Cardiorespiratory fitness and the metabolic syndrome: Roles of inflammation and abdominal obesity
Cardiorespiratory fitness and the metabolic syndrome: Roles of inflammation and abdominal obesity
Anne-Sophie Wedell-Neergaard 0 1
Rikke Krogh-Madsen 0 1
Gitte Lindved Petersen 1
Åse Marie Hansen 1
Bente Klarlund Pedersen 0 1
Rikke Lund 1
Helle Bruunsgaard 0 1
0 The Centre of Inflammation and Metabolism/ the Centre for Physical Activity Research, Rigshospitalet, University of Copenhagen , Copenhagen , Denmark , 2 Section of Social Medicine in Department of Public Health, University of Copenhagen, Copenhagen, Denmark , 3 Center for Healthy Ageing , University of Copenhagen , Copenhagen , Denmark , 4 National Research Centre for the Working Environment, Copenhagen, Denmark, 5 Department of Clinical Immunology, Rigshospitalet, University Hospital of Copenhagen , Copenhagen , Denmark
1 Editor: Guillermo LoÂpez Lluch, Universidad Pablo de Olavide , SPAIN
Data Availability Statement: The study is based
on data from The Copenhagen Aging Midlife
Biobank (CAMB) at the University of Copenhagen,
Denmark. General data availability is restricted to
the public due to both ethical and legal limitations.
Thus, unrestricted and uncontrolled access to data
compromises the confidentiality and privacy of
participants, and it would be in violation of The Act
on Processing of Personal Data under the authority
of the Danish Data Protection Agency who
approved the establishment and management of
CAMB. Additionally, unrestricted and uncontrolled
Individuals with metabolic syndrome have increased risk of type 2 diabetes and
cardiovascular disease. We aimed to test the hypothesis that a high level of cardiorespiratory fitness
(CR-fitness), counteracts accumulation of visceral fat, decreases inflammation and lowers
risk factors of the metabolic syndrome.
The study sample included 1,293 Danes (age 49±52 years) who from 2009 to 2011
participated in the Copenhagen Aging and Midlife Biobank, including a questionnaire, physical
tests, and blood samples. Multiple linear regression models were performed with CR-fitness
as exposure and plasma levels of cytokines and high sensitive C-reactive protein as
outcomes and measures of abdominal obesity were added to test if they explained the potential
association. Similarly, multiple linear regression models were performed with CR-fitness as
exposure and factors of the metabolic syndrome as outcomes and the potential explanation
by inflammatory biomarkers were tested. All models were adjusted for the effect of age, sex,
smoking, alcohol consumption, socio-economic status, and acute inflammatory events
within the preceding two weeks.
CR-fitness was inversely associated with high sensitive C-reactive protein, Interleukin
(IL)6, and IL-18, and directly associated with the anti-inflammatory cytokine IL-10, but not
associated with tumor necrosis factor alpha, interferon gamma or IL-1β. Abdominal obesity could
partly explain the significant associations. Moreover, CR-fitness was inversely associated
with an overall metabolic syndrome score, as well as triglycerides, glycated haemoglobin
A1c, systolic blood pressure, diastolic blood pressure and directly associated with
highaccess to data goes against the approval obtained
from The Ethical Committee of the Capital Region
of Denmark. The Steering Committee at CAMB
holds data and responds to requests for data. Data
access can be obtained by a project application to
the CAMB Steering Committee for data delivery.
Request for data can be sent to . Any
application will be processed by the Steering
Committee who manages CAMB data on behalf of
University of Copenhagen, Denmark. The daily
project leader is Associate Professor Rikke Lund
. The authors of this manuscript
followed the defined procedures and rules for
project application to data access and the authors
did not have special access to the data.
Funding: The Copenhagen Aging and Midlife
Biobank has been supported by a generous grant
from the VELUX FOUNDATION (VELUX 26145 and
31539). The Centre for Physical Activity Research
(CFAS) is supported by a grant from TrygFonden.
CFAS is a member of DD2 - the Danish Center for
Strategic Research in Type 2 Diabetes (the Danish
Council for Strategic Research, grant no.
09067009 and 09-075724). The funders had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
Competing interests: The authors have declared
that no competing interests exist.
density lipoprotein. Single inflammatory biomarkers and a combined inflammatory score
partly explained these associations.
Data suggest that CR-fitness has anti-inflammatory effects that are partly explained by a
reduction in abdominal obesity and a decrease in the metabolic syndrome risk profile. The
overall inflammatory load was mainly driven by high sensitive C-reactive protein and IL-6.
