The Association Between Epigenetic Clocks and Physical Functioning in Older Women: A 3-Year Follow-up

Epigenetic clocks are composite markers developed to predict chronological age or mortality risk from DNA methylation (DNAm) data. The present study investigated the associations between 4 epigenetic clocks (Horvath’s and Hannum’s DNAmAge and DNAm GrimAge and PhenoAge) and physical functioning during a 3-year follow-up.We studied 63- to 76-year-old women (N = 413) from the Finnish Twin Study on Aging. DNAm was measured from blood samples at baseline. Age acceleration (AgeAccel), that is, discrepancy between chronological age and DNAm age, was determined as residuals from linear model. Physical functioning was assessed under standardized laboratory conditions at baseline and at follow-up. A cross-sectional analysis was performed with path models, and a longitudinal analysis was conducted with repeated measures linear models. A nonrandom missing data analysis was performed.In comparison to the other clocks, GrimAgeAccel was more strongly associated with physical functioning. At baseline, GrimAgeAccel was associated with lower performance in the Timed Up and Go (TUG) test and the 6-minute walk test. At follow-up, significant associations were observed between GrimAgeAccel and lowered performance in the TUG, 6-minute and 10-m walk tests, and knee extension and ankle plantar flexion strength tests.The DNAm GrimAge, a novel estimate of biological aging, associated with decline in physical functioning over the 3-year follow-up in older women. However, associations between chronological age and physical function phenotypes followed similar pattern. Current epigenetic clocks do not provide strong benefits in predicting the decline of physical functioning at least during a rather short follow-up period and restricted age range.Peer reviewe

Subjective stress, objective heart rate variability-based stress, and recovery on workdays among overweight and psychologically distressed individuals : a cross-sectional study

Background: The present study aimed to investigate how subjective self-reported stress is associated with objective heart rate variability (HRV)-based stress and recovery on workdays. Another aim was to investigate how physical activity (PA), body composition, and age are associated with subjective stress, objective stress, and recovery. Methods: Working-age participants (n = 221; 185 women, 36 men) in this cross-sectional study were overweight (body mass index, 25.3-40.1 kg/m(2)) and psychologically distressed (>= 3/12 points on the General Health Questionnaire). Objective stress and recovery were based on HRV recordings over 1-3 workdays. Subjective stress was assessed by the Perceived Stress Scale. PA level was determined by questionnaire, and body fat percentage was assessed by bioelectrical impedance analysis. Results: Subjective stress was directly associated with objective stress (P = 0.047) and inversely with objective recovery (P = 0.046). These associations persisted after adjustments for sex, age, PA, and body fat percentage. Higher PA was associated with lower subjective stress (P = 0.037). Older age was associated with higher objective stress (P <0.001). After further adjustment for alcohol consumption and regular medication, older age was associated with lower subjective stress (P = 0.043). Conclusions: The present results suggest that subjective self-reported stress is associated with objective physiological stress, but they are also apparently affected by different factors. However, some of the found associations among these overweight and psychologically distressed participants with low inter-individual variation in PA are rather weak and the clinical value of the present findings should be studied further among participants with greater heterogeneity of stress, PA and body composition. However, these findings suggest that objective stress assessment provides an additional aspect to stress evaluation. Furthermore, the results provide valuable information for developing stress assessment methods.Peer reviewe

Liikunta ja hyvä kunto vähentävät työstressiä

Stressi ymmärretään tavallisesti yksilön omakohtaisena stressikokemuksena. Stressiä voidaan kuitenkin myös arvioida objektiivisesti, elimistön fysiologisia toimintoja mittaamalla. Tieto kehon stressistä ja siitä palautumisesta antaa mahdollisuuden ehkäistä terveysongelmia.nonPeerReviewe

Does the epigenetic clock GrimAge predict mortality independent of genetic influences : an 18 year follow-up study in older female twin pairs

