Effects of Competitive Triathlon Training on Telomere Length
Marcus Colon, Andrew Hodgson, Eimear Donlon, and James E.J. Murphy
Telomeres act as a mitotic clock and telomere-related senescence has been linked to age-related physiological decline. There is
increasing evidence lifestyle factors can inﬂuence telomere length (TL). The purpose of this study was to determine the effect
of competitive triathlon training on TL. Seven competitive male triathletes and seven recreationally active males participated in
the study. Relative TL was measured using quantitative polymerase chain reaction. Physiological parameters key to athletic
performance such as maximal oxygen intake, lactate threshold, and running economy were also measured. Triathletes had longer
telomeres than the recreationally active (1.257 ± 0.028 vs. 1.002 ± 0.014; p<.0001). Positive association was found between TL
and maximal oxygen intake, lactate threshold, and running economy (R
= .677, .683, and .696, respectively). This study
indicates that competitive triathlon training buffers against age-related telomere shortening, and there is a correlation between
exercise behaviors, higher maximal oxygen intake, and TL.
Keywords:aging, delayed senescence, physical activity, maximal aerobic capacity
Telomeres are a repeating nucleotide sequence consisting of
TTAGGG (T, thymine; A, adenine; and G, guanine) located at the
end of chromosomes, which help to ensure chromosomal stability
during cell division. Each time a cell replicates, the telomere is pro-
gressively shortened (Harley, Futcher, & Greider, 1990), and loss
of telomere length (TL) provides a measure of replicative senes-
cence (Benetos et al., 2001). Telomeres act as a mitotic clock and
have even been proposed as a marker of biological aging (Butler
et al., 2004). Telomere-related senescence has been linked to age-
related physiological decline and several age-related pathologies
(Bekaert et al., 2007).
There is increasing evidence that lifestyle factors are associ-
ated with TL such as exercise, psychological stress, body mass, and
smoking status (Ludlow, Ludlow, & Roth, 2013). This has led to
an increased number of studies exploring the effects of exercise on
TL. To date, the results of these studies have been inconsistent
(LaRocca, Seals, & Pierce, 2010;Østhus et al., 2012;Shin, Lee,
Song, & Jun, 2008;Woo, Tang, & Leung, 2008). However, the
weight of evidence has begun to lean toward exercise having a
buffering effect on age-related telomere shortening with studies
(LaRocca et al., 2010;Østhus et al., 2012) showing a positive
correlation between maximal oxygen intake
Krauss et al. (2011) have even gone as far as showing increased
exercise capacity had longer telomeres equating to around 4 years
of biological age. While, Ludlow et al. (2008) and Savela et al.
(2013) have indicated that the relationship is an inverted Ucurve
where moderate-physical activity has beneﬁcial effects on TL
compared with both low- and high-intensity exercises.
Triathlon is an endurance multisport, consisting of three
separate disciplines (swimming, cycling, and running) performed
sequentially during a race. The popularity of triathlon has soared in
recent years with up to 300,000 active triathletes (TA) in the United
States (Burns, Keenan, & Redmond, 2003) and up to 17,000
members in Ireland (Triathlon Ireland, 2017). This surge in popu-
larity is also evidenced by triathlons inception into the Olympic
Games in 2000 where it has been contested at each summer games
since (Lenherr, Knechtle, Rüst, Rosemann, & Lepers, 2012). To
meet the demands of triathlon, athletes undergo rigorous high-
intensity training regimes. This training has been shown to convey
a host of physiological beneﬁts including improvements in aerobic
capacity and economy of motion (O’Toole & Douglas, 1995).
But there is a growing body of evidence showing that the intense
training and competition schedules carried out by endurance ath-
letes can have negative effects at the cellular level (Collins et al.,
2003). Accordingly, the hypothesis of this study was that TL is
positively correlated with physiological parameters key to athletic
performance and as such competitive triathlon training would
preserve TL over regular moderate-intensity exercise.
Subjects and Experimental Design
Seven competitive male TA and seven recreationally active (RA)
males participated in this study, following informed consent.
