Early-Life Intelligence Predicts Midlife Biological Age

Abstract

Early-life intelligence has been shown to predict multiple causes of death in populations around the world. This finding suggests that intelligence might influence mortality through its effects on a general process of physiological deterioration (i.e., individual variation in "biological age"). We examined whether intelligence could predict measures of aging at midlife before the onset of most age-related disease.
We tested whether intelligence assessed in early childhood, middle childhood, and midlife predicted midlife biological age in members of the Dunedin Study, a population-representative birth cohort.
Lower intelligence predicted more advanced biological age at midlife as captured by perceived facial age, a 10-biomarker algorithm based on data from the National Health and Nutrition Examination Survey (NHANES), and Framingham heart age (r = 0.1-0.2). Correlations between intelligence and telomere length were less consistent. The associations between intelligence and biological age were not explained by differences in childhood health or parental socioeconomic status, and intelligence remained a significant predictor of biological age even when intelligence was assessed before Study members began their formal schooling.
These results suggest that accelerated aging may serve as one of the factors linking low early-life intelligence to increased rates of morbidity and mortality.
© The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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© The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.
For permissions, please e-mail: journals.permissions@oup.com. 1
Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, 1–8
doi:10.1093/geronb/gbv035
Original Research Report
Original Research Report
Early-Life Intelligence Predicts Midlife Biological Age
Jonathan D.Schaefer,1 AvshalomCaspi,1,2 Daniel W.Belsky,3,4
HonaleeHarrington,1 RenateHouts,1 SalomonIsrael,1,5 Morgan
E.Levine,6 KarenSugden,1,7 BenjaminWilliams,1,7 RichiePoulton,8 and
Terrie E.Moffitt2,9
1Department of Psychology and Neuroscience, Duke University, Durham, North Carolina. 2Social, Genetic, and
Developmental Psychiatry Centre, Institute of Psychiatry, King’s College, London, UK. 3Social Science Research
Institute, Duke University, Durham, North Carolina. 4Department of Medicine, Duke University School of Medicine,
Durham, North Carolina. 5Department of Psychology, Hebrew University, Jerusalem, Israel. 6Department of Human
Genetics, University of California, Los Angeles, California. 7Center for Genomic and Computational Biology, Duke
University, Durham, North Carolina. 8Dunedin Multidisciplinary Health and Development Research Unit, Department
of Psychology, University of Otago, Dunedin, New Zealand. 9Department of Psychiatry and Behavioral Sciences,
Duke University, Durham, North Carolina.
Correspondence should be addressed to Jonathan Schaefer, BA, Department of Psychology and Neuroscience, Duke Uni-
versity, Durham, NC 27708. E-mail: jds116@duke.edu.
Abstract
Objectives. Early-life intelligence has been shown to predict multiple causes of death in populations
around the world. This finding suggests that intelligence might influence mortality through its
effects on a general process of physiological deterioration (i.e., individual variation in “biological
age”). We examined whether intelligence could predict measures of aging at midlife before the
onset of most age-related disease.
Methods. We tested whether intelligence assessed in early childhood, middle childhood,
and midlife predicted midlife biological age in members of the Dunedin Study, a population-
representative birth cohort.
Results. Lower intelligence predicted more advanced biological age at midlife as captured by
perceived facial age, a 10-biomarker algorithm based on data from the National Health and Nutrition
Examination Survey (NHANES), and Framingham heart age (r= 0.1–0.2). Correlations between
intelligence and telomere length were less consistent. The associations between intelligence and
biological age were not explained by differences in childhood health or parental socioeconomic
status, and intelligence remained a significant predictor of biological age even when intelligence
was assessed before Study members began their formal schooling.
Discussion. These results suggest that accelerated aging may serve as one of the factors linking
low early-life intelligence to increased rates of morbidity and mortality.
Key Words: Aging—Biomarkers—Cognition—IQ—Intelligence.
Decision Editor: Shevaun Neupert, PhD
Intelligence in early adulthood and middle age is an important risk
factor for early death, predicting risk of premature mortality better
than many other commonly assessed risk factors, including blood
pressure, dyslipidemia, and body mass index (Batty, Shipley, Gale,
Mortensen, & Deary, 2008). Arecent meta-analysis of 16 independ-
ent studies concluded that a 1 SD advantage in intelligence test scores
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assessed within the rst two decades of life is associated with a 24%
lower risk of death over a follow-up period of 17–69years (Calvin
etal., 2011). This body of work forms the backbone of “cognitive
epidemiology,” a new eld which seeks to document and explain the
ways in which intellectual differences inuence health and longevity
(Deary, 2010). Among the key developments in this eld are ndings
that low intelligence is associated not just with premature death, but
also with a range of health conditions, beginning with obesity and
the metabolic syndrome in the rst half of the life course, followed
by type 2 diabetes and heart disease in later life, and dementia in old
age (Arden, Gottfredson, & Miller, 2009; Batty etal., 2008; Belsky
etal., 2013; Der, Batty, & Deary, 2009; Wrulich etal., 2013).
