Research Article
Physical Activity Enhances Metabolic Fitness Independently of
Cardiorespiratory Fitness in Marathon Runners
M. J. Laye,1,2 M. B. Nielsen,1 L. S. Hansen,1 T. Knudsen,1 and B. K. Pedersen1
1Centre of Inflammation and Metabolism, Rigshospitalet, Department of Biomedical Sciences, University of Copenhagen,
2200 Copenhagen, Denmark
2The Buck Center for Research on Aging, Novato, CA 94945, USA
Correspondence should be addressed to M. J. Laye; mjlaye@gmail.com
Received 3 February 2015; Accepted 12 February 2015
Academic Editor: Francisco Blanco-Vaca
Copyright © 2015 M. J. Laye et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
High levels of cardiovascular fitness (CRF) and physical activity (PA) are associated with decreased mortality and risk to develop
metabolic diseases. The independent contributions of CRF and PA to metabolic disease risk factors are unknown. We tested the
hypothesis that runners who run consistently >50 km/wk and/or >2 marathons/yr for the last 5 years have superior metabolic fitness
compared to matched sedentary subjects (CRF, age, gender, and BMI). Case-control recruitment of 31 pairs of runner-sedentary
subjects identified 10 matched pairs with similar VO2max (mL/min/kg) (similar-VO2max). The similar-VO2max group was compared
with a group of age, gender, and BMI matched pairs who had the largest difference in VO2max (different-VO2max). Primary outcomes
that defined metabolic fitness including insulin response to an oral glucose tolerance test, fasting lipids, and fasting insulin were
superior in runners versus sedentary controls despite similar VO2max. Furthermore, performance (velocity at VO2max, running
economy), improved exercise metabolism (lactate threshold), and skeletal muscle levels of mitochondrial proteins were superior
in runners versus sedentary controls with similar VO2max. In conclusion subjects with a high amount of PA have more positive
metabolic health parameters independent of CRF. PA is thus a good marker against metabolic diseases.
1. Introduction
High levels of physical activity (PA) and cardiorespiratory
fitness (CRF) are independently associated with a low risk for
many chronic diseases [1] and mortality [2]. While related,
the distinction between these CRF and PA is critical. CRF
integrates many different physiological systems into a single
measure of function. On the other hand PA is comprised
of any body movements, which may not necessarily lead
to improvements in CRF. However, in general metareviews
and independent studies indicate that the protective effect
of CRF is greater when compared to PA, in most [3–5],
but not all cases [6]. One factor in the discrepancy between
the relative health effects for CRF versus PA is the level of
precision in measurements of CRF versus PA. Assessment of
CRF in large epidemiological studies is typically assessed by
a treadmill or ergometer test [3], while PA is assessed by a
questionnaire [7]. Indeed, the correlation between reported
PA using the international physical activity questionnaire and
CRF as measured by a treadmill test ranged from 0.24 to 0.32
in 3 reviewed studies [8]. Furthermore, the range and types
of PA vary dramatically, while CRF consists of a single well
characterized number.
CRF is a powerful predictor of early mortality inde-
pendent of PA levels, BMI, or other risk factors [9, 10].
CRF also varies largely within sedentary populations [11].
For instance in a population of 1707 men aged 20–49 the
difference between 20th and 80th percentile is more than
30% (36.8–48.5 mL/kg/min) [11]. Likewise the change in
CRF to a standardized exercise training program varies
largely from −4.7% to +58.0 [12]. While large cross-sectional
studies suggest higher CRF are associated with lower levels in
biomarkers for metabolic disease other intervention studies
show that improvements in CRF are only weakly correlated
with improved biomarkers [13].
VO2max in sedentary population of monozygotic twins
is highly heritable (77%) even with correction for various
Hindawi Publishing Corporation
Disease Markers
Volume 2015, Article ID 806418, 11 pages
http://dx.doi.org/10.1155/2015/806418
2
Disease Markers
anthropometric measures [14]. Similarly, gains in CRF fol-
lowing 20 weeks of endurance exercise training (3 d/wk at 55–
75% VO2max) are heritable (47%) and highly variable, with
some individuals showing no response in VO2max [15]. The
variability of the response to endurance training is in part
explained by differences in age, sex, race, and initial VO2max
[12, 16, 17], an explanation that is not universally found or
contributes in totality to the variation of gains in CRF [18].
On the other hand changes in PA can be accomplished
through behavioural strategies that are not subject to the
same variability that CRF is. Furthermore levels of PA are
associated with changes in disease risk. In general, results
from the Aerobics Center Longitudinal Study suggest that
within groups of individuals who have similar CRF, overall
health (cardiovascular health and cancer) is better in indi-
viduals with higher PA [6]. One specific example is that
PA, independent of CRF, is atheroprotective by improving
lipoprotein subclass distribution, postprandial lipoprotein
metabolism, inflammation, and endothelial function [19–23].