Physical inactivity has been identified as the fourth leading risk factor of mortality, causing 6%
of all deaths globally [
]. Physical inactivity leads to accumulation of visceral fat [
] and is
directly associated with systemic inflammation in cohort studies [3±5]. In accordance with
this, regular physical activity has beneficial effects on factors in the metabolic syndrome [
that reduces the risk of common chronic life style diseases such as cardiovascular diseases
(CVD)  and type 2 diabetes mellitus (T2DM) [
]. Anti-inflammatory effects of physical
activity are considered to play a role in protection of the metabolic health [
The metabolic syndrome is defined as a cluster of cardio-metabolic risk factors including
elevated waist circumference (waist), elevated triglycerides, high blood pressure, insulin
resistance and reduced high-density lipoprotein (HDL) [
]. Metabolic syndrome is accompanied
by chronic systemic low-grade inflammation [
], characterized by a two- to fourfold elevation
in circulating levels of inflammatory cytokines and acute-phase reactants [
inflammation predicts the mortality risk in middle-aged and elderly populations [
inflammation is considered a central driver and a molecular link between components of the
metabolic syndrome and CVD as pro-inflammatory pathways promote atherosclerosis [16±
18]. Moreover, chronic inflammation links metabolic syndrome with T2DM as
pro-inflammatory pathways induce insulin resistance [
]. Most epidemiological studies focus on C-Reactive
Protein (CRP) and Interleukin (IL)-6 as biomarkers of chronic inflammation [
IL-6 and CRP are likely surrogate biomarkers of tumor necrosis factor alpha (TNF-α) and IL-1
mediated activities [
]. Thus, TNF-α and IL-1β initiate inflammatory processes and
constitute upstream triggers of IL-6 production, which again stimulates liver production of the acute
phase reactant CRP [
]. A regulatory anti-inflammatory response (i.e. IL-10) inhibits the
production or signalling of the pro-inflammatory cytokines [
]. IL-18 is a
pro-inflammatory cytokine implicated in insulin resistance and the risk of T2DM [
]. IL-18 stimulates the
production of interferon gamma (IFN-γ) [
], which in turn is likely to be implicated in the
pathogenesis of atherosclerosis [29±31]. Both IL-18 and IFN-γ are cytokines that have been
shown to be related to the metabolic syndrome .
Circulating levels of cytokines such as TNF-α and IL-6 are directly correlated with fat mass
]. This relationship is considered to reflect spill over to the circulation of inflammatory
mediators produced by adipocytes and cytokine producing immune cells, infiltrating the
adipose tissue [
]. Visceral fat is more inflamed than subcutaneous fat [
] and the amount of
visceral fat is more strongly correlated to the risk of developing CVD and T2DM than the
amount of total body fat [
]. Measures of visceral fat mass is highly correlated with
measures of abdominal obesity [
]. In accordance with this, measures of abdominal obesity such
as waist, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and truncal fat percentage
(trunk fat%) are better risk markers than body mass index (BMI) [
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CR-fitness is partly determined by genetic traits and partly by the amount of physical
activity which can improve the maximal oxygen consumption and thereby increase the CR-fitness
]. We hypothesised that high levels of CR-fitness prevents accumulation of
abdominal obesity and consequently inhibits the network of inflammatory pathways. We also
hypothesised that this anti-inflammatory effect of CR-fitness would inhibit the development of
the metabolic syndrome. The aim of this study was to test these hypotheses in a cohort of
middle-aged Danes from the Copenhagen Aging and Midlife Biobank (CAMB) with an available
detailed profile of inflammatory biomarkers including TNF, IL-1β, IL-18, IFN-γ, IL-6, hsCRP,
and IL-10 . Moreover, we investigated if an overall combined load of inflammation was
more robust in statistical models, than single inflammatory biomarkers.
Study population and design
This study was based on data from CAMB, a cohort designed to study signs of early ageing in
middle-aged Danes [
]. CAMB includes participants from three existing cohorts of which
only one, The Copenhagen Perinatal Cohort (CPC), was used for the present study since it
provides data on CR-fitness levels. CPC contains data from individuals born at the National
University Hospital of Copenhagen between 1959 and 1961.
CPC included a total of 8,102 men and women in 2009±2011 at the CAMB follow-up. Of
these, 5,196 individuals (age 49±53 years) were living in eligible geographical areas. A total of
1,718 individuals accepted the invitation and participated in the clinical examination,
constituting a 33% participation rate in CAMB. For the present study, participants lacking
CR-fitness data (n = 310) or blood samples (n = 90) or with BMI 18.5 (n = 25) were excluded,
leaving data on 1,293 participants (580 men and 713 women) for analysis.
