profile Scores (RAPS).\u003csup\u003e21\u003c/sup\u003e We also tested the strength of genetic SNPs with the F-statistic, considering F\u0026thinsp;\u0026lt;\u0026thinsp;10 as indicative of low power for the SNP-exposure association, which could introduce bias when weak instrumental variables are present.\u003c/p\u003e \u003cp\u003eAll MR analyses were conducted using the \"MungeSumstats\", \"TwosampleMR\", \"MR-PRESSO\", R packages in R statistical software (version 4.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 a bi-directional two-sample MR analysis\u003c/h2\u003e\n \u003cp\u003eWe first calculated the F-statistics for the SNPS for usual walking speed, walking time, and walking frequency in the last four weeks, each of which had an F-statistic above 10 (Supplementary file 1). It can be considered that the selected SNPS are not weak instrumental variables. As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, after removal of linkage disequilibrium and deletion of repetitive SNPS, usual walking pace was inversely associated with the acceleration of the four classical aging clocks, GrimAge (-1.842(-2.937, -0.747), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PhenoAge (1.567 (3.052, 0.082), P\u0026thinsp;=\u0026thinsp;0.039) Horvath (1.089 (2.142, 0.035), P\u0026thinsp;=\u0026thinsp;0.043), Hannum (-1.626(-2.695, -0.557), P\u0026thinsp;=\u0026thinsp;0.003) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). There is often a genetic causal relationship between walking speed and epigenetic age acceleration. The results obtained using RAPs method also proved this point, GrimAge (-1.721(-2.731, -0.711) P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PhenoAge (-1.261(-2.722, -0.178), P\u0026thinsp;=\u0026thinsp;0.025), Horvath (-1.105079(-2.131, -0.079) P\u0026thinsp;=\u0026thinsp;0.035), Hannum (-1.359455(-2.353, -0.365) P\u0026thinsp;=\u0026thinsp;0.007) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). However, walking time and the frequency of walking in the last four weeks did not have a causal effect on the four epigenetic age accelerations (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We observed a significant positive association between leisure sedentary behavior and GrimAge EAA. However, there was a lack of reliable causality with the other three classical epigenetic clocks acceleration. (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) We then performed inverse MR Tests, which showed no causal effects of acceleration of the four epigenetic clocks on walking speed, time, near-four-week frequency or leisure sedentary behavior (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e Primary mendelian randomization estimates of Walking and Sedentarism on EAA.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eCI, Confdent interval; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Stage II: sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eWe calculated the statistical values of SNPs for the acceleration of genetic time in the four methylation time clocks, and the F values were also all above 10. In the subsequent sensitivity analysis, the heterogeneity results revealed significant heterogeneity between walking time and PhenoAge EAA SNPs (P\u0026thinsp;=\u0026thinsp;0.042), as well as between leisure-time sedentary behavior and Hannum EAA SNPs (P\u0026thinsp;=\u0026thinsp;0.023). The newly calculated results using the random effects model indicated that there may still be no causal relationship between walking time and PhenoAge EAA (P\u0026thinsp;=\u0026thinsp;0.956) or between leisure-time sedentary behavior and Hannum EAA (P\u0026thinsp;=\u0026thinsp;0.559). All other results showed no heterogeneity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among the horizontal pleiotropy results, MR-Egger results showed that there may be horizontal pleiotropy between SNPs with accelerated PhenoAge and those with usual walking pace (P\u0026thinsp;\u0026lt;\u0026thinsp;0.019). MR-PRESSO was employed to identify potential pleiotropic SNPs, revealing the presence of pleiotropic SNPs between leisure sedentary behavior and Hannum EAA (P\u0026thinsp;=\u0026thinsp;0.030). However, the results remained unchanged even after the removal of outliers. The results of IVW method are reliable. There was no horizontal pleiotropy between the other SNPs for walking speed, time, frequency of walking in the last four weeks, or leisure sedentary behavior on the four epigenetic age accelerations. (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 2\u003c/strong\u003e Association Walking with Epigenetic age acceleration using heterogeneity test, pleiotropy test, MR-PRESSO and MR Egger.