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Quantification of biological aging in young adults

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Significance The global population is aging, driving up age-related disease morbidity. Antiaging interventions are needed to reduce the burden of disease and protect population productivity. Young people are the most attractive targets for therapies to extend healthspan (because it is still possible to prevent disease in the young). However, there is skepticism about whether aging processes can be detected in young adults who do not yet have chronic diseases. Our findings indicate that aging processes can be quantified in people still young enough for prevention of age-related disease, opening a new door for antiaging therapies. The science of healthspan extension may be focused on the wrong end of the lifespan; rather than only studying old humans, geroscience should also study the young.
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Quantification of biological aging in young adults
Daniel W. Belsky
a,b,1
, Avshalom Caspi
c,d,e,f
, Renate Houts
c
, Harvey J. Cohen
a
, David L. Corcoran
e
, Andrea Danese
f,g
,
HonaLee Harrington
c
, Salomon Israel
h
, Morgan E. Levine
i
, Jonathan D. Schaefer
c
, Karen Sugden
c
, Ben Williams
c
,
Anatoli I. Yashin
b
, Richie Poulton
j
, and Terrie E. Moffitt
c,d,e,f
a
Department of Medicine, Duke University School of Medicine, Durham, NC 27710;
b
Social Science Research Institute, Duke University, Durham, NC 27708;
c
Department of Psychology & Neuroscience, Duke University, Durham, NC 27708;
d
Department of Psychiatry & Behavioral Sciences, Duke University
School of Medicine, Durham, NC 27708;
e
Center for Genomic and Computational Biology, Duke University, Durham, NC 27708;
f
Social, Genetic, &
Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, London SE5 8AF, United Kingdom;
g
Department of Child &
Adolescent Psychiatry, Institute of Psychiatry, Kings College London, London SE5 8AF, United Kingdom;
h
Department of Psychology, The Hebrew
University of Jerusalem, Jerusalem 91905, Israel;
i
Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, University of
California, Los Angeles, CA 90095; and
j
Department of Psychology, University of Otago, Dunedin 9016, New Zealand
Edited by Bruce S. McEwen, The Rockefeller University, New York, NY, and approved June 1, 2015 (received for review March 30, 2015)
Antiaging therapies show promise in model organism research.
Translation to humans is needed to address the challenges of an
aging global population. Interventions to slow human aging will
need to be applied to still-young individuals. However, most human
aging research examines older adults, many with chronic disease. As
a result, little is known about aging in young humans. We studied
aging in 954 young humans, the Dunedin Study birth cohort,
tracking multiple biomarkers across three time points spanning
their third and fourth decades of life. We developed and validated
two methods by which aging can be measured in young adults,
one cross-sectional and one longitudinal. Our longitudinal mea-
sure allows quantification of the pace of coordinated physiological
deterioration across multiple organ systems (e.g., pulmonary,
periodontal, cardiovascular, renal, hepatic, and immune function).
We applied these methods to assess biological aging in young
humans who had not yet developed age-related diseases. Young
individuals of the same chronological age varied in their biological
aging(declining integrity of multiple organ systems). Already,
before midlife, individuals who were aging more rapidly were less
physically able, showed cognitive decline and brain aging, self-
reported worse health, and looked older. Measured biological
aging in young adults can be used to identify causes of aging
and evaluate rejuvenation therapies.
biological aging
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cognitive aging
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aging
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healthspan
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geroscience
By 2050, the world population aged 80 y and above will more
than triple, approaching 400 million individuals (1, 2). As the
population ages, the global burden of disease and disability is
rising (3). From the fifth decade of life, advancing age is asso-
ciated with an exponential increase in burden from many different
chronic conditions (Fig. 1). The most effective means to reduce
disease burden and control costs is to delay this progression by
extending healthspan, years of life lived free of disease and dis-
ability (4). A key to extending healthspan is addressing the prob-
lem of aging itself (58).
At present, much research on aging is being carried out with
animals and older humans. Paradoxically, these seemingly sen-
sible strategies pose translational difficulties. The difficulty with
studying aging in old humans is that many of them already have
age-related diseases (911). Age-related changes to physiology
accumulate from early life, affecting organ systems years before
disease diagnosis (1215). Thus, intervention to reverse or delay
the march toward age-related diseases must be scheduled while
people are still young (16). Early interventions to slow aging can
be tested in model organisms (17, 18). The difficulty with these
nonhuman models is that they do not typically capture the
complex multifactorial risks and exposures that shape human
aging. Moreover, whereas animalsbrief lives make it feasible to
study animal aging in the laboratory, humanslives span many
years. A solution is to study human aging in the first half of the
life course, when individuals are starting to diverge in their aging
trajectories, before most diseases (and regimens to manage
them) become established. The main obstacle to studying aging
before old ageand before the onset of age-related diseases
is the absence of methods to quantify the Pace of Aging in
young humans.
