Mediation role of cardiorespiratory ﬁtness on the association between
fatness and cardiometabolic risk in European adolescents:
The HELENA study
*, Javier Courel-Ib
, Francisco B. Ortega
, Jose Castro-Pi~
, Angela Polito
Luis M. Moreno
, Jonatan R. Ruiz
, on behalf of the HELENA study group
IRyS Group, Physical Education School, Pontiﬁcia Universidad Cat
olica de Valparai
so 2530388, Chile
Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, Murcia 30071, Spain
PROmoting FITness and Health through Physical Activity Research Group (PROFITH), Department of Physical Education and Sports,
Faculty of Sport Sciences, University of Granada, Granada 18001, Spain
Department of Physical Education, Faculty of Education Sciences, University of C
adiz, Puerto Real 11003, Spain
Department of Health and Human Performance, School of Health Sciences, University of Zaragoza, Zaragoza 50001, Spain
Growth, Exercise, Nutrition and Development (GENUD) Research Group, Zaragoza 50001, Spain
National Institute for Food and Nutrition Research, Rome 80070, Italy
Lille Inﬂammation Research International Center, University of Lille, Lille 59000, France
Immunonutrition Research Group, Department Metabolism and Nutrition, Institute of Food Science and Technology and Nutrition (ICTAN),
Spanish National Research Council (CSIC), Madrid E-28040 Spain
Received 20 September 2018; revised 25 February 2019; accepted 9 June 2019
Available online 16 August 2019
2095-2546/Ó2021 Published by Elsevier B.V. on behalf of Shanghai University of Sport. This is an open access article under the CC BY-NC-ND license.
Purpose: This study was aimed to analyze the mediation role of cardiorespiratory ﬁtness (CRF) on the association between fatness and cardiome-
tabolic risk scores (CMRs) in European adolescents.
Methods: A cross-sectional study was conducted in adolescents (n= 525; 46% boys; 14.1 §1.1 years old, mean §SD) from 10 European cities
involved in the Healthy Lifestyle in Europe by Nutrition in Adolescence study. CRF was measured by means of the shuttle run test, while fatness
measures included body mass index (BMI), waist to height ratio, and fat mass index estimated from skinfold thicknesses. A clustered CMRs was
computed by summing the standardized values of homeostasis model assessment, systolic blood pressure, triglycerides, total cholesterol/high-
density lipoprotein cholesterol ratio, and leptin.
Results: Linear regression models indicated that CRF acted as an important and partial mediator in the association between fatness and CMRs in
1217-year-old adolescents (for BMI: coefﬁcients of the indirect role b= 0.058 (95% conﬁdence interval (95%CI): 0.0230.101), Sobel test
z= 3.11 (10.0% mediation); for waist to height ratio: b= 4.279 (95%CI: 2.2427.059), z=3.86 (11.5% mediation); and for fat mass index:
b= 0.060 (95%CI: 0.0200.106), z= 2.85 (9.4% mediation); all p<0.01).
Conclusion: In adolescents, the association between fatness and CMRs could be partially decreased with improvements to ﬁtness levels; therefore, CRF
contribution both in the clinical ﬁeld and public health could be important to consider and promote in adolescents independently of their fatness levels.
Keywords: Cardiovascular disease; Children; Fat mass; Fitness; Health; Physical activity
Obesity in adolescents has increased dramatically in recent
decades and has reached worldwide pandemic proportions.
Obese youths are likely to become obese adults and are at
Peer review under responsibility of Shanghai University of Sport.
* Corresponding author.
E-mail address: email@example.com (C. Cristi-Montero).
Cite this article: Cristi-Montero C, Courel-Ib
nez J, Ortega FB, et al. Mediation role of cardiorespiratory ﬁtness on the association between fatness and cardiome-
tabolic risk in European adolescents: The HELENA study. J Sport Health Sci 2021;10:3607.
Available online at www.sciencedirect.com
Journal of Sport and Health Science 10 (2021) 360367
higher risk of developing cardiovascular diseases (CVD), dys-
lipidemia, and type 2 diabetes,
which together constitute the
main cause of death in adults.
Both unhealthy dietary behav-
iors and physical inactivity
seem to be the key determinants
of obesity in youth; thus, early interventions for preventing the
development of obesity-related CVD are needed not only for
present health but also for future health.
To date, there has been no absolute clarity regarding the
reverse or bidirectional relationship (causeeffectresponse)
between obesity and physical inactivity because the direction of
causality cannot be inferred from cross-sectional associations.
There is evidence from longitudinal studies that shows reciprocal
associations and synergistic interactions among fatness with
motor skill competence, physical activity, and cardiorespiratory
In contrast, there are other studies that show
that fatness leads to inactivity in children.
expose the ambiguity of the scientiﬁc literature in this matter and
the need to investigate the interaction between fatness and ﬁtness.
Of note is that physically inactive adolescents have lower
and, in turn, this physiological marker is cru-
cial for attenuating the association between fatness and
increased cardiometabolic risk from childhood to adoles-
Hence, nowadays there is a special focus on improv-
especially in obese adolescents.