Conversely, physical inactivity increases the relative risk for at
least 35 pathological and clinical conditions [1]. Remarkably,
physical inactivity as defined by sitting time is a risk factor
for premature death, a number of chronic diseases and
pathologies [24]. For example, 20 days of bed rest [25] or 14
days of reduced step count (and thus increased sitting) [26]
reduce CRF 27% and 7%, respectively. However, it remains
difficult to isolate the independent roles of PA, inactivity, and
CRF in health parameters as most studies are not designed to
explicitly control for CRF, while changing just PA.
In the present study, we hypothesized that PA would
improve metabolic fitness independent of CRF. We there-
fore sought to identify two groups of people who were
closely matched with regard to age, gender, and CRF, but
who differed markedly with regard to their PA level. We
recruited an endurance-trained group (consisting of recre-
ational marathon runners) and a control group, matched
for BMI, age, and gender, and tested various metabolic
health parameters (blood lipids, glucose tolerance, and body
composition) as our primary outcome. The endurance group
fulfilled at least one of two inclusion criteria: (1) they had had
an average training volume of at least 50 kilometers per a week
for at least the past 5 years or (2) they completed at least 10
marathons within the past 5 years including 2 within the last
14 months. Because of the known variability in CRF it was our
aim to compare a subgroup (runners versus controls) with
similar-VO2max (similar-VO2max) and a subgroup (runners
versus controls) with different-VO2max (different-VO2max).
2. Methods
2.1. Recruitment of Subjects. Subjects were recruited through
newspaper adverts and emails listserves in 2008-2009 from
the greater Copenhagen metro area. Marathon runners ful-
filled at least one of two inclusion criteria: (1) having an
average training volume of at least 50 kilometers per week for
at least the past 5 years, as determined by questionnaire or (2)
having ran at least 10 marathons in the past 5 years including
2 within the last 14 months. After a marathon runner had
completed the physiological testing a sedentary subject was
recruited to match the BMI, age, and sex of the marathon
runner. Sedentary subjects were limited to individuals who
did not obtain more than 1 hour of structured exercise per
week. Exclusion criteria prior to inclusion in the study for
both groups included chronic diseases, pregnancy within the
last 3 months, abuse of alcohol, use of cigarettes, or use of
performance enhancing drugs. In total, 31 pairs of subjects
were recruited. No subjects were excluded after testing. The
purpose of the study, possible risks, and discomforts were
explained to the subjects before written consent was obtained.
The study was approved by the Local Ethical Committee of
Copenhagen and Frederiksberg and was in accordance with
the Declaration of Helsinki.
Subjects underwent two days of testing. On day 1, subjects
arrived to the lab in a fasted condition and underwent a
physical examination during which blood pressure, resting
heart rate (HR), and blood were taken for standard laboratory
measurements including blood lipids.
Following the examination subjects underwent a muscle
biopsy. Briefly, muscle biopsies from vastus lateralis were
obtained at rest using the percutaneous Bergstrom needle
method with suction under local anaesthesia, using 3–5 mL of
20 mg mL−1 lidocaine (SAD, Denmark Copenhagen). Muscle
tissue was immediately frozen in liquid nitrogen and stored
at −80∘C until further analysis. Following the muscle biopsy,
subjects underwent a dual-energy X-ray absorptiometry
(DEXA) measurement and an oral glucose tolerance test,
which was administered at ∼10:00 AM.
2.2. Separation of Subjects into “Similar-VO2𝑚𝑎𝑥” and “Differ-
ent-VO2𝑚𝑎𝑥”. After all subjects had undergone the standard
testing, each runner-sedentary pair of subjects was ranked
by their relative difference in VO2max (mL/kg/min). Runners
VO2max ranged from −5% to 64% higher than their sedentary
pair. We separated the cohort into thirds and focused our
analysis on the 10 pairs of marathon runner and sedentary
controls with closest VO2max (−5% to 15%, mean 5% higher,
and absolute difference 2.2 mL/kg/min) and the 10 pairs with
the largest difference in VO2max (27% to 64%, mean of 44%
high, and absolute difference 17.3 mL/kg/min). We refer to
these two groups as similar-VO2max and different-VO2max,
respectively.
2.3. Body Composition. Whole body fat and fat-free tissue
mass measurements were performed using a dual-energy
X-ray absorptiometry (DXA) scanner (Lunar Prodigy, GE
Healthcare, WI, Madison USA, software v. 8.8).
