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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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www.agingus.com  AGING2018,Vol.10,No.4
Research Paper
Anepigeneticbiomarkerofagingforlifespanandhealthspan
MorganE.Levine1,AkeT.Lu1,AustinQuach1,BrianH.Chen2,ThemistoclesL.Assimes3,Stefania
Bandinelli4,LifangHou5,AndreaA.Baccarelli6,JamesD.Stewart7,YunLi8,EricA.Whitsel7,9,James
GWilson10,AlexPReiner11,AbrahamAviv12,KurtLohman13,YongmeiLiu14,LuigiFerrucci2,*,Steve
Horvath1,15,*
1DepartmentofHumanGenetics,DavidGeffenSchoolofMedicine,UniversityofCaliforniaLosAngeles,Los
Angeles,CA90095,USA
2LongitudinalStudiesSection,TranslationalGerontologyBranch,NationalInstituteonAging,NationalInstitutesof
Health,USA.Baltimore,MD21224,USA
3DepartmentofMedicine,StanfordUniversitySchoolofMedicine,Stanford,CA94305,USA
4GeriatricUnit,AziendaToscanaCentro,Florence,Italy
5CenterforPopulationEpigenetics,RobertH.LurieComprehensiveCancerCenterandDepartmentofPreventive
Medicine,NorthwesternUniversityFeinbergSchoolofMedicine,Chicago,IL60611,USA
6LaboratoryofEnvironmentalEpigenetics,DepartmentsofEnvironmentalHealthSciencesandEpidemiology,
ColumbiaUniversityMailmanSchoolofPublicHealth,NewYork,NY10032,USA
7DepartmentofEpidemiology,GillingsSchoolofGlobalPublicHealth,UniversityofNorthCarolina,ChapelHill,NC
27599,USA
8DepartmentofGenetics,DepartmentofBiostatistics,DepartmentofComputerScience,UniversityofNorth
Carolina,ChapelHill,NC 27599,USA
9DepartmentofMedicine,SchoolofMedicine,UniversityofNorthCarolina,ChapelHill,NC27599,USA
10DepartmentofPhysiologyandBiophysics,UniversityofMississippiMedicalCenter,Jackson,MS39216,USA
11PublicHealthSciencesDivision,FredHutchinsonCancerResearchCenter,Seattle,WA98109,USA
12CenterofHumanDevelopmentandAging,NewJerseyMedicalSchool,RutgersStateUniversityofNewJersey,
Newark,NJ07103,USA
13DepartmentofBiostatistics,DivisionofPublicHealthSciences,WakeForrestSchoolofMedicine,Winston
Salem,NC27157,USA
14DepartmentofEpidemiology&Prevention,DivisionofPublicHealthSciences,WakeForrestSchoolofMedicine,
WinstonSalem,NC27157,USA
15DepartmentofBiostatistics,FieldingSchoolofPublicHealth,UniversityofCaliforniaLosAngeles,LosAngeles,
CA90095,USA
*Coseniorauthors
Correspondenceto:SteveHorvath;email:shorvath@mednet.ucla.edu
Keywords:epigeneticclock,DNAmethylation,biomarker,healthspan
Received:March20,2018Accepted:April8,2018 Published:April17,2018
Copyright:Levineetal.ThisisanopenaccessarticledistributedunderthetermsoftheCreativeCommonsAttribution
License(CCBY3.0),whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginal
authorandsourcearecredited.
ABSTRACT
Identifyingreliablebiomarkersofagingisamajorgoalingeroscience.Whilethefirstgenerationofepigenetic
biomarkersofagingweredevelopedusingchronologicalageasasurrogateforbiologicalage,wehypothesized
thatincorporationofcompositeclinicalmeasuresofphenotypicagethatcapturedifferencesinlifespanand
healthspanmayidentifynovelCpGsandfacilitatethedevelopmentofamore powerful epigenetic biomarker of
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INTRODUCTION
One of the major goals of geroscience research is to
define ‘biomarkers of aging’[1, 2], which can be
thought of as individual-level measures of aging that
capture inter-individual differences in the timing of
disease onset, functional decline, and death over the life
course. While chronological age is arguably the
strongest risk factor for aging-related death and disease,
it is important to distinguish chronological time from
biological aging. Individuals of the same chronological
age may exhibit greatly different susceptibilities to age-
related diseases and death, which is likely reflective of
differences in their underlying biological aging
processes. Such biomarkers of aging will be crucial to
enable evaluation of interventions aimed at promoting
healthier aging, by providing a measurable outcome,
which unlike incidence of death and/or disease, does not
require extremely long follow-up observation.
One potential biomarker that has gained significant
interest in recent years is DNA methylation (DNAm).
Chronological time has been shown to elicit predictable
hypo- and hyper-methylation changes at many regions
across the genome [3-7], and as a result, the first
generation of DNAm based biomarkers of aging were
developed to predict chronological chronological age
[8-13]. The blood-based algorithm by Hannum [10] and
the multi-tissue algorithm by Horvath [11] produce age
estimates (DNAm age) that correlate with chronological
age well above r=0.90 for full age range samples.
Nevertheless, while the current epigenetic age
estimators exhibit statistically significant associations
with many age-related diseases and conditions [14-27],
the effect sizes are typically small to moderate. One
explanation is that using chronological age as the
reference, by definition, may exclude CpGs whose
methylation patterns don’t display strong time-
dependent changes, but instead signal the departure of
biological age from chronological age. Thus, it is
important to not only capture CpGs that display changes
with chronological time, but also those that account for
differences in risk and physiological status among
individuals of the same chronological age.
Previous work by us and others have shown that
“phenotypic aging measures”, derived from clinical
biomarkers [28-32], strongly predict differences in the
risk of all-cause mortality, cause-specific mortality,
physical functioning, cognitive performance measures,
and facial aging among same-aged individuals. What’s
more, in representative population data, some of these
measures have been shown to be better indicators of
remaining life expectancy than chronological age [28],
suggesting that they may be approximating individual-
level differences in biological aging rates. As a result,
we hypothesize that a more powerful epigenetic
biomarker of aging could be developed by replacing
prediction of chronological age with prediction of a
surrogate measure of "phenotypic age" that, in and of
itself, differentiates morbidity and mortality risk among
same-age individuals.
RESULTS
Overview of the statistical model and analysis
Our development of the new epigenetic biomarker of
aging proceeded along three main steps (Fig. 1). In step
1, a novel measure of ‘phenotypic age’ was developed
using clinical data from the third National Health and
Nutrition Examination Survey (NHANES). Details on
the phenotypic age estimator can be found in Table 1
and in Supplement 1. In step 2, DNAm from whole
blood was used to predict phenotypic age, such that:
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The coefficient values of this model can be found in
Supplement 2 (Table S6). Predicted estimates from this
model represent a person’s epigenetic age, which we
refer to as ‘DNAm PhenoAge’. Using multiple indepen-
dent datasets, we then tested whether DNAm PhenoAge
was associated with a number of aging-related outcomes.
We also tested whether it differed as a function of social,
behavioral, and demographic characteristics, and whether
it was applicable to tissues other than whole blood.
aging.Usinganinnovativetwostepprocess,wedevelopanewepigeneticbiomarkerofaging,DNAm
PhenoAgethatstronglyoutperformspreviousmeasuresinregardstopredictionsforavarietyofaging
outcomes,includingallcausemortality,cancers,healthspan,physicalfunctioning,andAlzheimer'sdisease.
Whilethisbiomarkerwasdevelopedusingdatafromwholeblood,itcorrelatesstronglywithageineverytissue
andcelltested.Basedonanindepthtranscriptionalanalysisinsortedcells,wefindthatincreasedepigenetic,
relativetochronologicalage,isassociatedwithincreasedactivationofproinflammatoryandinterferon
pathways,anddecreasedactivationoftranscriptional/translationalmachinery,DNAdamageresponse,and
mitochondrialsignatures.Overall,thissingleepigeneticbiomarkerofagingisabletocapturerisksforanarray
ofdiverseoutcomesacrossmultipletissuesandcells,andprovideinsightintoimportantpathwaysinaging.
