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R E S E A R CH A R T I C L E
Testing for paternal influences on offspring telomere length ina human cohort in the Philippines
Dan T. A. Eisenberg1,2 | Peter H. Rej1 | Paulita Duazo3 | Delia Carba3 |
M. Geoffrey Hayes4,5,6 | Christopher W. Kuzawa6,7
1Department of Anthropology, University of
Washington, Seattle, Washington
2Center for Studies in Demography and
Ecology, University of Washington, Seattle,
Washington
3USC-Office of Population Studies
Foundation, Inc., University of San Carlos,
Cebu City, Philippines
4Division of Endocrinology, Metabolism and
Molecular Medicine, Department of Medicine,
Northwestern University Feinberg School of
Medicine, Chicago, IL
5Center for Genetic Medicine, Northwestern
University Feinberg School of Medicine,
Chicago, IL
6Department of Anthropology, Northwestern
University, Chicago, IL
7Institute for Policy Research, Northwestern
University, Chicago, IL
Correspondence
Dan T. A. Eisenberg, Department of
Anthropology, University of Washington,
Campus Box 353100, Seattle, WA 98195.
Email: dtae@dtae.net
Funding information
National Institutes of Health, Grant/Award
Numbers: DK056350, DK078150, ES10126,
RR20649, TW05596; National Science
Foundation, Grant/Award Numbers: BCS-
0962282, BCS-1519110; Wenner-Gren
Foundation, Grant/Award Number: 8111
Abstract
Objectives: Telomeres, emerging biomarkers of aging, are comprised of DNA repeats
located at chromosomal ends that shorten with cellular replication and age in most
human tissues. In contrast, spermatocyte telomeres lengthen with age. These
changes in telomere length (TL) appear to be heritable, as older paternal ages of con-
ception (PAC) predict longer offspring TL. Mouse-model studies raise questions
about the potential for effects of paternal experiences on human offspring TL, as
they suggest that smoking, inflammation, DNA damage, and stressors all shorten
sperm TL. Here, we examined whether factors from the paternal environment predict
offspring TL as well as interact with PAC to predict offspring TL.
Materials and Methods: Using data from the Philippines, we tested if smoking, psy-
chosocial stressors, or shorter knee height (a measure of early life adversity) predict
shorter offspring TL. We also tested if these interacted with PAC in predicting
offspring TL.
Results: While we did not find the predicted associations, we observed a trend toward
fathers with shorter knee height having offspring with longer TL. In addition, we found
that knee height interacted with PAC to predict offspring TL. Specifically, fathers with
shorter knee heights showed a stronger positive effect of PAC on offspring TL.
Discussion: While the reasons for these associations remain uncertain, shorter knee
height is characteristic of earlier puberty. Since spermatocyte TL increases with the produc-
tion of sperm, we speculate that individuals with earlier puberty, and its concomitant com-
mencement of production of sperm, had more time to accumulate longer sperm telomeres.
K E YWORD S
epigenetics, intergenerational effects, intergenerational inertia, intergenerational plasticity,
senescence
1 | INTRODUCTION
Telomeres are repeating segments of DNA at the ends of chromo-
somes that shorten with each cell division, with age, and poten-
tially with environmental exposures such as stress. Short telomere
length (TL) is implicated as a cause of impaired immune function
and accelerated senescence (Blackburn, Greider, & Szostak, 2006;
Cawthon, Smith, O'Brien, Sivatchenko, & Kerber, 2003; Cohen
et al., 2013; Wilbourn et al., 2018). TL has a high heritability in
humans (Broer et al., 2013). While shorter TL is thought to be
selected against due to its negative effects on multiple aspects of
health and longevity, long TL might increase cancer risk and/or
promote energetically expensive maintenance efforts (Eisenberg &
Kuzawa, 2018).
