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Early-Life Intelligence Predicts Midlife Biological Age


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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:
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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: 1
Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, 1–8
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:
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
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
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.,
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
2 Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, Vol. 00, No. 00
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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.
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
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”.
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).
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
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
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
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
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.
Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, Vol. 00, No. 00 5
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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
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
−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.
6 Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, Vol. 00, No. 00
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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.
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).
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.
Arden, R., Gottfredson, L. S., & Miller, G. (2009). Does a tness factor con-
tribute to the association between intelligence and health outcomes? Evi-
dence from medical abnormality counts among 3654 US Veterans. Intel-
ligence, 37, 581–591. doi:10.1016/j.intell.2009.03.008
Barker, D. J., Osmond, C., Golding, J., Kuh, D., & Wadsworth, M. E. (1989).
Growth in utero, blood pressure in childhood and adult life, and mor-
tality from cardiovascular disease. BMJ, 298, 564–567. doi:10.1136/
Batty, G. D., Deary, I. J., Schoon, I., & Gale, C. R. (2007). Mental ability across
childhood in relation to risk factors for premature mortality in adult life:
The 1970 British Cohort Study. Journal of Epidemiology and Community
Health (1979-), 61, 997–1003. doi:10.1136/jech.2006.054494
Batty, G. D., Gale, C. R., Mortensen, L. H., Langenberg, C., Shipley, M. J.,
& Deary, I. J. (2008). Pre-morbid intelligence, the metabolic syndrome
and mortality: The Vietnam Experience Study. Diabetologia, 51, 436–443.
Batty, G. D., Shipley, M. J., Gale, C. R., Mortensen, L. H., & Deary, I. J.
(2008). Does IQ predict total and cardiovascular disease mortality as
strongly as other risk factors? Comparison of effect estimates using
the Vietnam Experience Study. Heart, 94, 1541–1544. doi:10.1136/
Belsky, D. W., Caspi, A., Goldman-Mellor, S., Meier, M. H., Ramrakha, S.,
Poulton, R., & Moftt, T. E. (2013). Is obesity associated with a decline
in intelligence quotient during the rst half of the life course? American
Journal of Epidemiology, 178, 1461–1468. doi:10.1093/aje/kwt135
Figure1. The association between early childhood intelligence and biological
age as measured by the NHANES biomarker algorithm. The histogram depicts
the normal distribution of Study members’ early childhood intelligence
scores, whereas the scatter plot and regression line show the association
between early childhood intelligence and age 38 biological age as measured
by the NHANES biomarker algorithm. The dots and standard error bars show
average biological age for Study members with early childhood intelligence
scores falling <−1.5, −1.5 to −1, −1 to −0.5, −0.5 to 0, 0–0.5, 0.5–1, 1–1.5, and
> 1.5 SDs relative to the mean. NHANES= National Health and Nutrition
Examination Survey (III).
Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, Vol. 00, No. 00 7
at Acq/Serials Dept-Periodicals on July 24, 2015 from
Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease
epidemiology: conceptual models, empirical challenges and interdiscipli-
nary perspectives. International Journal of Epidemiology, 31, 285–293.
Brinch, C. N., & Galloway, T. A. (2012). Schooling in adolescence raises IQ
scores. Proceedings of the National Academy of Sciences of the United
States, 109, 425–430. doi:10.1073/pnas.1106077109
Butler, R. N., Sprott, R., Huber, W., Bland, J., Feuers, R., Forster, M., … Wolf,
N. (2004). Biomarkers of aging: From primitive organisms to humans. The
Journals of Gerontology, 59A, B560–7. doi:10.1093/gerona/59.6.b560
Calvin, C. M., Batty, G. D., Lowe, G. D., & Deary, I. J. (2011). Childhood
intelligence and midlife inammatory and hemostatic biomarkers: The
National Child Development Study (1958) cohort. Health Psychology, 30,
710–718. doi:10.1037/a0023940
Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., &
Batty, G. D. (2011). Intelligence in youth and all-cause-mortality: System-
atic review with meta-analysis. International Journal of Epidemiology, 40 ,
626–644. doi:10.1093/ije/dyq190
Campbell, F., Conti, G., Heckman, J. J., Moon, S. H., Pinto, R., Pungello, E.,
& Pan, Y. (2014). Early childhood investments substantially boost adult
health. Science, 343, 1478–1485. doi:10.1126/science.1248429
Christensen, K., Thinggaard, M., McGue, M., Rexbye, H., Hjelmborg, J.v B.,
Aviv, A., … Vaupel, J. W. (2009). Perceived age as clinically useful bio-
marker of ageing: cohort study. BMJ, 339, b5262. doi:10.1136/bmj.b5262
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Mas-
saro, J. M., & Kannel, W. B. (2008). General cardiovascular risk prole
for use in primary care: The Framingham Heart Study. Circulation, 117,
743–753. doi:10.1161/circulationaha.107.699579
Deary, I. J. (2010). Cognitive epidemiology: Its rise, its current issues, and
its challenges. Personality and Individual Differences, 49, 337–343.