The collection of data for CAMB included a comprehensive questionnaire, a health
interview, a physical examination and blood sampling. The questionnaire collected data about
health, use of medication, occupational social class and health behaviour. The physical
examination was performed by educated staff following a standardized protocol that covered
estimated CR-fitness, blood pressure, anthropometrics and a bioelectrical impedance analysis.
Furthermore, non-fasting blood samples were collected [
]. The Ethical Committee of the
Capital Region of Denmark approved the study (No: H-A-2008-126), and written, informed
consent was obtained from all participants. CAMB is also registered at the Danish Data
Protection Agency as a combined database (No: 2008-41-2938).
Physical tests and metabolic syndrome
CR-fitness. To estimate oxygen consumption, a submaximal test was performed using an
ergometer bike and CR-fitness level was calculated using the Aastrand nomogram [
height of the seat was adjusted and participants were informed to keep a cadence of 60 pedal
rounds per minute (Ergometer bike, Monark 939 E, Serial number: WBK 2740 20, Denmark,
2010). Heart rate was measured by a pulse belt (T31, Polar1, 2010) during the 16 minutes
biking session. Subjects initiated the test by eight minutes light warm up on an ergometer cycle.
Subsequently, the workload was increased in order to yield a stable pulse just exceeding 120
beats per minute, which was typically obtained after six minutes. When a steady state heart
rate was achieved, pulse data was registered every half minute for two minutes, before the
concluded. If a steady state was not achieved within six minutes the test was extended one minute
or until steady state was reached. An average pulse was calculated from the four measurements
made at the end of the session, and maximum oxygen consumption was estimated from the
average pulse, workload and gender using the Aastrands nomogram [
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Inclusion criteria covered all participants from CPC, who lived in eligible geographic areas
and accepted the invitation as well as participated in the clinical examination.
Exclusion criteria included 1) moderately increased blood pressure (systolic >160mmHg
and/or diastolic >100mmHg), 2) angina pectoris, 3) fever, 4) heart medication (beta blockers),
5) resting heart rate >120, and 6) having eaten a big meal, smoked cigarettes or performed
hard physical work within the last hour before the test. After the test, data on CR-fitness were
missing for a total of 310 participants, either due to the above listed exclusion criteria (n = 201)
or to technical problems with the bikes (n = 109).
Body composition. Waist [
], WHtR [
], WHR [
] and bioelectrical impedance
analysis of trunk fat% (SECA 201, Germany, 2009) (TANITA1 MC 180, Japan, 2010) were used
as measures of abdominal obesity to increase the robustness of our findings [
]. BMI was also
]. Underweight participants (n = 25) were excluded because they were expected
to show different trends in the investigated associations than normal weight or overweight
Blood pressure. After 10 minutes of rest, blood pressure was measured twice on each arm
(OMRON M6 COMFORT, Denmark, bought 2009±2010). If a difference was detected
between the four blood pressures (>20 mmHg systolic or >10 mmHg diastolic), a fifth
measurement was conducted and average systolic and diastolic blood pressures were calculated
from the last two measurements [
Haemoglobin A1c (HbA1c). A high performance liquid chromatography (HPLC) method
was used for determination of HbA1c. The HPLC consisted of a Waters 625 LC system together
with a Waters photo-diode-array detector model 996 and a WISP 717 auto sampler for
automatic injection of the samples. Millennium chromatography software was used for calculation
of concentrations (Waters Associates Inc., Milford, United States). A cation exchange column
Mono S HR 5/5 from Pharmacia Biotech AB, Uppsala, Sweden was used to separate HbA1c
from other components in the samples [
]. HbA1c is a standard indicator of long-term
glycaemic control and an HbA1c level of 5.6% corresponds to a fasting glucose level of 100 mg/
dL, which is a measure of insulin resistance [
HDL and triglycerides. Blood lipids levels were assessed using a COBAS Mira Plus. The
measures of HDL and triglycerides were based on ABX Pentra assays from Triolab
Metabolic syndrome score. The metabolic syndrome was characterised by a large waist
circumference ( 102 cm for men, 88cm for women), dyslipidaemia (triglycerides
1.7mmol/L for men or women or HDL <1 mmol/L for men or HDL <1.3 mmol/L for
women), a blood pressure of 130/85 mmHg or higher and insulin resistance (HbA1c 5.6%,
as no fasting blood samples were taken) [
]. For every positive indicator of the metabolic
syndrome, one point was added to the overall metabolic syndrome score resulting in a categorical
score from 0 to 4.
