Vol. 274, Issue 2, H701-H708, February 1998
MODELING IN PHYSIOLOGY
Modeling the influence of body size and composition on M-mode
echocardiographic dimensions
Alan M.
Batterham and
Keith P.
George
Department of Exercise and Sport Science, Manchester Metropolitan
University, Crewe and Alsager Faculty, Alsager, Cheshire ST7 2HL,
United Kingdom
 |
ABSTRACT |
The purpose of this study was to determine the
optimal index for normalizing left ventricular (LV) echocardiographic
dimensions for differences in body size. M-mode echocardiograms defined
LV internal dimension at end diastole (LVIDD) and LV wall thickness (LVWT) in 107 adults (59 male, 48 female). Allometric relations were
assessed between cardiac dimensions
(Y) and body size variables (X) of fat-free mass (FFM), height
(H), body surface area (BSA), and fat mass (FM). Further to
confirmation of homogeneity of regression slopes, size exponents common
to both genders were fitted by a log-linear model: ln
Y = ln
a + c · gender + b · ln
X, where
a is the proportionality coefficient,
b is the size exponent, and c is the gender
coefficient. For LVIDD, mean body size exponents (95%
confidence interval) were FFM0.35
(0.22-0.47),
H0.68
(0.32-1.03), and BSA0.44
(0.26-0.62). For LVWT, the derived exponents were
FFM0.43 (0.20-0.65),
H0.65
(0-1.3), and BSA0.56
(0.23-0.89). Body fatness (expressed by FM) had no influence on LV
dimensions, with exponents not different from zero
(P > 0.05). The root-mean-squares
error from the separate regression models indicated that the FFM index
was the optimal solution. Indexation of LV dimensions by
H was associated with the greatest error. Because the 95% confidence interval for the FFM exponents included 0.33, we recommend that linear LV dimensions be indexed by the
cube root of FFM. In the absence of FFM data, the root of BSA was found
to be the best surrogate index.
allometric relations; heart size; log-linear models
| |
INTRODUCTION |
DESPITE THE ADVENT of two- and even three-dimensional
echocardiography (32), M-mode echocardiography continues to be a useful tool in the diagnosis and management of cardiovascular disease (26). In
addition, M-mode techniques have been widely applied in the exercise
and sport sciences to examine such issues as gender differences in
cardiac dimensions (3) and the "athletic heart syndrome" (16,
40). Because of the strong relationship between heart size and body
size, cardiac dimensions must be scaled for body size differences to
establish reference standards for normality (35) and to permit
meaningful intersubject or intergroup comparisons (3).
In the cardiology literature, a variety of different methods of
normalizing heart size to account for the influence of body size have
been proposed. Traditionally in clinical practice, cardiac dimensions
have been divided by body surface area (BSA), although this "cardiac
index" has been criticized on theoretical (19) and mathematical (44)
grounds. To address these concerns, a number of cross-sectional studies
have modeled echocardiographic dimensions using a general allometric
equation (e.g., see Refs. 7-10, 23, 24, 30, 45, 47). The
allometric model assumes a nonlinear relationship between the body size
and heart size variables of the form Y = aXb
(where
Y is the cardiac dimension,
X is the indicator of body size,
b is the "dimensionless" size
exponent, a is the proportionality coefficient, and is the multiplicative residual error term). Derivation of b permits the
construction of a power function ratio standard
(Y/Xb),
which is allegedly size independent.
