DNA Methylation-derived biological age and long-term mortality risk in subjects with type 2 diabetes.

BACKGROUND: Individuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown. METHODS: Among a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients. RESULTS: Deceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05-1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43-9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived. CONCLUSIONS: These findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.

PhenoAge and PhenoAgeAccel in the context of dyslipidemia: association with stroke and potential as a new predictor in the US general population.

BACKGROUND: The challenges posed by an aging society are increasingly recognized, particularly regarding the potential impacts of high-fat diets and the trend toward younger ages of disease onset. The purpose of this study was to clarify the relationships between the prevalence of stroke in the general US population and PhenoAge (PA) and PhenoAgeAccel (PAA). METHODS: Key methods included a correlation heatmap to explore linear relationships between some of the variables and weighted multivariable logistic regression to assess associations between PA, PAA, and stroke. Restricted cubic spline (RCS) analysis was used to investigate nonlinear relationships and dose-response effects. The ability of the PAA to predict stroke risk was assessed by a nomogram model and a receiver operating characteristic curve. RESULTS: After adjusting for confounding variables, both continuous and categorical PA and PAA were significantly associated with stroke prevalence. RCS analysis revealed a significant nonlinear relationship between PA and stroke prevalence. Further subgroup RCS analysis indicated that maintaining PA below 31.6 could provide greater benefits across all races and genders, specifically for individuals aged 40-80 years and those who are overweight or obese. CONCLUSION: This study highlights the importance of stroke prevention in overweight and obese populations. Monitoring intermediary factors such as high-density lipoprotein cholesterol and waist circumference may reduce stroke risk. Additionally, PA and PAA may serve as novel markers for stroke, offering new directions for health management in an aging society.

Association of biological aging with prostate cancer: insights from the National Health and Nutrition Examination Survey.

The link between biological aging and prostate cancer (PCa) risk, particularly as indicated by elevated prostate-specific antigen (PSA) levels, remains uncertain. This study utilized data from the National Health and Nutrition Examination Survey (2001-2010) to explore this association. Biological age was assessed using Klemera-Doubal method age (KDMAge) and phenotypic age (PhenoAge). PCa was identified through self-reported diagnoses, and highly probable PCa was determined by PSA levels. We analyzed the prevalence of PCa and PSA-defined highly probable PCa across quartiles of biological age measures using weighted chi-square and linear trend tests. Associations were evaluated using weighted multiple logistic regression models. Among 7,209 and 6,682 males analyzed, the overall weighted prevalence of PCa was 2.86%, increasing to 9.60% in those aged 65 and above. A significant rise in PCa prevalence was observed with higher quartiles of KDMAge or PhenoAge (P for trend < 0.001), particularly in those under 65. In this younger group, higher PhenoAge acceleration quartiles were linked to increased PCa prevalence and higher risk of PCa (OR = 1.50, P = 0.015) as well as highly probable PCa in those without a diagnosis (OR = 1.28, P = 0.031). These findings suggest that accelerated biological aging is associated with an increased risk of PCa and may indicate early risk as signaled by PSA levels, even in those without a PCa diagnosis.

[Epigenetics of aging: a breakthrough direction in gerontology?]

Whereas genome is identical in all types of tissue and relatively stable during the lifetime, the epigenome of the multicellular organism changes during development and aging. The strong effects of age on one of the central epigenetic modifications - DNA cytosine methylation levels have been identified by Dr. B.F.Vanyushin et al., in the A.N.Belozersky Institute of Physico-Chemical Biology of MSU, Moscow, Russia in the 1960s. This discovery served as an impetus to numerous studies of DNA methylation, as a result of which it became possible not only to calculate with an amazing accuracy the age of the organism regardless of its physiological indices, but also to reveal pathological changes in it. Moreover, in the future, these discoveries can promote the development of a new direction of therapy - epigenetic therapy.

Phenoage and longitudinal changes on transthoracic echocardiography in Alström syndrome: a disease of accelerated ageing?