Cytokines, C-reactive protein and inflammatory load
Non-fasting blood samples were collected at the day of the clinical examination and stored at
-80ÊC for up to 2 years. An electro-chemiluminescence multiplex system was used to measure
the below-mentioned cytokines on a Sector 2400 Imager from Meso Scale Discovery
(Gaithersburg, USA) according to the manufacturer's instructions. IL-6, TNF-α, IL-1β, IFN-γ, and
IL-10 were measured in a multi-plex and IL-18 in a single-plex system. Lower limit of
detection was the calculated concentration of the signal that was 2.5 standard deviations over the
zero calibrator (the blank). All samples were run as duplicates. The intra-assay variation had to
be <20% to be accepted. The same two internal laboratory controls were included in all runs:
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Control A was a fasting plasma sample from a healthy young subject and control B was a
plasma sample after endotoxin administration in vivo. The same lot number was used for all
analyses (IL-18: Z001057; 6-plex: Z000042839), and incubation periods, pipettes, freezing/
thawing cycles of buffers and controls were always performed in the same way to limit the
inter assay variation as much as possible. Additionally, only two laboratory technicians ran all
Values below the limit of detection (LOD) for the remaining cytokines were substituted by
simple imputation of a value between 0 (zero) and a LOD from a uniform distribution. With
regard to TNF-α the in-house LOD was 0.28 pg/ml and the inter assay correlation of variance
(CV) in house was 9±13%. For IL-6 the LOD was 0.21 pg/ml and the inter assay CV was 11±
21%. Regarding IL-18, LOD was 1.98 pg/ml and inter assay CV was 14±18%. For IL-10 LOD
was 0.21pg/ml and inter assay CV was 15±28% and for INF-γ the LOD was 0.19 pg/ml and the
inter assay CV was 24±28%.
Levels of hsCRP was assessed with a high sensitive assay (Tina quant, Roche Diagnostics
GmbH, Mannheim, Germany) using latex-entrenched immune-turbidimetry analysis (Roche/
Hitachi automatic instrument COBAS1), measuring hsCRP.
In total, data from blood samples were missing on 90 participants. In 51 cases, it was not
possible to draw blood from the participants. In 39 cases, the participants were excluded after
analysis of the blood due to outlying plasma values (hsCRP 40 (2 participants), IL-18 775
(15 participants), IL-10 170 (19 participants), IL-6 400 (3 participants)), as these values
must be assumed to reflect acute inflammation and expected to show different associations
than the rest of the cohort.
Inflammatory load. The inflammatory load variable included hsCRP, IL-6, IL-18 and
IL10. The inflammatory load variable was categorical (0±4) and one point was added for every
pro-inflammatory biomarker that had a high value (hsCRP 3mmol/L, IL-6 75% percentile
and IL-18 75% percentile) or if the anti-inflammatory biomarker had a low value
(IL10 25% percentile). The cut-off limit for hsCRP was chosen to be 3mg/L, since this limit is
correlated with high risk of CVD [
]. There is no consensus on relevant cut-off limits for
IL6, IL-18, and IL-10. In the present study, we decided to use quartiles as cut-off limits for these
biomarkers. We decided not to include inflammatory biomarkers without a significant effect
on a single parameter basis (please see result section) in the inflammatory load variable.
Covariates obtained from questionnaire
Acute inflammatory events. Participants were registered as having experienced acute
inflammatory events if they had had broken bones or surgery during the last month, fever,
cold, influenza, pneumonia, digestive tract infection or other infections during the last 3
weeks, urinary tract infection during the last 2 weeks or visit to a dentist during the last week
prior to the test.
Occupational social class. Based on information about occupation, social class was
ranked into a categorical variable with 5 groups (I to V) according to the standards of the
Danish Occupational Social Class classification [
]. Social class I reflected professional occupation
and social class V reflected unskilled occupation. An additional separate category represented
people on transfer income, including sickness benefits and disability pension.
Smoking and alcohol consumption. Participants were asked to classify themselves as
current, previous, or never smokers. The amount of alcohol consumed per week was given in
units with 1 unit defined as 1 bottle of beer, 1 glass of wine or 1 drink of strong alcohol.
The entire questionnaire was validated and thoroughly documented. The face validity was
established by experts. The questionnaire was pilot tested on a subset of participants prior to
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the main data collection, and pilot data indicated satisfactory internal consistency. All answers
All statistical analyses were performed using STATA 13.0. T-tests and chi2-tests were used to
test differences between men and women in the descriptive analyses and between participants
and non-participants. Multiple linear regression models were performed to investigate the
association between CR-fitness and different measures of abdominal obesity (waist, WHtR,
WHR, trunk fat%), adjusted for confounders (age, social class, alcohol consumption, smoking
status and sex).