In studies of these allometric relations, "height" has been the
most popular indicator of general body size because it is simple to use
and allegedly a surrogate of lean body mass (24). In modeling left
ventricular (LV) mass, a variety of different height exponents have
been reported, including
height1.97 in young,
"apparently healthy" men and women from the Framingham Heart
Study (23), height2.7 in a large
population study of normotensive children and adults (9), and
height3 in children and
adolescents aged 6-17 yr (7). For linear echocardiographic dimensions, Lauer et al. (24) reported mean height exponents in males
and females of 0.57 and 0.50, respectively, for LV internal dimension
and 0.84 and 0.41, respectively, for LV wall thickness [LVWT; sum
of posterior free wall (PWT) and interventricular septum (ST)
thicknesses]. It is clear that the majority of studies have focused on echocardiographically predicted LV mass, with less attention
paid to LVWT and LV internal dimensions. Allometric relations between
body size and linear echocardiographic dimensions, however, are of
critical importance in establishing normal limits for quantification of
specific disease states (35) and in comparative studies when attempting
to differentiate between "concentric" (due to increased LVWT) and
"eccentric" (due to increased LV internal dimension) LV
hypertrophy (16).
Despite the apparent popularity of indexing cardiac dimensions
according to their allometric relations with height, the most appropriate indicator of body size is yet to be defined and remains controversial. It has been suggested that height is superior to BSA
normalization, because the latter masks the observed independent influence of obesity on heart size (22, 25). Others have suggested that
the impact of obesity on cardiac dimensions is negligible (2, 8, 17).
We believe that the controversy relates to how obesity is operationally
defined. Lauer et al. (22, 24) and others (25) have represented obesity
by threshold values for body mass index (BMI). It is well known,
however, that BMI is influenced to an almost equal degree by the lean
and fat compartments of the body (21), indicating that this ratio is
unable to distinguish muscularity from adiposity (36). Because a strong
link between cardiac and skeletal muscularity has been assumed (16), it
is possible that the observed positive relationships between BMI and LV
mass (22, 24, 25) are due to the influence of fat-free mass (FFM)
rather than to obesity per se. On the basis of this critique, to assess
the influence of overfatness or obesity on heart size, we suggest that
it is more appropriate to model the influence of estimated fat mass
(FM) on cardiac dimensions. Indeed, in contrast to the observed
influence of BMI on LV hypertrophy, FM has been found to be of only
minor importance in determining cardiac dimensions in children and
adolescents (8) and in older adults (2).
Recently, Daniels et al. (8) and Roman (35) have suggested that FFM may
indeed represent the optimal parameter for allometric normalization of
cardiac dimensions. Estimated FFM has been found to be the strongest
predictor of heart size in children and adolescents (7, 47), older
adults (2), and younger adults (3, 11, 20, 29). Criterion methods for
assessing body composition, such as dual-energy X-ray absorptiometry
(DEXA), clearly offer greater precision and accuracy. Indeed, DEXA may
be superior to the traditional gold standard of hydrodensitometry,
because it is capable of separating the FFM into bone and bone-free
compartments (28). Estimation of body composition using DEXA thus
circumvents two key assumptions of the two-component model, that the
density of the FFM is known and constant and that the components of the FFM normally exist in constant proportions (21).
In clinical practice, however, concerns have been voiced about the
practicality of obtaining accurate measurements of FFM using criterion
techniques that may be expensive and cumbersome and require highly
skilled technicians (35). Methods that are safe, rapid, and acceptably
precise and accurate are required for clinical and field testing. For
the busy clinician, the choice falls between anthropometry and
bioelectrical impedance analysis (BIA). In the field, percent body fat
can be estimated using skinfold equations with acceptable accuracy
[Ref. 27; standard error of the estimate (SEE) 3-4%
fat]. Estimation of body composition using BIA, although rapid
and noninvasive, may have limited general applicability because of the
lack of appropriately cross-validated prediction equations (15). In a
recent report (15), the prediction of body fat percentage using
skinfolds (SEE 2.6% fat compared with the criterion derived from
densitometry) was found to be more accurate than any of 10 published
BIA equations in a population of young adult males. The authors
considered that the BIA equations developed by Guo et al. (18) (SEE
2.9% fat) and Segal et al. (39) (SEE 3.3% fat) were acceptable
alternatives to the skinfold method. However, both of these equations
require additional anthropometry, along with resistance measures, to
improve the predictive accuracy of the model. Indeed, the Segal
fatness-specific BIA equations (39) require the a priori estimation of
body fat percentage from skinfolds to determine which equation to
apply. Moreover, results of multiple regression analyses including
anthropometric and BIA variables (15, 18) indicate that anthropometric
measures account for the majority of the variance in the prediction of body fat percentage. The addition of resistance measures from BIA
contributes little to the explained variance and has a marginal effect
on the SEE. On the basis of these findings, we believe that, despite
the inherent and well-documented limitations (31), anthropometric
estimates of FFM can be an extremely useful indicator of general body
size. Recently, we have demonstrated (3) the utility of FFM, estimated
by skinfolds, in allometric scaling of LV mass in adult males and
females, and this is the preferred field method for the current study.
