|
RESPIRATORY GAS EXCHANGE INDICES FOR ESTIMATING THE ANAEROBIC
THRESHOLD
|
1Faculty Division, Ullevål University Hospital, University of
Oslo, Norway
2Department
of Pulmonary Medicine, Ullevål University Hospital, Oslo, Norway
| Received |
|
05 October 2004 |
| Accepted |
|
16
December 2004 |
| Published |
|
01
March 2005 |
©
Journal of Sports Science and Medicine (2005) 4, 29 - 36
Search
Google Scholar for Citing Articles
| ABSTRACT |
| Several
methods are used for estimating the anaerobic threshold (AT) during
exercise. The aim of the present study was to compare AT values based
on blood lactate measurements with those obtained from computerised
calculations of different respiratory gas indices. Twelve healthy,
well-trained men performed a stepwise incremental test on both treadmill
and cycle ergometer. Respiratory gases were measured continuously,
and blood samples were drawn every third minute. AT was determined,
based on 1) blood lactate concentrations (Lactate-AT), 2) respiratory
exchange ratio (RER-AT), 3) V- slope method (Vslope-AT), and 4) ventilatory
equivalent for VO2 (EqO2-AT). Lactate-AT and RER-AT values showed
similar values, both on treadmill and on cycle ergometer. EqO2-AT
showed a trend towards lower values for AT, while Vslope-AT gave significantly
lower values for AT for both exercise modes. Bland-Altman plots showed
an even distribution of data for RER-AT, while a more scattered and
skewed distribution of data was observed when EqO2-AT and Vslope-AT
were compared with Lactate-AT. The study demonstrates that RER-based
estimates of AT correlate well with the blood lactate-based AT determination.
The RER method is non-invasive and simple to perform, and, in the
present study, seemed to be the best respiratory index for estimation
of AT.
KEY
WORDS: Anaerobic threshold, exercise test.
|
| INTRODUCTION |
Exercise
physiologists have for many years had knowledge about a critical work
intensity above which lactate accumulation occurs (Owles, 1930;
Wasserman and McIlroy, 1964).
Although somewhat controversial, AT has been widely used as an objective
measure for aerobic work capacity in both athletes and patients (Wasserman
and McIlroy, 1964;
Katz et al., 1992;
Roecker et al., 1998;
Coen et al., 2001).
AT has also been used to help in discriminating between cardiovascular
and pulmonary limitation to exercise (Wasserman 1984).
Several methods, both invasive and non-invasive, have been used for
estimating the AT, and there is no international consensus about what
is the best procedure.
Invasive methods require repeated measurements of blood lactate concentration
and give a lactate defined anaerobic threshold (Lactate-AT). Both
the point of abrupt increase in lactate level and the non-linear rise
in blood lactate concentration have been used (Beaver et al., 1985;
Katz et al. 1992),
inferring visual inspection of plotted curves of data. Others define
a specific blood lactate cut-off value, such as 2.0, 3.0 or 4.0 mmol·l-1
(Yeh et al., 1983;
Hech et al., 1985;
Borch et al., 1993). Some laboratories
calculate an individual anaerobic threshold based on for example the
resting value of blood lactate plus 1.5 mmol·l-1 (Roecker
et al., 1998,
Helgerud et al., 2001).
Whether lactate is measured in arterial, capillary or venous blood
will also influence the results (Yeh et al., 1983).
Non-invasive methods are based on continuous measurement of respiratory
gases and give a respiratory gases defined anaerobic threshold (RAT).
In an old method for estimating RAT, the relationship between expired
CO2 and inspired O2 (respiratory exchange ratio,
RER) is used, and AT is detected either at the point where RER starts
to rise, or where RER exceeds a certain defined cut-off value, such
as 1.0 (RER-AT) (Yeh et al., 1983;
Anderson and Rhodes, 1989;
Dickstein et al., 1990;
Myers and Ashley, 1997).
This method has later been considered inaccurate (Caiozzo et al.,
1982),
and several other methods have been proposed (Anderson and Rhodes,
1989;
Myers and Ashley, 1997),
for example the V-slope method (Beaver at al., 1986),
and the ventilation equivalent for O2 (EqO2)
(Reinhard et al., 1979).
In the present study we wanted to compare three different computer-based
methods for estimating AT from respiratory gases analysis, with the
more invasive method of Lactate-AT. We also wanted to investigate
whether the relationship between these methods were dependent on the
mode of exercise. Therefore the study subjects were exercised both
on treadmill and cycle ergometer. |
| METHODS |
|
Subjects
Twelve healthy men were recruited (Table
1). All subjects were endurance trained and were familiar with
treadmill running and ergometer cycling. The study was approved
by the Regional Ethics Committee and in accordance with the Helsinki
Declaration.