We studied aging in a population-representative 19721973 birth
cohort of 1,037 young adults followed from birth to age 38 y with
95% retention: the Dunedin Study (SI Appendix). When they were
38 y old, we examined their physiologies to test whether this young
population would show evidence of individual variation in aging
despite remaining free of age-related disease. We next tested the
hypothesis that cohort members with olderphysiologies at age 38
had actually been aging faster than their same chronologically aged
peers who retained youngerphysiologies; specifically, we tested
whether indicators of the integrity of their cardiovascular, meta-
bolic, and immune systems, their kidneys, livers, gums, and lungs,
and their DNA had deteriorated more rapidly according to mea-
surements taken repeatedly since a baseline 12 y earlier at age 26.
We further tested whether, by midlife, young adults who were
aging more rapidly already exhibited deficits in their physical
functioning, showed signs of early cognitive decline, and looked
older to independent observers.
Results
Are Young Adults Aging at Different Rates? Measuring the aging pro-
cess is controversial. Candidate biomarkers of aging are numerous,
Significance
The global population is aging, driving up age-related disease
morbidity. Antiaging interventions are needed to reduce the
burden of disease and protect population productivity. Young
people are the most attractive targets for therapies to extend
healthspan (because it is still possible to prevent disease in the
young). However, there is skepticism about whether aging
processes can be detected in young adults who do not yet have
chronic diseases. Our findings indicate that aging processes can
be quantified in people still young enough for prevention of
age-related disease, opening a new door for antiaging thera-
pies. The science of healthspan extension may be focused on
the wrong end of the lifespan; rather than only studying old
humans, geroscience should also study the young.
Author contributions: D.W.B., A.C., R.P., and T.E.M. designed research; D.W.B., A.C., R.H.,
H.J.C., D.L.C., A.D., H.H., S.I., M.E.L., J.D.S., K.S., B.W., A.I.Y., R.P., and T.E.M. performed
research; M.E.L. contributed new reagents/ analytic tools; D.W.B., A.C., R.H., H.H. , and
T.E.M. analyzed data; and D.W.B., A.C., and T.E.M. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1
To whom correspondence should be addressed. Email: dbelsky@duke.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1506264112/-/DCSupplemental.
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but findings are mixed (1922). Multibiomarker algorithms have
been suggested as a more reliable alternative to single-marker aging
indicators (2325). A promising algorithm is the 10-biomarker US
National Health and Nutrition Survey (NHANES)-based measure of
Biological Age.In more than 9,000 NHANES participants aged
3075 y at baseline, Biological Age outperformed chronological age
in predicting mortality over a two-decade follow-up (26). Be-
cause NHANES participants were all surveyed at one time point,
age differences in biomarker levels were not independent of co-
hort effects; measured aging also included secular trends in en-
vironmental and behavioral influences on biomarkers. Similarly,
most deaths observed during follow-up occurred to the oldest
NHANES participants, leaving the algorithms utility for quantifi-
cation of aging in younger persons uncertain. We therefore applied
this algorithm to calculate the Biological Age of Dunedin Study
members, who all shared the same birth year and birthplace, and
were all chronologically 38 y old at the last assessment (SI Appen-
dix). Even though the Dunedin cohort remained largely free of
chronic disease, Biological Age took on a normal distribution,
ranging from 28 y to 61 y (M =38 y, SD =3.23; Fig. 2). This dis-
tribution was consistent with the hypothesis that some 38-y-old
cohort members were biologically older than others.
Biological Age is assumed to reflect ongoing longitudinal
change within a person. However, it is a cross-sectional measure
taken at a single point in time. Therefore, we next tested the
hypothesis that young adults with older Biological Age at age
38 y were actually aging faster. To quantify the pace at which an
individual is aging, longitudinal repeated measures are needed
that track change over time. The Dunedin Study contains lon-
gitudinal data on 18 biomarkers established as risk factors or
correlates of chronic disease and mortality. Our selection of 18
biomarkers was constrained by measures available 15 y ago at
time one, that can be assayed with high throughput, and that are
scalable to epidemiologic studies. Still, these biomarkers track
the physiological integrity of study memberscardiovascular,
metabolic, and immune systems, their kidneys, livers, and lungs,
their dental health, and their DNA (SI Appendix). We analyzed
within-individual longitudinal change in these 18 biomarkers
across chronological ages 26 y, 32 y, and 38 y to quantify each
study members personal rate of physiological deterioration,
their Pace of Aging.