Deary, I. J. (2012). Looking for system integrity in cognitive epidemiology.
Gerontology, 58, 545–553. doi:10.1159/000341157
Deary, I. J., Weiss, A., & Batty, G. D. (2010). Intelligence and personality as
predictors of illness and death: How researchers in differential psychol-
ogy and chronic disease epidemiology are collaborating to understand and
address health inequalities. Psychological Science in the Public Interest,
11, 53–79. doi:10.1177/1529100610387081
Deelen, J., Beekman, M., Codd, V., Trompet, S., Broer, L., Hägg, S., … Slag-
boom, P. E. (2014). Leukocyte telomere length associates with prospec-
tive mortality independent of immune-related parameters and known
genetic markers. International Journal of Epidemiology, 43, 878–886.
Der, G., Batty, G. D., & Deary, I. J. (2009). The association between IQ in
adolescence and a range of health outcomes at 40 in the 1979 US National
Longitudinal Study of Youth. Intelligence, 37, 573–580. doi:10.1016/j.
D’Onofrio, B. M., Class, Q. A., Rickert, M. E., Larsson, H., Långström, N.,
& Lichtenstein P. (2013). Preterm birth and mortality and morbidity:
A population-based quasi-experimental study. JAMA Psychiatry, 70,
1231–1240. doi:10.1001/jamapsychiatry.2013.2107
Dubal, D. B., Yokoyama, J. S., Zhu, L., Broestl, L., Worden, K., Wang, D.,
Mucke, L. (2014). Life extension factor Klotho enhances cognition. Cell
Reports, 7, 1065–1076. doi:10.1016/j.celrep.2014.03.076
Dunn, L. M. (1965). Expanded manual for the peabody picture vocabulary
test. Minneapolis, MN: American Guidance Service.
Harris, S. E., Martin-Ruiz, C., von Zglinicki, T., Starr, J. M., & Deary, I. J.
(2012). Telomere length and aging biomarkers in 70-year-olds: The
Lothian Birth Cohort 1936. Neurobiology of Aging, 33, 1486.e3–1486.
e8. doi:10.1016/j.neurobiolaging.2010.11.013
Klemera, P., & Doubal, S. (2006). A new approach to the concept and com-
putation of biological age. Mechanisms of Ageing and Development, 127,
240–248. doi:10.1016/j.mad.2005.10.004
Levine, M. E. (2013). Modeling the rate of senescence: Can estimated bio-
logical age predict mortality more accurately than chronological age? The
Journals of Gerontology Series A: Biological Sciences and Medical Sci-
ences, 68, 667–674. doi:10.1093/gerona/gls233
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G.
(2013). The Hallmarks of Aging. Cell, 153, 1194–1217. doi:10.1016/j.
Lubinski, D., & Humphreys, L. G. (1992). Some bodily and medical correlates
of mathematical giftedness and commensurate levels of socioeconomic
status. Intelligence, 16, 99–115. doi:10.1016/0160-2896(92)90027-O
Mitnitski, A., & Rockwood, K. (2013). Biological age revisited. The Journals
of Gerontology Series A: Biological Sciences and Medical Sciences, 69,
295–296. doi:10.1093/gerona/glt137
Molfese, V. J. (2013). Perinatal risks across infancy and early childhood: What
are the lingering effects on high and low risk samples? In L. F. DiLalla &
S. M. C. Dollinger (Eds.), Assessment of biological mechanisms across the
life span. Hillsdale, NJ: Erlbaum.
