Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration

DNA methylation at AHRR as a master predictor of smoke exposure and a biomarker for sleep and exercise

BACKGROUND

DNA methylation profiling may provide a more accurate measure of the smoking status than self-report and may be useful in guiding clinical interventions and forensic investigations. In the current study, blood DNA methylation profiles of nearly 800 Polish individuals were assayed using Illuminia EPIC and the inference of smoking from epigenetic data was explored. In addition, we focused on the role of the AHRR gene as a top marker for smoking and investigated its responsiveness to other lifestyle behaviors.

RESULTS

We found > 450 significant CpGs associated with cigarette consumption, and overrepresented in various biological functions including cell communication, response to stress, blood vessel development, cell death, and atherosclerosis. The model consisting of cg05575921 in AHRR (p = 4.5 × 10-32) and three additional CpGs (cg09594361, cg21322436 in CNTNAP2 and cg09842685) was able to predict smoking status with a high accuracy of AUC = 0.8 in the test set. Importantly, a gradual increase in the probability of smoking was observed, starting from occasional smokers to regular heavy smokers. Furthermore, former smokers displayed the intermediate DNA methylation profiles compared to current and never smokers, and thus our results indicate the potential reversibility of DNA methylation after smoking cessation. The AHRR played a key role in a predictive analysis, explaining 21.5% of the variation in smoking. In addition, the AHRR methylation was analyzed for association with other modifiable lifestyle factors, and showed significance for sleep and physical activity. We also showed that the epigenetic score for smoking was significantly correlated with most of the epigenetic clocks tested, except for two first-generation clocks.

CONCLUSIONS

Our study suggests that a more rapid return to never-smoker methylation levels after smoking cessation may be achievable in people who change their lifestyle in terms of physical activity and sleep duration. As cigarette smoking has been implicated in the literature as a leading cause of epigenetic aging and AHRR appears to be modifiable by multiple exogenous factors, it emerges as a promising target for intervention and investment.

Common DNA sequence variation influences epigenetic aging in African populations

Aging is associated with genome-wide changes in DNA methylation in humans, facilitating the development of epigenetic age prediction models. However, most of these models have been trained primarily on European-ancestry individuals, and none account for the impact of methylation quantitative trait loci (meQTL). To address these gaps, we analyzed the relationships between age, genotype, and CpG methylation in 3 understudied populations: central African Baka (n = 35), southern African ‡Khomani San (n = 52), and southern African Himba (n = 51). We find that published prediction methods yield higher mean errors in these cohorts compared to European-ancestry individuals, and find that unaccounted-for DNA sequence variation may be a significant factor underlying this loss of accuracy. We leverage information about the associations between DNA genotype and CpG methylation to develop an age predictor that is minimally influenced by meQTL, and show that this model remains accurate across a broad range of genetic backgrounds. Intriguingly, we also find that the older individuals and those exhibiting relatively lower epigenetic age acceleration in our cohorts tend to carry more epigenetic age-reducing genetic variants, suggesting a novel mechanism by which heritable factors can influence longevity.

The Impact of Childhood Mental Health and Substance Use on Methylation Aging Into Adulthood

OBJECTIVE

To test whether childhood mental health symptoms, substance use, and early adversity accelerate the rate of DNA methylation (DNAm) aging from adolescence to adulthood.

METHOD

DNAm was assayed from blood samples in 381 participants in both adolescence (mean [SD] age = 13.9 [1.6] years) and adulthood (mean [SD] age = 25.9 [2.7] years). Structured diagnostic interviews were completed with participants and their parents at multiple childhood observations (1,950 total) to assess symptoms of common mental health disorders (attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, anxiety, and depression) and common types of substance use (alcohol, cannabis, nicotine) and early adversities.

RESULTS

Neither childhood mental health symptoms nor substance use variables were associated with DNAm aging cross-sectionally. In contrast, the following mental health symptoms and substance variables were associated with accelerated DNAm aging from adolescence to adulthood: depressive symptoms (b = 0.314, SE = 0.127, p = .014), internalizing symptoms (b = 0.108, SE = 0.049, p = .029), weekly cannabis use (b =1.665, SE = 0.591, p = .005), and years of weekly cannabis use (b = 0.718, SE = 0.283, p = .012). In models testing all individual variables simultaneously, the combined effect of the variables was equivalent to a potential difference of 3.17 to 3.76 years in DNAm aging. A final model tested a variable assessing cumulative exposure to mental health symptoms, substance use, and early adversities. This cumulative variable was strongly associated with accelerated aging (b = 0.126, SE = 0.044, p = .005).

CONCLUSION

Mental health symptoms and substance use accelerated DNAm aging into adulthood in a manner consistent with a shared risk mechanism.

PLAIN LANGUAGE SUMMARY

Using data from 381 participants in the Great Smoky Mountains Study, the authors examined whether childhood mental health symptoms, substance use, and early adversity accelerate biological aging, as measured by DNA methylation age, from adolescence to adulthood. Depressive symptoms and cannabis use were found to significantly accelerate biological aging. Models that tested the combined effect of mental health symptoms, substance use, and early adversity demonstrated that there was a shared effect across these types of childhood problems on accelerated aging.

Epigenetic Aging Helps Explain Differential Resilience in Older Adults

Past research suggests that resilience to health hazards increases with age, potentially because less resilient individuals die at earlier ages, leaving behind their more resilient peers. Using lifetime cigarette smoking as a model health hazard, we examined whether accelerated epigenetic aging (indicating differences in the speed of individuals' underlying aging process) helps explain age-related resilience in a nationally representative sample of 3,783 older U.S. adults from the Health and Retirement Study. Results of mediation moderation analyses indicated that participants aged 86 or older showed a weaker association between lifetime cigarette smoking and mortality relative to participants aged 76-85 and a weaker association between smoking and multimorbidity relative to all younger cohorts. This moderation effect was mediated by a reduced association between smoking pack-years and epigenetic aging. This research helps identify subpopulations of particularly resilient individuals and identifies epigenetic aging as a potential mechanism explaining this process. Interventions in younger adults could utilize epigenetic aging estimates to identify the most vulnerable individuals and intervene before adverse health outcomes, such as chronic disease morbidity or mortality, manifest.

Objective Assessments of Smoking and Drinking Outperform Clinical Phenotypes in Predicting Variance in Epigenetic Aging

The reliability of the associations of the acceleration of epigenetic aging (EA) indices with clinical phenotypes other than for smoking and drinking is poorly understood. Furthermore, the majority of clinical phenotyping studies have been conducted using data from subjects of European ancestry. In order to address these limitations, we conducted clinical, physiologic, and epigenetic assessments of a cohort of 278 middle-aged African American adults and analyzed the associations with the recently described principal-components-trained version of GrimAge (i.e., PC-GrimAge) and with the DunedinPACE (PACE) index using regression analyses. We found that 74% of PC-GrimAge accelerated aging could be predicted by a simple baseline model consisting of age, sex, and methylation-sensitive digital PCR (MSdPCR) assessments of smoking and drinking. The addition of other serological, demographic, and medical history variables or PACE values did not meaningfully improve the prediction, although some variables did significantly improve the model fit. In contrast, clinical variables mapping to cardiometabolic syndrome did independently contribute to the prediction of PACE values beyond the baseline model. The PACE values were poorly correlated with the GrimAge values (r = 0.2), with little overlap in variance explained other than that conveyed by smoking and drinking. The results suggest that EA indices may differ in the clinical information that they provide and may have significant limitations as screening tools to guide patient care.

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

Early-life tobacco smoke exposure and stroke risk: a prospective study of 341,783 and 352,737 UK Biobank participants

INTRODUCTION

Stroke is a life-threatening condition that causes a major medical burden globally. The currently used methods for the prevention or prediction of stroke have certain limitations. Exposure to tobacco in early life, including smoking during adolescence and maternal smoking during pregnancy, can affect adolescent development and lead to several negative outcomes. However, the association between early-life tobacco exposure and stroke is not known.