An interesting approach to studying the role and importance of
a variable over the association between others is mediation analy-
sis, which can determine whether a predictor variable in an out-
come is mediated through an intermediate variable (mediator).
This innovative method has been applied in studies of children to
examine the mediator role of obesity in the relationship between
CRF and cardiometabolic risk
of these studies reported that, when including body mass index
(BMI) as a mediator, the association between CRF and cardiome-
tabolic risk disappears completely in girls and is considerably miti-
These results do not fully support the “fat-but-ﬁt”
paradigm, which suggests that a person with excess adiposity but
with a high CRF (fat-ﬁt phenotype) has a better cardiometabolic
proﬁle than a person with excess adiposity but with a low
Accordingly, the role of ﬁtness and fatness as mediators
of cardiometabolic health still needs to be clariﬁed, especially in
children and adolescents.
Based on the assumption that fatness leads to inactivity, the
purpose of this study was to analyze the mediation role of CRF
on the association between fatness markers and cardiometabolic
risk scores (CMRs) clustering in European adolescents and to
analyze the magnitude of CRF mediation in this relationship.
We hypothesized that CRF would only partially mediate the
relationship between fatness and CMRs. This investigation con-
tributes to the ﬁeld of public health by providing evidence about
the bidirectional relationship between obesity and CRF.
The Healthy Lifestyle in Europe by Nutrition in Adoles-
cence (HELENA) cross-sectional study was designed to obtain
reliable and comparable data on nutrition and health-related
parameters from a sample of adolescents aged
12.517.5 years from 10 European cities in 9 countries. Data
collection took place between 2006 and 2008. A sample of
3528 adolescents met the HELENA general inclusion criteria
(age of 13.0016.99 years, schooling in one of the 10 Euro-
pean cities, informed consent signed, had at least weight and
height measured, and completed at least 75% of the other
In the present study, 525 adolescents (54% girls) with
valid data on the main variables and covariates were included
in the analysis: cardiometabolic risk factors, including total
cholesterol to high-density lipoprotein cholesterol ratio
(TC/HDL-C), triglycerides (TG), homeostasis model assess-
ment (HOMA), and systolic blood pressure (SBP); as well as
leptin, CRF, age, sex, pubertal stage, and center (city involved
in the study from each country). This subsample was represen-
tative to the total sample (assuming an error of 5%, a conﬁ-
dence interval (CI) of 95%, and 50% of heterogeneity).
Moreover, there were no differences between the present sub-
sample and the whole sample (boys and girls separately) in
terms of age, body mass, height, and BMI (all p>0.05).
The study was performed following the ethical guidelines
of the 1964 Declaration of Helsinki (revision of Edinburgh
2000), Good Clinical Practice, and legislation regarding clini-
cal research in humans in each of the participating countries.
The protocol was approved by the Human Research Review
Committees of the involved centers. Furthermore, all parents
and guardians signed an informed consent form, and the ado-
lescents agreed to participate in the study.
2.1. Physical examination
Adolescents participating in the study were barefoot and
dressed in light clothing during anthropometric measurements.
Body weight was measured to the nearest 0.1 kg with an elec-
tronic scale (Seca 861; Seca GmbH & Co., Hamburg, Ger-
many), and body height was measured with a telescopic
stadiometer (Seca 225; Seca GmbH & Co.) to the nearest
0.1 cm. BMI was calculated as body weight divided by the
square of height (kg/m
Waist circumference (WC) was
measured with a non-elastic tape (Seca 200; Seca GmbH &
Co.) to the nearest 0.1 cm. Waist to height ratio (WHtR) was
then calculated by dividing WC (cm) by height (cm). Bicipital,
tricipital, subscapular, and suprailiac skinfold thicknesses on
the left side of the body were measured with a Holtain Calliper
(Holtain Ltd., Crymych, Wales, UK) to the nearest 0.2 mm.
All the anthropometric measurements were taken 3 times and
the mean was scored.
Percentage of body fat was calculated
using the equation of Slaughter et al.,
which has proven to be
the most suitable equation for use with adolescents.
(kg) was estimated by multiplying body fat percent by weight
(kg) and dividing by 100, and fat-free mass (kg) was calculated
as the difference between body weight and fat mass. Fat mass
index (FMI) was determined by dividing fat mass (kg) by
height squared (m
). Identiﬁcation of pubertal maturation
(Stages IV) was assessed by direct observation by a medical
doctor, according to Tanner and Whitehouse.
SBP was mea-
sured (OMRONÒM6, HEM 70001; Omron, Kyoto, Japan) fol-
lowing the recommendations for adolescent populations,
Mediation role of cardiorespiratory ﬁtness 361
with participants seated in a separate quiet room for 10 min
with their backs supported and feet on the ground. Two SPB
readings were taken at 10-min intervals, and the lowest mea-
sure was recorded.
2.2. Blood samples
Blood samples were collected as previously described.