The Pace of Aging was calculated from longitudinal analysis of
the 18 biomarkers in three steps (SI Appendix). First, all bio-
markers were standardized to have the same scale (mean =0,
SD =1 based on their distributions when study members were 26 y
old) and coded so that higher values corresponded to older levels
(i.e., scores were reversed for cardiorespiratory fitness, lung func-
tion, leukocyte telomere length, creatinine clearance, and high
density lipoprotein cholesterol, for which values are expected to
decline with increasing chronological age). Even in our cohort of
young adults, biomarkers showed a pattern of age-dependent de-
cline in the functioning of multiple organ system over the 12-y fol-
low-up period (Fig. 3). Second, we used mixed-effects growth
models to calculate each study members personal slope for each of
the 18 biomarkers; 954 individuals with repeated measures of bio-
markers contributed data to this analysis. Of the 51,516 potential
observations (n=954 study members ×18 biomarkers ×3time
points), 44,475 (86.3%) were present in the database and used to
estimate longitudinal growth curves modeling the Pace of Aging.
The models took the form Bit =γ0+γ1Ageit +μ0i+μ1iAgeit +eit,
where B
it
is a biomarker measured for individual iat time t,γ
0
and γ
1
are the fixed intercept and slope estimated for the cohort, and μ
0i
and μ
1i
are the random intercepts and slopes estimated for each
individual i. Finally, we calculated each study membersPaceof
Aging as the sum of these 18 slopes: PaceofAgingi=P18
B=1μ1iB.
We calculated Pace of Aging from slopes because our goal was to
quantify change over time. We summed slopes across biomarkers
because our goal was to quantify change across organ systems. An
additive model reduces the influence of temporary change isolated
to any specific organ system, e.g., as might arise from a transient
infection. The resulting Pace of Aging measure was normally
Fig. 1. Burden of chronic disease rises exponentially with age. To examine the association between age and disease burden, we accessed data from the
Institute for Health Metrics and Evaluation Global Burden of Disease database (www.healthdata.org/gbd) (43). Data graph (A) disability-adjusted life years
(DALYs) and (B) deaths per 100,000 population by age. Bars, from bottom to top, reflect the burden of cardiovascular disease (navy), type-2 diabetes (light
blue), stroke (lavender), chronic respiratory disease (red), and neurological disorders (purple).
Fig. 2. Biological Age is normally distributed in a cohort of adults aged 38 y.
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distributed in the cohort, consistent with the hypothesis that some
cohort members were aging faster than others.
The Pace of Aging can be scaled to reflect physiological
change relative to the passage of time. Because the intact birth
cohort represents variation in the population, it provides its own
norms. We scaled the Pace of Aging so that the central tendency
in the cohort indicates 1 y of physiological change for every one
chronological year. On this scale, cohort members ranged in
their Pace of Aging from near 0 y of physiological change per
chronological year to nearly 3 y of physiological change per
chronological year.
Study members with advanced Biological Age had experienced
a more rapid Pace of Aging over the past 12 y compared with
their biologically younger age peers (r=0.38, P<0.001; Fig. 4).
Each year increase in Biological Age was associated with a 0.05-y
increase in the Pace of Aging relative to the population norm.
Thus, a 38-y-old with a Biological Age of 40 y was estimated
to have aged 1.2 y faster over the course of the 12-y follow-up
period compared with a peer whose chronological age and
Biological Age were 38. This estimate suggests that a substantial
component of individual differences in Biological Age at midlife
emerges during adulthood.
We next tested whether individual variation in Biological Age
and the Pace of Aging related to differences in the functioning of
study membersbodies and brains, measured with instruments
commonly used in clinical settings (SI Appendix).
Does Accelerated Aging in Young Adults Influence Indicators of
Physical Function? In gerontology, diminished physical capability
is an important indication of aging-related health decline that
cuts across disease categories (27, 28). Study members with ad-
vanced Biological Age performed less well on objective tests of
physical functioning at age 38 than biologically younger peers
(Fig. 5). They had more difficulty with balance and motor tests
(for unipedal stance test of balance, r=0.22, P<0.001; for
grooved pegboard test of fine motor coordination, r=0.13, P<
0.001), and they were not as strong (grip strength test, r=0.19,
P<0.001). Study membersBiological Ages were also related to
their subjective experiences of physical limitation. Biologically
older study members reported having more difficulties with
physical functioning than did biologically younger age peers
(SF-36 physical functioning scale, r=0.13, P<0.012). We re-
peated these analyses using the Pace of Aging measure. Consistent
with findings for Biological Age, study members with a more rapid
Pace of Aging exhibited diminished capacity on the four measures
of physical functioning relative to more slowly aging age peers.