2.4. Oral Glucose Tolerance Test. Subjects underwent a three-
hour oral glucose tolerance test (OGTT). Within 2 minutes,
the subject ingested a drink containing 75 g glucose (Dextrose
Anhydre, Roquette Freres, France) dissolved in 300 mL of
water. Venous blood samples were collected from an antecu-
bital venous catheter before and 10, 20, 30, 60, 90, 120, 150,
and 180 minutes after ingestion of the solution. Blood was
drawn into tubes (Vacuette, Serum clot activator and Sodium
Fluoride/Potassium Oxalate, Hettich, Labinstruments APS)
for determination of glucose and insulin to each time point.
Disease Markers
3
Blood samples were analyzed for standard biochemical mea-
surements by the biochemical department of Rigshospitalet.
2.5. Exercise Testing. The treadmill (Runrace, Technogym,
Italy) test consisted of a lactate threshold and maximal oxygen
consumption (VO2max) portion, which took less than 20
minutes. Indirect calorimetry measurements were collected
throughout the test (Quark b2, CosMed, Rome, Italy). For
marathon runners the test began at 3 km⋅h−1 slower than their
current marathon pace. The controls started at 6-7 km⋅h−1.
Every third minute we increased the speed 1 km⋅h−1, the third
minute of which VO2 was averaged until lactate threshold was
reached. After each stage subjects stepped off the treadmill for
the 15 s required to obtain a blood sample, after which they
began the next stage while the measurement was conducted.
Blood samples were collected within 15 sec of the end of
the stage by wiping sweat, ethanol cleaning, and drying of
a nonlanced finger. Lactate was measured by a handhold
lactate device (Lactate Scout, EKF Senslab GmbH, Leipzig,
Germany), which requires 0.5 𝜇L whole blood and 10 seconds
for a measurement. The workload at which the concen-
tration of blood lactate reached 4.0 mmol/mL or began to
increase exponentially was selected as lactate threshold. We
performed posttest analysis to ensure that the stage at which
lactate threshold was reached showed agreement with the
ventilatory threshold.
Without a rest subjects began the VO2max test at the
speed lactate threshold was reached. The speed was increased
1 km⋅h−1 every minute until the participant was unable to
keep up with speed and/or increasing the workload no longer
increased VO2 (mL/L). All subjects reached exhaustion or
plateaued VO2max and had an RER > 1.10. Heart rate was
recorded during the whole test.
2.6. Immunoblotting. Immunoblots were completed as pre-
viously reported [27]. Briefly, skeletal muscle biopsies were
weighed and homogenized using a Tissuelyser (Qiagen)
(50 mM Tris⋅HCl, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA,
50 mM NaF, 5 mM NaP, and 0.2% Ipegal-CA-630) supple-
mented with complete protease inhibitor cocktail (Roche)
and phosphatase inhibitors (Sigma). Protein concentrations
were measured with the Bradford assay [28]. Equal amounts
of proteins were subjected to SDS-PAGE using Invitrogen 8%
precast gels and an I-blot dry transfer machine according to
the manufacturer’s instructions. Each GEL contained either
the different-VO2max group or the similar-VO2max group with
pairs loaded in adjacent lanes. 30 𝜇g of protein was loaded in
each well. Polyvinylidene fluoride membranes were probed
with primary antibodies at the following concentrations:
MnSOD (#06-984; Upstate) 1 : 2000, GPX1 (number 3206;
Cell Signaling) 1 : 5000, HSP72 (SPA-810F; Gentaur) 1 : 1000,
COXIV-3E11 (number 4850; Cell Signaling) 1 : 2000, GLUT4
(PA1-1065; Thermo Fischer) 1 : 1000, VEGF (sc-152; Santa
Cruz) 1 : 500, and MHCIIa (number 3403; Cell Signaling)
Detection of primary antibodies was performed using either a
mouse (Pierce) or rabbit (Dako) peroxidase-conjugated IgG,
and protein signals were visualized using FEMTO-enhanced
chemiluminescence and a Bio-Rad Chemidoc XRS imager.
Equal protein loading and transfer was verified by beta-
tubulin signal and total lane reactive brown signal, which
stains for total protein. Quantification of the immunoblots
was done using Image J (National Institutes of Health,
Bethesda, MD, http://rsb.info.nih.gov/ij/) and corrected for
total signal on each blot to correct across blots.
2.7. Statistics. All statistics were performed in Graph Pad
(version 5.00 for Windows, GraphPad Software, San Diego,
California, USA, http://www.graphpad.com/). Unless noted
in the text, a two-way ANOVA with marathon runner/control
and similar-VO2max/different-VO2max as the two factors was
performed. If either factor showed significance post hoc
analysis was done by Bonferroni correction with significance
set at 𝑃 < 0.05.
