Epigenetic age quantifies biological age using DNA methylation information and is a potential pathway by which physical activity benefits general health. We aimed to assess the cross-sectional and longitudinal associations between physical activity and epigenetic age in middle-aged and older Australians. Blood DNA methylation data for 6208 participants (40% female) in the Melbourne Collaborative Cohort Study (MCCS) were available at baseline (1990–1994, mean age, 59 years) and, of those, for 1009 at follow-up (2003–2007, mean age, 69 years). Physical activity measurements (weighted scores at baseline and follow-up and total MET hours per week at follow-up) were calculated from self-reported questionnaire data. Five blood methylation–based markers of ageing (PCGrimAge, PCPhenoAge, bAge, DNAmFitAge, and DunedinPACE) and four fitness-related markers (DNAmGaitspeed, DNAmGripmax, DNAmVO2max, and DNAmFEV1) were calculated and adjusted for age. Linear regression was used to examine the cross-sectional and longitudinal associations between physical activity and epigenetic age. Effect modification by age, sex, and BMI was assessed. At baseline, a standard deviation (SD) increment in physical activity was associated with 0.03-SD (DNAmFitAge, 95%CI = 0.01, 0.06, P = 0.02) to 0.07-SD (bAge, 95%CI = 0.04, 0.09, P = 2 × 10⁻⁸) lower epigenetic age. These associations were attenuated after adjustment for other lifestyle variables. Only weak evidence was found for the longitudinal association (N = 1009) of changes in physical activity and epigenetic age (e.g. DNAmFitAge: adjusted β = − 0.04, 95%CI = − 0.08, 0.01). The associations were not modified by age, sex, or BMI. In middle-aged and older Australians, higher levels of self-reported physical activity were associated with slightly lower epigenetic age.

表观遗传年龄使用 DNA 甲基化信息量化生物年龄，是身体活动有益于整体健康的潜在途径。我们旨在评估中老年人澳大利亚人身体活动与表观遗传年龄之间的横断面和纵向关联。墨尔本协作队列研究 （MCCS） 中 6208 名参与者 （40% 为女性） 的血液 DNA 甲基化数据在基线时 （1990-1994 年，平均年龄为 59 岁） 可用，其中 1009 名参与者在随访时 （2003-2007 年，平均年龄 69 岁）。身体活动测量 （基线和随访时的加权评分以及随访时每周的总 MET 小时数） 是根据自我报告的问卷数据计算的。计算了 5 个基于血液甲基化的衰老标志物 （PCGrimAge、PCPhenoAge、bAge、DNAmFitAge 和 DunedinPACE） 和 4 个与健身相关的标志物 （DNAmGaitspeed、DNAmGripmax、DNAmVO2max 和 DNAmFEV1） 并根据年龄进行了调整。线性回归用于检查体力活动与表观遗传年龄之间的横断面和纵向关联。评估了年龄、性别和 BMI 的影响改变。在基线时，身体活动的标准差 （SD） 增加与 0.03-SD （DNAmFitAge， 95%CI = 0.01， 0.06， P = 0.02） 到 0.07-SD （bAge， 95%CI = 0.04， 0.09， P = 2 × ^10-8） 的表观遗传年龄相关。在调整其他生活方式变量后，这些关联减弱。仅发现微弱的证据证明身体活动变化和表观遗传年龄的变化 （N = 1009） （例如 DNAmFitAge：调整后的 β = − 0.04,95%CI = − 0。08, 0.01).这些关联未因年龄、性别或 BMI 而改变。在中老年人澳大利亚人中，较高水平的自我报告的身体活动与略低的表观遗传年龄相关。