METHODS

In this prospective cohort study, for the analyses involving exposure to maternal smoking during pregnancy and age of smoking initiation, we included 304,984 and 342,893 participants, respectively., respectively from the UK Biobank. Cox proportional hazard regression model and subgroup analyses were performed to investigate the association between early-life tobacco exposure and stroke. Mediation analyses were performed to identify the mediating role of biological aging in the association between early tobacco exposure and stroke.

RESULTS

Compared with participants whose mothers did not smoke during pregnancy, participants whose mothers smoked during pregnancy showed an 11% increased risk of stroke (HR: 1.11, 95% CI: 1.05-1.18, P < 0.001). Compared with participants who never smoked, participants who smoked during adulthood, adolescence and childhood showed a 22%, 24%, and 38% increased risk of stroke during their adulthood, respectively. Mediation analysis indicated that early-life tobacco exposure can cause stroke by increasing biological aging.

CONCLUSION

This study reveals that exposure to tobacco during early life is associated with an increased risk of experiencing a stroke, and increased biological aging can be the underlying mechanism.

Inference of tobacco and alcohol consumption habits from DNA methylation analysis of blood

DNA methylation has become a biomarker of great interest in the forensic and clinical fields. In criminal investigations, the study of this epigenetic marker has allowed the development of DNA intelligence tools providing information that can be useful for investigators, such as age prediction. Following a similar trend, when the origin of a sample in a criminal scenario is unknown, the inference of an individual's lifestyle such as tobacco use and alcohol consumption could provide relevant information to help in the identification of DNA donors at the crime scene. At the same time, in the clinical domain, prediction of these trends of consumption could allow the identification of people at risk or better identification of the causes of different pathologies. In the present study, DNA methylation data from the UK AIRWAVE study was used to build two binomial logistic models for the inference of smoking and drinking status. A total of 348 individuals (116 non-smokers, 116 former smokers and 116 smokers) plus a total of 237 individuals (79 non-drinkers, 79 moderate drinkers and 79 drinkers) were used for development of tobacco and alcohol consumption prediction models, respectively. The tobacco prediction model was composed of two CpGs (cg05575921 in AHRR and cg01940273) and the alcohol prediction model three CpGs (cg06690548 in SLC7A11, cg0886875 and cg21294714 in MIR4435-2HG), providing correct classifications of 86.49% and 74.26%, respectively. Validation of the models was performed using leave-one-out cross-validation. Additionally, two independent testing sets were also assessed for tobacco and alcohol consumption. Considering that the consumption of these substances could underlie accelerated epigenetic ageing patterns, the effect of these lifestyles on the prediction of age was evaluated. To do that, a quantile regression model based on previous studies was generated, and the potential effect of tobacco and alcohol consumption with the epigenetic age was assessed. The Wilcoxon test was used to evaluate the residuals generated by the model and no significant differences were observed between the categories analyzed.

Adherence to Life's Essential 8 is associated with delayed biological aging: a population-based cross-sectional study

INTRODUCTION AND OBJECTIVES

The aim of this study was to explore the potential of adhering to the American Heart Association's updated Life's Essential 8 (LE8) scores in delaying biological aging amid growing concerns about aging populations and related diseases.

METHODS

A total of 18 261 adults (≥ 20 years old) were examined using National Health and Nutrition Examination Survey data from 2005-2010 and 2015-2018. The LE8 includes 8 components, covering health behaviors and factors. Acceleration of biological aging was defined as an excess of biological/phenotypic age over chronological age, assessed by using clinical biomarkers. The association between LE8 score and biological aging was explored through regression analyses.

RESULTS

Each 10-point increase in LE8 scores was associated with a 1.19-year decrease in biological age and a 1.63-year decrease in phenotypic age. Individuals with high cardiovascular health (CVH) had a 90% reduction in their risk of accelerated aging based on biological age and an 81% reduction based on phenotypic age compared with individuals with low CVH. Bootstrap-based model estimates and weighted quantile sum regression suggested that health factors, particularly blood glucose, had strong impact on delaying aging. The association between smoking and biological aging seemed to differ depending on the definition of aging used. Among all subgroups, LE8 consistently correlated negatively with biological aging, despite observed interactions. Three sensitivity analyses confirmed the robustness of our conclusions.

CONCLUSIONS

A higher CVH is associated with a lower risk of biological aging. Maintaining elevated LE8 levels across demographics, regardless of cardiovascular history, is recommended to delay aging and promote healthy aging, with significant implications for primary health care.

A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data

Differentially methylated regions (DMRs) are genomic regions with methylation patterns across multiple CpG sites that are associated with a phenotype. In this study, we proposed a Principal Component (PC) based DMR analysis method for use with data generated using the Illumina Infinium MethylationEPIC BeadChip (EPIC) array. We obtained methylation residuals by regressing the M-values of CpGs within a region on covariates, extracted PCs of the residuals, and then combined association information across PCs to obtain regional significance. Simulation-based genome-wide false positive (GFP) rates and true positive rates were estimated under a variety of conditions before determining the final version of our method, which we have named DMRPC. Then, DMRPC and another DMR method, coMethDMR, were used to perform epigenome-wide analyses of several phenotypes known to have multiple associated methylation loci (age, sex, and smoking) in a discovery and a replication cohort. Among regions that were analysed by both methods, DMRPC identified 50% more genome-wide significant age-associated DMRs than coMethDMR. The replication rate for the loci that were identified by only DMRPC was higher than the rate for those that were identified by only coMethDMR (90% for DMRPC vs. 76% for coMethDMR). Furthermore, DMRPC identified replicable associations in regions of moderate between-CpG correlation which are typically not analysed by coMethDMR. For the analyses of sex and smoking, the advantage of DMRPC was less clear. In conclusion, DMRPC is a new powerful DMR discovery tool that retains power in genomic regions with moderate correlation across CpGs.

Effects of accelerated biological age on depressive symptoms in a causal reasoning framework

BACKGROUND

Depression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation.

METHODS

We utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly.

RESULTS

In the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives.

LIMITATIONS

Depressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population.

CONCLUSIONS

This study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.

Accelerated epigenetic age, inflammation, and gene expression in lung: comparisons of smokers and vapers with non-smokers

BACKGROUND

Cigarette smoking and aging are the main risk factors for pulmonary diseases, including cancer. Epigenetic aging may explain the relationship between smoking, electronic cigarette vaping, and pulmonary health. No study has examined smoking and vaping-related epigenetic aging in relation to lung biomarkers.

METHODS

Lung epigenetic aging measured by DNA methylation (mAge) and its acceleration (mAA) was assessed in young (age 21-30) electronic cigarette vapers (EC, n = 14, including 3 never-smoking EC), smokers (SM, n = 16), and non-EC/non-SM (NS, n = 39). We investigated relationships of mAge estimates with chronological age (Horvath-mAge), lifespan/mortality (Grim-mAge), telomere length (TL-mAge), smoking/EC history, urinary biomarkers, lung cytokines, and transcriptome.

RESULTS

Compared to NS, EC and SM had significantly older Grim-mAge, shorter TL-mAge, significantly accelerated Grim-mAge and decelerated TL-mAge. Among SM, Grim-mAA was associated with nicotine intake and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). For EC, Horvath-mAA was significantly correlated with puffs per day. Overall, cytokines (IL-1β, IL-6, and IL-8) and 759 transcripts (651 unique genes) were significantly associated with Grim-mAA. Grim-mAA-associated genes were highly enriched in immune-related pathways and genes that play a role in the morphology and structures of cells/tissues.

