Paternal age at birth is associated with offspring leukocyte telomere length in the nurses' health study
Advanced Access publication on August
Paternal age at birth is associated with offspring leukocyte telomere length in the nurses' health study
J. Prescott 1 2
M. Du 1 2
J.Y.Y. Wong 1 2
J. Han 0 1 2
I. De Vivo 1 2
0 Department of Dermatology, Brigham and Women's Hospital and Harvard Medical School , Boston, MA 02115 , USA
1 Department of Epidemiology, Program in Molecular and Genetic Epidemiology, Harvard School of Public Health , Boston, MA 02115 , USA
2 Channing Division of Network Medicine, Departments of Medicine, Brigham and Women's Hospital and Harvard Medical School , 181 Longwood Ave., Boston, MA 02115 , USA
study question: Is the association between paternal age at birth and offspring leukocyte telomere length (LTL) an artifact of early life socioeconomic status (SES)? summary answer: Indicators of early life SES did not alter the relationship between paternal age at birth and offspring LTL among a population of white female nurses. what is known already: Telomere length is considered a highly heritable trait. Recent studies report a positive correlation between paternal age at birth and offspring LTL. Maternal age at birth has also been positively associated with offspring LTL, but may stem from the strong correlation with paternal age at birth. study design, size and duration: The Nurses' Health Study (NHS) is an ongoing prospective cohort study of 121 700 female registered nurses who were enrolled in 1976. Great effort goes into maintaining a high degree of follow-up among our cohort participants (.95% of potential person-years). In 1989 - 1990, a subset of 32 826 women provided blood samples from which we selected participants for several nested case - control studies of telomere length and incident chronic disease. We used existing LTL data on a total of 4250 disease-free women who also reported maternal and paternal age at birth for this study. participants/materials, setting and methods: Nested case - control studies of stroke, myocardial infarction, cancers of the breast, endometrium, skin, pancreas and colon, as well as colon adenoma, were conducted within the blood sub-cohort. Each study used the following study design: for each case of a disease diagnosed after blood collection, a risk-set sampling scheme was used to select from one to three controls from the remaining participants in the blood sub-cohort who were free of that disease when the case was diagnosed. Controls were matched to cases by age at blood collection (+1 year), date of blood collection (+3 months), menopausal status, recent postmenopausal hormone use at blood collection (within 3 months, except for the myocardial infarction case - control study), as well as other factors carefully chosen for each individual study. The current analysis was limited to healthy controls. We also included existing LTL data from a small random sample of women participating in a cognitive sub-study. LTL was measured using the quantitative PCR-based method. Exposure and covariate information are extracted from biennial questionnaires completed by the participants. main results and the role of chance: We found a strong association between paternal age at birth and participant LTL (P ¼ 1.6 × 10 - 5) that remained robust after controlling for indicators of early life SES. Maternal age at birth showed a weak inverse association with participant LTL after adjusting for age at blood collection and paternal age at birth (P ¼ 0.01). We also noted a stronger association between paternal age at birth and participant LTL among premenopausal than among postmenopausal women (Pinteraction ¼ 0.045). However, this observation may be due to chance as premenopausal women represented only 12.6% (N ¼ 535) of the study population and LTL was not correlated with age at menopause, total or estrogen-only hormone therapy (HT) use suggesting that changes in in vivo estrogen exposure do not influence telomere length regulation. limitations and reasons for caution: The women in our study are not representative of the general US female population, with an underrepresentation of non-white and low social class groups. Although the interaction was not significant, we noted that the
paternal age at birth association with offspring LTL appeared weaker among women whose parents did not own their home at the time of the
participant’s birth. As telomere dynamics may differ among individuals who are most socioeconomically deprived, SES indicators may have
more of an influence on the relationship between paternal age at birth and offspring LTL in such populations.
