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O2 max
in predicting blood volume: implications for the effect of fitness
on aging
United States Army Institute of Surgical Research, Fort Sam Houston, Texas 78234; and University of North Carolina, Greensboro, Department of Mathematical Sciences, Greensboro, North Carolina 27402
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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A
multiple regression model was constructed to investigate the premise
that blood volume (BV) could be predicted using several anthropometric
variables, age, and maximal oxygen uptake
(
O2 max). To test this hypothesis, age,
calculated body surface area (height/weight composite), percent body
fat (hydrostatic weight), and O2 max were regressed on to BV using data obtained from 66 normal healthy men.
Results from the evaluation of the full model indicated that the most
parsimonious result was obtained when age and
O2 max were regressed on BV expressed
per kilogram body weight. The full model accounted for 52% of the
total variance in BV per kilogram body weight. Both age and
O2 max were related to BV in the
positive direction. Percent body fat contributed <1% to the explained
variance in BV when expressed in absolute BV (ml) or as BV per kilogram
body weight. When the model was cross validated on 41 new subjects and
BV per kilogram body weight was reexpressed as raw BV, the results
indicated that the statistical model would be stable under cross
validation (e.g., predictive applications) with an accuracy of ± 1,200 ml at 95% confidence. Our results support the hypothesis that BV
is an increasing function of aerobic fitness and to a lesser extent the
age of the subject. The results may have implication as to a mechanism
by which aerobic fitness and activity may be protective against reduced
BV associated with aging.
maximal oxygen uptake; age; body surface area; body fat
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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SEVERAL INVESTIGATORS
HAVE reported relatively high correlations between maximal oxygen
uptake (O2 max) and blood volume (BV)
(9, 11, 21, 24, 28, 29, 33), suggesting that BV may be
predicted from, and contribute significantly to, O2 max. This relationship was not
unexpected since an expansion of BV typically accompanies an increase
in O2 max with exercise training
(9). However, other investigations produced lower
correlations between O2 max and BV
(28) or increased O2 max
without alteration in BV (28, 30, 32, 38). These studies
provide evidence that aerobic capacity may not necessarily be related
to circulating vascular volume. Unfortunately, interpretation of
results from these experiments may have been heavily influenced by the
small sample size (11, 28), methodological differences between control and experimental groups (32), posture
(27), and gravity (38) conditions during the
measurement of BV and O2 max. When
sample size was adequate, regression techniques did not include
multiple variables (21, 28, 33), or significant variability may have been introduced by inclusion of BV and
O2 max data into models that were
measured using a mixture of different techniques (28).
Many of these studies used data that were collected for other purposes
(i.e., secondary data sets) or that were collected on a restricted
range of subjects (e.g., college-age subjects). Additionally,
evaluation of these regression models was accomplished by using the
same data that were originally used to construct these models and
without cross-validation with different data sets. Taken together,
these limitations suggest that further studies designed to
systematically define the relationship between
O2 max and BV are warranted.
It was therefore the purpose of this investigation to examine the
relationship between BV and O2 max
while controlling for covariates known to be related to both variables.
This was accomplished by constructing a multivariate statistical model using data that were specifically collected for this purpose and cross
validating the resulting model using new observations.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects
Sixty-six healthy, nonsmoking, normotensive Caucasian men with a mean ± SD age of 37 ± 10 yr, a mean height of 178 ± 6 cm, a mean weight of 76.7 ± 8.5 kg, and a meanO2 max of 47.5 ± 10.3 ml · kg
1 · min1 gave
written consent to participate in this study. All subjects were
thoroughly briefed on the test procedures before consenting to
participate. The complete study protocol was approved by the Institutional Human Research Review Boards at the National Aeronautics and Space Administration (NASA)-Ames Research Center (Mountain View,
CA) and NASA-Kennedy Space Center. Subjects were volunteers who were
recruited from a general population that was large and diverse, ranging
from unskilled laborers to doctoral-level research scientists.
Participation in the study was based on a screening evaluation that
consisted of a detailed medical history, physical examination, complete
blood count, a panel of blood chemistry analyses, urinalysis, a resting
and treadmill electrocardiogram, pulmonary function tests, and body
composition. Subjects were disqualified as participants if any of the
above medical markers indicated values outside normal ranges.
