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University Laboratory of Physiology, University of Oxford, Oxford OX1 3PT, United Kingdom
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Measurement
of the acute hypoxic ventilatory response (AHVR) requires careful
choice of the hypoxic stimulus. If the
stimulus is too brief, the response may be incomplete; if the stimulus is too long, hypoxic ventilatory depression may ensue. The purpose of
this study was to compare three different techniques for assessing AHVR, using different hypoxic stimuli, and also to examine the between-day variability in AHVR. Ten subjects were studied, each on six
different occasions, which were 
1 wk apart. On each occasion, AHVR
was assessed using three different protocols: 1) protocol SW, which uses square waves of hypoxia; 2) protocol
IS, which uses incremental steps of hypoxia; and 3)
protocol RB, which simulates an isocapnic rebreathing test.
Mean values for hypoxic sensitivity were 1.02 ± 0.48, 1.15 ± 0.55, and 0.93 ± 0.60 (SD)
l · min
1 · %1
for protocols SW, IS, and RB, respectively. These
differed significantly (P < 0.01). The coefficients of
variation for measurement of AHVR were 20, 23, and 36% for the three
protocols, respectively. These were not significantly different. There
was a significant physiological variation in AHVR
(F 50,100 = 3.9, P < 0.001), with a
coefficient of variation of 26%. We conclude that there was relatively
little systematic variation between the three protocols but that AHVR varies physiologically over time.
acute hypoxic ventilatory response; ventilation
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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THE VENTILATORY RESPONSE of humans to isocapnic hypoxia
is triphasic. First, there is a rapid increase in expired minute
ventilation (
E)
known as the acute hypoxic ventilatory response (AHVR), which
occurs with a time constant in the order of seconds (6, 16). Second,
there is a fall in E known
variously as hypoxic ventilatory depression (HVD), hypoxic ventilatory
decline, or ventilatory "roll-off" with a time constant in the
order of minutes (4, 16). Third, there is a progressive rise in
E with a time constant in the order of
hours (10) that appears to be related to ventilatory acclimatization to
altitude. A major methodological problem in studies relating to the
first component of the response (AHVR) has been obtaining adequate sets
of data while avoiding contamination of results from the later phases
of the response to hypoxia.
Most techniques used to assess AHVR can loosely be classified into four
main groups. Steady-state methods are really those in which the
inspired gases are fixed; the experimenter then waits for ventilation
and for the end-tidal gases to settle before determining the response
(2, 14). The development of these techniques predates our current
understanding of HVD, and it is essentially inevitable that data that
have been collected using such techniques have been
"contaminated" by a varying degree of HVD. The second group of
methods involves either transient hypoxia or hyperoxia, lasting no more
than a few breaths (3, 5, 13). These methods avoid the problems
associated with HVD, but the duration of the stimulus is so brief that
the methods do not allow the ventilatory response to fully develop
(19). Furthermore, because the response only involves a few breaths,
the data are very susceptible to random variations in
E. The third group of methods involves progressive hypoxia such as can be achieved with a rebreathing circuit
(19, 26). By careful choice of the rebreathing volume, these methods
are likely to generate a more appropriate total exposure to hypoxia for
measuring AHVR than either the steady-state or transient techniques. By
careful manipulation of an absorbing circuit, end-tidal
PCO2
(PETCO2) may also be controlled. The fourth group of methods involves the technique of
dynamic end-tidal forcing (21, 24). This technique makes it possible to
generate a wider range of stimuli in the end-tidal gas concentrations,
but it does so at the expense of requiring a much more elaborate
experimental system. It has often been combined with the use of a
dynamic model of the ventilatory response to hypoxia to extract a
numerical value for AHVR from the data (1, 16).