Multiple linear regression models were performed to investigate the association between
CR-fitness and different biomarkers of chronic low-grade inflammation (hsCRP, IL-6, IL-18,
IL-10, TNF-α, IFN-γ, IL-1β), and the combined inflammatory load, respectively. Analyses
were adjusted for confounders (age, social class, alcohol consumption, smoking status, acute
inflammatory events and sex). To test if the potential relationship between CR-fitness and
inflammation was independent of abdominal obesity, measures of abdominal obesity were
added to the model (waist, WHtR, WHR, trunk fat%) one by one.
Multiple linear regression models were performed to investigate the association between
CR-fitness and factors of the metabolic syndrome (HbA1c, triglycerides, HDL, systolic and
diastolic blood pressure) adjusted for confounders (age, social class, alcohol consumption,
smoking status and sex). Inflammatory biomarkers were added to the model one by one (hsCRP,
IL-6, IL-18, IL-10, TNF-α, IFN-γ, IL-1β and inflammatory load). Interactions between sex and
CR-fitness were tested in models with low-grade inflammation or factors of the metabolic
syndrome as outcomes to evaluate whether pooling of men and women were justified to obtain
maximal power in analyses.
In all regression-models, the assumptions of linearity, homogeneity of variance and normal
distributed residuals were tested. Inflammatory biomarkers, factors of the metabolic syndrome
and measures of abdominal obesity showed skewed distributions and were consequently
log10transformed in regression models. Estimates in regression analyses were back transformed by
using the relevant antilogarithm and interpreted as the percentage change in CR-fitness as
described by The Institute for Digital Research and Education [
]. We found no other
violations of the model assumptions.
Association between CR-fitness and abdominal obesity
An increase in CR-fitness of +5 mL/min/kg was associated with a decrease in waist (-3.65%;
95%CI: -4.0 to -3.3; p<0.0001), WHtR (-3.65%; 95%CI: -4.0 to -3.3; p<0.0001), WHR (-1.45%;
95%CI: -1.65 to -1.25; p<0.0001) and trunk fat% (-15.1%; 95%CI: -16.4 to -13.85; p<0.0001)
after adjustment for age, sex, social class, alcohol consumption and smoking status.
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Values are presented as median (IQ 25%; IQ 75%) or number (%). P values for comparison of sex characteristics are based on independent t-test or chi-square test.
TI, transfer income.
Inflammatory load: A combined score of hsCRP, IL-6, IL-18 and IL-10.
Association between CR-fitness and biomarkers of inflammation
An increase in CR-fitness of +5 mL/min/kg was associated with a decrease in hsCRP (-18.25%;
95%CI: -21.45 to -15.05; p<0.0001), IL-6 (-10.45%; 95%CI: -12.6 to -8.35; p<0.0001), IL-18
(-2.65%; 95%CI: -3.85 to -1.4; p<0.0001), the overall inflammatory load (-5.6%; 95%CI: -7.15
to -4.1; p< 0.0001) and an increase in IL-10 (4.4%; 95%CI: 0.1 to 8.7; p = 0.04), adjusted for
the effect of age, sex, social class, alcohol consumption, smoking status and acute inflammatory
events (Table 2). There were no associations between CR-fitness and TNF-α, IFN-γ or IL-1β,
respectively (data not shown). When different markers of abdominal obesity were added to the
model, the estimate for inflammation decreased (Fig 1). Only one statistically significant
interaction was found between CR-fitness and sex. This was in relation to IL-6. An increase in
CRfitness of +1 mL/min/kg was associated with a decrease in IL-6 in both men (-1.44%; 95%CI:
-2.05 to -0.83; p<0.0001) and women (-2.80%; 95%CI: -3.39 to -2.21; p<0.0001).
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Multiple linear regression analyses were performed with CR-fitness as exposure and plasma levels of inflammatory biomarkers as outcomes, adjusted for the effect of
age, sex, social class, alcohol consumption, smoking status and acute inflammatory events. Inflammatory load: A combined score of hsCRP, IL-6, IL-18 and IL-10.
Association between CR-fitness and factors of the metabolic syndrome
An increase in CR-fitness of +5 mL/min/kg was associated with a decrease in the overall
metabolic syndrome score (-11.2%; 95% CI: -12.65 to -9.75; p<0.0001), triglycerides (-8.95%; 95%
Fig 1. Associations between cardiorespiratory fitness (CR-fitness) and inflammatory biomarkers. Multiple linear regression analyses were performed with
CRfitness as exposure and plasma levels of inflammatory biomarkers as outcomes, adjusted for the effect of age, sex, social class, alcohol consumption, smoking status and
acute inflammatory events. Relative differences (%) in estimates of inflammatory biomarkers, adjusted for measures of abdominal obesity. Y-axis: Regression estimates
of inflammatory biomarkers from models containing abdominal obesity have been normalised to estimates from the initial model (100%) to show relative percentage
changes. P<0.05 on all regression estimates shown. Waist = waist circumference; WHtR = waist-to-height-ratio; WHR = waist-hip-ratio; trunk fat% = truncal fat
percentage; MS = metabolic syndrome.
