Received: 19 April 2022 |
Accepted: 16 November 2022
DOI: 10.1111/cpf.12800
O R I G I N A L A R T I C L E
Detailed investigation of multiple resting cardiovascular
parameters in relation to physical fitness
Lars Lind1
|
Karl Michaëlsson2
1Department of Medical Sciences, Uppsala
University, Uppsala, Sweden
2Department of Surgical Sciences, Uppsala
University, Uppsala, Sweden
Correspondence
Lars Lind, Department of Medical Sciences,
Uppsala University, Uppsala 75185, Sweden.
Email: lars.lind@medsci.uu.se
Funding information
Akademiska Sjukhuset
Abstract
Objective: Maximal oxygen consumption at an exercise test (VO2‐max) is a
commonly used marker of physical fitness. In the present study, we aimed to find
independent clinical predictors of VO2‐max by use of multiple measurements of
cardiac, respiratory and vascular variables collected while resting.
Methods: In the Prospective study of Obesity, Energy and Metabolism (POEM), 420
subjects aged 50 years were investigated regarding endothelial function, arterial
compliance, heart rate variability, arterial blood flow and atherosclerosis, left
ventricular structure and function, lung function, multiple blood pressure measure-
ments, lifestyle habits, body composition and in addition a maximal bicycle exercise
test with gas exchange (VO2 and VCO2).
Results: When VO2‐max (indexed for lean mass) was used as the dependent variable
and the 84 hemodynamic or metabolic variables were used as independent variables
in separate sex‐adjusted models, 15 variables showed associations with p < 0.00064
(Bonferroni‐adjusted). Eight independent variables explained 21% of the variance in
VO2‐max. Current smoking and pulse wave velocity (PWV) were the two major
determinants of VO2‐max (explaining each 7% and 3% of the variance; p < 0.0001
and p = 0.008, respectively). They were in order followed by vital capacity, fat mass,
pulse pressure, and high‐density lipoprotein (HDL)‐cholesterol. The relationships
were inverse for all these variables, except for vital capacity and HDL.
Conclusion: Several metabolic, cardiac, respiratory and vascular variables measured
at rest explained together with smoking 21% of the variation in VO2‐max in middle‐
aged individuals. Of those variables, smoking and PWV were the most important.
K E Y W O R D S
exercise test, physical fitness, pulse wave velocity, smoking, VO2‐max
Clin Physiol Funct Imaging. 2023;43:120–127.
120
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wileyonlinelibrary.com/journal/cpf
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2022 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and
Nuclear Medicine.
1
|
INTRODUCTION
Maximal oxygen consumption at an exercise test (VO2‐max) is
commonly used as a marker of physical fitness. VO2‐max has further
been shown to be related to all‐cause mortality in a dose response
fashion (Kodama, et al., 2009). VO2‐max is generally considered to
have a strong genetic component and twin studies report heritability
estimates of 0.5–0.7, although fitness is naturally also affected by
lifestyle habits (Maes et al., 1996).
There are sex differences in VO2‐max, and VO2‐max declines with
age (Amara et al., 2000; Serrano‐Sánchez et al., 2010) and increasing body
fat (Serrano‐Sánchez et al., 2010). Other important determinants or
consequences of low fitness are lung function at rest (forced vital capacity
[FVC] and forced expiratory volume at 1 s [FEV1]) (Laukkanen et al.,
2009; Mendelson et al., 2016; Nakamura et al., 2004) and smoking
(Bernaards et al., 2003; de Borba et al., 2014; Suminski et al., 2009).
As reviewed by Rost (1997), cardiac enlargement in athletes was first
described by Henshen in 1899 comparing cross‐country skiers with
sedentary controls (Henschen, 1899). Later studies have also evaluated
total heart size in physical fitness (Bouchard et al., 1977), but in most
other studies, the heart size has been divided into left atrial (LA) size, left
ventricular (LV) end‐diastolic diameter and LV mass using echo-
cardiography to give more detailed information. All of these indices of
heart size have been linked to cardiorespiratory fitness (Brinker et al.,
2014; Gidding et al., 2010; Lam et al., 2010; Rogers et al., 2020).