Received: 9 September 2019 Revised: 25 November 2019 Accepted: 26 November 2019
DOI: 10.1002/ajpa.23983
Am J Phys Anthropol. 2019;1–9. wileyonlinelibrary.com/journal/ajpa © 2019 Wiley Periodicals, Inc. 1
Unlike somatic tissues, sperm telomeres paradoxically lengthen
with age. Males produce a constant supply of sperm via cell division
across the adult life course, which without compensatory mechanisms
would be expected to lead to a cumulative shortening of sperm TL
with age. The reverse-transcriptase enzyme telomerase is a promising
candidate to explain telomere lengthening in sperm (for an alternative
explanation see Hjelmborg et al., 2015; Kimura et al., 2008). Telome-
rase is generally inactive in postnatal human somatic tissues, but is
active across adult life at high levels in men's testes (Wright,
Piatyszek, Rainey, Byrd, & Shay, 1996) and appears to be critical for
continued sperm production (Eisenberg & Kuzawa, 2018). If testicular
telomerase activity is sufficiently high, it could not only maintain TL
despite continued sperm production but also progressively lengthen
telomeres with age with each round of cell replication. Consistent
with this idea, in humans, sperm TL appears to increase with age
(reviewed in Eisenberg & Kuzawa, 2018), and later paternal age at
conception (PAC) predicts longer TL in not only offspring but also
grandoffspring (Eisenberg, Lee, Rej, Hayes, & Kuzawa, 2019). We have
suggested that this PAC effect could represent an intergenerational
predictive adaptive response which allows organisms to pass on TLs
that induce more optimal maintenance allocations to their offspring
based on shifting ecological conditions (Eisenberg & Kuzawa, 2018).
The plasticity in sperm TL with male age raises the possibility
that other environmental and physiological factors might influence
sperm TL, and thereby modify the length of telomeres inherited by
offspring (Eisenberg, 2011). One candidate influence that is
supported by several studies is smoking. In mice, nicotine exposure
increased testicular cell apoptosis, decreased testicular telomerase
activity, and shortened spermatozoal TL (Gu et al., 2016). In humans,
smoking has been shown to predict lower sperm concentration and
lower sperm vitality (Adashi, Vine, Margolin, Morrison, & Hulka,
1994; Shelko, Hamad, Montenarh, & Hammadeh, 2016), as well as
differences in offspring health (Pembrey, Saffery, Bygren,, & Net-
work in Epigenetic Epidemiology, 2014; but see Carslake, Pinger,
Romundstad, & Davey Smith, 2016). These lines of evidence lead to
the prediction that smoking shortens sperm TL in humans and
thereby impacts TL inherited by offspring.
Another plausible influence on sperm TL is oxidative stress, which
may be induced by commonly experienced factors like infection, inflam-
mation, psychosocial stress, and smoking (Agarwal et al., 2018; Black,
Bot, Scheffer, Cuijpers, & Penninx, 2015; Tsatsoulis & Fountoulakis,
2006). Male fertility appears to be negatively impacted by oxidative
stress (Agarwal et al., 2018). Oxidative stress has been posited to affect
the male germ line (Metcalfe & Alonso-Alvarez, 2010) and to reduce
sperm TL (Haussmann & Heidinger, 2015). In mice, doxorubicin, an anti-
biotic that inhibits DNA synthesis and generates reactive oxygen spe-
cies, has been found to decrease sperm density and sperm motility, and
reduce testicular telomerase expression (Sato et al., 2010). Green tea
extracts, which have antioxidant properties, partially restored normal
function (Sato et al., 2010). Furthermore, in male mice, administration of
TNF-α, a pro-inflammatory cytokine produced in response to many
stressors including psychological and oxidative stress, similarly shortens
TL in male offspring (Liu et al., 2019).
It is unclear at what point in the life course the father's biology is
likely to be sensitive to environmental influences that lead to changes
in the offspring's TL. Contrary to common belief, the testes and sperm
stem cells (spermatogonia) are not quiescent before puberty (Chemes,
2001). Both overall testicular volume and spermatogonia density
change substantially beginning in infancy and continuing through the
prepubertal years (Chemes, 2001; Masliukaite et al., 2016; Paniagua &
Nistal, 1984; Wistuba, Neuhaus, Sharma, Pock, & Schlatt, 2019).
