An epigenetic biomarker of aging for lifespan and healthspan · 38 Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, 695 39 Charles E. Young Drive

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An epigenetic biomarker of aging for lifespan and healthspan 1

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Morgan E. Levine1, Ake T. Lu1, Austin Quach1, Brian H. Chen2, Themistocles L. Assimes3, 3

Stefania Bandinelli4, Lifang Hou5, Andrea A. Baccarelli6, James D. Stewart7, Yun Li7, Eric A. 4

Whitsel7,8, James G Wilson9, Alex P Reiner10, Abraham Aviv11, Kurt Lohman12, Yongmei Liu13, 5

Luigi Ferrucci2*, Steve Horvath1,14* 6

Affiliations 7

1 Dept. of Human Genetics, David Geffen School of Medicine, University of California Los 8

Angeles, Los Angeles, CA 90095, USA 9

2 Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, 10

National Institutes of Health, USA. Baltimore, MD 21224, USA 11

3 Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA 12

4 Geriatric Unit, Azienda Toscana Centro, Florence, Italy 13

5 Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and 14

Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 15

Chicago, IL 60611, USA 16

6 Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences 17

Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, 18

USA 19

7 Dept. of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 20

Chapel Hill, NC, USA 21

.CC-BY-NC 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted March 5, 2018. . https://doi.org/10.1101/276162doi: bioRxiv preprint

https://doi.org/10.1101/276162

http://creativecommons.org/licenses/by-nc/4.0/

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8 Dept. of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA 22

9 Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, 23

MS 39216, USA 24

10 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 25

98109, USA1 26

11 Center of Development and Aging, New Jersey Medical School, Rutgers State University of 27

New Jersey, Newark, NJ 07103, USA 28

12 Department of Biostatistics, Division of Public Health Sciences, Wake Forrest School of 29

Medicine, Winston-Salem, NC 27157, USA 30

13 Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forrest 31

School of Medicine, Winston-Salem, NC 27157, USA 32

14Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, 33

Los Angeles, CA 90095, USA 34

*Denotes joint senior-author status 35

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Corresponding author: Steve Horvath, PhD, E-mail: [email protected]. Address: 37

Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, 695 38

Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, USA 39

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Abstract 43

Identifying 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

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BACKGROUND 65

One 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. 75

One 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 [3-7], and as a result, the first generation of DNAm 78

based biomarkers of aging were developed to predict chronological age [8-13]. 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 chronological age well above r=0.90 for full age range samples. 81

Nevertheless, while the current epigenetic age estimators exhibit statistically significant 82

associations with many age-related diseases and conditions [15-26], the effect sizes are typically 83

small to moderate. One explanation is that by using chronological age as the reference, by 84

definition, may exclude CpGs whose methylation patterns don’t display strong time-dependent 85

changes, but instead signal the departure of biological age from chronological age. Thus, it is 86

important to not only capture CpGs that display changes with chronological time, but also those 87


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that account for differences in risk and physiological status among individuals of the same 88

chronological age. 89

Previous work by us and others have shown that “phenotypic aging measures”, derived from 90

clinical biomarkers[27-31], strongly predict differences in the risk of all-cause mortality, cause-91

specific mortality, physical functioning, cognitive performance measures, and facial aging among 92

same-aged individuals. What’s more, in representative population data, some of these measures 93

have been shown to be better indicators of remaining life expectancy than chronological age[27], 94

suggesting that they may be approximating individual-level differences in biological aging rates. 95

As a result, we hypothesize that a more powerful epigenetic biomarker of aging could be generated 96

by replacing prediction of chronological age with prediction of a surrogate measure of "phenotypic 97

aging" that, in and of itself, differentiates morbidity and mortality risk among same-age 98

individuals. 99

RESULTS 100

Overview of the statistical model and analysis 101

Our development of the new epigenetic biomarker of aging proceeded along three main steps (Fig. 102

1). In step 1, a novel measure of ‘phenotypic age’ was developed using clinical data from the third 103

National Health and Nutrition Examination Survey (NHANES). In step 2, DNAm from whole 104

blood was used to predict phenotypic age, such that: 105

𝐷𝑁𝐴𝑚 𝑃ℎ𝑒𝑛𝑜𝐴𝑔𝑒 = 𝐶𝑝𝐺1 × 𝛽1 + 𝐶𝑝𝐺2 × 𝛽2 + ⋯ 𝐶𝑝𝐺513 × 𝛽513 + 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 106

Predicted estimates from this model represent a person’s epigenetic age, which we refer to as 107

‘DNAm PhenoAge’. Using multiple independent datasets, we then tested whether DNAm 108

PhenoAge was associated with a number of aging-related outcomes. We also tested whether it 109


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differed as a function of social, behavioral, and demographic characteristics, as whether it was 110

applicable to tissues other than whole blood. Finally, in step 3, we examine the underlying biology 111

of the 513 CpGs in the DNAm PhenoAge measure by examining differential expression, GO and 112

pathway enrichment, chromosomal locations, and heritability. 113

Estimating phenotypic age from clinical biomarkers 114

For step 1, NHANES III was used to generate a measure of phenotypic age. NHANES III is a 115

nationally-representative sample, with over twenty-three years of mortality follow-up, from which 116

our analytical sample included 9,926 adults with complete biomarker data. A Cox penalized 117

regression model—where the hazard of mortality was regressed on forty-two clinical markers and 118

chronological age—was used to select variables for inclusion in our phenotypic age score. The 119

forty-two biomarkers considered represent those that were available in both NHANES III and IV. 120

Based on 10-fold cross-validation, ten variables (including chronological age) were selected for 121

the phenotypic age predictor (Additional file 1: Table S1). These nine biomarkers and 122

chronological age were then combined in a phenotypic age estimate (in units of years) as detailed 123

in Methods. 124

Validation data for phenotypic age came from NHANES IV, and included up to 17 years of 125

mortality follow-up for n=6,209 national representative US adults. In this population, phenotypic 126

age is correlated with chronological age at r=0.94. Results from all-cause and cause-specific 127

(competing risk) mortality predictions, adjusting for chronological age (Table 1), show that a one 128

year increase in phenotypic age is associated with a 9% increase in the risk of all-cause mortality 129

(HR=1.09, p=3.8E-49), a 9% increase in the risk of mortality from aging-related diseases 130

(HR=1.09, p=4.5E-34), a 10% increase in the risk of CVD mortality (HR=1.10, p=5.1E-17), a 7% 131

increase in the risk of cancer mortality (HR=1.07, p=7.9E-10), a 20% increase in the risk of 132


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diabetes mortality (HR=1.20, p=1.9E-11), and a 9% increase in the risk of lung disease mortality 133

(HR=1.09, p=6.3E-4). Further, phenotypic age is highly associated with comorbidity count 134

(p=3.9E-21) and physical functioning measures (p=2.1E-10, Additional file 1: Fig. S1). 135

An epigenetic biomarker of aging (DNAm PhenoAge) 136

For step 2 (Fig. 1), data from n=456 participants at two time-points in the Invecchiare in Chianti 137

(InCHIANTI) study was used to relate blood DNAm levels to phenotypic age. InCHIANTI was 138

used as training data for the new epigenetic biomarker because the study assessed all clinical 139

measures needed to estimate phenotypic age, contained data on DNAm, and had a large age range 140

population (21-100 years). A total of 20,169 CpGs were considered when generating the new 141

DNAm measure. They represented those CpGs available on all three chips (27k, 450k, EPIC), so 142

as to facilitate usability across platforms. Elastic net regression, with 10-fold cross-validation, 143

produced a model in which phenotypic age is predicted by DNAm levels at 513 of the 20,169 144

CpGs. The linear combination of the weighted 513 CpGs yields a DNAm based estimator of 145

phenotypic age that we refer to as ‘DNAm PhenoAge’ (mean=58.9, s.d.=18.2, range=9.1-106.1), 146

in contrast to the previously published Hannum and Horvath ‘DNAm Age’ measures. 147

While our new clock was trained on cross-sectional data in InCHIANTI, we capitalized on the 148

repeated time-points to test whether changes in DNAm PhenoAge are related to changes in 149

phenotypic age. As expected, between 1998 and 2007, mean change in DNAm PhenoAge was 150

8.51 years, whereas mean change in phenotypic age was 8.88 years. Moreover, participants’ 151

phenotypic age (adjusting for chronological age) at the two time-points was correlated at r=0.50, 152

whereas participants’ DNAm PhenoAge (adjusting for chronological age) at the two time-points 153

was correlated at r=0.68 (Additional file 1: Fig. S2). Finally, we find that the change in phenotypic 154


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age between 1998 and 2007 is highly correlated with the change in DNAm PhenoAge between 155

these two time-points (r=0.74, p=3.2E-80, Additional file 1: Fig. S2). 156

DNAm PhenoAge strongly relates to all-cause mortality 157

In step 3 (Fig. 1), the epigenetic biomarker, DNAm PhenoAge, was calculated in five independent 158

large-scale samples—two samples from Women’s Health Initiative (WHI) (n=2,016; and 159

n=2,191), the Framingham Heart Study (FHS) (n=2,553), the Normative Aging Study (NAS) 160

(n=657), and the Jackson Heart Study (JHS) (n=1,747). The first four studies used the Illumina 161

450K array while the JHS employed the latest Illumina EPIC array platform. In these studies, 162

DNAm PhenoAge correlated with chronological age at r=0.66 in WHI (Sample 1), r=0.69 in WHI 163

(Sample 2), r=0.78 in FHS, r=0.62 in the NAS, and r=0.89 in JHS. The five validation samples 164

were then used to assess the effects of DNAm PhenoAge on mortality in comparison to the Horvath 165

and Hannum DNAm Age measures. DNAm PhenoAge was significantly associated with 166

subsequent mortality risk in all studies (independent of chronological age), such that, a one year 167

increase in DNAm PhenoAge is associated with a 4.5% increase in the risk of all-cause mortality 168

(Meta(FE)=1.045, Meta p=7.9E-47, Fig. 2). To better conceptualize what this increase represents, 169

we compared the predicted life expectancy and mortality risk for person’s representing the top 5% 170

(fastest agers), the average, and the bottom 5% (slowest agers). Results suggest that those in the 171

top 5% of fastest agers have a mortality hazard of death that is about 1.62 times that of the average 172

person, i.e. your hazard of death is 62% higher than that of an average person. Further, contrasting 173

the 5% fastest agers with the 5% slowest agers, we find that the hazard of death of the fastest agers 174

is 2.58 times higher than that of the bottom 5% slowest agers (HR=1.04511.0/1.045-10.5). 175