Regarding other cardiovascular parameters, impaired endothelial
vasodilatory capacity (Montero, 2015), increased aortic augmentation
index (AIx) (Binder et al., 2006), increased arterial stiffness (Augustine
et al., 2016; Boreham et al., 2004; Fernberg et al., 2017), poor LV
diastolic function (Brinker et al., 2014), low haemoglobin level
(Laukkanen et al., 2009) and carotid artery atherosclerosis (Rauramaa
et al., 1995) have all been associated with poor VO2‐max.
A major disadvantage with previous studies is that they mainly have
investigated a limited number of cardiovascular and lung function
parameters in the same individuals. Accordingly, no comprehensive
picture of the determinants of VO2‐max has been presented.
With the present study, we, therefore, aimed to measure multiple
cardiovascular and lung function parameters in the same individuals
and to relate those to VO2‐max. We used data from the population‐
based Prospective study of Obesity, Energy and Metabolism (POEM),
in which multiple cardiovascular and lung function parameters have
been measured in the same individuals at the age of 50. We included
all measured cardiovascular and lung function parameters in the
analysis to capture as many facets of cardiorespiratory function as
possible. The hypothesis tested was that we by this approach could
explain a great proportion of the variance in VO2‐max.
2
|
METHODS
In a population‐based study of individuals, all aged 50 years, in
Uppsala City, Sweden, the POEM (Lind, 2013), a random sample of
men and women were invited to participate 1 month following their
50th birthday. The inclusion in the study started in 2012 and was
stopped in 2017. The participation rate was 25%, and the inclusion
was stopped after 502 participants. The study was approved by the
Ethics Committee of the University of Uppsala, and the subjects gave
their written informed consent to participate.
The participants were asked how many times a week they
performed mild (such as walking) and harder (to produce perspiration,
like running) exercises for at least 30 min. Based on these data, four
groups were defined (see Lind et al., 2021 for details): sedentary (13%
of the sample), mild exercise only (24%), some harder exercise (33%)
and harder exercise (30%).
All individuals were investigated in the morning after an
overnight fast. An arterial cannula was inserted in the brachial artery
for blood sampling and was later used for regional infusions of
vasodilators. Lipid variables and fasting blood glucose were measured
by standard laboratory techniques. Height was recorded by a ruler
and body weight was measured on a scale (Tanita BC‐418).
Thereafter, multiple physiological tests were performed.
Endothelial function and arterial compliance/stiffness were both
measured by three different techniques: acetylcholine‐mediated
increase in forearm blood flow, flow‐mediated vasodilation (FMD)
and peripheral artery vasodilation (EndoPath). The carotid arteries
were investigated by ultrasound for anatomy (intima‐media thickness
and echolucency and blood flow. The myocardial LV was evaluated
by ultrasound for LV geometry (LV mass, end‐diastolic volume, wall
thickness), systolic (ejection fraction) and diastolic function (isovolu-
metric relaxation time, E/A‐ratio, Doppler e′/a′ ratio). Blood pressure
was measured by four different techniques (conventional, invasive,
derived central pressure, 24 h ambulatory). Basal energy expenditure
was measured by indirect calorimetry and heart rate variability (HRV)
was recorded for 5 min. Arterial compliance was measured by three
techniques (carotid‐femoral pulse wave velocity [PWV], carotid
artery distensibility and the stroke volume to pulse pressure ratio).
Radial artery pulse wave was recorded for the AIx and reflectance
index. Blood flow of the brachial artery was recorded at rest and
following 5 min of hyperaemia.
Total and regional body fat and lean mass were estimated using
dual‐energy X‐ray absorptiometry (DXA; Lunar Prodigy, GE Health-
care). To minimize the potential operator bias, all scans were
performed in the same room by one experienced nurse. Total fat
and lean mass had a precision error of 1.5% and 1.0%, respectively.
For analysis, automatic edge detection was always used; however, all
scans were thoroughly checked for errors and manually corrected if
needed.
On a separate day, close to the first investigations, the
participants returned to the nonfasted state to evaluate lung function
(FVC and FEV1) and to perform a maximal bicycle ergometer test
with gas exchange recordings. Also, the recoveries of the heart rate,
blood pressure and VO2 and VCO2 during 5 min were recorded.
Smoking was identified as current smoking.
All the physical investigations have previously been described
by Lind and Lampa (2019) and are given in detail in the Supporting
Information.