TG, TC, and HDL-C were measured using enzymatic methods
(Dade Behring, Schwalbach, Germany). All blood parameters
were measured after an overnight fast. HOMA index calculation
was used as a measure of insulin resistance
using the formula
HOMA-index = (insulin (mUI/mL) £glucose (mg/dL))/405.
The ratio of TC/HDL-C was also calculated. Leptin con-
centrations were measured by the RayBio (RayBiotech
Inc., GA, USA) Human Leptin enzyme-linked immunosor-
bent assay. Leptin assay sensitivity was set at less than 6
pg/mL, with intraclass and interclass coefﬁcients of varia-
CRF was measured by the progressive 20-m shuttle run test.
The test has high validity and reliability in adolescents
(testretest reliability coefﬁcients were 0.89; standard error of
estimation was 5.9 mL/kg/min).
In the shuttle run test, par-
ticipating adolescents ran between 2 lines 20 m apart, while
keeping the pace with audio signals emitted from a pre-recorded
CD with an initial speed of 8.5 km/h and increasing by 0.5 km/h
every minute (1 min equals 1 stage). The test ended either when
the adolescent failed to reach the end line concurrent with the
audio signals on 2 consecutive occasions or when he or she
stopped owing to fatigue. The last stage completed (precision of
0.5 stages) was used to calculate the maximal oxygen consump-
tion from the equation developed by L
eger et al.
A summative cardiometabolic risk index that includes sev-
eral factors has proven to be a better marker of cardiovascular
health in children than an index that only includes a single risk
CMRs were created from the sum of SBP, TG,
TC/HDL-C ratio, HOMA, and leptin z-scores. The standard-
ized value (z-scores) of each variable was calculated as
follows: (valuemean)/SD, separately for boys and girls, and
for each 1-year age group. Lower values are indicative of a
This risk proﬁle has been used previously in a
cross-sectional study of 1732 randomly selected 9-year-old
and 15-year-old school children from 3 European countries
and in 2015.
Andersen et al.
conﬁrmed that the composite
risk score improved substantially in a sample of 15,794 youths
aged 618 years that included the HOMA index rather than
fasting glucose, leptin, the sum-of-4-skinfolds (instead of BMI
or WC), and CRF. Both the sum-of-4-skinfolds and CRF were
removed from this CMRs because these were part of the medi-
ation analysis and could have generated a methodological
2.5. Statistical analysis
The normality of the distribution of the variables was
tested using both statistical (KolmogorovSmirnov test) and
graphical methods (normal probability plot). Adolescents’
characteristics are presented as the mean §SD and the fre-
quency (%) for continuous and categorical variables, respec-
tively. The Student ttest and the Pearson’s x
test were used
to test sex differences in relation to adolescents’ CRF, fatness
(BMI, WHtR, and FMI), and cardiometabolic biomarkers
(TC/HDL-C, TG, HOMA, SBP, and leptin) and pubertal
stage. Partial correlation coefﬁcients (r), adjusted for age,
sex, pubertal stage, and center, were used as a preliminary
analysis to examine the associations between CRF, cardiome-
tabolic, and fatness biomarkers.
Multicollinearity was tested before completing the media-
tion analysis through tolerance value and variance inﬂation
We analyzed the mediation role of CRF on the
association of 3 fatness markers (BMI, WHtR, and FMI) with
CMRs through bootstrapped (10,000 samples) linear regres-
using the PROCESS SPSS script,
for age, sex, pubertal stage, fat-free mass, and center. Each
model included 3 equations.
Equation 1 regressed the mediator (CRF) on the indepen-
dent variable (BMI, WHtR, and FMI). Equation 2 regressed
the dependent variable (CMRs) on the independent variable
(BMI, WHtR, and FMI). Equation 3 regressed the dependent
variable (CMRs) on both the independent (BMI, WHtR, and
FMI) and the mediator variable (CRF), and Equation 30took
into account the indirect (mediating) role. We calculated
the percentage of the total contribution that is accounted for by
mediation using the standardized coefﬁcients of the Equation
1£Equation 2 / Equation 3. A signiﬁcant “indirect role”
(mediation) was established when (a) the independent variable
was signiﬁcantly related to the mediator, (b) the independent
variable was signiﬁcantly related to the dependent variable, (c)
the mediator was signiﬁcantly related to the dependent vari-
able, and (d) the association between the independent and
dependent variable (“direct role”) was attenuated when the
mediator was included in the regression model.
test was used to test the hypothesis that the indirect role was
equal to 0.
Point estimates and 95% conﬁdence intervals
(95%CIs) were estimated for the indirect role. Complete medi-
ation was established when the independent variable (fatness)
was not associated with the dependent variable (CMRs) after
mediator (CRF) has been controlled, making path Equation 30
zero. Partial mediation is when the path from fatness to CMRs
is reduced in absolute size but is still different from 0 when the
mediator is introduced. The percentage of mediation was cal-
culated as 1(Equation 30/ Equation 3) where Equation 3 is
the role for the independent variable in predicting the main
outcome and Equation 30is the role of the independent variable
in predicting the dependent variable with the mediator vari-
able. Analyses were completed using the IBM SPSS (Version
21.0; IBM Corp., Armonk, NY, USA). The level of signiﬁ-
cance was set at p<0.05.