Murray, C. A. (1998). Income inequality and IQ. Washington, DC: AEI Press.
Murray, C. J.L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud,
C., … Grant, B. (2012). Disability-adjusted life years (DALYs) for 291
diseases and injuries in 21 regions, 1990–2010: A systematic analysis for
the Global Burden of Disease Study 2010. The Lancet, 380, 2197–223.
Newcombe, R., Milne, B. J., Caspi, A., Poulton, R., & Moftt, T. E. (2007).
Birthweight predicts IQ: fact or artefact? Twin Research and Human
Genetics, 10, 581–586. doi:10.1375/twin.10.4.581
Pearce, M. S., Mann, K. D., Martin-Ruiz, C., Parker, L., White, M., von Zglinicki,
T., & Adams, J. (2012). Childhood growth, IQ and education as predictors of
white blood cell telomere length at age 49 -51 years: The Newcastle Thousand
Families Study. PLoS One, 7, e40116. doi:10.1371/journal.pone.0040116
Reynell, J. (1969). The Reynell developmental language scales. London, UK:
National Foundation for Educational Research.
Richards, M., Strachan, D., Hardy, R., Kuh, D., & Wadsworth, M. (2005).
Lung function and cognitive ability in a longitudinal birth cohort
study. Psychosomatic Medicine, 67, 602–608. doi:10.1097/01.
Salthouse, T. A. (2014). Selectivity of attrition in longitudinal studies of cogni-
tive functioning. The Journals of Gerontology Series B: Psychological Sci-
ences and Social Sciences, 69, 567–574. doi:10.1093/geronb/gbt046
Sanders, J. L., & Newman, A. B. (2013). Telomere length in epidemiology:
Abiomarker of aging, age-related disease, both, or neither? Epidemiologic
Reviews, 35, 112–131. doi:10.1093/epirev/mxs008
Shalev, I., Moftt, T. E., Braithwaite, A. W., Danese, A., Fleming, N. I., Gold-
man-Mellor, S., … Caspi, A. (2014). Internalizing disorders and leukocyte
telomere erosion: A prospective study of depression, generalized anxiety
disorder and post-traumatic stress disorder. Molecular Psychiatry, 19,
1163–1170. doi:10.1038/mp.2013.183
Silva, P. (1981). Developmental and educational experiences and activities. In P. Silva,
R. McGee, & S. M. Williams (Eds.), From birth to seven: Child development in
Dunedin: A multidisciplinary study. Otago, NZ: Otago University Press.
Strand, B. H., Groholt, E.-K., Steingrimsdottir, O. A., Blakely, T., Graff-Iversen,
S., & Naess, O. (2010). Educational inequalities in mortality over four
decades in Norway: Prospective study of middle aged men and women
followed for cause specic mortality, 1960–2000. BMJ, 340, c654.
Terman, L. M. & Merrill, M. A. (1960). Stanford-Binet intelligence scale:
Manual for the third revision form L-M. Boston, MA: Houghton Mifin.
Von Stumm, S., & Plomin, R. (2015). Socioeconomic status and the growth
of intelligence from infancy through adolescence. Intelligence, 48, 30–36.
Wechsler, D. (1974). Manual for the Wechsler Intelligence Scale for Children,
Revised. New York, NY: Psychological Corp.
Wechsler, D. (2008). Wechsler Adult Intelligence Scale (4th ed.). San Antonio,
TX: Pearson Assessment.
Whalley, L. J., & Deary, I. J. (2001). Longitudinal cohort study of child-
hood IQ and survival up to age 76. BMJ, 322, 819–822. doi:10.1136/
Wrulich, M., Brunner, M., Stadler, G., Schalke, D., Keller, U., Chmiel, M., &
Martin, R. (2013). Childhood intelligence and adult health: The mediating
roles of education and socioeconomic status. Intelligence, 41, 490–500.