Sampling Plan and Cross-Validation
To guard against the problem of restricted variation across both independent and dependent variables, the sampling plan was set up with age stratification to assure that a wide variety of ages were included. Subjects were recruited from each of these age strata as possible candidates for this study. This sampling plan produced an age range from 18 to 55 yr.A statistical power analysis was performed to determine the sample size needed to detect an approximate change in full model R2 of 0.10 for any effect in the model given a type I error rate of 5%. It was determined that a sample of at least 60 subjects would be needed for 90% power for any effect when the full model R2, estimated from previous research, was ~0.50 (7). The final sample consisted of 66 subjects.
Data on 41 additional subjects available from previous research studies
were used to cross validate the model derived from the original group
of 66 subjects. Subjects in the cross-validation group met all of the
informed consent and medical entrance criteria described above. All of
the cross-validation subjects were nonsmoking normotensive men, with a
mean ± SD age of 36 ± 4 yr, a mean height of 177 ± 5 cm, a mean weight of 80.2 ± 11.7 kg, and a mean
O2 max of 44.5 ± 7.0 ml · kg1 · min1.
Independent Effects and Measurements
O2 max.
A treadmill protocol consisting of stepwise elevations in grade
and speed until each subject reached volitional exhaustion (4) was used to elicit
O2 max. Subjects breathed through a
low-resistance valve, and the volume and composition of the expired gas
was collected continuously and analyzed for the fractions of mixed
expired oxygen (O2) and carbon dioxide.
The O2 calculated from these data
collected during the final 30 s of the treadmill protocol was used
to identify O2 max.
Body surface area.
Height was measured to the nearest centimeter using a standard medical
scale height ruler, and body weight was measured to the nearest ±5 g
with a digital-load cell scale (Sartorius Scale, Goettingen,
Germany). Body surface area (BSA) was calculated from height and
weight using the following standard surface area formula of Du Bois and
Du Bois (12)
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Percent body fat. Body density and percent body fat (PBF) were determined by underwater densitometry, with correction for residual lung volume (36). After the measurement of nude body weight in air, each subject was seated in an aluminum weighing seat that was suspended in a tank of water (temperature = 34°C). The subject was instructed to exhale completely and expel all of the air from his lungs while he lowered his head toward his legs in a tucked position until he was completely immersed. Underwater weighings were repeated until maximal underwater weight was maintained. Residual lung volume was measured by the nitrogen washout method with an Ohio model 700 nitrogen analyzer. Lean body mass was derived by subtracting calculated total body fat from total body weight.
Age. Age was measured in years from the last birthday and was obtained by interviewing the subject.
Dependent Effect and Measurements
BV. Total BV was calculated from the plasma volume and peripheral venous hematocrit measurements. BV was standardized for body size by dividing total BV by body weight in kilograms (BV per kilogram body weight). Plasma volume was determined by a modified dilution technique (10, 17) using sterile solutions of Evans blue dye contained in 10-ml ampules (The New World Trading, DeBary, FL). After each subject was stabilized in the supine position for 30 min, a preinjection control blood sample was drawn followed by an intravenous injection of 11.5 mg of dye diluted with isotonic saline solution (2.5 ml) that was administered through a sterile 0.45-µm filter. One milliliter of plasma from a 10-min postinjection blood sample was passed through a wood-cellulose powder (Solka-Floc SW-40A) chromatographic column so that the dye could be absorbed. The absorbed dye was eluted from the column using a 1:1 water-acetone solution (pH = 7.0) and was collected in a 10-ml volumetric flask. The postinjection solution was compared with 1-ml samples from a preinjection time (zero control) and a standard dye solution (1:50 dilution with distilled water), and all samples were read at 615 nm with a spectrophotometer. With the use of these procedures in our laboratory, the test-retest correlation coefficient for BV was 0.969 (n = 12) and the average changes were 82 ml (average %change = 1.5%, n = 17), 75 ml (average %change = 1.5%, n = 19), 56 ml (average %change = 1.1%, n = 23), and 25 ml (average %change = 0.7%, n = 7) when measurements were determined 4, 8, 15, and 330 days apart, respectively (10, 17).
Statistical Methods
A multiple linear regression model was used to determine whether BV per kilogram body weight could be predicted from age, PBF, andO2 max. A full model regression using
all three independent variables was constructed, and the unique
contribution of each variable to the prediction of BV per kilogram body
wt was assessed using regression leverage plots, partial correlations, and R2 values. These plots and associated
partial correlation coefficients were used to reflect the relationship
between each of the predictor variables and BV per kilogram body weight
after the variance shared between BV per kilogram body weight and the
other predictors in the model had been removed. Multicollinearity was
then checked for each independent variable in the model by calculating
the variance inflation factor (VIF; see Ref. 26). The tenability of the
underlying model assumptions was then assessed followed by tests on the
individual parameters using partial F statistics. All
statistical probabilities associated with statistical tests are given
as exact probabilities and reflect the chances of observing a
parameter(s)-shared variance with the dependent variable given an
estimated error-only system.