wider implications of the findings: As of yet, our and prior studies have not identified childhood or adult characteristics
that confound the paternal age at birth association with offspring LTL, supporting the hypothesis that offspring may inherit the longer
telomeres found in sperm of older men. The biological implications of the paternal age effect are unknown. A recent theory proposed that the
inheritance of longer telomere from older men may be an adaptive signal of reproductive lifespan, while another theory links telomere length
attrition to female reproductive senescence. However, we are unaware of any data to substantiate a relationship between paternal age at
birth and daughter’s fertility. Generalizability of our study results to other white female populations is supported by prior reports of paternal
age at birth and offspring telomere length. Furthermore, a confounding relationship between paternal or maternal age at birth and SES was
not observed in a study of SES and telomere length.
study funding/competing interest(s): This work was supported by the National Institutes of Health (grants numbers:
CA87969, CA49449, CA065725, CA132190, CA139586, HL088521, CA140790, CA133914, CA132175, ES01664 to M.D.); and by the
American Health Association Foundation. We have no competing interests to declare.
A fundamental biological mechanism present in nearly all eukaryotes is
the telomere maintenance system
(de Lange, 2004)
. The main known
function of telomeres is to protect the physical integrity of linear
chromosomes (Abbott, 2009), by masking chromosome ends from being
recognized as double-stranded DNA breaks, which would otherwise
result in chromosomal rearrangements
(d’Adda di Fagagna et al.,
. These capping structures, composed from a long stretch of
telomeric (TTAGGG)n DNA repeats complexed to
(de Lange, 2005)
, also create a buffer to
prevent the loss of genomic DNA as a result of the end replication
. On average, 50 – 100 bp of telomeric
DNA are lost per mitotic division, limiting the replicative capacity of
(Allsopp et al., 1992)
. Exposure to inflammation and oxidative
stress may accelerate telomere loss
(von Zglinicki, 2002)
The highly conserved and tightly regulated catalytic component of
the telomerase enzyme complex, telomerase reverse transcriptase
(TERT), restores the length of telomeres by reverse transcribing
telomeric repeats from an RNA template
(Smogorzewska and de Lange,
. However, postnatally, only germline stem cells express
sufficient levels of telomerase to maintain telomere lengths
(Kim et al.,
1994; Wright et al., 1996)
. Telomerase activity has not been detected
in most adult somatic tissues, and where the enzyme is expressed, the
levels are not sufficient to prevent attrition
(Broccoli et al., 1995;
Counter et al., 1995; Kyo et al., 1997; Yasumoto et al., 1996;
Masutomi et al., 2003)
and replication-associated senescence. As a result,
telomere length declines with age
(Hastie et al., 1990; Slagboom
et al., 1994; Butler et al., 1998; Frenck et al., 1998; Friedrich et al.,
2000; Takubo et al., 2002)
and the accumulation of senescent cells
is thought to contribute to age-related tissue deterioration and
(Campisi, 2001; Campisi et al., 2001; Stewart
and Weinberg, 2006)
. Therefore, telomeres may serve as a ‘molecular
clock’ of biological aging
(Shay and Wright, 1996)
Although considered highly heritable with estimates in the range of
36 – 86%
(Slagboom et al., 1994; Jeanclos et al., 2000; Vasa-Nicotera
et al., 2005; Andrew et al., 2006; Bakaysa et al., 2007; Njajou et al.,
2007; Atzmon et al., 2010)
, little is known regarding the inheritance
of telomere length. Mean maternal
(Nawrot et al., 2004; Akkad
et al., 2006)
(Nordfjall et al., 2005, 2010; Njajou et al.,
telomere lengths have been significantly correlated with
offspring telomere length, with an apparent stronger paternal
contribution. Several studies have also observed positive correlations
between paternal age at birth, which is a surrogate for age at
conception, and offspring telomere length
(Unryn et al., 2005; De Meyer
et al., 2007; Njajou et al., 2007; Kimura et al., 2008; Arbeev et al.,
. Longer telomere lengths have also been noted among offspring
of older mothers
(Unryn et al., 2005; De Meyer et al., 2007; Njajou
et al., 2007; Kimura et al., 2008; Arbeev et al., 2011)
. However, this
may reflect the strong correlation between maternal and paternal
age at birth
(De Meyer et al., 2007; Kimura et al., 2008)
cross-sectional studies have observed longer telomeres in sperm from
older men compared with sperm from younger men
(Allsopp et al.,
1992; Baird et al., 2006; Kimura et al., 2008)
. The inheritance of
longer telomere lengths from the sperm of older men is considered
one potential mechanism contributing to this association
et al., 2008)
Low socioeconomic status (SES) has been linked to worse health