8 Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 2015, Vol. 00, No. 00
at Acq/Serials Dept-Periodicals on July 24, 2015 from
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... Interestingly, lower childhood and adolescent intelligence has been found to predict more advanced biological age in mid-and late adulthood (Schaefer et al., 2015;Stevenson et al., 2019). Potentially, this explains the increased rates of later-life morbidity and mortality in individuals with lower IQ earlier in life (Batty et al., 2007;Schaefer et al., 2015). Furthermore, complex links have been described between personality traits in childhood and adolescence and adult outcome: For example, childhood sociability was found to be related to better subjective well-being and family relationships in mid-adulthood, and better relationships in mid-adulthood and childhood conscientiousness were predictive of a longer life . ...
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Evidence is accumulating that individual and environmental factors in childhood and adolescence should be considered when investigating adult health and aging-related processes. The data required for this is gathered by comprehensive long-term longitudinal studies. This article describes the protocol of the Zurich Longitudinal Studies (ZLS), a set of three comprehensive cohort studies on child growth, health, and development that are currently expanding into adulthood. Between 1954 and 1961, 445 healthy infants were enrolled in the first ZLS cohort. Their physical, motor, cognitive, and social development and their environment were assessed comprehensively across childhood, adolescence, and into young adulthood. In the 1970s, two further cohorts were added to the ZLS and assessed with largely matched study protocols: Between 1974 and 1979, the second ZLS cohort included 265 infants (103 term-born and 162 preterm infants), and between 1970 and 2002, the third ZLS cohort included 327 children of participants of the first ZLS cohort. Since 2019, the participants of the three ZLS cohorts have been traced and invited to participate in a first wave of assessments in adulthood to investigate their current health and development. This article describes the ZLS study protocol and discusses opportunities, methodological and conceptual challenges, and limitations arising from a long-term longitudinal cohort recruited from a study about development in early life. In the future, the ZLS will provide data to investigate childhood antecedents of adult health outcomes and, ultimately, will help respond to the frequent call of scientists to shift the focus of aging research into the first decades of life and, thus, to take a lifespan perspective on aging.
... Childhood cognitive developmental outcomes predict midlife biological age, too (Schaefer et al. 2016). For this reason, our revitalization efforts of the LTS are ideal for exploring whether individual differences in childhood cognitive developmental trajectories predict midlife biological age. ...
... We will attempt to correct for sampling bias in the full midlife study of the Louisville twins using information previously collected from the twins. Yet, the findings in the current pilot report suggest that cognitive developmental trajectories, lower biological age, higher functional ability, and higher physical functioning correlate in expected directions with midlife FSIQ and episodic memory, consistent with results found in larger studies (Deary et al. 2000;Singh-Manoux et al. 2005;Emery et al. 2012;Kimhy et al. 2013;Karlamangla et al. 2014;Schaefer et al. 2016). ...
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The Louisville Twin Study (LTS) began in 1958 and became a premier longitudinal twin study of cognitive development. The LTS continuously collected data from twins through 2000 after which the study closed indefinitely due to lack of funding. Now that the majority of the sample is age 40 or older (61.36%, N = 1770), the LTS childhood data can be linked to midlife cognitive functioning, among other physical, biological, social, and psychiatric outcomes. We report results from two pilot studies in anticipation of beginning the midlife phase of the LTS. The first pilot study was a participant tracking study, in which we showed that approximately 90% of the Louisville families randomly sampled (N = 203) for the study could be found. The second pilot study consisted of 40 in-person interviews in which twins completed cognitive, memory, biometric, and functional ability measures. The main purpose of the second study was to correlate midlife measures of cognitive functioning to a measure of biological age, which is an alternative index to chronological age that quantifies age as a function of the breakdown of structural and functional physiological systems, and then to relate both of these measures to twins’ cognitive developmental trajectories. Midlife IQ was uncorrelated with biological age (− .01) while better scores on episodic memory more strongly correlated with lower biological age (− .19 to − .31). As expected, midlife IQ positively correlated with IQ measures collected throughout childhood and adolescence. Additionally, positive linear rates of change in FSIQ scores in childhood significantly correlated with biological age (− .68), physical functioning (.71), and functional ability (− .55), suggesting that cognitive development predicts lower biological age, better physical functioning, and better functional ability. In sum, the Louisville twins can be relocated to investigate whether and how early and midlife cognitive and physical health factors contribute to cognitive aging.