All participants were nonsmokers and in good health. All partici-
pants were recruited by e-mail advertisement. TAs were recruited
through a local triathlon club respondents were eligible to be
included if they had participated competitively in triathlons for
the previous 3 years, trained a minimum of 10 hr per week, were
nonsmokers, had never used performance enhancing drugs, and
were not in a period of competition training at the time of study
commencement. The RA males were recruited through the Institute
of Technology Sligo and respondents respondents were eligible to
participate if they performed a minimum of 3 hr of moderate-
intensity exercise per week and did not participate competitively in
any sports. Prior to inclusion, all participants completed a preex-
ercise health questionnaire and were eligible for inclusion if they
were nonsmokers, in good health, and suffered from no chronic
Triathletes were an average of 35.11 ± 5.86 years old,
(height: 180.04 cm ± 6.02 cm; weight: 73.90 ± 5.753), trained
an average of 12.67 ± 3.32 hr per week and had competed at
Colon and Murphy are with Mitochondrial Biology and Radiation Research Centre,
Institute of Technology Sligo, Sligo, Ireland. Colon, Donlon, and Murphy are with
the Dept. of Life Sciences, Institute of Technology Sligo, Sligo, Ireland. Hodgson is
with Sligo University Hospital, Sligo, Ireland. Address author correspondence to
James E.J. Murphy at firstname.lastname@example.org.
Journal of Aging and Physical Activity, (Ahead of Print)
© 2018 Human Kinetics, Inc. ORIGINAL RESEARCH
national or international level in triathlons for a minimum of
3 years. All TAs were tested during triathlon season in a period of
“out of competition training.”RA were deﬁned as people who
were “engaged in regular endurance-based exercise but do not
participate competitively in any sport.”RA group were an average
of 34.14 ± 10.29 years old, (height: 177.13 cm ± 4.96 cm; weight:
78.51 ± 9.657) and exercised and average of 5 ± 1.15 hr per week.
Blood samples were taken by venous blood draw 24 hr post-
exercise testing. All procedures were approved by the research
and ethics committee of Sligo University Hospital in accordance
with the Declaration of Helsinki.
Participants were tested in a semifasted state (no food or water
for 3 hr prior). All measurements were performed on individuals
in a rested state. Height was measured in bare feet using a
stadiometer. Body composition was measured using whole-body
densitometry in a BodPod (Cosmed, Concord, CA) using man-
ufacturer protocols. Weight was also measured using a calibrated
scale that accompanies the BodPod. All instruments were cali-
brated on the day of testing using manufacturer’s quality control
Treadmill Test for
VO2max, Lactate Threshold, and
The treadmill test was performed in two stages, the ﬁrst stage
was used to calculate lactate threshold and running economy. This
employed a discontinuous incremental test of 3 min stages, start
speed was determined as 2–3 km/hr below participant 5 km or
10 km race pace and increasing by 1 km/hr after each stage until
blood lactate concentration exceeded 4 mmol·L
carried out ﬁve to seven stages, with a 30-s break in between when
aﬁnger prick blood sample was taken for blood lactate concen-
tration and measured using a portable blood lactate analyzer
(Arkray Factor Ltd., Ipswich, United Kingdom). Participants had
a 5-min rest period before completing a second incremental test
VO2max. This was initiated at the ﬁnal speed of
the ﬁrst stage with the gradient increasing by 1% every minute
until voluntary exhaustion. Heart rate was recorded using a T31
heart rate monitor (Polar Electro, Kempele, Finland), and oxygen
uptake and carbon dioxide expiration was recorded using a
(Cosmed). Heart rate, O
VO2max are expressed for each partic-
ipant as a 30-s average. Lactate thresholds were calculated using
the software previously described (Newell et al., 2007; available
DNA Extraction and Quantitative Polymerase Chain
DNA was extracted from whole blood using a DNeasy blood and
tissue kit (Qiagen, Crawley, United Kingdom) and quantiﬁed using
a biophotometer (Eppendorf, Hamburg, Germany). Each 20-μL
reaction contained 18–20 ng DNA, 1 ×SYBR green fast mix and 6-
Carboxy-X-Rhodamine (ROX) (Quanta Biosciences, Gaithers-
burg, MD), and 1-μM primers in ultrapure water. All reactions
were analyzed on a StepOnePlus system (Applied Biosystems,
Foster City, CA). Housekeeper used for normalization was β-2
microglobulin used at a ﬁnal concentration of 500 nM of both
forward and reverse primer. β-2 microglobulin primers were:
forward, 5′–CGGCTCTGCTTCCCTTAGA –3′and reverse,
5′–TCACAGCCAAGCATTCTACAAAC –3′. Data were cal-
culated relative to the RA group using the 2
Threshold) equation (Livak & Schmittgen, 2001).