Does Accelerated Aging in Young Adults Influence Indicators of Brain
Aging? In neurology, cognitive testing is used to evaluate age-
related decline in brain integrity. The Dunedin Study conducted
cognitive testing when study members were children and re-
peated this testing at the age-38 assessment (29). Study members
with older Biological Ages had poorer cognitive functioning at
midlife (r=0.17, P<0.001). Moreover, this difference in
cognitive functioning reflected actual cognitive decline over the
years. When we compared age-38 IQ test scores to baseline test
scores from childhood, study members with older Biological Age
showed a decline in cognitive performance net of their baseline
level (r =0.09, P=0.010). Results were similar for the Pace of
Aging (Fig. 6). The literature on cognitive aging divides the
composite IQ into crystallized versus fluidconstituents (30, 31).
Crystallized abilities (such as the Information subtest) peak in the
fifties and show little age-related decline thereafter. In contrast,
fluid abilities (such as the digit symbol coding subtest) peak in the
twenties and show clear decline thereafter (31). The overall IQ
aggregates these age trends. This aggregation makes it a highly
reliable measure, albeit a conservative choice as a correlate of
Fig. 3. Healthy adults exhibit biological aging of multiple organ systems over 12 y of follow-up. Biomarker values were standardized to have mean =0and
SD =1 across the 12 y of follow-up (Z scores). Z scores were coded so that higher values corresponded to older levels of the biomarkers; i.e., Z scores for
cardiorespiratory fitness, lung function (FEV
1
and FEV
1
/FVC), leukocyte telomere length, creatinine clearance, and HDL cholesterol, which decline with age,
were reverse coded so that higher Z scores correspond to lower levels.
Fig. 4. Dunedin Study members with older Biological Age at 38 y exhibited
an accelerated Pace of Aging from age 2638 y. The figure shows a binned
scatterplot and regression line. Plotted points show means for bins of data
from 20 Dunedin Study members. Effect size and regression line were cal-
culated from the raw data.
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Biological Age and Pace of Aging. Therefore, we also report
results for individual subtests in SI Appendix. As expected, the
largest declines in cognitive functioning, and the largest corre-
lations between decline and Pace of Aging, were observed for
tests of fluid intelligence, in particular the digit symbol coding
test (r=0.15, P<0.001).
Neurologists have also begun to use high-resolution 2D pho-
tographs of the retina to evaluate age-related loss of integrity of
blood vessels within the brain. Retinal and cerebral small vessels
share embryological origin and physiological features, making
retinal vasculature a noninvasive indicator of the state of the
brains microvasculature (32). Retinal microvascular abnormali-
ties are associated with age-related brain pathology, including
stroke and dementia (3335). Two measurements of interest are
the relative diameters of retinal arterioles and venules. Narrower
arterioles are associated with stroke risk (36). Wider venules are
associated with hypoxia and dementia risk (37, 38). We calcu-
lated the average caliber of study membersretinal arterioles and
venules from images taken at the age-38 assessment. Consistent
with the cognitive testing findings, study members with advanced
Biological Age had older retinal vessels (narrower arterioles, r=
0.20, P<0.001; wider venules, r=0.17, P<0.001). Results
were similar for the Pace of Aging measure (Fig. 6).
Do Young Adults Who Are Aging Faster Feel and Look Older? Beyond
clinical indicators, a persons experience of aging is structured by
their own perceptions about their well-being and by the per-
ceptions of others. Consistent with tests of aging indicators, study
members with older Biological Age perceived themselves to be in
poorer health compared with biologically younger peers (r=
0.22, P<0.001). In parallel, these biologically older study
members were perceived to be older by independent observers.
We took a frontal photograph of each study members face at
age 38, and showed these to a panel of Duke University un-
dergraduates who were kept blind to all other information about
the study members, including their age. Based on the facial im-
ages alone, student raters scored study members with advanced
Biological Age as looking older than their biologically younger
peers (r=0.21, P<0.001). Results for self-perceived well-being
and facial age were similar when analyses were conducted using
the Pace of Aging measure (Fig. 7).
Discussion
Aging is now understood as a gradual and progressive de-
terioration of integrity across multiple organ systems (7, 39).