In relation to gauging an appropriate amount of hypoxia for assessing AHVR, Mou et al. (15) used an end-tidal forcing technique to compare the ventilatory response to ascending and descending steps of hypoxia, each of 50-s duration, that were evenly spaced in terms of the saturation of arterial blood (SaO2). With the particular protocol employed, they found that the ventilatory response to the final 20 s of hypoxia was independent of whether the steps in hypoxia were ascending or descending. This suggested that the exposure to hypoxia was sufficient to allow the response to develop fully but insufficient for HVD to develop. The first purpose of this study was to compare the protocol involving incremental steps of hypoxia of Mou et al. ( protocol IS in the present study) with two other protocols. One of these protocols was designed to simulate the commonly used technique of rebreathing to induce progressive hypoxia ( protocol RB). The second of these was an end-tidal forcing technique that used a sequence of square waves of hypoxia at constant PETCO2 ( protocol SW). This protocol has been used in a number of studies in the hope that the variance associated with measurements of AHVR would be reduced (18, 20, 25).
AHVR varies widely among individuals (7). However, repeat determinations of AHVR within an individual are often also very different. In relation to this, Sahn et al. (22) reported that the between-day variability in AHVR was greater than the within-day variability in AHVR in seven of eight subjects studied and that this difference was significant in four of the eight subjects. This finding would seem to suggest that there is a genuine slow physiological variation in AHVR within subjects over time. A second aim of this study was to try to confirm the findings of Sahn et al. in relation to our subjects.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects
Ten healthy adults (8 men and 2 women), mean age of 23.4 ± 3.9 (SD) yr, mass of 71.3 ± 11.9 kg, and height of 1.77 ± 0.09 m, volunteered to take part in the study. Subjects received both a written and verbal explanation of the experiment before they gave their consent. This study was approved by the Central Oxford Research Ethics Committee.Protocols
Each subject was studied on six different occasions, which were separated from one another by at least 1 wk. Subjects were either always studied in the morning, starting at 9:00 AM, or always in the afternoon, starting at 1:00 PM. Female subjects were only studied in the first 2 wk of their menstrual cycle. On each study day, AHVR was measured three times, once using each of three different techniques or protocols. The protocols for determining AHVR were between 12 and 18 min in duration and were undertaken in varied order. The protocols were separated from one another by 60 min during which time the subject sat quietly while breathing room air. This design resulted in a total of 180 separate determinations of AHVR with 60 determinations associated with each of the different protocols. For any given individual, there was a total of 18 measurements of AHVR with 6 measurements associated with each protocol.The individual protocols for determining AHVR are described below. For each protocol, there was a 5-min period immediately preceding it in which the subject's end-tidal PO2 (PETO2) was maintained constant at 100 Torr and the subject's PETCO2 was maintained 2 Torr above the quiet air-breathing value as determined on the day of the experiment. The slight elevation in PETCO2 was used to improve the degree of control obtained over the end-tidal gases.
Square-wave protocol (protocol SW). The hypoxic stimulus associated with this protocol consisted of six square waves in PETO2, each with a period of 120 s, and with PETO2 stepping between 60 s at 50 Torr and 60 s at 100 Torr. The calculated values for SaO2 corresponding to these values for PETO2 were 97.8 and 84.8%, respectively (23). PETCO2 was held constant throughout at 2 Torr above the subject's quiet air-breathing value, as determined on the day of the experiment.
Incremental step protocol (protocol IS). The hypoxic stimulus associated with this protocol consisted of seven different levels of PETO2 in descending order within the range from 100 to 45 Torr. The levels of PETO2 were calculated to provide equal reductions in SaO2 and were 100.0, 75.2, 64.0, 57.0, 52.0, 48.2, and 45.0 Torr. The equivalent values for SaO2 were calculated (23) as 97.8, 94.9, 92.0, 89.1, 86.2, 83.3, and 80.3%, respectively. The duration of each level of PETO2 was 50 s. PETCO2 was held constant throughout at 2 Torr above the subject's quiet air-breathing value, as determined on the day of the experiment.
