Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study

Sleep disturbances, shift work, and epigenetic ageing in working-age adults: findings from the Young Finns study

BACKGROUND

Sleep disturbances are known to have adverse effects on health, but knowledge on the effect of sleep disturbances on epigenetic ageing is limited. We investigated (1) whether symptoms of insomnia, obstructive sleep apnoea, sleep deprivation, and circadian rhythm lateness are associated with epigenetic ageing, and (2) whether years spent in shift work moderates these associations.

METHODS

We used the population-based Young Finns data (n = 1618). Epigenetic clocks such as AgeDevHannum, AgeDevHorvath, AgeDevPheno, AgeDevGrim, and DunedinPACE were utilized to measure epigenetic ageing. Sleep was evaluated using various validated self-report questionnaires. Covariates included sex, array type, smoking status, health behaviours, socioeconomic factors, and cardiovascular health factors.

RESULTS

Among the various sleep measures, obstructive sleep apnoea symptoms were most consistently linked to accelerated epigenetic ageing, as measured by AgeDevGrim and DunedinPACE. Insomnia, sleep deprivation, and years spent in shift work were not associated with epigenetic ageing after adjusting for health-related or socioeconomic covariates. Additionally, we found interactions between years spent in shift work and sleep disturbances when accounting for epigenetic ageing. Among those with little to no history of shift work, both insomnia and sleep deprivation were associated with more accelerated epigenetic ageing in AgeDevGrim when compared to long-term shift workers. However, the pace of epigenetic ageing (measured with DunedinPACE) appears to be higher in those with both sleep deprivation and longer history of shift work.

CONCLUSIONS

Among various sleep measures, symptoms of obstructive sleep apnoea appear to be most consistently associated with accelerated epigenetic ageing even after adjusting for various health-related and socioeconomic factors. Shift work seems to have a crucial role in the relationship between sleep disturbances and epigenetic ageing in working-age adults.

Causal Associations of Epigenetic Age Acceleration With Stroke and Its Functional Outcome: A Two-Sample, Two-Step Mendelian Randomization Study

BACKGROUND

Emerging evidence from observational studies suggested that epigenetic age acceleration may result in an increased incidence of stroke and poorer functional outcomes after a stroke. However, the causality of these associations remains controversial and may be confounded by bias. We aimed to investigate the causal effects of epigenetic age on stroke and its functional outcomes.

METHODS

We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal relationships between epigenetic age and stroke and its outcomes. Additionally, a two-step MR analysis was performed to investigate whether lifestyle factors affect stroke via epigenetic age. Datasets of epigenetic age were obtained from a recent meta-analysis (n = 34,710), while those of stroke and its outcomes were sourced from the MEGASTROKE (n = 520,000) consortium and Genetics of Ischaemic Stroke Functional Outcome (GISCOME) network (n = 6165).

RESULTS

Two-sample MR analysis revealed a causal relationship between PhenoAge and small vessel stroke (SVS) (OR = 1.07; 95% CI, 1.03-1.12; p = 2.01 × 10-3). Mediation analysis through two-step MR indicated that the increased risk of SVS due to smoking initiation was partially mediated by PhenoAge, with a mediation proportion of 9.5% (95% CI, 1.6%-20.6%). No causal relationships were identified between epigenetic age and stroke outcomes.

CONCLUSIONS

Our study supports using epigenetic age as a biomarker to predict stroke occurrence. Interventions specifically aimed at decelerating epigenetic aging, such as specific lifestyle changes, offer effective strategies for reducing stroke risk.

