Multi-tissue DNA methylation age: Molecular relationships and perspectives for advancing biomarker utility

Bidirectional causal associations between aging and major mental disorders: A population-based study using the two-sample mendelian randomization method from the UK biobank (AM-SRNMA 002)

AIMS

While observational studies have suggested associations linking aging and mental disorders, the question of causality has remained unclear. This study aimed to explore the causal relationship between aging level and major mental disorders.

METHODS

We utilized Two-Sample Mendelian randomization (2SMR) with mental disorders data and aging indicators information from an extensive genome-wide association study (GWAS) database. The GWAS database is a comprehensive resource that compiles genetic association data, encompassing a sample size of over 450,000 individuals. We employed five methods for 2SMR and single nucleotide polymorphisms were chosen as instrumental variables.

RESULTS

Our analyses consistently supported a bidirectional causal association between the Frailty Index (FI) and Major Depressive Disorder (MDD). Furthermore, our findings indicated potential influences, such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Bipolar Disorder (BD) affecting GrimAge, and Anxiety Disorder (AD) impacting Left Hand Grip Strength (LHGS). In contrast, we observed no significant correlations for other mental disorders on FI, Telomere Length (TL), GrimAge, Appendicular Lean Mass (ALM), and LHGS. In the reverse direction, FI showed a significant impact on the risk of MDD, AD, and ADHD, while LHGS affected the risk of MDD. Importantly, no significant associations were found between other factors and the risk of MDD, BD, AD, Schizophrenia (SZ), and ADHD.

CONCLUSIONS

This 2SMR analysis has presented evidence for a bidirectional causal relationship between FI and MDD, while the relationship between ADHD, BD, and GrimAge should be more considered. Our study provides genetic evidence supporting a causal link between aging indicators and several mental illnesses.

Epigenetic Age Acceleration Markers Are Associated With Physiologic Frailty and All-Cause Mortality in People With Human Immunodeficiency Virus

BACKGROUND

Biomarkers that provide insight into drivers of aging are needed for people with human immunodeficiency virus (PWH). The study objective was to determine if epigenetic age acceleration (EAA) markers are associated with physiologic frailty measured by the Veterans Aging Cohort Study (VACS) Index and predict all-cause mortality for PWH.

METHODS

Epigenome-wide DNA methylation was profiled in VACS total white blood cell samples collected during 2005-2007 from 531 PWH to generate 6 established markers of EAA. The association of each EAA marker was tested with VACS Index 2.0. All-cause mortality was assessed over 10 years. For each EAA marker, the hazard ratio per increased year was determined using Cox regression. To evaluate mortality discrimination, C-statistics were derived.

RESULTS

Participants were mostly men (98.5%) and non-Hispanic Black (84.4%), with a mean age of 52.4 years (standard deviation [SD], 7.8 years). Mean VACS Index score was 59.3 (SD, 16.4) and 136 deaths occurred over a median follow-up of 8.7 years. Grim age acceleration (AA), PhenoAA, HannumAA, and extrinsic epigenetic AA were associated with the VACS Index and mortality. HorvathAA and intrinsic epigenetic AA were not associated with either outcome. GrimAA had the greatest mortality discrimination among EAA markers and predicted mortality independently of the VACS Index. One-year increase in GrimAA was associated with a 1-point increase in VACS Index and a 10% increased hazard for mortality.

CONCLUSIONS

The observed associations between EAA markers with physiologic frailty and mortality support future research to provide mechanistic insight into the accelerated aging process and inform interventions tailored to PWH for promoting increased healthspan.

Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging

Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.

Effect of metformin on the epigenetic age of peripheral blood in patients with diabetes mellitus

Background: Metformin has been proven to have an antiaging effect. However, studies on how metformin affects global epigenetic regulation and its effect on the epigenetic clock in diabetes mellitus (DM) patients are limited. This study aims to investigate the impact of metformin on the epigenetic age in subjects with type 2 DM.

Results: We collected the peripheral blood of the metformin group and the no-metformin group of the 32 DM patients. Three previously established epigenetic clocks (Hannum, Horvath, and DNAmPhenoAge) were used to estimate the epigenetic age acceleration of the two groups. We defined biological age acceleration for each group by comparing the estimated biological age with the chronological age. Results were presented as follows: 1) all three epigenetic clocks were strongly correlated with chronological age. 2) We found a strong association between metformin intake and slower epigenetic aging by Horvath’s clock and Hannum’s clock.