The challenge for cognitive epidemiology now is to identify why
low childhood intelligence is associated with such a diverse array of
negative health outcomes. One possibility is that associations between
intelligence, disease, and mortality arise because less intelligent peo-
ple actually “age” faster than their more intelligent peers. The con-
cept of accelerated aging arises from observations that age-related
chronic diseases are preceded by a gradual accumulation of damage
to multiple organ systems that begins in the rst half of the life course
(Ben-Shlomo & Kuh, 2002). Consequently, if children with lower
intelligence are aging faster, evidence of this acceleration should be
detectable even before the onset of chronic diseases that ultimately
causedeath.
One way to observe accelerated aging before the onset of disease
is to examine measures of “biological age.” Measures of biological
age capture the progressive deterioration in physiological functioning
that transforms the physical and cognitive tness of healthy adult-
hood into frailty characterized by increasing vulnerability to injury,
disease, and death (Butler etal., 2004). Examples of such measures
include specic biomarkers such as leukocyte telomere length (LTL),
as well as composite indices that synthesize information from mul-
tiple biomarkers, like the Framingham heart age. Importantly, these
measures can be taken at any chronological age, and can therefore
help to identify individuals who are aging more rapidly than their
peers even at younger ages before pathology presents.
We tested the hypothesis that low intelligence predisposes to
accelerated aging using four measures of biological age: perceived
facial age, a 10-biomarker algorithm developed using data from the
National Health and Nutritional Examination Survey (NHANES
III; Levine, 2013), an estimate of cardiovascular disease (CVD) risk
translated into a measure of “vascular age” using data from the
Framingham group (D’Agostino etal., 2008), and LTL. We exam-
ined data from the Dunedin Study of a complete birth cohort. The
Dunedin Study measured intelligence beginning in early childhood,
when cohort members were 3years old. Biological age was assessed
at midlife, when cohort members were aged 38years—before the
onset of most age-related disease.
Whereas associations between certain components (e.g., lung
function, C-reactive protein) of our two composite measures of bio-
logical age have been explored in relation to intelligence in previous
studies (Batty, Deary, Schoon, & Gale, 2007; Calvin, Batty, Lowe, &
Deary, 2011; Richards, Strachan, Hardy, Kuh, & Wadsworth, 2005),
we chose to examine these markers as constituents of larger compos-
ites (where appropriate) because doing so allows for capture of the
concurrent age-related decline of multiple biomarkers across a vari-
ety of bodily systems (the sign of advancing biological age) as well
as more accurate prediction of mortality (D’Agostino etal., 2008;
Levine, 2013). In addition, our composite measures are less suscepti-
ble to “noise” generated by transient uctuations in individual mark-
ers due to temporary illness or stochastic variation, and minimize the
inuence of non-error sources of variation seen in specic markers
while aggregating the common variance cutting across markers, fur-
ther enhancing construct validity.
To strengthen the inference that low intelligence contributes to
accelerated aging, we also tested whether the association between
intelligence and biological age could be accounted for by early-life
exposures known to decrease intelligence as well as increase the
risk of ill health and disease. For example, preterm birth and low
birth weight are risk factors for low IQ (Newcombe, Milne, Caspi,
Poulton, & Moftt, 2007), age-related diseases (Barker, Osmond,
Golding, Kuh, & Wadsworth, 1989), and early mortality (D’Onofrio
etal., 2013). Thus, infants who suffer more perinatal problems may
later display both reduced intelligence and accelerated aging, creat-
ing the false impression of a causal relationship. Similarly, childhood
illness may interfere with a child’s performance on cognitive tests
as well as inuence later measures of aging. We therefore included
statistical adjustments for perinatal complications and childhood ill
health to address these possibilities.
Research designs aimed at untangling socioeconomic status
(SES) and intelligence suggest that physical health appears to be
more closely associated with intellectual ability than socioeconomic
privilege, at least in adolescence (Lubinski & Humphreys, 1992).