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Finally, in step 3, we examine the underlying biology of
the 513 CpGs in the DNAm PhenoAge measure by
examining differential expression, GO and pathway
enrichment, chromosomal locations, and heritability.
Estimating phenotypic age from clinical biomarkers
For step 1, NHANES III was used to generate a
measure of phenotypic age. NHANES III is a
nationally-representative sample, with over twenty-
three years of mortality follow-up, from which our
analytical sample included 9,926 adults with complete
biomarker data. A Cox penalized regression model—
where the hazard of mortality was regressed on forty-
two clinical markers and chronological age—was used
to select variables for inclusion in our phenotypic age
score. The forty-two biomarkers considered represent
those that were available in both NHANES III and IV.
Based on 10-fold cross-validation, ten variables
(including chronological age) were selected for the
phenotypic age predictor (Table 1, Table S1). These
nine biomarkers and chronological age were then
combined in a phenotypic age estimate (in units of
years) as detailed in Methods.
Validation data for phenotypic age came from
NHANES IV, and included up to 12 years of mortality
follow-up for n=6,209 national representative US
adults. In this population, phenotypic age is correlated
with chronological age at r=0.94. Results from all-cause
and cause-specific (competing risk) mortality pre-
dictions, adjusting for chronological age (Table 2),
show that a one year increase in phenotypic age is
associated with a 9% increase in the risk of all-cause
Figure1.RoadmapfordevelopingDNAmPhenoAge.Theroadmapdepictsouranalyticalprocedures.Instep1,we
developedanestimateof‘PhenotypicAge’basedonclinicalmeasure.PhenotypicagewasdevelopedusingtheNHANESIIIas
trainingdata,inwhichweemployedaproportionalhazardpenalizedregressionmodeltonarrow42biomarkersto9biomarkers
andchronologicalage.ThismeasurewasthenvalidatedinNHANESIVandshowntobeastrongpredictorofbothmorbidityand
mortalityrisk.Instep2,wedevelopedanepigeneticbiomarkerofphenotypicage,whichwecallDNAmPhenoAge,byregressing
phenotypicage(fromstep1)onbloodDNAmethylationdata,usingtheInCHIANTIdata.ThisproducedanestimateofDNAm
PhenoAgebasedon513CpGs.Wethenvalidatedournewepigeneticbiomarkerofaging,DNAmPhenoAge,usingmultiple
cohorts,agingrelatedoutcomes,andtissues/cells.Instep3,weexaminedtheunderlyingbiologyofthe513CpGsandthe
compositeDNAmPhenoAgemeasure,usingavarietyofcomplementarydata(geneexpression,bloodcellcounts)andvarious
genomeannotationtoolsincludingchromatinstateanalysisandgeneontologyenrichment.
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mortality (HR=1.09, p=3.8E-49), a 9% increase in the
risk of mortality from aging-related diseases (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% increase in the
risk of cancer mortality (HR=1.07, p=7.9E-10), a 20%
increase in the risk of diabetes mortality (HR=1.20,
p=1.9E-11), and a 9% increase in the risk of chronic
lower respiratory disease mortality (HR=1.09, p=6.3E-
4). Further, phenotypic age is highly associated with
comorbidity count (p=3.9E-21) and physical
functioning measures (p=2.1E-10, Supplement 1: Fig.
S1).
An epigenetic biomarker of aging (DNAm
PhenoAge)
For step 2 (Fig. 1), data from n=456 participants at two
time-points in the Invecchiare in Chianti (InCHIANTI)
study was used to relate blood DNAm levels to
phenotypic age. InCHIANTI was used as training data
for the new epigenetic biomarker because the study
assessed all clinical measures needed to estimate
phenotypic age, contained data on DNAm, and had a
large age range population (21-100 years). A total of
20,169 CpGs were considered when generating the new
DNAm measure. They represented those CpGs
available on all three chips (27k, 450k, EPIC), so as to
facilitate usability across platforms. Elastic net regres-
sion, with 10-fold cross-validation, produced a model in
which phenotypic age is predicted by DNAm levels at
513 of the 20,169 CpGs. The linear combination of the
weighted 513 CpGs yields a DNAm based estimator of
phenotypic age that we refer to as ‘DNAm PhenoAge’
(mean=58.9, s.d.=18.2, range=9.1-106.1), in contrast to
the previously published Hannum and Horvath ‘DNAm
Age’ measures.
While our new clock was trained on cross-sectional data
in InCHIANTI, we capitalized on the repeated time-
points to test whether changes in DNAm PhenoAge are
related to changes in phenotypic age. As expected,
between 1998 and 2007, mean change in DNAm
PhenoAge was 8.51 years, whereas mean change in
clinical phenotypic age was 8.88 years. Moreover,
participants’ clinical phenotypic age (adjusting for
chronological age) at the two time-points was correlated
at r=0.50, whereas participants’ DNAm PhenoAge
(adjusting for chronological age) at the two time-points
was correlated at r=0.68 (Supplement 1: Fig. S2). We
also find that the change in phenotypic age between
Table1.PhenotypicagingmeasuresandGompertzcoefficients.
Variable Units Weight
Albumin Liver g/L -0.0336
Creatinine Kidney umol/L 0.0095
Glucose, serum Metabolic mmol/L 0.1953
C-reactive protein (log) Inflammation mg/dL 0.0954
Lymphocyte percent Immune % -0.0120
Mean (red) cell volume Immune fL 0.0268
Red cell distribution width Immune % 0.3306
Alkaline phosphatase Liver U/L 0.0019
White blood cell count Immune 1000 cells/uL 0.0554
Age Years 0.0804
Table2.Mortalityvalidationsforphenotypicage.
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
Chronic lower respiratory
diseases 53 1.09 6.3E-4
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1998 and 2007 is highly correlated with the change in
DNAm PhenoAge between these two time-points
(r=0.74, p=3.2E-80, Supplement 1: Fig. S2).
DNAm PhenoAge strongly relates to all-cause
mortality
In step 2 (Fig. 1), the epigenetic biomarker, DNAm
PhenoAge, was calculated in five independent large-
scale samples—two samples from Women’s Health
Initiative (WHI) (n=2,016; and n=2,191), the
Framingham Heart Study (FHS) (n=2,553), the
Normative Aging Study (NAS) (n=657), and the
Jackson Heart Study (JHS) (n=1,747). The first four
studies used the Illumina 450K array while the JHS
employed the latest Illumina EPIC array platform. In
these studies, DNAm PhenoAge correlated with
chronological age at r=0.66 in WHI (Sample 1), r=0.69
in WHI (Sample 2), r=0.78 in FHS, r=0.62 in the NAS,
and r=0.89 in JHS. The five validation samples were
then used to assess the effects of DNAm PhenoAge on
mortality in comparison to the Horvath and Hannum
DNAm Age measures. DNAm PhenoAge was
significantly associated with subsequent mortality risk
in all studies (independent of chronological age), such
that, a one year increase in DNAm PhenoAge is
associated with a 4.5% increase in the risk of all-cause
mortality (Meta(FE)=1.045, Meta p=7.9E-47, Fig. 2).
To better conceptualize what this increase represents,
we compared the predicted life expectancy and
mortality risk for person’s representing the top 5%
(fastest agers), the average, and the bottom 5% (slowest
agers). Results suggest that those in the top 5% of
fastest agers have a mortality hazard of death that is
about 1.62 times that of the average person, i.e. the
hazard of death is 62% higher than that of an average
person. Further, contrasting the 5% fastest agers with
the 5% slowest agers, we find that the hazard of death
of the fastest agers is 2.58 times higher than that of the
bottom 5% slowest agers (HR=1.04511.0/1.045-10.5).
Additionally, both observed and predicted Kaplan-
Meier survival estimates showed that faster agers had
much lower life expectancy and survival rates compared
to average and/or slow agers (Fig. 2).