Testing procedure
The subjects were randomised to perform either the treadmill test
(n = 6) or the cycle ergometer test (n = 6) first. All participants
had two days of rest between the two tests. Restrictions against
intensive training two days ahead of testing, and in the two days
between testing, were given. The subjects fasted for 4 hours prior
to each exercise. All subjects were examined by a physician. Dynamic
spirometry and electrocardiogram (ECG) were performed, confirming
normal lung function and ECG.
The respiratory gas measurements were done using a Jaeger Oxycon
Champion spirometric analysator (Erich Jaeger GmBH & Co Wuerzburg,
Germany). Calibrations of flow transducer and gas analysers were
performed daily. A Triple V transducer with mouthpiece detecting
breath-by-breath- registrations of oxygen uptake (VO2),
expired CO2 (VCO2), minute ventilation (VE)
was used. The dead space in this unit was 42 ml. Breath-by-breath-registrations
were registered, and mean values of registrations from every minute
were used in further statistical analyses. Double capillary samples
were drawn from a finger using an Outlet 2000 pistol and Unilet
2000 lancets. The samples were taken two minutes after each increase
in workload and immediately analysed for lactate concentration (YSI
1500 sport, USA). The analysator was calibrated between each subject
with five- and 15 mmol·l-1 standard solutions. The double lactate
samples showed a mean difference of 0.02 mmol·l-1 with
a standard deviation of 0.25 mmol·l-1. The participants
stated their subjective feeling of exhaustion by using Borg's CR10
exhaustion scale (Borg, 1982).
Heart rate was monitored at the end of each exercise level using
a Polar Sport Tester (HRM, Finland).
Before starting the exercise test, resting values of VO2
were to be stabilized below 3.5 ml-1·min-1·kg-1
for three minutes (1 MET). The subjects were instructed to warm
up for ten minutes without mouthpiece, and a capillary blood sample
for lactate analysis was taken. An incremental test with increasing
workload every three minutes was employed.
The treadmill test
The subjects exercised on a Jaeger LE 300 C treadmill (Erich Jaeger
GmBH & Co Wuerzburg, Germany). A five percent slope was used
during the whole test. Starting speed was eight km·h-1,
and the speed was increased by two km·h-1 every three
minutes to subjective exhaustion. One subject lost the mouthpiece,
and his treadmill test was excluded from further analyses.
The cycle ergometer test
The subjects exercised on a Jaeger ER 900 cycle ergometer (Erich
Jaeger GmBH & Co Wuerzburg, Germany). The cycling position was
standardised with a 160º knee angle with pedal in lowest position.
During exercise the cadence should be held at constant 70 r·min-1.
The first workload was 150 W, increasing by 50 W every three minutes
to subjective exhaustion.
The AT-calculations
Lactate-AT: The lactate threshold was defined as the mean
value of lactate concentrations in capillary blood during warm-up
and the first workload + 1.5 mmol·l-1 (Helgerud et al.,
1990;
2001).
From the lactate concentrations, which were determined every three
minutes, a third order curve linear regression was calculated. From
this regression curve, Lactate-AT was expressed as the VO2
at the time when the lactate threshold was superseded (example in
Figure 1a).
RER-AT: The software calculated the RER automatically. RER-AT
was defined as the VO2 at the time when RER stabilized
above 1.0 not returning to levels below (Yeh et al., 1983;
Dickstein et al., 1990).
Mean values for every minute were used in the calculations (example
in Figure 1b).
EqO2-AT: The point of AT was detected using the
EqO2 method (Reinhard et al., 1979).
The ventilation equivalent of oxygen (VE/VO2)
was computed, and the point where VE/VO2 starts
rising was automatically detected by the computer software (example
in Figure 1c).
Vslope-AT: The point of AT was detected using v-slope technique
as described earlier (Beaver at al., 1986).
The VCO2 versus VO2 curve is divided into
two regions, each of which is fitted by linear regression, and the
intersection between the two regression lines is regarded as the
Vslope-AT. The software of the Jaeger equipment automatically established
the regression lines and their crossing points (example in Figure
1d).
Statistics
Data are presented as mean (±standard
deviation, SD) for description purpose, and as mean (±standard
error of the mean, SE) for comparison purpose. Data were analysed
by SPSS version 10.0. Histograms and Q-Q plots were used to check
for normal distribution of data. To visualise the relationship between
data, a Bland-Altman plot was used (Bland an Altman, 1986).
A paired Student's t-test to test for significant differences between
the two test models was employed for ordinal data. Pearson's two-tailed
correlation test with 95 % confidence interval was used to test
the correlation between Lactate-AT and the other methods of AT determination.
