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.

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

43Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation 44 of epigenetic biomarkers of aging were developed using chronological age as a surrogate for 45 biological age, we hypothesized that incorporation of composite clinical measures of phenotypic 46 age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the 47 development of a more powerful epigenetic biomarker of aging. Using a innovative two-step 48 process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly 49 outperforms previous measures in regards to predictions for a variety of aging outcomes, including 50 all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this 51 biomarker was developed using data from whole blood, it correlates strongly with age in every 52 tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that 53 increased epigenetic, relative to chronological age, is associated increased activation of pro-54 inflammatory and interferon pathways, and decreased activation of transcriptional/translational 55 machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic 56 biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues 57 and cells, and provide insight into important pathways in aging. 58 Keywords: aging; life expectancy; biological age; epigenetic clock; DNA methylation 59 60 61 62 63 64 4 BACKGROUND 65One of the major goals of geroscience research is to define 'biomarkers of aging' [1, 2], which can 66 be thought of as individual-level measures of aging that capture between-person differences in the 67 timing of disease onset, functional decline, and death over the life course. While chronological age 68 is arguably the strongest risk factor for aging-related death and disease, it is important to 69 distinguish chronological time from biological aging. Individuals of the same chronological age 70 may exhibit greatly different susceptibilities to age-related diseases and death, which is likely 71 reflective of differences in their underlying biological aging processes. Such biomarkers of aging 72 will be crucial to enable evaluation of interventions aimed at promoting healthier aging, by 73 providing a measurable outcome, that unlike incidence of death and/or disease, does not require 74 extremely long follow-up observation. 75One potential biomarker that has gained significant interest in recent years is DNA methylation 76 (DNAm). Chronological time has been shown to elicit predictable hypo-and hyper-methylation 77 changes at many regions across the genome [3][4][5][6][7], and as a result, the first generation of DNAm 78 based biomarkers of aging were developed to predict chronological age [8][9][10][11][12][13]. The blood-based 79 algorithm by Hannum[10] and the multi-tissue algorithm by Horvath [14] produce age estimates 80 (DNAm age) that correlate with chronologica...

Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-cause mortality, chronic conditions and age-related functional decline. We analyze cross-sectional data from 4,173 postmenopausal female participants from the Women's Health Initiative, as well as 402 male and female participants from the Italian cohort study, Invecchiare nel Chianti.Extrinsic epigenetic age acceleration (EEAA) exhibits significant associations with fish intake (p=0.02), moderate alcohol consumption (p=0.01), education (p=3×10-5), BMI (p=0.01), and blood carotenoid levels (p=1×10-5)—an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin—the first-line medication for the treatment of type 2 diabetes—does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.

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.

Metabolism and ageing are intimately linked. Compared to ad libitum feeding, dietary restriction (DR) or calorie restriction (CR) consistently extends lifespan and delays age-related diseases in evolutionarily diverse organisms1,2. Similar conditions of nutrient limitation and genetic or pharmacological perturbations of nutrient or energy metabolism also have longevity benefits3,4. Recently, several metabolites have been identified that modulate ageing5,6 with largely undefined molecular mechanisms. Here we show that the tricarboxylic acid (TCA) cycle intermediate α-ketoglutarate (α-KG) extends the lifespan of adult C. elegans. ATP synthase subunit beta is identified as a novel binding protein of α-KG using a small-molecule target identification strategy called DARTS (drug affinity responsive target stability)7. The ATP synthase, also known as Complex V of the mitochondrial electron transport chain (ETC), is the main cellular energy-generating machinery and is highly conserved throughout evolution8,9. Although complete loss of mitochondrial function is detrimental, partial suppression of the ETC has been shown to extend C. elegans lifespan10–13. We show that α-KG inhibits ATP synthase and, similar to ATP synthase knockdown, inhibition by α-KG leads to reduced ATP content, decreased oxygen consumption, and increased autophagy in both C. elegans and mammalian cells. We provide evidence that the lifespan increase by α-KG requires ATP synthase subunit beta and is dependent on the target of rapamycin (TOR) downstream. Endogenous α-KG levels are increased upon starvation and α-KG does not extend the lifespan of DR animals, indicating that α-KG is a key metabolite that mediates longevity by DR. Our analyses uncover new molecular links between a common metabolite, a universal cellular energy generator, and DR in the regulation of organismal lifespan, thus suggesting new strategies for the prevention and treatment of ageing and age-related diseases.

Telomere length (TL) is associated with several aging-related diseases. Here, we present a DNA methylation estimator of TL (DNAmTL) based on 140 CpGs. Leukocyte DNAmTL is applicable across the entire age spectrum and is more strongly associated with age than measured leukocyte TL (LTL) (r ~-0.75 for DNAmTL versus r ~ -0.35 for LTL). Leukocyte DNAmTL outperforms LTL in predicting: i) time-to-death (p=2.5E-20), ii) time-to-coronary heart disease (p=6.6E-5), iii) time-to-congestive heart failure (p=3.5E-6), and iv) association with smoking history (p=1.21E-17). These associations are further validated in large scale methylation data (n=10k samples) from the Framingham Heart Study, Women's Health Initiative, Jackson Heart Study, InChianti, Lothian Birth Cohorts, Twins UK, and Bogalusa Heart Study. Leukocyte DNAmTL is also associated with measures of physical fitness/functioning (p=0.029), age-at-menopause (p=0.039), dietary variables (omega 3, fish, vegetable intake), educational attainment (p=3.3E-8) and income (p=3.1E-5). Experiments in cultured somatic cells show that DNAmTL dynamics reflect in part cell replication rather than TL per se. DNAmTL is not only an epigenetic biomarker of replicative history of cells, but a useful marker of age-related pathologies that are associated with it.

Although epigenetic processes have been linked to aging and disease in other systems, it is not yet known whether they relate to reproductive aging. Recently, we developed a highly accurate epigenetic biomarker of age (known as the "epigenetic clock"), which is based on DNA methylation levels. Here we carry out an epigenetic clock analysis of blood, saliva, and buccal epithelium using data from four large studies: the Women's Health Initiative (n = 1,864); Invecchiare nel Chianti (n = 200); Parkinson's disease, Environment, and Genes (n = 256); and the United Kingdom Medical Research Council National Survey of Health and Development (n = 790). We find that increased epigenetic age acceleration in blood is significantly associated with earlier menopause (P = 0.00091), bilateral oophorectomy (P = 0.0018), and a longer time since menopause (P = 0.017). Conversely, epigenetic age acceleration in buccal epithelium and saliva do not relate to age at menopause; however, a higher epigenetic age in saliva is exhibited in women who undergo bilateral oophorectomy (P = 0.0079), while a lower epigenetic age in buccal epithelium was found for women who underwent menopausal hormone therapy (P = 0.00078). Using genetic data, we find evidence of coheritability between age at menopause and epigenetic age acceleration in blood. Using Mendelian randomization analysis, we find that two SNPs that are highly associated with age at menopause exhibit a significant association with epigenetic age acceleration. Overall, our Mendelian randomization approach and other lines of evidence suggest that menopause accelerates epigenetic aging of blood, but mechanistic studies will be needed to dissect cause-and-effect relationships further.