However, previous research also suggests that children’s early SES
inuences their intelligence (Von Stumm & Plomin, 2015), and that
socioeconomically advantaged children may benet from resources
that promote healthy aging (Strand etal., 2010). To control for a
possible confounding effect of some Study members’ early economic
privilege, we thus included an additional statistical adjustment for
childhoodSES.
Finally, education is also likely to affect intelligence test scores
(Brinch & Galloway, 2012). However, because the effects of intelli-
gence and educational attainment are reciprocal over the life course,
it is difcult to disentangle their effects in observational studies.
Consequently, instead of using a statistical covariate to control for
educational attainment, we exploited our prospective design to
examine whether biological age could be predicted by intelligence
tested prior to the start of Study members’ formal schooling. This
exceptionally early measure of intelligence provides us with a signi-
cant advantage over previous research, which has typically assessed
intelligence in early adolescence or young adulthood (Calvin etal.,
2011).
Methods
Sample
Participants are members of the Dunedin Multidisciplinary Health
and Development Study, a longitudinal investigation of health and
behavior in a complete birth cohort. Study members (N= 1,037;
91% of eligible births; 52% male) were all individuals born between
April 1972 and March 1973 in Dunedin, New Zealand who were
eligible for the longitudinal study based on residence in the province
at age 3, and who participated in the rst follow-up assessment at
age 3. The cohort represents the full range of SES in the general
population of New Zealand’s South Island and is primarily white.
Assessments were carried out at birth and at ages 3, 5, 7, 9, 11, 13,
15, 18, 21, 26, 32, and, most recently, 38years, when 95% of the
1,007 Study Members still alive took part. At each assessment wave,
each Study member is brought to the Dunedin research unit for a
full day of interviews and examinations. There were 30 deaths in the
cohort between assessment waves at ages 3 and 38; however, in each
case the cause of death was not due to age-related disease. By age
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38, only 11 Study members had been diagnosed with an age-related
condition such as type II diabetes, myocardial infarction, or stroke.
The Otago Ethics Committee approved each phase of the Study and
informed consent was obtained from all Study members.
Measures of Intelligence
Intellectual assessments were conducted in early childhood (ages 3
and 5), middle childhood (ages 7, 9, and 11), and again at midlife
(age 38). Correlations among our 3 measures of intelligence ranged
from 0.577 (early childhood and midlife) to 0.791 (middle child-
hood and midlife).
Early-childhood intelligence
At age 3, we measured intelligence using two measures of ver-
bal comprehension: the Peabody Picture Vocabulary Test (PPVT;
Dunn, 1965) and the Receptive Language Scale from the Reynell
Developmental Language Scales (RDLS; Reynell, 1969). On the
PPVT, the child is asked to point to one of four pictures in response
to a stimulus word; in this way, a measure of verbal comprehen-
sion is made. On the RDLS, verbal comprehension is assessed by
presenting the child with toys and asking him or her to respond to
questions. At age 5, we measured participants’ intelligence using the
Stanford–Binet Intelligences Scales (Terman & Merrill, 1960), which
involve a variety of tasks set out in age levels from age two to supe-
rior adult level centering largely on language comprehension and
expression. We then averaged standardized versions of Study mem-
bers’ ages 3 and 5 intelligence test scores to create a single measure
of intelligence in early childhood.
Middle-childhood intelligence
At ages 7, 9, and 11, we report results from the Wechsler Intelligence
Scale for Children—Revised (WISC-R; Wechsler, 1974), using par-
ticipants’ total scores averaged over the three assessment points to
represent intelligence in middle-to-late childhood.
Midlife intelligence
At age 38, we report results from the Wechsler Adult Intelligence
Scale, 4th Edition (WAIS-IV; Wechsler, 2008).
Midlife Aging Outcomes
We used clinical biomarkers alongside other sources of information
to create four measures of age 38 biological age. Physical exami-
nations were conducted during the age 38 assessment day at the
Dunedin Study Research Unit, with 4-hour postprandial blood
draws between 4:15 and 4:45 pm. Table1 shows the correlations
among these four outcome measures.
Perceived facialage
Perceived facial age is an assessment of how old a person appears
relative to his or her chronological age, reecting tissue integrity.