Relative TL was measured using a quantitative polymerase chain
reactionmethodaspreviouslydescribed(Cawthon, 2002). Telomere
primers used were: forward, 5′–GGTTTTTGAGGGTGAGGGT-
GAGGGTGAGGGTGAGGGT –3′and reverse, 5′–TCCCGAC-
TATCCCTATCCCTATCCCTATCCCTATCCCTA –3′. Cycling
conditions were: 95 °C for 10 min followed by 30 cycles of 95 °C
All statistical analyses were performed on Prism software (version
5.0; Graphpad, San Diego, CA). All data were found to be normally
distributed as determined by a Shapiro–Wilks test for normality
(p>.05). Signiﬁcance was determined using an independent sam-
ples ttest with p<.05 as the limit. Effect sizes were calculated as
Cohen’sd(Cohen, 1988) to determine the magnitude of the dif-
ference between groups. Linear regression was used to assess the
relationship between Telomere/Single copy gene (T/S) ratio and
physiological variables using Pearson’scoefﬁcient of determina-
) and relationship strength interpreted as previously described
Physiological and Anthropometric Characteristics
Physiological and anthropometric analysis results are shown in
Table 1. TAs had signiﬁcantly greater
VO2max (p= .0066, d=
1.753) and lactate threshold speed (p= .0002, d= 2.791). TAs also
had signiﬁcantly lower percentage body fat (p= .0374, d=−1.250)
and running economy (p=<.0001, d=−3.060). There were no
Table 1 Physiological Characteristics of the Recreationally Active and Competitive Triathlete Participants Tested
During Out of Competition Training
(average result ± SD)
(average result ± SD)
pvalue, [95% CI],
) 53.89 ± 4.325 65.44 ± 4.240 .0003, [−16.54, −6.564], 2.697
Body fat (%) 18.70 ± 8.346 10.89 ± 2.906 .0374, [0.536, 15.09], 1.250
Lactate threshold—speed (km/hr) 11.66 ± 0.953 16.08 ± 2.026 .0002, [−6.266, −2.577], 2.791
Lactate threshold—heart rate (bpm) 156.6 ± 14.77 164.0 ± 10.74 .3012, [−22.50, 7.582], 0.573
Running economy (ml·kg
) 221.2 ± 9.190 193.2 ± 9.109 <.0001, [17.34, 38.66], 3.060
Note. Data are shown as mean ± SD.pvalues and CIs are calculated from an independent samples ttest and effect sizes are calculated as Cohen’sd.CI=conﬁdence
(Ahead of Print)
2Colon et al.
signiﬁcant differences found between the group’s bodyweight and
heart rate at lactate threshold (p>.05). However, Cohen’s effect
sizes show that triathlon training had a moderate effect on body-
weight and pronounced effect on heart rate at lactate threshold.
Figure 1shows that the RA group (1.002 ± 0.014) had signiﬁcantly
shorter telomeres than the TA group (1.257 ± 0.028), (p<.0001,
95% conﬁdence interval [−0.2806, −0.2288], Cohen’sd= 11.519).
There were signiﬁcant positive relationships found between
VO2max, lactate threshold speed, running economy, and relative
TL with moderate- to high-positive relationships, as seen in Table 2.
A low-positive relationship was also found with percentage body
fat. There was no relationship found between age, bodyweight, and
lactate threshold heart rate.
The main ﬁndings of this study are (a) TA group had signiﬁcantly
longer telomeres than the RA group; (b) a positive relationship
was seen between
VO2max and TL; and (c) TA group showed
the physiological markers associated with high-intensity endur-
ance training (when compared with the RA group) with greater
VO2max, higher lactate threshold, superior running economy, and
lower percentage body fat.
When the physiological test results of the TA and RA groups
are compared, the key performance indicators (
threshold, running economy, and percentage body fat) were as
expected and conﬁrm the validity of the cohort recruitment process.