Simulated rebreathing protocol (protocol RB). The hypoxic stimulus associated with this protocol involved a linear reduction in PETO2 from 100 to 45 Torr over a period of 330 s. Values for SaO2 associated with the maximum and minimum values for PETO2 of 100 and 45 Torr were calculated as 97.8 and 80.3%, respectively (23). PETCO2 was held constant throughout at 2 Torr above the subject's quiet air-breathing value, as determined on the day of the experiment.
Experimental Technique
Before the series of AHVR determinations began, each subject undertook one or two preliminary experiments at the laboratory, which familiarized them with the apparatus and procedures. Subjects were requested to refrain from alcohol and caffeine-containing beverages starting from the evening before each experimental day. Once at the laboratory, subjects sat quietly for a period of at least 15 min before any measurements were made. After this, the subject's value for PETCO2 during quiet air breathing was determined using a fine nasal catheter connected to a mass spectrometer. During this measurement and the measurements of AHVR, the subject's attention, insofar as was possible, was diverted by reading or watching television.During each determination of AHVR, the subject was seated comfortably in an upright position and breathed through a mouthpiece with the subject's nose occluded by a clip. A pulse oximeter (Ohemeda Biox 3740) was attached to the forefinger to monitor SaO2 as a safety precaution. Respiratory volumes were measured with a turbine volume-measuring device (12) fixed in series with the mouthpiece. Breath durations were obtained using a pneumotachograph from the time points at which respiratory flow was zero. Gas at the mouth was continuously sampled and analyzed by mass spectrometry for PCO2 and PO2. All experimental data were recorded in real time at a sampling frequency of 50 Hz by a computer that also determined PETCO2 and PETO2 together with the inspiratory and expiratory volumes and durations. Throughout the experiments, breath-by-breath data for inspired and expired tidal volume, PETCO2, PETO2, and SaO2 were displayed on a six-channel recorder.
Once detected by the data acquisition computer, the end-tidal gas values were passed to a second computer that controlled a fast-responding gas-mixing system. The measured end-tidal values were compared with the desired end-tidal values; the composition of a new inspiratory gas mixture was then calculated so as to reduce the error for the following breath. The controlling computer adjusted the inspired partial pressures of N2, O2, and CO2 through a series of outlet valves connected to a gas supply. The gas-mixing system and control scheme have been described in greater detail elsewhere (11, 21).
Data Processing
For each protocol, data from the 4th and 5th min of the 5-min period immediately preceding the hypoxic stimulation were used to obtain CO2 production (CO2) and O2
consumption (O2). The calculations associated with obtaining these values have been described
elsewhere (17).
Estimation of AHVR from protocol SW.
To quantify AHVR from these data, the responses to the square-wave
hypoxia were fitted by a single compartment model of the ventilatory
response to hypoxia, as detailed by Clement and Robbins (1). The use of
such a dynamic model enables the whole of the data to be used for
estimating AHVR, rendering it unnecessary to select portions of the
response when E is approximately
steady. In this model, a linear steady-state relationship between
E and hypoxia was obtained by using 100 
SaO2 as the hypoxic stimulus, where SaO2 was calculated from
PETO2 via the relationship described by Severinghaus (23). This relationship does not include any
allowance for variations in
PCO2, and this would seem appropriate because our use of saturation is simply as a function
of PO2 for which the
ventilatory response is approximately linear.
E.
Estimation of AHVR from protocol IS.
E and
PETO2 were averaged over the
last 20 s of exposure to each level of hypoxia. The values for
PETO2 so obtained were
converted to SaO2 via the relation
given by Severinghaus (23). A linear regression was then performed between the mean values for E
and 100 SaO2. The slope from this relation yielded a numerical value for AHVR
[Gp(IS), where Gp is hypoxic sensitivity], and
the intercept (at SaO2 = 100%) yielded
a numerical value for the residual ventilation in hyperoxia [s(IS)].
Estimation of AHVR from protocol RB.