CONCLUSIONS

Faster lung mAge for SM is consistent with prior studies of blood. Faster lung mAge for EC compared to NS indicates possible adverse pulmonary effects of EC on biological aging. Our findings support further research, particularly on epigenetic markers, on effects of smoking and vaping on pulmonary health. Given that most EC are former smokers, further study is needed to understand unique effects of electronic cigarettes on biological aging.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging

INTRODUCTION

An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions.

METHODS

We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions.

RESULTS

BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected).

CONCLUSIONS

Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.

Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing

Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F₁-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.

Healthy lifestyle, DNA methylation age acceleration, and incident risk of coronary heart disease

BACKGROUND

DNA methylation clocks emerged as a tool to determine biological aging and have been related to mortality and age-related diseases. Little is known about the association of DNA methylation age (DNAm age) with coronary heart disease (CHD), especially in the Asian population.

RESULTS

Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip for 491 incident CHD cases and 489 controls in the prospective China Kadoorie Biobank. We calculated the methylation age using a prediction model developed among Chinese. The correlation between chronological age and DNAm age was 0.90. DNA methylation age acceleration (Δage) was defined as the residual of regressing DNA methylation age on the chronological age. After adjustment for multiple risk factors of CHD and cell type proportion, compared with participants in the bottom quartile of Δage, the OR (95% CI) for CHD was 1.84 (1.17, 2.89) for participants in the top quartile. One SD increment in Δage was associated with 30% increased risk of CHD (OR = 1.30; 95% CI 1.09, 1.56; Ptrend = 0.003). The average number of cigarette equivalents consumed per day and waist-to-hip ratio were positively associated with Δage; red meat consumption was negatively associated with Δage, characterized by accelerated aging in those who never or rarely consumed red meat (all P < 0.05). Further mediation analysis revealed that 10%, 5% and 18% of the CHD risk related to smoking, waist-to-hip ratio and never or rarely red meat consumption was mediated through methylation aging, respectively (all P for mediation effect < 0.05).

CONCLUSIONS

We first identified the association between DNAm age acceleration and incident CHD in the Asian population, and provided evidence that unfavorable lifestyle-induced epigenetic aging may play an important part in the underlying pathway to CHD.

Global and promoter specific hypermethylation of tumor suppressor genes P16, SOCS1, and SHP1 in oral squamous cell carcinoma and oral submucous fibrosis

ABSTRACTS

Aberrant methylation pattern leads to altered gene expression, that is, involved in the transformation of various cancers, including oral squamous cell carcinoma (OSCC). In the present study, an attempt has been made to examine the association of global and promoter-specific methylation of tumor suppressor genes in patients with OSCC and oral submucous fibrosis (OSMF). Promoter-specific methylation of tumor suppressor genes P16, SOCS1, and SHP1 had been studied earlier for their aberrant methylation patterns in other cancers; however, these studies were mainly conducted in-vitro or in animal models, and as such, only a few studies are available on human samples. In the present study evaluation of promoter-specific methylation of genes P16, SOCS1, and SHP1 in 76 patients' blood and tissue samples was done and compared with methylation of 35 healthy control samples using qPCR. Further, these samples were analyzed for global methylation patterns using ELISA. The results have shown a significant decreasing trend of promoter methylation (OSCC > OSMF > Controls); the methylation indices (MI) were significantly higher in OSCC than in the controls. The median MI of three genes for OSCC were P16MI (0.96), SHP1MI (0.79), and SOCS1 (0.80). Similarly, median MIs for OSMF were P16MI (0.18), SHP1 MI (0.19), and SOCS1 MI (0.5) against controls with MI (0) for each of the three genes. The global methylation %mC values were 1.9, 0.5, and 0.1, respectively. The values of MI and %mC were found to correlate with various risk factors such as tobacco, smoking, and alcohol consumption, which are positively involved in OSMF pathogenesis followed by oral cancer progression. Further, the methylation trend in tissue was reflected in blood samples, proving a window for methylation load to be used as a lesser invasive biomarker. The sensitivity and specificity of methylation load were also found reasonable. Therefore, the current study suggests that there may be a role of global and promoter-specific methylation load in the transition of OSMF to OSCC.

Within subject cross-tissue analyzes of epigenetic clocks in substance use disorder postmortem brain and blood

There is a possible accelerated biological aging in patients with substance use disorders (SUD). The evaluation of epigenetic clocks, which are accurate estimators of biological aging based on DNA methylation changes, has been limited to blood tissue in patients with SUD. Consequently, the impact of biological aging in the brain of individuals with SUD remains unknown. In this study, we evaluated multiple epigenetic clocks (DNAmAge, DNAmAgeHannum, DNAmAgeSkinBlood, DNAmPhenoAge, DNAmGrimAge, and DNAmTL) in individuals with SUD (n = 42), including alcohol (n = 10), opioid (n = 19), and stimulant use disorder (n = 13), and controls (n = 10) in postmortem brain (prefrontal cortex) and blood tissue obtained from the same individuals. We found a higher DNAmPhenoAge (β = 0.191, p-value = 0.0104) and a nominally lower DNAmTL (β = -0.149, p-value = 0.0603) in blood from individuals with SUD compared to controls. SUD subgroup analysis showed a nominally lower brain DNAmTL in subjects with alcohol use disorder, compared to stimulant use disorder and controls (β = 0.0150, p-value = 0.087). Cross-tissue analyzes indicated a lower blood DNAmTL and a higher blood DNAmAge compared to their respective brain values in the SUD group. This study highlights the relevance of tissue specificity in biological aging studies and suggests that peripheral measures of epigenetic clocks in SUD may depend on the specific type of drug used.

Association between depression and epigenetic age acceleration: A co-twin control study

INTRODUCTION

Prior studies have shown inconsistent findings of an association between depression and epigenetic aging. DNA methylation (DNAm) age acceleration can measure biological aging. We adopted a robust co-twin control study design to examine whether depression is associated with DNAm age acceleration after accounting for the potential confounding influences of genetics and family environment.

METHODS

We analyzed data on a sub-cohort of the Vietnam Era Twin Registry. A total of 291 twins participated at baseline and 177 at follow-up visit after a mean of 11.7 years, with 111 participants having DNA samples for both time points. Depression was measured using the Beck Depression Inventory II (BDI-II). Six measures of DNAm age acceleration were computed at each time point, including Horvath's DNAm age acceleration (HorvathAA), intrinsic epigenetic age acceleration (IEAA), Hannum's DNAm age acceleration (HannumAA), extrinsic epigenetic age acceleration (EEAA), GrimAge acceleration (GrimAA), and PhenoAge acceleration (PhenoAA). Mixed-effects modeling was used to assess the within-pair association between depression and DNAm age acceleration.

RESULTS

At baseline, a 10-unit higher BDI-II total score was associated with HannumAA (0.73 years, 95% confidence interval [CI] 0.13-1.33, p = .019) and EEAA (0.94 years, 95% CI 0.22-1.66, p = .012). At follow-up, 10-unit higher BDI-II score was associated with PhenoAA (1.32 years, 95% CI 0.18-2.47, p = .027).

CONCLUSION

We identified that depression is associated with higher levels of DNAm age acceleration. Further investigation is warranted to better understand the underlying mechanisms for the potential causal relationship between depression and accelerated aging.

A review of epigenetics and its association with ageing of muscle and bone

Ageing is defined as the 'increasing frailty of an organism with time that reduces the ability of that organism to deal with stress'. It has been suggested that epigenetics may underlie the observation that some individuals appear to age faster than others. Epigenetics is the study of changes which occur in an organism due to changes in expression of the genetic code rather than changes to the genetic code itself; that is, epigenetic mechanisms impact upon the function of DNA without changing the DNA sequence. It is important to recognise that epigenetic changes, in contrast to genetic changes, can vary according to different cell types and therefore can demonstrate significant tissue-specificity. There are different types of epigenetic mechanisms: histone modification, non-coding RNAs and DNA methylation. Epigenetic clocks have been developed using statistical techniques to identify the optimal combination of CpG sites (from methylation arrays) to correlate with chronological age. This review considers how epigenetic factors may affect rates of ageing of muscle and bone and provides an overview of current understanding in this area. We discuss studies using first-generation epigenetic clocks, as well as the second-generation iterations, which appear to show stronger associations with the ageing muscle phenotype. We also review epigenome-wide association studies that have been performed in various tissues examining relationships with osteoporosis and fracture. It is hoped that an understanding of this area will lead to interventions that might prevent or reduce rates of musculoskeletal ageing in later life.