Here we show that this process can be quantified already in
young adults. We followed a birth cohort of young adults over 12 y,
from ages 2638, and observed systematic change in 18 bio-
markers of risk for age-related chronic diseases that was con-
sistent with age-dependent decline. We were able to measure
these changes even though the typical age of onset for the related
diseases was still one to two decades in the future and just 1.1%
of the cohort members had been diagnosed with an age-related
chronic disease.
Measuring aging remains controversial. We measured aging in
two ways. First, we used a biomarker scoring algorithm pre-
viously calibrated on a large, mixed-age sample. We applied this
algorithm to cross-sectional biomarker data collected when our
study members were all chronologically aged 38 y to calculate
their Biological Age. Second, we conducted longitudinal analysis
of 18 biomarkers in our population-representative birth cohort
when they were aged 26 y, 32 y, and 38 y. We used this longi-
tudinal panel dataset to model how each individual changed over
the 12-y period to calculate their personal Pace of Aging.
Pace of Aging and Biological Age represent two different ap-
proaches to quantifying aging. Pace of Aging captures real-time
longitudinal change in human physiology across multiple systems
and is suitable for use in studies of within-individual change. For this
analysis, we examined all 18 biomarkers with available longitudinal
Fig. 5. Healthy adults who were aging faster exhibited deficits in physical functioning relative to slower-aging peers. The figure shows binned scatter plots
of the associations of Biological Age and Pace of Aging with tests of physical functioning (unipedal stance test, grooved pegboard test, grip strength) and
study membersreports of their physical limitations. In each graph, Biological Age associations are plotted on the left in blue (red regression line) and Pace of
Aging associations are plotted on the right in green (navy regression line). Plotted points show means for bins of data from 20 Dunedin Study members. Effect
size and regression line were calculated from the raw data.
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data in the Dunedin Study biobank. The other approach, Bi-
ological Age, provides a point-in-time snapshot of physiological
integrity in cross-sectional samples. For this analysis, we used the
published 10-biomarker set developed from the NHANES.
These two approaches yielded consistent results. Study members
with older Biological Age had evidenced faster Pace of Aging
over the preceding 12 y. Based on Pace of Aging analysis, we
estimate that roughly 1/2 of the difference in Biological Age
observed at chronological age 38 had accumulated over the past
12 y. Our analysis shows that Biological Age can provide a
summary of accumulated aging in cases where only cross-sectional
data are available. For purposes of measuring the effects of risk
exposures and antiaging treatments on the aging process, Pace-
of-Aging-type longitudinal measures provide a means to test within
individual change.
Biological measures of study membersaging were mirrored in
their functional status, brain health, self-awareness of their own
physical well-being, and their facial appearance. Study members
who had older Biological Age and who experienced a faster Pace
of Aging scored lower on tests of balance, strength, and motor
coordination, and reported more physical limitations. Study
members who had an older Biological Age and who experienced
a faster Pace of Aging also scored lower on IQ tests when they
were aged 38 y, showed actual decline in full-scale IQ score from
childhood to age-38 follow-up, and exhibited signs of elevated
risk for stroke and for dementia measured from images of micro-
vessels in their eyes. Further, study members who had an older
Biological Age and who experienced a faster Pace of Aging re-
ported feeling in worse health. Undergraduate student raters
who did not know the study members beyond a facial photograph
were able to perceive differences in the aging of their faces.
Together, these findings constitute proof of principle for the
measures of Biological Age and Pace of Aging studied here to
serve as technology to measure aging in young people. Further
research is needed to refine and elaborate this technology. Here
we identify several future directions that can build on our initial
proof-of-principle for measuring accelerated aging up to midlife.
First, our analysis was limited to a single cohort, and one that
lacked ethnic minority populations. Replication in other cohorts
is needed, in particular in samples including sufficient numbers
of ethnic minority individuals to test the weathering hypothesis
that the stresses of ethnic minority status accelerate aging (40, 41).
Larger samples can also help with closer study of relatively rare
aging trajectories. Three Dunedin Study members had Pace of
Aging less than zero, appearing to grow physiologically younger
during their thirties. In larger cohorts, study of such individuals
may reveal molecular and behavioral pathways to rejuvenation.
Second, data were right censored (follow-up extended only to
age 38); aging trajectories may change at older ages. Some co-
hort members experienced negligible aging per year, a pace that
cannot be sustained throughout their lives. Future waves of data
collection in the Dunedin cohort will allow us to model these
nonlinear patterns of change. A further issue with right censoring
is that we lack follow-up data on disability and mortality with
which to evaluate the precision of the Pace of Aging measure.
Continued follow-up of the Dunedin cohort and analysis of
other cohorts with longer-range follow-up can be used to con-
duct, e.g., receiver operating characteristic curve and related
analyses (42) to evaluate how well Pace of Aging forecasts health-
span and lifespan.