Breath-by-breath values for
PETO2 were converted to
SaO2 using the relationship given by
Severinghaus (23). A linear regression was then performed between the
breath-by-breath values for E and 100 SaO2. The slope from this
relation yielded a numerical value for AHVR
[Gp(RB)], and the intercept (at
SaO2 = 100%) yielded a numerical value
for the residual ventilation in hyperoxia
[s(RB)].
Statistical Analysis
Differences in the responses to hypoxia between the protocols were assessed statistically using ANOVA, treating subjects as a random factor and protocol as a fixed factor and including the interactive term. To assess whether there was a physiological variation in the responses over time, the variance was partitioned so as to compare the within-subject, between-day variance with the within-subject, within-day variance. Post hoc comparisons were undertaken with Bonferroni's correction to allow for multiple comparisons. Correlations between variables were assessed using the Pearson correlation coefficient. All statistical analysis was undertaken by using the SPSS statistical software package (Chicago, IL). Differences were considered significant at P < 0.05.| |
RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects
All the subjects completed the study, and none reported discomfort from the measurement of AHVR during any of the protocols. Table 1 gives the mean values together with standard deviations for each subject for the measurements of initial air-breathing PETCO2,CO2, and
O2.
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Quality of Gas Control
Figure 1, A and B, shows breath-by-breath plots for PETCO2 and PETO2 against time from a single experiment in one subject (1091) for each of the three protocols ( protocols SW, IS, and RB). PETCO2 was well controlled throughout for all three protocols. For protocol SW, the transitions between euoxia and hypoxia took two to three breaths and were well controlled. For protocol IS, there was a little instability around the mean value for three of the seven levels of hypoxia; otherwise, the control was good. For protocol RB, there was a steady linear fall in PETO2 over time. These results were reasonably typical for the experiments overall.
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Ventilatory Responses
General.
Figure 1C shows breath-by-breath plots for
E against time from a single
experiment in one subject (1091) for each of the three
protocols ( protocols SW, IS, and RB). For
protocol SW, there was a clear variation in ventilation caused
by the square waves in
PETO2. The fit of the model
used to determine Gp and s from these data is also
shown. For protocol IS, the rise in
E appeared linear as might be expected
from the fact that the step decreases in
PETO2 were selected to
provide evenly matched reductions in
SaO2. For protocol RB, the rise
in E appeared curvilinear, as might be
expected from the linear fall in
PETO2.
s for each
subject on each test day were plotted in Fig.
2. From Fig. 2, it is clear that different
subjects possess different mean values. This finding was highly
significant for both Gp and s for all three protocols
(P < 0.001, ANOVA). Also apparent is the considerable day-to-day variation in the measurements obtained. This had no significant relationship with the sequential number of the test (ANOVA).
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Comparison between protocols.
Table 2 lists the mean values for Gp and
s for each subject, together with the overall mean
values for each of the three protocols. There were significant
differences between protocols for Gp (P < 0.01) but not for
s. Post hoc analysis revealed that the ~20%
difference in value for Gp between protocols IS and RB
was significant but that the intermediate value for Gp obtained from
protocol SW did not differ significantly from the values
obtained in either protocol IS or RB.
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s were determined by linear regression without
modelling the dynamics, there is no intrinsic reason why the model used
to determine Gp and s from protocol SW cannot
also be used with these data. Mean values for Gp obtained in this
manner were 1.19 ± 0.58 (SD)
l · min1 · %1
and 1.18 ± 0.76 l · min1 · %1
for protocols IS and RB, respectively, and mean values
for s were 15.1 ± 4.0 l/min and 15.2 ± 3.7 l/min,
respectively. For protocol IS, the difference in results
between the two techniques was small. For protocol RB, the
differences between the two techniques were much more substantial. With
the use of the dynamic model, the values obtained for Gp were ~27%
higher (P < 0.005) and those for s were
~5% lower (P < 0.001) than those obtained using linear regression. ANOVA revealed no significant differences between the three
protocols for either Gp or s when all parameters had been obtained using the model that included the dynamics.
