No abstract

Physical fitness is a well-known correlate of health and the aging process and DNA methylation (DNAm) data can capture aging via epigenetic clocks. However, current epigenetic clocks did not yet use measures of mobility, strength, lung, or endurance fitness in their construction. We develop blood-based DNAm biomarkers for fitness parameters gait speed (walking speed), maximum handgrip strength, forced expiratory volume in one second (FEV1), and maximal oxygen uptake (VO2max) which have modest correlation with fitness parameters in five large-scale validation datasets (average r between 0.16-0.48). We then use these DNAm fitness parameter biomarkers with DNAmGrimAge, a DNAm mortality risk estimate, to construct DNAmFitAge,

DNA methylation‐based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age‐adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65–72 years were used to appraise first‐ (i.e., DNAmHorvath and DNAmHannum) and second‐ (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2/kg at VT) and peak oxygen uptake (VO2/kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2/kg at VT and VO2/kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle‐related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.

DNAmPhenoAge, DNAmGrimAge, and the newly developed DNAmFitAge are DNA methylation (DNAm)-based biomarkers that reflect the individual aging process. Here, we examine the relationship between physical fitness and DNAm-based biomarkers in adults aged 33–88 with a wide range of physical fitness (including athletes with long-term training history). Higher levels of VO2max (ρ = 0.2, p = 6.4E − 4, r = 0.19, p = 1.2E − 3), Jumpmax (p = 0.11, p = 5.5E − 2, r = 0.13, p = 2.8E − 2), Gripmax (ρ = 0.17, p = 3.5E − 3, r = 0.16, p = 5.6E − 3), and HDL levels (ρ = 0.18, p = 1.95E − 3, r = 0.19, p = 1.1E − 3) are associated with better verbal short-term memory. In addition, verbal short-term memory is associated with decelerated aging assessed with the new DNAm biomarker FitAgeAcceleration (ρ: − 0.18, p = 0.0017). DNAmFitAge can distinguish high-fitness individuals from low/medium-fitness individuals better than existing DNAm biomarkers and estimates a younger biological age in the high-fit males and females (1.5 and 2.0 years younger, respectively). Our research shows that regular physical exercise contributes to observable physiological and methylation differences which are beneficial to the aging process. DNAmFitAge has now emerged as a new biological marker of quality of life.

Highlights MicroRNAs have been linked to redox sensitive cellular processes. MicroRNAs associated control of mitochondrial function, includes regulation of reactive oxygen species. Exercise-associated redox- sensitive microRNAs regulation is an important element for exercise-mediated adaption.