We chose to include this measure in our analyses because per-
ceived age is widely used as a general indicator of health by clini-
cians, and is correlated with early mortality and telomere length
(Christensen etal., 2009). Because there is no consensus regarding
which approach is the best measure of perceived age, we used two
methods. First, age range was assessed by a panel of four under-
graduate raters blind to Study members’ actual ages. Raters were
presented with standardized (non-smiling) facial photographs of
Study members divided into sex-segregated slideshow batches con-
taining approximately 50 photos, viewed for 10s each. Raters were
randomized to viewing the slideshow batches in either forward
progression or backwards progression and used a Likert scale to
categorize each Study member into a 5-year age range (i.e., from
20–24years old up to 65–70years). Scores for each Study member
were averaged across all raters (α= 0.71; range: 25–29 to 53–57).
The second measure, relative age, was assessed by a different panel
of four undergraduates. These raters were told that all photos were
of people aged 38years old. Raters then used a 7-item Likert scale
to assign a “relative age” to each Study member (1=“young look-
ing”, 7=“old looking”). Scores for each Study member were aver-
aged across all raters (α=0.72; range: 2–6). Because age range and
relative age were highly correlated (r= 0.73), we standardized and
averaged both variables to create a composite measure of perceived
age at 38years (N=956).
Biomarker algorithm
Calculating human biological age is a relatively recent enterprise
and there is disagreement about methods (Mitnitski & Rockwood,
2013). Our goal was to borrow and implement the most validated
approaches. Recently, Levine (2013) used data from a nationally
representative, cross-sectional sample of adults aged 30–75 years
(NHANES III) to compare the ability of ve Biological Age algo-
rithms to predict mortality. Results showed that Klemera and Doubal
(2006) method performed the best (i.e., it predicted mortality, did so
signicantly better than chronological age, and accounted for the
association between chronological age and mortality). We chose to
include this measure in our analyses because it predicts mortality
better than any single biomarker considered in isolation.
BA
()
CA
1
EC
12
A
2
2
BA
2
1
=
−+




+
=
=
∑
∑
xq
k
ss
k
ss
jj
j
mj
j
j
j
j
m
B
This equation takes information from m number of regression
lines of chronological age regressed on m number of biomarkers,
where x is the value of biomarker j measured for an individual in
the Dunedin cohort. For each biomarker j, the parameters k, q, and
sBA are estimated from a regression of chronological age on the bio-
marker using data from NHANES III. Parameters k, q, and sBA, rep-
resent the regression intercept, slope, and root mean squared error,
respectively, from the age and biomarker-specic regression models.
CA represents chronological age (38 for all Dunedin cohort mem-
bers). Biomarkers used to calculate biological age in the Dunedin
cohort are the same as those used in Levine’s (2013) original analy-
sis. (Levine analyzed a panel of 21 biomarkers in the NHANES III
sample and included the 10 that were signicantly correlated with
chronological age at r > 0.1 in the biomarker algorithm.) The bio-
markers are: C-reactive protein, glycated hemoglobin, total cho-
lesterol, forced expiratory volume, systolic blood pressure, serum
creatinine, serum albumin, serum urea nitrogen, serum alkaline
phosphatase, and cytomegalovirus optical density. We excluded
Study members who did not consent to phlebotomy or were preg-
nant at the time of assessment, leaving us with data from 904 Study
members (Biomarker algorithm age range=28.33–61.01years).
Framingham heartage
Heart age is an estimate of vascular age based on the Framingham
CVD risk score, a single multivariable function that predicts risk
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of developing all CVD and its constituents. We chose to include
Framingham heart age in our analyses because the score is com-
monly used by physicians to communicate cardiovascular disease
risk to their patients. The 10-year CVD risk for each Study mem-
ber was computed using sex-specic factors collected at the age
38 assessment phase including: total cholesterol, HDL cholesterol,
systolic blood pressure, treatment for hypertension, diabetes status,
and smoking status. Framingham CVD risk was then translated to
Heart age using the Heart-age calculators made available by the
Framingham group (D’Agostino et al., 2008). We excluded Study
members who did not consent to phlebotomy, were pregnant at the
time of assessment, or were missing any of the individual variables,
leaving us with data from 900 Study members (Framingham heart
age range=22–85years).