LIND AND MICHAËLSSON
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121
FIGURE 1
(See caption on next page)
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2.1
|
Statistical analysis
All variables were checked for a normal distribution, and some
variables such as the E/A ratio, serum triglycerides, most HRV vari-
ables, were skewed to the right and therefore ln‐transformed to
achieve a normal distribution to be used in the models.
First, the relationship between VO2‐max and sex was investi-
gated by ANOVA (same age of all subjects). Second, the relationships
between VO2‐max (adjusted for lean mass) and the 84 hemodynamic
or metabolic variables were investigated one by one in sex‐adjusted
linear regression models. Third, the relationships between VO2‐max
and the 84 hemodynamic or metabolic variables were investigated
one by one with sex and fat mass included in the model. Fourth, the
interactions between sex and the hemodynamic or metabolic
variables were investigated one by one. Fifth, a multiple linear model
with VO2‐max as the outcome and sex together with eight other
hemodynamic
or
metabolic
variables,
which
were
Bonferroni‐
significant in the initial analyses, as independent variables were
evaluated. In this model, variables being closely related (correlation
coefficient > 0.3) to other more significant variables, such as FEV1,
and several blood pressure and heart rate measurements, were not
included in this multiple model due to the risk of co‐linearity. In the
second to fourth steps, the relationships between VO2‐max and the
84 hemodynamic or metabolic variables were investigated one by
one, and therefore, Bonferroni adjustment for these tests was
performed resulting in a critical p‐value of 0.00064. In step five, we
regarded p < 0.05 to be significant.
STATA14 was used for the calculations (Stata Inc.).
3
|
RESULTS
The median and interquartile ranges of measured variables are given
in Supporting Information: Table 1.
VO2‐max (alone and when adjusted for lean mass) was a normally
distributed variable. The mean for unadjusted VO2‐max was 2.79 (SD
0.54) L/min in men and 1.85 (0.40) in women (p < 0.0001). Sex
explained 49% of the variation in unadjusted VO2‐max. VO2‐max
adjusted for lean mass was 0.46 (SD 0.079) L/min/kg lean mass in
men and 0.39 (0.083) in women (p = 0.0043). Sex explained only 1.7%
of the variation in VO2‐max after adjustment for lean mass. In the
following analysis, VO2‐max adjusted for lean mass was used. Current
smoking was reported by 9.8% of the individuals.
When VO2‐max was used as the dependent variable and the
84 hemodynamic or metabolic variables were used as indepen-
dent variables in separate sex‐adjusted models for each hemo-
dynamic or metabolic variable, 15 variables showed associations
with p < 0.00064 (Bonferroni adjusted threshold, see Supporting
Information: Table 2 and Figure 1 for details). Vital capacity, FEV1
and high‐density lipoprotein (HDL) were positively related to
VO2‐max, while the pulse rate, pulse pressure, diastolic night‐
time dipping at 24 h ambulatory recording, BMI, fat mass,
triglycerides, office recordings of the pulse rate, diastolic blood
pressure and calculated central systolic and diastolic blood
pressure all were related to VO2‐max in a negative fashion. All
of these variables displayed p < 0.05 when additional adjustment
for fat mass was made. No interaction term between sex and any
hemodynamic or metabolic variable was significant following
adjustment for multiple testing.
Together
with
smoking,
eight
hemodynamic
or
metabolic
variables being Bonferroni‐significant in the initial analyses explained
21% of the variation in VO2‐max. This held true also after omitting
the variable sex, which was included in the first version of the model.
In this model with VO2‐max as the outcome, smoking and
PWV were the two major determinants of VO2‐max (explaining 7%,
p < 0.0001 and explaining 3%, p = 0.008, respectively). They were
followed by vital capacity, fat mass, pulse pressure and HDL‐
cholesterol, which all showed p < 0.05 in this multiple model (see
Table 1 for details). The relationships were inverse for all these
variables,
except
for
vital
capacity
and
HDL.
Sex
(p = 0.97),
triglycerides and the resting heart rate showed p > 0.05. Figure 2
displays some of these main relationships more in detail.
4
|
DISCUSSION
The present study showed that smoking and an increased PWV at
rest were most closely related to VO2‐max, but lung function, fat
mass, pulse pressure and HDL‐cholesterol were also related to this
commonly used marker of physical fitness.