As shown in Fig. 2, the DNAm age based measures
from Hannum and Horvath also related to all-cause
mortality, consistent with what has been reported
previously [15, 19, 23, 33, 34]. To directly compare the
three epigenetic measures, we contrasted their accuracy
in predicting 10-year and 20-year mortality risk, using
receiver operating characteristics (ROC) curves. DNAm
PhenoAge (adjusted for age) predicts both 10-year
mortality and 20-year mortality significantly better than
the Horvath and Hannum DNAmAge measures (Sup-
plement 1: Table S2). When examining a model that
includes all three measures (Supplement 1: Table S3),
we find that only DNAm PhenoAge is positively
associated with mortality (HR=1.04, p=1.33E-8),
whereas Horvath DNAm Age is now negatively
associated (HR=0.98, p=2.72E-2), and Hannum DNAm
Age has no association (HR=1.01, p=4.66E-1).
DNAm PhenoAge strongly relates to aging-related
morbidity
Given that aging is believed to also influence disease
incidence/prevalence, we examined whether DNAm
PhenoAge relates to diverse age-related morbidity
outcomes. We observe strong associations between
DNAm PhenoAge and a variety of other aging out-
comes using the same five validation samples (Table 3).
For instance, independent of chronological age, higher
DNAm PhenoAge is associated with an increase in a
person’s number of coexisting morbidities (β=0.008 to
0.031; Meta P-value=1.95E-20), a decrease in
likelihood of being disease-free (β=-0.002 to -0.039;
Meta P-value=2.10E-10), an increase in physical
functioning problems (β=-0.016 to -0.473; Meta P-
value=2.05E-13), an increase in the risk of coronary
heart disease (CHD) risk (β=0.016 to 0.073; Meta P-
value=3.35E-11).
DNAm PhenoAge and smoking
Cigarette exposure has been shown to have an
epigenetic fingerprint[35-37], which has been reflected
in previous DNAm risk predictors[38]. Similarly, we
find that DNAm PhenoAge significantly differs
between never (n=1,097), current (n=209), and former
smokers (n=710) (p=0.0033) (Supplement 1, Fig. S3A);
however, conversely, we do not find a robust
association between pack-years and DNAm PhenoAge
(Supplement 1, Fig. S3B-D). Given the association
between DNAm PhenoAge and smoking, we re-
evaluated the morbidity and mortality associations
(fully-adjusted) in our four samples, stratifying by
smoking status (Supplement 1: Fig. S4 and Table S4).
We find that DNAm PhenoAge is associated with
mortality among both smokers (adjusted for pack-
years) (Meta(FE)=1.050, Meta p=7.9E-31), and non-
smokers (Meta(FE)=1.033, Meta p=1.2E-10). DNAm
PhenoAge relates to the number of coexisting
morbidities, physical functioning status, disease free
status, and CHD for both smokers and non-smokers
(Supplement 1: Table S4). In previous work we showed
that Horvath DNAm age of blood predicts lung cancer
risk in the first WHI sample [20]. Using the same data,
we find that a one year increase in DNAm PhenoAge
(adjusting for chronological age, race/ethnicity, pack-
years, and smoking status) is associated with a 5%
increase in the risk of lung cancer incidence and/or
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mortality (HR=1.05, p=0.031). Further, when restricting
the model to current smokers only, we find that the
effect of DNAm PhenoAge on future lung cancer
incidence and/or mortality is even stronger (HR=1.10,
p=0.014).
DNAm PhenoAge in other tissues
One advantage of developing biological aging estimates
based on molecular markers (like DNAm), rather than
clinical risk measures (e.g. those in the phenotypic age
variable), is that they may lend themselves to measuring
tissue/cell specific aging. Although DNAm PhenoAge
was developed using samples from whole blood, our
empirical results show that it strongly correlates with
chronological age in a host of different tissues and cell
types (Fig. 3). For instance, when examining all tissues
concurrently, the correlation between DNAm PhenoAge
and chronological age was 0.71. Age correlations in
brain tissue ranged from 0.54 to 0.92, while correlations
were also found in breast (r=0.47), buccal cells (r=0.88),
dermal fibroblasts (r=0.87), epidermis (r=0.84), colon
(r=0.88), heart (r=0.66), kidney (r=0.64), liver (r=0.80),
lung (r=055), and saliva (r=0.81).
Figure2.MortalityPredictionbyDNAmPhenoAge.
(A)Usingfivesamplesfromlargeepidemiologicalcohorts—twosamples
fromtheWomen’shealthInitiative,theFraminghamHeartStudy,theNormativeAgingStudy,andtheJacksonHeartStudy—we
testedwhetherDNAmPhenoAgewaspredictiveofallcausemortality.TheFig.displaysaforestplotforfixedeffectmetaanalysis,
basedonCoxproportionalhazardmodels,andadjustingforchronologicalage.ResultssuggestthatDNAmPhenoAgeispredictiveo
f
mortalityinallsamples,andthatoverall,aoneyearincreaseinDNAmPhenoAgeisassociatedwitha4.5%increaseintherisko
f
death(p=9.9E47).ThisiscontrastedagainstthefirstgenerationofepigeneticbiomarkersofagingbyHannumandHorvath,which
exhibitlesssignificantassociationswithlifespan(p=1.7E21andp=4.5E5,respectively).(BandC)UsingtheWHIsample1,weplotted
KaplanMeiersurvivalestimatesusingactualdatafromthefastestversustheslowestagers(panelB).Wealsoappliedtheequation
fromtheproportionalhazardmodeltopredictremaininglifeexpectancyandplottedpredictedsurvivalassumingachronologicalage
of50andaDNAmPhenoAgeofeither40(slowager),50(averageager),or60(fastager)(panelC).Medianlifeexpectancyatage50
waspredictedtobeapproximately81yearsforthefastestagers,83.5yearsforaverageagers,and86yearsfortheslowestagers.
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Alzheimer's disease and brain samples
Based on the accuracy of the age prediction in other
tissues/cells, we examined whether aging in a given
tissue was associated with tissue-associated outcomes.
For instance, using data from approximately 700 post-
mortem samples from the Religious Order Study (ROS)
and the Memory and Aging Project (MAP) [39, 40] we
tested the association between pathologically diagnosed
Alzheimer’s disease and DNAm PhenoAge in dorsolate-
ral prefrontal cortex (DLPFX). Results suggest (Fig. 4)
that those who are diagnosed with Alzheimer’s disease
(AD), based on postmortem autopsy, have DLPFX that
appear more than one year older than same aged
individuals who are not diagnosed with AD postmortem
(p=4.6E-4). Further, age adjusted DNAm PhenoAge was
found to be positively associated with neuropathological
hallmarks of Alzheimer’s disease, such as amyloid load
(r=0.094, p=0.012), neuritic plaques (r=0.11, p=0.0032),
and neurofibrillary tangles (r=0.10, p=0.0073).
Table3.MorbidityvalidationforDNAmPhenoAge.
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 White 0.025 1.53E-03 -0.020 1.55E-01 0.022 3.30E-01 -0.284 1.43E-02
WHI EMPC Black 0.022 6.34E-02 -0.008 6.62E-01 0.055 6.12E-02 -0.323 9.56E-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
Meta P-value (Stouffer) 1.50E-08 1.64E-04 1.40E-05 2.03E-05
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 White 0.031 1.99E-04 -0.043 5.56E-03 0.000 9.88E-01 -0.288 1.74E-02
WHI EMPC Black 0.016 1.93E-01 -0.003 8.56E-01 0.033 2.87E-01 -0.144 4.68E-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
Meta P-value (Stouffer) 3.26E-06 6.36E-07 1.49E-01 1.43E-03
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Lifestyle and demographic variables
In evaluating the relationship between DNAm
PhenoAge in blood and additional characteristics we
observe significant differences between racial/ethnic
groups (p=5.1E-5), with non-Hispanic blacks having the
highest DNAm PhenoAge on average, and non-
Hispanic whites having the lowest (Supplement 1: Fig.
S5). We also find evidence of social gradients in DNAm
PhenoAge, such that those with higher education
(p=6E-9) and higher income (p=9E-5) appear younger
(Figure 5). DNAm PhenoAge relates to exercise and
dietary habits, such that increased exercise (p=7E-5)
and markers of fruit/vegetable consumption (such as
carotenoids, p=2E-27) are associated with lower DNAm
PhenoAge (Figure 5, Supplement 1: Fig. S6). Cross-
sectional studies in the WHI also revealed that
DNAmPhenoAge acceleration is positively correlated
with C-reactive protein (r=0.18, p=5E-22, Figure 5),
insulin (r=0.15, p=2E-20), glucose (r=0.10, p=2E-10),
triglycerides (r=0.09, p=5E-9), waist to hip ratio
(r=0.15, p=5E-22) but it is negatively correlated with
HDL cholesterol (r=-0.09, p=7E-9).