Ninety-five percent confidence intervals (CI) for difference between
methods were computed. P-values below 0.05 were considered statistical
significant.
|
| RESULTS |
|
The test was not designed as a VO2max test, but all the
test persons had high peak VO2 with 61.8 ±
4.6
ml-1·min-1·kg-1 on the treadmill
and 58.6 ±
4.8
ml-1·min-1·kg-1 on the cycle ergometer.
The maximum attained lactate concentrations of 8.70 ±
1.47
mmol·l-1 on the treadmill and 8.71 ±
1.77
mmol·l-1 on the cycle ergometer indicated that all of
the subjects exceeded their AT as defined in this study. This was
also confirmed by the RER measurements which all exceeded 1.0 (1.22
±
0.09
on the treadmill and 1.21 ±
0.06
on the cycle ergometer). Mean exhaustion time was 14.5 minutes and
perceived dyspnoe at maximal workload was rated 6 on Borg's scale
(Borg,
1982)
on cycle ergometer, versus 13.5 minutes and 7 on treadmill test.
In Table 2 the AT estimations
by the four different methods are presented. There were no significant
differences between VO2 values at attained Lactate-AT
and RER-AT, neither for treadmill test nor for ergometer cycle test.
The difference between RER-AT and Lactate-AT was 53 ml·min-1
(CI -258 to 152) for ergometer cycle test and 51 ml·min-1
(CI -79 to 182) for treadmill test.
EqO2-AT and Vslope-AT gave lower values for AT, especially
for Vslope-AT, where a significant difference from Lactate-AT was
found. The difference between EqO2-AT and Lactate-AT
was -198 ml·min-1 (CI -741 to 345) for ergometer cycle
test and -329 ml·min-1 (CI -688 to 30) for treadmill test. The difference
between Vslope-AT and Lactate-AT was -680 for ergometer cycle test
(CI -1033 to -327) and -646 (CI -1111 to -181) for treadmill test.
Including data from both exercise modes we found a significant correlation
between Lactate-AT and RER-AT (r = 0.87, p < 0.001). Similarly
a positive correlation for Lactate-AT and EqO2-AT was
found (r = 0.45, p < 0.05) and between Lactate-AT and Vslope-AT
(r = 0.42, p < 0.05).
The Bland-Altman plot illustrates an even distribution of Lactate-AT
and RER-AT without deviation depending on the mean value of AT (Figure
2). Vslope-AT and EqO2-AT, however, tends to show
lower values than Lactate-AT, especially in the lower range of their
mean values.
|
| DISCUSSION |
|
In this
study we have compared well-known methods for estimating AT, one invasive
method with direct measurement of lactate in blood, and three computer
based, non-invasive methods. We have used standard test procedures
and recruited a homogeneous group of subjects. The results indicated
minimal difference between the obtained Lactate-AT and RER-AT data,
both on treadmill and on cycle ergometer. The RER-AT method, which
is simple to perform, thus seems to give a valuable estimate of the
anaerobic threshold.
The V-slope method and the EqO2 method did not correspond with the
lactate-AT as well as the RER defined method. Both the EqO2-AT and
Vslope-AT methods had a tendency to show lower values than the lactate-AT,
especially in the lower range of AT. This is in accordance to the
findings of Cheng et al (Cheng et al., 1992)
where the ratio between Lactate-AT, Vslope-AT and EqO2-AT was similar
to ours. One would perhaps expect closer estimations of RER-AT and
Vslope-AT since they both originate from VCO2 versus VO2 values. Our
values for Vslope-AT are generally lower than for RER-AT, and this
might be due to the chosen cut-off value for RER.
Maximal oxygen uptake is usually higher on treadmill than on bicycle
(Zeballos and Weisman, 1994),
and anaerobic threshold is reported to occur at slightly higher values
of VO2 during treadmill running (Medelli et al., 1993).
Studies have also indicated adaptation to mode of exercise regarding
both work economy and energy turnover (Hirakoba et al., 1992;
Roecker et al., 2000).
Ten out of twelve of our test subjects reported aerobic training with
preference for running, and all the test subjects achieved higher
VO2-values at AT on the treadmill than on the cycle ergometer. However,
the ratio between AT estimated by the different methods were not essentially
affected by mode of exercise.
Several methods for detecting the Lactate-AT are used. In order to
avoid inter personal errors in determination, we have chosen to use
a definite cut off value rather than trying to define the point at
which blood lactate concentration starts to rise. This can probably
result in somewhat higher estimates of Lactate-AT than in other studies.
Using the initial rise in lactate might have decreased the difference
between EqO2-AT and Lactate-AT and between Vslope-AT and Lactate-AT.
In the Bland Altman plots, this would result in a shift along the
y-axis towards higher values for all the respiratory parameters. However,
still there is a problem with more scattered data for Vslope- AT,
and the skewed distribution of data for and EqO2-AT in the Bland-Altman
plots.