... Differences between individuals in the population emerge early in the life course and they exist across a wide array of traits (Clegg et al., 2015;Moffitt et al., 2011;Schaefer et al., 2016). Beyond just physiological measures of growth and biological development, variation across psychological, cognitive, emotional, and temperamental traits is detectable within the first few years of childhood (Cvencek et al., 2020;Dixon & Smith, 2000;Moffitt et al., 2011). ...
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Children’s impulse control and intelligence are important for health and social functioning later in life, but the degree to which they impact later outcomes via common vs. unique pathways is still unclear, particularly in high-risk samples. The Fragile Families and Child Wellbeing Study (n = 4,898; 48% female; 18% White) was used to examine the plausibility of common or “global” dimensions of health problems and antisocial behavior at approximately age 15, as well as to examine the possible roles of impulsivity and intelligence in their etiologies. To this end, we employed structural equation modeling, controlled for covariates, and leveraged ratings from parents, teachers, observers, and children. Findings suggest that childhood impulsivity forecasted higher levels on dimensions of health problems and antisocial behaviors in adolescence, whereas with impulsivity controlled, childhood intelligence forecasted greater general risk for age-typical antisocial behavior and did not significantly predict global health. Future studies aiming to elucidate the degree to which adolescent outcomes emerge via common pathways will benefit from the use of latent variable modeling.
... To estimate 10-year cardiometabolic risk (FCMR10), the Framingham algorithm uses systolic blood pressure (SBP), body mass index (BMI), and diabetes (HbA1C > 6 or taking diabetes medication), plus it adjusts for an individual's chronological age and gender, and whether they currently smoke (young adults: 1 = yes, 24.3%; middle-age adults: 1 = yes, 27.1%) and whether they are taking antihypertensive medication (young adults: 1 = yes, 7.8%; middle-age adults: 1 = yes, 65.5%). This measure has been shown to have high validity and reliability [16] and has been commonly used by physicians to monitor their patient's health condition [17][18][19][71][72][73]. ...
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Background . Methylation of FKBP5 is involved in the regulation of the stress response and is influenced by early stress exposure. Two CpG sites, cg20813374 and cg00130530, have been identified as potential reporters of early stress. We examined whether FKBP5 methylation was associated with accelerated DNA methylation aging and indirectly predicted poorer cardiovascular health among both young adult and middle-aged Black Americans. Methods . 449 young adults, with a mean age of 28.67 and N = 469 middle-age parents and their current partners with a mean age of 57.21, provided self-reports, biometric assessments, and blood draws. Methylation values were obtained using the Illumina Epic Array. Cardiometabolic risk was calculated by summing the standardized log-transformed scores for the body mass index, mean arterial blood pressure, and HbA1c. We also used a more standard index of risk, the Framingham 10-year cardiometabolic risk index, as an alternative measure of cardiometabolic risk. To measure accelerated aging, four widely used indices of accelerated, DNA-methylation based aging were used controlling sex, age, other variation in FKBP5, and cell-type. Results Exposure to community danger was associated with demethylation of FKBP5. FKBP5 methylation was significantly associated with accelerated aging for both young-adult and middle-aged samples, with significant indirect effects from FKBP5 methylation to cardiometabolic risk through accelerated aging for both. Conclusions Early exposure to danger may influence FKBP5 methylation. In turn, FKBP5 methylation may help explain intrinsic accelerated aging and elevated cardiometabolic risk in adulthood for Black Americans.
... Standardized tests were designed to assess human intelligence, resulting in a total score termed intelligence quotient (IQ). People with higher intelligence have been shown to live longer, enjoy better health, and have more favorable health behaviors [2]. Lower IQ has been associated with an increased risk of morbidity and mortality [3][4][5], while higher cognitive scores predict better general and mental health, including lower odds of having self-reported "severe tooth or gum trouble" [6]. ...