An idea of the inherent variability associated with each protocol for
determining Gp and s can be obtained from the mean squared error remaining after the between-subject variability is
removed. This variance may be thought of as the sum of variance associated with the between-test variation and the between-day variability of subjects. The latter should be approximately constant across methods and may be estimated as 0.07 (l · min1 · %1)2
for Gp and 8.9 (l/min)2 for s (see next
section). For Gp, the mean squared error remaining for protocol
SW was 0.11 (l · min1 · %1)2;for
protocol IS, it was 0.14 (l · min1 · %1)2;
and, for protocol RB, it was 0.18 (l · min1 · %1)2.
Although these results suggest that protocol SW yielded the most repeatable results for Gp and protocol RB the least, the differences did not quite reach significance. By subtracting the between-day variance for subjects, estimates for the actual
between-test variance may be obtained as 0.04, 0.07, and 0.11 (l · min1 · %1)2
for protocols SW, IS, and RB, respectively.
These yield coefficients of variation of 20, 23, and 36% for the three
protocols, respectively.
For s, the mean squared error remaining for
protocol SW was 10.0 (l/min)2; for protocol
IS, it was 16.0 (l/min)2; and, for protocol RB,
it was 21.5 (l/min)2. For this variable, the mean squared
error associated with protocol SW was significantly smaller
than with either protocol IS (P < 0.05) or
protocol RB (P < 0.01). By subtracting the estimate
for the variance associated with the between-day variability of 8.9 (l/min)2 (see next section), estimates for the actual
between-test variance may be obtained as 1.1, 7.1, and 12.6 (l/min)2 for protocols SW, IS, and
RB, respectively. These yield coefficients of variation of 7, 18, and 22% for the three protocols, respectively.
Day-to-day variability in values for Gp and s
within subjects.
To examine the physiological variability in Gp and s
over time, we first compared the within-subject, between-day mean
squared error with the within-subject, within-day mean squared error. This was done by first removing the variability associated with subjects and protocols and the interactive term between these two
factors and then partitioning the remaining error into between-day and
within-day components. The within-subject, between-day mean squared
error for Gp was 0.283 (l · min1 · %1)2
and that for s was 33.7 (l/min)2. The
within-subject, within-day mean squared error for Gp was 0.073 (l · min1 · %1)2
and that for s was 6.9 (l/min)2. For both
Gp and s, the within-subject, between-day mean
squared error was significantly larger than the within-subject
within-day mean squared error (Gp: F50,100 = 3.9, P < 0.001; s: F50,100 = 4.9, P < 0.001). The expectation for the within-subject,
between-day mean squared error is that it should equal three times the
physiological between-day variance (because there are three
observations in a day) plus the residual mean squared error
[0.073
(l · min1 · %1)2
for Gp and 6.9 (l/min)2 for s].
Thus the physiological between-day variance for Gp may be calculated as
0.070 (l · min1 · %1)2
and that for s may be calculated as 8.9 (l/min)2. These variances are equivalent to coefficients of
variation for the physiological between-day variability of 26% and
19% for Gp and s, respectively.
s for each subject, plotted so that the deviations
from different protocols on the same day can be compared. The data from
all plots for both Gp and s show a positive
correlation, such that large values for either Gp or
s obtained on a given day from one protocol tend to
be associated with large values for either Gp or s
obtained on the same day from the other two protocols and vice versa.
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Relationship between Gp and s.
For single determinations of Gp and s from
protocol SW, the correlation matrix between model parameters
obtained as part of the fitting process did not reveal any consistent
relation between the value returned for Gp and the value returned for
s (R = 0.06 ± 0.23, mean ± SD).
For single determinations of Gp and s from
protocols IS and RB, the correlation between Gp and s was significantly negative (R = 0.88 ± 0.00 and R = 0.84 ± 0.00, respectively).
These results suggest that the model and the process of fitting the
model to the data introduce either no correlation or else quite a
strong negative correlation between the values for Gp and
s.
s
around the subject mean on different days (Fig.