Third, data were left censored (biomarker follow-up began at
age 26); when and how aging trajectories began to diverge was
not observed. Studies tracking Pace of Aging earlier in adulthood
and studies of children are needed.
Fourth, measurements were taken only once every 6 y. Con-
tiguous annual measurements would provide better resolution
Fig. 6. Healthy adults who were aging faster showed evidence of cognitive decline and increased risk for stroke and dementia relative to slower-aging peers.
The figure shows binned scatter plots of the associations of Biological Age and Pace of Aging with cognitive functioning and cognitive decline (Top) and with the
calibers of retinal arterioles and venules (Bottom). The yaxes in the graphs of cognitive functioning and cognitivedecline are denominated in IQ points. The yaxes
in the graphs of arteriolar and venular caliber are denominated in SD units. In each graph, Biological Age associations are plotted on the left in blue
(red regression line) and Pace of Aging associations are plotted on the right in green (navy regression line). Plotted points show means for bins of data from
20 Dunedin Study members. Effect size and regression line were calculated from the raw data.
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to measure aging, but neither our funders nor our research
participants favored this approach. Medical record datasets
comprising primary care health screenings may provide annual
follow-up intervals (although patients seeking annual physicals
will not fully represent population aging).
Fifth, Pace of Aging analyses applied a unit weighting scheme
to all biomarkers. We weighted all biomarkers equally to trans-
parently avoid assumptions, and to avoid sample-specific find-
ings. Nonetheless, aging is likely to affect different bodily systems
to differing degrees at different points in the lifespan. Further
study is needed to refine weightings of biomarker contributions
to Pace of Aging measurement. For example, longitudinal data
tracking biomarkers could be linked with follow-up records of
disability and mortality to estimate weights for biomarker change.
Sixth, biomarkers used to measure aging in our study were
restricted to those scalable to a cohort based on technology
available during the measurement period (19982012). They
necessarily provide an incomplete picture of age-related changes
to physiology. Similarly, it is possible that not every biomarker in
our set of 18 is essential to measure aging processes. We used all
of the biomarkers that were repeatedly measured in the Dunedin
Study, some of which may become more (or less) important for
modeling Pace of Aging as our cohort grows older. Our leave-one-
out analysis showed that associations between Pace of Aging and
measures of physical and cognitive functioning and subjective
aging did not depend on any one biomarker. A next step is add-
one-in-type analysis to test the relative performance of bio-
marker subsets with the aim of identifying a short formof the
Pace of Aging. This analysis will require multiple datasets so that
an optimal short-form Pace of Aging identified in a training
dataset can be evaluated in an independent test dataset.
Seventh, methods are not available to estimate confidence
intervals for a persons Pace of Aging score. Datasets with re-
peated measures of multiple biomarkers are becoming available.
Our findings suggest that future studies of aging incorporate
longitudinal repeated measures of biomarkers to track change.
They also suggest that these studies of aging incorporate multiple
biomarkers to track change across different organ systems. Such
studies will require new statistical methods to calculate confi-
dence intervals around Pace of Aging-type scores.
Within the bounds of these limitations, the implication of the
present study is that it is possible to quantify individual differences
in aging in young humans. This development breaks through two
blockades separating model organism research from human
translational studies. One blockade is that animals age quickly
enough that whole lifespans can be observed whereas, in humans,
lifespan studies outlast the researchers. A second blockade is that
humans are subject to a range of complex social and genomic
exposures impossible to completely simulate in animal experiments.
If aging can be measured in free-living humans early in their
lifespans, there are new scientific opportunities. These include
testing the fetal programming of accelerated aging (e.g., does in-
trauterine growth restriction predispose to faster aging in young
adulthood?); testing the effects of early-life adversity (e.g., does
child maltreatment accelerate aging in the decades before chronic
diseases develop?); testing social gradients in health (e.g., do
children born into poor households age more rapidly than their
age-peers born into rich ones and can such accelerated aging be
slowed by childhood interventions?); and searching for genetic
regulators of aging processes (e.g., interrogating biological aging
using high throughput genomics). There are also potential clinical
applications. Early identification of accelerated aging before
chronic disease becomes established may offer opportunities for
prevention. Above all, measures of aging in young humans allow for
testing the effectiveness of antiaging therapies (e.g., caloric restric-
tion) without waiting for participants to complete their lifespans.
Materials and Methods
A more detailed description of study measures, design, and analysis is pro-
vided in SI Appendix.