3. Results
3.1. Matching of Healthy Controls and Marathon Runners. A
total of 40 subjects, 20 runners, and 20 sedentary runners
were included for analysis to take advantage of similarities
or differences in VO2max. The subjects were further divided
into similar-VO2max (𝑛 = 10 for runners and sedentary)
and different-VO2max (𝑛 = 10 for runners and sedentary).
Basic anthropometrics can be found in Table 1, indicating that
the experimental design resulted in well matched subjects
by age (𝑃 = 0.97 for interaction) and body weight (𝑃 =
0.76 for interaction). The similar-VO2max group runners and
sedentary subjects had similar body fat percentages (𝑡-test),
while the different-VO2max group runners had significantly
lower body fat than the sedentary group.
3.2. Separation of Paired Subjects Based on Their VO2𝑚𝑎𝑥
Similarity. As expected runners as a group had a higher
VO2max than healthy controls (Figure 1, 𝑃 < 0.0001). In
ranking VO2max difference between one marathon runners
and one sedentary subject the 31 differences ranged from
−5% to 64%, with one-third of the pairs showing a 10% or
less difference in VO2max. In this subgroup, which we call
similar-VO2max, runners had on average only a 5.2% higher
VO2max (range of −5% to 15% for runners/controls, Figure 1,
𝑃 = 0.22 by 𝑡-test). The similar-VO2max group allowed us
to compare a group of individuals with the same CRF, but
different levels of PA. At the other extreme in the 10 pairs
of subjects with the largest difference in VO2max, which we
call different-VO2max, runners had on average a 43% higher
VO2max (range of 27%–64% higher, Figure 1, 𝑃 < 0.001).
The different-VO2max subgroup allowed us to examine the
degree and magnitude to which high CRF and high PA were
associated with better metabolic health.
3.3. Runners Metabolic Profile Was Improved Relative to
Healthy Controls. To assess the overall metabolic health of
the subjects, blood lipids and oral glucose tolerance tests
were conducted. Fasting levels of insulin, total cholesterol,
LDL, and triglycerides were all significantly lower, and
HDL was significantly higher in the runners versus controls
(Table 1). Similarly, the insulin response during an OGTT was
4
Disease Markers
Table 1
𝑛
Age
BMI
Fat-free mass
(kg)
Fat %
Fasting glucose
(mM)
Fasting insulin
(pmol)
Total
cholesterol
(mM)
HDL (mM)
LDL (mM)
TAGs (mM)
Similar-VO2max
Controls
10
(9 M, 1 F)
46.8 ± 2.9
(32–61)
23.6 ± 0.5
(21.0–26.9)
60.5 ± 3.2
19.0 ± 1.9
(10.6–27.6)
5.9 ± 0.1
35 ± 5
5.5 ± 0.3
1.5 ± 0.1
3.5 ± 0.2
1.13 ± 0.17
Runners
10
(9 M, 1 F)
46.2 ± 2.7
(31–60)
23.8 ± 0.6
(21.7–25.7)
58.4 ± 2.1
19.1 ± 2.2#
(8.3–26.7)
6.1 ± 0.1
29 ± 5#
4.9 ± 0.3#
1.7 ± 0.2#
2.9 ± 0.3#
0.90 ± 0.15#
Different-VO2max
Controls
10
(7 M, 3 F)
42.5 ± 3.1
(32–64)
23.4 ± 0.6
(20.8–26.1)
57.7 ± 3
26.7 ± 2.2
(17.0–36.6)
6.2 ± 0.2
38 ± 5
5.7 ± 0.4
1.5 ± 0.1
3.4 ± 0.3
1.28 ± 0.23
Runners
10
(7 M, 3 F)
42.4 ± 3.1
(29–62)
23.0 ± 0.5
(21.4–27.4)
60.3 ± 2.7
14.9 ± 1.9#∧
(6.9–24.0)
5.8 ± 0.2
25 ± 4#
4.6 ± 0.2#∧
1.9 ± 0.1#
2.5 ± 0.2#
0.76 ± 0.08#
All data presented as mean ± SEM, with the range of values within parentheses. # indicates significant effect of marathon running. ∧ indicates post hoc difference in runner versus control within either similar or
divergent group. M = males, F = females, BMI = body mass index, HDL = high density lipoproteins, LDL = low density lipoproteins, and TAGs = triglycerides.