The stability of the final multiple regression model and subsequent estimates was tested using data from 41 subjects not included in the initial model formulation (i.e., cross validation). The process of cross validation assesses the stability and utility of the model when applied to data other than that which derived it. An additional check of model stability was performed by fitting a second multiple regression model using absolute BV (ml) as the dependent variable while adding BSA as an independent effect (i.e., model reparameterization). All statistical procedures were performed using JMP Statistical Discovery Software (Version 3.2, SAS Institute, Cary, NC).
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Full Model Estimation
Descriptive statistics for all of the variables used in the modeling of BV are presented in Table 1. Minimum and maximum values listed in Table 1 reflected a wide range and good distributional properties (e.g., symmetric box plots, etc.) and assured adequate variation in all variables. The univariate correlation matrix for all six variables used in the modeling is presented in Table 2 for descriptive purposes.
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The summary of fit for the full model using BV per kilogram body weight
as the dependent variable is presented in Table
3. The full model accounted for 54% of
the total variance in BV per kilogram body weight
[F(3,62) = 23.9, P = 0.0001]. The partial r statistic listed in Table 3 reflects
the correlation of each of the independent variables after BV per
kilogram body weight had been corrected (adjusted) for the other
effects in the model. The percent reduction in error (PRE) for each
variable in the full model listed in Table 3 equals the square of the
partial r statistic multiplied by 100 and reflects the PRE
that would result if that particular variable were added to a model
consisting only of the other two remaining effects. The change in full
model R2 (
R2)
resulting from the omission of a particular variable from the three-variable model is also listed in Table 3. For this full model,
omission of age and O2 max would reduce
the full model R2 by 0.09 (PRE = 16%) and
0.18 (PRE = 27%), respectively. This result is confirmed by the
VIF statistics presented in Table 3, since VIF values departing from
1.0 indicate some degree of multicollinearity for a particular effect
in the model. PBF and O2 max demonstrated slightly elevated VIF values due to their high
intercorrelation (
0.81, see Table 2).
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The partial F ratios for each effect in the model are also
presented in Table 3. The F ratios reflected the degree of
R2 uniquely attributable to a particular
variable in the model, with a ratio of 1.0 indicating that additional
variance accounted for by an effect was no greater than that variance
attributed to experimental error (i.e., the estimated "noise" in
the system). All independent variables, with the exception of PBF, had
substantial partial F ratios
[F(1,62) 
11.86, P = 0.001].
Model Reduction
Given the results of the multivariate regression analysis, PBF was dropped from the model. The remaining parameters of this reduced model were then reestimated and evaluated for multicollinearity. The results are presented in Table 4. The reduced model demonstrated advantages over the full model. Because PBF was removed, the VIF statistics approached one, and the unique effects of each of the independent variables are more defined with no loss in overall explained variance [R2 = 0.53, F(2,63) = 35.6, P = 0.0001]. The results indicate thatO2 max adds the most weight in the
model for prediction of BV per kilogram body weight, contributing a
percentage reduction in error of ~53%. After
O2 max is considered, age accounts for
an additional 9% of the total variation in BV. The prediction model
for BV per kilogram body weight using age and
O2 max is as follows
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Because BV is expressed per kilogram of body weight, the prediction accuracy [root mean square error (RMSE)] is presented in compound units (i.e., a compound function of both BV and body weight). However, if predicted BV per kilogram body weight is unstandardized through postmultiplication by weight, the estimated prediction error is ~598 ml (1,200 ml at 95% confidence).