Additionally, both observed and predicted Kaplan-Meier survival estimates showed that faster 176


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agers had much lower life expectancy and survival rates compared to average and/or slow agers 177

(Fig. 2). 178

As shown in Fig. 2, the DNAm age based measures from Hannum and Horvath also related to all-179

cause mortality, consistent with what has been reported previously [16, 24, 32-34]. To directly 180

compare the three epigenetic measures, we contrasted their accuracy in predicting 10-year and 20-181

year mortality risk, using receiver operating characteristics curves. DNAm PhenoAge (adjusted 182

for age) predicts both 10-year mortality and 20-year mortality significantly better than the other 183

two measures (Additional file 1: Table S2). Finally, when examining a model that includes all 184

three measures (Additional file 1: Table S3), we find that only DNAm PhenoAge is positively 185

associated with mortality (HR=1.04, p=1.33E-8), whereas Horvath DNAm Age is now negatively 186

associated (HR=0.98, p=2.72E-2), and Hannum DNAm Age has no association (HR=1.01, 187

p=4.66E-1). 188

DNAm PhenoAge strongly relates to aging-related morbidity 189

Given that aging is believed to also influence diseases incidence/prevalence, we examined whether 190

DNAm PhenoAge relate to diverse ag-related morbidity outcomes. We observe strong associations 191

between DNAm PhenoAge and a variety of other aging outcomes using the same five validation 192

samples (Table 2). For instance, independent of chronological age, higher DNAm PhenoAge is 193

associated with an increase in a person’s number of coexisting morbidities (=0.008 to 0.031; 194

Meta P-value=1.95E-20), a decrease in likelihood of being disease-free (=-0.002 to -0.039; Meta 195

P-value=2.10E-10), an increase in physical functioning problems (=-0.016 to -0.473; Meta P-196

value=2.05E-13), an increase in the risk of CHD risk (=0.016 to 0.073; Meta P-value=3.35E-11). 197

DNAm PhenoAge and smoking 198


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Cigarette exposure has been shown to have an epigenetic fingerprint[35-37], which has been 199

reflected in previous DNAm risk predictors[38]. Similarly, we find that DNAm PhenoAge 200

significantly differs between never (n=1,097), current (n=209), and former smokers (n=710) 201

(p=0.0033) (Additional file 1, Fig. S3A); however, conversely, we do not find a robust association 202

between pack-years and DNAm PhenoAge (Additional file 1, Fig. S3B-D). Given the association 203

between DNAm PhenoAge and smoking, we re-evaluated the morbidity and mortality associations 204

(fully-adjusted) in our four samples, stratifying by smoking status (Additional file 1: Fig. S4 and 205

Table S4). We find that DNAm PhenoAge is associated with mortality among both smokers 206

(adjusted for pack-years) (Meta(FE)=1.050, Meta p=7.9E-31), and non-smokers 207

(Meta(FE)=1.033, Meta p=1.2E-10). DNAm PhenoAge relates to the number of coexisting 208

morbidities, physical functioning status, disease free status, and CHD for both smokers and non-209

smokers (Additional file 1: Table S4). Finally, in previous work we showed that Horvath DNAm 210

age of blood predicts lung cancer risk in the first WHI sample[21]. Using the same data, we find 211

that a one year increase in DNAm PhenoAge (adjusting for chronological age, race/ethnicity, pack-212

years, and smoking status) is associated with a 5% increase in the risk lung cancer incidence and/or 213

mortality (HR=1.05, p=0.031). Further, when restricting the model to current smokers only, we 214

find that the effect of DNAm PhenoAge on future lung cancer incidence and/or mortality is even 215

stronger (HR=1.10, p=0.014). 216

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In evaluating the relationship between DNAm PhenoAge and additional characteristics we observe 218

significant differences between racial/ethnic groups (p=5.1E-5), with non-Hispanic blacks having 219

the highest DNAm PhenoAge on average, and non-Hispanic whites having the lowest (Additional 220

file 1: Fig. S5). We also find evidence of social gradients in DNAm PhenoAge, such that those 221


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with higher education (p=6E-9) and higher income (p=9E-5) appear younger. DNAm PhenoAge 222

relates to exercise and dietary habits, such that increased exercise (p=7E-5) and markers of 223

fruit/vegetable consumption (such as carotenoids, p=2E-27) are associated with lower DNAm 224

PhenoAge (Additional file 1: Fig. S6A & Additional file 1: Fig. S6B). Cross sectional studies in 225

the WHI also revealed that DNAmPhenoAge acceleration is positively correlated with C-reactive 226

protein (r=0.18, p=5E-22, Additional file 1: Fig. S6B), insulin (r=0.15, p=2E-20), glucose (r=0.10, 227

p=2E-10), triglycerides (r=0.09, p=5E-9), waist to hip ratio (r=0.15, p=5E-22) but it is negatively 228

correlated with the "good" cholesterol HDL (r=-0.09, p=7E-9). 229

DNAm PhenoAge in other tissues 230

One advantage of developing biological aging estimates based on molecular markers (like 231

DNAm), rather than clinical risk measures (e.g. those in the phenotypic age variable), is that they 232

may lend themselves to measuring tissue/cell specific aging. Although DNAm PhenoAge was 233

developed using samples from whole blood, our empirical results show that it strongly correlates 234

with chronological age in a host of different tissues and cell types (Fig. 3). For instance, when 235

examining all tissues concurrently, the correlation between DNAm PhenoAge and chronological 236

age was 0.71. Age correlations in brain tissue ranged from 0.54 to 0.92, while correlations were 237

also found in breast (r=0.47), buccal cells (r=0.88), dermal fibroblasts (r=0.87), epidermis (r=0.84), 238

colon (r=0.88), heart (r=0.66), kidney (r=0.64), liver (r=0.80), lung (r=055), and saliva (r=0.81). 239

Alzheimer's disease and brain samples 240

Based on the accuracy of the age prediction in other tissues/cells, we examined whether aging in 241

a given tissue was associated with tissue-associated outcomes. For instance, using data from 242

approximately 700 post-mortem samples from the Religious Order Study (ROS) and the Memory 243

and Aging Project (MAP)[39, 40] we tested the association between pathologically diagnosed 244


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Alzheimer’s disease and DNAm PhenoAge in dorsolateral prefrontal cortex (DLPFX). Results 245

suggest (Fig. 4) that those who are diagnosed with Alzheimer’s disease (AD), based on 246

postmortem autopsy, have DLPFX that appear more than one year older than same aged 247

individuals who are not diagnosed with AD postmortem (p=4.6E-4). Further, age adjusted DNAm 248

PhenoAge was found to be positively associated with neuropathological hallmarks of Alzheimer’s 249

disease, such as amyloid load (r=0.094, p=0.012), neuritic plaques (r=0.11, p=0.0032), and 250

neurofibrillary tangles (r=0.10, p=0.0073). 251

Comparison with other DNAm biomarkers of aging 252

Several additional DNAm biomarkers have been described in the literature CpGs [12, 13]. A direct 253

comparison of 6 DNAm biomarkers (including DNAm PhenoAge) reveals that DNAm PhenoAge 254

stands out in terms of its predictive accuracy for lifespan, its relationship with smoking status, its 255

relationship with leukocyte telomere length, naïve CD8+ T cells and CD4+ T cells (Additional file 256

1: Table S5). 257

DNAm PhenoAge and Immunosenescence 258

To test the hypothesis that DNAm PhenoAge captures aspects of the age-related decline of the 259

immune system, we correlated DNAm PhenoAge with estimated blood cell count (Additional file 260

1, Fig. S7). After adjusting for age, we find that DNAm PhenoAgeAccel is negatively correlated 261

with naïve CD8+ T cells (r=-0.35, p=9.2E-65), naïve CD4+ T cells (r=-0.29, p=4.2E-42), CD4+ 262

helper T cells (r=-0.34, p=3.6E-58), and B cells (r=-0.18, p=8.4E-17). Further, DNAm 263

PhenoAgeAccel is positively correlated with the proportion of granulocytes (r=0.32, p=2.3E-51), 264

exhausted CD8+ (defined as CD28-CD45RA-) T cells (r=0.20, p=1.9E-20), and plasma blast cells 265

(r=0.26, p=6.7E-34). These results are consistent with age related changes in blood cells [41] and 266

suggest that DNAm PhenoAge may capture aspects of immunosenescence in blood. However, 267


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three lines of evidence suggest that DNAm PhenoAge is not simply a measure of 268

immunosenescence. First, another measure of immunosenescence, leukocyte telomere length, is 269

only weakly correlated with DNAm PhenoAgeAccel (r=-0.13 p=0.00019 in the WHI and r=-0.087, 270

P=7.6E-3 in Framingham Heart study, Additional file 1, Fig. S8). Second, the strong association 271

between DNAm PhenoAge and mortality does not simply reflect changes in blood cell 272

composition, as can be seen from the fact that in Additional file 1, Fig. S9 the robust association 273

remains even after adjusting for estimates of seven blood cell count measures (Meta(FE)=1.036, 274

Meta p=5.6E-21). 275

DNA sequence characteristics of the 513 CpGs in DNAm PhenoAge 276

Of the 513 CpGs in DNAm PhenoAge, we find that, 41 CpGs were also in the Horvath DNAm 277

Age measure (Additional file 2: Table S6). This represents a 4.88-fold increase over what would 278

be expected by chance (p=8.97E-15). Of the 41 overlapping CpGs, the average absolute value for 279

their age correlations was r=0.40, and 31 had age correlations with absolute values in the top 20% 280

of what is found for among the 513 CpGs in the DNAm PhenoAge score. We also observed 6 281

CpGs that overlapped between the Hannum DNAm Age score and the DNAm PhenoAge score—282

five of which were also found in the Horvath DNAm Age measure. All six CpGs had extremely 283

high age correlations (half positive, half negative), with absolute values between r=0.49 and 284

r=0.76. The five CpGs that are found in all three epigenetic aging measures were: cg05442902 285

(P2RXL1), cg06493994 (SCGN), cg09809672 (EDARADD), cg19722847 (IPO8), and 286

cg22736354 (NHLRC1). 287

Finally, we conducted a functional enrichment analysis of the chromosomal locations of the 513 288

CpGs, we found that 149 CpGs whose age correlation exceeded 0.2 tended to be located in CpG 289

islands (p=0.0045, Additional file 1: Fig. S10) and were significantly enriched with polycomb 290