Disease Markers
5
30
40
50
60
70
Controls
Runners
All subjects
VO 2max (mL/kg/min)
∗
(a)
Similar-VO2max
Different-VO2max
Controls
Runners
Controls
Runners
Separated groups
30
40
50
60
70
VO2max (mL/kg/min)
∗∗
(b)
Figure 1: VO2max (mL/kg/min) was determined in marathon runners and controls who were individually matched for age, BMI, and gender
with one runner. Runners as a group had a significantly higher VO2max than their sedentary paired control (Panel (a)) (𝑛 = 31/group, 𝑃 <
0.0001, 𝑡-test). When we stratified the pairs by difference in VO2max between each runner and their control the 10 pairs with the most similar-
VO2max did not significantly differ in VO2max (Panel (b)) (𝑛 = 10, 𝑃 = 0.22, 𝑡-test). In 10 pairs with the most different-VO2max the runners had
a significantly higher VO2max (𝑛 = 10, 𝑃 < 0.0001, 𝑡-test).
Table 2
VO2max
(mL/kg/min)
VO2max
(mL/kg FFM/min)
Predicted
VO2max
VVO2max
(k/h)
Lactate
threshold (k/h)
RE
(mL O2/kg/km)
Resting HR
(bpm)
Similar-VO2max
Controls
46.3 ± 1.6
58.2 ± 1.6
39.2 ± 2.4
13.2 ± 0.6
10.1 ± 0.5
234.2 ± 6.9
58 ± 2
Runners
48.5 ± 1.2#
62.5 ± 1.4#
51.6 ± 4.4#∧
15.7 ± 0.5#∧
13.0 ± 0.6#∧
205.7 ± 8.8#∧
50 ± 3#
Different-VO2 max
Controls
40.2 ± 2.2
54.2 ± 1.8
36.9 ± 3.5
11.8 ± 0.6
8.2 ± 0.5
224.4 ± 3.5
59 ± 3
Runners
57.5 ± 2.2#∧
69.9 ± 2.2#∧
57.3 ± 4.0#∧
17.2 ± 0.9#∧
14.5 ± 0.5#∧
210.8 ± 5.1#∧
47 ± 3#∧
All data presented as mean ± SEM. # indicates significant effect of marathon running. ∧ indicates post hoc difference in runner versus control within either
similar or divergent group. VO2max = ventilation of maximum oxygen consumption, FFM = fat-free mass, VVO2max = velocity at VO2max, k/h = kilometers
per hour, RE = running economy, HR = heart rate, and bpm = beats per a minute.
significantly lower in both the similar-VO2max and different-
VO2max (Figures 2(b) and 2(d)), while the glucose response
to an OGTT did not differ (Figures 2(a) and 2(c)). Together
these data indicate that in subjects matched for CRF high
levels of PA are necessary for improved metabolic risk factors.
3.4. Runners Markers of Performance Are Better Than Healthy
Controls Despite Similar-VO2𝑚𝑎𝑥. VO2max is not necessarily a
predictor of performance so we examined additional markers
of exercise performance to see whether runners perform
better regardless of their VO2max. The velocity at VO2max and
speed at lactate threshold was higher in the runners from both
the similar-VO2max and different-VO2max groups (Table 2).
What may account for the higher velocity at VO2max, but
not absolute VO2max in the runners from the similar-VO2max
group was a difference in running economy. The similar-
VO2max group, but not the different-VO2max, uses less relative
oxygen at a given speed compared to their matched sedentary
control group.
3.5. Runners Skeletal Muscle Has Higher Antioxidant and
Oxidative Enzyme Content Relative to Healthy Controls. In
addition to systemic measures of exercise capacity such
as VO2max, velocity and VO2max, and lactate threshold we
wanted to examine whether skeletal muscle markers for
antioxidant and mitochondrial content were higher regard-
less of VO2max in vastus lateralis skeletal muscle biopsies
of runners (Figure 3). The skeletal muscle of marathon
runners had significantly higher protein content of the
antioxidant enzymes glutathione peroxidase 1 (GPX1) and
mitochondrial superoxide dismutase (mnSOD), but not heat
shock protein 72 (HSP70) (Figure 3). The difference in GPX1
(Figure 3(a)) was higher in runners from both the similar-
VO2max and different-VO2max group. Similarly, cytochrome
oxidase subunit 4 (COXIV), a mitochondrial enzyme in
electron transport chain, was higher in skeletal muscle from
the runners in both the similar-VO2max and different-VO2max
group (Figure 3(f)). However, runners and sedentary subjects
had no differences in protein content glucose transporter 4
6
Disease Markers
Similar-VO2max
0
0.0
2.5
5.0
7.5
10.0
30
60
90
120
150
180
Time (min)
Glucose (mmol)
(a)
Similar-VO2max
0
0
100
200
300
400
500
30
60
90
120
150
180
Time (min)
Insulin (pmol)
∗
∗
PA P < 0.0001
(b)
Different-VO2max
0
30
60
90
120
150
180
Time (min)
0.0
2.5
5.0
7.5
10.0
Glucose (mmol)
Control
Runner
(c)
Different-VO2max
0
100
200
300
400
500
Insulin (pmol)
0
30
60
90
120
150
180
Time (min)
Control
Runner
PA P = 0.0004
(d)
Figure 2: Oral glucose tolerance tests were conducted on marathon runners and sedentary paired controls with similar-VO2max ((a, b), 𝑛 =
10) and with different-VO2max ((c, d), 𝑛 = 10). While no differences in plasma glucose were seen between runners and sedentary controls
regardless of VO2max stratification, runners regardless of VO2max stratification had a significantly lower insulin response (𝑃 < 0.0005 for both
groups, One-Way Repeated Measures ANOVA).