Sample. Participants are members of the Dunedin Multidisciplinary Health
and Development Study, which tracks the development of 1,037 individuals
born in 19721973 in Dunedin, New Zealand.
Measuring Biological Age. We calculated each Dunedin Study members
Biological Age at age 38 y using the KlemeraDoubal equation (23) and param-
eters estimated from the NHANES-III dataset (26) for 10 biomarkers. Biological Age
took on a normal distribution, ranging from 28 y to 61 y (M =38 y, SD =3.23).
Measuring the Pace of Aging. We measured Pace of Aging from repeated
assessments of a panel of 18 biomarkers, 7 of which overlapped with the
Biological Age algorithm. We modeled change over time in each biomarker
and composited results within each individual to calculate their Pace of
Aging. Study members ranged in their Pace of Aging from near 0 y of
physiological change per chronological year to nearly 3 y of physiological
change per chronological year.
Measuring Diminished Physical Capacity. We measured physical capacity as
balance, strength, motor coordination, and freedom from physical limitations
when study members were aged 38 y.
Measuring Cognitive Aging. We measured cognitive aging using neuropsy-
chological tests in childhood and at age 38 y and images of retinal
microvessels.
Measuring Subjective Perceptions of Aging. We measured subjective percep-
tions of aging using study membersself reports and evaluations of facial
photographs of the study members made by independent raters.
ACKNOWLEDGMENTS. We thank Dunedin Study members, their families,
unit research staff, and Dunedin Study founder Phil Silva. The Dunedin
Multidisciplinary Health and Development Research Unit is supported by the
New Zealand Health Research Council. This research received support from
US National Institute on Aging (NIA) Grants AG032282 and AG048895 and
Fig. 7. Healthy adults who were aging faster felt less healthy and were
rated as looking older by independent observers. The figure shows binned
scatter plots of the associations of Biological Age and the Pace of Aging with
self-rated health (Top) and with facial aging (Bottom). The yaxes are
denominated in SD units. In each graph, Biological Age associations are
plotted on the left in blue (red regression line) and Pace of Aging associa-
tions are plotted on the right in green (navy regression line). Plotted points
show means for bins of data from 20 Dunedin Study members. Effect size
and regression line were calculated from the raw data.
6of7
|
www.pnas.org/cgi/doi/10.1073/pnas.1506264112 Belsky et al.
UK Medical Research Council Grant MR/K00381X. Additional support was provided
by the Jacobs Foundation. D.W.B. received support from NIA through a post-
doctoral fellowship T32 AG000029 and P30 AG028716-08. S.I. was supported by a
Rothschild Fellowship from the Yad Hanadiv Rothschild Foundation.
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... However, most of the evidence comes from older adults, especially those aged over 65 years, many of whom are living with chronic illness or experiencing cognitive impairment. Since there is increasing evidence that active interventions for risk factors in midlife may reduce the incidence of dementia, especially Alzheimer's disease (AD) (2,6,7), it is necessary to know whether these associations still exist in the middle-aged and older population, which is essential for early intervention or delaying the dysfunction and AD (8). ...
... The geroscience hypothesis suggests that interventions to delay the biological processes of aging could prevent age-related diseases and extend healthy lifespan (9,10). Prior studies found that people who experienced an accelerated aging process tended to have poorer cognitive or memory function (8,(11)(12)(13). On the other hand, laboratory studies indicated that sleep disorders can affect the biological aging process through various pathways, such as mitochondrial metabolism, DNA damage, telomeric shortening, and chronic inflammation (14). ...
... People with higher values of KDM-BAacc or PD are more likely to experience worsening cognitive performance. These findings are consistent with those of previous studies (8,10,11). Biologically older individuals had poorer cognitive performance at midlife, and this difference mirrored a real decrease in cognitive function over time (8). Notably, our results suggested that the two measures revealed inconsistent associations with specific domains. ...
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Objective The complicated association of daytime napping, biological aging and cognitive function remains inconclusive. We aimed to evaluate the cross-sectional and longitudinal associations of daytime napping and two aging measures with cognition and to examine whether napping affects cognition through a more advanced state of aging. Methods Data was collected from the China Health and Retirement Longitudinal Study. Napping was self-reported. We calculated two published biological aging measures: Klemera and Doubal biological age (KDM-BA) and physiological dysregulation (PD), which derived information from clinical biomarkers. Cognitive z-scores were calculated at each wave. Linear mixed models were used to explore the longitudinal association between napping, two aging measures, and cognitive decline. Mediation analyses were performed to assess the mediating effects of biological age acceleration on the association between napping and cognition. Results Participants aged over 45 years were included in the analyses. Non-nappers had greater KDM-BA and PD [LS means (LSM) = 0.255, p = 0.007; LSM = 0.085, p = 0.011] and faster cognitive decline (LSM = −0.061, p = 0.005)compared to moderate nappers (30–90 min/nap). KDM-BA ( β = −0.007, p = 0.018) and PD ( β = −0.034, p < 0.001) showed a negative association with overall cognitive z scores. KDM-BA and PD partially mediated the effect of napping on cognition. Conclusion In middle-aged and older Chinese, compared to moderate nappers, non-nappers seem to experience a more advanced state of aging and increased rates of cognitive decline. The aging status possibly mediates the association between napping and cognition. Moderate napping shows promise in promoting healthy aging and reducing the burden of cognitive decline in Chinese middle-aged and older adults.