362 C. Cristi-Montero et al.
Table 1 shows the characteristics of the sample. The partial
correlations coefﬁcients between the fatness variables, CRF,
and cardiometabolic biomarkers are presented in Table 2.
There was no difference in the CMRs between sex (p= 0.992)
when both boys and girls were grouped. CRF was negatively
associated with all the study measures of fatness as well as
with cardiometabolic biomarkers (p<0.05), except for SBP.
BMI, WHtR, and FMI were associated positively with all car-
diometabolic biomarkers (p<0.05). We observed no multi-
collinearity. The tolerance value ranged between 0.829 and
0.888 for collinearity tolerance and the variance inﬂation fac-
tor ranged between 1127 and 1206 for collinearity.
The results from mediation analysis are shown in Fig. 1.
Overall, fatness (BMI, WHtR, and FMI) was positively associ-
ated with CMRs. Mediation analysis including CRF revealed
that the association between fatness and CMRs was mediated
via CRF. The role of this mediation adjusted for potential con-
founders accounted for around 10%. More precisely, CRF medi-
ates 10.0% of CMRs for BMI (z= 3.11; indirect role = 0.058
(95%CI: 0.0230.101); p= 0.001), 11.5% for WHtR (z=3.86;
indirect role = 4.279 (95%CI: 2.2427.059); p<0.001), and
9.4% for FMI (z= 2.85; indirect role = 0.060 (95%CI:
0.0200.106); p= 0.004) parameters.
The main ﬁndings of the current study indicate that CRF
acts as a partial mediator in the association between 3 different
fatness variables and clustered CMRs in European adolescents.
Therefore, although it is true that CRF is an important health
biomarker, it does not seem to completely counteract the nega-
tive role of fatness on cardiometabolic health. To our knowl-
edge, this is one of the few studies that have used CRF as a
and the ﬁrst to establish the mediation role of
Characteristics of adolescents.
Overall (n= 525) Boys (n= 241) Girls (n= 284)
Age (year) 14.1 §1.1 14.2 §1.2 14.1 §1.1
Body mass (kg) 56.9 §12.3 59.0 §13.3 55.2 §11.2*
Height (cm) 164.7 §9.7 168.9 §10.2 161.1 §7.5*
) 20.8 §3.4 20.5 §3.3 21.1 §3.5*
Pubertal stage I/II/III/IV/V (%) 0.8/6.8/21.3/37.4/29.0 1.7/9.1/20.9/34.1/30.3 0.0/5.1/21.6/39.9/28.0
Fat mass (kg) 13.5 §8.2 11.6 §9.0 15.1 §7.2*
Fat-free mass (kg) 43.4 §8.1 47.4 §8.9 40.1 §5.5*
Waist circumference (cm) 71.1 §8.2 72.3 §8.1 70.0 §8.2*
WHtR 0.43 §0.04 0.43 §0.04 0.43 §0.04
CRF (mL/kg/min) 41.1 §7.8 46.0 §7.2 37.0 §5.6*
) 4.9 §2.9 4.0 §3.0 5.7 §2.6*
TC (mg/dL) 161.0 §26.9 153.1 §24.0 167.7 §27.4*
HDL-C (mg/dL) 55.6 §10.5 53.4 §9.7 57.5 §10.9*
Triglycerides (mg/dL) 69.1 §35.4 62.4 §29.4 74.8 §38.9*
HOMA 2.4 §2.0 2.3 §2.4 2.4 §1.6
SBP (mmHg) 117.0 §13.1 120.9 §13.8 113.8 §11.6*
Leptin (ng/dL) 19.3 §22.0 9.3 §14.6 27.8 §23.5*
Notes: Values are mean §SD or frequency (%). Independent two-tailed ttests or x
tests were applied to compare unadjusted means by sex.
*p<0.05, compared with boys.
Abbreviations: BMI = body mass index; CRF = cardiorespiratory ﬁtness; FMI = fat mass index; HDL-C = high-density lipoprotein cholesterol; HOMA = homeosta-
sis model assessment; SBP = systolic blood pressure; TC = total cholesterol; WHtR = waist to height ratio.
Partial correlations coefﬁcients (r) among fatness, CRF, and cardiometabolic biomarkers.
CRF BMI WHtR FMI SBP TC/HDL-C TG HOMA Leptin
CRF — — — — — — — — —
BMI 0.318*————— —— —
WHtR 0.323*0.892*———— —— —
FMI 0.409*0.909*0.852*——— —— —
SBP 0.030 0.356*0.339*0.288*—— —— —
TC/HDL-C 0.161*0.233*0.253*0.219*0.026 — — — —
TG 0.148*0.196*0.218*0.216*0.011 0.449*—— —
Note: Model was adjusted for age, sex, pubertal stage, and center.
*p<0.05, signiﬁcant association.