In the absence of FFM estimates, an accurate body size surrogate of FFM
is required to appropriately normalize cardiac dimensions. To date,
insufficient attention has been paid to the determination and critical
evaluation of these surrogates.
The aim of the current study was to determine the most appropriate and
practical method of normalizing linear echocardiographic dimensions. We
modeled the influence of general body size (FFM, BSA, and height) and
"fatness" (FM) on LVWT and LV internal dimension. The best
potential surrogate of FFM was predicted from a dimensional analysis of
the allometric relations between the different body size indicators.
| |
METHODS |
Subjects.
One hundred and seven Caucasian adults [59 males, age 23.5 ± 3.2 (mean ± SD) yr and 48 females, age 23.7 ± 2.7 yr]
volunteered for the study. Subjects were screened medically with a
standard laboratory questionnaire, and all were found to be apparently healthy, asymptomatic, and free from cardiovascular disease and major
risk factors for coronary heart disease. Exclusion
criteria also included the chronic use of medications that may
influence resting echocardiographic dimensions. Previous testing in the same laboratory had revealed no evidence of resting or exertional hypertension or electrocardiographic abnormalities. Subject
characteristics are displayed in Table 1.
Institutional ethics approval for the project and written informed
consent from all subjects were obtained.
A simple, "global" self-report assessment of habitual physical
activity was obtained through personal interview. The instrument was
modified from that used in the Allied Dunbar National Fitness Survey
(1), with the frequency and intensity of 
20-min exercise or activity
sessions in the previous 4 wk documented. All subjects were found to be
moderately recreationally active, with 49% of males and 46% of
females reporting an average of three 20-min sessions per week at a
"vigorous" exercise intensity (7.5 kcal/min). The remainder of
the sample reported an equivalent frequency of "moderate"
intensity activity (~5 kcal/min). Male and female activity levels
were not significantly different (P > 0.05). There was no relationship
(P > 0.05) between physical activity
status and LV dimensions. Moreover, no subjects reported physical
activity levels of sufficient frequency, intensity, and duration to be associated with concentric or eccentric LV hypertrophy.
Anthropometry.
Stature (height) to the nearest 0.005 m was measured using a Harpenden
stadiometer. Body mass (in light underwear) was assessed to the nearest
0.1 kg using Avery beam balance scales. Height (H) and body mass measurements were
used to predict BSA according to the formula of DuBois and DuBois (13).
Body density was estimated by a generalized age- and gender-specific
regression equation, using the log of the sum of bicep, tricep,
subscapular, and suprailiac skinfolds (14). Body density was converted
to percent fat using the formula of Siri (41). All skinfolds were
measured, using Harpenden calipers, by an investigator who had
previously demonstrated technical errors of measurement (TEM) and
intraclass correlations (R) for
repeated measures on each site ranging from TEM = 0.6 mm for tricep,
with an R of 0.98, to TEM = 1.4 mm for
suprailiac (R = 0.96) skinfolds. The
median of three measurement rotations that agreed within 10% was used
for subsequent analyses. Total body mass and fat percent were used to
partition body mass into its FM and FFM components. BMI data revealed
that 15 males (25%) and 7 females (15%) could be classified as
"overweight" according to the criteria of the Royal College of
Physicians report on obesity (Ref. 37; BMI 25.1-29.9 for men and
23.9-28.5 for women). One male and one female (~2% of the
sample) were classified as "obese" (BMI >30 for men and >28.6
for women).