Background Epigenetic clocks are based on DNA methylation (DNAm). It has been suggested that these clocks are useable markers of biological aging and premature mortality. Because genetic factors explain variations in both epigenetic aging and mortality, this association could also be explained by shared genetic factors. We investigated the influence of genetic and lifestyle factors (smoking, alcohol consumption, physical activity, chronic diseases, body mass index) and education on the association of accelerated epigenetic aging with mortality using a longitudinal twin design. Utilizing a publicly available online tool, we calculated the epigenetic age using two epigenetic clocks, Horvath DNAmAge and DNAm GrimAge, in 413 Finnish twin sisters, aged 63-76 years, at the beginning of the 18-year mortality follow-up. Epigenetic age acceleration was calculated as the residuals from a linear regression model of epigenetic age estimated on chronological age (AA(Horvath), AA(GrimAge), respectively). Cox proportional hazard models were conducted for individuals and twin pairs. Results The results of the individual-based analyses showed an increased mortality hazard ratio (HR) of 1.31 (CI95: 1.13-1.53) per one standard deviation (SD) increase in AA(GrimAge). The results indicated no significant associations of AA(Horvath) with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI95: 1.02-2.20) per 1 SD increase in AA(GrimAge). However, after adjusting for smoking, the HR attenuated substantially and was statistically non-significant (1.29; CI95: 0.84-1.99). Similarly, in multivariable adjusted models the HR (1.42-1.49) was non-significant. In AA(Horvath), the non-significant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AA(GrimAge) there were no systematic differences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair difference in AA(GrimAge) was associated with a higher all-cause mortality risk. Conclusions In conclusion, the findings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic influences. Smoking, which is known to alter DNAm levels and is built into the DNAm GrimAge algorithm, attenuated the association between epigenetic aging and mortality risk.Peer reviewe

Mortality associations with DNA methylation-based biological aging and physical functioning measures across a 20-year follow-up period

Background Measures of biological aging range from DNA methylation (DNAm)-based estimates to measures of physical abilities. The purpose of this study was to compare DNAm- and physical functioning-based measures of biological aging in predicting mortality. Methods We studied 63- to 76-year-old women (N = 395) from the Finnish Twin Study on Aging (FITSA). Participants’ biological age (epigenetic clocks DNAm GrimAge and DunedinPACE) was estimated using blood DNAm data. Tests of physical functioning conducted under standardized laboratory conditions included the Timed Up and Go (TUG) test and 10-m walk test. Mortality hazard ratios (HRs) were calculated per every one standard deviation (SD) increase in the predictor. Cox regression models were conducted for individuals and twin pairs, the latter controlling for underlying genetic effects. The models were adjusted for known lifestyle predictors of mortality. Results During the follow-up period (mean 17.0 years, range 0.2–20.3), 187 participants died. In both the individual-based and pairwise analyses, GrimAge and both functional biomarkers of aging were associated with mortality independent of family relatedness, chronological age, physical activity, body mass index, smoking, education, or chronic diseases. In a model including both the DNAm-based measures and functional biomarkers of aging, GrimAge and TUG remained predictive. Conclusions The findings suggest that DNAm GrimAge and the TUG test are strong predictors of mortality independent of each other’s and genetic influences. DNAm-based measures and functional tests capture different aspects of the aging process and thus complement each other as measures of biological aging in predicting mortality.peerReviewe

Physical Activity : Absolute Intensity vs. Relative-to-Fitness-Level Volumes

Purpose: This study aimed to investigate in a real-life setting how moderate- and vigorous-intensity physical activity (PA) volumes differ according to absolute intensity recommendation and relative to individual fitness level by sex, age, and body mass index. Methods: A total of 23,224 Finnish employees (10,201 men and 13,023 women; ages 18–65 yr; body mass index = 18.5–40.0 kg·m−2) participated in heart rate recording for 2+ d. We used heart rate and its variability, respiration rate, and on/off response information from R-R interval data calibrated by participant characteristics to objectively determine daily PA volume, as follows: daily minutes of absolute moderate (3–<6 METs) and vigorous (≥6 METs) PA and minutes relative to individual aerobic fitness for moderate (40%–<60% of oxygen uptake reserve) and vigorous (≥60%) PA. Results: According to absolute intensity categorization, the volume of both moderate- and vigorous-intensity PA was higher in men compared with women (P < 0.001), in younger compared with older participants (P < 0.001), and in normal weight compared with overweight or obese participants (P < 0.001). When the volume of PA intensity was estimated relative to individual fitness level, the differences were much smaller. Mean daily minutes of absolute vigorous-intensity PA were higher than those of relative intensity minutes in normal weight men ages 18–40 yr (17.7, 95% confidence interval [CI] = 16.9–18.6, vs 8.6, 95% CI = 8.0–9.1; P < 0.001), but the reverse was the case for obese women ages 41–65 yr (0.3, 95% CI = 0.2–0.4, vs 7.8, 95% CI = 7.2–8.4; P < 0.001). Conclusion: Compared with low-fit persons, high-fit persons more frequently reach an absolute target PA intensity, but reaching the target is more similar for relative intensity.peerReviewe