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Relatively few studies have analyzed the association between cognitive performance and dental status. This study aimed to analyze the association between cognitive performance and dental caries. Included were data from the dental, oral, medical epidemiological (DOME) study; cross-sectional records-based research, which integrated large socio-demographic, medical, and dental databases of a nationally representative sample of young to middle-aged military personnel (N = 131,927, mean age: 21.8 ± 5.9 years, age range: 18-50). The cognitive function of draftees is routinely measured at age 17 years using a battery of psychometric tests termed general intelligence score (GIS). The mean number of decayed teeth exhibited a gradient trend from the lowest (3.14 ± 3.58) to the highest GIS category (1.45 ± 2.19) (odds ratio (OR) lowest versus highest = 5.36 (5.06-5.68), p < 0.001). A similar trend was noted for the other dental parameters. The associations between GIS and decayed teeth persisted even after adjusting for socio-demographic parameters and health-related habits. The adjustments attenuated the OR but did not eliminate it (OR lowest versus highest = 3.75 (3.38-4.16)). The study demonstrates an association between cognitive performance and caries, independent of the socio-demographic and health-related habits that were analyzed. Better allocation of resources is recommended, focusing on populations with impaired cognitive performance in need of dental care.
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É notório que existem muitas dificuldades no ensino de matemática no Brasil, sendo muitas destas relacionadas ao fato de os estudantes não serem capazes de relacionar os conteúdos apresentados em sala de aula com suas vivências cotidianas, o que acaba estimulando o desinteresse deles na matéria. O professor é uma peça-chave na mediação do conhecimento, devendo saber articular e conduzir discussões, transformando as aulas tradicionais de matemática, tornando-as atrativas, atuais e divertidas. Porém, como fazer para que as crianças sejam curiosas em uma aula de matemática? Como despertar seu interesse? Neste artigo, procuramos examinar alguns números da educação brasileira à luz da contribuição do Círculo da Matemática do Brasil, cuja abordagem é fundamentada na construção e no estímulo à participação de todos estudantes em sala de aula, para que a matemática seja trabalhada de uma maneira prazerosa e divertida.
The field of cognitive epidemiology studies the prospective associations between cognitive abilities and health outcomes. We review research in this field over the past decade and describe how our understanding of the association between intelligence and all-cause mortality has consolidated with the appearance of new, population-scale data. To try to understand the association better, we discuss how intelligence relates to specific causes of death, diseases/diagnoses and biomarkers of health through the adult life course. We examine the extent to which mortality and health associations with intelligence might be attributable to people’s differences in education, other indicators of socioeconomic status, health literacy and adult environments and behaviours. Finally, we discuss whether genetic data provide new tools to understand parts of the intelligence–health associations. Social epidemiologists, differential psychologists and behavioural and statistical geneticists, among others, contribute to cognitive epidemiology; advances will occur by building on a common cross-disciplinary knowledge base. Cognitive epidemiology studies prospective associations between cognitive abilities and health outcomes. Deary et al. review research in this field over the past decade, synthesizing evidence and outlining open questions.
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Aging is the primary risk factor for cognitive decline, an emerging health threat to aging societies worldwide. Whether anti-aging factors such as klotho can counteract cognitive decline is unknown. We show that a lifespan-extending variant of the human KLOTHO gene, KL-VS, is associated with enhanced cognition in heterozygous carriers. Because this allele increased klotho levels in serum, we analyzed transgenic mice with systemic overexpression of klotho. They performed better than controls in multiple tests of learning and memory. Elevating klotho in mice also enhanced long-term potentiation, a form of synaptic plasticity, and enriched synaptic GluN2B, an N-methyl-D-aspartate receptor (NMDAR) subunit with key functions in learning and memory. Blockade of GluN2B abolished klotho-mediated effects. Surprisingly, klotho effects were evident also in young mice and did not correlate with age in humans, suggesting independence from the aging process. Augmenting klotho or its effects may enhance cognition and counteract cognitive deficits at different life stages.