(Figure 3(g)), myosin heavy chain isoform IIa (Figure 3(d)),
or vascular endothelial growth factor (Figure 3(e)). Thus,
high levels of PA, rather than whole body VO2max, are asso-
ciated with higher levels of specific markers of antioxidant
capacity and mitochondrial content in skeletal muscle.
4. Discussion
The present case-control study clearly shows that a high
amount of physical activity (PA) is associated with benefits
on metabolic health parameters independent of cardiores-
piratory fitness (CRF). Conversely a higher level of CRF
without high levels of PA was associated with lower levels of
metabolic fitness. Our measures of metabolic fitness included
improved blood lipid profile, lower insulin response to an
OGTT, and increased skeletal muscle mitochondrial markers.
Furthermore, runners within the similar-VO2max had supe-
rior exercise performance as measured by velocity at VO2max,
lactate threshold, and submaximal running economy.
It is well accepted that PA and CRF reduce the risk of
cardiovascular disease, diabetes, and all-cause mortality [29–
31]. However, the relative contributions of PA and CRF to
the reduction in risk are less well known and few studies
have been designed to pre hoc separate the effects of PA
and CRF from each other. Still analyses of larger cohorts
have in cross-sectional and longitudinal studies attempted to
separate the effects of CRF and PA on health. For instance,
with regard to mortality, the associations of PA and CRF
were examined separately and in combination in a cohort
of relatively healthy 20–82-year-old men and women [32].
Subjects with high levels CRF had lower mortality, while no
association between PA and mortality was found. Conversely,
data from another study showed higher levels of both PA and
CRF associated with reduced risk factors for cardiovascular
disease in a cohort of randomly selected 20–65-year-old
Swedish men and women [33]. Furthermore, in attempt to
separate the effects of CRF and PA, the author’s analysis
showed that subjects with a low CRF, but who were physically
Disease Markers
7
0
20
40
60
∗
∗
GPX1
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
Similar-VO2max
Different-VO2max
(a)
0
20
40
60
80
∗
MnSOD
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
Similar-VO2max
Different-VO2max
(b)
0
20
40
60
∗
∗
COXIV3E11
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
Similar-VO2max
Different-VO2max
(c)
0
50
10
20
30
40
MHCIIa
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
Similar-VO2max
Different-VO2max
(d)
0
50
10
20
30
40
VEGF
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
Similar-VO2max
Different-VO2max
(e)
Similar-VO2max
Different-VO2max
0
10
20
30
40
HSP70
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Controls
Runners
(f)
Figure 3: Continued.
8
Disease Markers
0
50
10
20
30
40
Controls
GLUT4
Runners
AU (mean intensity normalized to
𝛽-tubulin ± SEM)
Similar-VO2max
Different-VO2max
(g)
C
R
C
R
GPX1
MnSOD
HSP70
𝛽-tubulin
𝛽-tubulin
𝛽-tubulin
COXIV
GLUT4
VEGF
MHCIIa
VO2max
VO2max
Similar-
Different-
(h)
Figure 3: Protein levels as measured by western blot of traditional training markers in runners (solid bars) or pair sedentary controls (open
bars) in resting vastus lateralis biopsies. Glutathione peroxidase 1 (a) and manganese superoxide dismutase (MnSOD) (b) were significantly
higher in runners (𝑛 = 10, 𝑃 < 0.001, 2-way ANOVA). Mitochondrial complex IV (COXIV) (c) was significantly higher in runners (𝑛 = 10,
𝑃 < 0.001, 2-way ANOVA). Post hoc significance as determined by Bonferroni corrected 𝑡-test is indicated by a ∗(𝑃 < 0.05). Myosin heavy
chain IIa (d), vascular endothelial growth factor (VEGF) (e), heat shock protein 70 (HSP70) (f), nor glucose transporter 4 (GLUT4) (g) did not
differ between groups. Proteins of interest are normalized to 𝛽-tubulin as a loading control that did not differ between groups. Representative
blots are shown in (h), with C indicating control and R indicating runner.
activity, had a 50% reduction in risk factors. However, the
design of the study only groups subjects as only fit/unfit
active/nonactive, which is a less robust method to match
groups as we employed in the current study. While useful
for large cohorts the dichotomization of groups by fit/unfit
and active/nonactive may not best represent the complex
interaction between CRF and PA.