outcomes, which may stem from a greater likelihood of unhealthy
exposures and adverse events, while having a limited access to
(Adler and Rehkopf, 2008)
. Several studies have
found short telomere lengths associated with various factors related to
(Cherkas et al., 2006; Batty et al., 2009; Shiels et al., 2011)
particularly if experienced during early life
(Kananen et al., 2010;
Kiecolt-Glaser et al., 2011; Steptoe et al., 2011; Needham et al.,
2012; Surtees et al., 2012)
. Fathers who are older at the time of
their offspring’s birth may be more established financially and socially,
and therefore able to contribute more resources toward the physical
and psychological well-being of the child (Vigil and Geary, 2006),
bestowing better health and coping behaviors for life. Prior studies
indicated that the paternal age at birth association with the offspring
telomere length is independent of the age of the offspring at blood
(Unryn et al., 2005; De Meyer et al., 2007; Kimura et al., 2008;
Arbeev et al., 2011)
, maternal age at birth
(Kimura et al., 2008; Arbeev
et al., 2011)
and adult body mass index (BMI), smoking status, and
household SES of the offspring
(Kimura et al., 2008)
none have considered whether early childhood or adolescent
exposures may account for some or all of the observed association. To
address the possibility of confounding by indicators of early life SES,
we investigated the relationship within the Nurses’ Health Study
(NHS), which has collected an extensive array of information from
female nurses since inception of the cohort in 1976. Existing leukocyte
telomere length (LTL) and covariate data were available on 4250
healthy women for this analysis.
Materials and Methods
The NHS is a prospective cohort study of 121 700 female registered
nurses in 11 US states who were 30 – 55 years of age at enrollment. In
1976 and biennially thereafter, self-administered questionnaires gather
detailed information on lifestyle, menstrual and reproductive factors, and
medical history. Self-reports of major chronic diseases are confirmed by
medical records and pathology report reviews, telephone interviews or
supplemental questionnaires. From 1989 to 1990, 32 826 women
provided blood samples and completed a short questionnaire asking about
day and time of collection, current weight and use of medications.
The details of blood collection methods have been previously described
(Hankinson et al., 1995)
A number of nested case – control studies examining LTL associations
with cancer and cardiovascular disease have been conducted in the
blood sub-cohort. For the current analysis, we used existing LTL data
on participants selected as controls from nested case – control studies of
stroke, myocardial infarction, cancers of the breast, endometrium, skin,
pancreas and colon, as well as colon adenoma
(De Vivo et al., 2009;
Han et al., 2009; Prescott et al., 2010; Nan et al., 2011)
. We also included
existing LTL data from a small random sample of women participating in
the NHS cognitive sub-study
(Devore et al., 2011)
Completion of the self-administered questionnaire and submission of the
blood sample were considered to imply informed consent. The NHS
protocol was approved by the Human Research Committee of Brigham
and Women’s Hospital, Boston, MA.
Participants provided personal and lifestyle characteristics on the 1976
baseline questionnaire. Women reported their current weight (pounds),
height (inches), age at menarche, age when menstrual periods stopped
and for what reason (natural or surgery), use and duration of
postmenopausal hormone therapy (HT), smoking status (never, past, current), age
started smoking, number of cigarettes per day in categories (1 – 4, 5 – 14,
15 – 24, 25 – 34, 35 – 44 and ≥45 cigarettes per day) and age stopped
smoking. Weight, menopausal status, HT use and smoking habits were
updated on each subsequent questionnaire. Women who provided a
blood sample reported current weight and HT use within the past 3
months on an accompanying short questionnaire. Height and weight at
blood draw were used to calculate BMI (kg/m2). Participants were
defined as postmenopausal if they reported having a natural menopause
or bilateral oophorectomy. Women who reported a hysterectomy with
either one or both ovaries remaining were defined as postmenopausal
when they were 56 years old (if a nonsmoker) or 54 years old (if a
current smoker), ages at which natural menopause had occurred in 90%
of the respective cohorts. Age at menopause in the NHS is reported
with a high degree of reproducibility and accuracy
(Colditz et al., 1987)
Smoking duration in years multiplied by packs of cigarettes smoked per
day (20 cigarettes per pack) was used to calculate pack-years of
smoking. Total pack-years of smoking were assessed in the questionnaire
cycle prior to blood collection.