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This monograph describes research findings linking intelligence and personality traits with health outcomes, including health behaviors, morbidity, and mortality. The field of study of intelligence and health outcomes is called cognitive epidemiology, and the field of study of personality traits and health outcomes is known as personological epidemiology. Intelligence and personality traits are the principal research topics studied by differential psychologists, so the combined field could be called differential epidemiology. This research is important for the following reasons: The findings overviewed are relatively new, and many researchers and practitioners are unaware of them; the effect sizes are on par with better-known, traditional risk factors for illness and death; mechanisms of the associations are largely unknown, so they must be explored further; and the findings have yet to be applied, so we write this to encourage diverse interested parties to consider how applications might be achieved. To make this research accessible to as many relevant researchers, practitioners, policymakers, and laypersons as possible, we first provide an overview of the basic discoveries regarding intelligence and personality. We describe the nature and structure of the measured phenotypes (i.e., the observable characteristics of an individual) in both fields. Although both areas of study are well established, we recognize that this may not be common knowledge outside of experts in the field. Human intelligence differences are described by a hierarchy that includes general intelligence (g) at the pinnacle, strongly correlated broad domains of cognitive functioning at a lower level, and specific abilities at the foot. The major human differences in personality are described by five personality factors that are widely agreed on with respect to their number and nature: neuroticism, extraversion, openness, agreeableness, and conscientiousness. As a foundation for health-related findings, we provide a summary of research showing that intelligence and personality differences can be measured reliably and validly and are stable across many years (even decades), substantially heritable, and related to important life outcomes. Cognitive and personality traits are fundamental aspects of a person, and they have relevance to life chances and outcomes, including health outcomes. We provide an overview of major and recent research on the associations between intelligence and personality traits and health outcomes. These outcomes include mortality from all causes, specific causes of death, specific illnesses, and others, such as health-related behaviors. Intelligence and personality traits are significantly and substantially (by comparison with traditional risk factors) related to all of these outcomes. The studies we describe are unusual in psychology: They have large sample sizes (typically thousands of subjects, sometimes ~ 1 million), the samples are more representative of the background population than in most studies, the follow-up times are long (sometimes many decades, almost the whole human life span), and the outcomes are objective health measures (including death), not just self-reports. In addition to the associations, possible mechanisms for the associations are described and discussed, and some attempts to test these mechanisms are illustrated. It is relatively early in this research field, so a significant amount of work remains to be done. Finally, we make some preliminary remarks about possible applications, with the knowledge that the psychological predictors addressed are somewhat stable aspects of the person, with substantial genetic causes. Nevertheless, we believe differential epidemiology can be a useful component of interventions to improve individual and public health. Intelligence and personality differences are possible causes of later health inequalities; the eventual aim of cognitive andpersonological epidemiology is to reduce or eliminate these inequalities, to the extent that it is possible, and provide information to help people toward their own optimal health through the life course. We present these findings to a wider audience so that more associations will be explored, a better understanding of the mechanisms of health inequalities will be produced, and inventive applications will follow on the basis of what we hope will be seen as practically useful knowledge.
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Look After the Child Investing in children has been demonstrated to improve their lives, both during the school-age years and afterward, as assessed by outcomes such as employment and income; furthermore, these investments often help those in the most need. Campbell et al. (p. 1478 ) report that these investments can also lead to improved adult health. Results from a randomized and intensive intervention that involved 122 children in four cohorts recruited in the 1970s suggest that full-day child care for the first 5 years of life has produced adults in their 30s with better metabolic and cardiovascular health measures.
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Human leukocyte telomere length (LTL) decreases with age and shorter LTL has previously been associated with increased prospective mortality. However, it is not clear whether LTL merely marks the health status of an individual by its association with parameters of immune function, for example, or whether telomere shortening also contributes causally to lifespan variation in humans. We measured LTL in 870 nonagenarian siblings (mean age 93 years), 1580 of their offspring and 725 spouses thereof (mean age 59 years) from the Leiden Longevity Study (LLS). We found that shorter LTL is associated with increased prospective mortality in middle (30-80 years; hazard ratio (HR) = 0.75, P = 0.001) and highly advanced age (≥90 years; HR = 0.92, P = 0.028), and show that this association cannot be explained by the association of LTL with the immune-related markers insulin-like growth factor 1 to insulin-like growth factor binding protein 3 molar ratio, C-reactive protein, interleukin 6, cytomegalovirus serostatus or white blood cell counts. We found no difference in LTL between the middle-aged LLS offspring and their spouses (β = 0.006, P = 0.932). Neither did we observe an association of LTL-associated genetic variants with mortality in a prospective meta-analysis of multiple cohorts (n = 8165). We confirm LTL to be a marker of prospective mortality in middle and highly advanced age and additionally show that this association could not be explained by the association of LTL with various immune-related markers. Furthermore, the approaches performed here do not further support the hypothesis that LTL variation contributes to the genetic propensity for longevity.