Abbreviations: BMI = body mass index; CRF = cardiorespiratory ﬁtness; FMI = fat mass index; HOMA = homeostasis model assessm ent; SBP = systolic blood pressure;
TC/HDL-C = total cholesterol to high-density lipoprotein cholesterol ratio; TG = triglycerides; WHtR = waist to height ratio.
Mediation role of cardiorespiratory ﬁtness 363
CRF on the relationship between fatness and cardiometabolic
risk in European adolescents.
The evidence shows a close relationship among fatness,
low CRF, and cardiometabolic health in youths.
CRF and obesity as predictors of CVD should be monitored
to identify children and adolescents with potential CVD
Despite the importance of both markers on cardio-
metabolic health, mediation analysis studies have frequently
used fatness as a mediator,
assuming a possible unique
association way (physical inactivity as causal of fatness).
However, there is also literature showing otherwise.
looking at the 2 sides of the coin seems to be adequate for
achieving a broader and objective understanding of this prob-
lem to improve the effectiveness of intervention programs in
The results of the present mediation analysis show that CRF
is capable of partially attenuating the association between fat-
ness and a cluster of cardiometabolic risk factors in adoles-
cents. In this sense, the magnitude of the mediation of CRF
could support—up to a point—the fat-but-ﬁt hypothesis,
Fig. 1. Mediation analysis. Contribution of fatness on CMRs through CRF, adjusting for potential confounders (age, sex, pubertal stage, center, and fat-free mass).
BMI = body mass index; CMRs = cardiometabolic risk scores; CRF = cardiorespiratory ﬁtness; FMI = fat mass index; WHtR = waist to height ratio.
364 C. Cristi-Montero et al.
because a high level of CRF would not fully protect the inde-
pendent action of fatness (BMI, WHtR, and FMI) on cardio-
metabolic risk in adolescents. However, the independent and
beneﬁcial role of high CRF may indeed offset more of the fat-
ness risk than indicated by the mediator analysis—just by
mechanisms not related to fatness. It should be noted that this
mediation role is around 10% for the 3 fatness variables stud-
ied in this approach, which gives it an important role both in
public health strategies and for clinical purposes.
This outcome is in line with previous prospective research
reporting that in schoolchildren (aged 711 years) the associa-
tion of CRF with change in CVD risk factor levels after adjust-
ment for adiposity (total body fat, BMI, and WC) was less
strong and concluding that the association of CRF with CVD
risk factors was largely explained by adiposity.
study of schoolchildren (aged 1014 years) showed that chil-
dren with low ﬁtness levels had increased odds of presenting
both individual and clustered cardiometabolic risk factors, but
these associations no longer remained after adjusting for
The authors suggested that abdominal
adiposity may be a more important determinant of adverse car-
diometabolic health in this age group,
and especially in
Notwithstanding the foregoing, childhood
CRF seems to decrease the long-term (20-year follow-up of
1792 adults) cardiometabolic risks associated with childhood
Therefore, more studies exploring the symbiotic
interaction between fatness and CRF on cardiometabolic
health are warranted to establish insight into the bidirectional
causality between these variables.
The physiological interpretation of these ﬁndings is com-
plex because fatness and CRF are separately and indepen-
dently associated with cardiometabolic risk factors,
possibly exerting their effects through different causal path-
For example, obesity may result in the develop-
ment of a chronic, low-grade inﬂammatory state caused by
an increased expression of pro-inﬂammatory adipokines and
diminished expression of anti-inﬂammatory adipokines.
Moreover, obesity has been associated with elevated oxida-
tive stress, which can lead to endothelial dysfunction.
has also been related to a decrease in oxygen respiration in
skeletal muscle associated with an impairment of mitochon-
Each condition mentioned (chronic low-grade inﬂammatory
state, diminished antioxidant capacity, and decrease in oxygen
respiration in skeletal muscle) can be improved or even
reversed, to some extent, by adequate levels of physical activ-
ity and exercise
and by enhancing CRF.
meta-analysis showed that high-intensity interval training
seems to be more effective for improving cardiometabolic risk
and CRF than other forms of exercise in overweight and obese
High-intensity activities, particularly among adoles-
cents, seems to be more effective in reducing a clustered meta-
bolic syndrome for both ﬁt and unﬁt individuals.
our results contribute to the existing evidence by suggesting
that if younger individuals maintain adequate CRF levels, this
can play a protective role that partially counteracts the harmful
role of obesity.
This work has some important strengths, such as the size of
the European sample used (multicentric study) and the use of
high-quality harmonization methods. It also seems to be the
ﬁrst study that has used CRF as a mediator to analyze CRF’s
role in relation to a cluster of cardiometabolic risk factors in
adolescents. However, this study also has certain limitations.
The present mediation analysis should be interpreted as
exploratory given the cross-sectional nature of our study; thus,
it does not allow conclusions about the causality relationships
to be drawn. Future longitudinal and experimental studies are
needed to conﬁrm our results.
Although it is true that CRF was measured with a validated
and reliable test, this variable depends heavily on body weight.