DNAm PhenoAge and Immunosenescence
To test the hypothesis that DNAm PhenoAge captures
aspects of age-related decline of the immune system, we
correlated DNAm PhenoAge with estimated blood cell
count (Supplement 1, Fig. S7). After adjusting for age,
we find that DNAm PhenoAgeAccel is negatively
correlated with naïve CD8+ T cells (r=-0.35, p=9.2E-
65), naïve CD4+ T cells (r=-0.29, p=4.2E-42), CD4+
helper T cells (r=-0.34, p=3.6E-58), and B cells (r=-
0.18, p=8.4E-17). Further, DNAm PhenoAgeAccel is
positively correlated with the proportion of granulocytes
(r=0.32, p=2.3E-51), exhausted CD8+ (defined as
CD28-CD45RA-) T cells (r=0.20, p=1.9E-20), and
plasma blast cells (r=0.26, p=6.7E-34). These results are
consistent with age related changes in blood cells [41]
and suggest that DNAm PhenoAge may capture aspects
of immuno-senescence in blood. However, three lines
of evidence suggest that DNAm PhenoAge is not
simply a measure of immunosenescence. First, another
measure of immunosenescence, leukocyte telomere
length, is only weakly correlated with DNAm
PhenoAgeAccel (r=-0.13 p=0.00019 in the WHI; r=-
Figure3.ChronologicalageversusDNAmPhenoAgeinavarietyoftissuesandcells.AlthoughDNAmPhenoAgewasdeveloped
usingmethylationdatafromwholeblood,italsotrackschronologicalageinawidevarietyoftissuesandcells.(A)Thecorrelationacrossall
tissues/cellsweexaminedisr=0.71.(BZJ)reportresultsindifferentsourcesofDNAasindicatedinpanelheadings.Thenumberscorrespond
tothedatasetsfrom(Horvath2013).Overall,correlationsrangefromr=0.35(breast,panelO)tor=0.92(temporalcortexinbrain,panelL).
www.agingus.com581AGING
0.087, P=7.6E-3 in Framingham Heart study; JHS
p=7.83E-7, Supplement 1, Fig. S8). Second, the strong
association between DNAm PhenoAge and mortality
does not simply reflect changes in blood cell
composition, as can be seen from the fact that in
Supplement 1, Fig. S9 the robust association remains
even after adjusting for estimates of seven blood cell
count measures (Meta(FE)=1.036, Meta p=5.6E-21).
Third, DNAmPhenoAge correlates with chronological
age in non-blood tissue.
DNA sequence characteristics of the 513 CpGs in
DNAm PhenoAge
Of the 513 CpGs in DNAm PhenoAge, we find that, 41
CpGs were also in the Horvath DNAm age measure
(Supplement 2: Table S6). This represents a 4.88-fold
increase over what would be expected by chance
(p=8.97E-15). Of the 41 overlapping CpGs, the average
absolute value for their age correlations was r=0.40, and
31 had age correlations with absolute values in the top
20% of what is found among the 513 CpGs in the
DNAm PhenoAge score. We also observed 6 CpGs that
overlapped between the Hannum DNAm Age score and
the DNAm PhenoAge score—five of which were also
found in the Horvath DNAm Age measure. All six
CpGs had extremely high age correlations (half positive,
half negative), with absolute values between r=0.49 and
r=0.76. The five CpGs that are found in all three
epigenetic aging measures were: cg05442902 (P2RXL1),
cg06493994 (SCGN), cg09809672 (EDARADD),
cg19722847 (IPO8), and cg22736354 (NHLRC1).
Several additional DNAm biomarkers have been
described in the literature [12, 13]. A direct comparison
of 6 DNAm biomarkers (including DNAm PhenoAge)
reveals that DNAm PhenoAge stands out in terms of its
predictive accuracy for lifespan, its relationship with
smoking status, its relationship with leukocyte telomere
length, naïve CD8+ T cells and CD4+ T cells
(Supplement 1: Table S5).
Next, we conducted a functional enrichment analysis of
the chromosomal locations of the 513 CpGs and found
that 149 CpGs whose age correlation exceeded 0.2
tended to be located in CpG islands (p=0.0045, Supple-
Figure4.DNAmPhenoAgemeasuredindorsolateralprefrontalcortexrelatestoAlzheimer’sdiseaseandrelated
neuropathologies.UsingpostmortemdatafromtheReligiousOrderStudy(ROS)andtheMemoryandAgingProject(MAP),wefind
amoderate/highcorrelationbetweenchronologicalageandDNAmPhenoAge(panelA).WealsoestimatethePproportiono
f
neuronsviatheCETSalgorithmandshowthatitcorrelateswithDNAmPhenoAge(B).Further,thecorrelationbetweenchronological
agenandDNAmPhenoAgeisincreasedafteradjustingfortheestimatedproportiononneuronsineachsample(panelC).Wealso
findthatDNAmPhenoAgeissignificantlyhigher(p=0.00046)amongthosewithAlzheimer’sdiseaseversuscontrols(panelD),and
thatitpositivelycorrelateswithamyloidload(p=0.012,panelE),neuriticplaques(p=0.0032,panelF),diffuseplaques(p=0.036,panel
G),andneurofibrillarytangles(p=0.0073,panelH).
Figure5.LifestylefactorsversusDNAmPhenoAgeaccelerationinbloodintheWHI.Inthiscross
sectionalanalysis,thecorrelationtestanalysis(bicor,biweightmidcorrelation)betweenselectvariablesand
DNAmPhenoAgeAccelrevealsthateducation,income,exercise,proxiesoffruit/vegetableconsumption,and
HDLcholesterolarenegativelyassociated(blue)withDNAmPhenoAge,i.e.youngerepigeneticage.
Conversely,CRP,insulin,glucose,triglycerides,BMI,waisttohipratio,systolicbloodpressure,andsmokinghave
apositiveassociation(red)withDNAmPhenoAge.Allresultshavebeenadjustedforethnicityandbatch/data
set.SimilarresultsbasedonmultivariateregressionmodelscanbefoundinSupplementaryFigure6B.
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ment 1: Fig. S10) and were significantly enriched with
polycomb group protein targets (p=8.7E-5, Supplement
1: Fig. S10), which in line with results of epigenome
wide studies of aging effects [4, 5, 42].
Transcriptional and genetic studies of DNAm
PhenoAge
Using the genome-wide data from FHS and WHI, we
estimated the heritability of DNAm PhenoAge. The heri-
tability estimated by the SOLAR polygenic model for
those of European ancestry in the FHS was =0.33,
while the heritability estimated for those of European
ancestry in WHI, using GCTA-GREML analysis [43]
was =0.51.
Using the monocyte data mentioned above, as well as
PBMC expression data on 2,188 persons from the FHS,
we conducted a transcriptional analysis to identify dif-
ferential expression associated with DNAm PhenoAge-
Figure5.LifestylefactorsversusDNAmPhenoAgeaccelerationinbloodintheWHI.Inthiscross
sectionalanalysis,thecorrelationtestanalysis(bicor,biweightmidcorrelation)betweenselectvariablesand
DNAmPhenoAgeAccelrevealsthateducation,income,exercise,proxiesoffruit/vegetableconsumption,and
HDLcholesterolarenegativelyassociated(blue)withDNAmPhenoAge,i.e.youngerepigeneticage.
Conversely,CRP,insulin,glucose,triglycerides,BMI,waisttohipratio,systolicbloodpressure,andsmokinghave
apositiveassociation(red)withDNAmPhenoAge.Allresultshavebeenadjustedforethnicityandbatch/data
set.SimilarresultsbasedonmultivariateregressionmodelscanbefoundinSupplementaryFigure6B.
www.agingus.com583AGING
Accel (Supplement 3: Table S7). Overall, we find that
genes show similar associations with chronological age
and DNAm PhenoAgeAccel. DNAm PhenoAgeAccel
represents aging differences among same-aged
individuals and is adjusted so as to exhibit a correlation
of r=0.0 with chronological age. Thus, this observation
suggests that genes whose transcription increases with
age are upregulated among epigenetically older
compared to epigenetically younger persons of the same
chronological age (Supplement 1: Fig. S11); the same
applies for genes that show decreases with chrono-
logical age being downregulated in epi-genetically older
versus younger persons of the same age.