Echocardiography.
A Hewlett-Packard (Andover, MA) Sonos 100 ultrasound imaging system
(2.5-MHz transducer) in sector two-dimensional mode was used to image a
longitudinal axis view of the left ventricle from the parasternal
window. M-mode recordings were derived from a cursor line crossing the
left ventricle just below the tips of the mitral valve leaflets. All
echocardiograms were conducted and analyzed by a single experienced
technician. The ST, PWT, and LVIDD measurements were made in
centimeters according to the Penn convention (12). The ST and PWT
measurements were then summed to form LVWT. All readings were obtained
in held expiration, at the peak of a simultaneous electrocardiogram R
wave, with subjects in a standardized left lateral decubitus position.
Measurements represented an average of three to five heart cycles and
met standard criteria of technical quality (12).
Allometric modeling.
All analyses were carried out by using the statistical package SPSS
(release 6.0 for Windows; SPSS, Chicago, IL). Allometric relationships
were derived from natural log transformations (base e) of
the absolute data. The general curvilinear allometric equation Y = aXb
can be linearized by taking natural logarithms of both sides: ln
Y = ln
a + bln X + ln . The exponent b is simply the
slope of the log-linear plot, and a is
derived from the antilog of the Y-intercept. All exponents were
calculated as mean point estimates, with 95% confidence intervals
(CI). Statistical significance of the coefficients was tested at an
-level of 0.05.
Allometric relations between body size indicators.
To predict the optimal surrogate of FFM, a dimensional analysis was
conducted of the allometric relations between the different body size
variables. Estimated FFM was modeled as the dependent variable
(Y), with
H and BSA included separately as
independent variables (X). To check
whether a single model common to both genders could be identified,
homogeneity of regression slopes (43) was confirmed by including gender
(G, coded "0" for males, "1" for females), and a G × lnBSA (or G × ln H)
interaction term in a multiple log-linear regression model
The
interaction terms (d) for both ln
BSA and ln H were not significant
(P > 0.05), indicating commonality
of slopes between gender for the relationships between ln FFM and ln
BSA and between ln FFM and ln
H. Single solutions,
common to both males and females, could therefore be obtained from the
following model, omitting the interaction term
The
separate BSA and H prediction models
were evaluated by comparing the model
R2 and
root-mean-squares error and the relative width and stability of the
95% CI surrounding the mean b
exponents.
Allometric relations between body size and cardiac dimensions.
To evaluate whether allometric normalization models can reduce the
between-gender variability of cardiac dimensions, it is necessary to
derive a common power function ratio standard
(Y/Xb).
With this index, male and female cardiac dimensions can be compared in
the same units of measurement. Clearly, this is possible only if
homogeneity of regression slopes is confirmed, that is, there is no
significant gender difference in the slopes of the log-linear
relationships between body size indicators and cardiac dimensions.
Therefore, allometric relations between the indicators of body size
(X) and the echocardiographic
dimensions (Y) were determined, as
per the previous dimensional analysis
All
G × ln X interaction terms were
nonsignificant (P > 0.05),
indicating that allometric models common to males and females could be
fitted by omitting the interaction
term
|
(1)
|
|
(2)
|
By taking antilogs of the constant (ln
a) and the constant plus gender
coefficients (ln a + c), the "proportionality
coefficients" (a) in the
allometric relationships Y = aXb were
obtained for males and females, respectively. Direct gender comparisons
could then be made with respect to appropriately normalized cardiac
dimension, to evaluate the reduction in between-gender variability due
to each scaling model. Additional information regarding the optimal
body size indicator was again obtained by comparing the model
R2 and
root-mean-squares error from the separate regression models, together
with the relative width and stability of the CI surrounding the mean
size exponents.