The Socioeconomic Gradient in Epigenetic Ageing Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study

Epigenetic clocks have been widely used to predict disease risk in multiple tissues or cells. Their success as a measure of biological ageing has prompted research on the connection between epigenetic pathways of ageing and the socioeconomic gradient in health and mortality. However, studies examining social correlates of epigenetic ageing have yielded inconsistent results. We conducted a comprehensive, comparative analysis of associations between various dimensions of socioeconomic status (SES) (education, income, wealth, occupation, neighbourhood environment, and childhood SES) and eight epigenetic clocks in two well-powered US ageing studies: The Multi-Ethnic Study of Atherosclerosis (MESA) (n = 1,211) and the Health and Retirement Study (HRS) (n = 4,018). In both studies, we found robust associations between SES measures in adulthood and the GrimAge and DunedinPoAm clocks (Bonferroni-corrected p-value < 0.01). In the HRS, significant associations with the Levine and Yang clocks were also evident. These associations were only partially mediated by smoking, alcohol consumption, and obesity, which suggests that differences in health behaviours alone cannot explain the SES gradient in epigenetic ageing in older adults. Further analyses revealed concurrent associations between polygenic risk for accelerated intrinsic epigenetic ageing, SES, and the Levine clock, indicating that genetic risk and social disadvantage may contribute additively to faster biological aging.

Lifetime exposure to smoking, epigenetic aging, and morbidity and mortality in older adults

BACKGROUND

Cigarette smoke is a major public health concern. Epigenetic aging may be an important pathway by which exposure to cigarette smoke affects health. However, little is known about how exposure to smoke at different life stages affects epigenetic aging, especially in older adults. This study examines how three epigenetic aging measures (GrimAge, PhenoAge, and DunedinPoAm38) are associated with parental smoking, smoking in youth, and smoking in adulthood, and whether these epigenetic aging measures mediate the link between smoke exposure and morbidity and mortality. This study utilizes data from the Health and Retirement Study (HRS) Venous Blood Study (VBS), a nationally representative sample of US adults over 50 years old collected in 2016. 2978 participants with data on exposure to smoking, morbidity, and mortality were included.

RESULTS

GrimAge is significantly increased by having two smoking parents, smoking in youth, and cigarette pack years in adulthood. PhenoAge and DunedinPoAm38 are associated with pack years. All three mediate some of the effect of pack years on cancer, high blood pressure, heart disease, and mortality and GrimAge and DunedinPoAm38 mediate this association on lung disease.

CONCLUSIONS

Results suggest epigenetic aging is one biological mechanism linking lifetime exposure to smoking with development of disease and earlier death in later life. Interventions aimed at reducing smoking in adulthood may be effective at weakening this association.

Age, Education, and Stress Affect Ageing Males' Symptoms More than Lifestyle Does: The Wroclaw Male Study

An increasing number of subjects are affected by health problems related to the advanced involutional processes. It is extremely important to identify the determinants of the rate of occurrence of physiological, psychological, and social manifestations of aging. The aim was to determine how factors such as lifestyle, level of education, or severity of stressful life events indicate the appearance of aging symptoms in adult men. The material consisted of data of ethnically homogeneous group of 355 men (32−87 years), invited to the study as a part of the Wroclaw Male Study research project. The analyzed features included (1) socioeconomic status: age, educational level, marital status, and having children; (2) elements of lifestyle: alcohol drinking, cigarette smoking, and physical activity; (3) major and most important stressful life events—the Social Readjustment Rating Scale; (4) symptoms related to male aging—the Aging Males’ Symptoms. The backward stepwise regression models, the Kruskal−Wallis test, and multiple comparisons of mean ranks were used. Noncentrality parameter δ (delta), two-tailed critical values of the test, and test power with α = 0.05 were calculated. Among the analyzed variables, age was most strongly associated with the intensity of almost all groups of andropausal symptoms in men (p = 0.0001), followed by the level of education (p = 0.0001) and the intensity of stressful life events (p = 0.0108). Selected lifestyle elements turned out to be much less important (p > 0.01). Preventive actions aimed at slowing down the intensification of involutional processes, including teaching strategies for coping with stressful life events, should be implemented in groups of men with specific risk factors from an early age.

Hibernation slows epigenetic ageing in yellow-bellied marmots

Species that hibernate generally live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor–arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause ageing suppression. Metabolic rate is also a known correlate of longevity; we thus proposed the ‘hibernation–ageing hypothesis’ whereby ageing is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7–8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modelled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic ageing over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (two years old). With respect to circannual patterns, the epigenetic age increased during the active season and essentially stalled during the hibernation period. Taken together, our results are consistent with the hibernation–ageing hypothesis and may explain the enhanced longevity in hibernators.

Species that hibernate generally have longer lifespans than expected based on their body size. The authors show epigenetic ageing patterns from a natural population of hibernating yellow-bellied marmots consistent with the hypothesis that ageing is suspended during hibernation.

Epigenome-wide association study and epigenetic age acceleration associated with cigarette smoking among Costa Rican adults

Smoking-associated DNA methylation (DNAm) signatures are reproducible among studies of mostly European descent, with mixed evidence if smoking accelerates epigenetic aging and its relationship to longevity. We evaluated smoking-associated DNAm signatures in the Costa Rican Study on Longevity and Healthy Aging (CRELES), including participants from the high longevity region of Nicoya. We measured genome-wide DNAm in leukocytes, tested Epigenetic Age Acceleration (EAA) from five clocks and estimates of telomere length (DNAmTL), and examined effect modification by the high longevity region. 489 participants had a mean (SD) age of 79.4 (10.8) years, and 18% were from Nicoya. Overall, 7.6% reported currently smoking, 35% were former smokers, and 57.4% never smoked. 46 CpGs and five regions (e.g. AHRR, SCARNA6/SNORD39, SNORA20, and F2RL3) were differentially methylated for current smokers. Former smokers had increased Horvath's EAA (1.69-years; 95% CI 0.72, 2.67), Hannum's EAA (0.77-years; 95% CI 0.01, 1.52), GrimAge (2.34-years; 95% CI1.66, 3.02), extrinsic EAA (1.27-years; 95% CI 0.34, 2.21), intrinsic EAA (1.03-years; 95% CI 0.12, 1.94) and shorter DNAmTL (- 0.04-kb; 95% CI - 0.08, - 0.01) relative to non-smokers. There was no evidence of effect modification among residents of Nicoya. Our findings recapitulate previously reported and novel smoking-associated DNAm changes in a Latino cohort.

Dog Models of Aging

As the most phenotypically diverse mammalian species that shares human environments and access to sophisticated healthcare, domestic dogs have unique potential to inform our understanding of the determinants of aging. Here we outline key concepts in the study of aging and illustrate the value of research with dogs, which can improve dog health and support translational discoveries. We consider similarities and differences in aging and age-related diseases in dogs and humans and summarize key advances in our understanding of genetic and environmental risk factors for morbidity and mortality in dogs. We address health outcomes ranging from cancer to cognitive function and highlight emerging research opportunities from large-scale cohort studies in companion dogs. We conclude that studying aging in dogs could overcome many limitations of laboratory models, most notably, the ability to assess how aging-associated pathways influence aging in real-world environments similar to those experienced by humans.