Alström syndrome (AS) is an ultra-rare disorder characterised by early-onset multi-organ dysfunction, such as insulin resistance, obesity, dyslipidaemia, and renal and cardiovascular disease. The objective is to explore whether AS is a disease of accelerated ageing and whether changes over time on echocardiography could reflect accelerated cardiac ageing. Cross-sectional measurement of Phenoage and retrospective analysis of serial echocardiography were performed between March 2012 and November 2022. The setting is a single national tertiary service jointly run by health service and patient charity. Forty-five adult patients aged over 16 years were included, 64% were male and 67% of White ethnicity. The median Phenoage was 48 years (interquartile range [IQR]: 35-72) in the 34 patients for whom this was calculable, which was significantly higher than the median chronological age of 29 years (IQR: 22-39, p<0.001). Phenoage was higher than chronological age in 85% (N=29) of patients, with a median difference of +18 years (IQR: +4, +34). On echocardiography, significant decreases were observed over time in left ventricular (LV) size at end-diastole (average of 0.046 cm per year, p<0.001) and end-systole (1.1% per year, p=0.025), with significant increase in posterior wall thickness at end-diastole (0.009 cm per year, p=0.008). LV systolic function measured by global longitudinal strain reduced (0.34 percentage points per year, p=0.020) and E/e'lat increased (2.5% per year, p=0.019). Most AS patients display a higher Phenoage compared to chronological age. Cardiac changes in AS patients were also reflective of accelerated ageing, with a reduction in LV size and increased wall thickening. AS may be a paradigm disease for premature ageing.

Epigenetic Aging and Rheumatoid Arthritis.

This is the first known comparative assessment of the associations of epigenetic age estimates with the prevalence of rheumatoid arthritis (RA). We used data available in Gene Expression Omnibus (GSE42861) from the Swedish Epidemiological Investigation of Rheumatoid Arthritis study. Information regarding RA diagnosis and 450K DNA methylation (DNAm) of 18- to 70-year-old participants was available. Utilizing Horvath's online DNAm Age Calculator, we determined the DNAm estimate of Telomere length (DNAmTL), Hannum's epigenetic age, Horvath's 2013 and 2018 epigenetic ages, PhenoAge, GrimAge, and the respective age-acceleration measures. The association of RA prevalence with epigenetic age measures was assessed using linear regression, adjusting for sex and smoking status. The p values were corrected for multiple testing using a false discovery rate. We identified statistically significant associations of RA with Horvath 2013 age acceleration (estimate: -1.34; FDR p value: 1.0 × 10-2), Horvath 2018 age acceleration (estimate: -1.32; FDR p value: 4.0 × 10-5), extrinsic age acceleration (estimate: 1.34; FDR p value: 1.0 × 10-2), PhenoAge acceleration (estimate: 2.31; FDR p value: 1.1 × 10-5), GrimAge (estimate: 2.54; FDR p value: 1.0 × 10-2), and GrimAge acceleration (estimate: 3.15; FDR p-value: 1.7 × 10-17). Of note, the raw and age-adjusted GrimAge surrogate DNAm protein components were significantly higher in RA cases than controls. Interestingly, the first-generation measures were associated only with women. No sex-specific effects were identified for PhenoAge or GrimAge accelerations. In this cross-sectional assessment, the second-generation clocks show promise as markers of biological aging, with higher epigenetic age acceleration observed in RA cases compared with healthy controls.

Association of Neighborhood Deprivation and Depressive Symptoms With Epigenetic Age Acceleration: Evidence From the Canadian Longitudinal Study on Aging.

BACKGROUND: Neighborhood deprivation and depression have been linked to epigenetic age acceleration. The next-generation epigenetic clocks including the DNA methylation (DNAm) GrimAge, and PhenoAge have incorporated clinical biomarkers of physiological dysregulation by selecting cytosine-phosphate-guanine sites that are associated with risk factors for disease, and have shown improved accuracy in predicting morbidity and time-to-mortality compared to the first-generation clocks. The aim of this study is to examine the association between neighborhood deprivation and DNAm GrimAge and PhenoAge acceleration in adults, and assess interaction with depressive symptoms. METHODS: The Canadian Longitudinal Study on Aging recruited 51 338 participants aged 45-85 years across provinces in Canada. This cross-sectional analysis is based on a subsample of 1 445 participants at baseline (2011-2015) for whom epigenetic data were available. Epigenetic age acceleration (years) was assessed using the DNAm GrimAge and PhenoAge, and measured as residuals from regression of the biological age on chronological age. RESULTS: A greater neighborhood material and/or social deprivation compared to lower deprivation (b = 0.66; 95% confidence interval [CI] = 0.21, 1.12) and depressive symptoms scores (b = 0.07; 95% CI = 0.01, 0.13) were associated with higher DNAm GrimAge acceleration. The regression estimates for these associations were higher but not statistically significant when epigenetic age acceleration was estimated using DNAm PhenoAge. There was no evidence of a statistical interaction between neighborhood deprivation and depressive symptoms. CONCLUSIONS: Depressive symptoms and neighborhood deprivation are independently associated with premature biological aging. Policies that improve neighborhood environments and address depression in older age may contribute to healthy aging among older adults living in predominantly urban areas.