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group protein targets (p=8.7E-5, Additional file 1: Fig. S10), which echoes results of epigenome 291

wide studies of aging effects [4, 42, 43]. 292

Transcriptional and genetic studies of DNAm PhenoAge 293

Using the genome-wide data from FHS and WHI, we estimated the heritability of DNAm 294

PhenoAge. The heritability estimated by the SOLAR polygenic model for those of European 295

ancestry in the FHS was ℎ2=0.33, while the heritability estimated for those of European ancestry 296

in WHI, using GCTA-GREML analysis[44, 45] was ℎ2=0.51. 297

Using the monocyte data mentioned above, as well as PBMC expression data on 2,188 persons 298

from the FHS, we conducted a more thorough transcriptional analysis to identify differential 299

expression associated with DNAm PhenoAgeAccel (Additional file 3: Table S7). Overall, we find 300

that genes show similar associations to chronological age and DNAm PhenoAgeAccel. DNAm 301

PhenoAgeAccel represents aging differences among same-aged individuals and is adjusted so as 302

to exhibit a correlation of r=0.0 with chronological age. Thus, this observation can be taken to 303

suggest that genes whose transcription increases with age are upregulated among epigenetically 304

older compared to epigenetically younger persons of the same chronological age (Additional file 305

1: Fig. S11)—same applies for genes that show decreases with chronological age being 306

downregulated in epigenetically older versus younger persons of the same age. 307

Using the transcriptional data from monocytes described above (adjusting for array, sex, 308

race/ethnicity, age, and imputed cell counts), we tested for GO enrichment among genes that 309

positively associated with DNAm PhenoAge and those that negatively associated with DNAm 310

PhenoAge (Additional file 4: Table S8). Among those with positive aging associations 311

(overexpression among epigenetically older individuals), we observed enrichment for a number of 312

pro-inflammatory signaling pathways. These pathways included, but were not limited to: multiple 313


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toll-like receptor signaling pathways (7,9,3,2), regulation of inflammatory response, JAK-STAT 314

cascade, response to lipopolysaccharide, tumor necrosis factor-mediated signaling pathway, and 315

positive regulation of NF-kappaB transcription factor activity. Additionally, positively associated 316

genes were also enriched for a number anti-viral response pathways—type I interferon signaling, 317

defense response to virus, interferon-gamma-mediated signaling pathway, cellular response to 318

interferon-alpha, etc. Finally, other interesting GO terms enriched among positively associated 319

genes included: response to nutrient, JAK-STAT cascade involved in growth hormone signaling 320

pathway, multicellular organism growth, and regulation of DNA methylation. 321

When testing for enrichment among genes that were negatively associated with DNAm 322

PhenoAgeAccel (decreased expression among epigenetically older persons) we observed that 323

many were implicated in processes involving transcriptional and translational machinery, as well 324

as damage recognition and repair. These included: translational initiation; regulation of 325

translational initiation; ribosomal large subunit assembly; ribosomal small subunit assembly; 326

translational elongation; transcription initiation from RNA polymerase I promoter; transcription-327

coupled nucleotide-excision repair; nucleotide-excision repair, DNA incision, 5'-to lesion; 328

nucleotide-excision repair, DNA damage recognition; DNA damage response, detection of DNA 329

damage; and regulation of DNA damage checkpoint. 330

DISCUSSION 331

Using a novel two-step method, we were successful in developing a DNAm based biomarker of 332

aging that is highly predictive of nearly every morbidity and mortality outcome we tested. Training 333

an epigenetic predictor of phenotypic age instead of chronological age led to substantial 334

improvement in mortality/healthspan predictions over the first generation of DNAm based 335

biomarkers of chronological age from Hannum[10], Horvath[14] and other published DNAm 336


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biomarkers. In doing so, this is the first study to conclusively demonstrate that DNAm biomarkers 337

of aging are highly predictive of CVD and coronary heart disease. DNAm PhenoAge also tracks 338

chronological age and relates to disease risk in samples other than whole blood. Finally, we find 339

that an individual’s DNAm PhenoAge, relative to his/her chronological age, is moderately 340

heritable and is associated with activation of pro-inflammatory, interferon, DNAm damage repair, 341

transcriptional/translational signaling, and various markers of immunosenescenc: a decline of 342

naïve T cells and shortened leukocyte telomere length. 343

The ability of our measure to predict multifactorial aging conditions is consistent with the 344

fundamental underpinnings of Geroscience research [1, 46], which posits that aging mechanisms 345

give rise to multiple pathologies and thus, differences in the rate of aging will have implications 346

for a wide array of diseases and conditions. Further, these results answer a fundamental biological 347

question of whether differences in multi-system dysregulation (estimated using clinical phenotypic 348

age measures), healthspan, and lifespan are reflected at the epigenetic level, in the form of 349

differential DNAm at specific CpG sites. 350

The improvement over previous epigenetic biomarkers, likely comes down to the types of CpGs 351

selected for the various measures. Only 41 of the 513 CpGs in DNAm PhenoAge were shared with 352

the Horvath clock, while only five CpGs were shared between all three clocks (DNAm PhenoAge, 353

Horvath, and Hannum). In general, these CpGs did not tend to be drivers of the DNAm PhenoAge 354

score, and instead represented those with large age correlations, but lower weights. This may 355

explain the improvements of DNAm PhenoAge over previous epigenetic biomarkers of aging. 356

While the previous DNAm age estimators selected CpGs to optimize prediction of chronological 357

age, the CpGs in DNAm PhenoAge were optimized to predict a multi-system proxy of 358

physiological dysregulation (phenotypic age). In doing so, we were able to not only capture CpGs 359


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that exhibited changes in DNAm with age, but also those that captured variations in risk of death 360

and disease among same aged individuals. In general, the CpGs with the highest weights in the 361

new clock did not correlate with chronological age (Additional file 1: Fig. S12), but instead were 362

related to the difference between phenotypic and chronological age—i.e. divergence in the rate of 363

aging. Interestingly, the CpGs that contributed the most to the DNAm PhenoAge score, tended to 364

have low age correlations. 365

While DNAm PhenoAge greatly outperformed all previous DNAm biomarkers of aging 366

(Additional file 1: Table S5), the utility of DNAm PhenoAge for estimating risk does not imply 367

that it should replace clinical biomarkers when it comes to informing medical and health-related 368

decisions. In fact, but perhaps not surprisingly, the phenotypic age measure used to select CpGs is 369

a better predictor of morbidity and mortality outcomes than DNAm PhenoAge. While the addition 370

of error in performing a two-step process, rather than training a DNAm predictor directly on 371

mortality may contribute, we don’t believe this accounts for the difference in predictive 372

performance. In fact, a recent DNAm measure by Zhang et al.[38] was trained to directly predict 373

mortality risk, yet it appears to be a weaker predictor than both our DNAm PhenoAge measure 374

and our clinical phenotypic age measure (Additional file 1: Table S9). The first generation of 375

DNAm age estimators only exhibit weak associations with clinical measures of physiological 376

dysregulation [47, 48]. Physiological dysregulation, which is more closely related to our clinical 377

age measure “phenotypic age” than to chronological age, is not only the result of 378

exogenous/endogenous stress factors (such as obesity, infections) but also a result of age related 379

molecular alterations, one example of which are modifications to the epigenome. Over time, 380

dysregulation within organ systems leads to pathogenesis of disease (age-related molecular 381

changes physiological dysregulation morbidity mortality)[49]. However, stochasticity 382


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and variability exist at each of these transitions. Therefore, measures of physiological 383

dysregulation, will be better predictors of transition to the next stage in the aging trajectory (i.e. 384

morbidity and mortality) than will measures of age related molecular alterations, like DNAm 385

PhenoAge. Similarly, quantification of disease pathogenesis (cancer stage, Alzheimer’s stage) is 386

likely a better predictor of mortality risk than clinical phenotypic aging measures. As a result, 387

clinical phenotypic aging measures may be preferable to epigenetic measures when the goal is risk 388

prediction, and all samples come from blood. 389

That being said, when the aim is to study the mechanisms of the aging process, DNAm measures 390

have advantages over clinical measures. First, they may better capture “pre-clinical aging” and 391

thus may be more suited for differentiating aging in children, young adults, or extremely healthy 392

individuals, for whom measures like CRP, albumin, creatinine, glucose, etc. are still fairly 393

homogenous. Second, as demonstrated, these molecular measures can capture cell and/or tissue 394

specific aging rates and therefore may also lend themselves to in vitro studies of aging, studies for 395

which blood is not available, studies using postmortem samples, and/or studies comparing aging 396

rates between tissues/cells. While the fundamental drivers of aging are believed to be shared across 397

cells/tissues, that is not to say that all the cells and tissues within an individual will age at the same 398

rate. In fact, it is more likely that individuals will vary in their patterning of aging rates across 399

tissues, and that this will have implications for death and disease risk. Relatedly, it is not known 400

how predictions based on DNAm PhenoAge measures from non-blood samples will compare to 401

phenotypic age predictions. It may be the case that various outcomes will be more tightly related 402

to aging in specific cells/tissues, rather than blood. Finally, examination of DNAm based aging 403

rates facilitates the direct study of the proposed mechanisms of aging, of which “epigenetic 404

alterations” is one of the seven hypothesized “pillars of aging” [1]. 405


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While more work needs to be done to model the biology linking DNAm PhenoAge and aging 406

outcomes, we began to explore this using differential expression, functional enrichment, 407

heritability estimates, and network analysis. Overall, we found that CpGs that had larger increases 408

with aging tended to be located in CpG islands and enriched with polycomb group protein targets, 409

consistent with what has been reported in previous epigenome wide studies of aging effects [4, 6, 410

7, 42, 43]. While typically DNAm of CpG islands and/or polycomb recruitment is linked to 411

transcriptional silencing [50], for the most part, we did not observe associations between DNAm 412

and expression for co-locating CpG-gene pairs—this was also true when only considering CpGs 413

located in islands. These findings may suggest that the genes annotated to the CpGs in our score 414

are not part of the link between changes in DNAm and aging. Nevertheless, we also recognize that 415

these null results could stem from the fact that 1) associations were only tested in monocytes, 2) 416

DNAm and expression represents what is present globally for each sample, rather than on a cell-417

by-cell bases, and 3) stronger associations between DNAm and gene expression levels may only 418

exist early in life. 419

Nevertheless, we do identify potentially promising transcriptional pathways when considering 420