Using the transcriptional data from monocytes
described above (adjusting for array, sex, race/ethnicity,
age, and imputed cell counts), we tested for GO
enrichment among genes that are positively associated
with DNAm PhenoAge and those that are negatively
associated with DNAm PhenoAge (Supplement 4:
Table S8). Among those with positive aging
associations (over-expression among epigenetically
older individuals), we observed enrichment for a
number of pro-inflammatory signaling pathways. These
pathways included, but are not limited to: multiple toll-
like receptor signaling pathways (7,9,3,2), regulation of
inflammatory response, JAK-STAT cascade, response
to lipopoly-saccharide, tumor necrosis factor-mediated
signaling pathway, and positive regulation of NF-
kappaB transcription factor activity. Additionally,
positively associated genes were also enriched for a
number anti-viral response pathways—type I interferon
signaling, defense response to virus, interferon-gamma-
mediated signaling pathway, cellular response to
interferon-alpha, etc. Other interesting GO terms
enriched among positively associated genes included:
response to nutrient, JAK-STAT cascade involved in
growth hormone signaling pathway, multicellular
organism growth, and regulation of DNA methylation.
When testing for enrichment among genes that were
negatively associated with DNAm PhenoAgeAccel
(decreased expression among epigenetically older
persons) we observed that many were implicated in
processes involving transcriptional and translational
machinery, as well as damage recognition and repair.
These included: translational initiation; regulation of
translational initiation; ribosomal large subunit assemb-
ly; ribosomal small subunit assembly; translational
elongation; transcription initiation from RNA
polymerase I promoter; transcription-coupled nucleo-
tide-excision repair; nucleotide-excision repair, DNA
incision, 5'-to lesion; nucleotide-excision repair, DNA
damage recognition; DNA damage response, detection
of DNA damage; and regulation of DNA damage
checkpoint.
DISCUSSION
Using a novel two-step method, we were successful in
developing a DNAm based biomarker of aging that is
highly predictive of nearly every morbidity and mortali-
ty outcome we tested. Training an epigenetic predictor
of phenotypic age instead of chronological age led to
substantial improvement in mortality/healthspan
predictions over the first generation of DNAm based
biomarkers of chronological age from Hannum[10],
Horvath[11] and other published DNAm biomarkers. In
doing so, this is the first study to conclusively demons-
trate that DNAm biomarkers of aging are highly
predictive of cardiovascular disease and coronary heart
disease. DNAm PhenoAge also tracks chronological
age and relates to disease risk in samples other than
whole blood. Finally, we find that an individual’s
DNAm PhenoAge, relative to his/her chronological age,
is moderately heritable and is associated with activation
of pro-inflammatory, interferon, DNAm damage repair,
transcriptional/ translational signaling, and various
markers of immuno-senescence: a decline of naïve T
cells and shortened leukocyte telomere length
(Supplementary Information).
The ability of our measure to predict multifactorial
aging conditions is consistent with the fundamental
underpinnings of Geroscience research [1, 44], which
posits that aging mechanisms give rise to multiple
pathologies and thus, differences in the rate of aging
will have implications for a wide array of diseases and
conditions. Further, these results answer a fundamental
biological question of whether differences in multi-
system dysregulation (estimated using clinical
phenotypic age measures), healthspan, and lifespan are
reflected at the epigenetic level, in the form of
differential DNAm at specific CpG sites.
The improvement over previous epigenetic biomarkers,
likely comes down to the types of CpGs selected for the
various measures. Only 41 of the 513 CpGs in DNAm
PhenoAge were shared with the Horvath clock, while
only five CpGs were shared between all three clocks
(DNAm PhenoAge, Horvath, and Hannum). In general,
these CpGs did not tend to be drivers of the DNAm
PhenoAge score, and instead represented those with
large age correlations. This may explain the
improvements of DNAm PhenoAge over previous
epigenetic biomarkers of aging. While the previous
DNAm age estimators selected CpGs to optimize
prediction of chronological age, the CpGs in DNAm
PhenoAge were optimized to predict a multi-system
proxy of physiological dysregulation (phenotypic age).
In doing so, we were able to not only capture CpGs that
exhibited strong correlations with age, but also those
that captured variations in risk of death and disease
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among same aged individuals. In general, the CpGs
with the highest weights in the new clock did not
correlate with chronological age (Supplement 1: Fig.
S12), but instead were related to the difference between
phenotypic and chronological age—i.e. divergence in
the rate of aging.
While DNAm PhenoAge greatly outperformed all
previous DNAm biomarkers of aging (Supplement 1:
Table S5), the utility of DNAm PhenoAge for
estimating risk does not imply that it should replace
clinical biomarkers when it comes to informing medical
and health-related decisions. In fact, but perhaps not
surprisingly, the phenotypic age measure used to select
CpGs is a better predictor of morbidity and mortality
outcomes than DNAm PhenoAge. While the addition of
error in performing a two-step process, rather than
training a DNAm predictor directly on mortality may
contribute, we don’t believe this accounts for the
difference in predictive performance. In fact, a recent
DNAm measure by Zhang et al. [38] was trained to
directly predict mortality risk, yet it appears to be a
weaker predictor than both our DNAm PhenoAge
measure and our clinical phenotypic age measure
(Supplement 1: Table S9). The first generation of
DNAm age estimators only exhibit weak associations
with clinical measures of physiological dysregulation
[24, 45]. Physiological dysregulation, which is more
closely related to our clinical age measure “phenotypic
age” than to chronological age, is not only the result of
exogenous/endogenous stress factors (such as obesity,
infections) but also a result of age related molecular
alterations, one example of which are modifications to
the epigenome. Over time, dysregulation within organ
systems leads to pathogenesis of disease (age-related
molecular changes Æ physiological dysregulation Æ
morbidity Æ mortality)[46]. However, stochasticity and
variability exist at each of these transitions. Therefore,
measures of physiological dysregulation, will be better
predictors of transition to the next stage in the aging
trajectory (i.e. morbidity and mortality) than will
measures of age related molecular alterations, like
DNAm PhenoAge. Similarly, quantification of disease
pathogenesis (cancer stage, Alzheimer’s stage) is likely
a better predictor of mortality risk than clinical
phenotypic aging measures. As a result, clinical pheno-
typic aging measures may be preferable to epigenetic
measures when the goal is risk prediction, and samples
come from blood.
That being said, when the aim is to study the
mechanisms of the aging process, DNAm measures
have advantages over clinical measures. First, they may
better capture “pre-clinical aging” and thus may be
more suited for differentiating aging in children, young
adults, or extremely healthy individuals, for whom
measures like CRP, albumin, creatinine, glucose, etc.
are still fairly homogenous. Second, as demonstrated,
these molecular measures can capture cell and/or tissue
specific aging rates and therefore may also lend
themselves to in vitro studies of aging, studies for which
blood is not available, studies using postmortem
samples, and/or studies comparing aging rates between
tissues/cells. While the fundamental drivers of aging are
believed to be shared across cells/tissues, that is not to
say that all the cells and tissues within an individual will
age at the same rate. In fact, it is more likely that
individuals will vary in their patterning of aging rates
across tissues, and that this will have implications for
death and disease risk. Relatedly, it is not known how
predictions based on DNAm PhenoAge measures from
non-blood samples will compare to phenotypic age
predictions. It may be the case that various outcomes
will be more tightly related to aging in specific
cells/tissues, rather than blood. Finally, examination of
DNAm based aging rates facilitates the direct study of
the proposed mechanisms of aging, of which
“epigenetic alterations” is one of the seven hypo-
thesized “pillars of aging” [1].
While more work needs to be done to model the biology
linking DNAm PhenoAge and aging outcomes, we
began to explore this using differential expression,
functional enrichment, and heritability estimates.