The best surrogate of the optimal body size parameter was determined by
comparing the 95% agreement limits (5) between the LVIDD and LVWT
values predicted by the obtained optimal model and the corresponding
values predicted by the surrogate models.
| |
RESULTS AND DISCUSSION |
The physical and physiological characteristics of the subjects in the
current study (Table 1) are remarkably consistent with those reported
in the healthy, body size-restricted sample of men and women from the
Framingham Heart Study (24). This suggests that our sample is
adequately representative of a normal adult population. The prevalence
of overweight and obesity in men in the current study compares well
with that reported in the Allied Dunbar National Fitness Survey (1) for
the equivalent age group. For women, the data indicate that the
subjects in our sample were slightly leaner than the national average.
Clearly, therefore, caution must be exercised in extrapolating the
findings of the current study to populations with different body size
and composition characteristics. All body size and heart size variables
demonstrated significant gender differences. On average, females were
0.11 m shorter and 15 kg lighter, with 18.9 kg less FFM. In absolute terms, females possessed ~91% of the LVIDD and 79% of the LVWT of
males.
Kolmogorov-Smirnov one-sample tests revealed that the independent and
dependent log-transformed variables, together with the allometric model
residuals, were normally distributed
(P > 0.1). In addition, no
correlation was found between the absolute residuals and the predictor
(independent) variables for any of the allometric models analyzed
(P > 0.05), indicating that the
model assumption of homoscedasticity had been satisfied.
Allometric relations between body size indicators.
For estimated FFM modeled by BSA, the following solution was obtained
where
model R2 = 0.96, P = 0.0000, and model
root-mean-squares error = 0.04. Separate 
-coefficients for constant,
gender, and BSA were all significant at
P = 0.0000. Allometric relations between estimated FFM and H were best
represented by the equation below
where
model R2 = 0.84, P = 0.0000, and model
root-mean-squares error = 0.08. Separate -coefficients for constant,
gender, and H were all significant at
P = 0.0000.
This simple dimensional analysis of the allometric relations between
indicators of body size suggests that BSA is superior to
H as a surrogate of FFM in young,
moderately active adults of average-to-lean body fatness. This finding
is in agreement with previous literature (42) in which stature was
found to be an ineffective predictor of FFM when used alone. The BSA
model is able to explain 12% more of the variance in FFM and has a
much smaller root-mean-squares error. In addition, the width of the confidence interval around the mean body size exponent is much broader
for H, suggesting that the
H exponent is relatively less stable.
The mean exponent for BSA of 1.32 (95% CI 1.20-1.43) is
representative of slight "negative allometry." Simple
dimensionality theory (38) predicts that FFM (a 3-dimensional
construct) would be proportional to BSA (a 2-dimensional construct) to
the power of 1.5, to maintain dimensional consistency or
"isometry." The upper limit of the 95% CI for the BSA exponent,
however, excludes 1.5.
The mean exponent for H of 1.63 (95%
CI 1.21-2.06) again indicates negative allometry. Dimensional
consistency would require that FFM be proportional to the cube of
height (a linear dimension), a value precluded in the current analysis.
The 95% CI for the H exponent
includes 2, indicating that in this sample FFM is approximately proportional to height squared. This result is remarkably consistent with previous findings in adults. In a population study of >3,000 adult males and females (age 16-64 yr), Nevill and Holder (33) reported that estimated FFM was proportional to the square of height in
all groups except female subjects aged 55 yr (where the
H exponent was <2). In contrast,
Daniels et al. (7) reported that FFM, determined by DEXA, was
proportional to height cubed in children and adolescents. Such
differences in the reported allometric relations between FFM and
H in different populations may well
explain the wide range of H exponents
obtained in allometric modeling of LV mass. Previously reported
H exponents for LV mass of 1.97 in
adults (23) and 3.0 in children and adolescents (7) are entirely
consistent with the foregoing analysis. Clearly, these findings lend
indirect support to the pivotal role of FFM in determining
echocardiographic dimensions.