DNAm PhenoAge as a whole. For instance, we observe that higher DNAm PhenoAge is associated 421

with increases in the activation of proinflammatory pathways, such as NF-kappaB; increased 422

interferon (IFN) signaling; decreases in ribosomal–related and translational machinery pathways; 423

and decreases in damage recognition and repair pathways. These findings are consistent with 424

previous work describing aging associated changes, comprising increases in dysregulated 425

inflammatory activation, increased DNA damage, and loss of translational fidelity. For instance, 426

there exists a large body of literature highlighting the importance of an increased low-grade pro-427

inflammatory status as a driver of the aging process, termed inflamm-aging [41, 51, 52]. IFN 428


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signaling pathways have been shown to be markers of DNA damage and mediators of cellular 429

senescence[53]. Additionally, it has been shown that breakdown of the transcriptional and 430

translational machinery may play a central role in the aging process[54, 55]. For instance, the 431

ribosome is believed to be a key regulator of proteostasis, and in turn, aging[54, 56]. Relatedly, 432

loss of integrity in DNA damage repair pathways is considered another hallmark of the aging 433

process[57-59]. 434

In general, many of these pathways will have implications for adaptation to exogenous and 435

endogenous stressors. Factors related to stress resistance and response have repeatedly been shown 436

to be drivers of differences in lifespan and aging[60-65]. This may partially account for our 437

findings related to smoking. In general, it is not surprising that a biomarker of aging and mortality 438

risk relates to smoking, given that life expectancies of smokers are on average ten years shorter 439

than never smokers, and smoking history is associated with a drastic increase in the risk of a 440

number of age-related conditions. However, perhaps more interestingly, we find that the effects of 441

DNAm PhenoAge on mortality appear to be higher for smokers than non-smokers, which could 442

suggest that DNAm PhenoAge represent differences in innate resilience/vulnerability to pro-aging 443

stressors, such as cigarette smoke. 444

Interestingly, we observed moderately high heritability estimates for DNAm PhenoAge. For 445

instance, we estimated that genetic differences accounted for one-third to one-half of the variance 446

in DNAm PhenoAge, relative to chronological age. In moving forward, it will be useful to identify 447

the genetic architecture underlying differences in epigenetic aging. Finally, we reported that 448

individuals’ DNAm PhenoAges—relative to their chronological ages—remained fairly stable over 449

a nine-year period. However, it is unclear whether it is attributable to genetic influences, or the 450

fact that social and behavioral characteristics tend to also remain stable for most individuals. 451


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If the goal is to utilize accurate quantifiable measures of the rate of aging, such as DNAm 452

PhenoAge, to assess the efficacy of aging interventions, more work will be needed to evaluate the 453

dynamics of DNAmPhenoAge following various treatments. For instance, it remains to be seen 454

whether interventions can reverse DNAmPhenoAge in the short term. Along these lines, it will be 455

essential to determine causality—does DNAm drive the aging process, or is it simply a surrogate 456

marker of senescence? If the former is true, DNAm PhenoAge could provide insight into promising 457

targets for therapies aimed at lifespan, and more importantly, healthspan extension. 458

Conclusion 459

Overall, DNAm PhenoAge is an attractive composite biomarker that captures organismal age and 460

the functional state of many organ systems and tissues, above and beyond what is explained by 461

chronological time. Our validation studies in multiple large and independent cohorts demonstrate 462

that DNAm PhenoAge is a highly robust predictor of both morbidity and mortality outcomes, and 463

represents a promising biomarker of aging, which may prove to be beneficial to both basic science 464

and translational research. 465

METHODS 466

Using the NHANES training data, we applied a Cox penalized regression model—where the 467

hazard of aging-related mortality (mortality from diseases of the heart, malignant neoplasms, 468

chronic lower respiratory disease, cerebrovascular disease, Alzheimer’s disease, Diabetes mellitus, 469

nephritis, nephrotic syndrome, and nephrosis) was regressed on forty-two clinical markers and 470

chronological age to select variables for inclusion in our phenotypic age score. Ten-fold cross-471

validation was employed to select the parameter value, lambda, for the penalized regression. In 472

order to develop a sparse phenotypic age estimator (the fewest biomarker variables needed to 473

produce robust results) we selected a lambda of 0.0192, which represented a one standard deviation 474


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increase over the lambda with minimum mean-squared error during cross-validation (Additional 475

file 1, Fig. S13). Of the forty-two biomarkers included in the penalized Cox regression model, this 476

resulted in ten variables (including chronological age) that were selected for the phenotypic age 477

predictor. 478

These nine biomarkers and chronological age were then included in a parametric proportional 479

hazards model based on the Gompertz distribution. Based on this model, we estimated the 10-year 480

(120 months) mortality risk of the j-the individual. Next, the mortality score was converted into 481

units of years (Additional file 1). The resulting phenotypic age estimate was regressed on DNA 482

methylation data using an elastic net regression analysis. The penalization parameter was chosen 483

to minimize the cross validated mean square error rate (Additional file 1, Fig. S14), which resulted 484

in 513 CpGs. 485

Estimation of blood cell counts based on DNAm levels 486

We estimate blood cell counts using two different software tools. First, Houseman's estimation 487

method [66] was used to estimate the proportions of CD8+ T cells, CD4+ T, natural killer, B cells, 488

and granulocytes (mainly neutrophils). Second, the Horvath method, implemented in the advanced 489

analysis option of the epigenetic clock software [11, 18], was used to estimate the percentage of 490

exhausted CD8+ T cells (defined as CD28-CD45RA-), the number (count) of naïve CD8+ T cells 491

(defined as CD45RA+CCR7+) and plasmablasts. We and others have shown that the estimated 492

blood cell counts have moderately high correlations with corresponding flow cytometric measures 493

[66, 67]. 494

Additional descriptions of methods and materials can be found in Additional file 1. 495

496


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Competing interests 497

The Regents of the University of California is the sole owner of a provisional patent application 498

directed at this invention for which MEL, SH are named inventors. 499

Ethics approval 500

This study was reviewed by the UCLA institutional review board (IRB#13-000671, IRB#15-501

000697, IRB#16-001841, IRB#15-000682) 502

Author contributions 503

ML and SH developed the DNAmPhenoAge estimator and wrote the article. SH, ML, LF 504

conceived of the study. ML, SH, AL, AQ carried out the statistical analysis. The remaining authors 505

contributed data and participated in the interpretation of the results. 506

ACKNOWLEDGEMENTS 507

We would like to acknowledge The WHI Investigators listed below: 508

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques 509

Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller. 510

Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet 511

Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg. 512

Investigators and Academic Centers: (Brigham and Women's Hospital, Harvard Medical School, 513

Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, 514

Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA). 515

Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University 516

of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) 517


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Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; 518

(University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, 519

Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) 520

Sally Shumaker. 521

The Framingham Heart Study is funded by National Institutes of Health contract N01-HC-25195 522

and HHSN268201500001I. The laboratory work for this investigation was funded by the Division 523

of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health. 524

The analytical component of this project was funded by the Division of Intramural Research, 525

National Heart, Lung, and Blood Institute, and the Center for Information Technology, National 526

Institutes of Health, Bethesda, MD. The Framingham Heart Study is conducted and supported by 527

the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University. 528

This manuscript was not prepared in collaboration with investigators of the Framingham Heart 529

Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, 530

Boston University, or the NHLBI. 531

The United States Department of Veterans Affairs (VA) Normative Aging Study (NAS) is 532

supported by the Cooperative Studies Program/ERIC and is a research component of the 533

Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston 534

Massachusetts. 535

The MESA Epigenomics and Transcriptomics Studies were funded by R01HL101250, R01 536

DK103531-01, R01 DK103531, R01 AG054474, and R01 HL135009-01 to Wake Forest 537

University Health Sciences. 538

We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this 539

work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, 540


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HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National 541

Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health 542

Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General 543

Medical Sciences. 544

545

FUNDING 546

This study was supported by NIH/NIA U34AG051425-01 (Horvath) and NIH/NIA 547

K99AG052604 (Levine). The WHI epigenetic studies were supported by NIH/NHLBI 60442456 548

BAA23 (Assimes, Absher, Horvath) and by the National Institute of Environmental Health 549

Sciences R01-ES020836 WHI-EMPC (Whitsel, Baccarelli, Hou). The WHI program is funded by 550

the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of 551

Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, 552

HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and 553

HHSN271201100004C. The authors thank the WHI investigators and staff for their dedication, 554

and the study participants for making the program possible. A full listing of WHI investigators can 555

be found at: www.whi.org/researchers/ 556

Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf. 557

The InCHIANTI study baseline (1998-2000) was supported as a "targeted project" 558

(ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on 559

Aging (Contracts: 263 MD 9164 and 263 MD 821336). 560

Funding for the DNA methylation in JHS provided by NHLBI R01HL116446. 561


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The funding bodies played no role in the design, the collection, analysis, or interpretation of the 562

data. 563

Availability of data and materials 564

The WHI data are available at dbGaP under the accession numbers phs000200.v10.p3. The FHS 565

data are available at dbGaP under the accession numbers phs000342 and phs000724. The 566

Normative Aging data are available from dbGAP phs000853.v1.p1. The Jackson Heart Study data 567

are available from https://www.jacksonheartstudy.org/Research/Study-Data 568

569

570

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50. Merlo A, Herman JG, Mao L, Lee DJ, Gabrielson E, Burger PC, Baylin SB, Sidransky D: 5’ CpG island 698 methylation is associated with transcriptional silencing of the tumour suppressor 699 p16/CDKN2/MTS1 in human cancers. Nat Med 1995, 1. 700

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53. Moiseeva O, Mallette FA, Mukhopadhyay UK, Moores A, Ferbeyre G: DNA damage signaling and 706 p53-dependent senescence after prolonged beta-interferon stimulation. Mol Biol Cell 2006, 707 17:1583-1592. 708

54. Gonskikh Y, Polacek N: Alterations of the translation apparatus during aging and stress response. 709 Mechanisms of Ageing and Development 2017, 168:30-36. 710

55. Wolters S, Schumacher B: Genome maintenance and transcription integrity in aging and disease. 711 Front Genet 2013, 4:19. 712

56. Steffen KK, Dillin A: A Ribosomal Perspective on Proteostasis and Aging. Cell Metab 2016, 713 23:1004-1012. 714

57. Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, Breit TM, van Steeg H, 715 Mullenders LH, van der Horst GT, Bruning JC, et al: Persistent transcription-blocking DNA lesions 716 trigger somatic growth attenuation associated with longevity. Nat Cell Biol 2009, 11:604-615. 717