This situation could lead to underestimations of CRF values in
people with overweight and obesity
and underestimate the
beneﬁt of the fat-but-ﬁt hypothesis.
Moreover, the use of
clustered CMRs is speciﬁc to the study sample, and each factor
is equally weighted in predicting future disease. Finally, the
sample used in this study was made up of relatively healthy
youths, which could hamper the sensitivity in detecting associ-
CRF in adolescents seems to act as a signiﬁcant and partial
mediator of the relationship between fatness and CMRs, which
supports, to a certain extent, the fat-but-ﬁt paradigm. These
results suggest that the independent role of CRF is associated
with other mechanisms that are not directly linked to fatness.
However, these ﬁndings also indicate that maintaining ade-
quate CRF levels among adolescents can counteract the devel-
opment of harmful obesity-related CVDs and thus is an
important public health strategy.
We thank the adolescents who participated in the study
and their parents and teachers for their collaboration. We also
acknowledge the HELENA study members involved in ﬁeld-
work for their efforts. The HELENA project was supported
by the European Community 6th Framework Programme for
Research and Technological Development (contract FOOD-
CT-2005-007034). The data for this study were gathered
under the aegis of the HELENA project, and further analysis
was additionally supported by the Spanish Ministry of Econ-
omy and Competitiveness (Grants RYC-2010-05957 and
RYC-2011-09011), the Spanish Ministry of Health: Maternal,
Child Health and Development Network (Grants RD08/0072
and RD16/0022), the Fondo Europeo de Desarrollo Regional
(MICINN-FEDER), and the University of Granada, Plan Pro-
pio de Investigaci
on 2016, Excellence Actions: Units of
Excellence; Unit of Excellence on Exercise and Health
(UCEES). The content of this article reﬂects the authors’
views alone, and the European Community is not liable for
any use that may be made of the information contained
Mediation role of cardiorespiratory ﬁtness 365
CCM and JRR had full access to all of the data in the
study and took responsibility for the integrity of the data and
the accuracy of the data analysis; CCM and JCI conceived
and designed the study and drafted the manuscript; FBO,
JCP, ASP, AP, JV, AM, LMM, and JRR carried out critical
revisions of the manuscript for important intellectual content.
All authors have read and approved the ﬁnal version of the
manuscript, and agree with the order of presentation of the
The authors declare that they have no competing interests.
1. Ng M, Fleming T, Robinson M, et al. Global, regional, and national preva-
lence of overweight and obesity in children and adults during 19802013:
A systematic analysis for the Global Burden of Disease Study 2013. The
2. Arellano-Ruiz P, Garc
ıa-Hermoso A, Mart
ıno V, Salcedo-
Aguilar F, Garrido-Miguel M, Solera-Martinez M. Trends in cardiometa-
bolic parameters among Spanish children from 2006 to 2010: The Cuenca
study. Am J Hum Biol 2017;29:e22970. doi:10.1002/ajhb.22970.
3. Ajala O, Mold F, Boughton C, Cooke D, Whyte M. Childhood predictors
of cardiovascular disease in adulthood. A systematic review and meta-
analysis. Obes Rev 2017;18:1061–70.
4. World Health Organization. Global status report on noncommunicable
diseases. Geneva: World Health Organization; 2014.
5. Vilallonga R, Moreno Villares JM, Yeste Fern
andez D, et al. Initial
approach to childhood obesity in Spain. A multisociety expert panel
assessment. Obes Surg 2017;27:997–1006.
6. Metcalf BS, Hosking J, Jeffery AN, Voss LD, Henley W, Wilkin TJ.
Fatness leads to inactivity, but inactivity does not lead to fatness: A
longitudinal study in children (EarlyBird 45). Arch Dis Child
7. Hjorth MF, Chaput JP, Ritz C, et al. Fatness predicts decreased physical
activity and increased sedentary time, but not vice versa: Support from a
longitudinal study in 8- to 11-year-old children. Int J Obes 2014;38:959–
8. Lima RA, Pfeiffer KA, Bugge A, Møller NC, Andersen LB, Stodden DF.
Motor competence and cardiorespiratory ﬁtness have greater inﬂuence on
body fatness than physical activity across time. Scand J Med Sci Sport
9. Lima RA, Bugge A, Ersbøll AK, Stodden DF, Andersen LB. The lon-
gitudinal relationship between motor competence and measures of fat-
ness and ﬁtness from childhood into adolescence. J Pediatr (Rio J)
10. Stodden DF, Goodway JD, Langendorfer SJ, et al. A developmental per-
spective on the role of motor skill competence in physical activity: An
emergent relationship. Quest 2008;60:290–306.
11. Santos R, Mota J, Okely AD, et al. The independent associations of seden-
tary behaviour and physical activity on cardiorespiratory ﬁtness. Br J
Sports Med 2014;48:1508–12.
12. Lagestad P, Mehus I. The importance of adolescents’ participation in
organized sport according to VO
A longitudinal study. Res Q Exerc
13. Brouwer SI, Stolk RP, Liem ET, Lemmink KA, Corpeleijn E. The role of
ﬁtness in the association between fatness and cardiometabolic risk from
childhood to adolescence. Pediatr Diabetes 2013;14:57–65.