4). For protocol SW, the variation
in value for Gp around the mean for each subject was very significantly
correlated (R = 0.65, P < 0.001) with the variation
in value for s around the mean for each subject.
For protocols IS and RB, the strong negative
correlation that existed between values for Gp and s
from individual determinations was lost. For protocol IS, no
correlation was detected (R = 0.00), and for protocol
RB there was instead a weak, positive correlation (R =
0.28, P < 0.05). Because, for individual fits of the model to
the data, the correlation between Gp and s
was either absent (protocol SW) or strongly negative
(protocols IS and RB), these results suggest a
physiological effect such that on days when the value for Gp was higher
then so was the value for s.
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Relationship of day-to-day variability in Gp and s
to day-to-day variability in
PETCO2 and metabolic
rate.
Values for Gp and s obtained on the same day were
first averaged across the three protocols. The variations in both Gp
and s around their mean values (separate mean values
were calculated for each subject) did not correlate significantly with
the variations around the subject means for
PETCO2. With respect to variations in metabolic rate, there was a weak correlation between the
day-to-day variation in Gp around the subject means and the day-to-day
variation in CO2 (R = 0.30, P < 0.05) around the subject means, but this
correlation was not present for the corresponding day-to-day variation
in O2. For variations in
s around the subject means, no correlation was
detected with the variations around the subject means for either
CO2 or
O2.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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This study provides two main findings. The first is that there was
relatively little systematic variation between the three protocols for
determining AHVR, and that which does occur seems to be related to data
handling rather than to real physiological differences. Protocol IS
gave the highest mean value for AHVR, which may suggest that this
form of hypoxic exposure is least affected by the problems
associated with the hypoxic exposure being too short or too long,
both of which would tend to reduce the measured value for AHVR.
Protocol SW was associated with the lowest variance or
greatest repeatability for the measurements. The second main finding is
that there was real physiological variation over time in the
values for both Gp and s.
Comparison Between Protocols
Overall, the mean values obtained from the different protocols for both Gp ands did not differ very greatly. As outlined in
the introduction, a major problem with measuring AHVR is that the
hypoxic exposure associated with the measurement may itself affect the
sensitivity. In relation to protocol IS, a separate study
showed that the results obtained are not excessively influenced either
by an inadequate period of time for the response to develop or by an
excessive exposure to hypoxia (15). Thus the similarity in results
between protocol IS and protocols SW and RB of
the present study also suggests that protocols SW and
RB do not suffer from either an inadequate or excessive
exposure to hypoxia.
The one significant difference detected was that the value for Gp from
protocol RB was ~20% below that associated with protocol IS. This difference vanished when Gp was estimated from
protocol RB via the dynamic model associated with protocol
SW rather than via simple linear regression. In contrast, values
obtained for Gp from protocol IS were affected very little by
whether they were estimated via linear regression or through the
dynamic model. These findings led us to hypothesize that determinations
of Gp via linear regression of E against
desaturation were more likely to be influenced by the dynamics of the
ventilatory response when it was curvilinear against time (as in
protocol RB) than when the response was linear against time (as
in protocol IS). To test this hypothesis, we simulated the
noise-free ventilatory response to protocols RB and IS
using the dynamic model together with the mean parameter values
obtained from protocol SW. We then attempted to recover the
value for Gp using linear regression of E
against desaturation. For protocol IS, the value for Gp that
was recovered was within 1% of the value used to generate the data,
but, for protocol RB, the value that was recovered was 14%
lower than the value used to generate the data.
One question that does arise is whether any of the three protocols may be considered to have been optimized in relation to the dynamics of the hypoxic exposure. In relation to protocol IS, a previous study using an identical sequence in PETO2, but with steps of 2-min duration rather than 50-s duration, demonstrated that the results were affected by a significant degree of HVD (8). In relation to protocol SW, in a previous study using identical levels in PETO2 but with a period of 90 s for the square-wave stimulation instead of 120 s, concern was expressed that this period was too short to show a clear plateau phase in the ventilatory response (9). The duration of protocol RB was not selected on the basis of previous experience available in the literature but rather to match the duration of protocol IS. In view of the curvilinear induction of hypoxia in protocol RB compared with the linear induction of hypoxia in protocol IS and the significant effects of the ventilatory dynamics in relation to estimating AHVR discussed above, it may be that a slightly longer protocol could provide superior results.