Allometric modeling of LVIDD and LVWT.
The findings from the specific allometric models are presented in
Tables 2 (LVIDD) and
3 (LVWT). All allometric models were successful in providing dimensionless size exponents, with no residual
correlations evident (P > 0.05)
between the power function ratio-scaled cardiac dimension variable
(Y/Xb)
and the body size variable (X). In
addition, examination of the model residuals revealed no (linear or
curvilinear) size-related distributional patterns
(P > 0.05), indicating that the
log-linear model is correctly specified, with the residuals randomly
scattered about zero.
The derived body size exponents are consistent with the foregoing
dimensional analysis. If LV mass (a 3-dimensional construct) is assumed
to be directly proportional to the first power of FFM, as we have
previously reported (3), then it follows that linear echocardiographic
dimensions should relate to the cube root of FFM
(FFM0.33). The 95% CI
surrounding the FFM exponents for LVIDD and LVWT both include 0.33 (Tables 2 and 3). Indeed, the point estimate for the FFM exponent for
LVIDD is very close to 1/3. The dimensional analysis revealed that FFM
in this sample was proportional to BSA to the power of 1.32, with the
upper boundary of the CI just excluding the value of 1.5 predicted from
simple dimensionality theory. If linear cardiac dimensions are
proportional to FFM0.33, it would
be predicted that LVIDD and LVWT would be related to BSA to the power
of 0.44 (1.32 × 0.33). For LVIDD (Table 2), the mean BSA exponent
is exactly 0.44. For the LVWT model (Table 3), the 95% CI for the BSA
exponent includes this value. In practice, however, because the 95% CI
for the exponent also includes the value of 0.5, it would be more
convenient to scale LVIDD and LVWT by the square root of BSA, as
recommended by Gutsegell and Rembold (19). Simple dimensionality theory
would predict that linear cardiac dimensions should be indexed by the
first power of height. However, this would only be so if FFM and LV
mass were related to the cube of height. Because we have stated that in
adults these variables appear to be proportional to height squared, it
follows that LVIDD and LVWT should scale with height to the power of
2/3. Consistent with this prediction, Tables 2 and 3 show that the mean
H exponents for both LVIDD and LVWT
are very close to 2/3. Within 95% confidence limits, however, these
height exponents are not different from unity
(P > 0.05) or from previously
reported values for the equivalent cardiac dimensions in a large
population sample (24).
For both linear echocardiographic dimensions, the model including FFM
as the body size indicator provides the highest
R2 and lowest
root-mean-squares error. This finding supports the well-documented
observation that FFM is the strongest univariate predictor of heart
size (e.g., see Refs. 3, 8, 29, 47). Conversely, modeling
echocardiographic dimensions by height results in the lowest
R2 and highest
root-mean-squares error of the three general body size indicators. All
size exponents are statistically significant, however, and the
differences in variance of cardiac dimension accounted for are
relatively modest. Additional insight can be gained from the width of
the CI surrounding the size exponent. The relatively wide CI
surrounding the mean H exponent,
especially for the LVWT model, suggests that this point estimate is
less stable and potentially less generalizable to other populations than either the FFM or BSA exponents.