14. Ross R, Blair SN, Arena R, et al. Importance of assessing cardiorespira-
tory ﬁtness in clinical practice: A case for ﬁtness as a clinical vital sign: A
scientiﬁc statement from the American Heart Association. Circulation
15. Cummings DM, DuBose KD, Imai S, Collier DN. Fitness versus fatness
and insulin resistance in U.S. Adolescents. J Obes 2010;2010: 195729.
16. Hayes A. Introduction to mediation, moderation, and conditional process
analysis: A regression-based approach. New York, NY: Guilford Press;
andez A, S
opez M, Mora-Rodr
ıguez R, Notario-
Pacheco B, Torrijos-Ni~
no C, Mart
ıno V. Obesity as a mediator
of the inﬂuence of cardiorespiratory ﬁtness on cardiometabolic risk: A
mediation analysis. Diabetes Care 2014;37:855–62.
18. Garcia-Hermoso A, Agostinis-Sobrinho C, Mota J, Santos RM, Correa-
Bautista JE, Ram
elez R. Adiposity as a full mediator of the inﬂu-
ence of cardiorespiratory ﬁtness and inﬂammation in schoolchildren: The
FUPRECOL study. Nutr Metab Cardiovasc Dis 2017;27:525–33.
19. Ortega FB, Ruiz JR, Labayen I, Lavie CJ, Blair SN. The fat but ﬁt paradox:
what we know and don’t know about it. Br J Sports Med 2017;52:151–3.
20. Moreno LA, De Henauw S, Gonz
alez-Gross M, et al. Design and imple-
mentation of the Healthy Lifestyle in Europe by Nutrition in Adolescence
cross-sectional study. Int J Obes 2008;32(Suppl. 5):S4–11.
21. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-
offs for thinness, overweight and obesity. Pediatr Obes 2012;7:284–94.
22. Nagy E, Vicente-Rodriguez G, Manios Y, et al. Harmonization process
and reliability assessment of anthropometric measurements in a multicen-
ter study in adolescents. Int J Obes 2008;32(Suppl. 5):S58–65.
23. Slaughter MH, Lohman TG, Boileau RA, et al. Skinfold equations for
estimation of body fatness in children and youth. Hum Biol 1988;60:709–
ıguez G, Moreno LA, Blay MG, et al. Body fat measurement in ado-
lescents: comparison of skinfold thickness equations with dual-energy
X-ray absorptiometry. Eur J Clin Nutr 2005;59:1158–66.
25. Tanner JM, Whitehouse RH. Clinical longitudinal standards for height,
weight, height velocity, weight velocity, and stages of puberty. Arch Dis
26. National High Blood Pressure Education Program Working Group on
High Blood Pressure in Children and Adolescents. The fourth report on
the diagnosis, evaluation, and treatment of high blood pressure in children
and adolescents. Pediatrics 2004;114:555–76.
alez-Gross M, Breidenassel C, G
ınez S, et al. Sampling
and processing of fresh blood samples within a European multicenter
nutritional study: Evaluation of biomarker stability during transport and
storage. Int J Obes 2008;32(Suppl. 5):S66–75.
28. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner
RC. Homeostasis model assessment: Insulin resistance and beta-cell func-
tion from fasting plasma glucose and insulin concentrations in man. Dia-
29. Labayen I, Ruiz J, Ortega F, et al. Association between the FTO
rs9939609 polymorphism and leptin in European adolescents: A possible
link with energy balance control. The HELENA study. Int J Obes
eger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre
shuttle run test for aerobic ﬁtness. J Sports Sci 1988;6:93–101.
31. Olds T, Tomkinson G, L
eger L, Cazorla G. Worldwide variation in the
performance of children and adolescents: An analysis of 109 studies of
the 20-m shuttle run test in 37 countries. J Sports Sci 2006;24:1025–38.
32. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered
cardiovascular risk in children: A cross-sectional study (The European
Youth Heart Study). The Lancet 2006;368:299–304.
33. Andersen LB, Lauersen JB, Brønd JC, et al. A new approach to deﬁne and
diagnose cardiometabolic disorder in children. J Diabetes Res 2015;2015:
34. Menard S. Applied logistic regression analysis: Sage university series on
quantitative applications in the social sciences. California, CA: Sage Pub-
lications Inc; 2002.
35. Myers RH. Classical and modern regression with applications. Boston,
MA: Duxbury Press;1990.
36. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assess-
ing and comparing indirect effects in multiple mediator models. Behav
Res Methods 2008;40:879–91.
366 C. Cristi-Montero et al.
37. Baron RM, Kenny DA. The moderator-mediator variable distinction in
social psychological research: Conceptual, strategic, and statistical con-
siderations. J Pers Soc Psychol 1986;51:1173–82.