These prepubertal changes are likely driven by a reduction in sper-
matogonia number, as spermatagonia that fail to reach the basal mem-
brane of the seminiferous tubules degenerate during the first three
postnatal years (Masliukaite et al., 2016). Afterward, spermatagonia
disperse across the seminiferous tubules and then proliferate and dif-
ferentiate into other subtypes of cells (Masliukaite et al., 2016). The
function of this prepubertal testicular activity is unclear but may
include sensitizing and programming effects (Chemes, 2001).
With respect to telomere biology, differential proliferation or
degeneration of spermatogonia with differing TL or telomere mainte-
nance activity could result in a pool of spermatogonia that produce
sperm with different average TL. Consistent with the dynamic and
potentially sensitive nature of the testes in the prepubertal period,
emerging evidence suggests that germ line mutation rates per cell divi-
sion are much higher in males before puberty than after (Forster et al.,
2015; Rahbari et al., 2016). Additionally, multiple lines of evidence sug-
gest that exposures in males before puberty predict aspects of offspring
and even grandoffspring health (Pembrey et al., 2014; Kuzawa &
Eisenberg, 2014; Franklin & Mansuy, 2010; but see Carslake et al.,
2016), suggesting that there are means by which prepubertal exposures
might affect offspring biology. The postpubertal period may also have
considerable influence on sperm TL, as this is when sperm production is
greatest. Although speculative, effects on testicular biology, such as
durable changes in telomerase activity, could also lead to modifications
in the rate of increase in sperm TL with age.
In this study, we use a multigenerational longitudinal cohort from
the Philippines to test the hypothesis that paternal exposures previously
shown to modify sperm/offspring TL, reviewed above, will predict TL in
their adult offspring. Specifically, we examine relationships between off-
spring TL and paternal smoking before the conception of the offspring,
paternal knee height (a physical measure of infancy and early childhood
nutritional sufficiency; Bogin & Varela-Silva, 2010; but see Kinra, Sarma,
Hards, Smith, & Ben-Shlomo, 2011), and psychosocial stressors mea-
sured in the fathers. We predict that stress exposures will tend to lead
to shorter offspring TL, consistent with a heritable alteration in paternal
sperm TL. Secondarily, we examine whether each exposure shows an
interaction effect with PAC, as would be consistent with a durable
change in the rate of age-related lengthening of TL.
2 | MATERIALS AND METHODS
Data come from the Cebu Longitudinal Health and Nutrition Survey
(CLHNS), which recruited pregnant women from randomly selected
neighborhoods in metropolitan Cebu, Philippines. In 1983–1984, a
2 EISENBERG ET AL.
baseline interview was conducted among 3,327 women during preg-
nancy. Follow-up surveys have been conducted periodically since (see
Adair et al., 2011). TLs were measured from venous blood samples
collected in 2005 when original cohort members were 20–22 years of
age. In 2016, a subset of fathers (n = 712) of the 1983–1984 born off-
spring were interviewed, and had blood collected from which TL was
measured (n = 640). Information about smoking, stressors, and anthro-
pometric measures were collected. Survey and biological sample col-
lection were conducted with informed consent and institutional
review board approvals from the University of North Carolina, North-
western University, and the University of Washington.
2.1 | Telomere length
DNA was extracted from venous blood. TLs were measured using the
monochrome multiplex quantitative polymerase chain reaction assay,
as described previously (Eisenberg et al., 2019; Eisenberg, Borja,
Hayes, & Kuzawa, 2017; Eisenberg, Kuzawa, & Hayes, 2015). Since
the coefficient of variation has recently been recognized to be an
invalid statistic to compare TL measurement reliability across studies,
we instead used the intraclass correlation coefficient (ICC; Eisenberg,
2016; Verhulst et al., 2015). Specifically, we assayed samples twice
and calculated interassay ICC values using their within-run mean
values (same sample run in triplicate within runs). In the analyses of
data from 2005, 873 samples were run separately in triplicate on two
separate runs because of initially high intra-assay CVs. We calculated
ICC using mean T/S values from the first and second run: ICC
(1) = 0.81 (95% CI: 0.79–0.84). For the 2016 samples, a plate of sam-
ples (n = 95) was assayed an additional time, which yielded an ICC
(1) of 0.79 (95% CI: 0.70–0.86).