Conclusions: Here, we found an association between metformin intake and slower epigenetic aging.

Can Night Shift Work Affect Biological Age? Hints from a Cross-Sectional Study on Hospital Female Nurses

Disruption of age-related processes seems to play a relevant role in health effects related to night shift (NS) work. We aim to verify whether NS work can influence biological age (BA), estimated through Zbieć-Piekarska's epigenetic signature, based on methylation of five CpG sites in ELOVL2, C1orf132/MIR29B2C, TRIM59, KLF14, and FHL2. Forty-six female nurses working in NS were matched by age and length of employment with 51 female colleagues not working in NS. Each subject filled in a questionnaire (including the Effort Reward Imbalance (ERI) index to assess job stress) and gave a blood sample. Age acceleration (AA) was estimated by regressing BA on chronological age and taking the residuals. Multivariate linear regression models were applied. BA was not associated with NS. However, we did observe an increase in AA per each year in NS in subjects with overweight/obesity (β = 0.46, 95% CI: 0.05; 0.87, p = 0.03), experiencing work-related stress (β = 0.58, 95% CI: 0.10; 1.06, p = 0.018), or both (β = 0.66, 95% CI: 0.03; 1.29, p = 0.041). Although based on a small sample size, our findings suggest an increased BA only among hypersusceptible subjects and is worth further investigation, also in light of recent results suggesting a higher breast cancer risk in women with increased AA.

The genomic loci of specific human tRNA genes exhibit ageing-related DNA hypermethylation

The epigenome has been shown to deteriorate with age, potentially impacting on ageing-related disease. tRNA, while arising from only ˜46 kb (<0.002% genome), is the second most abundant cellular transcript. tRNAs also control metabolic processes known to affect ageing, through core translational and additional regulatory roles. Here, we interrogate the DNA methylation state of the genomic loci of human tRNA. We identify a genomic enrichment for age-related DNA hypermethylation at tRNA loci. Analysis in 4,350 MeDIP-seq peripheral-blood DNA methylomes (16-82 years), identifies 44 and 21 hypermethylating specific tRNAs at study-and genome-wide significance, respectively, contrasting with none hypomethylating. Validation and replication (450k array and independent targeted Bisuphite-sequencing) supported the hypermethylation of this functional unit. Tissue-specificity is a significant driver, although the strongest consistent signals, also independent of major cell-type change, occur in tRNA-iMet-CAT-1-4 and tRNA-Ser-AGA-2-6. This study presents a comprehensive evaluation of the genomic DNA methylation state of human tRNA genes and reveals a discreet hypermethylation with advancing age.

Hallmarks of environmental insults

Environmental insults impair human health around the world. Contaminated air, water, soil, food, and occupational and household settings expose humans of all ages to a plethora of chemicals and environmental stressors. We propose eight hallmarks of environmental insults that jointly underpin the damaging impact of environmental exposures during the lifespan. Specifically, they include oxidative stress and inflammation, genomic alterations and mutations, epigenetic alterations, mitochondrial dysfunction, endocrine disruption, altered intercellular communication, altered microbiome communities, and impaired nervous system function. They provide a framework to understand why complex mixtures of environmental exposures induce severe health effects even at relatively modest concentrations.

Is your environment making you older? Molecular biomarkers and new approaches to investigate the influences of environmental chemicals through aging

Aging is characterized by a gradual and progressive decline in system integrity that occurs with advancing chronological age. Although it is a physiological process, aging is associated with a myriad of age-related diseases (ARDs), including frailty, sarcopenia, chronic obstructive pulmonary disease, cardiovascular disease, cancer, and neurodegenerative diseases. While not exclusively ARDs, many of these diseases lead to death, a lesser quality of life, and increased healthcare costs for individuals and systems. ARDs share several underlying molecular mechanisms, such as cellular damage, inflammation, DNA methylation changes, stem cells exhaustion, and DNA mutations, which have been outlined as hallmarks of aging. Evidence suggests that environmental exposures, including but not limited to metals, air pollution, endocrine-disrupting chemicals, and noise, may accelerate biological aging. Over the past few years, aging research has identified new molecular biomarkers of the aging process. When applied to investigate environmental influences, these biomarkers can help identify individuals who are particularly susceptible to the influences of environmental exposures on aging processes and therefore guide in implementing possible preventive measures.