Modeling of cardiac dimensions by FM sheds light on the influence of
body fatness on LV dimensions. Tables 2 and 3 reveal that FM was not a
significant determinant of LVIDD or LVWT in this sample, with
b exponents not significantly
different from zero. The magnitude of effect of FM on LV dimension can
be quantified by an analysis of the mean exponents. The -coefficient
for FM signifies the predicted increase in the specific LV dimension, associated with a 1-unit increase in log e FM. Because the
back-transformation of a log-transformed variable provides a ratio (4),
a 1-unit increase in log e FM represents a 2.718-fold
increment (antilog of 1 = 2.718, base e). For
LVIDD, the mean -coefficient of 0.02 indicates that a 2.718-fold
increase in FM is associated with only a 2% increase in the cardiac
dimension (antilog of 0.02 = 1.02). For LVWT, the equivalent increase
in FM is associated with only a 6% increment in the dependent variable
(antilog 0.06 = 1.06). In contrast, the corresponding predicted
increments in LV dimensions associated with a 2.718-fold increase in
FFM are 42% for LVIDD (antilog 0.35 = 1.42) and 54% for LVWT (antilog 0.43 = 1.54). Modeling of echocardiographic dimensions by gender and FM
was associated with the lowest
R2 and highest
root-mean-squares error overall. It would appear that in this sample of
young, healthy adults, body fat has a negligible impact on LV
hypertrophy, as has previously been reported in children and
adolescents (8) and older adults (2). We urge caution in extrapolating
this finding to larger and diverse population samples, however, in
which a greater degree and a higher prevalence of overweight or obesity
may influence the results.
As detailed in METHODS, the relative
reduction in between-gender variability due to different scaling models
can be evaluated by calculating the separate proportionality
coefficients (a) in the allometric
equations for males and females, assuming a common size exponent
(b). For comparison, findings are
presented for FFM and H (the body size
indicators associated with the lowest and highest root-mean-squares
error, respectively) in modeling LVIDD and LVWT. For modeling of LVIDD
by FFM, in males LVIDD (cm) = 1.25 · FFM
(kg)0.35 and in females LVIDD (cm) = 1.28 · FFM
(kg)0.35 (female/male percentage,
102%, P = 0.34). For modeling of
LVIDD by H, in males LVIDD (cm) = 3.60 · H
(m)0.67 and in females LVIDD (cm) = 3.41 · H
(m)0.67 (female/male percentage
95%, P = 0.002). For modeling of LVWT by FFM, in males LVWT (cm) = 0.33 · FFM
(kg)0.43 and in females LVWT (cm) = 0.30 · FFM
(kg)0.43 (female/male percentage
91%, P = 0.03). For modeling of LVWT by H, in males LVWT (cm) = 1.33 · H
(m)0.65 and in females LVWT (cm) = 1.08 · H
(m)0.65 (female/male percentage
81%, P = 0.0000).
The findings confirm that indexation of cardiac dimensions by FFM,
raised to its allometric power, is most effective in reducing between-gender variability. For LVIDD, significant absolute gender differences (females 91% of male value) disappear when the data are
appropriately normalized for differences in FFM. For LVWT, although the
differences in FFM-adjusted values remain significant, between-gender
variability is considerably reduced. Females possess 91% of the male
FFM-adjusted value compared with 79% in absolute terms. These results
are concordant with previous findings reported in the literature.
Devereux et al. (11) reported that scaling LV mass by lean body mass,
estimated by 24-h urinary creatinine excretion, eliminated gender
differences in absolute LV mass. Furthermore, we have previously
demonstrated the ability of an allometric model using FFM, estimated by
skinfolds, to reduce between-gender variability in LV mass (3). In
contrast, the present results for height-adjusted cardiac dimensions
illustrate that between-gender variability remains almost as high as
the unadjusted absolute values, especially for LVWT.
FFM surrogates: Bland-Altman agreement limits.
The preliminary dimensional analysis suggested that BSA would be the
best surrogate of FFM for modeling echocardiographic dimensions. The
LVIDD and LVWT predicted from the allometric models including gender
and BSA, and gender and H as predictor
variables, were compared with the predicted values from the criterion
(optimal) model (including gender and FFM). Bland-Altman plots (5)
revealed that for LVIDD, BSA was a more effective surrogate, with a
95% agreement limit of 98-102% of the FFM-predicted value.
The corresponding agreement limits for
H were 94-106%. For LVWT, a
similar picture emerged. Agreement limits for BSA were
96-104% compared with 94-106% for
H.