The finding that the highly PA marathon runners had
a more beneficial lipid profile than controls, independent
of CRF, is in accordance with previous findings in “at risk
groups” [34]. In a classic randomized-controlled trial by
Kraus et al. [20] they determined the effect of the amount
and intensity of training in 111 sedentary, overweight men
and women with mild-to-moderate dyslipidemia. The study
design randomly assigned subjects to a control group (no
exercise training) or to 8 months of physical training entail-
ing either high-amount-high-intensity exercise (32 km jog-
ging/week at 65–80% of peak oxygen uptake (VO2max)), low-
amount-high-intensity exercise (19 km jogging/week at 65–
80% percent of VO2max), or low-amount-moderate-intensity
exercise (19 km walking/week at 40–55% of VO2max). Regard-
less of which exercise group they were assigned to all subjects
showed improved lipoprotein profile (lower triglycerides,
lower vLDL, and lower vLDL particle size) relative to the
control group. However, which exercise group subjects were
assigned to did make a difference in whether they improved
CRF. The group that performed low-amount-high-intensity
exercise improved CRF more than the low-amount-low-
intensity group. Thus, both groups improved lipid profiles to
a similar degree but improved CRF to a different level. In
healthy subjects a change in PA without a change in CRF can
lead to improved metabolic fitness. For instance, subjects who
skied an average of 342 min/day on a 32 day cross country
skiing trip across Greenland had a decrease in maximal CRF
but still had an improvement in circulating lipoproteins [35].
While both of these studies were interventions lasting less
than 9 months, we were interested in whether a similar
finding would occur in long-term runners versus long-term
sedentary subjects. Indeed we found that sedentary and run-
ners with similar levels of CRF had different lipoprotein and
insulin responses to an OGTT test. One potential explanation
for this separation in CRF and metabolic health is the type
of exercise the marathon runners do. Many of the subjects
perform only low-intensity long duration types of exercise.
Disease Markers
9
This is in agreement with Houmard et al. [36] who showed
that insulin sensitivity was improved more by an exercise
training programme with high volume than high intensity.
Insulin sensitivity is highly dependent on skeletal muscle
specific adaptations, which may differ in response to PA or
exercise training that increases CRF.
Some types of PA, such as low intensity or limited to
small amount of tissue, may lead to significant local changes
important for metabolic health without improved whole
body CRF. Adaptation specific to the working muscle is
illustrated in a number of studies on one-legged exercise
training, which utilize a small fraction of whole body muscle
mass result in important local adaptations in trained muscle
such as increased capillarization, muscle glycogen content,
mitochondrial activity, insulin sensitivity, and transcription
of metabolic genes [37–40]. Vollaard et al. [41] find these local
changes in muscle metabolism are associated with changes in
maximal work capacity more so than VO2max. Furthermore,
an increase in VO2max with a PA intervention is not even
necessary to improve muscle mitochondrial content. For
instance, despite not improving CRF cross country, skiers sill
improved skeletal muscle mitochondrial [35]. Similarly, in
our study it was the high levels of PA, and not a high CRF, that
was associated with higher protein levels of the mitochondrial
protein COXIV and antioxidant enzyme mnSOD. The exact
physiological, genetic, and environmental contributions that
determine individuals VO2max vary tremendously and are not
completely known.
We were surprised that so many (one-third) of the
marathon runners did not have substantially higher VO2max
than their sedentary match subject. A number of twin
studies (reviewed in [1]) have determined a typical genetic
contribution for the baseline physical fitness characteristics
and responses to standardized aerobic and strength training
programs of approximately 50%. The largest single study
examining genetic and exercise training interactions with
health outcomes is the HERITAGE Family Study [16], which
suggested significant genetic variation in the interaction
between VO2max and risk factors such as resting systolic blood
pressure, fasting plasma HDL-cholesterol, triglycerides, and
insulin. The HERITAGE study showed that between 8%
and 13% of exercise intervention participants have worse
off risk factors following 20 weeks to 6 months of exer-
cise training programs [42]. Some specific genes that may
contribute to these differences in response to exercise have
been identified already. An ongoing study listed in 2005
that 165 autosomal gene entries were found to alter physical
performance and health parameters, some of which are only
present in response to PA, such as variants in AKT [43],
perilipin [44], and FTO [45]. However, the HERITAGE study
did not examine what proportion of the inactive groups
developed worse risk factors. Other studies which include an
inactive control group show worsening of 12 different risk
factors including 3 of the 4 examined above (blood pressure
was not measured) following 6 months of inactivity [46].