2.2 | Smoking
Fathers were asked how old they were when they started smoking
cigarettes regularly (at least one cigarette/day) as well as when they
quit smoking. The number of years a father smoked before the con-
ception of the child was calculated from these variables. Of the
fathers, 21.2% did not smoke cigarettes regularly. The mean starting
age of the regular smokers was 19.0 ± 6.8.
In the 2005 survey, the offspring were asked how old they were
when they first tried smoking and how often they smoke (sticks/day,
smokes but not daily, or stopped smoking). These responses were
used to calculate a maximum years of smoking variable (age at survey
minus age at first tried smoking) and a current smoking frequency var-
iable in sticks/day (stopped smoking coded as 0.1 sticks/day and
smokes but not daily as 0.3 sticks/day). Both of these variables and
their interaction term are included to measure offspring smoking.
Knee height (cm) was measured three times using a steel ruler
from seated subjects with bare feet measuring from the floor to the
top of the patella and averaged (Bogin et al., 2014). Overall height
(cm) was also recorded three times from standing subjects. Since knee
height is included in overall stature, it is not surprising that the two
variables are highly correlated (n = 704, r = .835 95% CI 0.815–0.859).
The correlation of knee height with non-knee height (height minus
knee height) is less (n = 704, r = .641, 95% CI 0.595–0.683), and non-
knee height is included as a control variable.
Stress was quantified using two scales—both from the 2016 sur-
vey of the fathers. First, the Childhood Trauma Questionnaire short
version (CTQ-SF) was administered (Bernstein et al., 2003). Addition-
ally, fathers were asked if they experienced a variety of stressors.
They were asked if before 18 they: moved residences, had a mother,
father, or sibling die. They were also asked if they experienced diffi-
culties/hardships due to war, insurgencies, typhoons, floods, fire,
long-term care for a disabled relative, or other (free response). Aside
from moving residences, we gathered information on the age of expe-
rience for all stressors. We only counted stressors if they occurred
before offspring conception. Each of these 11 variables was binary
coded and then summed to create a scale of stressors experienced
before conception of the offspring. The CTQ and stressors scale
showed only a small correlation with each other (n = 712, r = .110,
95% CI: 0.037–0.182).
Control variables included several variables indexing socioeco-
nomic status (SES) and other factors. The SES variables included years
of education in 2005 of offspring and their mothers and fathers. Addi-
tionally, we took mean values across survey administered in 1983,
1986, 1991, 1994, 1998, 2002, and 2005 of assets score, deflated
household income, and urbanicity. For each year, assets scores were a
sum of several assets reflective of social class in Cebu (e.g., ownership
of an air conditioner, car, home, jeepney, refrigerator, television, tape
recorder, and electric fan). Participants also reported their home build-
ing material (0–light, 1–mixed, 2–strong). Total assets score ranged
between 0 and 11. Mean household income values were log (base 10)
transformed. Urbanicity is a continuous measure derived from
community-level data to measure the urban–rural continuum in the
Philippines (Dahly & Adair, 2007) that has been found to be associ-
ated with TL in past analyses (Rej, Tennyson, Lee, & Eisenberg, 2019;
Tennyson et al., 2018).
This article is focused on discerning intergenerational effects and
not within-generation life course developmental effects. However,
since many characteristics, including height and smoking propensity,
have substantial heritable and shared environmental effects (Li,
Cheng, Ma, & Swan, 2003; Silventoinen et al., 2012), paternal traits
could be inadvertently indexing offspring traits/environments. For
example, if father's smoking predicts offspring smoking (due to envi-
ronmental and/or genetically mediated pathways), associations
between paternal smoking and offspring TL could actually be due to
offspring smoking. To minimize the potential for such confounding,
offspring smoking (defined above) was included as a control variable
in all maximally controlled models. Similarly, in analyzing effects of the
knee height of father (Table 2, Model 4), we also include a control var-
iable for offspring's height in 2005.