FIGURE 1
Relationships between hemodynamic and metabolic variables and VO2‐max (adjusted for lean mass) when the hemodynamic and
metabolic variables were evaluated one by one. The regression coefficient and 95% CIs are given for the sex‐adjusted analyses. a, atrial
contraction transmitral filling velocity; AIx, aortic augmentation index; AMBP, ambulatory monitoring of blood pressure; BMI, body mass index;
CI, cardiac index; DBP, diastolic blood pressure; e, early transmitral filling velocity; EDV, endothelium‐dependent vasodilatation; EE, energy
expenditure; EF, ejection fraction; EIDV, endothelium‐independent vasodilatation; FEV1, forced expiratory volume at 1 s; FMD, flow‐mediated
dilatation; HDL, high‐density lipoprotein; HR, heart rate; HRV, heart rate variability; IM‐GSM, echogenicity of the intima‐media complex; IMT,
intima‐media thickness; IVRT, isovolumetric relaxation time; IVS, intraventricular thickness; LA, left atrial diameter; LF, low frequency; LF/HF
ratio, low‐frequency/high‐frequency ratio; LVEDD, left ventricular end‐diastolic diameter; LVESD, left ventricular end‐systolic diameter; LVMI,
left ventricular mass index; PP, pulse pressure; PW, posterior wall thickness; PWV, pulse wave velocity; RHI, reactive hyperaemia index; RI,
reflectance index; RQ, respiratory quote; RWT, relative wall thickness; SBP, systolic blood pressure; SDFV, systolic to diastolic blood flow
velocity; SI, stroke index; SV/PP‐ratio, stroke volume to pulse pressure ratio; TPRI, total peripheral resistance index; VC, Vital capacity; VCO2,
carbon dioxin production; VO2, oxygen consumption.
LIND AND MICHAËLSSON
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123
4.1
|
Comparison with the literature
All of these variables have previously been shown to be related to
VO2‐max, as cited in the Introduction section. The novelty of the
present
study
is
that
we
by
the
measurements
of
multiple
cardiovascular and lung function variables in the same individuals
were able to compare these variables in terms of importance and
independence from each other.
We could not reproduce some other previous findings that
endothelial vasodilatory function (FMD) (Montero, 2015), a poor LV
diastolic function and a large LV end‐diastolic volume (Brinker et al.,
2014), a low haemoglobin level (Laukkanen et al., 2009) and carotid
artery atherosclerosis (Rauramaa et al., 1995) were related to
VO2‐max.
One major advantage of the present study is that we could
evaluate the independent contribution of indices reflecting different
aspects of physiology in the same model and found that several
different physiological pathways are determinants of VO2‐max. This
is not a surprise, since it is obvious that the heart, the lungs and the
skeletal muscles simultaneously all play important roles in the
determination of cardiorespiratory fitness.
Given that basic assumption, it was a surprise that no variable
reflecting myocardial function or structure was amongst the major
identified physiological indices. One explanation for this could be the
very strict Bonferroni adjustment applied to compensate for the
multiple statistical testing. It could be seen that both the s′ and e′ at
TDI, the e′/a′‐ratio at TDI, stroke index, LA diameter (inverse) and
relative wall thickness (RWT) (inverse) showed p < 0.05 (p = 0.054 for
RWT). Thus, if not using this strict adjustment for multiple testing, we
could replicate the findings of others that several myocardial indices
are linked to VO2‐max, although other factors might be more
important.
Only a small part of the variance in VO2‐max could be
explained by the evaluated variables despite that a great number
of cardiopulmonary variables were assessed. Several factors could
explain this finding. First, all variables have a certain lack of
precision and variability in measurements that could lower the
degree of explained variance, especially when several variables
seem to be of importance. Second, all variables were measured at
rest. It could be speculated that a better R2 for VO2‐max would be
obtained if the variables were measured during exercise instead.
Third, certain factors of particular interest were not measured.
One such very important feature is the mitochondrial function in
the heart and skeletal muscles during exercise. Another could be
diffusion capacity in the lungs. Yet another factor is skeletal
muscle composition, which is important for endurance capacity
(Hall et al., 2021). Fourth, it has been shown that genetic DNA
variations both at the global level (Gineviciene et al., 2022), as well
as at the mitochondrial level (Vellers et al., 2020), are important
determinants of VO2‐max. Fifth, we normalized VO2‐max for lean
mass measured at DXA. Most other studies have not performed
such rigorous normalization, and if no normalization would have
been performed, lean mass in itself would explain 60% of the
variance in VO2‐max.