Mean relativeLTL
Telomeres, the protective caps at the end of chromosomes, gradu-
ally erode in somatic tissues with each division of the cell. We chose
to include this measure in our analyses because both animal and
human studies show a link between telomere length and early mor-
tality (Deelen et al., 2014), and because telomere erosion can be
observed in midlife when most people are still healthy, leading some
to liken telomere length to a “biological clock” that captures cellular
aging across the lifespan (Lopez-Otin, Blasco, Partridge, Serrano, &
Kroemer, 2013). Leukocyte DNA was extracted from the blood of
non-Maori ancestry Study members at age 38 using standard pro-
cedures (for cultural reasons, DNA from Study members of Maori
ancestry are not transported to the United States for analysis). Study
members’ DNA was stored at −80°C until assayed to prevent degra-
dation of the samples. LTL was measured using a validated quantita-
tive PCR method, as previously described, which determines mean
telomere length across all chromosomes for all cells sampled (Shalev
etal., 2014). This method involves two quantitative PCRs for each
subject, one for a single-copy gene (S) and the other in the telom-
eric repeat region (T). All DNA samples were run in triplicate for
telomere and single-copy reactions—that is, six reactions per Study
member. We excluded Study members who only gave buccal swabs
and/or are of Maori ancestry, leaving us with data from 829 Study
members.
Additional Variables
Perinatal complications
We created a composite index of perinatal complications for each
Study member by combining prenatal information drawn from hos-
pital records with ndings from a physical examination performed
shortly after birth. The obstetric complications assessed in this Study
have been described previously (Shalev et al., 2014), and include
maternal diabetes, glycosuria, epilepsy, hypertension, eclampsia,
antepartum hemorrhage, accidental hemorrhage, placenta previa,
having had a previous small baby, gestational age younger than 37
weeks, birth weight less than 2.5 kg, small for gestational age, major
or minor neurologic signs, Rh incompatibility, ABO incompatibility,
non-hemolytic hyperbilirubinemia, hypoxia at birth (idiopathic res-
piratory distress syndrome or apnea), and low Apgar score at birth.
Based on evidence that the effects of adverse conditions are cumu-
lative (Molfese, 2013), each condition was weighted equally and
summed to yield an obstetric complications index. 650 Study mem-
bers (63%) had 0 perinatal complications, 271 (26%) had 1 perinatal
complication, and 116 (11%) had 2 or more.
Childhood ill health
Information about Study members’ childhood medical status was
gathered every 2years via standardized medical assessments and par-
ent reports. Examinations included assessment by a neurologist, motor
tests, and otological and opthalmological assessments. Parents were
interviewed about milestones, accidents and poisonings, loss of con-
sciousness, infections, and disease. In addition, home visits were con-
ducted by a Health Department nurse, and a pediatrician conducted
a general medical examination at the research unit. We compiled a
“medical portfolio” for each child from birth to age 5years, which
was independently evaluated by two staff members who were blind to
all other information about Study members. Each child’s health was
coded on a 5-point scale (1=“poor”, 5=“excellent”), with inter-rater
agreement=0.85. Using this method, 686 children (66%) were rated
as having health that was either “very good” or “excellent”.
ChildhoodSES
When Study members were born, we recorded parental SES on a scale
that places occupations into one of six categories (1=unskilled laborer,
6=professional) based on education and income associated with that
occupation in data from the New Zealand census. If both parents were
employed, we used the higher occupation (M=3.46, SD=1.36).
Results
Consistent with literature identifying low intelligence as a risk factor
for premature mortality (Calvin etal., 2011), the 30 Study members
in our cohort who were deceased by age 38 scored about one half of
a standard deviation below surviving cohort members on our meas-
ure of early-childhood intelligence, although this difference was not
signicant (d=0.42, p = .15). Cohort members with present data
for each of the four aging outcomes were representative of the 1,007
living cohort members with respect to early childhood intelligence
(all p’s ≥0.22).
Table1. Correlations Between Age 38 Aging Outcomes
Perceived facial age NHANES biomarker algorithm Framingham heart age Telomere length
Perceived facial age 1
956
NHANES algorithm 0.197*** 1
904 904
Framingham heart age 0.217*** 0.530*** 1
900 900 900
Telomere length −0.076* −0.059 −0.061 1
829 822 820 829
Notes. N for each correlation in italics. NHANES=National Health and Nutrition Examination Survey (III).
*p < .05, **p < .01, ***p < .001.
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Does Intelligence Predict Study Members’ Rate
ofAging?
At midlife, Study members with lower intelligence were biologically
“older” than their same-age peers with higher intelligence (Table2).
Study members with higher intelligence had younger-looking faces,
scored younger on the NHANES biomarker algorithm measure, had
“younger” cardiovascular systems, and, to a lesser extent, longer tel-
omeres. Results were similar regardless of whether intelligence was
assessed concurrently with the biological age measure (when Study
members were 38years old), in middle childhood (when Study mem-
bers were 7–11years old), or in early childhood (when Study mem-
bers were 3–5years old). Effect sizes were comparable for perceived
facial age, our biomarker algorithm, and heart age (r = 0.142–
0.182), but were more modest for telomere length (r=0.030–0.073).