Women provided some basic family information. On the baseline
questionnaire, women reported their mother’s and father’s years of birth.
Parental ages at birth were calculated by subtracting the mother’s or father’s
year of birth from the nurse’s year of birth. In 1996, women were asked
‘How many biological brothers and sisters do you have? (Include any
deceased siblings. Do not include 12 siblings.)’. The total number of full
biological siblings was calculated by summing the number of brothers and
number of sisters reported by the nurse.
Parental occupation and homeownership were used as indicators of SES
in early life. On the baseline questionnaire, women were asked what their
father’s occupation was when the nurse was 16 years of age. Responses
were grouped into the following categories: professional (10.2%),
managerial (15.3%), clerical (10.9%), sales (22.1%), craftsmen (14%), service
(5.1%), laborer (2.4%), farmer (10.9%), househusband (,1%) and
unknown / retired / deceased (9.2%). In 2004, women were asked
‘Did your parents own a home at the time of your birth or infancy?’. Of
the 3609 women who answered, 46% had parents who owned their
Leukocyte telomere length
Genomic DNA was extracted from peripheral blood leukocytes using the
QIAmp (Qiagen, Chatsworth, CA, USA) 96-spin blood protocol.
PicoGreen quantitation of genomic DNA was performed using a Molecular
Devices 96-well spectrophotometer (Sunnyvale, CA, USA). The ratio of
telomere repeat copy number to a single gene copy number (T/S) was
determined by a previously described modified, high-throughput version
(Wang et al., 2008)
of the quantitative real-time PCR telomere assay
run on the Applied Biosystems 7900HT Sequence
Detection System (Foster City, CA, USA). Triplicate reactions of each
assay were performed on each sample. LTL is reported as the
exponentiated sample T/S ratio corrected for a reference sample. In all nested
case – control studies, the telomere and single-gene assay coefficients of
variation (CVs) for triplicates were ,4%. CVs for the exponentiated T/
S ratio were ≤18%.
Several studies have found longer LTLs among blacks than whites
et al., 2008; Fitzpatrick et al., 2011; Hofmann et al., 2011; Zhu et al.,
. The LTL difference declines and may even reverse with aging.
This may be due to a faster rate of telomere attrition among blacks
(Hunt et al., 2008; Aviv et al., 2009; Roux et al., 2009)
, which might
stem from a higher prevalence of perceived stress and poverty
(Geronimus et al., 2010). Given potential racial differences in telomere length
dynamics and that the NHS cohort is composed predominantly of white
women ( 97%), our analysis was restricted to individuals of self-reported
European ancestry. Within each study, LTL values displayed a skewed
distribution. Telomere data generated by non-standard assay conditions were
excluded. Data were natural logarithm transformed to improve normality
(lnLTL). Participants with outlier lnLTL values were identified using an
extreme studentized deviate many-outlier procedure
excluded from the study populations. Because the distribution of lnLTL
values differed slightly between studies, we generated probit scores to
standardize for between-batch variability
(Hosmer and Lemeshow,
and created a standard normal distribution prior to pooling data.
Because older paternal age at birth may be associated with adverse
outcomes in offspring
(Sartorius and Nieschlag, 2010; Arbeev et al., 2011)
we restricted our study population to the women who were selected as
controls for the nested case – control studies, and the random sample of
women in the cognitive sub-study, as mentioned above (N ¼ 4593).