Molecular basis of ageing in chronic metabolic diseases

AIM

Over the last decades, the shift in age distribution towards older ages and the progressive ageing which has occurred in most populations have been paralleled by a global epidemic of obesity and its related metabolic disorders, primarily, type 2 diabetes (T2D). Dysfunction of the adipose tissue (AT) is widely recognized as a significant hallmark of the ageing process that, in turn, results in systemic metabolic alterations. These include insulin resistance, accumulation of ectopic lipids and chronic inflammation, which are responsible for an elevated risk of obesity and T2D onset associated to ageing. On the other hand, obesity and T2D, the paradigms of AT dysfunction, share many physiological characteristics with the ageing process, such as an increased burden of senescent cells and epigenetic alterations. Thus, these chronic metabolic disorders may represent a state of accelerated ageing.

MATERIALS AND METHODS

A more precise explanation of the fundamental ageing mechanisms that occur in AT and a deeper understanding of their role in the interplay between accelerated ageing and AT dysfunction can be a fundamental leap towards novel therapies that address the causes, not just the symptoms, of obesity and T2D, utilizing strategies that target either senescent cells or DNA methylation.

RESULTS

In this review, we summarize the current knowledge of the pathways that lead to AT dysfunction in the chronological ageing process as well as the pathophysiology of obesity and T2D, emphasizing the critical role of cellular senescence and DNA methylation.

CONCLUSION

Finally, we highlight the need for further research focused on targeting these mechanisms.

Serum dioxin levels and sperm DNA methylation age: Findings in Vietnam war veterans exposed to Agent Orange

Exposure to dioxin, a known endocrine disruptor and carcinogen, is associated with poor reproductive outcomes. Yet, few studies have explored the role of DNA methylation in these relationships. Utilizing a publicly available dataset from 37 male Air Force Health Study participants exposed to dioxin-contaminated Agent Orange during the Vietnam war, we cross-sectionally examined the relationship of serum dioxin levels with a novel DNA methylation-based measure of sperm age (DNAm-agesperm). DNAm-agesperm was calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. We estimated associations of dioxin levels with DNAm-agesperm using linear regression models adjusted for chronological age, body mass index, and smoking status. Chronological age was highly correlated with DNAmagesperm (r = 0.80). In fully-adjusted linear models, a one percent increase in serum dioxin levels was significantly associated with a 0.0126-year (i.e. 4.6-day) increase in DNAm-agesperm (95%CI: 0.003, 0.022, p = 0.01). Further analyses demonstrated significant negative associations of dioxin levels (β = -0.0005, 95%CI: -0.0010, 0.00004, P = 0.03) and DNAm-agesperm (β = -0.02, 95%CI: -0.04, -0.001, P = 0.03) with methylation levels of FOXK2 - a gene previously reported to be hypomethylated in infertile men. In sum, we demonstrate associations of dioxin with increased methylation aging of sperm. DNAm-agesperm may provide utility for understanding how dioxin levels impact sperm health and potentially male reproductive capacity in human population studies. Moreover, our pilot study contributes further evidence that some environmental toxicants are associated with methylation aging. Additional studies are necessary to confirm these findings, and better characterize dioxin and sperm methylation relationships with male reproductive health.

DNA methylation GrimAge strongly predicts lifespan and healthspan

It was unknown whether plasma protein levels can be estimated based on DNA methylation (DNAm) levels, and if so, how the resulting surrogates can be consolidated into a powerful predictor of lifespan. We present here, seven DNAm-based estimators of plasma proteins including those of plasminogen activator inhibitor 1 (PAI-1) and growth differentiation factor 15. The resulting predictor of lifespan, DNAm GrimAge (in units of years), is a composite biomarker based on the seven DNAm surrogates and a DNAm-based estimator of smoking pack-years. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim.Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death (Cox regression P=2.0E-75), time-to-coronary heart disease (Cox P=6.2E-24), time-to-cancer (P= 1.3E-12), its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count (P=3.45E-17). Similarly, age-adjusted DNAm PAI-1 levels are associated with lifespan (P=5.4E-28), comorbidity count (P= 7.3E-56) and type 2 diabetes (P=2.0E-26). These DNAm-based biomarkers show the expected relationship with lifestyle factors including healthy diet and educational attainment.Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.