... Health assessments in older adults are relevant and complex tasks, given the drastic increase in global aging, and great diversity of their capacities [1,2]. Aging is specially related to changes in physical [3][4][5] and cognitive domains [6,7]. ...
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Background Valid and reliable measurements are necessary to understand and monitor age-related changes. Aims To describe the factor structure and provide validity evidence of a neuropsychological and a physical testing batteries using factor analysis. Methods We performed a secondary analysis of data from the Epidemiology and Development of Alzheimer’s Disease (EDAD) project. Community-dwelling adults aged 55 to 85 years underwent comprehensive physical and neuropsychological assessments. An exploratory factor analysis was performed on both assessment batteries. The models were later confirmed with a random subsample using confirmatory factor analysis. Results Data from 238 adults (163 females and 75 males) was included. The neuropsychological model revealed a four-factor structure formed by “Executive Functioning”, “Verbal Memory”, “Logical Memory”, and “Labeling And Reading” (Extraction Sums of Squared Loadings [ESSL] = 56.41% explained variance; Standardized Root Mean Square Residual [SRMSR] = 0.06; Comparative Fit Index [CFI] = 0.98). The physical model was formed by a two-factor structure including “Health-related Fitness and “Functional Fitness” (ESSL = 50.54% explained variance; SRMSR = 0.07; CFI = 0.93). Discussion To our knowledge, this is the first study to analyze the structure of comprehensive testing batteries for the Latin-American older adults. Our analysis contributes to the understanding of theoretical constructs that are evaluated in the EDAD project. Conclusion Our findings provide validity evidence for simplified and reduced testing batteries, which imply shorter testing times and fewer resources.
... Third, findings on SPA and cognitive pathology may further strengthen the role of psychology in understanding the multifactorial pathways that are involved in neurodegenerative disorders. In fact, SPA may increasingly take on the status as 'biopsychosocial markers of aging' [47] that may be sensitive to a variety of aging-related processes [48]. Fourth, research on SPA may also offer new avenues for prevention and intervention with relevance for public health and the positive framing of aging and old age in society at large [49]. ...
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Background Aging is an inevitable phenomenon of biological processes, and frailty, one of its key symptoms, usually reflects a decline in the body's functional and adaptive capacity. In this study, we aimed to investigate the association between frailty index (FI) and phenotypic age using quantitative measures. Herein, a cross-sectional study in a U.S. population reinforces current clinical knowledge that frailty promotes accelerated aging in phenotypic age. Methods In this cross-sectional study, data from the National Health and Nutrition Examination Survey (NHANES) were utilized, encompassing 11,918 participants aged 20 years and older. The analyses employed multiple logistic regression and restricted cubic splines (RCS). Additionally, subgroup analyses stratified by covariates were performed. Results This study included 11,918 adult participants with complete data. After adjusting for all confounding factors, a significant positive correlation was observed between FI and phenotypic age [2.04 (1.89, 2.18)], indicating that for every 0.1 increase in FI score, the phenotypic age increased by 2.04 years. Further subgroup analysis demonstrated that this association was significant only in some subgroups. Conclusion We observed a correlation between FI and the accelerated aging represented by phenotypic age. Our findings warrant further confirmation in future, more extensive prospective studies.
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As our society ages, questions concerning the relations between generations gain importance. The quality of human relations depends on the quality of emotion communication, which is a significant part of our daily interactions. Emotion expressions serve not only to communicate how the expresser feels, but also to communicate intentions (whether to approach or retreat) and personality traits (such as dominance, trustworthiness, or friendliness) that influence our decisions regarding whether and how to interact with a person. Emotion Communication by the Aging Face and Body delineates how aging affects emotion communication and person perception by bringing together research across multiple disciplines. Scholars and graduate students in the psychology of aging, affective science, and social gerontology will benefit from this over-view and theoretical framework.
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