Cross Validation
To test the stability of the statistical estimates and the precision of the prediction equation, the model was applied to other data used to derive the model. The prediction model was applied to data from 41 new subjects, and various aspects of fit and prediction accuracy were evaluated. The full model R2 of 0.52 under cross validation was similar to the 0.54 from the original estimation. The cross-validated RMSE for the full model was 7.5 ml/kg compared with 8.4 ml/kg from the original estimation. When the prediction errors were converted to milliliters by postmultiplication of body weight, the cross-validated RMSE was ~590 ml. Thus the unbiased estimated individual error of prediction remained at approximately ±1,200 ml (~95% confidence). Only one (2.4%) of the 41 subjects used in the cross-validation had a predicted BV that was >1,200 ml from the observed BV.Figure 2B shows the predicted BV per kilogram body weight vs. the actual BV per kilogram body weight when the statistical model derived from the initial 66 subjects was applied to the 41 new individuals. The overall appearance of this scatter plot is similar to that in Fig. 1A, reflecting stability of model performance when data from new individuals are applied.
Model Reparameterization
As an additional check of model stability, we fit a second model using absolute BV (ml) as the dependent variable and included BSA as an independent effect. Originally, BSA was not included in the modeling of BV per kilogram body weight since both BV per kilogram body weight and BSA were derived from body weight. When the model is fit and reduced for prediction of absolute BV, age, BSA, andO2 max explained 52% of the variance
in BV. As in the original model, PBF explained <1% of the variance in BV. BSA and O2 max carried
approximately equal weight in the model, with both resulting in full
model R2 reduction of ~0.37 when dropped from
the full model. Age accounted for a reduction of 0.08 in full model
R2. The prediction error was virtually identical
between the BV per kilogram body weight and absolute BV models. For the
absolute BV model, the RMSE was 594 ml, resulting in an approximate
prediction interval of ±1,200 ml. Therefore, for any given individual,
the error in a prediction of absolute BV from age, BSA, and
O2 max was approximately ±1,200 ml
(95% confidence). The final model for predicting absolute BV is as
follows
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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The primary finding of the present investigation was that
O2 max represented a significant
predictor of BV. The univariate correlation coefficient of 0.67 between
O2 max and BV calculated from the data
of 66 male subjects in the present study was consistent with moderately
high zero-order correlation coefficients between
O2 max and BV (ml/kg body wt) reported
from several experiments (Table 5). These
relationships indicate a direct influence of BV on aerobic capacity
and/or vice versa. However, a major limitation to the application of
univariate correlations is that they do not reflect the manner in which
numerous variables relate to each other and/or to BV after the
variances associated with other variables have been accounted for
(i.e., multivariate relationships). Although previous investigations
have assessed relationships between
O2 max and BV with the use of
zero-order correlation coefficients (9, 11, 21, 24, 28, 29, 33) or cross-sectional comparisons of mean values (1, 3, 9, 16, 22, 35), we are unaware of any studies that have applied
a statistical approach that considered the collinearity between these
and other variables. Therefore, a unique feature of the present
investigation was the development of a predication model using
multivariate regression statistics in an attempt to account for various
covariance parameters associated with
O2 max and BV. Our results confirm that
O2 max is a primary contributor to the
prediction of BV.
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Contrary to the findings that lean body mass provides a strong index to
predict BV (2, 28), body density did not account for any
additional variation in BV in the present investigation once BV was
expressed per unit of body weight or BSA was considered. It has been
hypothesized that, because adipose tissue is less vascular than
fat-free mass, body density should account for additional variation in
BV beyond that which can be explained by overall size of the individual
(19). Although theoretically reasonable, the current
findings do not support this notion and suggest that body fat does not
contribute to the prediction of BV per kilogram body weight after age
and O2 max have been considered. Our
data suggest that individuals with high PBF were generally larger and,
subsequently, had larger somatotype and more surface area. The
observation that PBF was highly related to
O2 max in our subjects (Table 2) was
consistent with previous reports (11, 33) and provided
another explanation for the failure of PBF to contribute to the model
for prediction of BV per kilogram body weight in the present
investigation. This conclusion is contrary to that suggested by
univariate correlation between BV per kilogram body weight and PBF that
indicated a moderate negative relationship (Table 2). Because PBF was
positively related to age and negatively related to
O2 max in the present study, there was
no additional variance left in the BV per kilogram body weight model to
be accounted for by PBF once effects for
O2 max and age had been accounted for.
This result indicates that PBF is merely an alias of
O2 max and that PBF has no direct effect on BV.
Before conducting a multiple-regression statistic using PBF as a
measure of body density, several other measures of body density and/or
body type (including height, body weight, and BSA) proposed by other
investigators to be good predictors of BV were investigated for use in
our statistical model (2, 14, 15, 18-20, 28, 35).