Generations of epigenetic clocks and their links to socioeconomic status in the Health and Retirement Study.

Aim: This is a brief description of links between nine epigenetic clocks related to human aging and socioeconomic and behavioral characteristics as well as health outcomes.Materials & methods: We estimate frequently used and novel clocks from one data source, the Health and Retirement Study.Results: While all of these clocks are thought to reflect "aging," they use different CpG sites and do not strongly relate to each other. First and fourth generation clocks are not as linked to socioeconomic status or health outcomes as second and third generation clocks.Conclusion: Epigenetic clocks reflect exciting new tools and their continued evolution is likely to improve our understanding of how exposures get under the skin to accelerate aging.

Biological aging occurs much earlier than mortality and the onset of diseases associated with age that can be clinically diagnosed. In fact, changes in biology that accelerate aging can occur throughout life in response to adverse exposures, behaviors and experiences. One such change is methylation or the attachment of methyl groups to genetic markers to affect their activity. Epigenetic clocks are measures of the amount of methylation that is related to aging. They are called clocks because they are measured in years or ticks of time or in change in years relative to age. We show that not all epigenetic clocks are the same in how they relate to socioeconomic status and health behaviors as well as subsequent mortality and morbidity. There are now four generations of these clocks developed in a little more than 10 years. The second and third generation clocks are more closely associated with lifetime socioeconomic status, health behaviors and health outcomes probably because they have been developed by relating them to health indicators in contrast to epigenetic measures that were developed because of their relation to age. Incorporating epigenetic measures into population studies reflects the beginning of our ability to measure some aspects of aging long before old age; it also provides entry to monitoring, measuring and intervening on biological aging throughout life.

Moderate BMI accumulation modified associations between blood benzene, toluene, ethylbenzene and xylene (BTEX) and phenotypic aging: mediating roles of inflammation and oxidative stress.

The associations between blood benzene, toluene, ethylbenzene, and xylenes (BTEX) and biological aging among general adults remain elusive. The present study comprised 5780 participants from the National Health and Nutrition Examination Survey 1999-2010. A novel measure of biological aging, phenotypic age acceleration (PhenoAge.Accel), derived from biochemical markers was calculated. Weighted generalized linear regression and weighted quantile sum regression (WQS) were utilized to assess the associations between BTEX components and mixed exposure, and PhenoAge.Accel. The mediating roles of systemic immune-inflammation index (SII) and oxidative stress indicators (serum bilirubin and gamma-glutamyl transferase), along with the modifying effects of body mass index (BMI) were also examined. In the single-exposure model, the highest quantile of blood benzene (b = 0.89, 95%CI: 0.58 to 1.20), toluene (b = 0.87, 95%CI: 0.52 to 1.20), and ethylbenzene (b = 0.80, 95%CI: 0.46 to 1.10) was positively associated with PhenoAge.Accel compared to quantile 1. Mixed-exposure analyses revealed a consistent positive association between BTEX mixed exposure and PhenoAge.Accel (b = 0.88, 95%CI: 0.56 to 1.20), primarily driven by benzene (92.78%). The association between BTEX and PhenoAge.Accel was found to be partially mediated by inflammation and oxidative stress indicators (ranging from 3.2% to 13.7%). Additionally, BMI negatively modified the association between BTEX mixed exposure and PhenoAge.Accel, with a threshold identified at 36.2 kg/m^2. Furthermore, BMI negatively moderated the direct effect of BTEX mixed exposure on PhenoAge.Accel in moderated mediation models, while positively modified the link between SII and PhenoAge.Accel in the indirect path (binteraction = 0.04, 95%CI: 0.01 to 0.06). Overall, BTEX mixed exposure was associated with PhenoAge.Accel among US adults, with benzene may have reported most contribution, and inflammation and oxidative damage processes may partially explain this underlying mechanism. The study also highlighted the potential benefits of appropriate BMI increased. Additional large-scale cohort studies and experiments were necessary to substantiate these findings.