Mitochondria and aging in older individuals: an analysis of DNA methylation age metrics, leukocyte telomere length, and mitochondrial DNA copy number in the VA normative aging study

Population aging is a looming global health challenge. New biological aging metrics based on DNA methylation levels have been developed in addition to traditional aging biomarkers. The prospective relationships of aging biomarkers with mitochondrial changes are still not well understood. Here, we examined the prospective associations of mitochondrial copy number (mtDNAcn) with several aging biomarkers – DNAm-Age, DNAm-PhenoAge, DNAm-GrimAge, and leukocyte telomere length. We analyzed 812 individuals from Veteran Affairs Normative Aging Study (NAS) with available blood samples from 1999-2013. Whole blood mtDNAcn and relative leukocyte telomere length were measured via qPCR. DNA methylation was assessed and used to calculate DNAm-Age, DNAm-GrimAge, and DNAm-PhenoAge. Linear mixed models were used to quantify the associations of mtDNAcn with DNAm-Age, DNAm-GrimAge, DNAm-PhenoAge, and leukocyte telomere length. In multivariable cross-sectional analyses, mtDNAcn is negatively associated with DNAm-Age PhenoAge and DNAm-PhenoAge. In contrast, mtDNAcn is associated with prospective measures of higher DNAm-PhenoAge and shorter leukocyte telomere length. Our study shows that higher mtDNAcn is associated with prospective measures of greater DNAm-PhenoAge and shorter leukocyte telomere length independent of chronological age. This indicates a role for mitochondrial in aging-related disease and mortality, but not the departure of biological age from chronological age.

CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer

Many biological indicators related to chronological age have been proposed. Recent studies found that epigenetic clock or DNA methylation age is highly correlated with chronological age. In particular, a significant difference between DNA methylation age (m-age) and chronological age was observed in cancers. However, the prediction and characterization of m-age in pan-cancer remains an explored area. In this study, 1,631 age-related methylation sites in normal tissues were discovered and analyzed. A comprehensive computational model named CancerClock was constructed to predict the m-age for normal samples based on methylation levels of the extracted methylation sites. LASSO linear regression model was used to screen and train the CancerClock model in normal tissues. The accuracy of CancerClock has proved to be 81%, and the correlation value between chronological age and m-age was 0.939 (P < 0.01). Next, CancerClock was used to evaluate the difference between m-age and chronological age for 33 cancer types from TCGA. There were significant differences between predicted m-age and chronological age in large number of cancer samples. These cancer samples were defined as “age-related cancer samples” and they have some differential methylation sites. The differences between predicted m-age and chronological age may contribute to cancer development. Some of these differential methylation sites were associated with cancer survival. CancerClock provided assistance in estimating the m-age in normal and cancer samples. The changes between m-age and chronological age may improve the diagnosis and prognosis of cancers.

Epigenetic Clock and Relative Telomere Length Represent Largely Different Aspects of Aging in the Berlin Aging Study II (BASE-II)

DNA methylation age (DNAm age; "epigenetic clock") has recently been described as highly correlated with chronological age. Several studies suggest that DNAm age reflects, at least in part, biological age. Here, we adapted a recently published methylation-sensitive single nucleotide primer extension method for epigenetic age estimation and calculated the DNAm age based on only seven cytosine-phosphate-guanine sites in 1,895 DNA samples of the Berlin Aging Study II. In a second step, we explored the relationship between this new potential measure of biological age with an established marker of biological age, relative leukocyte telomere length (rLTL), in the same cohort. Our results showed a positive and significant correlation between DNAm age estimation and chronological age (N = 1,895, Rs2 = .47), which persisted after adjustment for covariates (sex, leukocyte distribution, alcohol and smoking). We found a significant but weak negative association between DNAm age acceleration and rLTL in linear regression analysis adjusted for age, sex, alcohol and smoking (β = -0.002, p = .007). Therefore, DNAm age appears to be a promising biomarker in the analysis of phenotypes of aging, which are not (only) related to pathways associated with mitotic age as measured by rLTL.

Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies

DNA methylation (DNAm)-based biomarkers of aging have been developed for many tissues and organs. However, these biomarkers have sub-optimal accuracy in fibroblasts and other cell types used in ex vivo studies. To address this challenge, we developed a novel and highly robust DNAm age estimator (based on 391 CpGs) for human fibroblasts, keratinocytes, buccal cells, endothelial cells, lymphoblastoid cells, skin, blood, and saliva samples. High age correlations can also be observed in sorted neurons, glia, brain, liver, and even bone samples. Gestational age correlates with DNAm age in cord blood. When used on fibroblasts from Hutchinson Gilford Progeria Syndrome patients, this age estimator (referred to as the skin & blood clock) uncovered an epigenetic age acceleration with a magnitude that is below the sensitivity levels of other DNAm-based biomarkers. Furthermore, this highly sensitive age estimator accurately tracked the dynamic aging of cells cultured ex vivo and revealed that their proliferation is accompanied by a steady increase in epigenetic age. The skin & blood clock predicts lifespan and it relates to many age-related conditions. Overall, this biomarker is expected to become useful for forensic applications (e.g. blood or buccal swabs) and for a quantitative ex vivo human cell aging assay.

Relationships of Long-Term Smoking and Moist Snuff Consumption With a DNA Methylation Age Relevant Smoking Index: An Analysis in Buccal Cells

INTRODUCTION

Currently, there is no widely accepted, non-self-report measure that simultaneously reflects smoking behaviors and is molecularly informative of general disease processes. Recently, researchers developed a smoking index (SI) using nucleated blood cells and a multi-tissue DNA methylation-based predictor of chronological age and disease (DNA methylation age [DNAm-age]). To better understand the utility of this novel SI in readily accessible cell types, we used buccal cell DNA methylation to examine SI relationships with long-term tobacco smoking and moist snuff consumption.

METHODS

We used a publicly available dataset composed of buccal cell DNA methylation values from 120 middle-aged men (40 long-term smokers, 40 moist snuff consumers, and 40 nonsmokers). DNAm-age (353-CpGs) and SI (66-CpGs) were calculated using CpG sites measured using the Illumina HumanMethylation450 BeadChip. We estimated associations of tobacco consumption habits with both SI and DNAm-age using linear regression models adjusted for chronological age, race, and methylation technical covariates.

RESULTS

In fully adjusted models with nonsmokers as the reference, smoking (β = 1.08, 95% CI = 0.82 to 1.33, p < .0001) but not snuff consumption (β = .06, 95% CI = -0.19 to 0.32, p = .63) was significantly associated with SI. SI was an excellent predictor of smoking versus nonsmoking (area under the curve = 0.92, 95% CI = 0.85 to 0.98). Four DNAm-age CpGs were differentially methylated between smokers and nonsmokers including cg14992253 [EIF3I], which has been previously shown to be differentially methylated with exposure to long-term fine-particle air pollution (PM2.5).

CONCLUSIONS

The 66-CpG SI appears to be a useful tool for measuring smoking-specific behaviors in buccal cells. Still, further research is needed to broadly confirm our findings and SI relationships with DNAm-age.

IMPLICATIONS

Our findings demonstrate that this 66-CpG blood-derived SI can reflect long-term tobacco smoking, but not long-term snuff consumption, in buccal cells. This evidence will be useful as the field works to identify an accurate non-self-report smoking biomarker that can be measured in an easily accessible tissue. Future research efforts should focus on (1) optimizing the relationship of the SI with DNAm-age so that the metric can maximize its utility as a tool for understanding general disease processes, and (2) determining normal values for the SI CpGs so that the measure is not as study sample specific.

DNA Methylation Clocks in Aging: Categories, Causes, and Consequences

Age-associated changes to the mammalian DNA methylome are well documented and thought to promote diseases of aging, such as cancer. Recent studies have identified collections of individual methylation sites whose aggregate methylation status measures chronological age, referred to as the DNA methylation clock. DNA methylation may also have value as a biomarker of healthy versus unhealthy aging and disease risk; in other words, a biological clock. Here we consider the relationship between the chronological and biological clocks, their underlying mechanisms, potential consequences, and their utility as biomarkers and as targets for intervention to promote healthy aging and longevity.