Control participants found to have had a diagnosis of cancer, diabetes,
myocardial infarction, multiple sclerosis or rheumatoid arthritis prior to
blood collection were excluded (N ¼ 39). For the present analysis, we
also excluded controls who were missing data on mother’s or father’s
year of birth (N ¼ 255), BMI at blood collection (N ¼ 17) or smoking
status (N ¼ 15). We additionally restricted our analyses to women
whose calculated maternal age at birth was in the range of 15 – 50 years
and paternal age at birth in the range of 15 – 70 years of age (out of
range N ¼ 17). Our final study population consisted of 4250 women.
We used Spearman’s partial rank correlation coefficients to examine the
relationship between LTL probit scores and various factors. We used
generalized linear models to estimate adjusted least-squares mean (LSM) LTL
probit scores by categories of paternal age at birth (15 – 24, 25 – 29, 30 –
34, 35 – 39, 40+ years of age). All models were adjusted for the nurse’s
age at blood collection (continuous). We considered BMI at blood
collection (continuous), smoking status (never, past, current), maternal age at
birth (continuous), father’s occupation when the nurse was 16 years of
age (professional, managerial, clerical, sales, craftsmen, service, laborer,
farmer, househusband and unknown/retired/deceased), whether the
nurse’s parents owned their home when the nurse was an infant (no,
yes), number of full biological siblings (continuous), menopausal status
(pre- and postmenopausal) and recent postmenopausal hormone use at
blood collection (never, past, current) as potential confounders. The
midpoints of categorical variables were used to test for trend. We conducted
stratified analyses by whether the nurse’s parents owned their home when
the nurse was an infant, median age at blood collection (,60, 60+ years),
and menopausal status. We used F-tests to compare nested models with
and without interaction terms between paternal age categories and these
variables. Generalized linear models were also used to estimate adjusted
LSM LTL probit score by categories of maternal age at birth (15 – 19,
20 – 24, 25 – 29, 30 – 34, 35+ years of age). Tests used an alpha level of
0.05, and all P-values are two-sided. We used SAS Version 9.2 software
(SAS Institute, Cary, NC, USA).
As expected, the participant’s age at blood collection was inversely
associated with the LTL probit score (P ¼ 1.1 × 10211; Table I).
Although maternal age at birth and paternal age at birth were highly
correlated (rs ¼ 0.77; Table II), we only observed a positive age-adjusted
ars ¼ Spearman’s partial rank correlation coefficient.
bNot adjusted for age.
cNever smokers assigned value of 0.
correlation for the participant’s LTL probit score with paternal age at
birth (P ¼ 0.006), but not with maternal age at birth (P ¼ 0.64;
Table I). Even so, the significant positive trend in age-adjusted LSM
LTL probit score (P ¼ 0.002) strengthened when we included
maternal age at birth in the analysis of paternal age (P ¼ 1.6 × 1025;
Table III). The mutual adjustment of parental ages at birth also
revealed an inverse relationship between maternal age at birth and
LTL probit score (P ¼ 0.01; Supplementary Table SI). Additionally
adjusting for the participant’s BMI and smoking status at blood
collection did not change estimates.
We next examined whether childhood and/or adolescent SES may
have confounded the paternal age at birth association with the LTL
probit score. We adjusted for father’s occupation when the nurse
was 16 years of age and whether or not the parents owned their
home at the time of the nurse’s birth as approximate measures of
childhood/adolescent SES. These factors did not alter the relationship
between paternal age at birth and the LTL probit score. While there
was an indication that paternal age at birth was more strongly
associated with the LTL probit score among nurses whose parents
owned their home (P ¼ 0.003) compared with those who did not
(P ¼ 0.09), the interaction was not significant (P ¼ 0.82; Table IV).
We noted that having a larger number of full biological siblings was
positively correlated with the LTL probit score (P ¼ 0.03; Table I) and
with paternal age at birth (rs ¼ 0.15, P ¼ 4.1 × 10223; Table II). Most
of the participants in our cohort were born just before or during the
great depression, a period when the fertility rates declined in the USA
(Martin et al., 2011)
. One potential explanation for the observed
correlations is that men of higher SES, who may possess longer telomeres
than men of lower SES, may have been more likely to continue to have
children later in life. However, when we adjusted the model for
number of full biological siblings, the paternal age association with
the LTL probit score remained the same.