To control for potential effects of population genetic structure,
we included principal components (PCs) of genome-wide genetic vari-
ation as controls. Briefly, PCs were derived from genotypes from a
EISENBERG ET AL. 3
microarray typing for 196,725 single-nucleotide polymorphisms from
each original cohort member (offspring) as previously described
(Croteau-Chonka et al., 2012). PCs are commonly used in genome-
wide association studies to control for confounding due to population
stratification (Hellwege et al., 2017) and may index social and/or bio-
logical differences among individuals, which may affect both our pre-
dictor and dependent variables. As in our previous analyses
(Bethancourt et al., 2017), the bivariate association between the first
10 PCs and TL were tested. The top PCs up to and including the last
one showing a significant bivariate association with TL were included
as control variables.
2.3 | Statistical approach
We ran a series of ordinary least squares regression models. PAC was
calculated as the offspring's date of birth minus 280 days
(to approximate date of conception), minus the father's date of birth.
PAC, knee height, non-knee height, and CTQ were mean centered on
zero to minimize nonessential collinearity of interaction terms and
make effects more interpretable (Cohen, Cohen, West, & Aiken,
2003). All models controlled for offspring age at blood draw, offspring
sex, and PAC. Odd-numbered models were minimally controlled
models, which included key environmental exposure variables of the
father. Even-numbered models were our maximally controlled models,
which added in other controls. If the coefficients of the key indepen-
dent variables (e.g., smoking and PAC × smoking interaction in Model
#1) reduced substantially from the minimal to maximum controlled
model, we interpreted this as potentially confounding of these envi-
ronmental effects by other aspects of SES. In Models 3 and 4, non-
knee height was included as a predictor to discern whether knee
height was a predictor above and beyond the remainder of stature.
All statistical models, unless otherwise noted (e.g., as post hoc),
were preregistered at the open science framework (OSF; https://osf.
io/mb6te/). Models were designed and coded with the outcome mea-
sure, TL, replaced with random numbers to allow designs to be atten-
tive to missingness. Only after we posted analysis methods to OSF
did we add real TL into the analysis script. We deviated slightly from
the preregistration by also centering other variables used with interac-
tion terms without meaningful zeros: knee height, non-knee height,
and CTQ.
3 | RESULTS
The sample and key variables used for analyses are described in
Table 1. Blood samples used for TL measurement were collected
when the offspring were 21.7 ± 0.3 years old. Our results (Table 2) do
not support our hypotheses. Paternal smoking, stressors, CTQ values
were all unrelated to offspring TL, as were their interactions with PAC
(Table 2, Models 1, 2, 5, and 6). There was a trend toward increased
paternal CTQ values (indexing more childhood trauma), predicting lon-
ger offspring TL (Model 5). However, this association, which was in
the opposite direction of expectations, attenuated when additional
controls were added into the model (Model 6).
Because animal model research shows that increased stressors
can shorten sperm TL (reviewed in Section 1), we expected that
greater paternal knee height (and to a lesser extent non-knee height)
would predict longer offspring TL. In contrast to these predictions,
greater paternal knee height trended toward predicting shorter off-
spring TL (Table 2, Models 3 and 4). Greater non-knee height (overall
height subtracting out knee height) trended toward predicting greater
offspring TL. Offspring's overall height was not predictive of the off-
spring's own TL (Model 4, p = .217). These same regression models
also showed a significant interaction between PAC and paternal knee
height in which fathers with greater knee height have an attenuated
PAC effect on offspring TL (Figure 1).
To better quantify effect sizes, and allow comparison across stud-
ies, the observed effect sizes were translated into interpolated base-
pair (bp) effect sizes based on a subset of 190 samples from the
Philippines, which were measured using southern blot terminal restric-
tion fragment analysis (Eisenberg et al., 2015, figure 3). The −0.0074
(95% CI −0.0153 to +0.0006) effect size of paternal knee height on
offspring TL (Table 2, Model 4) implies a −23.4 bp decrease (95% CI
−48.3 to +1.9 bp) for each centimeter increase in paternal knee
height, or a −49.6 bp decrease for a 1 SD increase in knee height.