58. Schumacher B, Garinis GA, Hoeijmakers JH: Age to survive: DNA damage and aging. Trends Genet 718 2008, 24:77-85. 719

59. McMurray CT, Vijg J: Editorial overview: Molecular and genetic bases of disease: the double life 720 of DNA. Curr Opin Genet Dev 2014, 26:v-vii. 721

60. Finch CE, Morgan TE, Longo VD, de Magalhaes JP: Cell resilience in species life spans: a link to 722 inflammation? Aging Cell 2010, 9:519-526. 723

61. Levine ME, Crimmins EM: A Genetic Network Associated With Stress Resistance, Longevity, and 724 Cancer in Humans. J Gerontol A Biol Sci Med Sci 2016, 71:703-712. 725

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64. Yu BP, Chung HY: Stress resistance by caloric restriction for longevity. Ann N Y Acad Sci 2001, 730 928:39-47. 731

65. Martin GM, Austad SN, Johnson TE: Genetic analysis of ageing: role of oxidative damage and 732 environmental stresses. Nat Genet 1996, 13:25-34. 733

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740

741

742

743

TABLES 744

Table 1: Mortality Validations for Phenotypic Age 745

Mortality Cause Cases HR P-Value

All-Cause 1052 1.09 3.8E-49

Aging-Related 661 1.09 4.5E-34

CVD 272 1.10 5.1E-17

Cancer 265 1.07 7.9E-10

Alzheimer's 30 1.04 2.6E-1

Diabetes 41 1.20 1.9E-11

Lung 53 1.09 6.3E-4

746

747

748

749

750

751

752

753

754

755

756

757

758

759

760

761

762

763

764

765

766


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31

767

768

769

770

Table 2: Morbidity Validation for DNAm PhenoAge 771

Comorbidity Disease Free CHD Risk Physical Functioning

Sample Coefficient P-value Coefficient P-value Coefficient P-value Coefficient P-value

DNAm PhenoAge

WHI BA23 White 0.008 2.38E-01 -0.002 3.82E-01 0.016 5.36E-02 -0.396 1.04E-04

WHI BA23 Black 0.013 6.15E-02 -0.006 2.40E-02 0.021 2.02E-02 -0.423 4.50E-04

WHI BA23 Hispanic 0.024 1.64E-02 -0.004 3.67E-01 0.033 5.07E-02 -0.329 7.37E-02

WHI EMPC White 0.031 2.95E-07 -0.026 1.63E-02 0.023 1.89E-01 -0.361 3.81E-05

WHI EMPC Black 0.014 7.67E-02 -0.023 6.98E-02 0.048 2.27E-02 -0.473 3.75E-04

WHI EMPC Hispanic 0.003 7.83E-01 0.002 9.28E-01 0.073 1.98E-01 -0.377 6.54E-02

FHS 0.022 3.93E-07 -0.034 1.59E-03 0.028 5.47E-06 -0.016 4.60E-01

NAS 0.023 7.59E-06 -0.062 2.00E-04 0.030 2.27E-02 NA NA

JHS 0.018 1.86E-08 -0.039 5.92E-05 0.033 4.73E-02 NA NA

Meta P-value (Stouffer) 1.95E-20 2.14E-10 3.35E-11 2.05E-13

DNAmAge Hannum

WHI BA23 White 0.007 3.90E-01 -0.003 3.48E-01 0.013 2.36E-01 -0.399 2.90E-03

WHI BA23 Black 0.022 2.72E-02 -0.007 6.03E-02 0.015 2.67E-01 -0.345 4.29E-02

WHI BA23 Hispanic 0.010 4.33E-01 -0.010 6.24E-02 0.011 6.10E-01 -0.599 1.16E-02



WHI EMPC Hispanic -0.012 4.17E-01 0.035 2.09E-01 -0.012 8.85E-01 -0.345 2.54E-01

FHS 0.019 5.94E-04 -0.030 2.55E-02 0.022 1.55E-02 0.040 1.32E-01

NAS 0.009 2.19E-01 -0.026 2.26E-01 0.025 1.83E-01 NA NA

JHS 0.020 2.09E-05 -0.036 9.91E-03 0.086 1.64E-04 NA NA


DNAmAge Horvath

WHI BA23 White 0.007 3.49E-01 -0.004 1.69E-01 0.001 9.12E-01 -0.440 5.10E-04

WHI BA23 Black 0.018 3.96E-02 -0.006 6.25E-02 0.009 4.07E-01 -0.305 4.52E-02

WHI BA23 Hispanic 0.012 3.65E-01 -0.007 1.86E-01 -0.001 9.78E-01 -0.204 4.12E-01



WHI EMPC Hispanic -0.025 8.99E-02 -0.016 5.70E-01 -0.064 4.63E-01 -0.012 9.70E-01

FHS 0.011 5.82E-02 -0.021 8.34E-02 0.007 5.19E-01 0.027 3.16E-01

NAS 0.011 7.90E-02 -0.039 4.53E-02 0.006 7.14E-01 NA NA

JHS 0.014 2.03E-03 -0.040 1.78E-03 0.049 3.93E-02 NA NA


772

773

774

775


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32

FIG. LEGENDS 776

Fig. 1. Roadmap for developing DNAm PhenoAge 777

The roadmap depicts our analytical procedures. In step 1, we developed an estimate of ‘Phenotypic 778

Age’ based on clinical measure. Phenotypic age was developed using the NHANES III as training 779

data, in which we employed a proportional hazard penalized regression model to narrow 42 780

biomarkers to 9 biomarkers and chronological age. This measure was then validated in NHANES 781

IV and shown to be a strong predictor of both morbidity and mortality risk. In step 2, we developed 782

an epigenetic biomarker of phenotypic age, which we call DNAm PhenoAge, by regressing 783

phenotypic age (from step 1) on blood DNA methylation data, using the InCHIANTI data. This 784

produced an estimate of DNAm PhenoAge based on 513 CpGs. We then validated our new 785

epigenetic biomarker of aging, DNAm PhenoAge, using multiple cohorts, aging-related outcomes, 786

and tissues/cells. In step 3, we examined the underlying biology of the 513 CpGs and the composite 787

DNAm PhenoAge measure, using a variety of complementary data (gene expression, blood cell 788

counts) and various genome annotation tools including chromatin state analysis and gene ontology 789

enrichment. 790

Fig. 2. Mortality Prediction by DNAm PhenoAge 791

A: Using five samples from large epidemiological cohorts—two samples from the Women’s health 792

Initiative, the Framingham Heart Study, the Normative Aging Study, and the Jackson Heart 793

Study—we tested whether DNAm PhenoAge was predictive of all-cause mortality. The Fig. 794

displays a forest plot for fixed-effect meta-analysis, based on Cox proportional hazard models, and 795

adjusting for chronological age. Results suggest that DNAm PhenoAge is predictive of mortality 796

in all samples, and that overall, a one year increase in DNAm PhenoAge is associated with a 4.5% 797

increase in the risk of death (p=9.9E-47). This is contrasted against the first generation of 798


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33

epigenetic biomarkers of aging by Hannum and Horvath, which exhibit less significant 799

associations with lifespan (p=1.7E-21 and p=4.5E-5, respectively). B & C: Using the WHI sample 800

1, we plotted Kaplan-Meier survival estimates using actual data from the fastest versus the slowest 801

agers (panel B). We also applied the equation from the proportional hazard model to predict 802

remaining life expectancy and plotted predicted survival assuming a chronological age of 50 and 803

a DNAm PhenoAge of either 40 (slow ager), 50 (average ager), or 60 (fast ager) (panel C). Median 804

life expectancy at age 50 was predicted to be approximately 81 years for the fastest agers, 83.5 805

years for average agers, and 86 years for the slowest agers. 806

Fig. 3. Chronological age versus DNAm PhenoAge in a variety of tissues and cells 807

Although DNAm PhenoAge was developed using methylation data from whole blood, it also 808

tracks chronological age in a wide variety of tissues and cells. A) The correlation across all 809

tissues/cells we examined is r=0.71. B-ZJ) report results in different sources of DNA as indicated 810

in panel headings. The numbers correspond to the data sets from (Horvath 2013). Overall, 811

correlations range from r=0.35 (breast, panel O) to r=0.92 (temporal cortex in brain, panel L). 812

Fig. 4. DNAm PhenoAge measured in dorsolateral prefrontal cortex relates to Alzheimer’s 813

disease and related neuropathologies 814

Using postmortem data from the Religious Order Study (ROS) and the Memory and Aging Project 815

(MAP), we find a moderate/high correlation between chronological age and DNAm PhenoAge 816

(panl A), that is further increased after adjusting for the estimated proportion on neurons in each 817

sample (panel C). We also find that DNAm PhenoAge is significantly higher (p=0.00046) among 818

those with Alzheimer’s disease versus controls (panel D), and that it positively correlates with 819

amyloid load (p=0.012, panel E), neuritic plaques (p=0.0032, panel F), diffuse plaques (p=0.036, 820

panel G), and neurofibrillary tangles (p=0.0073, panel H). 821


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All-CauseMortality

CauseSpecificMortality

CoexistingDiseaseCount

PhysicalFunctioning

ValidateAssociationswith:

All-CauseMortality

CoronaryHeartDiseaseRisk

#ofCoexistingDiseases

PhysicalFunctioning

FamilialLongevity

Dementia

DownSyndrome

Parkinson'sDisease

DiseaseFreeStatus

AgeatMenopause

HIVpositive

Chronological agein35tissues/cells

GOEnrichment

PathwayEnrichment

LTLCorrelation

DifferentialExpression

Cancer(Lung, Breast) Neuropathology (BrainDNAm)

CpG IslandEnrichment

HeritabilityAnalysis

DNAm NetworkAnalysis

ImmuneCellAssociations

CpG OverlapwithHannum/Horvath

Loci-SpecificDNAm vs.Transcription

Testfor:

MortalityAges20-64

MortalityAges65-79

MortalityAges85+

Polycomb GroupProteinTargets

ChromosomalLocations

ChangeinDNAm PhenoAge overTime

TranscriptionalAnalysisinMonocytes

All-CauseMortality

CauseSpecificMortality


PhysicalFunctioning

ValidateAssociationswith:

All-CauseMortality

CoronaryHeartDiseaseRisk


PhysicalFunctioning

ValidateAssociationswith:FamilialLongevity

Dementia

DownSyndrome

Parkinson'sDisease

DiseaseFreeStatus

AgeatMenopause

HIVpositive

Chronological agein35tissues/cells

GOEnrichment

PathwayEnrichment

LTLCorrelation

DifferentialExpression

Cancer(Lung, Breast) Neuropathology (BrainDNAm)

SocioeconomicStatus

Race/ethnicity

Diet

Physicalactivity

MetabolicSyndrome

Smoking Status

Obesity(LiverDNAm)

CpG IslandEnrichment

HeritabilityAnalysis

DNAm NetworkAnalysis

ImmuneCellAssociations

CpG OverlapwithHannum/Horvath

Loci-SpecificDNAm vs.Transcription

Testfor:

MortalityAges20-64

MortalityAges65-79

MortalityAges85+

Polycomb GroupProteinTargets

ChromosomalLocations

ChangeinDNAm PhenoAge overTime

TranscriptionalAnalysisinMonocytes

Train a composite epigenetic predictor of phenotypic age,

called “DNAmPhenoAge”.