Associations between lifestyle factors, physiological conditions, and epigenetic age acceleration in an Asian population

Epigenetic clocks use DNA methylation (DNAm) levels to predict an individual's biological age. However, relationships between lifestyle/biomarkers and epigenetic age acceleration (EAA) in Asian populations remain unknown. We here explored associations between lifestyle factors, physiological conditions, and epigenetic markers, including HannumEAA, IEAA, PhenoEAA, GrimEAA, DunedinPACE, DNAm-based smoking pack-years (DNAmPACKYRS), and DNAm plasminogen activator inhibitor 1 level (DNAmPAI1). A total of 2474 Taiwan Biobank (TWB) individuals aged between 30 and 70 provided physical health examinations, lifestyle questionnaire surveys, and blood and urine samples. Partial correlation analysis (while adjusting for chronological age, smoking, and drinking status) demonstrated that 29 factors were significantly correlated with at least one epigenetic marker (Pearson's correlation coefficient |r|> 0.15). Subsequently, by exploring the model with the smallest Akaike information criterion (AIC), we identified the best model for each epigenetic marker. As a DNAm-based marker demonstrated to predict healthspan and lifespan with greater accuracy, GrimEAA was also found to be better explained by lifestyle factors and physiological conditions. Totally 15 factors explained 44.7% variability in GrimEAA, including sex, body mass index (BMI), waist-hip ratio (WHR), smoking, hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), creatinine, uric acid, gamma-glutamyl transferase (GGT), hemoglobin, and five cell-type proportions. In summary, smoking, elevated HbA1c, BMI, WHR, GGT, and uric acid were associated with more than one kind of EAA. At the same time, higher HDL-C and hemoglobin were related to epigenetic age deceleration (EAD). These findings offer valuable insights into biological aging.

Insights to aging prediction with AI based epigenetic clocks

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.

ERJ advances: epigenetic ageing and leveraging DNA methylation in chronic respiratory diseases

Chronic respiratory diseases are the third leading cause of death and affect more than 450 million people worldwide [1]. Major risk factors such as cigarette smoking have long been studied in their pathogenesis, but as the global population ages, increasing attention must now be paid to the contributory role of ageing [2]. Epidemiological evidence indicates a decline in lung health over time with lung function classically reaching its peak between 20–30 years of age and starting an inevitable descent thereafter [3]. Modern paradigms suggest that this rise and descent may occur at different rates along the lifespan, which may indicate that the links between age and lung function may be variable between individuals [4]. Deciphering how lung ageing influences the development of chronic respiratory diseases may hold powerful clues into novel therapeutics and management strategies.

Epigenetic age is a novel biomarker utilising DNA methylation profiles that can detect accelerated biological ageing. Potential uses in respiratory disease include risk stratification for vulnerable patients and prognostication for poor clinical outcomes. https://bit.ly/3ZMTAK1

Younger epigenetic age is associated with higher cardiorespiratory fitness in individuals with airflow limitation

We hypothesized that increased cardiorespiratory fitness (CRF) slows down a person's aging, particularly in individuals with chronic airflow limitation (CAL). Participants aged ≥40 years (n = 78) had baseline blood DNA methylation profiled and underwent cardiopulmonary cycle exercise testing at baseline and at three years. Epigenetic clocks were calculated and tested for their association with CRF using linear regression. Differentially methylated genes associated with CRF were identified using a robust linear model. Higher CRF at baseline was associated with lower age acceleration in the epigenetic clocks DNAmAgeSkinBlood (p = 0.016), DNAmGrimAge (p = 0.012), and DNAmGrimAge2 (p = 0.011). These effects were consistent in individuals with CAL (DNAmGrimAge p = 0.009 and DNAmGrimAge2 p = 0.007). CRF at three years was associated with baseline DNAmGrimAge (p = 0.015) and DNAmGrimAge2 (p = 0.011). Differentially methylated genes associated with CRF enriched multiple aging-related pathways, including cellular senescence. Enhancing CRF may be one intervention that can slow biological aging and improve health outcomes in chronic respiratory diseases.

Influence of physical activity on the epigenetic clock: evidence from a Japanese cross-sectional study

BACKGROUND

Biological age, especially epigenetic age derived from the epigenetic clock, is a significant measure of aging, considering the differences in aging rates among individuals. The epigenetic clock, a machine learning-based algorithm, uses DNA methylation states to estimate biological age. Previous studies have reported inconsistent associations between physical activity (PA) and the epigenetic clock, especially second-generation clocks such as PhenoAge and GrimAge. This study aimed to clarify this relationship using cross-sectional data from Japanese participants aged 40-69.