38. Sobel ME. Asymptotic conﬁdence intervals for indirect effects in struc-
tural equation models. Sociol Methodol 1982;13:290–312.
enez V, Parrilla-Moreno F, Fern
andez-Santos JR, et al. Physi-
cal ﬁtness as a mediator between objectively measured physical activity
and clustered metabolic syndrome in children and adolescents: The
UP&DOWN study. Nutr Metab Cardiovasc Dis 2016;26:1011–9.
40. Muntaner-Mas A, Pere P, Vidal-Conti J, Esteban-Cornejo I. A media-
tion analysis on the relationship of physical ﬁtness components, obe-
sity, and academic performance in children. J Pediatr 2018;198:90–7.
41. Steele RM, Brage S, Corder K, Wareham NJ, Ekelund U. Physical activ-
ity, cardiorespiratory ﬁtness, and the metabolic syndrome in youth. J Ado-
42. Pozuelo-Carrascosa DP, S
opez M, Cavero-Redondo I, Torres-
Costoso A, Bermejo-Cantarero A, Mart
ıno V. Obesity as a
mediator between cardiorespiratory ﬁtness and blood pressure in pre-
schoolers. J Pediatr 2017;182:114–9.
43. Countryman AJ, Saab PG, Llabre MM, Penedo FJ, McCalla JR, Schnei-
derman N. Cardiometabolic risk in adolescents: Associations with physi-
cal activity, ﬁtness, and sleep. Ann Behav Med 2013;45:121–31.
nero J, Perez-Bey A, Segura-Jim
enez V, et al. Cardiorespiratory
ﬁtness cutoff points for early detection of present and future cardiovascu-
lar risk in children. Mayo Clin Proc 2017;92:1753–62.
45. Seo YG, Choi MK, Kang JH, et al. Cardiovascular disease risk factor clus-
tering in children and adolescents: A prospective cohort study. Arch Dis
andez A, S
opez M, Gul
alez R, et al. BMI as a
mediator of the relationship between muscular ﬁtness and cardiometabolic
risk in children: A mediation analysis. PLoS One 2015;10: e0116506.
47. Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circ
48. Klakk H, Grøntved A, Møller NC, Heidemann M, Andersen LB, Wedder-
kopp N. Prospective association of adiposity and cardiorespiratory ﬁtness
with cardiovascular risk factors in healthy children. Scand J Med Sci Sport
49. Bailey DP, Savory LA, Denton SJ, Kerr CJ. The association between car-
diorespiratory ﬁtness and cardiometabolic risk in children is mediated by
abdominal adiposity: The HAPPY study. J Phys Act Heal 2015;12:
om CD, Henriksson P, Mart
ıno V, et al. Does cardiorespi-
ratory ﬁtness attenuate the adverse effects of severe/morbid obesity on
cardiometabolic risk and insulin resistance in children? A pooled analysis.
Diabetes Care 2017;40:1580–7.
51. Schmidt MD, Magnussen CG, Rees E, Dwyer T, Venn AJ. Childhood ﬁt-
ness reduces the long-term cardiometabolic risks associated with child-
hood obesity. Int J Obes 2016;40:1134–40.
52. LaMonte MJ, Blair SN. Physical activity, cardiorespiratory ﬁtness, and
adiposity: contributions to disease risk. Curr Opin Clin Nutr Metab Care
53. Pedersen BK, Febbraio MA. Muscles, exercise and obesity: Skeletal mus-
cle as a secretory organ. Nat Rev Endocrinol 2012;8:457–65.
54. Nakamura K, Fuster JJ, Walsh K. Adipokines: A link between obesity and
cardiovascular disease. J Cardiol 2014;63:250–9.
55. Huang CJ, McAllister MJ, Slusher AL, Webb HE, Mock JT, Acevedo EO.
Obesity-related oxidative stress: The impact of physical activity and diet
manipulation. Sport Med Open 2015;1:32. doi:10.1186/s40798-015-0031-y.
56. Safdar A, Khrapko K, Flynn JM, et al. Exercise-induced mitochon-
drial p53 repairs mtDNA mutations in mutator mice. Skelet Muscle
57. Ruiz JR, Huybrechts I, Cuenca-Garcia M, et al. Cardiorespiratory ﬁtness
and ideal cardiovascular health in European adolescents. Heart
ıa-Hermoso A, Cerrillo-Urbina AJ, Herrera-Valenzuela T, Cristi-
Montero C, Saavedra JM, Mart
ıno V. Is high-intensity interval
training more effective on improving cardiometabolic risk and aerobic
capacity than other forms of exercise in overweight and obese youth? A
meta-analysis. Obes Rev 2016;17:531–40.
59. Krachler B, Savonen K, Komulainen P, Hassinen M, Lakka TA, Raura-
maa R. Cardiopulmonary ﬁtness is a function of lean mass, not total body
weight: The DRs EXTRA study. Eur J Prev Cardiol 2015;22:1171–9.
60. Silva DR, Werneck AO, Collings PJ, et al. Cardiorespiratory ﬁtness effect
may be under-estimated in “fat but ﬁt” hypothesis studies. Ann Hum Biol
Mediation role of cardiorespiratory ﬁtness 367