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eMR-PRESSO, pleiotropy Residual and Outlier; RAPS, Robust Adjusted Profile Scores\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis represents the inaugural large-scale two-sample Mendelian randomization (MR) study uncovering the causal link between walking and epigenetic aging. Our results illuminate a consistent and significant causal association, indicating that increased walking speed correlates with the deceleration of epigenetic aging. Essentially, brisk walking appears to exert a beneficial influence on slowing down the aging process. Notably, this causal relationship persists uniformly across all four classical epigenetic clocks. Furthermore, a thorough sensitivity analysis was conducted, underscoring the robustness of our findings. The results maintained stability even after rigorous testing for horizontal pleiotropy and adjustments for heterogeneity. In contrast, alternate facets of walking, including walking duration and frequency over the past four weeks, did not exhibit resilient causality concerning accelerated epigenetic aging. Additionally, a comparative analysis using sedentary behavior revealed that leisurely sedentary behavior induced GrimAge EAA. While no conclusive causal link was identified in the analysis of sedentary behavior on the remaining three epigenetic clocks, the heightened correlation of GrimAge with behavioral lifestyle suggests a potential association between sedentary behavior and accelerated aging.\u003c/p\u003e \u003cp\u003eMR results offer valuable insights into the precise relationship between walking and the aging process. In previous observational studies, walking has consistently been recognized as closely linked to aging, with walking speed serving as a significant indicator of the aging process. As individuals age, their walking speed tends to slow down significantly, emphasizing the role of walking in assessing age-related changes.\u003csup\u003e22, 23\u003c/sup\u003e Simultaneously, several studies have identified a strong correlation between gait speed and the onset of various age-related physical conditions and adverse events. For instance, a Chinese study involving 3,009 individuals with an average age of 66.4 years found that slower walking speed was associated with a more pronounced future cognitive decline. This underscores the significance of gait speed as a comprehensive marker for assessing not only the aging process but also a range of related health outcomes and cognitive changes. \u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese additional studies have consistently demonstrated that increased gait speed is associated with a reduced likelihood of cognitive impairment and enhanced cardiovascular and cerebrovascular function. These findings underscore the potential advantages of preserving or improving gait speed in promoting cognitive well-being and overall cardiovascular health.\u003csup\u003e25, 26\u003c/sup\u003e Furthermore, a lower incidence of movement disorders and reduced mortality rates have been closely linked to gait speed.\u003csup\u003e27, 28\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA new observational study has found that walking is associated not only with aging in older adults, but also with accelerated apparent age and older faces in younger adulthood.\u003csup\u003e29\u003c/sup\u003e This suggests that walking may be a potential intervention for senescence rather than just an aging feature. The above observational studies have provided many new suggestions for the relationship between walking and aging, but they still have some observational limitations because they cannot provide clear causality due to the lack of powerful interventions. Our MR results can provide further valuable insight into the precise relationship between walking and the aging process in the current study, suggesting that walking acceleration may be a potential daily measure to slow the rate of aging.\u003c/p\u003e \u003cp\u003eMR studies stand out as a powerful approach for establishing causal relationships, particularly rooted in genetic factors. This potency is exemplified by the observed larger effects of associations between risk factors and health outcomes in MR studies compared to what is suggested by observational or interventional studies, as seen in contexts such as blood pressure and lipid levels.\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e This difference is attributed to MR studies measuring lifetime exposure, while observational studies capture exposure at a single time point, and interventional studies track changes over relatively short time frames.\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe unique strengths of MR studies, including lifetime exposure measurement and extensive sample sizes, prove invaluable when investigating complex phenomena like gait speed. Routine intervention studies often struggle to achieve comparable sample sizes and comprehensive lifetime exposure data. Furthermore, the chronic effects of walking speed on the aging process pose challenges due to the lack of reliable markers based on age-related characteristics. MR studies, characterized by their genetic foundation and large-scale scope, bridge this research gap and provide a more thorough understanding of the causal relationship between walking speed and aging.\u003c/p\u003e \u003cp\u003eHorvath's epigenetic clock, among the earliest methylation clocks, demonstrates a remarkable correlation between DNAm age and chronological age (0.96).\u003csup\u003e14, 34\u003c/sup\u003e Renowned for its accuracy and versatility across diverse tissues and cell types, this model has been validated using hundreds of datasets.