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Multiple linear regression analyses were performed with CR-fitness as exposure and an overall metabolic syndrome
score or factors of the metabolic syndrome as outcomes, adjusted for the effect of age, sex, social class, alcohol
consumption and smoking status.
CI: -10.45 to -7.45; p<0.0001), HbA1c (-0.75%; 95%CI: -1.05 to -0.45; p<0.0001), systolic
blood pressure (-1.1%; 95% CI: -1.4 to -0,75; p<0.0001) diastolic blood pressure (-1.75%; 95%
CI: -2.1 to -1.4; p<0.0001) and an increase in HDL (3.05%; 95% CI: 2.25 to 3.85; p<0.0001),
adjusted for age, sex, social class, alcohol consumption and smoking status (Table 3). When
hsCRP or IL-6 were also added to the models, estimates of metabolic syndrome factors were
reduced, indicating that these pro-inflammatory biomarkers could explain part of the
association between CR-fitness and factors of the metabolic syndrome. Inclusion of IL-18 and IL-10
had a minor or no effect on the association between CR-fitness and factors of the metabolic
syndrome (Fig 2). TNF-α, IFN-γ and IL-1β showed no effect on the associations (data not
shown). The overall inflammatory load was mainly driven by hsCRP. No interaction was
found between CR-fitness and sex in these regression models.
Missing and excluded data
Underweight participants (BMI 18.5, n = 25) had significantly higher levels of hsCRP than
the rest of the participants in the CAMB cohort (57.2% difference in hsCRP levels; 95%CI 79.9
to 8.6; p<0.05). Participants with outlying plasma values had more often experienced acute
inflammatory events than participants who did not have outlying plasma values (Pearson chi2
= 3.9; p <0.05). However, by excluding the outlying plasma values (n = 39), the regression
estimates for inflammatory biomarkers only changed by the 3rd and 4th decimal. Participants
who did not perform a CR-fitness test had a higher level of hsCRP (51.2% difference in hsCRP
levels; 95%CI 26.5 to 80.7; p<0.0001) and a larger waist (4.5% difference in waist; 95%CI 2.3 to
6.8; p<0.0001) than the group who did perform the test. Participants with unsuccessful blood
sampling were no different than the rest of the cohort with regard to CR-Fitness level (-4.4%
difference in CR-Fitness; 95%CI 4.5 to -14.2; p = 0.3) and acute inflammatory events (Pearson
chi2 = 0.59; p = 0.4). Participants with data missing on CR-Fitness level because of technical
problems with the bikes were not different with regard to inflammation level (hsCRP) (-0.1%
difference in hsCRP; 95%CI 20.9 to -26.7; p = 1 and body composition (waist) (0.06%
difference in waist; 95%CI 2.9 to -2.9; p = 1) from the rest of participants.
In this cohort study of middle aged Danes from CPC, an inverse association was found
between CR-fitness and levels of hsCRP, IL-6, IL-18 and a direct association was found
between CR-fitness and levels of IL-10. Abdominal obesity could partly explain these
associations and the impact of abdominal obesity was robust and found throughout waist, WHtR,
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Fig 2. Associations between cardiorespiratory fitness (CR-fitness) and factors of the metabolic syndrome. Multiple linear regression analyses were performed with
CR-fitness as exposure and factors of the metabolic syndrome as outcomes, adjusted for the effect of age, sex and social class, alcohol consumption, smoking status.
Relative difference (%) in estimates of factors of the metabolic syndrome, adjusted for measures of inflammation Y-axis: Regression estimates of metabolic syndrome
factors from models containing inflammation have been normalised to estimates from the initial model (100%) to show relative percentage changes. P<0.01 on all
regression estimates shown. Inflammatory load is a combined score of hsCRP, IL-6, IL-18 and IL-10.
WHR, and trunk fat%. In addition, higher CR-fitness levels were associated with lower
measures of abdominal obesity. Moreover, a higher CR-fitness level was associated with a lower
overall metabolic syndrome score as well as a beneficial level of all individual factors of the
metabolic syndrome (triglycerides, HDL, HbA1c, systolic and diastolic blood pressure).