4.2
|
Clinical perspectives
Apart from an increase in endurance training, smoking cessation
would be the single most important action to improve VO2‐max, as
suggested by the present findings. We could not however find any
intervention studies to support that assumption.
It might also be warranted to reduce arterial stiffness, although
the causality is less clear in this case. In a small placebo‐controlled
trial in postmyocardial infarction patients, treatment with a combina-
tion of a statin and an angiotensin‐receptor blocker reduced PWV
(Turk Veselič et al., 2018). In an open trial of the combination of an
ACE inhibitor and a calcium channel blocker in patients with
hypertension, an improvement in PWV was seen after 12 months
(Radchenko et al., 2018). It would be of interest to see if such
interventions that improve arterial stiffness would also have an
impact on VO2‐max.
TABLE 1
Relationships between VO2‐
max (outcome, adjusted for lean mass) and
sex and eight hemodynamic or metabolic
variables as independent variables
Variables related to VO2‐max
Beta
95% CI low
95% CI high
p Value
Sex
−0.004335
−0.2405681
0.2318981
0.971
Ambulatory pulse pressure
−0.1310065
−0.2354139
−0.0265991
0.014
Smoking
−0.2153444
−0.2965848
−0.1341041
0.000
Fat mass
−0.1095866
−0.1923139
−0.0268592
0.010
Resting heart rate
−0.0134249
−0.1198909
0.093041
0.804
Triglycerides
−0.0539096
−0.1287491
0.02093
0.158
Pulse wave velocity
−0.112147
−0.1944053
−0.0298887
0.008
HDL
0.0920998
0.0008397
0.1833599
0.048
Resting vital capacity
0.1385489
0.0300567
0.2470411
0.012
Abbreviations: CI, cardiac index; HDL, high‐density lipoprotein; VO2‐max, maximal oxygen
consumption.
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FIGURE 2
Relationships between VO2‐max
(adjusted for lean mass) and variables were found to
be of major importance to explain the variation in
VO2‐max (adjusted for lean mass). VO2‐max versus
current smoking is given in the upper panel. VO2‐max
versus pulse wave velocity (PWV) is in the middle
panel and VO2‐max versus fat mass is given in the
lower panel.
LIND AND MICHAËLSSON
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125
Weight loss might also be a way to increase VO2‐max, and at
least in patients with class III obesity (BMI > 40 kg/m2), weight
reduction increased VO2‐max (Hakala et al., 1996).
4.3
|
Strengths and limitations
The major strength of the present study is the multitude of
cardiovascular and lung function variables measured at rest together
with VO2‐max in individuals of the same age. Since age is an
important determinant of VO2‐max (Amara et al., 2000; Serrano‐
Sánchez et al., 2010), standardization of age would remove the
impact of this very important variable on the variance in VO2‐max.
Another strength is that we could adjust VO2‐max for lean mass,
measured by the gold standard, DXA. As could be seen in our
analysis, this standardization removed most of the sex effect on the
variation in VO2‐max.
This is a cross‐sectional study, and as such causality can never be
proven and the directions of relationships are not clear.
A limitation of studying a homogeneous sample is that the
generalizability is low, so the present results have to be reproduced in
samples from other countries with other ethnical groups, as well as in
other age groups.
5
|
CONCLUSION
Several metabolic, cardiac, respiratory and vascular variables mea-
sured at rest explained together with smoking 21% of the variation in
VO2‐max in individuals aged 50 years.
ACKNOWLEDGEMENTS
The study was funded by the University Hospital of Uppsala, Sweden.
The funders had no role in study design, data collection and analysis,
decision to publish or preparation of the manuscript.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
According to Swedish law, personal health data cannot be made
publicly available. Data from this study are however available upon a
reasonable request by other researchers.
ORCID
Lars Lind
https://orcid.org/0000-0003-2335-8542
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Lind, L. & Michaëlsson, K. (2023)
Detailed investigation of multiple resting cardiovascular
parameters in relation to physical fitness. Clinical Physiology
and Functional Imaging, 43, 120–127.
https://doi.org/10.1111/cpf.12800
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Detailed investigation of multiple resting cardiovascular parameters in relation to physical fitness.
Lind, Lars,Michaëlsson, Karl