Overall, we found that CpGs that had larger increases
with aging tended to be located in CpG islands and
enriched with polycomb group protein targets,
consistent with what has been reported in previous
epigenome wide studies of aging effects [4-7, 42].
While typically DNAm of CpG islands and/or
polycomb recruitment is linked to transcriptional
silencing [47], for the most part, we did not observe
associations between DNAm and expression for co-
locating CpG-gene pairs—this was also true when only
considering CpGs located in islands. These findings
may suggest that the genes annotated to the CpGs in our
score are not part of the link between changes in DNAm
and aging. Nevertheless, we also recognize that these
null results could stem from the fact that 1) associations
were only tested in monocytes, 2) DNAm and
expression represents what is present globally for each
sample, rather than on a cell-by-cell basis, and 3)
stronger associations between DNAm and gene
expression levels may only exist early in life.
Nevertheless, we do identify potentially promising
transcriptional pathways when considering DNAm
PhenoAge as a whole. For instance, we observe that
higher DNAm PhenoAge is associated with increases in
the activation of proinflammatory pathways, such as
NF-kappaB; increased interferon (IFN) signaling;
decreases in ribosomal–related and translational
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machinery pathways; and decreases in damage
recognition and repair pathways. These findings are
consistent with previous work describing aging
associated changes, comprising increases in dysregulat-
ed inflammatory activation, increased DNA damage,
and loss of translational fidelity. For instance, there
exists a large body of literature highlighting the
importance of an increased low-grade pro-inflammatory
status as a driver of the aging process, termed inflamm-
aging [41, 48, 49]. IFN signaling pathways have been
shown to be markers of DNA damage and mediators of
cellular senescence[50]. Additionally, it has been shown
that breakdown of the transcriptional and translational
machinery may play a central role in the aging process
[51, 52]. For instance, the ribosome is believed to be a
key regulator of proteostasis, and in turn, aging [51, 53].
Relatedly, loss of integrity in DNA damage repair
pathways is considered another hallmark of the aging
process [54-56].
In general, many of these pathways will have implica-
tions for adaptation to exogenous and endogenous
stressors. Factors related to stress resistance and
response have repeatedly been shown to be drivers of
differences in lifespan and aging [49, 57-61]. This may
partially account for our findings related to smoking. In
general, it is not surprising that a biomarker of aging
and mortality risk relates to smoking, given that life
expectancies of smokers are on average ten years shorter
than never smokers, and smoking history is associated
with a drastic increase in the risk of a number of age-
related conditions. However, perhaps more interesting-
ly, we find that the effects of DNAm PhenoAge on
mortality appear to be higher for smokers than non-
smokers, which could suggest that DNAm PhenoAge
represent differences in innate resilience/ vulnerability to
pro-aging stressors, such as cigarette smoke.
Interestingly, we observed moderately high heritability
estimates for DNAm PhenoAge. For instance, we
estimated that genetic differences accounted for one-
third to one-half of the variance in DNAm PhenoAge,
relative to chronological age. In moving forward, it will
be useful to identify the genetic architecture underlying
differences in epigenetic aging. Finally, we reported
that individuals’ DNAm PhenoAges—relative to their
chronological ages—remained fairly stable over a nine-
year period. However, it is unclear whether it is
attributable to genetic influences, or the fact that social
and behavioral characteristics tend to also remain stable
for most individuals.
If the goal is to utilize accurate quantifiable measures of
the rate of aging, such as DNAm PhenoAge, to assess
the efficacy of aging interventions, more work will be
needed to evaluate the dynamics of DNAmPhenoAge
following various treatments. For instance, it remains to
be seen whether interventions can reverse
DNAmPhenoAge in the short term. Along these lines, it
will be essential to determine causality—does DNAm
drive the aging process, or is it simply a surrogate
marker of organismal senescence? If the former is true,
DNAm PhenoAge could provide insight into promising
targets for therapies aimed at lifespan, and more
importantly, healthspan extension.
Overall, DNAm PhenoAge is an attractive composite
biomarker that captures organismal age and the func-
tional state of many organ systems and tissues, above
and beyond what is explained by chronological time.
Our validation studies in multiple large and independent
cohorts demonstrate that DNAm PhenoAge is a highly
robust predictor of both morbidity and mortality
outcomes, and represents a promising biomarker of
aging, which may prove to be beneficial to both basic
science and translational research.
METHODS
Using the NHANES training data, we applied a Cox
penalized regression model—where the hazard of
aging-related mortality (mortality from diseases of the
heart, malignant neoplasms, chronic lower respiratory
disease, cerebrovascular disease, Alzheimer’s disease,
Diabetes mellitus, nephritis, nephrotic syndrome, and
nephrosis) was regressed on forty-two clinical markers
and chronological age to select variables for inclusion in
our phenotypic age score. Ten-fold cross-validation was
employed to select the parameter value, lambda, for the
penalized regression. In order to develop a sparse
parsimonious phenotypic age estimator (fewer
biomarker variables preferred to produce robust results)
we selected a lambda of 0.0192, which represented a
one standard deviation increase over the lambda with
minimum mean-squared error during cross-validation
(Supplement 1, Fig. S13). Of the forty-two biomarkers
included in the penalized Cox regression model, this
resulted in ten variables (including chronological age)
that were selected for the phenotypic age predictor.
These nine biomarkers and chronological age were then
included in a parametric proportional hazards model
based on the Gompertz distribution. Based on this
model, we estimated the 10-year (120 months)
mortality risk of the j-the individual. Next, the
mortality score was converted into units of years
(Supplement 1). The resulting phenotypic age estimate
was regressed on DNA methylation data using an
elastic net regression analysis. The penalization
parameter was chosen to minimize the cross validated
mean square error rate (Supplement 1, Fig. S14), which
resulted in 513 CpGs.
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Estimation of blood cell counts based on DNAm
levels
We estimate blood cell counts using two different
software tools. First, Houseman's estimation method
[62] was used to estimate the proportions of CD8+ T
cells, CD4+ T, natural killer, B cells, and granulocytes
(mainly neutrophils). Second, the Horvath method,
implemented in the advanced analysis option of the
epigenetic clock software [11, 18], was used to estimate
the percentage of exhausted CD8+ T cells (defined as
CD28-CD45RA-), the number (count) of naïve CD8+ T
cells (defined as CD45RA+CCR7+) and plasmablasts.
We and others have shown that the estimated blood cell
counts have moderately high correlations with
corresponding flow cytometric measures [62, 63].
Additional descriptions of methods and materials can be
found in Supplement 1.
Ethics approval
This study was reviewed by the UCLA institutional
review board (IRB#13-000671, IRB#15-000697,
IRB#16-001841, IRB#15-000682).
Availability of data and materials
The WHI data are available at dbGaP under the
accession numbers phs000200.v10.p3. The FHS data
are available at dbGaP under the accession numbers
phs000342 and phs000724. The Normative Aging data
are available from dbGAP phs000853.v1.p1. The
Jackson Heart Study data are available from
https: // www.jacksonheartstudy.org/Research/Study-
Data.
AUTHOR CONTRIBUTIONS
ML and SH developed the DNAmPhenoAge estimator
and wrote the article. SH, ML, LF conceived of the
study. ML, SH, AL, AQ carried out the statistical
analysis. The remaining authors contributed data and
participated in the interpretation of the results.
ACKNOWLEDGEMENTS
We would like to acknowledge The WHI Investigators
listed below:
Program Office: (National Heart, Lung, and Blood
Institute, Bethesda, Maryland) Jacques Rossouw, Shari
Ludlam, Dale Burwen, Joan McGowan, Leslie Ford,
and Nancy Geller.
Clinical Coordinating Center: (Fred Hutchinson Cancer
Research Center, Seattle, WA) Garnet Anderson, Ross
Prentice, Andrea LaCroix, and Charles Kooperberg.
Investigators and Academic Centers: (Brigham and
Women's Hospital, Harvard Medical School, Boston,
MA) JoAnn E. Manson; (MedStar Health Research
Institute/Howard University, Washington, DC) Barbara
V. Howard; (Stanford Prevention Research Center,
Stanford, CA).