\u003csup\u003e35\u003c/sup\u003e Its applicability spans various tissues and organs, including whole blood, cerebellum, colon, kidney, liver, and lung. As a result, its acceleration serves as a reflective indicator of aging across different tissues within the body.\u003csup\u003e14\u003c/sup\u003e The Hannum Epigenetic clock, based on 450K methylation data from whole blood samples, complements Horvath's epigenetic clock by exhibiting increased accuracy in predicting adult blood samples.\u003csup\u003e13\u003c/sup\u003e PhenoAge, constructed from 513 age-related CpG sites across three methylation chip platforms, distinguishes itself with cross-platform applicability, mortality risk differentiation among individuals of the same chronological age, and a stronger correlation with behavioral lifestyle compared to Horvath's epigenetic clock.\u003csup\u003e5\u003c/sup\u003e GrimAge, featuring DNA methylation-based plasma protein markers and smoking pack-years, prioritizes lifestyle and age-related diseases, resulting in more accurate lifespan predictions.\u003csup\u003e15\u003c/sup\u003e Its acceleration mitigation by walking speed suggests an association between higher gait speed and lower mortality.\u003c/p\u003e \u003cp\u003eThe investigation into the causal effects of walking on four classical epigenetic clocks provides a nuanced understanding of how walking influences accelerated aging. The emergence of the aging clock not only serves as a crucial assessment tool for aging research but also presents an avenue for interventions aimed at delaying or potentially reversing the aging process. Our findings indicate that increasing daily walking speed may serve as a viable lifestyle strategy to slow down the aging process. However, the effects of walking duration and frequency on aging deceleration lack convincing evidence.\u003c/p\u003e \u003cp\u003eThe study boasts technical and conceptual strengths, being the first to explore the bidirectional relationship between epigenetic aging acceleration and walking speed. Leveraging multiple large datasets, including summary statistics from the UK Biobank GWAS meta-analysis and apparent time-clock acceleration data from a substantial GWAS meta-analysis, enhances the robustness of our findings. However, limitations include the exclusive use of data from individuals of European ancestry, impacting population diversity, and the unknown mechanism through which walking affects aging, necessitating further exploration of potential mediators. Additionally, future studies are crucial to determining the specific relationship between different walking speeds and the degree of aging acceleration, paving the way for the development of more scientific anti-aging walking protocols.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrates that a faster usual walking speed is significantly associated with a delay in epigenetic age acceleration. This finding highlights the potential benefits of maintaining a brisk walking pace for slowing down the aging process, as supported by our analysis of the four classical epigenetic ages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Research Project of Human HealthCommission (grant number 202204073071) and Fundamental Research Funds for Central Universities of the Central South University (grant number 2024ZZTS0937).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data used in this study were obtained from openly available databases and consortiums. The authors thank all investigators for providing the data publicly.\u003c/p\u003e\n\u003cp\u003eSupplementary material\u003c/p\u003e\n\u003cp\u003eThe Supplementary Material for this article can be found online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMoqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell 2023; 186: 3758\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature 2018; 561: 45\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJylhava J, Pedersen NL, Hagg S. Biological Age Predictors. EBioMedicine 2017; 21: 29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HY, Lee SD, Shin KJ. Forensic DNA methylation profiling from evidence material for investigative leads. 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STATISTICAL INFERENCE IN TWO-SAMPLE SUMMARY-DATA MENDELIAN RANDOMIZATION USING ROBUST ADJUSTED PROFILE SCORE. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagnani PE, Freire Junior RC, Zanellato NFG, Genovez MB, Alvarenga IC, Abreu DCC. The influence of aging on the spatial and temporal variables of gait during usual and fast speeds in older adults aged 60 to 102 years. Hum Mov Sci 2019; 68: 102540.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreira BS, Andrade ACS, Bastone AC, et al. Home-based gait speed and the association with sociodemographic and anthropometric variables: A national study (ELSI-Brazil). Geriatr Nurs 2023; 51: 400\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Zhang J, Zou X, et al. The Bidirectional Association Between Cognitive Function and Gait Speed in Chinese Older Adults: Longitudinal Observational Study. 