Because smoking is one of the constituent items used to calcu-
late Framingham heart age and may inuence our other outcome
variables, we repeated these analyses using pack-years smoked as a
covariate (a pack-year represents the number of cigarettes consumed
during a year spent smoking 20 cigarettes per day). This adjustment
left the pattern of results largely unchanged (Supplementary Table A).
Can the Association Between Early-Life Intelligence
and Biological Age be Explained by Differences in
Study Members’ Early Environments?
It is possible that the association between intelligence and biologi-
cal age is driven partly by early educational experiences. Our data
allowed us to investigate this possibility in two ways. First, we were
able to examine Study members’ intelligence in early childhood, before
they began formal schooling. Study members with lower intelligence
at these early assessments were biologically older at midlife (Table2).
(Some Dunedin cohort members were enrolled in preschool by age 5,
but this did not increase their tested intelligence; Silva, 1981.)
Second, the correlations between biological age and the components
of intelligence that are more affected by schooling (e.g., verbal skills)
were roughly equivalent to the correlations between biological age and
the components of intelligence that are less affected by schooling (e.g.,
processing speed) (Table2). This pattern also suggests that the intelli-
gence-aging association is not simply a spurious artifact of education.
Is the Link Between Early-Life Intelligence and
Aging Partly Attributable to Initial Differences in
Early-Life Health or Early-LifeSES?
Children with more perinatal complications performed signi-
cantly worse on early childhood intelligence tests (r= −0.131) and
displayed more signs of aging (Table3). Children with ill health in
childhood showed a similar pattern, scoring lower on intelligence
tests (r=0.221) and “older” on measures of biological age (Table3).
Conversely, children born into upper-class families tended to per-
form better on intelligence tests than children from lower class fami-
lies (r=0.334), and scored “younger” on our measures of biological
age (Table3).
To determine whether the associations between intelligence and
aging outcomes are artifacts due to differences in our set of childhood
confounds that pre-dated intellectual assessment, we rst estimated
associations between intelligence in early childhood and each of the
midlife aging outcomes in two “utopian” subsamples (cf. Murray,
1998), one excluding all Study members with any history of peri-
natal complications whatsoever, and another including only Study
members with “very good” or “excellent” childhood health. Despite
reducing the sample size by almost half, Study members with lower
intelligence in these healthy groups still tended to show signs of more
advanced biological age (Table3). In addition, we repeated our analy-
ses in a subset of Study members who grew up in middle-class fami-
lies (whose breadwinners had occupations such as building inspector,
aircraft mechanic), excluding low-SES families (whose breadwinners
had low-skill occupations such as foodpacker), as well as high-SES
families (professional occupations such as dentist), thus precluding
confounding by SES inequalities. The association between childhood
IQ and our aging indicators again remained unaltered (Table3).
As a further test, we again calculated correlations between
Study members’ intelligence in early childhood and their scores
on each midlife aging measure in the full cohort, but this time
controlling for Study members’ histories of perinatal compli-
cations, childhood ill health, and childhood SES. Associations
between early childhood intelligence and midlife biological age
were largely unchanged (Table 3). Taken together, these nd-
ings support our hypothesis that the association between early
life intelligence and aging cannot be directly attributed to differ-
ences in childhood health or SES that preceded intelligence test
administration.
Discussion
In this longitudinal study of a birth cohort, we found that lower intel-
ligence manifest as early as the preschool years (ages 3–5) was pre-
dictive of more advanced biological age measured more than three
decades later. When followed up at age 38, Study members with lower
intelligence looked older, scored as biologically older on a 10-bio-
marker algorithm reecting metabolic, hepatic, renal, cardiovascular,
Table2. Correlations Between Intelligence Assessed throughout the First Half of the Life Course and Biological Age at Age38
Intelligence measures and age of
assessment
Measures of Aging
Perceived facial age NHANES biomarker algorithm Framingham heart age Telomere length
Early childhood (ages 3–5) −0.160*** −0.164*** −0.182*** 0.030
Middle childhood (ages 7–11) −0.161*** −0.149*** −0.142*** 0.073*
Midlife (age 38) −0.163*** −0.173*** −0.175*** 0.059
Verbal comprehension −0.172*** −0.140*** −0.166*** 0.042
Perceptual reasoning −0.101** −0.158*** −0.104** 0.026
Working memory −0.117*** −0.110** −0.090** 0.075*
Processing speed −0.098** −0.124*** −0.189*** 0.054
Notes. Weschler Adult Intelligence Scale, 4th Edition (WAIS-IV) indices listed in italics. There were no signicant sex differences in the associations between
intelligence and biological aging. NHANES=National Health and Nutrition Examination Survey (III).