Using the same method, the effect of a 1 cm increase in non-knee
height implies an 11.4 bp increase (95% CI −2.0 to +25.0 bp) in off-
spring TL, or a +44.7 bp increase for a 1 SD increase. These estimated
TABLE 1 Descriptive statistics
Variable Mean SD Min Max
Offspring: telomere length 0.77 0.17 0.03 1.42
Dad: telomere length 1.03 0.26 0.41 1.94
Offspring age 21.7 0.3 20.9 22.5
PAC 27.5 5.6 15.1 52.6
Offspring: Education (years) 11.1 3.6 0 23
Mom: Education (years) 7.4 3.8 0 17
Dad: Education (years) 7.6 3.8 0 19
Urbanicity 33.8 13.2 8.1 53.9
Log-income 1983–2005 5.9 0.6 4.1 10.5
Assets 1983–2005 3.9 1.6 0.9 9.3
Dad: Years smoked 6.9 7.4 0 45.6
Dad: Knee height (cm) 50.3 2.1 43.0 57.0
Dad: Non-knee height (cm) 111.8 3.9 100.9 124.2
Offspring: Height (cm) 157.2 8.2 135.1 181.2
Dad: CTQ 49.3 12.1 28 105
Dad: Stressors 1.2 1.1 0 6
Offspring sticks/day
smoked
1.8 4.2 0 25
Offspring max smoke years 3.3 3.4 0 14.1
Abbreviations: CTQ, Childhood Trauma Questionnaire; PAC, paternal age
at conception.
4 EISENBERG ET AL.
effect sizes should be interpreted as effects when PAC is at the mean
value because of the significant interaction term between PAC and
knee height in these models (both variables are mean centered at
zero). The interaction effect between knee height and PAC in
predicting offspring TL (Figure 1) equates to a 1 SD increase in PAC
over the mean PAC at the mean knee height predicting a 14.5 bp
increase in TL, while the same PAC increase at a knee height of 1 SD
above the mean would predict a −25.2 bp reduction in TL. By compar-
ison, a 1 year increase in age in the mothers of the offspring in this
population (age range 36–69) predicts a 13.6 bp decrease in the
mothers own blood TL (Eisenberg et al., 2017).
To examine the robustness of these knee height and non-knee
height findings, we conducted multiple post hoc tests. Running Model
4 without the PAC by paternal knee height interaction term yielded
similar direct effects of knee height (β = −.0068, p = .093) and non-knee
height (β = .0040, p = .063) on offspring TL. Modifying the same model
to also include an interaction term of non-knee height with PAC yielded
similar results and a nonsignificant association of greater non-knee
height with an increased PAC effect (β = .00055, p = .15). Finally, we
modified Model 4 to exclude paternal knee height, PAC by paternal
knee height interaction, and non-knee height and added overall paternal
height and overall paternal height interacted with PAC. Neither overall
paternal height nor the height by PAC interaction predicted offspring
TL (β = .00036, p = .787, and β = −.00021, p = .317, respectively).
Paternal knee height and non-knee height could be indexing
these same body proportions in offspring (Chatterjee, Das, & Chatter-
jee, 1999). As such, these offspring anthropometric measures might
be more proximately related to offspring TL. Because we do not have
measures of knee height in the offspring generation, we instead exam-
ined whether paternal knee height and non-knee height predicted
TABLE 2 Regression models predicting telomere lengtha
(1) (2)b (3) (4)b (5) (6)b
Age −0.044+ −0.029 −0.047+ −0.032 −0.048+ −0.033
Sex (0 = female, 1 = male) 0.067 −0.053 0.078 −0.048 0.053 −0.052
Sex × age −0.0036 0.0021 −0.0040 0.00098 −0.0030 0.0020
PAC 0.0029+ 0.0034* 0.00069 0.00082 0.00074 0.0014
Dad: Years smoked −0.00087 −0.00060
PAC × Dad: Years smoked −0.00013 −0.00018
Dad: Knee height −0.0067+ −0.0074+
PAC × Dad: Knee height −0.0014* −0.0011*
Dad: Non-knee height 0.0038+ 0.0036+
Offspring: Height 0.0016
Stressors 0.0028 −0.0016
PAC × stressors 0.00020 −0.000033
CTQ 0.0010+ 0.00077
PAC × CTQ 0.0000021 0.000023
Observations 695 695 687 687 695 695
Adjusted R2 0.007 0.034 0.016 0.042 0.006 0.028
Abbreviations: CTQ, Childhood Trauma Questionnaire; PAC, paternal age at conception.aMore complete regression statistics in Supplementary Table 1.bIncludes additional controls for offspring education, mother's education, father's education, urbanicity, Log-income 1983–2005, Assets 1983–2005,genome-wide principal components 1–10, offspring cigarettes/day smoked, offspring maximum smoke years, offspring sticks/day smoked × offspring
maximum smoke year.+p < .10 (italics); *p < .05.