Dietary diversity score and the acceleration of biological aging: a population-based study of 88,039 participants.

OBJECTIVES: Our study aimed to investigate the association of dietary diversity score (DDS), as reflected by five dietary categories, with biological age acceleration. DESIGN: A cross-sectional study. SETTING AND PARTICIPANTS: This study included 88,039 individuals from the UK Biobank. METHODS: Biological age (BA) was assessed using Klemerae-Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age represents the age acceleration (AgeAccel), termed as "KDMAccel" and "PhenoAgeAccel". AgeAccel > 0 indicates faster aging. Generalized linear regression models were performed to assess the associations of DDS with AgeAccel. Similar analyses were performed for the five dietary categories. RESULTS: After adjusting for multiple variables, DDS was inversely associated with KDMAccel (ßHigh vs Low= -0.403, 95%CI: -0.492 to -0.314, P < 0.001) and PhenoAgeAccel (ßHigh vs Low= -0.545, 95%CI: -0.641 to -0.450, P < 0.001). Each 1-point increment in the DDS was associated with a 4.4% lower risk of KDMAccel and a 5.6% lower risk of PhenoAgeAccel. The restricted cubic spline plots demonstrated a non-linear dose-response association between DDS and the risk of AgeAccel. The consumption of grains (ßKDMAccel = -0.252, ßPhenoAgeAccel = -0.197), vegetables (ßKDMAccel = -0.044, ßPhenoAgeAccel = -0.077) and fruits (ßKDMAccel = -0.179, ßPhenoAgeAccel = -0.219) was inversely associated with the two AgeAccel, while meat and protein alternatives (ßKDMAccel = 0.091, ßPhenoAgeAccel = 0.054) had a positive association (All P < 0.001). Stratified analysis revealed stronger accelerated aging effects in males, smokers, and drinkers. A strengthening trend in the association between DDS and AgeAccel as TDI quartiles increased was noted. CONCLUSIONS: This study suggested that food consumption plays a role in aging process, and adherence to a higher diversity dietary is associated with the slowing down of the aging process.

Association of sleep duration, chronotype, social jetlag, and sleep disturbance with phenotypic age acceleration: A cross-sectional analysis.

OBJECTIVE: Sleep is a critical health-related behavior; research evidence has shown that sleep duration, poor sleep quality and insomnia are associated with aging and relevant age-related diseases. However, the associations between sleep duration, chronotype, sleep disturbance, and biological age have not been comprehensively assessed. This study aimed to examine sleep characteristics with biological age. METHODS: The study included 6534 participants aged 20 years and older from the National Health and Nutrition Examination Survey between 2017 and March 2020. Sleep questionnaires were used to collect information on sleep duration and wake behavior on workdays and workfree days and sleep disturbance. Phenotypic age acceleration (PhenoAgeAccel) was estimated as a biological age measure using 9 blood chemistry biomarkers. RESULTS: Long sleep (>9 hours) and extremely short sleep (≤4 hours) on workdays were positively associated with PhenoAgeAccel, compared with optimal sleep duration (7-8 hours). Similar positive associations with PhenoAgeAccel were observed for sleep duration on workfree days and across the whole week. Both slightly evening and evening chronotypes were associated with faster PhenoAgeAccel compared to morning chronotype. Social jetlag and sleep disturbance were not associated with PhenoAgeAccel, while long corrected social jetlag was associated with faster PhenoAgeAccel. The associations of sleep duration, chronotype, and corrected social jetlag with PhenoAgeAccel appeared stronger among females than among males. CONCLUSIONS: Findings suggest a U-shape relationship between sleep duration and biological aging; slightly evening and evening chronotypes may be risk factors for aging. Further studies are needed to confirm these findings.