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Spatiotemporal gait characteristics and risk of mortality in community-dwelling older adults. Maturitas 2021; 151: 31\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasmussen LJH, Caspi A, Ambler A, et al. Association of Neurocognitive and Physical Function With Gait Speed in Midlife. JAMA Netw Open 2019; 2: e1913123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArvanitis M, Qi G, Bhatt DL, et al. Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation 2021; 143: 895\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerence BA, Yoo W, Alesh I, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. J Am Coll Cardiol 2012; 60: 2631\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomsen M, Varbo A, Tybjaerg-Hansen A, Nordestgaard BG. Low nonfasting triglycerides and reduced all-cause mortality: a mendelian randomization study. Clin Chem 2014; 60: 737\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerence BA. How to use Mendelian randomization to anticipate the results of randomized trials. Eur Heart J 2018; 39: 360\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOblak L, van der Zaag J, Higgins-Chen AT, Levine ME, Boks MP. A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration. Ageing Res Rev 2021; 69: 101348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet 2018; 19: 371\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aging, Walking, Epigenetic Clock, Mendelian Randomization Study, Epigenetic Age Acceleration, Usual Walking Pace, GrimAge, Hannum, PhenoAge, Horvath","lastPublishedDoi":"10.21203/rs.3.rs-4085508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4085508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003e: Walking stands as the most prevalent physical activity in the daily lives of individuals and is closely associated with physical functioning and the aging process. Nonetheless, the precise cause-and-effect connection between walking and aging remains unexplored. The epigenetic clock emerges as the most promising biological indicator of aging, capable of mirroring the biological age of the human body and facilitating an investigation into the association between walking and aging. Our primary objective is to investigate the causal impact of walking with epigenetic age acceleration (EAA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample two-way Mendelian randomization (MR) study to investigate the causal relationship between walking and EAA. Walking and Leisure sedentary behaviour data were sourced from UK Biobank, while EAA data were gathered from a total of 28 cohorts. The MR analysis was carried out using several methods, including the inverse variance weighted (IVW), weighted median, MR-Egger, and Robust Adjusted Profile Score (RAPS). To ensure the robustness of our findings, we conducted sensitivity analyses, which involved the MR-Egger intercept test, Cochran\u0026rsquo;s Q test, and MR-PRESSO, to account for and mitigate potential pleiotropy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe IVW MR results indicate a significant impact of usual walking pace on GrimAge (BETA = -1.84, 95% CI (-2.94, -0.75)), PhenoAge (BETA = -1.57, 95% CI (-3.05, -0.08)), Horvath (BETA = -1.09 (-2.14, -0.04)), and Hannum (BETA = -1.63, 95% CI (-2.70, -0.56)). Usual walking pace is significantly associated with a delay in Epigenetic Aging Acceleration (EAA) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the direction of effect predicted by the gene remained consistent across RAPs outcomes and sensitivity MR Analyses. There is a lack of robust causal relationships between other walking conditions, such as walking duration and walking frequency, on EAA (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur evidence demonstrates that a higher usual walking pace is associated with a deceleration of the acceleration of all four classical epigenetic clocks acceleration.\u003c/p\u003e","manuscriptTitle":"Effects of walking on epigenetic age acceleration: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 18:19:32","doi":"10.21203/rs.3.rs-4085508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-03T09:01:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-29T15:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0bd285ec-cfad-436d-88e8-82bd4df425a3","date":"2024-04-19T11:01:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6d8c62b5-b214-4495-8695-b9fef1d9dae3","date":"2024-03-22T22:11:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-21T05:12:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-19T23:24:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-19T23:23:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2024-03-12T13:57:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"188416c1-8275-45a6-bcb8-3e2ffe667f14","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T16:19:24+00:00","versionOfRecord":{"articleIdentity":"rs-4085508","link":"https://doi.org/10.1186/s13148-024-01707-w","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2024-07-18 16:13:13","publishedOnDateReadable":"July 18th, 2024"},"versionCreatedAt":"2024-03-22 18:19:32","video":"","vorDoi":"10.1186/s13148-024-01707-w","vorDoiUrl":"https://doi.org/10.1186/s13148-024-01707-w","workflowStages":[]},"version":"v1","identity":"rs-4085508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4085508","identity":"rs-4085508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}