Our current cross-sectional study design only allows us to
make association and future studies that trained subjects
over a period of time or followed the same subjects over
a number of years could provide information about the
relative contribution of environment and genetics.
We believe our study has distinctions over previous
studies. First is that we selected runners that have been
running for at least 5 years in a fairly consistent manner, either
completing two marathons per a year or having averaged
more than 50 km/week over the past 5 years. This is sub-
stantially more exercise for a much longer duration than the
large clinical intervention studies such as the HERITAGE and
STRRIDE studies [15, 46]. Second, we recruited everyday ath-
letes rather than elites, which have been studied at older ages
previously. For instance, Trappe et al. [47] examined the per-
formance abilities of elite octotarian lifelong athletes versus
healthy untrained men. Octotarian athletes had VO2max and
max workloads that were 80% and 51% higher than a matched
sedentary group. While our subjects spanned a much larger
age range the subjects most runners had significantly higher
VO2max and max workload (running speed). Excluding the
runners in the similar-VO2max group (𝑟2 = 0.04) VO2max
and max workload were highly correlated (𝑟2 = 0.48–0.80),
a relationship that is also present in octotarian athletes and
sedentary controls. In general the runners in the similar-
VO2max group had higher max workloads than their VO2max
would predict. This discrepancy between max workload and
VO2max is likely due to the superior running economy it the
similar-VO2max group compared to their sedentary controls.
Together the data suggests that multiple types of physiological
adaptations may occur to result in an improved work capacity
following years of high levels of PA.
There are a number of limitations in the present study
that are worth discussing. First, although within our similar-
VO2max group there was no statistical difference between
the runners and the sedentary group there was a 5% rela-
tive (2.2 mL/kg/min absolute) higher-VO2max that may have
contributed to some of the metabolic benefits seen in the
runners. However, runners in both the similar-VO2max and
different-VO2max had similar improvements in metabolic
fitness, arguing against the relative difference in VO2max
being important for metabolic health. Second, a larger subject
population number would have allowed for more advanced
statistical analysis that could model the variables as contin-
uous and more precisely determine the relative contribution
of VO2max to different metabolic phenotypes. However, given
our difficult inclusion criteria for the runner group and
difficulty in finding BMI and age matched sedentary controls,
it would still remain difficult to recruit hundreds of subjects.
Similarly because the training that the runners did was not
controlled there are likely a number of individual differences
within the runner group that may contribute positively or
negatively to the findings seen. Finally, while we observed
improved metabolic health of the runners in our group, the
metabolic health of the sedentary group remained within
clinically normal ranges. However, even within the normal
range of clinical variables, small variations in lipoproteins
[48, 49] and blood pressure [50] are associated with varied
outcomes and risks for cardiovascular disease and death.
In summary the present cross-sectional case-control
study suggests that a high amount of PA independent of CRF
10
Disease Markers
is associated with positive benefits on a number of metabolic
health parameters as well as markers of performance.
Conflict of Interests
No conflict of interests is reported.
Acknowledgments
The Centre of Inflammation and Metabolism (CIM) is
supported by a grant from the Danish National Research
Foundation (no. 02-512-55) and is part of the UNIK Project:
Food, Fitness & Pharma for Health and Disease, supported by
the Danish Ministry of Science, Technology, and Innovation.
M. J. Laye was supported by a postdoctoral grant from the
Danish Ministry of Science, Technology, and Innovation and
additional support was provided by Augustinus Fonden. The
authors wish to thank Miss Hanne Villumsen and Miss Ruth
Rovsing for their technical support and assistance. They also
thank the subjects for taking part in their study. M. J. Laye
and B. K. Pedersen formulated the study design and wrote
the paper. M. J. Laye takes responsibility for the integrity
of the data. M. B. Nielsen, L. S. Hansen, and T. Knudsen
conducted the clinical studies. M. J. Laye planned the analyses
and performed laboratory assays and analyzed the data. All
authors were involved in editing and approval of the final
version of the paper.
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Physical activity enhances metabolic fitness independently of cardiorespiratory fitness in marathon runners.
Laye, M J,Nielsen, M B,Hansen, L S,Knudsen, T,Pedersen, B K