Composite dietary antioxidant index associated with delayed biological aging: a population-based study.

OBJECTIVE: The objective of this study was to explore the potential correlation between the composite dietary antioxidant index (CDAI) and biological aging, addressing the insufficient epidemiological evidence in this area. METHODS: Participants meeting eligibility criteria were selected from the National Health and Nutrition Examination Surveys (NHANES) conducted between 2001 and 2018. CDAI was determined based on dietary antioxidants obtained from 24-hour dietary recalls. Biological age was determined using PhenoAge algorithms incorporating various clinical features. Weighted multiple models were employed to investigate and assess the association between CDAI and biological age. RESULTS: Analysis of the CDAI quartile revealed disparities in terms of age, gender, ethnicity, educational level, marital status, poverty, dietary calories intakes, smoking, drinking status, BMI, physical activity, and PhenoAge. After adjusting for potential confounding factors, a significant inverse relationship was found between CDAI and Phenotypic Age, with each standard deviation increase in CDAI score correlating with a 0.18-year decrease in Phenotypic Age. These negative correlations between CDAI and PhenoAge advancement were observed regardless of age, gender, physical activity status, smoking status, and body mass index. CONCLUSIONS: Our findings demonstrate a positive relationship between higher CDAI scores and delayed biological aging. These results have significant implications for public health initiatives aimed at promoting healthy aging through dietary interventions.

Association of biological age acceleration with cardiac morphology, function, and incident heart failure: insights from UK Biobank participants.

AIMS: Advanced age is associated with an increased risk of adverse cardiovascular events. The relationship between biological age acceleration (BAA), cardiac size, cardiac function, and heart failure (HF) is not well-defined. METHODS AND RESULTS: Utilizing the UK Biobank cohort, we assessed biological age using the Klemera-Doubal and PhenoAge methods. BAA was quantified by residual analysis compared with chronological age. Cardiovascular magnetic resonance (CMR) imaging provided detailed insights into cardiac structure and function. We employed multivariate regression to examine links between BAA and CMR-derived cardiac phenotypes. Cox proportional hazard regression models analysis was applied to explore the causative relationship between BAA and HF. Additionally, Mendelian randomization was used to investigate the genetic underpinnings of these associations. A significant correlation was found between increased BAA and deleterious changes in cardiac structure, such as diminished left ventricular mass, lower overall ventricular volume, and reduced stroke volumes across ventricles and atria. Throughout a median follow-up of 13.8 years, participants with greater biological aging showed a heightened risk of HF [26% per standard deviation (SD) increase in KDM-BA acceleration, 95% confidence intervals (CI): 23-28%; 33% per SD increase in PhenoAge acceleration, 95% CI: 32-35%]. Mendelian randomization analysis suggests a likely causal link between BAA, vital cardiac metrics, and HF risk. CONCLUSION: In this cohort, accelerated biological aging may serve as a risk indicator for altered cardiac dimensions, functionality, and the onset of heart failure among middle-aged and elderly adults. It holds promise as a focal point for evaluating risk and developing targeted interventions.

Blood-based biological ageing and red cell distribution width are associated with prevalent Parkinson's disease: findings from a large Italian population cohort.

Background: Aging clocks tag the actual underlying age of an organism and its discrepancy with chronological age and have been reported to predict incident disease risk in the general population. However, the relationship with neurodegenerative risk and in particular with Parkinson's Disease (PD) remains unclear, with few discordant findings reporting associations with both incident and prevalent PD risk. Objective: To clarify this relationship, we computed a common aging clock based on blood markers and tested the resulting discrepancy with chronological age (ΔPhenoAge) for association with both incident and prevalent PD risk. Methods: In a large Italian population cohort - the Moli-sani study (N=23,437; age ≥ 35 years; 52% women) - we carried out both Cox Proportional Hazards regressions modelling ΔPhenoAge as exposure and incident PD as outcome, and linear models testing prevalent PD as exposure and ΔPhenoAge as outcome. All models were incrementally adjusted for age, sex, education level completed and other risk/protective factors previously associated with PD risk in the same cohort (prevalent dysthyroidism, hypertension, diabetes, use of oral contraceptives, exposure to paints, daily coffee intake and cigarette smoking). Results: No significant association between incident PD risk (209 cases, median (IQR) follow-up time 11.19 (2.03) years) and PhenoAging was observed (Hazard Ratio [95% Confidence Interval] = 0.98 [0.71; 1.37]). However, a small but significant increase of ΔPhenoAge was observed in prevalent PD cases vs healthy subjects (ß (Standard Error) = 1.39 (0.70)). An analysis of each component biomarker of PhenoAge revealed a significant positive association of prevalent PD status with red cell distribution width (RDW; ß (SE) = 0.46 (0.18)). All the remaining markers did not show any significant evidence of association. Conclusion: The reported evidence highlights systemic effects of prevalent PD status on biological aging and red cell distribution width. Further cohort and functional studies may help shedding a light on the related pathways altered at the organism level in prevalent PD, like red cells variability, inflammatory and oxidative stress mechanisms.