Overall, we consider that all three protocols were reasonably satisfactory for determining Gp. Protocol RB did appear to underestimate Gp slightly, unless the dynamics of the response were taken into account, and it was also associated with the largest variance. However, it is also probably the easiest function to generate without access to the technique of dynamic end-tidal forcing.
Between-Day Variability in Gp and s
O2,
CO2, temperature,
PCO2, or
bicarbonate levels. They did find a significant inverse relationship
between AHVR and an "arterialized" venous pH, which in turn
correlated with PCO2.
They considered that this might provide a partial explanation for the
day-to-day variability in AHVR.
As part of the protocol for the present study, we decided we should
assess PETCO2 under quiet
air-breathing conditions on each day of study and undertake the
protocols at 2 Torr above this value. Clearly, this led to some
variation in the PETCO2 associated with the protocols over different days. One possibility is
that these variations in
PETCO2 were measurement
errors rather than real physiological variations, in which case,
because of the interaction between CO2 and hypoxia as
respiratory stimulants, it might be predicted that higher values for Gp
and s would be associated with higher values for
PETCO2. Another possibility
is that the variations in Gp and s were related to
variations in acid-base state, possibly induced by variations in diet.
In this case, because acidosis lowers
PETCO2 and alkalosis
elevates PETCO2, it might be
predicted that Gp and s might show some
negative correlation with
PETCO2. However, no
correlation was detected between variations in
PETCO2 and variations in Gp
or s, keeping with the results of Sahn et
al. (22). Furthermore, the degree of between-day variability, even
after subtracting the within-day, between-test variability, was too
great to be explained by either the measured variability in
PETCO2 or any likely
variability in arterial pH.
Both Gp and s are likely to vary with metabolic rate.
Assessments of CO2 and
O2 were obtained during the
control periods of breathing for each of the protocols. There was a
weak correlation of Gp with
CO2 but not
O2. s
correlated with neither CO2 nor O2. As for the variations
in PETCO2, the variability in CO2 and
O2 appeared
quantitatively too small to explain the between-day variability in Gp
and s.
Gp and s both increase considerably after exposure to
hypoxia (9, 25), and recent unpublished observations from our laboratory suggest that more moderate levels of hypoxia or even hyperoxia can influence these values. Thus a further possibility is
that the variations in Gp and s arose through
variations in PETO2 over the
days leading up to the measurement. If so, it would seem likely that it
would have been the
PO2 during sleep that
would have been most important, as this is the time when
PO2 is at its lowest.
Although we have no evidence in relation to this hypothesis, it
is interesting to note that sleep deprivation results in a substantial
reduction of the acute ventilatory sensitivity to hypoxia (27).
In summary, we found no large differences in measured values for AHVR among the three different techniques studied. We have, however, confirmed the presence of substantial between-day variation in the respiratory controller. The origins of this variation remain to be determined.
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ACKNOWLEDGEMENTS |
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S. Zhang thanks Professor T. Stapleton, who provided financial support. We thank Dr. M. Fatemian for assistance with programming, Dr. F. Marriott for help with statistical analyses, and D. F. O'Connor for expert technical assistance.
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FOOTNOTES |
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This study was supported by the Wellcome Trust.
Original submission in response to a special call for papers on "Hypoxia Influence on Gene Expression."
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Address for reprint requests and other correspondence: P. A. Robbins, Univ. Laboratory of Physiology, Parks Road, Oxford OX1 3PT, UK (E-mail: peter.robbins{at}physiol.ox.ac.uk).
Received 17 November 1999; accepted in final form 4 January 2000.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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