.7.7
5.8
.85
Telo
mere
Length
−10 −5 0 5 10 15 20
Paternal Age at Conception (mean centered)
knee height −1 SD knee height at mean
knee height +1 SD
F IGURE 1 Paternal age at conception by paternal knee heightpredicting offspring telomere length. From Table 2, Model 4. X axis ismean centered paternal age at conception. Y axis is predictedtelomere length. Green solid line represents predicted values when
knee height is one standard deviation above the mean (+2.12 cm),blue dotted line when knee height is at mean, and black dashed linerepresents one standard deviation below the mean (−2.12 cm)
EISENBERG ET AL. 5
paternal TL while controlling for paternal age at blood sampling (and
with both height components in the same regression model). In these
post hoc analyses, neither knee height (β = .0049, p = .437) nor
non-knee height (β = .0012, p = .734) predicted the father's own
TL (n = 633).
4 | DISCUSSION
Contrary to our predictions, we did not find that father's smoking,
decreased knee height, or stress exposure predicted shorter offspring
TL. We found an association in the opposite direction of our hypothe-
sis wherein decreased paternal knee height predicted longer offspring
TL (p = .07), that increased non-knee height predicted longer offspring
TL (p = .09), and an interaction between knee height and PAC
predicting offspring TL (p = .05). These findings did not appear to be
driven by obvious confounders, as the associations changed little after
the inclusion of several control variables (SES, urbanicity, measures of
genetic ancestry, and offspring smoking). Overall paternal height did
not predict offspring TL, consistent with the contrasting direction of
effects of the knee height and non-knee height on offspring
TL. Because the fathers' body proportions did not predict his own
blood TL, it is unlikely that these intergenerational associations are
confounded by a relationship between the offspring's own body pro-
portions and their blood TL.
The borderline association linking greater knee height with
shorter offspring TL and the contrasting association of greater non-
knee height predicting longer offspring TL were not expected. We
predicted that knee height would show a positive association with
offspring TL, and that short knee height would be a more sensitive
indicator than non-knee height of early life nutritional stress. We also
anticipated that knee height and non-knee height, which both reflect
favorable early nutrition to varying degrees, would relate to TL in the
same direction. Body proportions vary in relation to maturational
tempo, which could provide insights into this pattern of findings. Since
more leg growth generally occurs prior to puberty, while trunk growth
is fastest during the pubertal growth phase, individuals who reach
puberty earlier tend to have shorter relative leg length and a longer
relative trunk length (Cools, Rooman, Op De Beeck, & Du Caju, 2008;
Gunnell, Smith, Frankel, Kemp, & Peters, 1998; Nielsen et al., 1986;
Schooling et al., 2008; Wadsworth, Hardy, Paul, Marshall, & Cole,
2002; Schooling et al., 2010). As such, the associations present in the
Cebu cohort might be due to fathers who transitioned into puberty
earlier, and who could have started experiencing the PAC effect on
their sperm TL at an earlier age as a result, passing on longer telo-
meres to offspring. This account does not explain the significant inter-
action between PAC and paternal knee-height as predictors of
offspring TL, which suggests that men with shorter knee height (and
perhaps earlier puberty) tend to have altered testicular biology such
that each year of age corresponds with a relatively greater increase in
sperm TL than those with longer knee height. If these findings are rep-
licated in other cohorts, it will be important to explore candidate path-
ways to explain them, such as durable alterations in testicular
telomerase activity in relation to early nutrition or maturational
tempo.