*p < .05, **p < .01, ***p < .001.
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pulmonary, and immune functioning, and had older cardiovascular
systems—but not necessarily shorter telomeres. Moreover, our results
suggest that the associations between intelligence and midlife biologi-
cal age did not arise from early-life health problems or early socio-
economic disadvantage, and can be seen even when intelligence is
assessed before the start of Study members’ formal education.
While previous studies have established a link between low intel-
ligence and increased morbidity and mortality (Calvin etal., 2011;
Der et al., 2009; Whalley & Deary, 2001), our study provides an
initial demonstration that lower early-life intelligence may actually
accelerate the aging process—and that evidence of this acceleration
can be observed even in people assessed before the onset of most age-
related disease. This nding suggests that accelerated aging may be
one of the mechanisms linking low early-life intelligence to an array
of negative, age-related health outcomes.
Our study has several methodological strengths. First, we tested
intelligence repeatedly at different developmental stages and with
different instruments, beginning as early as age 3.We found that
the magnitude of the association between intelligence and biological
age remained consistent across all assessment ages, possibly reect-
ing the long-term stability of intelligence throughout the life course.
Second, our study included four distinct measures of accelerated
aging: perceived facial age, biomarker-assessed biological age, heart
age, and telomere length. Although smoking history is one of the
variables used to calculate Framingham heart age, we were able
to show that the associations between intelligence and our aging
indicators were independent of this well-established risk factor. And
third, the extraordinary retention rate of the Dunedin Study (with
95% of surviving Study members participating in the most recent
assessment wave at age 38) allows us to largely avoid problems
that commonly limit the generalizability of ndings from longitu-
dinal studies, such as selective attrition on the basis of intelligence
(Salthouse, 2014).
Nevertheless, we acknowledge limitations. First, although we were
able to rule out plausible artifactual explanations for why intelligence
is associated with biological age (i.e., differences in early education,
childhood health, and childhood SES), our data did not allow us to
determine whether this association is causal. Second, our results were
drawn from a single, largely Caucasian cohort born in the 1970s, and
thus may not generalize to other populations. However, our results are
consistent with ndings connecting intelligence to health and mortal-
ity in other cohorts born in different time periods and in different
countries (Arden etal., 2009; Wrulich etal., 2013).
Third, because we could examine only cross-sectional differences
in biological age at midlife rather than change from an early-life
baseline, it is possible that our midlife aging measures reect stable
individual differences rather than individual differences in change. In
other words, less intelligent people may score higher on aging meas-
ures because they were biologically “older” from early life, rather
than because they aged more rapidly. This hypothesis will need to
be explored by studies with repeated measurements of aging indica-
tors taken across the life course. Nevertheless, our observation that
early-life intelligence predicts biological age independent of baseline
differences in childhood health argues against the notion that intel-
lectual differences predict biological age simply because less intelli-
gent children are at greater risk of exhibiting poor health frombirth.
A fourth limitation, illustrated in Figure1, is that the associa-
tion between early-life intelligence and biological age had a rela-
tively small effect size in the population as a whole (r= 0.1–0.2).
For example, Study members who scored more than 1 SD above
or below the cohort mean for early childhood intelligence differed
in NHANES biological age by about 1year (Figure 1). Although a
year’s difference in biological age may not seem consequential for
individuals in their late 30s, this difference may have greater practi-
cal signicance in late life, as risk of mortality increases exponen-
tially with age. Furthermore, because biological aging measures have
Table3. Correlations between Early Childhood Intelligence and Aging Measures Assessed at Age 38, Controlling for Potential Childhood
Confounds
Childhood confounds and age of assessment Measures of aging
Perceived facial age NHANES biomarker algorithm Framingham heart age Telomere length
Perinatal complications (birth) 0.110*** 0.104** 0.052 −0.092**
Childhood Ill health (ages 3, 5) −0.124*** −0.072* −0.108** 0.012
Childhood SES (birth) −0.155*** −0.091** −0.102** −0.014
Early childhood intelligence (ages 3, 5) −0.160*** −0.164*** −0.182*** 0.030
in subsamples
With no history of perinatal complications
(birth)
−0.171*** −0.177**** −0.225*** 0.002
With “very good” or “excellent” childhood
health (ages 3, 5)
−0.106** −0.164*** −0.170*** 0.030
Born to middle-class families (birth) −0.146** −0.177*** −0.245*** 0.021
controlling for
Perinatal complications (birth) −0.147*** −0.152*** −0.177*** 0.020
Childhood ill health (ages 3, 5) −0.140*** −0.154*** −0.167*** 0.028
Childhood SES (birth) −0.111** −0.159*** −0.164*** 0.025
All three potential confoundsa−0.097** −0.158*** −0.163*** 0.016
Notes. Top panel: Correlations between potential childhood confounds and aging measures assessed at age 38. Middle panel: Correlations between early child-
hood intelligence and aging measures assessed at age 38 calculated in three restricted subsamples of Study members. Bottom panel: Correlations between early
childhood intelligence and aging measures assessed at age 38 calculated in the full cohort, adjusted for perinatal complications, childhood ill health, and childhood
SES. aStandardized betas from a general linear model using early childhood intelligence to predict each aging measure controlling for all three potential confounds.