In the current study we chose a modified Bruce protocol (Zeballos
and Weisman, 1994)
using a three minutes stepwise incremental exercise test (Roecker
et al., 1998;
Coen et al., 2001).
Others have used intervals of one minute to make the test shorter
and easier to execute (Wasserman et al., 1973;
Zeballos et al. 1998).
It should be stressed that the current study is performed on well-trained
healthy subjects, and that the results cannot automatically be applied
to patients. However there are data from previous studies indicating
that Lactate-AT and RAT correspond reasonable well also in patients
with heart diseases (Dickstein et al., 1990;
Katz et al., 1992).
Several studies have investigated the relation between respiratory
gases exchange indices and blood lactate levels in order to determine
which respiratory gases index predict the Lactate-AT best (Wasserman
et al., 1973;
Davis et al., 1976;
Caiozzo et al., 1982;
Katz et al., 1992;
Patessio et al., 1993).
In the majority of these studies the anaerobic threshold has been
detected by visual inspection of plotted data in search for an abrupt
or non-linear rise in parameter values, either regarding Lactate-AT,
RAT, or both. This may result in bias in the interpretation of data,
and inter-observer errors have been a concern (Yeh et al., 1983;
Garrard and Das, 1987).
Computerized treatment of data may overcome this problem by computing
regression lines of measured data, indices or log-converted data (Dickstein
et al., 1990;
Katz et al., 1992
von Duvillard et al., 1993;
Miyahara et al., 2000
). However, this does not necessarily improve the validity and reproducibility
of the results. In this study we have shown that a simple parameter
as RER, with a predefined cut-off value, can give a reasonable good
estimate of the AT.
There are few recent studies treating RER as a single parameter predicting
the AT. Yeh et al. (1983)
found that RER = 1.0 gives a valuable estimate of Lactate-AT in healthy
subjects. Dickstein et al. (1990)
found that RER = 1 corresponded well with Lactate-AT
in patients with heart disease, although the RER-AT occurred at somewhat
higher levels of VO2 than Lactate-AT. With regard to the present study,
this discrepancy might be due to different definitions of the Lactate-AT.
RER data have shown a low inter-observer variability (Cohen-Solal
et al., 1994), and are easy to interpret. When calculating
AT from respiratory gases, a standardised timetable for sampling is
not necessary as it is for lactate measurements, since the measurement
of respiratory gases is performed continuously. Hence, the risk of
reaching AT in the interval between two sampling procedures, which
can be a problem using blood samples in the calculations, is eliminated.
Moreover invasive methods may require fixation of the arm with adjustment
of the running technique during blood sampling. This was reported
as a problem during the treadmill test since it resulted in a subjective
feeling of muscle soreness. |
| CONCLUSIONS |
| Computerised
determination of AT by the RER method gives comparable results with
AT determined by blood lactate concentrations. Estimation of AT, by
using a pre-defined cut-off value for RER, is non-invasive and simple
to perform in a respiratory laboratory. |
| KEY
POINTS |
- Anaerobic
threshold can reliably be estimated by respiratory gas indices
in well fit subjects.
- Sophisticated
computerassisted equations are not superior to the use of a simple
cut-off value of Respiratory Exchange Ratio in estimating the
anaerobic threshold.
- Estimation
of anaerobic threshold, by using a pre-defined cut-off value for
Respiratory Exchange Ratio, is non- invasive and simple to perform
in a respiratory laboratory.
|
| AUTHORS
BIOGRAPHY |
Geir SOLBERG
Employment: SHO, Department of Pulmonary Medicine, Ullevål
Univ. Hospital, and Faculty of Medicine, University of Oslo,
Norway.
Degree: MD
Research interests: Respiratory physiology and sports
medicine. |
|
Bjørn ROBSTAD
Employment: SHO, Department of Pulmonary Medicine, Ullevål
Univ. Hospital, and Faculty of Medicine, University of Oslo,
Norway.
Degree: MD
Research interests: Respiratory physiology and sports
medicine.
|
|
Ole Henning SKJØNSBERG
Employment: Professor of pulmonary medicine at the University
of Oslo, and consultant, Department of Pulmonary Medicine, Ullevål
Univ. Hospital, Oslo, Norway.
Degree: MD, PhD
Research interests: Respiratory physiology and respiratory
insufficiency.
E-mail: o.h.skjonsberg@medisin.uio.no
|
|
Fredrik
BORCHSENIUS
Employment: The head of the Department of Pulmonary Medicine,
Ullevål Univ. Hospital, Oslo, Norway
Degree: MD
Research interests: Respiratory physiology.
E-mail: fborchse@online.no
|
|
|
|
|