These factors included lean body mass, desirable weight, body mass
index, body density, and several indexes involving body weight and
height (e.g., Quetelet Index, ponderal index, BSA). Because many of
these measures are simple linear transformations of PBF, and to avoid
issues of multicollinearity, only one measure of "body density"
could be applied to the statistical model. None of these measures
explained any more of the variation in BV than PBF or body weight once
O2 max was considered. Because BV was
expressed per kilogram of body weight, weight could not be used as an
independent variable in the model. Height accounted for <1% of the
variance in the prediction of BV per kilogram body weight once age and
O2 max were considered in the model.
Previous investigations have revealed that BV either decreased
(6, 11, 15, 21, 31) or was unaffected (8, 34, 36) by age. These findings may represent more of a statistical artifact than a true relationship since statistical analyses that supported the notion that BV decreased with age have been limited to
use of simple cross-sectional comparison of mean values between BV and
age. When applying a single correlation to our data, we also found a
negative relationship between age and BV (Table 2). However, when the
correlation between BV and age is partialed for
O2 max, the relationship is moderately
positive partial correlation = 0.39, Table 4. Therefore,
contrary to previous findings, our multivariate model revealed that BV
increased with age (Table 3 and Fig. 1A). The regression
model presented in Table 4 represents one of classic suppression
(cooperative) since the effect of age is not apparent unless
O2 max is included in the model.
Suppression is a common model for homeostatic mechanisms in biology in
which factors such as BV, O2 max, and
age occur together and have counteractive effects (7).
Therefore, some of the variance in BV cannot be explained unless age
and O2 max are considered jointly.
Suppression occurs since, in the univariate correlations, age and
O2 max are related, but only
O2 max is related to BV (Table 2). As a
result, the effect of age is "suppressed" unless
O2 max is considered.
The suppression effects acting in the model presented in Table 4 are
graphically depicted in Fig. 3, where BV
(ml/kg) is plotted against age for all 107 observations made in the
present study. Points on this scatter plot have been identified by
O2 max level. Low
O2 max values (<40
ml · kg1 · min1, 25th
percentile) are shown as green dots, high
O2 max values are shown as red squares
(>52
ml · kg1 · min1,
75th percentile), and the remaining middle 50% of the data are shown
as blue crosses. When an overall regression line is fit to these data,
ignoring O2 max level, the slope is
negative (black line). However, when separate regressions are fit to
the data within each O2 max level (red,
green, and blue lines), the relationship between age and BV is
positive. The overall negative trend is a result of younger subjects
having higher O2 max values (Fig. 3,
top left), whereas older subjects have lower
O2 max values (Fig. 3, bottom
right). When all subjects are considered together, the negative
(left) side of the regression line is pulled up by the younger
subjects, and the positive (right) side of the regression line is
pulled down by the older subjects. When
O2 max is not considered, the
relationship between age and BV appears to be negative; when
O2 max is considered in the regression, the relationship between BV and age is actually positive.
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Our conclusions are in direct conflict with those of Sawka et al.
(28), who concluded that
O2 max does not correlate with BV. We
believe that the difference in the findings between the two
investigations is primarily attributable to the statistical approach
and sample used by Sawka and co-workers. As previously discussed, the
BV model is one of cooperative suppression in which the relative
contributions of age and O2 max to the
prediction of BV can only be evaluated when age and
O2 max are included in a multivariate
model at the same time. Rather than applying a multivariate model
analysis, Sawka et al. used a series of simple regression models (i.e.,
univariate) in an attempt to find a suitable model. Unfortunately,
univariate models are notoriously weak when quantifying patterns of
association and/or causal relationships between variables
(13). Even with proper specification of the model, we
believe the sample of subjects used by Sawka and co-workers was biased
to highly fit individuals (41.9-65.4
ml · kg1 · min1) of a
relatively young age group (18-35 yr). This restricted range of
both age and O2 max may have
effectively masked the interrelationship that actually exists between
O2 max, age, and BV (7).
The present experiment was designed to assure adequate variation in all
variables, especially age. Another unique feature of our multivariate
model was the use of an independent subject population with a large
sample size to test the validity of the model. The full model
R2 of 0.52 as well as the scatter plot (Fig.
2B) under cross validation were similar to the
R2 of 0.53 and scatter plot (Fig. 2A)
from the original estimation. These comparisons indicate that overall
the model performed well when it was applied to new individuals.