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There is evidence that persistent psychiatric disorders lead to age-related disease and premature mortality. Telomere length has emerged as a promising biomarker in studies that test the hypothesis that internalizing psychiatric disorders are associated with accumulating cellular damage. We tested the association between the persistence of internalizing disorders (depression, generalized anxiety disorder and post-traumatic stress disorder) and leukocyte telomere length (LTL) in the prospective longitudinal Dunedin Study (n=1037). Analyses showed that the persistence of internalizing disorders across repeated assessments from ages 11 to 38 years predicted shorter LTL at age 38 years in a dose-response manner, specifically in men (β=-0.137, 95% confidence interval (CI): -0.232, -0.042, P=0.005). This association was not accounted for by alternative explanatory factors, including childhood maltreatment, tobacco smoking, substance dependence, psychiatric medication use, poor physical health or low socioeconomic status. Additional analyses using DNA from blood collected at two time points (ages 26 and 38 years) showed that LTL erosion was accelerated among men who were diagnosed with internalizing disorder in the interim (β=-0.111, 95% CI: -0.184, -0.037, P=0.003). No significant associations were found among women in any analysis, highlighting potential sex differences in internalizing-related telomere biology. These findings point to a potential mechanism linking internalizing disorders to accelerated biological aging in the first half of the life course, particularly in men. Because internalizing disorders are treatable, the findings suggest the hypothesis that treating psychiatric disorders in the first half of the life course may reduce the population burden of age-related disease and extend health expectancy.Molecular Psychiatry advance online publication, 14 January 2014; doi:10.1038/mp.2013.183.
Low socioeconomic status (SES) children perform on average worse on intelligence tests than children from higher SES backgrounds, but the developmental relationship between intelligence and SES has not been adequately investigated. Here, we use latent growth curve (LGC) models to assess associations between SES and individual differences in the intelligence starting point (intercept) and in the rate and direction of change in scores (slope and quadratic term) from infancy through adolescence in 14,853 children from the Twins Early Development Study (TEDS), assessed 9 times on IQ between the ages of 2 and 16 years. SES was significantly associated with intelligence growth factors: higher SES was related both to a higher starting point in infancy and to greater gains in intelligence over time. Specifically, children from low SES families scored on average 6 IQ points lower at age 2 than children from high SES backgrounds; by age 16, this difference had almost tripled. Although these key results did not vary across girls and boys, we observed gender differences in the development of intelligence in early childhood. Overall, SES was shown to be associated with individual differences in intercepts as well as slopes of intelligence. However, this finding does not warrant causal interpretations of the relationship between SES and the development of intelligence.
The longitudinal relation between childhood intelligence and various health outcomes in adulthood is now well-established. One mediational model that accounts for this relation proposes that intelligence has cumulative indirect effects on adult health via subsequent educational attainment and adult socioeconomic status (SES). The aim of the present study was to examine whether and the extent to which educational attainment and SES mediate the impact of childhood intelligence on three dimensions of adult health in Luxembourg, a country with high-quality universal public health care. We used data from 745 participants in the Luxembourgish MAGRIP study. At the age of 12, participants completed a comprehensive intelligence test. At the age of 52, they reported their educational careers, SES, and functional, subjective, and physical health status. Using structural equation modeling, we investigated the direct and indirect effects (via educational attainment and adult SES) of childhood intelligence on adult health. We found that higher childhood intelligence predicted better functional, subjective, and physical health in adulthood. These effects were entirely mediated via educational attainment and SES. The mediational processes differed depending on the health dimension under investigation: Whereas SES was crucial in mediating the effect of intelligence on functional and subjective health, educational attainment was crucial in mediating the effect on physical health. These findings held up when considering adult intelligence and were similar for women and men. Our results suggest that even excellent public health care cannot fully offset the cumulative effects of childhood intelligence on adult health. Further studies are needed to investigate the relative importance of different mediators in the intelligence–health relation while including a broader set of objective health measures.