Menopause has been suggested as a potential confounder of
telomere dynamics in women
(Kimura et al., 2008)
. In our dataset,
menopausal status did not confound the relationship between the paternal
age at birth and the LTL probit score. However, when we stratified
our analysis by menopausal status, the association appeared stronger
among premenopausal women (Pinteraction ¼ 0.045; Table IV).
Premenopausal women with the oldest fathers had LTL probit scores 241%
greater than premenopausal women with the youngest fathers (LSM of
0.48 versus 20.34, respectively). The percent difference in LTL probit
score between the same categories was 188% among postmenopausal
women (LSM of 0.14 versus 20.16, respectively; Table IV). The
estimates remained unchanged after further adjusting for postmenopausal
hormone use at blood collection among postmenopausal women.
Using a population of 4250 healthy women, aged 42 – 69 at blood
collection, from the well-characterized NHS, we found a positive
association between paternal age at birth and participant’s LTL (P ¼ 1.6 ×
1025; Table 3) that remained robust after adjusting for potential
indicators of early life SES. After adjusting for age at blood collection and
paternal age at birth, we also observed a weak inverse association
between maternal age at birth and participant’s LTL.
Overall, our results are consistent with the published literature.
Taking the weighted average of estimates from prior studies suggests
Values are means (SD) or percentages and are standardized to the age distribution of the study population.
aValue is not age adjusted.
bAmong ever smokers.
an increase in telomere length of 17.7 bp (range: 10 – 55 bp)
associated with each year of older paternal age at birth
(Unryn et al.,
2005; De Meyer et al., 2007; Njajou et al., 2007; Kimura et al.,
2008; Arbeev et al., 2011)
. While the PCR-based assay does not
generate absolute measures of telomere length, using estimates from the
age at blood collection (b ¼ 20.016) and paternal age at birth (b ¼
0.014) analysis we could approximate an absolute difference in
telomere length. If we assume that the telomere length declines 23 bp
on average for every 1 year increase in age at blood collection
(Unryn et al., 2005; De Meyer et al., 2007; Kimura et al., 2008;
Arbeev et al., 2011)
, we calculate that a woman in our study
population has an additional 20 bp of telomere length for every one year
increase in the father’s age at the time of her birth. Similar to the
results of Kimura et al.
(Kimura et al., 2008)
, adjusting for the
participant’s BMI and smoking status at blood collection did not change
Individuals from disadvantaged environments are hypothesized to
age more rapidly as a result of greater exposure to physiological and
psychological stressors such as uncertainty, adverse events, poor
housing and nutrition, lack of adequate health care and being less
likely to engage in healthful behaviors
(Adler and Rehkopf, 2008;
Needham et al., 2012)
. Several studies have examined the relationship
between indicators of SES and telomere length. In a study of adult
female twins, women classified to manual social classes had shorter
telomeres than those in non-manual social classes (Cherkas et al.,
2006). Others did not find a relationship between adult telomere
length and indicators of SES at the time of blood collection
et al., 2007; Batty et al., 2009; Kananen et al., 2010; Shiels et al.,
2011; Steptoe et al., 2011; Surtees et al., 2012)
. Instead two studies
observed a positive association between telomere length and
educational attainment, an indicator of early life SES
(Steptoe et al., 2011;
Surtees et al., 2012)
. Another two studies reported that a greater
number of negative childhood events was independently associated
with adult telomere length
(Kananen et al., 2010; Kiecolt-Glaser
et al., 2011)
, with chronic/serious childhood illness and parental
unemployment noted as the most significant adverse events in one of
the studies (Kananen et al., 2010). Furthermore, among children
aged 7 – 13, children whose parents never attended college were
estimated to have telomeres 1178 base pairs shorter than children with at
least one college educated parent
(Needham et al., 2012)
As fathers who are older at the time of their child’s birth may be
able to provide a more protective and health-promoting childhood
environment (Vigil and Geary, 2006), we hypothesized that early life SES
could account for the association between paternal age at birth and
offspring LTL. However, when we adjusted for father’s occupation
when the nurse was 16 years old or whether parents owned their
........................................................ 401 ........................................................
.... –3539 ..........
.... –3034 ..........