Overview of Polyamines as Nutrients for Human Healthy Long Life and Effect of Increased Polyamine Intake on DNA Methylation

Polyamines, spermidine and spermine, are synthesized in every living cell and are therefore contained in foods, especially in those that are thought to contribute to health and longevity. They have many physiological activities similar to those of antioxidant and anti-inflammatory substances such as polyphenols. These include antioxidant and anti-inflammatory properties, cell and gene protection, and autophagy activation. We have first reported that increased polyamine intake (spermidine much more so than spermine) over a long period increased blood spermine levels and inhibited aging-associated pathologies and pro-inflammatory status in humans and mice and extended life span of mice. However, it is unlikely that the life-extending effect of polyamines is exerted by the same bioactivity as polyphenols because most studies using polyphenols and antioxidants have failed to demonstrate their life-extending effects. Recent investigations revealed that aging-associated pathologies and lifespan are closely associated with DNA methylation, a regulatory mechanism of gene expression. There is a close relationship between polyamine metabolism and DNA methylation. We have shown that the changes in polyamine metabolism affect the concentrations of substances and enzyme activities involved in DNA methylation. I consider that the increased capability of regulation of DNA methylation by spermine is a key of healthy long life of humans.

DNA Methylation Abnormalities and Altered Whole Transcriptome Profiles after Switching from Combustible Tobacco Smoking to Heated Tobacco Products

BACKGROUND

The use of heated tobacco products (HTP) has increased exponentially in Japan since 2016; however, their effects on health remain a major concern.

METHODS

Tsuruoka Metabolome Cohort Study participants (n = 11,002) were grouped on the basis of their smoking habits as never smokers (NS), past smokers (PS), combustible tobacco smokers (CS), and HTP users for <2 years. Peripheral blood mononuclear cells were collected from 52 participants per group matched to HTP users using propensity scores, and DNA and RNA were purified from the samples. DNA methylation (DNAm) analysis of the 17 smoking-associated DNAm biomarker genes (such as AHRR, F2RL3, LRRN3, and GPR15), as well as whole transcriptome analysis, was performed.

RESULTS

Ten of the 17 genes were significantly hypomethylated in CS and HTP users compared with NS, among which AHRR, F2RL3, and RARA showed intermediate characteristics between CS and NS; nonetheless, AHRR expression was significantly higher in CS than in the other three groups. Conversely, LRRN3 and GPR15 were more hypomethylated in HTP users than in NS, and GPR15 expression was markedly upregulated in all the groups when compared with that in NS.

CONCLUSIONS

HTP users (switched from CS <2 years) display abnormal DNAm and transcriptome profiles, albeit to a lesser extent than the CS. However, because the molecular genetic effects of long-term HTP use are still unknown, long-term molecular epidemiologic studies are needed.

IMPACT

This study provides new insights into the molecular genetic effects on DNAm and transcriptome profiles in HTP users who switched from CS.

DNA methylation ageing clocks and pancreatic cancer risk: pooled analysis of three prospective nested case-control studies

DNA methylation (DNAm) age may reflect age-related variations in biological changes and abnormalities related to ageing. DNAm age acceleration measures have been associated with a number of cancers, but to our knowledge, have not been examined in relation to pancreatic cancer risk or survival. DNAm levels in leukocytes of prediagnostic blood samples of 393 pancreatic cancer cases and 431 matched controls, pooled from three large prospective cohort studies, were used to estimate DNAm age, epigenetic age acceleration (AA), and intrinsic epigenetic age acceleration (IEAA) metrics. Logistic regression and Cox proportional hazard regression models were used to examine the relationship between the various AA and IEAA metrics and pancreatic cancer risk and survival, respectively. The results showed that pancreatic cancer risk was significantly increased across all IEAA metrics, ranging from 83% to 95% increased risk when comparing the third and highest quartiles to the lowest quartile of IEAA. Consistent with these findings, the results from multivariate spline regression analyses showed non-linear relationships between all three IEAA metrics and pancreatic cancer risk with apparent threshold effect including two turning points at minimal and at maximal risks, respectively. There is no evidence of a significant association between pancreatic cancer survival and any of the epigenetic AA or IEAA metrics. Our results indicate DNAm age acceleration, measured in blood prior to cancer diagnosis, is associated with an increased risk of pancreatic cancer in a complex nonlinear, dose-response manner. Epigenetic IEAA metrics may be a useful addition to current methods for pancreatic cancer risk prediction.

Exploring the impact of night shift work on methylation of circadian genes

Night shift work is associated with increased breast cancer risk, but the molecular mechanisms are not well-understood. The objective of this study was to explore the relationship between night shift work parameters (current status, duration/years, and intensity) and methylation in circadian genes as a potential mechanism underlying the carcinogenic effects of night shift work. A cross-sectional study was conducted among 74 female healthcare employees (n = 38 day workers, n = 36 night shift workers). The Illumina Infinium MethylationEPIC beadchip was applied to DNA extracted from blood samples to measure methylation using a candidate gene approach at 1150 CpG loci across 22 circadian genes. Linear regression models were used to examine the association between night shift work parameters and continuous methylation measurements (β-values) for each CpG site. The false-discovery rate (q = 0.2) was used to account for multiple comparisons. Compared to day workers, current night shift workers demonstrated hypermethylation in the 5'UTR region of CSNK1E (q = 0.15). Individuals that worked night shifts for ≥10 years exhibited hypomethylation in the gene body of NR1D1 (q = 0.08) compared to those that worked <10 years. Hypermethylation in the gene body of ARNTL was also apparent in those who worked ≥3 consecutive night shifts a week (q = 0.18). These findings suggest that night shift work is associated with differential methylation in core circadian genes, including CSNK1E, NR1D1 and ARNTL. Future, larger-scale studies with long-term follow-up and detailed night shift work assessment are needed to confirm and expand on these findings.

The early-life exposome and epigenetic age acceleration in children

The early-life exposome influences future health and accelerated biological aging has been proposed as one of the underlying biological mechanisms. We investigated the association between more than 100 exposures assessed during pregnancy and in childhood (including indoor and outdoor air pollutants, built environment, green environments, tobacco smoking, lifestyle exposures, and biomarkers of chemical pollutants), and epigenetic age acceleration in 1,173 children aged 7 years old from the Human Early-Life Exposome project. Age acceleration was calculated based on Horvath's Skin and Blood clock using child blood DNA methylation measured by Infinium HumanMethylation450 BeadChips. We performed an exposure-wide association study between prenatal and childhood exposome and age acceleration. Maternal tobacco smoking during pregnancy was nominally associated with increased age acceleration. For childhood exposures, indoor particulate matter absorbance (PM_abs) and parental smoking were nominally associated with an increase in age acceleration. Exposure to the organic pesticide dimethyl dithiophosphate and the persistent pollutant polychlorinated biphenyl-138 (inversely associated with child body mass index) were protective for age acceleration. None of the associations remained significant after multiple-testing correction. Pregnancy and childhood exposure to tobacco smoke and childhood exposure to indoor PM_abs may accelerate epigenetic aging from an early age.

Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood

DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (β = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Maternal biological age assessed in early pregnancy is associated with gestational age at birth

Maternal age is an established predictor of preterm birth independent of other recognized risk factors. The use of chronological age makes the assumption that individuals age at a similar rate. Therefore, it does not capture interindividual differences that may exist due to genetic background and environmental exposures. As a result, there is a need to identify biomarkers that more closely index the rate of cellular aging. One potential candidate is biological age (BA) estimated by the DNA methylome. This study investigated whether maternal BA, estimated in either early and/or late pregnancy, predicts gestational age at birth. BA was estimated from a genome-wide DNA methylation platform using the Horvath algorithm. Linear regression methods assessed the relationship between BA and pregnancy outcomes, including gestational age at birth and prenatal perceived stress, in a primary and replication cohort. Prenatal BA estimates from early pregnancy explained variance in gestational age at birth above and beyond the influence of other recognized preterm birth risk factors. Sensitivity analyses indicated that this signal was driven primarily by self-identified African American participants. This predictive relationship was sensitive to small variations in the BA estimation algorithm. Benefits and limitations of using BA in translational research and clinical applications for preterm birth are considered.