We have previously suggested that the PAC effect on offspring
TL may represent a genetically mediated intergenerational predictive
adaptive response, wherein men who survived and reproduced at
later ages transmit longer telomeres to their descendants (Eisenberg,
2011). Assuming that a later age at male reproduction predicts a
greater likelihood of descendants occupying a socio-ecological con-
text in which they may also have late age reproductive opportunities
(e.g., a lower rate of adult extrinsic mortality or cultural norms that
allow reproduction among older males), then it could be adaptive for
descendants to inherit longer TLs that promote increased somatic
maintenance (Eisenberg, 2011). Conversely, in environments where it
is unlikely that individuals will live to reproduce into late life (e.g., a
high rate of adult extrinsic mortality), it may be adaptive to inherit
shorter TL that reduce energetic investments in maintaining a dura-
ble soma. The knee height association with offspring TL shows some
consistency with this adaptive signaling hypothesis. Earlier puberty
tends to occur with better nourishment and decreased infections
(Bribiescas, 2006, p. 111; Kyweluk, Georgiev, Borja, Gettler, &
Kuzawa, 2017; Gettler, McDade, Bragg, Feranil, & Kuzawa, 2015;
Goldstein, 2011). Assuming earlier puberty is likely to predict similar
better-quality environments in descendant generations (e.g., better
nutritional availability and lower infection risk), the trade-offs
involved in investing energetic resources in somatic maintenance
may be lessened. Thus, men with earlier puberty passing on longer
TL to their offspring could tend to promote increased fitness in
those offspring.
The results of this study should be interpreted cautiously in light
of the exploratory nature of the hypotheses, the relatively high p-
values of the findings, and the multiple statistical tests conducted.
Our failure to detect associations between smoking or stressors and
offspring TL might be due to the retrospective nature of these mea-
sures. We also note that while knee height might be indexing physio-
logical effects on the developing testes and germ cells, it could also be
correlated with other biological, genetic, or social pathways that influ-
ence TL. These caveats aside, we found suggestive evidence that a
father's pattern of growth and body proportions, as reflected in knee
height and non-knee height, might influence offspring TL. We specu-
late that the divergent associations between these height components
and offspring TL could be driven by variation in pubertal age. These
tentative findings should be explored in future research by examining
how men's body proportions predict their own sperm TL, and how
pubertal age measured more directly (e.g., by Tanner stages) predicts
sperm TL and offspring TL.
ACKNOWLEDGMENTS
We thank, the anonymous reviewers and editors of AJPA for valu-
able feedback, Karen Mohlke for sharing aliquots of 2005
extracted DNA and genetic information, many researchers at the
USC-Office of Population Studies Foundation, University of San
Carlos, Cebu, the Philippines, for their central role in study design
and data collection, and the Filipino participants, who provided
6 EISENBERG ET AL.
their time and samples for this study. Funding from NSF (BCS-
1519110 and BCS-0962282), the Wenner-Gren Foundation
(Gr. 8111), and NIH (TW05596, DK078150, RR20649, ES10126,
and DK056350).
AUTHOR CONTRIBUTIONS
D.T.A.E. conducted all statistical analyses and wrote the manuscript.
D.T.A.E. and C.W.K. co-wrote the grant for and designed the 2016 data
and sample collection protocols. M.G.H. and D.T.A.E. supervised the
2005 and 2016 telomere length analyses, respectively. D.T.A.E. and
P.H.R. conducted the 2005 and 2016 telomere length analyses, respec-
tively. All authors commented on and approved this manuscript.
DATA AVAILABILITY STATEMENT
Some of the Cebu Longitudinal Health and Nutrition Survey surveys
and data are available on UNC Dataverse (https://dataverse.unc.edu/
dataverse/cebu). Complete data cannot be provided due to the sensi-
tive nature of these human data which could allow identification of
individuals.
ORCID
Dan T. A. Eisenberg https://orcid.org/0000-0003-0812-1862
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of this article.
How to cite this article: Eisenberg DTA, Rej PH, Duazo P,
Carba D, Hayes MG, Kuzawa CW. Testing for paternal
influences on offspring telomere length in a human cohort in
the Philippines. Am J Phys Anthropol. 2019;1–9. https://doi.
org/10.1002/ajpa.23983
EISENBERG ET AL. 9
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