Lowgrade inflammation could partly explain these associations. These findings support the
hypotheses that a high CR-fitness level promotes health by preventing abdominal obesity and
lower systemic inflammation which reduces the risk of the metabolic syndrome and thereby
the risk of developing diseases such as CVD and T2DM. The impact of inflammation was
strongest when hsCRP or IL-6 was used as biomarkers of systemic inflammation.
The association between CR-fitness and hsCRP was in accordance with findings in studies
of smaller cohorts [54±56]. The present study contributes with a large cohort, detailed
inflammatory data and adjustments for possible confounders, supporting that the anti-inflammatory
effect of CR-fitness is partly mediated by a reduction in abdominal obesity. In accordance with
data in the present study, human experimental studies using sedentary lifestyle interventions
in young healthy males have shown to increase the amount of visceral adipose tissue, decrease
the level of fitness, and impair glycaemic control [
]. Since in our present study the
antiinflammatory effect of a high CR-fitness level was only partly explained by abdominal obesity,
we speculate that physical activity per se also plays an important role by inducing
anti10 / 16
inflammatory cytokines. Thus, it has previously been demonstrated that each bout of physical
activity represents a natural, strong anti-inflammatory response as contracting muscle releases
IL-6 which induces a subsequent increase in the production of IL-1 receptor antagonist
(IL1ra) and IL-10, thus stimulating the occurrence of anti-inflammatory cytokines [
inhibits IL-1 signal transduction [
], and IL-10 is capable of inhibiting synthesis of
proinflammatory cytokines such as TNF-α [
]. Moreover, muscle released IL-6 inhibits TNF
The present study demonstrates that chronic inflammation may in part explain the
association between CR-fitness and factors of the metabolic syndrome. In consistence with this
finding, associations between inflammation and the metabolic syndrome have previously been
] and so has the beneficial impact of CR-fitness on the inflammatory
response in patients with metabolic syndrome . The latter study [
] evaluated white
blood cell count as biomarker of inflammation whereas the data presented here include an
extensive list of inflammatory biomarkers with known biological and/or epidemiological
impacts on the metabolic syndrome [
]. Measures of hsCRP and/or IL-6 are often used as
biomarkers of inflammation in epidemiological studies, although cytokines such as IL-18,
IL10, TNF-α, IFN-γ and IL-1β could be more important from a pathophysiological point of
view. However, despite the inclusion of several inflammatory biomarkers and the suggested
combination of these into an overall score of the inflammatory load, hsCRP was still the
strongest biomarker in relation to CR-fitness and the metabolic syndrome. Still, hsCRP is probably
not the biological driver [
]. Importantly, we demonstrated systemic inflammation to be
one of the explanatory mediators in the association between CR-fitness and the metabolic
syndrome. The recently published CANTOS study showed that anti-IL-1β therapy leads to a
lower rate of recurrent cardiovascular events  and that anti-IL-1β therapy reduces the risk
of and mortality from lung cancer in patients with atherosclerosis [
], supporting the concept
of persistent inflammation as a pathological driver in disease networks. The chronic
inflammation related co-morbidities may contribute to disability and decreased CR-fitness level,
inducing accumulation of visceral fat and thereby enhancing inflammation in a positive feedback
]. In this vicious cycle of persistent inflammation, our data support the notion that
physical activity is a possible strong physiological brake of disease development due to the
anti-inflammatory and anti-obesity effects.
Underweight middle-aged individuals have a higher prevalence of comorbidity [
they were expected to show different associations between the investigated parameters than
normal- or overweight participants. When comparing them to the rest of the cohort, the 25
underweight participants had higher levels of hsCRP, confirming the difference and
accordingly they were excluded from the analyses. Men and women were pooled to obtain maximal
power in this study and sex was included in all analysis. Possible interactions between sex and
CR-fitness were explored and only one interaction was found significant. This interaction was
in relation to IL-6 and since the trend was similar for men and women, it was decided to keep
men and women pooled. Considering the large number of tests, this one significant association
could be a chance finding, however, that has to be confirmed in future studies.
Limitations of the study must be taken into account. This study represents a narrow age
range and only includes participants of BMI>18.5. Thus, our results cannot be generalized to
other age groups or underweight individuals. CR-fitness levels were calculated from
submaximal tests made on an ergometer bicycle [
], which are not as exact as a VO2max test.
However, it is well-documented that results from submaximal tests correlate well with results from
VO2max tests [
]. In addition, as the submaximal test is less challenging, it increases the
number of middle-aged individuals who will be able to perform the test successfully. Thirty-one
percent of participants had experienced acute inflammatory events in the weeks preceding the
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data collection (Table 1). This could potentially influence the level of low-grade inflammation.