DNA methylation-based age clocks: From age prediction to age reversion

Aging as an irretrievable occurrence throughout the entire life is characterized by a progressive decline in physiological functionality and enhanced disease vulnerability. Numerous studies have demonstrated that epigenetic modifications, particularly DNA methylation (DNAm), correlate with aging and age-related diseases. Several investigations have attempted to predict chronological age using the age-related alterations in the DNAm of certain CpG sites. Here we categorize different studies that tracked the aging process in the DNAm landscape to show how epigenetic age clocks evolved from a chronological age estimator to an indicator of lifespan and healthspan. We also describe the health and disease predictive potential of estimated epigenetic age acceleration regarding different clinical conditions and lifestyle factors. Considering the revealed age-related epigenetic changes, the recent age-reprogramming strategies are discussed which are promising methods for resetting the aging clocks.

DNAm-based signatures of accelerated aging and mortality in blood are associated with low renal function

BACKGROUND

The difference between an individual's chronological and DNA methylation predicted age (DNAmAge), termed DNAmAge acceleration (DNAmAA), can capture life-long environmental exposures and age-related physiological changes reflected in methylation status. Several studies have linked DNAmAA to morbidity and mortality, yet its relationship with kidney function has not been assessed. We evaluated the associations between seven DNAm aging and lifespan predictors (as well as GrimAge components) and five kidney traits (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [uACR], serum urate, microalbuminuria and chronic kidney disease [CKD]) in up to 9688 European, African American and Hispanic/Latino individuals from seven population-based studies.

RESULTS

We identified 23 significant associations in our large trans-ethnic meta-analysis (p < 1.43E-03 and consistent direction of effect across studies). Age acceleration measured by the Extrinsic and PhenoAge estimators, as well as Zhang's 10-CpG epigenetic mortality risk score (MRS), were associated with all parameters of poor kidney health (lower eGFR, prevalent CKD, higher uACR, microalbuminuria and higher serum urate). Six of these associations were independently observed in European and African American populations. MRS in particular was consistently associated with eGFR (β =  - 0.12, 95% CI = [- 0.16, - 0.08] change in log-transformed eGFR per unit increase in MRS, p = 4.39E-08), prevalent CKD (odds ratio (OR) = 1.78 [1.47, 2.16], p = 2.71E-09) and higher serum urate levels (β = 0.12 [0.07, 0.16], p = 2.08E-06). The "first-generation" clocks (Hannum, Horvath) and GrimAge showed different patterns of association with the kidney traits. Three of the DNAm-estimated components of GrimAge, namely adrenomedullin, plasminogen-activation inhibition 1 and pack years, were positively associated with higher uACR, serum urate and microalbuminuria.

CONCLUSION

DNAmAge acceleration and DNAm mortality predictors estimated in whole blood were associated with multiple kidney traits, including eGFR and CKD, in this multi-ethnic study. Epigenetic biomarkers which reflect the systemic effects of age-related mechanisms such as immunosenescence, inflammaging and oxidative stress may have important mechanistic or prognostic roles in kidney disease. Our study highlights new findings linking kidney disease to biological aging, and opportunities warranting future investigation into DNA methylation biomarkers for prognostic or risk stratification in kidney disease.

An update in toxicology of ageing

The field of ageing research has been rapidly advancing in recent decades and it had provided insight into the complexity of ageing phenomenon. However, as the organism-environment interaction appears to significantly affect the organismal pace of ageing, the systematic approach for gerontogenic risk assessment of environmental factors has yet to be established. This puts demand on development of effective biomarker of ageing, as a relevant tool to quantify effects of gerontogenic exposures, contingent on multidisciplinary research approach. Here we review the current knowledge regarding the main endogenous gerontogenic pathways involved in acceleration of ageing through environmental exposures. These include inflammatory and oxidative stress-triggered processes, dysregulation of maintenance of cellular anabolism and catabolism and loss of protein homeostasis. The most effective biomarkers showing specificity and relevancy to ageing phenotypes are summarized, as well. The crucial part of this review was dedicated to the comprehensive overview of environmental gerontogens including various types of radiation, certain types of pesticides, heavy metals, drugs and addictive substances, unhealthy dietary patterns, and sedentary life as well as psychosocial stress. The reported effects in vitro and in vivo of both recognized and potential gerontogens are described with respect to the up-to-date knowledge in geroscience. Finally, hormetic and ageing decelerating effects of environmental factors are briefly discussed, as well.

Klotho, PTSD, and advanced epigenetic age in cortical tissue

This study examined the klotho (KL) longevity gene polymorphism rs9315202 and psychopathology, including posttraumatic stress disorder (PTSD), depression, and alcohol-use disorders, in association with advanced epigenetic age in three postmortem cortical tissue regions: dorsolateral and ventromedial prefrontal cortices and motor cortex. Using data from the VA National PTSD Brain Bank (n = 117), we found that rs9315202 interacted with PTSD to predict advanced epigenetic age in motor cortex among the subset of relatively older (>=45 years), white non-Hispanic decedents (corrected p = 0.014, n = 42). An evaluation of 211 additional common KL variants revealed that only variants in linkage disequilibrium with rs9315202 showed similarly high levels of significance. Alcohol abuse was nominally associated with advanced epigenetic age in motor cortex (p = 0.039, n = 114). The rs9315202 SNP interacted with PTSD to predict decreased KL expression via DNAm age residuals in motor cortex among older white non-Hispanics decedents (indirect β = -0.198, p = 0.027). Finally, in dual-luciferase enhancer reporter system experiments, we found that inserting the minor allele of rs9315202 in a human kidney cell line HK-2 genomic DNA resulted in a change in KL transcriptional activities, likely operating via long noncoding RNA in this region. This was the first study to examine multiple forms of psychopathology in association with advanced DNA methylation age across several brain regions, to extend work concerning the association between rs9315202 and advanced epigenetic to brain tissue, and to identify the effects of rs9315202 on KL gene expression. KL augmentation holds promise as a therapeutic intervention to slow the pace of cellular aging, disease onset, and neuropathology, particularly in older, stressed populations.

DNA Methylation-Based Biomarkers of Environmental Exposures for Human Population Studies

PURPOSE OF REVIEW

This manuscript orients the reader to the underlying motivations of environmental biomarker development for human population studies and provides the foundation for applying these novel biomarkers in future research. In this review, we focus our attention on the DNA methylation-based biomarkers of (i) smoking, among adults and pregnant women, (ii) lifetime cannabis use, (iii) alcohol consumption, and (iv) cumulative exposure to lead.

RECENT FINDINGS

Prior environmental exposures and lifestyle modulate DNA methylation levels. Exposure-related DNA methylation changes can either be persistent or reversible once the exposure is no longer present, and this combination of both persistent and reversible changes has essential value for biomarker development. Here, we present available biomarkers representing past and cumulative exposures using individual DNA methylation profiles. In the present work, we describe how the field of environmental epigenetics can leverage machine learning algorithms to develop exposure biomarkers and reduce problems of misreporting exposures or limited access technology. We emphasize the crucial role of the individual DNA methylation profiles in those predictions, providing a summary of each biomarker, and highlighting their advantages, and limitations. Future research can cautiously leverage these DNA methylation-based biomarkers to understand the onset and progression of diseases.

Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

The Reversion of cg05575921 Methylation in Smoking Cessation: A Potential Tool for Incentivizing Healthy Aging

Smoking is the largest preventable cause of mortality and the largest environmental driver of epigenetic aging. Contingency management-based strategies can be used to treat smoking but require objective methods of verifying quitting status. Prior studies have suggested that cg05575921 methylation reverts as a function of smoking cessation, but that it can be used to verify the success of smoking cessation has not been unequivocally demonstrated. To test whether methylation can be used to verify cessation, we determined monthly cg05575921 levels in a group of 67 self-reported smokers undergoing biochemically monitored contingency management-based smoking cessation therapy, as part of a lung imaging protocol. A total of 20 subjects in this protocol completed three months of cotinine verified smoking cessation. In these 20 quitters, the reversion of cg05575921 methylation was dependent on their initial smoking intensity, with methylation levels in the heaviest smokers reverting to an average of 0.12% per day over the 3-month treatment period. In addition, we found suggestive evidence that some individuals may have embellished their smoking history to gain entry to the study. Given the prominent effect of smoking on longevity, we conclude that DNA methylation may be a useful tool for guiding and incentivizing contingency management-based approaches for smoking cessation.

A Systematic Review and Meta-analysis of Environmental, Lifestyle, and Health Factors Associated With DNA Methylation Age

DNA methylation (DNAm) algorithms of biological age provide a robust estimate of an individual's chronological age and can predict their risk of age-related disease and mortality. This study reviewed the evidence that environmental, lifestyle and health factors are associated with the Horvath and Hannum epigenetic clocks. A systematic search identified 61 studies. Chronological age was correlated with DNAm age in blood (median .83, range .13-.99). In a meta-analysis body mass index (BMI) was associated with increased DNAm age (Hannum β: 0.07, 95% CI 0.04 to 0.10; Horvath β: 0.06, 95% CI 0.02 to 0.10), but there was no association with smoking (Hannum β: 0.12, 95% CI -0.50 to 0.73; Horvath β:0.18, 95% CI -0.10 to 0.46). DNAm age was positively associated with frailty (three studies, n = 3,093), and education was negatively associated with the Hannum estimate of DNAm age specifically (four studies, n = 13,955). For most other exposures, findings were too inconsistent to draw conclusions. In conclusion, BMI was positively associated with biological aging measured using DNAm, with some evidence that frailty also increased aging. More research is needed to provide conclusive evidence regarding other exposures. This field of research has the potential to provide further insights into how to promote slower biological aging and ultimately prolong healthy life.

The use of DNA methylation clock in aging research

One of the key characteristics of aging is a progressive loss of physiological integrity, which weakens bodily functions and increases the risk of death. A robust biomarker is important for the assessment of biological age, the rate of aging, and a person's health status. DNA methylation clocks, novel biomarkers of aging, are composed of a group of cytosine-phosphate-guanine dinucleotides, the DNA methylation status of which can be used to accurately measure subjective age. These clocks are considered accurate biomarkers of chronological age for humans and other vertebrates. Numerous studies have demonstrated these clocks to quantify the rate of biological aging and the effects of longevity and anti-aging interventions. In this review, we describe the purpose and use of DNA methylation clocks in aging research.

DNA methylation biomarker selected by an ensemble machine learning approach predicts mortality risk in an HIV-positive veteran population

Background: With the improved life expectancy of people living with HIV (PLWH), identifying vulnerable subpopulations at high mortality risk is important. Evidences showed that DNA methylation (DNAm) is associated with mortality in non-HIV populations. Here, we established a panel of DNAm biomarkers that can predict mortality risk among PLWH.

Methods: 1,081 HIV-positive participants from the Veterans Ageing Cohort Study (VACS) were divided into training (N = 460), validation (N = 114), and testing (N = 507) sets. VACS index was used as a measure of mortality risk among PLWH. Model training and fine-tuning were conducted using the ensemble method in the training and validation sets and prediction performance was assessed in the testing set. The survival analysis comparing the predicted high and low mortality risk groups and the Gene Ontology enrichment analysis of the predictive CpG sites were performed.

Results: We selected a panel of 393 CpGs for the ensemble prediction model that showed excellent performance in predicting high mortality risk with an auROC of 0.809 (95%CI: 0.767,0.851) and a balanced accuracy of 0.653 (95%CI: 0.611, 0.693) in the testing set. The high mortality risk group was significantly associated with 10-year mortality (hazard ratio = 1.79, p = 4E-05) compared with low risk group. These 393 CpGs were located in 280 genes enriched in immune and inflammation response pathways.

Conclusions: We identified a panel of DNAm features associated with mortality risk in PLWH. These DNAm features may serve as predictive biomarkers for mortality risk among PLWH.

Abbreviations: AUC: Area Under Curve; CI: Confidence interval; DMR: differentially methylated region; DNA: Deoxyribonucleic acid; DNAm: DNA methylation; DAVID: Database for Annotation, Visualization, and Integrated Discovery; EWA: epigenome-wide association; FDR: False discovery rate; FWER: Family-wise error rate; GLMNET: elastic-net-regularized generalized linear models; GO: Gene ontology; HIV: Human immunodeficiency virus; HM450K: Human Methylation 450 K BeadChip; k-NN: k-nearest neighbours; NK: Natural killer; PC: Principal component; PLWH: people living with HIV; QC: Quality control; SVM: Support Vector Machines; VACS: Veterans Ageing Cohort Study; XGBoost: Extreme Gradient Boosting Tree

Impact of age-related T cell dynamics on the identification of biomarkers predictive of immunotherapy discontinuation: a prospective cohort study

Background

The impact of biologic aging on immune checkpoint inhibitor (ICI) toxicity and efficacy is underexplored in metastatic melanoma (MM). In peripheral blood T-lymphocytes (PBTLs), biologic aging is characterized by changes in T-cell composition and cellular senescence. Whether indicators of PBTL biologic aging vary in MM patients or can be used to predict premature ICI discontinuation (pID) is unknown.

Methods

We prospectively collected PBTLs from 117 cancer-free controls and 46 MM patients scheduled to begin pembrolizumab or nivolumab monotherapy. 74 mRNAs indicative of T-cell subsets, activation, co-stimuation/inhibition and cellular senescence were measured by Nanostring. Relationships between each mRNA and chronologic age were assessed in patients and controls. Candidate biomarkers were identified by calculating the hazard ratio (HR) for pID in patients divided into low and high groups based on log-transformed mRNA levels or the magnitude by which each mRNA measurement deviated from the control trend (Δage). Area under the curve (AUC) analyses explored the ability of each biomarker to discriminate between patients with and without pID at 6 months and 1 year.

Results

Fifteen mRNAs correlated with chronologic age in controls, including markers of T-cell subsets, differentiation, cytokine production and co-stimulation/inhibition. None of these mRNAs remained correlated with age in patients. Median follow-up was 94.8 (1.6-195.7) weeks and 35 of 46 patients discontinued therapy (23 progression, 7 toxicity, 5 comorbidity/patient preference). Elevated pre-therapy CD8A (HR 2.2[1.1-4.9]), CD45RB (HR 2.9[1.4-5.8]) and TNFRSF14 (HR 2.2[1.1-4.5]) levels predicted pID independent of Δage-correction. CD3ε, CD27 and FOXO1 predicted pID only after Δage-correction (HR 2.5[1.3-5.1]; 3.7[1.8-7.8]; 2.1[1.1-4.3]). AUC analysis identified Δage-CD3ε and -CD27 as candidate predictors of pID (AUC=0.73; 0.75).

Conclusions

Correlations between transcriptional markers of PBTL composition and chronologic age are disrupted in MM. Correcting for normal, age-related trends in biomarker expression unveils new biomarker candidates predictive of ICI outcomes.