Based on DNAm at 513 CpGs.

Identify underlying biology of the 513 CpGs in the DNAmPhenoAge Score

Develop a multi-system estimate of

“Phenotypic Age”. Predictor of aging-related mortality based on clinical

measures.


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0 20 40 60 80 100

−50

100

A All Tissues cor=0.71, p<1e−200

Age

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43434343 434343434343 43434343 434343 43434343 4343 43434343 4343 4343 43434343 4343 434343 434343 434343 43434343 434343 43

4343

4343 434343 434343 4343 43434343434343 434343434343 43434343 4343434343 43 434343 434343 434343 4343 4343 4343 4343 434343 43 4343 434343 4343 434343434343434343 43434343 434343 43

4343 4343 4343434343 43 43434343 4343

4343 434343 434343 434343 4343 4343 434343 434343434343

4343 43434343 4343 43 4343444444 4444 44 4444444444 444444 4444444444444444444444 44 44444444 444444

444444 444444 44444444 4444 4444

4444 444444 444444 444444 4444444444444444 44444444 44 444444

444444 44444444444444 444444 444444444444444444 44

4444 44444444 444444444444 4444 4444 444444444444 4444444444 4444 4444

444444 4444

44 44444444

444444

44 444444444444444444 44444444 44 44444444 4444 44 444444 44444444 444444

44 44444444 4444444444 4444

44 44 4444 444444 444444 444444 44444444 44444444 444444 44

4444

444444444444 44 444444 44 4444 4444 444444 44 44444444

4444 4444 44444444444444 44 444444 44 44

44 444444444444

44 44 4444444444444444444444

4444444444

444444 444444

4444 44

4444 4444

44444444 4444

4444 4444 444444444444 44 4444

44 4444 4444444444 4444 4444 4444 444444 44 4444 44444444

44 4444 44444444 4444 444444 4444 44444444 444444 444444 444444 44 4444

444444444444 4444 4444 44 444444 44444444

4444 444444 444444

44 444444 444444 444444

4444 44 44

444444 4444

444444

4444 44 44444444 4444 4444

4444

44 44444444 44 4444

44 44444444 44 44444444 4444 4444444444 444444 444444 4444

44444444

444444 444444 4444

4444

4444

444444 44444444

44444444 4444

444444

44 44 4444 444444444444

4444 44444444 4444 4444 4444 44444444 44

4444

4444

4444 44444444444444

4444 44 44

444444

4444 4444 4444 44444444 444444

444444 4444 44 444444 44444444 444444

4444 4444

444444 4444 4444 44444444

44 444444 444444 44444444 4444 4444

4444444444 4444 444444 4444 444444 4444

4444 44 4444 444444

4444 4444 4444 4444

4444

444444

4444444444 44444444

44444444444444 444444444444

4444 44 4444444444

44 444444 44 444444 4444

4444444444444444

444444444444 4444

45 4545 454545 454545 454545

4545454545454545 454545454545454545454545454545454545454545454545454545454545

4545454545454545454545454545 4545454545454545454545454545454545454545454545 45454545454545454545

4545454545 4545 4545 4545

45454545 454545 4545 454545

45

4545 454545454545454545454545454545

45454545

4545454545454545

45454545454545 454545 45454545 45454545454545454545454545454545454545

454545454545454545 4545454545

45454545 45454545 45454545454545 4545 454545454545454545454545454545 454545454545454545 45454545454545454545454545454545 45454545454545 4545454545454545454545

4545 4545454545 45454545454545454545454545 454545454545 454545

454545 454545454545454545 4545454545454545

454545 454545454545 4545454545454545 45454545

45454545

454545454545454545454545 454545454545 45454545454545 454545

454545454545454545454545

46464646464646464646464646

464646464646

46464646464646464646464646464646464646

4747 47474747 47474747 47474747 47 47474747474747474747474747 474747 47474747 474747 47474747474747 4747474747474747474747 474747 474747

474747 4747474747 4747474747474747 4747 4747474747 47474747474747484848484848484848484848484848484848

4848

4848484848484848484848484848484848484848484848484848484849

494949494949494949494949

49

49494949494949494949

49

49494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949495050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151

52 525252 525252

5252525252 52

52525252 525252525252 52

5252

525252

52 52 525252 525252 5252 5252 525252525252 5252

525353 5353

5353

53535353

5353

535353 53

54 54 545454

54545454 5454 5454

54545454545454 555555 5555 5555

555555 5555

55555555

5656565656 5656 56 5656 5656

56 565656 5656565656 5656

5656

5656

56 56 5656 5656 56 56

565656

5656 56565656 565656565656565656 5656 565656 56 565656 5656 565656

56

5656 5656 565656 5656 565656

56575757 57575757 57 5757 57 57575757

57575757 57575757 5757

5757585858585858585858585858585858585858

58585858585858585858585858585858585858585858

5858585858585858585858

595959595959

5959

5959 5959 5959595959 5959 595959

5959595959

5959

59595959 59595959 59

60606060606060606060

61 616161616162 62626262 6262

6262626262626262 6262 6262 62 626262 6262 6262 6262 6262 62 6262 6262 626262

6262626262 62626262626262 62 6262 6262626262 62626262 626262 6262626262 62 62 6262 6262 6262 626262 626262 626262 626262 62 6262 626262 62626262626262 6262 6262 62 6262626262 62626262 626262 626262 62

6262 62626262 6262626262 62 62626262 62 626262 626262 626262

62626262 6262626262 62626262

6262 6262626262 6262 6262 6262 62626262

626262

6262 6262 626262 62 626262 6262 626363 6363636363 636363 636363 63 63 6363 63 636363 63

63

6363

63 6363 63636363 63 636363 636464 6464 646464 6464646464 6464 646464 6464 6464 6464

64 64 64656565656565656565656565 656565656565656565656666666666666666666666666666666666666666666666666666666666666666666666666666666666

66666667676767676767676767676767676767676767676767676767676767676767676767676767676767686868 68686868 68

6868 6868 6868 68 68686868

68 6868686868 68

686868 6868 6868686868 686868

6868686868

6868

6868 68686868696969 696969 6969

6969696969 696969696969 6969696969 69696969 69 6969

69696969696969 696969 69 6969

6969696969 69696969696969 6969 696969696969 69696969 696969696969 6969 69

6969 6969 69696969696969696969 696969696969 69 6969 696969 69696969 6969 696969696969 69 6969 696969 6969696969 696969696969 69 696969 696969696969696969 69696969696969 70