NHANES=National Health and Nutrition Examination Survey (III); SES=socioeconomic status.
*p < .05, **p < .01, ***p < .001.
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stronger associations with mortality than chronological age, individ-
ual differences in biological age should exert more dramatic effects
on age-related disease and mortality than equivalent differences in
chronological age, particularly when such outcomes are considered
at the populationlevel.
Consistent with previous studies examining associations between
telomere length and childhood intelligence (Harris, Martin-Ruiz,
von Zglinicki, Starr, & Deary, 2012; Pearce et al., 2012), telomere
length showed the weakest association with intelligence in our
cohort. Interestingly, telomere length also showed only weak asso-
ciations with our three other aging measures, which adds to existing
evidence suggesting that the relationship between telomere length
and “normal” aging parameters such as physical, sensory, and cogni-
tive functioning is controversial (Sanders & Newman, 2013). The
relatively weak associations between telomere length and intelli-
gence seen here may also be due to differences in aggregation among
our four outcome measures: Unlike perceived facial age, NHANES
biomarker age, and Framingham heart age (which all combine either
multiple variables or multiple ratings from independent observers),
telomere length reects a single indicator.
The reason(s) why early-life intelligence predicts biological age
at midlife remain unclear. The literature connecting intelligence to
health outcomes suggests at least four nonexclusive possibilities:
First, the association between intelligence and biological age may
arise because more intelligent people typically gain access to better
health care, which may retard the aging process. Second, more intel-
ligent people may obtain access to safer occupational and residential
environments, which may, in turn, decrease their exposure to poten-
tially age-accelerating conditions such as chronic job stress, danger-
ous working conditions, environmental toxins, and/or interpersonal
violence. Third, intelligence may contribute to slower aging through
several health-related behaviors such as sleep, physical activity, and
dietary choices (Deary, Weiss, & Batty, 2010). And nally, intelli-
gence may function as a measure of “brain health,” which reects
overall somatic integrity (Deary, 2012). Proponents of this last
view have suggested that highly intelligent people age more slowly
because of genetic factors such as a decreased mutation load (Arden
etal., 2009) or pleiotropy at genetic loci associated with both higher
intelligence and a longer lifespan (Dubal etal., 2014).
Aging is increasingly conceptualized as a unitary phenomenon
that increases one’s risk of multiple age-related diseases simultane-
ously. Although life expectancy is increasing, people are living more
years with disability from age-related conditions in 2010 than they
were two decades ago (Murray etal., 2012). Identifying behavioral
and psychological risk factors for accelerated aging thus constitutes
a signicant public health interest. Along with research demonstrat-
ing that early-life educational interventions can affect later health
(Campbell etal., 2014), our study suggests the hypothesis that early-
life cognitive enhancement interventions may help to decrease or
delay age-related morbidity.
Supplementary Material
Supplementary material can be found at: http://psychsocgerontology.
oxfordjournals.org/
Funding
The Dunedin Multidisciplinary Health and Development Research
Unit is supported by the New Zealand Health Research Council. This
work was supported by the National Institute on Aging (AG032282,
AG048895), the Medical Research Council (MR/K00381X), the
Economic and Social Research Council (ES/M010309/1), and the
Jacobs Foundation. J.D. S.and D.W. B.were supported by the NIA
(T32-AG000139-25, T-32AG000029), and (P30-AG028716-08).
Acknowledgments
We thank the Dunedin Study members, their families, Study staff, and Study
founder Phil Silva. We also thank Dana Kotter-Grühn, for her helpful com-
ments on an earlier draft.
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