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