7070 70707070 707070

70 7070 7070707070

707070 7070 70707070

707171

717171

717171

7171

717171 717171

717171

71 7171 7171717171

A

0.0 0.2 0.4 0.6 0.8 1.0

−70

−50

Blood CD4 Tcells cor=0.62, p=2.6e−06

Age

DN

Am

Phe

noA

ge

51

5151

51515151

5151

5151

51

5151

51

5151

515151

51 51

51

5151

5151

51515151

5151515151

515151

515151

51

5151

5151

51

B

20 30 40 50 60 70

−20

2060

Blood CD4+CD14 cor=0.6, p=4.1e−06

Age

DN

Am

Phe

noA

ge

525252

525252

52

52 5252

5252

52

52

525252

525252525252 52

52

52

52

5252

52 525252

5252 5252

525252

525252

52525252 52

52

52

C

20 30 40 50 60

2060

Blood Cell Types cor=0.29, p=0.28

Age

DN

Am

Phe

noA

ge

535353

53

53

53

53

535353

53

53

53

53 5353

D

0 20 40 60 80 100

−50

50

Blood PBMC cor=0.9, p<1e−200

Age

DN

Am

Phe

noA

ge

44

44

44444

4

444

44

4444444

44444

4444

4444444444

44444

4444

444

44

4444

444444

444444

4

555555

5555

5555

5 55555 55555 555

555555

55 55

555

55

555

4545

45454545 4545

45 4545454545454545454545 454545454545454545454545

45454545

4545454545454545454545454545

45454545454545454545

454545

45 45454545

4545454545 4545454545

45454545454545454545

4545454545 45454545

45454545

4545

45 4545 454545

45454545

45454545 454545

45

4545 4545454545454545

454545454545

4545

454545

4545454545454545

454545

45454545 454545 45454545 454545454545454545454545454545454545

4545454545454545

4545 4545454545

45454545

45454545454545454545

4545

45 4545454545

45454545454545454545 454545454545454545 45454545454545454545454545

45454545454545

454545 4545454545454545454545

4545 4545454545 45454545454545454545454545 454545454545 4545

4545

4545 4545454545 45454545 4545454545454545

454545 454545454545 4545454545454545

45454545

454545

4545

4545454545454545454545

45454545

4545 45454545454545 45454545

4545454545454545454545

46464646464646464646464646

464646464646

46464646464646464646464646464646464646

4747 47474747 47474747 47474747 47 474747474747474747474747

47 474747 47474747474747 47474747474747 4747474747474747474747

474747 474747

474747 4747474747 4747474747474747

4747 47474747

47 47474747474747

E

0 20 40 60 80 100

−50

50

Blood WB cor=0.92, p<1e−200

Age

DN

Am

Phe

noA

ge

11

11

11 11 1111 11 11 1111 11 11 11 1 11 111

1111

1 1 1 111

11111 11 11 1 11 1 11

111 111 1111 11 11 1

1111

11 11 1

111

1 1111111

11

1 11 11 11

1 11 111111 11

111

11

11 111

11

1

111111

1111

1 111 1 1

1 1 1111

1 11

11 111 1

11

11 111 11111 11 11 11 1

11

11 11111 1 11 11

11 1 1

11

11 11 11 11111 111

1 1111

111 1 11 11 11 1

111

1 11 1 11 111 11

111

111

111111

11 111 11 111

111

1 1 111 11 1

1 11

11 11 111 11 1111

111

1

111

1

1 11 11 111111

11 1 1 1 11

111

1111 11111

111

11 111 1111 11

1111 11 11 1 11 11 1111111 111

111 11 111 111

1111111 111 11

111 1 111 1

1111

1 1111

11 1 1111 11111

111 111 1111

11 111 1 11

1 1111 111 111 1 11111 11

1 11 1

11

1 1 11111

111

111 1 111 11

111

1111 1 11

11

11111

11 11

11 11 111 11 1 11 111111

1111111 1111111 1

1 1 1111111 11

1 11111

111 1111

1 11 11 1111

11 11 111 11 11

111 11 1111111 111111

1 111 11111 111 1111 111111 11 1

11111 111 1

111 1 1

11 11 1 11 1 111 111 11

1111 1 1111 11 1

11

11 1111 111 1 111 1 11 111 111 11 111 1111 111

1 11 111 11111 11 1

11 111

2222 222 2222 222

2 22 22 22 22 2222 2222 2222

222 22222 2 2

222 2 22 22 222

2222

22

22

222 222

222 2 222222 2222 2222 2

222

22

33333 3333 33

33

3 3 33 33

3 33 33333333 33 33 33

33 333333 3333 33 33 33 3 33 33 3 3 33

333

3333 3 33 33 33 333 333 33

3 33 333

333 33 33 333 33

3 3 3 3333 3 3333 33 33 333

33 33 333

333 3 333 3 3

3 3 333333 33

33 33 33 333

3 3 333 3333 3333 33 3

3 33 33 33333 33 333 33 333

333 333 333333 3 333 33

3 333 33 333 33 3

33 33 3 333 3 33333

33333 333 3 333 3333 33 33 33 3 33 333 33 33 3

3 3 3333 3 33 333 3 33 3 333 3 3333 3333 33 3 333 3

3 3333 33 33 3333 333

333 3 333 3333 3

3 3333333 33 33333 33333

333 3333 333 33 3333333 3 33 3 3333 3333 33 3333 3 333 33 33 3333 33

33 3333 333 333 3333 3333 3 33333 3 33 3 333 333 33 3 333 33 333 3333 33 3

3 333333 333 3 33

33

33 33 33 3

33 33

333 333 3 33 3 3 33 333

3 333

3 333 3

333 33

33 3 33

3 3 33 333 333 33 33

3 33333 3 3 333 3

333 3333

33 3 3

3 3 33 3 33 333 33 3

3 33

33 3333 33 3333 33 3333 333 33 33 33 33333 33 333 33 3 33 3

3333 33 3 3 33 3 333

3 333 33333 333

3 3 3333 3 33 33333 3

666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666

4040 4040 404040 404040404040 404040404040 404040 40 4040

404040 404040404040 4040

404040 40404040 40404040 40404040 404040 4040 40404040 4040 40404040

404040404040

4040 4040 40 40

404040404040 4040 404040 404040 404040

4040 4040404040 404040 40404040

40 4040 4040 40404040 404040 40404040 4040

4040404040 4040404040

40404040 4040 4040

404040

40404040 404040 4040

4040 40

4040404040 4040 404040 40 4040

4040 404040 4040

40404040 404040 40 4040 4041414141 41 4141 4141 4141 4141 4141 4141 4141414141 4141414141 41 4141 414141 41

414141414141 414141414141 414141 4141414141 41414141414141 4141 4141 41414141 4141 4141

414141

41414141 41414141414141 4141414141 4142 424242

4242 42 42 42

42 42424242

42 42 42424242 424242

4242

4242 42424242

42424242 42424242

42 4242 42424242 4242 4242 42 42

424242424242 42

4242424242 424242 424242 4242424242 42424242

4242 42 4242 424242 424242

42 4242

42 424242 424242424242 4242 42 42

42 424242

42424242 424242 42424242 4242 424242 4242424242 42424242 424242424242 42

42 4242 4242 42 4242424242

424242424242 424242

4242 42424242 4242

42424242

4242 42

4242 4242424242424242 4242424242 4242 42 42 424242424242 424242 4242 4242424242 42424242 4242

42

4242 42 424242 4242 42424242 4242 4242

4242 42424242424242 4242 42

4242

424242 42424242

4242 4242 4243 43

434343 434343 434343 4343434343

434343434343 4343 4343 4343 434343 434343 4343 434343434343434343 4343434343

4343 43434343 43 43

4343

43434343434343434343 434343 4343 4343 434343 4343 434343434343

4343434343434343

4343 434343 43434343 4343 43434343 4343 4343 434343434343 434343 434343 4343

43 43434343 43434343

4343

4343 434343 434343 4343 43434343434343

434343434343 43434343 4343434343 43 43

4343 434343 434343 4343 4343 4343 4343 434343 43 4343 434343 4343 434343434343434343 43434343 434343

434343 4343 4343434343 43 434343

43 434343

43 434343 434343 434343 4343 4343 434343 4343434343434343 43434343 43

43 43 4343444444 4444 44 4444444444

444444 4444444444444444444444 44 44444444 44444444

444444

4444 44444444 4444 44

444444 444444 444444 444444 4444444444444444 4444

4444 44 4444

44444444 44

444444444444 444444

444444444444444444 44

4444 44444444 444444444444 4444 4444 444444444444 4444444444 4444 4444

444444 4444

44 44444444

444444

44 4444

44444444444444 44444444 44 44444444 4444 44 444444 44444444 4444

4444 44

444444 4444444444 444444 44 4444 444444

444444 444444 44444444 44444444 444444 44

4444

4444444444

44 44444444 44 4444 44

44444444 44 44444444

4444 4444 44444444444444 44

444444 44 4444 4444444444

4444

44 4444444444444444444444

4444444444

444444 444444

4444 44

4444 44

44444444

4444

4444

44 4444 444444444444 44 4444

44 4444 4444444444 4444 4444 4444 444444 444444 4444

444444 4444 4444

4444 4444444444 4444 44444444 444444

444444 444444 44 4444

444444444444

4444

4444 44 444444 44444444

4444 444444 4444

4444 444444 444444

444444

4444 44 44

444444 4444

444444

4444 44 44444444 44

44 444444

4444 44

444444 44 4444

44 44444444 44 44444444 4444 4444444444 444444 444444 44

44444444

444444

44444444 4444

4444

4444

444444 44444444

4444444444

444444

4444 44 44

4444444444

4444

4444 44444444 4444 4444 4444 44444444 44

4444

4444

4444 44444444444444

4444 44 44

444444

44444444

4444 44444444

4444

44444444 4444 44 444444 44444444 44

44444444 44

4444

4444 4444 4444 4444444444 444444 444444 44444444 44

44 44444444444444 4444 4444

444444 444444 4444

4444 44 4444 4444

4444

44 4444 4444 444444

4444

44444444444444 4444

444444444444

444444 444444444444

4444 44 4444444444

44 444444 44 4444

44 44444444

444444444444444444444444

4444

484848484848484848484848484848484848

4848

4848484848484848484848484848484848484848484848484848484849

494949494949494949494949

49

49494949494949494949

49

49494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949495050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050505050