Marcia L. Stefanick; (The Ohio State University,
Columbus, OH) Rebecca Jackson; (University of
Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson;
(University at Buffalo, Buffalo, NY).
Jean Wactawski-Wende; (University of Florida,
Gainesville/Jacksonville, FL) Marian Limacher;
(University of Iowa, Iowa City/Davenport, IA) Robert
Wallace; (University of Pittsburgh, Pittsburgh, PA)
Lewis Kuller; (Wake Forest University School of
Medicine, Winston-Salem, NC) Sally Shumaker.
The Framingham Heart Study is funded by National
Institutes of Health contract N01-HC-25195 and
HHSN268201500001I. The laboratory work for this
investigation was funded by the Division of Intramural
Research, National Heart, Lung, and Blood Institute,
National Institutes of Health. The analytical component
of this project was funded by the Division of Intramural
Research, National Heart, Lung, and Blood Institute,
and the Center for Information Technology, National
Institutes of Health, Bethesda, MD. The Framingham
Heart Study is conducted and supported by the National
Heart, Lung, and Blood Institute (NHLBI) in
collaboration with Boston University. This manuscript
was not prepared in collaboration with investigators of
the Framingham Heart Study and does not necessarily
reflect the opinions or views of the Framingham Heart
Study, Boston University, or the NHLBI.
The United States Department of Veterans Affairs (VA)
Normative Aging Study (NAS) is supported by the
Cooperative Studies Program/ERIC and is a research
component of the Massachusetts Veterans Epidemio-
logy Research and Information Center (MAVERIC),
Boston Massachusetts.
The MESA Epigenomics and Transcriptomics Studies
were funded by R01HL101250, R01 DK103531-01,
R01 DK103531, R01 AG054474, and R01 HL135009-
01 to Wake Forest University Health Sciences.
We thank the Jackson Heart Study (JHS) participants
and staff for their contributions to this work. The JHS is
supported by contracts HHSN268201300046C,
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HHSN268201300047C, HHSN268201300048C,
HHSN268201300049C, HHSN268201300050C from
the National Heart, Lung, and Blood Institute and the
National Institute on Minority Health and Health
Disparities. Dr. Wilson is supported by U54GM115428
from the National Institute of General Medical
Sciences.
CONFLICTS OF INTEREST
The Regents of the University of California is the sole
owner of a provisional patent application directed at this
invention for which MEL, SH are named inventors.
FUNDING
This study was supported by NIH/NIA U34AG051425-
01 (Horvath) and NIH/NIA K99AG052604 (Levine).
The WHI epigenetic studies were supported by
NIH/NHLBI 60442456 BAA23 (Assimes, Absher,
Horvath) and by the National Institute of Environmental
Health Sciences R01-ES020836 WHI-EMPC (Whitsel,
Baccarelli, Hou). The WHI program is funded by the
National Heart, Lung, and Blood Institute, National
Institutes of Health, U.S. Department of Health and
Human Services through contracts
HHSN268201100046C, HHSN268201100001C,
HHSN268201100002C, HHSN268201100003C,
HHSN268201100004C, and HHSN271201100004C.
The authors thank the WHI investigators and staff for
their dedication, and the study participants for making
the program possible. A full listing of WHI
investigators can be found at: www.whi.org/researchers/
Documents%20%20Write%20a%20Paper/WHI%20Inv
estigator%20Short%20List.pdf.
The InCHIANTI study baseline (1998-2000) was
supported as a "targeted project" (ICS110.1/RF97.71)
by the Italian Ministry of Health and in part by the U.S.
National Institute on Aging (Contracts: 263 MD 9164
and 263 MD 821336).
Funding for the DNA methylation in JHS provided by
NHLBI R01HL116446.
The funding bodies played no role in the design, the
collection, analysis, or interpretation of the data.
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SUPPLEMENTARY MATERIAL
Please browse the Full Text version of this manuscript
to see Supplementary Methods, Tables, and Figures
presented in Supplements 1-4.
... It remains to be determined if longitudinal cohort studies will validate epigenetic clocks as biomarkers of aging and whether interventions directly targeting DNA methylate age will slow biologic aging (Horvath and Raj 2018; Jylhävä et al. 2017). Heritability estimates of DNA methylate age surmise that genetic differences account for one-third to one-half of the variance in DNA mAge relative to chronological age (Levine et al. 2018;Marioni et al. 2015), suggesting a substantial contribution from behavioral and other factors. If DNA methylation age is found to causally drive the aging process, and is hence validated as a measurable aging outcome with the advantage of a shorter time frame than incidence of death or disease, further investigation can be undertaken into of the role of stress and resilience interventions in healthspan and lifespan extension. ...
... It has since been employed in assessing systemic inflammation in psychiatric diagnoses ranging from dementia, alcohol dependence, schizophrenia, bipolar affective disorder, depression, non-phobic anxiety disorders to mild mental retardation (Brinn and Stone 2020). Since stress reactivity has been suggested to advance cellular aging through oxidative stress and inflammatory pathways (Lavretsky and Newhouse 2012), and higher DNAmAge has been associated with increased activation of proinflammatory pathways (Levine et al. 2018), we explored the NLR ratio of participants in the four perceived stress and resilience categories for potential association with DNAm GrimAge acceleration. Although we observed a trend towards lower NLR ratio in those categorized with high compared to low resilience among those with low PSS, this observation did not reach statistical significance. ...
Article
Full-text available
Chronic stress is associated with deleterious health outcomes and mortality risk. A potential mechanism by which stress affects healthspan and lifespan is acceleration of cellular aging. Biologic age prediction models, termed epigenetic clocks, have been developed to estimate biologic age differences among people with the same chronologic age. This study evaluates the simultaneous impact of perceived chronic stress and resilience on Grim Age acceleration. The perceived stress score (PSS) and Connor-Davidson Resilience Scale (CD-RISC) were used to measure chronic stress and resilience, respectively. DNA was extracted from whole blood and analyzed using the MethylationEPIC BeadChip. GrimAge estimates were calculated using the methylation age calculator. Forty-seven business executives were categorized by levels of high or low stress and resilience scores. Compared to participants with low stress and high resilience, those with low stress and low resilience demonstrated the strongest association with Grim Age acceleration (p = 0.044), after controlling for age and estimated cellular proportions. Interestingly, among participants with low resilience, those with high perceived stress had a weaker association with Grim Age acceleration than participants with low perceived stress. However, among participants with high resilience, low perceived stress had a weaker association with Grim Age acceleration than high perceived stress. Our findings suggest that the impact of perceived stress on epigenetic age acceleration may differ based on resilience capacity, with a potential paradoxical beneficial effect among those with low resilience.
... Considerable effort has been put into establishing the concept and markers of biological aging which is designed to incorporate the general deterioration of multiple biological functions [2][3][4]. Specifically, the relationship between chronological age and DNA methylation has been rigorously investigated [5,6] with multiple predictive models (deemed "epigenetic clocks") presented [7][8][9][10][11][12][13][14][15] that aim to estimate the chronological age by observing DNA methylation levels in various genomic positions. ...
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Epigenetic changes have long been investigated in association with the process of aging in humans. DNA methylation has been extensively used as a surrogate measure of biological age and correlations between “DNA methylation age” and chronological age have been established. A wide variety of epigenetic clocks has been designed to predict age in different tissues and on data obtained from different methylation platforms. We aimed to extend the scope of one of the most used epigenetic age predictors, the Horvath pan-tissue epigenetic clock, to improve its accuracy on data acquired from the latest Illumina methylation platform (BeadChip EPIC). We present three models trained on close to 6,000 samples of various source tissues and platforms and demonstrate their superior performance (Pearson correlation (r) = 0.917–0.921 and median absolute error (MAE) = 3.60–3.85 years) compared to the original model (r = 0.880 and MAE = 5.13 years) on a test set of more than 4,000 samples. The gain in accuracy was especially pronounced on EPIC array data (r = 0.89, MAE = 3.54 years vs. r = 0.83, MAE = 6.09 years), which was not available at the time when the original model was created. Our updated epigenetic clocks predict chronological age with great precision in an independent test cohort of samples on multiple tissue types and data platforms. Two of the three presented models exclusively use the covariates of the original epigenetic clock, albeit with different coefficients, allowing for straightforward adaptation for prefiltered datasets previously processed with the original predictor.