METHODS

We used two datasets from the Saga J-MICC study, of which 867 samples were available for analysis. DNA methylation data from peripheral blood samples were used to calculate the epigenetic age using the epigenetic clocks PhenoAge and GrimAge. PA and sedentary time were measured using a single-axis accelerometer, while self-reported PA, sedentary time, and covariates were assessed using a self-administered questionnaire. The association between PA or sedentary time and epigenetic age acceleration was assessed using multiple linear regression.

RESULTS

Pearson's correlation coefficients between accelerometer-based and self-reported PA variables ranged from 0.09 to 0.20. Multivariable regression analysis showed that accelerometer-based PA and sedentary time were associated with epigenetic age decelerations and accelerations, respectively. However, self-reported PA was not associated with the epigenetic age accelerations.

CONCLUSIONS

These results indicate that reducing sedentary time and increasing PA were associated with slowing both PhenoAge and GrimAge, even in East Asian populations with different exercise habits, body shapes, and lifestyles. This study highlights the potential of objective second-generation epigenetic age acceleration as an outcome index for healthcare interventions and clinical applications.

Polygenic Risk Score, Cardiorespiratory Fitness, and Cardiometabolic Risk Factors: WASEDA'S Health Study

PURPOSE

This study estimated an individual's genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high cardiorespiratory fitness (CRF) modifies the association between PRS and cardiometabolic risk factors.

METHODS

This cross-sectional study enrolled 1296 Japanese adults aged ≥40 yr. The PRS for each cardiometabolic trait (blood lipids, glucose, hypertension, and obesity) was calculated using the LDpred2 and clumping and thresholding methods. Participants were divided into low-, intermediate-, and high-PRS groups according to PRS tertiles for each trait. CRF was quantified as peak oxygen uptake (V̇O 2peak ) per kilogram body weight. Participants were divided into low-, intermediate-, and high-CRF groups according to the tertile V̇O 2peak value.

RESULTS

Linear regression analysis revealed a significant interaction between PRS for triglyceride (PRS TG ) and CRF groups on serum TG levels regardless of the PRS calculation method, and the association between PRS TG and TG levels was attenuated in the high-CRF group. Logistic regression analysis revealed a significant sub-additive interaction between LDpred2 PRS TG and CRF on the prevalence of high TG, indicating that high CRF attenuated the genetic predisposition to high TG. Furthermore, a significant sub-additive interaction between PRS for body mass index and CRF on obesity was detected regardless of the PRS calculation method. These significant interaction effects on high TG and obesity were diminished in the sensitivity analysis using V̇O 2peak per kilogram fat-free mass as the CRF index. Effects of PRSs for other cardiometabolic traits were not significantly attenuated in the high-CRF group regardless of PRS calculation methods.

CONCLUSIONS

The findings of the present study suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.

Physical Activity and Epigenetic Aging in Breast Cancer Treatment

Biological age, reflecting the cumulative damage in the body over a lifespan, is a dynamic measure more indicative of individual health than chronological age. Accelerated aging, when biological age surpasses chronological age, is implicated in poorer clinical outcomes, especially for breast cancer (BC) survivors undergoing treatments. This preliminary study investigates the impact of a 16-week online supervised physical activity (PA) intervention on biological age in post-surgery female BC patients. Telomere length was measured using qPCR, and the ELOVL2-based epigenetic clock was assessed via DNA methylation pyrosequencing of the ELOVL2 promoter region. Telomere length remained unchanged, but the ELOVL2 epigenetic clock indicated a significant decrease in biological age in the PA group, suggesting the potential of PA interventions to reverse accelerated aging processes in BC survivors. The exercise group showed improved cardiovascular fitness, highlighting PA's health impact. Finally, the reduction in biological age, as measured by the ELOVL2 epigenetic clock, was significantly associated with improvements in cardiovascular fitness and handgrip strength, supporting improved recovery. Epigenetic clocks can potentially assess health status and recovery progress in BC patients, identifying at-risk individuals in clinical practice. This study provides potential and valuable insights into how PA benefits BC survivors' health, supporting the immediate benefits of a 16-week exercise intervention in mitigating accelerated aging. The findings could suggest a holistic approach to improving the health and recovery of post-surgery BC patients.