F

0 10 20 30 40 50 60

−40

−10

Brain Cerebellar cor=0.82, p=9.6e−06

Age

DN

Am

Phe

noA

ge

5454

54

54

54

5454

5454

545454

54

5454

5454

545454

G

20 40 60 80

−30

020

Brain CRBLM cor=0.54, p=9.6e−23

AgeD

NA

mP

heno

Age

77777

7

777 7

77

77 7

7777 77

7

77

77 7

77 7

77

7

7

777777 7

7 777

777

777 77

77 7 77777

77 77

7777

777

7 77 7777

77 777 77

7 777

7777 77 7

7777 777 777 7

7

77 7

77

7 77

777

77 77

7 77

77

77 7

7

777 7 777

7

7

777

777

77 777

77

77

7777

77

7

7

7778

8 8

8

8

888

88

88

8 888

8

8888 88

8

88

88 8

8

888

888

88

88 88

8

8

888 88

8 88

8 88 8

888

88 88

8

8

8 88 88

88 888 8 8

8

8 888

888 88

8

8888

888 8

888

88 88

8

88

88 88

8 8

8 8

H

0 10 20 30 40 50 60

−50

−30

Brain Occipital Cortex cor=0.86, p=1.9e−05

Age

DN

Am

Phe

noA

ge

555555 55

55

5555

55

5555

55

55

55

55

55

55

I

20 40 60 80 100

−40

0

Brain PONS cor=0.88, p=1.4e−41

Age

DN

Am

Phe

noA

ge

10

1010

1010

10

101010

10 101010

101010

10

1010

1010 1010 10 10

10101010 10

10 10

10

10

1010

10 101010

101010

10 101010

101010

10 1010 10 10

10

1010

101010 1010

10

10

10

10

101010

10

1010

10

10

1010

1010

10

1010

10

1010

101010 10

10 1010

1010

1010

1010

1010 1010

10

10

101010

10

10

10

101010

1010 10

101010

1010

10

101010

J

0 20 40 60 80

−60

0

Brain Prefr.CTX cor=0.83, p=1.2e−28

Age

DN

Am

Phe

noA

ge

1111

11

1111111111

11

1111

11

11

11

11111111

1111

11111111

1111 1111

11

11 1111 1111 11 1111 1111 11

111111

11

11

1111

1111

11 1111

11

1111

1111

1111111111111111111111111111111111111111111111111111111111

111111

1111

111111

11

11

11 1111 1111

11

111111

11

1111

K

20 40 60 80 100

−40

0

Brain TCTX cor=0.92, p=1.1e−52

Age

DN

Am

Phe

noA

ge

13

13

13

13

13

13

131313

1313 131313

1313 1313

13 131313

13 13131313

131313

1313

13

1313

13

131313

1313 1313

13

13

1313

13

13

1313

13

131313 1313

13 13

1313

13

13 13131313

13131313

131313

131313131313

13

13

1313

13

13131313

1313

13

13

13

1313

13

13

131313

13131313

13

131313 13

131313

1313

13 131313

13

13

131313

13

13

13

L

20 30 40 50 60 70 80

−40

−10

Brain Cell Types err=55 cor=0.48, p=1e−09

Age

DN

Am

Phe

noA

ge

M

G

NNNN

GG G

N

GGNN

NN

N

G

N

G NG

GGG

N

GG

NN

NG

N

GGN

G G

N

G

N

G

N NG

NN

G G

NN NN

GG

GG

NN

GG

N

G

NNG

B

NB

G

GG

NG N

GG

NNGN

G

N

G

N

N

G

NNN

GGG

G

N

G GG

N

N

G NN

N

GGN

NNN

GGG

N

GNN

GG

MMM

MM

MMMMBB

BBBB

BB

BBB

BB

BBB

BB

20 30 40 50 60 70

030

Breast NL cor=0.47, p=0.024

Age

DN

Am

Phe

noA

ge

14

1414

14

14

1414

1414

141414 14

1414

1414141414

14 1414

N

30 40 50 60 70 80 90

2060

Breast NL Adj cor=0.35, p=2e−04

Age

DN

Am

Phe

noA

ge

56

56565656

5656 565656

56

56

56565656

56

5656

56565656

56

56

56

56

5656

5656

56

56

56 56

56

5656

5656

5656565656

5656565656

565656

5656

565656 56

565656 56

5656

5656

56

56

565656 565656 5656 56

5656

565757 57 57

5757

57

57 5757 57

5757 5757

57

575757

5757

5757 57

57

5757

O

0 10 20 30 40 50 60

030

60

Buccal cor=0.88, p=9.5e−73

Age

DN

Am

Phe

noA

ge

151515151515151515

15151515

1515151515

151515

15

151515151515151515151515151515151515151515151515151515151515151515

15

15

1515151515151515151515151515151515151515151515151515151515151515151515151515

1515151515151515151515151515

1616

161616

1616

16

17171717171717

17

17

1717

17

1717171717

17

171717171717

1717

17

1717

17

1717

17

17171717171717

17

17

171717

1717

171717

171717

58585858585858

58

58

5858

58

5858585858

58

585858585858

5858

58

5858

5858

58

585858585858

58

58

585858

5858

585858

585858

P

40 50 60 70 80

−20

0

Cartilage Knee cor=0.48, p=0.0015

Age

DN

Am

Phe

noA

ge

18

18

1818

1818

18

18

1818

1818

18

18 18

1818 18

18

18

18181818

18

181818

1818

18

181818

1818

18

181818

18

Q

20 40 60 80

040

100

Colon cor=0.88, p=1.8e−32

Age

DN

Am

Phe

noA

ge

19

19

19 1919

1919 19

19

19 1919191919

19 19

19

19

1919

1919 191919

19

1919

1919 1919 1919

20 2020202020

20202020

202020

20 202020

2020202020

2020

595959595959

59

595959

5959

595959

5959

5959 595959

59595959

59

59

59

59595959 59595959 59

R

10 20 30 40 50 60 70

−20

10

Dermal fibroblast cor=0.87, p=5.2e−05

Age

DN

Am

Phe

noA

ge

21 21

21

21

21

21

21

21

21

21

21

21

2121

S

30 40 50 60 70

3050

70

Epidermis cor=0.84, p=0.0024

Age

DN

Am

Phe

noA

ge

2222 22

2222

22

22

22

22

22

T

73 74 75 76 77 78

1030

Fat Adip cor=0.4, p=0.25

AgeD

NA

mP

heno

Age

6060

6060

60

60

60

60 6060

U

30 40 50 60 70 80 90

060

140

Gastric cor=0.42, p=0.0022

Age

DN

Am

Phe

noA

ge

2323

2323

23 23 23232323

23

23

2323

232323 23232323

23 23

23

232323

2323 23

23

2323 23

23

23 232323

2323232323

2323

23

23

2323

23

V

30 40 50 60 70 80

−20

40

Head+Neck cor=0.36, p=0.01

Age

DN

Am

Phe

noA

ge

24242424

24242424 24

2424 24242424

2424

24

24

24

2424

2424

24

242424

24

24

24

24

24 242424

2424 24

24

24

24242424242424

24

24

W

50 55 60 65 70

−15

015

Heart cor=0.66, p=0.0074

Age

DN

Am

Phe

noA

ge

2525

25

2525

2525

2525

616161 61

6161

X

30 40 50 60 70 80 90

030

60

Kidney cor=0.64, p=1.4e−47

Age

DN

Am

Phe

noA

ge

2626

2626

26

262626 26

2626

2626

26

2626

262626

2626 26

2626

2626

2626

2626 26

2626

26

26

26

2626

26

2626

2626

2727

27272727

27272727 27 27

2727

2727

2727

2727 2727

27272727 27

272727 27

27

272727

272727272727 27

27272727

272727 272727

27

2727

2727 272727 2727 27 27

2727

272727 272727

27

27 2727

27

2727 2727272727

27

27 2727

27

2727

27

27 272727

2727

2727

2727

272727

272727 2727

272727

2727

2727 2727

2727

2727 2727 2727 272727 27 27

27

2727 2727

272727

2727

2727

272727

2727

272727

2727

27

27

2727 2727

62 62626262 6262

6262626262

62

6262

62

62

6262

62 626262 6262 62

6262

62 62

6262 6262

6262 6262

62

62

6262

6262 62

62626262

6262 62

6262 626262

6262

6262

6262 626262 6262

6262

6262

6262

626262 6262 6262

626262

62

62

6262 626262

6262

62

62

6262 62

62626262

62

62

6262 62

6262

62626262

6262

62

626262

62

62

626262 62

62

62

62

626262 62

62

6262

62 62 62 62

6262 62

6262 62

6262

6262

62

62

6262

62

6262

62626262

62626262

62

6262

62626262 6262

6262

6262

62 6262

62

62

6262

6262 62

62 626262

62 626262 6262

62

Y

20 30 40 50 60 70 80

−10

30

Liver cor=0.8, p=4e−22

Age

DN

Am

Phe

noA

ge

2828 282828

28

28

2828

28

2828

2828

28

2828 282828 28

28 28

2828

28

2828

2828

28

28 2828

28282828 2828

28

2828

28

2828

28

2828282828 28 28

2828

28 6363 63

63636363

636363

63

6363 6363 63

6363

636363

63

63

63

63

6363

63 636363

63

63 6363

63

63

Z

40 50 60 70 80

1040

Lung NL Adj cor=0.55, p=1.3e−10

Age

DN

Am

Phe

noA

ge

2929 2929 292929 29 29

2929

2929 29 2929292929

292929 2929

29292930 30

3030 30 30

30

30

303030

30303030

30

30

30303030

3030 30

31

31313131

31 3131

3131 31

3131

31

3131313131

3131

31

3131

3131 31

3131

3131 31

31 31 3131 31313131

6464 64

646464

6464

6464

646464

64646464 64

6464

646464

64

6464

ZA

20 30 40 50 60 70 80

020

50

MSC (bonemarrow) cor=0.93, p=1.8e−07

Age

DN

Am

Phe

noA

ge

3232

32323232 3232

3232

3232

32

32 32

32

ZB

30 40 50 60 70 80

−10

10

Muscle cor=0.52, p=7.6e−06

Age

DN

Am

Phe

noA

ge

65656565

6565

6565

6565

6565

6565

656565

656565

6565

666666666666666666

66666666666666666666666666666666

66

66

6666666666666666666666

666666

66

6666

ZC

45 50 55 60 65 70

1040

70

Prostate NL cor=0.36, p=9e−05

Age

DN

Am

Phe

noA

ge

343434

34343434

3434 34

34

34 3434 3434 34

34

343434

343434 34

343434

3434

34

3434 343434

3434

3434

34

3434

3434

3434

34 34 34 3434 3434 34

34

3434

34 343434

34

34

3434

34

34

343535

35

353535

353535

3535 3535

35

35

35

3535 3535 353535

35

35 35353535

3535

353535

35 3535

35

35

3535 35

3535

ZD

20 25 30 35 40 45 50 55

1040

Saliva cor=0.81, p=7e−60

Age

DN

Am

Phe

noA

ge

3636

3636 36

36

36

36 363636

36 3636

3636

36

36

36

36 3636

363636

363636

36

3636 36

36

36

36 36 36363636

36

36

36

36363636

36

3636

36

36

36

363636 36

36

36

3636

3636

3636

363636

3636 36

36

36363636

3636 3636

36

36

36

36

36

36

3636

3636

36

363636

36 36 3636

3636

36

36

363636

36 3636

363636

36

36

3636

36

36 3636

3636

36

36

3636 36

363636

3636 3737

3737373737

3737

37373737

37373737373737

3737 3737

37373737

37373737

3737

373737373737

373737

37373737

373737

37

3737

3737

3737 37373737

373737373737

3737

6868

68 68

6868

6868

6868 686868

68

686868

6868

68

6868

6868

68 68

6868

686868

6868

6868 68

68

6868

6868

68

6868

68

68

6868 68

686868

ZE

50 60 70 80

060

Stomach cor=0.37, p=0.017

Age

DN

Am

Phe

noA

ge

3838

38

38

38383838

38 3838

38

3838

383838

38

38

3838

38 3838

3838 3838

3838 38

38

38 38

38383838

3838

38

ZF

20 30 40 50 60 70

−30

020

Thyroid cor=0.49, p=0.013

Age

DN

Am

Phe

noA

ge

3939

39

39

39

39

3939

393939

39

39393939

3939

393939

39

39

39 39

ZG

20 25 30 35 40 45 50 55

1030

50

Uterine Cervix cor=0.58, p=4.9e−15

AgeD

NA

mP

heno

Age

69

6969

6969

69

69

69

69

6969

6969 69

69

69696969 69

69696969 69696969

69 6969

69

696969

69

69

69

696969

6969

69

696969

69

6969

6969

69

6969

69 696969

69

69

696969

69696969 6969

69

6969

69 69

69

69

6969 6969

6969

69

69

6969 69696969 6969 696969

69 696969 69

6969 69 696969

69

6969

69

6969

69

6969

69

69 6969

69

696969

696969

696969696969 6969

6969

6969

696969 696969

69696969

6969

69

ZH

40 50 60 70 80 90

030

60

Uterine Endomet cor=0.39, p=0.04

Age

DN

Am

Phe

noA

ge

70

707070 707070 7070

70

7070

70

7070

707070

70

7070

70

70

7070

7070

70

ZI

0 20 40 60 80

−40

20

Various Tissues cor=0.71, p=3.4e−05

Age

DN

Am

Phe

noA

ge

7171

71

7171

717171

7171

71

7171

717171

717171

717171 7171

7171

71

ZJ


https://doi.org/10.1101/276162



https://doi.org/10.1101/276162