O2 max proved to be the single best
predictor of BV (ml/kg) and when considered negates the effect of PBF
(i.e., body density). Although the effect was somewhat small, when
O2 max is held constant, BV tends to
increase with age. The prediction accuracy of BV models for
standardized (ml/kg) and absolute (ml) values are, for all practical
purposes, identical. Although the model parameters differ, the relative
information regarding body size remains intact in both models. The
absolute BV model is a bit more complex since it requires the
additional calculation of BSA, whereas the standardized BV model
requires postmultiplication by weight to obtain BV in milliliters. When
both models are applied to the cross-validation data (41 new subjects),
and predicted BV per kilogram body weight is converted to milliliters,
the correlation between the two sets of predicted values was
significantly high.
Our experiment was not without limitation. We recognize that the present study represents an observational investigation that was dependent on inclusion of relevant independent variables. Thus it is possible that our model could have presented specific bias in the estimates for regression coefficients of the included independent variables if there were additional variables that had important contribution but were not included in the model. For this reason, we went to extremes to assure that there was adequate variance across both independent and dependent variables and that we considered in our analysis all known variables that have been identified or suggested as important to the prediction of BV (1-3, 5, 6, 8, 9, 11, 14-16, 18-22, 25-28, 31, 32, 34). We then checked our models through cross validation. Given our design characteristics, the observed effects are so large that it is difficult to account for more than a small part of this effect by other variables not considered in this analysis. This does not mean that our conclusions and eventual generalizations are not restricted by our sample (Caucasian males) but that statistical bias in our estimation procedure should be small.
Perspectives
Our findings have particular implications for the effect of physical activity and fitness on aging. Our data support the notion that BV actually increases with age but that this relationship may be masked by a sedentary "Western" lifestyle that can often accompany the aging process. This notion is further supported by the observations that BV can be increased with regular physical activity in elderly individuals to the same relative degree compared with younger people (5) and that reduction in BV associated with aging in sedentary subjects was removed in physically active subjects (21). The contraction of BV may be associated with adverse impacts on risk factors for cardiovascular disease such as elevated low-density lipoprotein cholesterol, increased whole blood viscosity, and stimulation of sympathetic nervous activity (33). In turn, sympathetic hyperactivity is typically reported in patients with essential hypertension and chronic renal failure and is associated with poor prognosis and increased risk of sudden death (23). The relationships between BV, sympathetic activity, and progressive cardiovascular disease may reflect a protective nature of increased BV against development of coronary heart disease (CHD) with aging. If it is true that BV increases with age when a sedentary lifestyle has been removed (21), then perhaps a sedentary lifestyle is actually acting to remove a natural CHD protective factor. For example, if less viscous circulating blood or sympathetic activity results from increasing BV with regular physical activity during the aging process, then various risk factors associated with CHD such as platelet aggregation, arterial thrombosis, or cardiac arrhythmias are less likely to occur. The literature supports the observation that, in the Western world, BV does decrease with age (6, 11, 15, 21, 30). However, our data provide compelling evidence that reduced BV with age may be a result of a sedentary, high-caloric lifestyle rather than the aging process. Therefore, perhaps one of many important benefits of maintaining physical activity and fitness during aging is the resultant expansion of plasma and BV that provides a protective effect against development of cardiovascular disease.| |
ACKNOWLEDGEMENTS |
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We thank the following individuals for making this project a success: Drs. Harold Sandler and Paul Buchanan for administrative support; Drs. Ralph Pelligra and G. Wyckliffe Hoffler for providing medical supervision; Dee O'Hara, Jill Polet, and Mary Lasley for subject recruitment, scheduling, and medical monitoring during the experiments; Donald Doerr, Marc Duvoisin, Art Maples, and Sandy Reed for engineering support; Dick Triandifils for technical support; Dr. Tom Sather for assistance with data collection; Marion Merz for analysis of plasma volume; and the subjects for cheerful cooperation.
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FOOTNOTES |
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The views expressed herein are the private views of the authors and are not to be construed as representing those of the Department of Defense or the Department of the Army.
This research was supported by the National Aeronautics and Space Administration (NASA) administered under Grants 199-14-17-07 and W-19,515. D. A. Ludwig was supported by a NASA-ASEE Summer Faculty Fellowship Award.
Address for reprint requests and other correspondence: Library Branch, US Army Institute of Surgical Research, 3400 Rawley E. Chambers Ave., Bldg. 3611, Fort Sam Houston, TX 78234-6315 (E-mail:victor.convertino{at}amedd.army.mil).
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.
Received 14 December 1999; accepted in final form 24 April 2000.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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