All statistical models containing biomarkers of inflammation were therefore adjusted for this
factor. However, if any residual confounding relates to this measurement, this could
potentially influence our results and lead to an underestimation of the association between fitness
and inflammation. Residual confounding could exist if our measurement of acute
inflammatory events did not capture all potential cases of such. If we excluded the 31% participants
categorized with an acute inflammatory event and repeated the presented analysis, the trend and
significance level of the associations did not change, which indicates that a potential residual
confounding is not an issue in this context. The exclusion of participants with missing data on
CR-fitness, missing blood samples, or outlying plasma values could potentially cause a
selection bias. As expected, the group of participants who were excluded due to the pre-defined
criteria from the CR-fitness test had higher levels of hsCRP and a larger waist than the group who
did perform the test. Participants with data missing on CR-fitness due to technical problems
with the bikes did not have different hsCRP levels or waist than the rest of the participants.
Likewise, participants who had unsuccessful blood drawings had no different CR-fitness levels
or acute inflammatory events than the rest of the cohort, while participants with outlying
plasma values were registered with more acute inflammatory events than the rest of the cohort.
These findings support the assumption that outliers in plasma levels of inflammation reflect
various acute health conditions rather than chronic low grade inflammation. The
bioimpedance analysis also had some limitations as it is influenced by time and hydration status.
Bioimpedance has, however, shown to provide reliable estimates of truncal fat mass in
middleaged and older subjects [
]. Finally, the cross-sectional study design does not allow us to
draw conclusions on the causal directions of the found associations. In order to conclude on
causality, the results of this study need to be confirmed in follow-up or in human intervention
In conclusion, CR-fitness and low-grade inflammation were inversely related and this
association was partly explained by abdominal obesity in middle-aged individuals. Moreover,
associations between CR-fitness and factors of the metabolic syndrome could in part be explained
by systemic low-grade inflammation. These findings support the notion that the beneficial
effect of physical activity on metabolic health is partially due to an anti-inflammatory effect of
CR-fitness mediated by a reduction in abdominal obesity. A combined score of inflammatory
biomarkers was not more robust in mathematical models than single measurements of hsCRP
The authors thank the staff at the Department of Public Health and the National Research
Center for the Working Environment, who undertook the data collection. Further thanks to
Kirsten Avlund, Nils-Erik Fiehn, Poul Holm-Pedersen, Erik Lykke Mortensen and Merete
Osler, who initiated and established CAMB from 2009 to 2011 together with Helle
Bruunsgaard, Åse Marie Hansen and Rikke Lund. The authors acknowledge the crucial role of the
initiators and steering groups of the Metropolit Cohort, the CPC, and the Danish Longitudinal
Study on Work Unemployment and Health.
Conceptualization: Anne-Sophie Wedell-Neergaard, Rikke Krogh-Madsen, Bente Klarlund
Pedersen, Rikke Lund, Helle Bruunsgaard.
Data curation: Anne-Sophie Wedell-Neergaard.
12 / 16
Formal analysis: Anne-Sophie Wedell-Neergaard, Gitte Lindved Petersen.
Funding acquisition: Bente Klarlund Pedersen, Rikke Lund, Helle Bruunsgaard.
Investigation: Anne-Sophie Wedell-Neergaard, Rikke Lund, Helle Bruunsgaard.
Methodology: Anne-Sophie Wedell-Neergaard, Rikke Krogh-Madsen, Gitte Lindved
Petersen, Rikke Lund, Helle Bruunsgaard.
Project administration: Anne-Sophie Wedell-Neergaard, Rikke Lund, Helle Bruunsgaard.
Resources: Åse Marie Hansen, Rikke Lund, Helle Bruunsgaard.
Software: Anne-Sophie Wedell-Neergaard, Gitte Lindved Petersen.
Supervision: Rikke Krogh-Madsen, Bente Klarlund Pedersen, Rikke Lund, Helle
Validation: Anne-Sophie Wedell-Neergaard, Helle Bruunsgaard.
Visualization: Anne-Sophie Wedell-Neergaard, Gitte Lindved Petersen, Helle Bruunsgaard.
Writing ± original draft: Anne-Sophie Wedell-Neergaard, Helle Bruunsgaard.
Writing ± review & editing: Anne-Sophie Wedell-Neergaard, Rikke Krogh-Madsen, Gitte
Lindved Petersen, Åse Marie Hansen, Bente Klarlund Pedersen, Rikke Lund, Helle
13 / 16
14 / 16
15 / 16
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