.... –2529 ..........
home at the time of the nurse’s birth as potential indicators of
childhood or adolescent SES, the paternal age at birth association with LTL
did not change. This is compatible with the female twin study, where
maternal and paternal age at birth did not confound the association
between LTL and social class
(Cherkas et al., 2006)
. Although the
interaction with parental home ownership was not significant in our
study (P ¼ 0.82), it is noteworthy that the paternal age trend
appeared stronger among women whose parents owned their home
at the time of birth, suggesting stress associated with low SES may
accelerate telomere shortening and reduce the paternal age effect.
While area-based deprivation and employment status were not
associated with telomere length in the cross-sectional study by Shiels and
colleagues, the authors noted steeper age-related telomere length
declines associated with low income, renting a home and poor diet
(Shiels et al., 2011)
A couple of novel observations were made by our study: (i) the
inverse association of offspring telomere length with maternal age at
birth and (ii) the effect modification of the paternal age at birth
association by menopausal status. Prior studies found evidence of longer
offspring telomere length associated with older maternal age at birth
(Unryn et al., 2005; De Meyer et al., 2007; Njajou et al., 2007;
Kimura et al., 2008; Arbeev et al., 2011)
. However, this appeared
to be an artifact of the very high correlation between maternal and
paternal age at birth (r ¼ 0.72 – 0.85)
(De Meyer et al., 2007; Kimura
et al., 2008)
. Once paternal age at birth was taken into account,
maternal age at birth was no longer independently associated with
offspring telomere length
(De Meyer et al., 2007; Kimura et al., 2008;
Arbeev et al., 2011)
. Although not significant, after adjusting for
paternal age at birth, inverse trends were noticeable for maternal age at
birth and offspring telomere length in two of the studies
et al., 2008; Arbeev et al., 2011)
. In contrast to the idea that sperm
of older fathers pass on longer telomere lengths to offspring
(Kimura et al., 2008)
, oocytes ovulated late in reproductive life are
hypothesized to have shortened telomeres
(Keefe et al., 2006)
which would coincide with an inverse association between maternal
age at birth and offspring telomere length.
The association of paternal age at birth with offspring telomere
length was consistently observed among sons from each cohort, but
was seen among daughters in only two of the four cohorts in the
(Kimura et al., 2008)
. The authors suggested
menopause, which is characterized by a decrease in estrogen levels
(Burger et al., 2007), may be a potential confounder given the
various age distributions of the cohorts included in their study
(Kimura et al., 2008)
. While much is still unknown regarding
transcriptional regulation of TERT, the rate-limiting component of the
telomerase enzyme complex, in vitro studies have shown that estrogen
treatment induces estrogen receptor binding to the promoter region
with a subsequent increase in mRNA expression and telomerase
activity in a variety of cell types
(Kyo et al., 1999; Misiti et al., 2000;
Nanni et al., 2002; Kimura et al., 2004; Sato et al., 2004; Boggess
et al., 2006; Grasselli et al., 2008; )
. Furthermore, long-term HT
users were found to have longer telomeres than never users (Lee
et al., 2005), and a recent small study found a non-significant positive
relationship between age at menopause and telomere length
et al., 2011)
, suggesting that greater estrogen exposure may contribute
to longer LTL. In our analytic population, menopausal status did not
confound the relationship between paternal age at birth and
participant’s LTL. Instead, we observed an interaction where the
paternal age at birth association with participant’s LTL appeared stronger
among premenopausal women than postmenopausal women
(Pinteraction ¼ 0.045). Menopausal status did not appear to simply be
a proxy for age, as the interaction was not significant (Pinteraction ¼
0.61) when stratified by median age at blood collection (,60, 60+
years). However, neither age at menopause, nor duration of total or
estrogen only HT use up until blood collection were correlated with
LTL (P ≥ 0.29). These results are not supportive of in vivo estrogen
regulation of telomere length in peripheral blood leukocytes. Thus,
the interaction by menopausal status may suggest that non-hormonal
menopausal changes influence LTL maintenance or our results could
be due to chance.