Targeting neuronal epigenomes for brain rejuvenation

Aging is associated with a progressive decline of brain function, and the underlying causes and possible interventions to prevent this cognitive decline have been the focus of intense investigation. The maintenance of neuronal function over the lifespan requires proper epigenetic regulation, and accumulating evidence suggests that the deterioration of the neuronal epigenetic landscape contributes to brain dysfunction during aging. Epigenetic aging of neurons may, however, be malleable. Recent reports have shown age-related epigenetic changes in neurons to be reversible and targetable by rejuvenation strategies that can restore brain function during aging. This review discusses the current evidence that identifies neuronal epigenetic aging as a driver of cognitive decline and a promising target of brain rejuvenation strategies, and it highlights potential approaches for the specific manipulation of the aging neuronal epigenome to restore a youthful epigenetic state in the brain.

Underlying Mechanisms of the Protective Effects of Lifestyle Factors On Age-Related Diseases

The rise in life expectancy has significantly increased the occurrence of age-related chronic diseases, leading to escalating expenses for both society and individuals. Among the main factors influencing health and lifespan, lifestyle takes a forefront position. Specifically, nutrition, mental activity, and physical exercise influence the molecular and functional mechanisms that contribute to the prevention of major age-related diseases. Gaining deeper insights into the mechanisms that drive the positive effects of healthy lifestyles is valuable for creating interventions to prevent or postpone the development of chronic degenerative diseases. This review summarizes the main mechanisms that underlie the positive effect of lifestyle factors in counteracting the major age-related diseases involving brain health, musculoskeletal function, cancer, frailty, and cardiovascular diseases, among others. This knowledge will help to identify high-risk populations for targeted intervention trials and discover new biomarkers associated with healthy aging.

Healthy Japanese dietary pattern is associated with slower biological aging in older men: WASEDA'S health study

Aging is the greatest risk factor for numerous diseases and mortality, and establishing geroprotective interventions targeting aging is required. Previous studies have suggested that healthy dietary patterns, such as the Mediterranean diet, are associated with delayed biological aging; however, these associations depend on nationality and sex. Therefore, this study aimed to investigate the relationship between dietary patterns identified through principal component analysis and biological aging in older men of Japan, one of the countries with the longest life expectancies. Principal component analysis identified two dietary patterns: a healthy Japanese dietary pattern and a Western-style dietary pattern. Eight epigenetic clocks, some of the most accurate aging biomarkers, were identified using DNA methylation data from whole-blood samples. Correlation analyses revealed that healthy Japanese dietary patterns were significantly negatively or positively correlated with multiple epigenetic age accelerations (AgeAccel), including AgeAccelGrim, FitAgeAccel, and age-adjusted DNAm-based telomere length (DNAmTLAdjAge). Conversely, the Western-style dietary pattern was observed not to correlate significantly with any of the examined AgeAccels or age-adjusted values. After adjusting for covariates, the healthy Japanese dietary pattern remained significantly positively correlated with DNAmTLAdjAge. Regression analysis showed that healthy Japanese dietary pattern contributed less to epigenetic age acceleration than smoking status. These findings suggest that a Western-style dietary pattern may not be associated with biological aging, whereas a healthy Japanese dietary pattern is associated with delayed biological aging in older Japanese men. Our findings provide evidence that healthy dietary patterns may have mild beneficial effects on delayed biological aging in older Japanese men.

Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study

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.

Healthy aging: Linking causal mechanisms with holistic outcomes

Identifying and understanding the impact of differing exposures over the lifecourse necessitates contextualizing different levels of influence ranging from genetics, epigenetics, geography, and psychosocial networks.