... Multiple advanced aging biomarkers based on omics analyses and machine learning techniques, most notably epigenetic aging clocks but also other aging biomarkers, lend themselves for studying this process in greater detail. These biomarkers have been shown to predict the biological age of mammalian species including humans, mice, and naked mole rats, as well as many other species, and have become invaluable tools for tracking the aging trajectories during various biological processes [10][11][12][13][14][15][16][17][18][19][20][21][22] . ...
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Recent studies suggest the existence of a natural rejuvenation event during early embryonic development of mice, followed by epigenetic aging. Here, by profiling embryonic DNA methylation in the African clawed frog, Xenopus laevis, we found that the epigenetic entropy basepoint maps to the gastrulation stage of embryogenesis and corresponds to a rapid increase in embryo transcript abundance. We further developed a frog aging clock, revealing that this species epigenetically ages. Application of this clock to developmental stages identified a decrease in epigenetic age during early embryogenesis, with the minimal age reached around gastrulation. By examining individual developmental trajectories of 6,457 embryos, we found that this stage is also accompanied by a higher incidence of disrupted development. Taken together, our data point to gastrulation as a critical stage for aging and natural rejuvenation, characterized by the lowest epigenetic age, increased mortality, nadir of DNA methylation entropy and rapid increase in embryo transcript abundance, defining aging ground zero as the basepoint where rejuvenation ends and the aging process begins.
... These results clearly showed that CHH methylation could potentially be used as a signature of development time and aging in woody bamboos. Using DNA methylation to predict aging has been extensively reported in mammals (Levine et al. 2018;Lu et al. 2019;Wu et al. 2019). Recently, some patterns have been observed in the growth and development of plants. ...
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STUDY QUESTION Is reproductive aging in granulosa cells associated with markers of ovarian reserve? SUMMARY ANSWER Age acceleration was associated with anti-Mullerian hormone (AMH) levels, antral follicle count (AFC), oocyte yield and maturity, and the number of successfully fertilized embryos. WHAT IS KNOWN ALREADY The rate of reproductive aging varies among women of the same age. DNA methylation can be used to predict epigenetic age in a variety of tissues. STUDY DESIGN, SIZE, DURATION This was a cross-sectional study of 70 women at the time of oocyte retrieval. PARTICIPANTS/MATERIALS, SETTING, METHODS The 70 participants were recruited for this study at an academic medical center and they provided follicular fluid samples at the time of oocyte retrieval. Granulosa cells were isolated and assessed on the MethylationEPIC array. Linear regression was used to evaluate the associations between DNA methylation-based age predictions from granulosa cells and chronological age. Age acceleration was calculated as the residual of regressing DNA methylation-based age on chronological age. Linear regressions were used to determine the associations between age acceleration and markers of ovarian reserve and IVF cycle outcomes. MAIN RESULTS AND THE ROLE OF CHANCE Participants were a mean of 36.7 ± 3.9 years old. In regards to race, 54% were white, 19% were African American and 27% were of another background. Age acceleration was normally distributed and not associated with chronological age. Age acceleration was negatively associated with AMH levels (t = −3.1, P = 0.003) and AFC (t = −4.0, P = 0.0001), such that women with a higher age acceleration had a lower ovarian reserve. Age acceleration was also negatively correlated with the total number of oocytes retrieved (t = −3.9, P = 0.0002), the number of mature oocytes (t = −3.8, P = 0.0003) and the number of fertilized oocytes or two-pronuclear oocytes (t = −2.8, P = 0.008) in the main analysis. LIMITATIONS, REASONS FOR CAUTION This study used pooled follicular fluid, which does not allow for the investigation of individual follicles. Infertility patients may also be different from the general population, but, as we used granulosa cells, the participants had to be from an IVF population. WIDER IMPLICATIONS OF THE FINDINGS This study demonstrated that epigenetic age and age acceleration can be calculated from granulosa cells collected at the time of oocyte retrieval. GrimAge most strongly predicted chronological age, and GrimAge acceleration was associated with baseline and cycle characteristics as well as cycle outcomes, which indicates its potential clinical relevance in evaluating both oocyte quantity and quality. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the National Institutes of Health (UL1TR002378) and the Building Interdisciplinary Research Careers in Women’s Health Program (K12HD085850) to A.K.K. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding source had no role in any aspect of this study. J.B.S. serves as Vice Chair for the American Society for Reproductive Medicine Education Committee, is a Medical Committee Advisor for the Jewish Fertility Foundation and works with Jscreen. J.B.S. has received funding from Georgia Clinical Translational Research Alliance. H.S.H., J.B.S. and A.K.S. have received NIH funding for other projects. A.K.K., S.A.G., S.G., Q.S.K., L.J.M. and W.S. have no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.
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Purpose As the population ages rapidly, the incidence of age-related diseases (ARDs) is also increasing fast. Predicting the incidence of ARDs is a challenge since the rates of individual aging vary, and objective assessments of the stages of aging based on chronological age (CA) may be inaccurate. Thus, in this study, we developed a biological age (BA) model based on the National Health Examination (NHE) data and analyzed the model prediction results for the incidence of 16 ARDs. Methods This study was based on the 2002–2019 National Health Information Databases of the National Health Insurance Service (NHIS-NHID). The data from a total of 10,002,494 subjects were selected between 2009 and 2010, and the principal component analysis (PCA) was performed to develop the BA model. The Cox-proportional hazard model was used to perform predictive analysis of the ARD incidence. Results For the unit increase in the difference between corrected biological age (cBA) and chronological age (CA), the hazard ratios (HRs) of ARDs increased significantly for both sexes (p<0.001). In descending order, the corresponding ARDs’ HRs were obesity (1.655), chronic renal failure (1.362), hypertension (1.301), hyperlipidemia (1.264), diabetes mellitus (1.261), fracture (1.119), dementia (1.163), cataract (1.116), myocardial infarction (1.097), stroke (1.169), macular degeneration (1.075), osteoarthritis (1.059), osteoporosis (1.124), Parkinson's disease (1.048), and chronic obstructive pulmonary disease (1.026). Conclusions In this study, the incidence of 16 ARDs were analyzed based on BA. Therefore, conducting the NHIS health examination can facilitate the prevention of ARDs by estimating HRs for at least 16 diseases.
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T2D is a clinically challenging disease to predict, largely due to its complex genetic and environmental architecture that underlies its development and clinical presentation. The work presented in this thesis reflects the workflow required to determine the clinical utility and translatability of DNA methylation biomarkers and suggests that it is inappropriate to use the methylation status of cg19693031 for T2D medicine in its current form. Together, the work presented here advances our understanding of the nuclear network regulatory effects underlying the clinical presentation and reflecting the etiological heterogeneity of T2D and provides new avenues for investigation to improve the prognostication and risk stratification for diabetic patients.
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The association between aging and cancer is complex. Recent studies have developed measures of biological aging based on DNA methylation and called them ‘age acceleration'. We aimed to assess associations of age acceleration with risk of and survival from seven common cancers. Seven case-control studies of DNA methylation and colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma, nested in the Melbourne Collaborative Cohort Study were conducted. Cancer cases, vital status and cause of death were ascertained through linkage with cancer and death registries. Conditional logistic regression and Cox models were used to estimate odds ratios (OR) and hazard ratios (HR) and 95% confidence intervals (CI) for associations of five age acceleration measures derived from the Human Methylation 450K Beadchip assay with cancer risk (N=3,216 cases) and survival (N=1,726 deaths), respectively. Epigenetic aging was associated with increased cancer risk, ranging from 4% to 9% per five-year age acceleration for the 5 measures considered. Heterogeneity by study was observed, with stronger associations for risk of kidney cancer and B-cell lymphoma. An associated increased risk of death following cancer diagnosis ranged from 2% to 6% per five-year age acceleration, with no evidence of heterogeneity by cancer site. Cancer risk and mortality were increased by 15 to 30% for the fourth vs. first quartile of age acceleration. DNA methylation-based measures of biological aging are associated with increased cancer risk and shorter cancer survival, independently of major health risk factors. This article is protected by copyright. All rights reserved.