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.

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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...

The telomere length of replicating somatic cells is inversely correlated with age and has been reported to be associated cross-sectionally with cardiovascular disease (CVD). Leukocyte telomere length, as expressed by mean terminal restriction fragment (TRF) length, was measured in 419 randomly selected participants from the Cardiovascular Health Study, comprising a community-dwelling cohort recruited in four US communities. The authors investigated associations between TRF length and selected measures of subclinical CVD/risk factors for CVD (data were collected at the 1992/1993 clinic visit) and incident CVD (ascertained through June 2002). In these participants (average age = 74.2 years (standard deviation, 5.2)), mean TRF length was 6.3 kilobase pairs (standard deviation, 0.62). Significant or borderline inverse associations were found between TRF length and diabetes, glucose, insulin, diastolic blood pressure, carotid intima-media thickness, and interleukin-6. Associations with body size and C-reactive protein were modified by gender and age, occurring only in men and in participants aged 73 years or younger. In younger (but not older) participants, each shortened kilobase pair of TRF corresponded with a threefold increased risk of myocardial infarction (hazard ratio = 3.08, 95% confidence interval: 1.22, 7.73) and stroke (hazard ratio = 3.22, 95% confidence interval: 1.29, 8.02). These results support the hypotheses that telomere attrition may be related to diseases of aging through mechanisms involving oxidative stress, inflammation, and progression to CVD.