Sleep and Microbiome in Psychiatric Diseases

Objectives: Disturbances in the gut–brain barrier play an essential role in the development of mental disorders. There is considerable evidence showing that the gut microbiome not only affects digestive, metabolic and immune functions of the host but also regulates host sleep and mental states through the microbiota–gut–brain axis. The present review summarizes the role of the gut microbiome in the context of circadian rhythms, nutrition and sleep in psychiatric disorders. Methods: A PubMed search (studies published between April 2015–April 2020) was conducted with the keywords: “sleep, microbiome and psychiatry”; “sleep, microbiome and depression”; “sleep, microbiome and bipolar disorder”, “sleep, microbiome and schizophrenia”, “sleep, microbiome and anorexia nervosa”, “sleep, microbiome and substance use disorder”, “sleep, microbiome and anxiety”; “clock gene expression and microbiome”, “clock gene expression and nutrition”. Only studies investigating the relationship between sleep and microbiome in psychiatric patients were included in the review. Results: Search results yielded two cross-sectional studies analyzing sleep and gut microbiome in 154 individuals with bipolar disorder and one interventional study analyzing the effect of fecal microbiota transplantation in 17 individuals with irritable bowel syndrome on sleep. In patients with bipolar disorder, Faecalibacterium was significantly associated with improved sleep quality scores and a significant correlation between Lactobacillus counts and sleep. Conclusion: Translational research on this important field is limited and further investigation of the bidirectional pathways on sleep and the gut microbiome in mood disorders is warranted.

Epigenetic clocks may come out of rhythm-implications for the estimation of chronological age in forensic casework

There is a growing perception that DNA methylation may be influenced by exogenous and endogenous parameters. Knowledge of these factors is of great relevance for the interpretation of DNA-methylation data for the estimation of chronological age in forensic casework. We performed a literature review to identify parameters, which might be of relevance for the prediction of chronological age based on DNA methylation. The quality of age predictions might particularly be influenced by lifetime adversities (chronic stress, trauma/post-traumatic stress disorder (PTSD), violence, low socioeconomic status/education), cancer, obesity and related diseases, infectious diseases (especially HIV and Cytomegalovirus (CMV) infections), sex, ethnicity and exposure to toxins (alcohol, smoking, air pollution, pesticides). Such factors may alter the DNA methylation pattern and may explain the partly high deviations between epigenetic age and chronological age in single cases (despite of low mean absolute deviations) that can also be observed with “epigenetic clocks” comprising a high number of CpG sites. So far, only few publications dealing with forensic age estimation address these confounding factors. Future research should focus on the identification of further relevant confounding factors and the development of models that are “robust” against the influence of such biological factors by systematic investigations under targeted inclusion of diverse and defined cohorts.

Changes at global and site-specific DNA methylation of MLH1 gene promoter induced by waterpipe smoking in blood lymphocytes and oral epithelial cells

Objective: Cigarette tobacco smoking has been shown to cause cancer through different mechanisms that include epigenetic modulation of tumor-suppressor genes. In the present study, the association between global and MLH1 gene promoter methylation and waterpipe tobacco smoking was investigated. Materials and Methods: Blood lymphocytes and oral epithelium were sampled from 150 pure waterpipe smokers and 150 never-smokers from Jordan. Methylation assessment was performed using the methylation-specific PCR technique for MLH1 gene and ELISA for global DNA methylation. Results: Significant increases were shown in global DNA methylation as measured in blood lymphocytes (p < 0.01). In addition, increases in MLH1 gene promoter methylation among waterpipe smokers compared to nonsmokers (p < 0.001) in both oral epithelium and blood lymphocytes was also observed. In addition, strong correlation was found between LWDS-10J dependence score and magnitude of promoter specific methylation of MLH1 (r2 = 0.74-0.78, p < 0.001). Moreover, the percentage of methylated MLH1 promoter was not affected by age or gender (p > 0.05). Discussion and Conclusion: Collectively, the results indicate that waterpipe tobacco use is associated with epigenetic changes that might predispose users to lung and blood cancers. The results highlight the need for actions to discourage waterpipe smoking and can be used in cessation interventions that target this type of smoking.

The Effect of Tobacco Smoking Differs across Indices of DNA Methylation-Based Aging in an African American Sample: DNA Methylation-Based Indices of Smoking Capture These Effects

Smoking is one of the leading preventable causes of morbidity and mortality worldwide, prompting interest in its association with DNA methylation-based measures of biological aging. Considerable progress has been made in developing DNA methylation-based measures that correspond to self-reported smoking status. In addition, assessment of DNA methylation-based aging has been expanded to better capture individual differences in risk for morbidity and mortality. Untested to date, however, is whether smoking is similarly related to older and newer indices of DNA methylation-based aging, and whether DNA methylation-based indices of smoking can be used in lieu of self-reported smoking to examine effects on DNA methylation-based aging measures. In the current investigation we examine mediation of the impact of self-reported cigarette consumption on accelerated, intrinsic DNA methylation-based aging using indices designed to predict chronological aging, phenotypic aging, and mortality risk, as well as a newly developed DNA methylation-based measure of telomere length. Using a sample of 500 African American middle aged smokers and non-smokers, we found that a) self-reported cigarette consumption was associated with accelerated intrinsic DNA methylation-based aging on some but not all DNA methylation-based aging indices, b) for those aging outcomes associated with self-reported cigarette consumption, DNA methylation-based indicators of smoking typically accounted for greater variance than did self-reported cigarette consumption, and c) self-reported cigarette consumption effects on DNA methylation-based aging indices typically were fully mediated by DNA methylation-based indicators of smoking (e.g., PACKYRS from GrimAge; or cg05575921 CpG site). Results suggest that when DNA methylation-based indices of smoking are substituted for self-reported assessments of smoking, they will typically fully reflect the varied impact of cigarette smoking on intrinsic, accelerated DNA methylation-based aging.

An epigenome-wide association study of posttraumatic stress disorder in US veterans implicates several new DNA methylation loci

BACKGROUND

Previous studies using candidate gene and genome-wide approaches have identified epigenetic changes in DNA methylation (DNAm) associated with posttraumatic stress disorder (PTSD).

METHODS

In this study, we performed an EWAS of PTSD in a cohort of Veterans (n = 378 lifetime PTSD cases and 135 controls) from the Translational Research Center for TBI and Stress Disorders (TRACTS) cohort assessed using the Illumina EPIC Methylation BeadChip which assesses DNAm at more than 850,000 sites throughout the genome. Our model included covariates for ancestry, cell heterogeneity, sex, age, and a smoking score based on DNAm at 39 smoking-associated CpGs. We also examined in EPIC-based DNAm data generated from pre-frontal cortex (PFC) tissue from the National PTSD Brain Bank (n = 72).

RESULTS

The analysis of blood samples yielded one genome-wide significant association with PTSD at cg19534438 in the gene G0S2 (p = 1.19 × 10-7, padj = 0.048). This association was replicated in an independent PGC-PTSD-EWAS consortium meta-analysis of military cohorts (p = 0.0024). We also observed association with the smoking-related locus cg05575921 in AHRR despite inclusion of a methylation-based smoking score covariate (p = 9.16 × 10-6), which replicates a previously observed PGC-PTSD-EWAS association (Smith et al. 2019), and yields evidence consistent with a smoking-independent effect. The top 100 EWAS loci were then examined in the PFC data. One of the blood-based PTSD loci, cg04130728 in CHST11, which was in the top 10 loci in blood, but which was not genome-wide significant, was significantly associated with PTSD in brain tissue (in blood p = 1.19 × 10-5, padj = 0.60, in brain, p = 0.00032 with the same direction of effect). Gene set enrichment analysis of the top 500 EWAS loci yielded several significant overlapping GO terms involved in pathogen response, including "Response to lipopolysaccharide" (p = 6.97 × 10-6, padj = 0.042).

CONCLUSIONS

The cross replication observed in independent cohorts is evidence that DNA methylation in peripheral tissue can yield consistent and replicable PTSD associations, and our results also suggest that that some PTSD associations observed in peripheral tissue may mirror associations in the brain.