It has long been known that TAs tend to have high-aerobic capacity
(O’Toole & Douglas, 1995) and that triathlon training leads to
a greater level of fat metabolism and lower levels of glycogen
depletion (O’Toole, Douglas, & Hiller, 1989), which allows ath-
letes to rely more on the oxidative energy pathways rather than
anaerobic metabolism leading to the production of lactic acid. The
TA group also showed greater economy of motion which is a key
determinant of race performance among athletes of similar abilities
(Conley & Krahenbuhl, 1980). Sleivert and Rowlands (1996) states
that these TAs would be classiﬁed as subelite according to their
VO2max, body mass, and percentage body fat.
It is now widely accepted that regular physical activity reduces
the risk of many age-related diseases and promotes a higher quality
of life. More recently, it has been hypothesized that exercise can
slow cellular aging by decreasing the rate of telomere shortening,
which has been seen in multiple studies showing a positive asso-
ciation between physical activity and TL (Cherkas et al., 2008;
LaRocca et al., 2010;Østhus et al., 2012). However, there have been
inconsistencies in the evidence to support this theory with studies
showing no association (Kadi et al., 2008;Mathur et al., 2013;Shin
et al., 2008). The relationship between physical activity levels and
quality of life/general health status is not necessarily a linear one;
however, several studies show an inverted U-shaped curve relation-
ship with moderate-physical activity having a beneﬁcial effect over
both low and high levels of activity (Ludlow et al., 2008;Savela
et al., 2013). The results from this study suggest that high-intensity
endurance training has a positive effect on TL over regular moderate
exercise. In this study, it may have been the case that the training
performed by the athletes was not of sufﬁcient intensity to the
detrimental effects seen in other studies, though the scope and
longevity of the study was too limited to evaluate this. It may
also be the case that as both Ludlow et al. (2008) and Savela et al.
(2013) used older athletes (65 years old), the negative effects ofhigh-
intensity exercise on TL may only be seen are only seen later in life.
Participants in this study were closely matched with respect
to their age, smoking status, body type, and were all in good
health with relatively minor intragroup variation of physiological
parameters. However, the present ﬁndings must be considered
limited due to the small sample sizes and the fact that our design
did not allow for the analysis of cause and effect. LaRocca et al.
(2010)ﬁrst showed that there was positive correlation between
maximal aerobic capacity and TL, which has also been shown in
Østhus et al. (2012). Here, it was also observed that there was a
strong correlation between a number of the physiological variables
including maximal aerobic capacity and TL but also with percent-
age body fat, lactate threshold speed, and running economy. These
results lend weight to the hypothesis put forward (LaRocca et al.,
2010) that habitual exercise behavior, higher
VO2max, and lon-
ger TL are part of the same phenotype, as all variables showing
positive correlation have been consistently shown to be directly
correlated with volume and intensity of exercise performed. These
results highlight the importance of exercise and exercise intensity
in healthy aging. TL has previously been shown to contribute
to mortality in age-related diseases such as heart and infectious
Figure 1 —Alterations in relative TL (T/S ratio) in blood DNA of the
RA and TA participants tested during a period out of competition training.
Data were calculated relative to the RA group and presented in a boxplot
with minimum to maximum whisker. Signiﬁcance was determined using
an independent samples ttest with p<.05 as the limit, ***p<.0001. TL =
telomere length; TA = triathlete; RA = recreationally active.
Table 2 Relationship of Physiological Variables With
Telomere Length in Recreationally Active and Triathlete
Participants During a Period of Out of Competition
Age .001, .914
) .677, .0003
Bodyweight (kg) .085, .313
Body fat (%) .318, .036
Lactate threshold—speed (km/hr) .683, .0003
Lactate threshold—heart rate (bpm) .096, .28
Running economy (ml·kg
) .696, .0002
Note. Linear regression was used to assess the relationship between relative
telomere length and physiological variables. Data are shown as Pearson’s coefﬁ-
cient of determination (R
(Ahead of Print)
Triathlon Training and Telomere Length 3
diseases (Cawthon, Smith, O’Brien, Sivatchenko, & Kerber,
2003); these results show that exercise could represent a tool to
help reduce mortality from age-related disease. Future research
should use large scale randomized control trials and longitudinal
studies to conﬁrm the relationship between endurance training,
aerobic ﬁtness, and TL.
Competitive triathlon training buffers against age-related telomere
shortening over regular moderate exercise. Our data also suggest
that TL is positively correlated with physiological parameters key
to athletic performance including
VO2max, lactate threshold, and
running economy. These results highlight the importance of exer-
cise in healthy aging.
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Triathlon Training and Telomere Length 5