Physical activity, body mass index and heart rate variability-based stress and recovery in 16 275 Finnish employees : a cross-sectional study

Background: Physical inactivity, overweight, and work-related stress are major concerns today. Psychological stress causes physiological responses such as reduced heart rate variability (HRV), owing to attenuated parasympathetic and/or increased sympathetic activity in cardiac autonomic control. This study’s purpose was to investigate the relationships between physical activity (PA), body mass index (BMI), and HRV-based stress and recovery on workdays, among Finnish employees. Methods: The participants in this cross-sectional study were 16 275 individuals (6863 men and 9412 women; age 18–65 years; BMI 18.5–40.0 kg/m2 ). Assessments of stress, recovery and PA were based on HRV data from beat-to-beat R-R interval recording (mainly over 3 days). The validated HRV-derived variables took into account the dynamics and individuality of HRV. Stress percentage (the proportion of stress reactions, workday and working hours), and stress balance (ratio between recovery and stress reactions, sleep) describe the amount of physiological stress and recovery, respectively. Variables describing the intensity (i.e. magnitude of recognized reactions) of physiological stress and recovery were stress index (workday) and recovery index (sleep), respectively. Moderate to vigorous PA was measured and participants divided into the following groups, based on calculated weekly PA: inactive (0 min), low (0 < 150 min), medium (150–300 min), and high (>300 min). BMI was calculated from self-reported weight and height. Linear models were employed in the main analyses. Results: High PA was associated with lower stress percentages (during workdays and working hours) and stress balance. Higher BMI was associated with higher stress index, and lower stress balance and recovery index. These results were similar for men and women (P < 0.001 for all). Conclusion: Independent of age and sex, high PA was associated with a lower amount of stress on workdays. Additionally, lower BMI was associated with better recovery during sleep, expressed by a greater amount and magnitude of recovery reactions, which suggests that PA in the long term resulting in improved fitness has a positive effect on recovery, even though high PA may disturb recovery during the following night. Obviously, several factors outside of the study could also affect HRV-based stress.peerReviewe

Predictors of increase in physical activity during a 6-month follow-up period among overweight and physically inactive healthy young adults

Background/Objective: The beneficial effects of physical activity (PA) are well known, but it remains challenging to increase PA among physically inactive and overweight young individuals. The present study aimed to examine how selected psychological and physical characteristics assessed at baseline predict the increase in total PA over a 6-month follow-up among 51 physically inactive and overweight adults (20 women, 31 men; age 26–40 years) who participated in a lifestyle counselling study without supervised PA sessions. Methods: Baseline measurements included a questionnaire assessment of sense of coherence and psychological flexibility, heart rate monitoring-based stress/recovery from stress (stress%/recovery% during 24 hours), and body composition. PA volume was elicited through interview. Participants who increased their PA by ≥ 500 metabolic equivalent of task-minutes/week during the follow-up compared with their prebaseline PA level were regarded as able to increase PA. Logistic regression was used to analyze associations of baseline characteristics with PA increase. Results: During the 6-month follow-up, 41% of the participants increased their total PA by ≥ 500 metabolic equivalent of task-minutes/week. The best predictors of the increase in PA were high meaningfulness subscores of the sense of coherence questionnaire (multivariate adjusted odds ratio 1.57, 95% confidence interval 1.04–2.35) and high recovery% during a day off (odds ratio 1.15, 95% confidence interval 1.02–1.30). Conclusion: A strong sense of meaningfulness and better recovery from stress predict an increase in PA among physically inactive and overweight young adults. Therefore, participants with a low sense of meaningfulness and low recovery from stress may require support from other interventions to be able to increase their PA