Telomere dysfunction in oocytes has been proposed as a common
mechanism underlying the increased rates of infertility, miscarriage and
birth defects among older women
(Keefe et al., 2006)
recently theorized that telomere length may serve as a
marker to balance the limited energy and resources invested in
maintaining a durable soma versus the potential reproductive life history of
an organism. Therefore, the paternal age effect may act as an adaptive
intergenerational male signal to offspring, reflecting the ‘rolling average’
reproductive lifespan of recent generations. Since menopause marks
the end of a woman’s reproductive life
(Burger et al., 2007)
might expect to find a positive association between paternal age at
birth and daughter’s age at menopause based on these hypotheses.
After adjusting for menopausal status at blood draw, paternal age at
birth showed a positive correlation with daughter’s age at menopause
in our study population (rs ¼ 0.04, P ¼ 0.01). However, maternal age
at birth was also positively correlated with daughter’s age at
menopause (rs ¼ 0.05, P ¼ 0.002). When maternal age at birth was taken
into account, paternal age at birth was no longer associated with
daughter’s age at menopause (P ¼ 0.88). Since women who undergo
menopause at an older age are capable of reproducing until later in
life, the relationship between maternal age at birth and daughter’s
age at menopause may reflect the strong correlation between
mother’s and daughter’s menopausal age
(He and Murabito, 2012)
Participants in our study were all registered nurses at the time of
cohort enrollment and therefore, not representative of the general
US population. The distribution of father’s occupation suggests that
the women were raised in diverse backgrounds. Even so, the strong
association observed between paternal age at birth and participant
LTL suggests that SES is not a major confounder in our population,
which presumably has a limited and higher range of childhood SES.
As different telomere dynamics may occur among those most
socioeconomically deprived, our results may not apply to lower social class
groups. Generalizability of our study results to other white female
populations is supported by prior reports of paternal age at birth
and offspring telomere length
(De Meyer et al., 2007; Njajou et al.,
2007; Kimura et al., 2008; Arbeev et al., 2011)
. However, it is still
undetermined whether a similar association exists in non-white
populations, who experience a greater prevalence of perceived stress and
poverty (Geronimus et al., 2010). Furthermore, the paternal age
effect is hypothesized to result from an overrepresentation of
sperm with longer telomeres as men age
(Kimura et al., 2008)
Acquiring sperm samples from the fathers of our participants is not
feasible. Such a task would be costly and introduce survival bias as the
youngest father would now be 86 years of age. Hence, we are
unable to confirm whether longer telomere lengths are inherited
from older fathers.
In conclusion, the paternal age association with the offspring
telomere length remained robust after considering indicators of early life
SES as potential confounding factors in a large, homogenous
population, consistent with inheritance of longer telomere lengths from the
sperm of older men. However, to-date, the observation of longer
sperm telomere lengths from older compared with younger men
has been made in cross-sectional studies. Longitudinal studies with
repeat sperm sample collections over time are needed to confirm
this observation. Further, alternate SES indicators or other
confounders not measured in our study that may more directly measure
childhood physiological and psychological stress may still account for the
paternal age at birth association. Thus, in addition to examining the
paternal age at birth relationship with offspring telomere length in
nonwhite populations, it would be informative to assess the effect in
populations with more detailed childhood SES data and of adopted
individuals. We also showed for the first time an inverse correlation
between maternal age at birth and telomere length as well as an
interaction by menopausal status. Additional studies are needed to confirm
these observations and to further investigate the significance of the
paternal age effect on offspring telomere length.
Supplementary data are available at http://humrep.oxfordjournals.
We thank Pati Soule for laboratory assistance and Carolyn Guo for
programming assistance. We gratefully acknowledge NHS participants
for their dedication and commitment.
J.P. and I.D.V. were involved in the conceptualization of the
manuscript. J.P. performed the analyses and drafted the manuscript. All
authors contributed toward data acquisition, critically revised the
manuscript and approved the final manuscript.
This work was supported by the National Institutes of Health (grants
numbers: CA87969, CA49449, CA065725, CA132190, CA139586,
HL088521, CA140790, CA133914, CA132175, ES01664 to M.D.);
and by the American Health Association Foundation.
Conflict of interest
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