Association of accelerated biological aging with brain volumes: A cross-sectional study.

BACKGROUND: Multiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes. METHODS: BA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed "KDM-acceleration" and "PhenoAge-acceleration". Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis. RESULTS: 14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (ß = -0.029), white matter (ß = -0.021), gray and white matter (ß = -0.026), and hippocampus (ß = -0.011 for left and ß = -0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (ß = -0.008), gray and white matter (ß = -0.010), thalamus (ß = -0.012 for left and ß = -0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging. CONCLUSIONS: Our research indicated that more advanced BA was associated with less brain tissue.

Poverty Status at Birth Predicts Epigenetic Changes at Age 15.

Epigenetic studies have provided new opportunities to better understand the biological effects of poverty and racial/ethnic minority status. However, little is known about sex differences in these processes. Methods: We used 15 years of follow up of 854 racially and ethnically diverse birth cohort who were followed from birth to age 15. Structural equation modeling (SEM) was used to examine the effects of race/ethnicity, maternal education, and family structure on poverty at birth, as well as the effects of poverty at birth on epigenetic changes at age 15. We also explored variations by sex. Results: Our findings indicate that Black and Latino families had lower maternal education and married family structure which in turn predicted poverty at birth. Poverty at birth then was predictive of epigenetic changes 15 years later when the index child was 15. This suggested that poverty at birth partially mediates the effects of race/ethnicity, maternal education, and family structure on epigenetic changes of youth at age 15. There was an effect of poverty status at birth on DNA methylation of male but not female youth at age 15. Thus, poverty at birth may have a more salient effect on long term epigenetic changes of male than female youth. Conclusions: Further studies are needed to understand the mechanisms underlying the observed sex differences in the effects of poverty as a mechanism that connects race/ethnicity, maternal education, and family structure to epigenetic changes later in life.

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

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 behavior 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.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults.

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults.

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

The association between adverse childhood experiences and epigenetic age acceleration in the Canadian longitudinal study on aging (CLSA).

Research examining the association between exposure to a wide range of adverse childhood experiences (ACEs) and accelerated biological aging in older adults is limited. The purpose of this study was to examine the association of ACEs, both as a cumulative score and individual forms of adversity, with epigenetic age acceleration assessed using the DNA methylation (DNAm) GrimAge and DNAm PhenoAge epigenetic clocks in middle and older-aged adults. This cross-sectional study analyzed baseline and first follow-up data on 1445 participants aged 45-85 years from the Canadian Longitudinal Study on Aging (CLSA) who provided blood samples for DNAm analysis. ACEs were assessed using a validated self-reported questionnaire. Epigenetic age acceleration was estimated by regressing each epigenetic clock estimate on chronological age. Cumulative ACEs score was associated with higher DNAm GrimAge acceleration (ß: 0.07; 95% CI: 0.02, 0.11) after adjusting for covariates. Childhood exposure to parental separation or divorce (ß: 0.06; 95% CI: 0.00, 0.11) and emotional abuse (ß: 0.06; 95% CI: 0.00, 0.12) were associated with higher DNAm GrimAge acceleration after adjusting for other adversities and covariates. There was no statistical association between ACEs and DNAm PhenoAge acceleration. Early life adversity may become biologically embedded and lead to premature biological aging, in relation to DNAm GrimAge, which estimates risk of mortality. Strategies that increase awareness of ACEs and promote healthy child development are needed to prevent ACEs.