In the current study, the high values for goodness of fit, the low
root-mean-squares error, the relatively narrow CIs and stable point
estimates, and the demonstrably superior ability to reduce
between-gender variability suggest strongly that estimated FFM is the
optimal body size parameter for modeling echocardiographic dimensions.
Therefore, despite the well-documented limitations (31), we believe
that a simple, anthropometric estimate of FFM may be more appropriate
than height in accounting for the influence of body size on heart size.
Future research, on larger samples and diverse populations, is
obviously required to substantiate this assertion. Anthropometric
variables can be measured rapidly, with acceptable precision, with
relatively little specialized training (34), making estimation of FFM
viable in clinical practice. If FFM estimates are unavailable or
impractical, however, allometric indexation by BSA appears to be the
most effective surrogate in young adults of average-to-lean body
composition. Scaling by various powers of height has a negligible
impact on between-gender variability in cardiac dimensions and
demonstrates relatively weaker agreement with criterion modeling by
FFM.
Because of the assumed close links between skeletal and cardiac
muscularity, with testosterone as the primary signal messenger (16), it
is likely that measures of "skeletal muscle mass" (MM) would
represent an improvement on FFM for modeling echocardiographic dimensions. Indeed, analyses from the Brussels Cadaver Study (6) have
revealed that the proportion of adipose tissue-free mass that is
composed of MM and bone mass demonstrates considerable intersubject
variability. In 25 cadavers, bone mass ranged from 16.3 to 25.7%, and
MM from 41.9 to 59.4% of adipose tissue-free mass. If this variability
were present in the current sample, an important assumption of the
existing allometric model, that MM represents a constant fraction of
FFM, would be violated. Hence, in the current study, even indexation of
cardiac dimensions by various powers of FFM may be prone to
considerable error because of violation of the assumptions of the
two-component (FM and FFM) body composition model. Unfortunately,
although methods such as DEXA and computerized axial tomography show
great promise, criterion methods for the in vivo measurement of whole
body MM have not been adequately developed and validated (46). At
present, it would seem that measures of FFM, preferably by DEXA or
alternatively by anthropometric techniques if a simple estimation is
required in clinical practice, are the best compromise available.
In conclusion, based on the current findings, together with those of
our previous study (3), we recommend indexation of LV mass by FFM to
the first power (LVM/FFM) and of linear M-mode echocardiographic
dimensions by FFM0.33.
Alternatively, for indexation by BSA, the power function ratio standards LV mass/BSA1.5 and
LVIDD/BSA0.5 (or
LVWT/BSA0.5) seem most
applicable, as originally proposed by Gutsegell and Rembold (19).
Further research is required to test the applicability of these
findings to diverse population samples, with different age,
racial/ethnic origin, and body size and composition characteristics.
| |
FOOTNOTES |
Address for reprint requests: A. Batterham, School of Social
Sciences, University of Teesside, Middlesbrough TS1 3BA, UK. E-mail:
A.Batterham{at}tees.ac.uk.
Received 28 May 1997; accepted in final form 23 October 1997.
| |
REFERENCES |
1.
Allied Dunbar.
The Allied Dunbar National Fitness Survey: Main Findings. London, UK: Sports Council and Health Education Authority, 1992.
2.
Andrén, B.,
L. Lind,
K. Larsson,
G. Hedenstierna,
S. Ljunghall,
and
H. Lithell.
The influence of body composition on left ventricular mass and other echocardiographic and Doppler measurements in 70-year old males.
Clin. Physiol.
15:
425-433,
1995[Medline].
3.
Batterham, A. M.,
K. P. George,
and
D. R. Mullineaux.
Allometric scaling of left ventricular mass by body dimensions in males and females.
Med. Sci. Sports Exerc.
29:
181-186,
1997[Medline].
4.
Bland, M.
An Introduction to Medical Statistics. Oxford, UK: Oxford Univ. Press, 1995, p. 268.
5.
Bland, J. M.,
and
D. G. Altman.
Statistical methods for assessing agreement between two methods of clinical measurement.
Lancet
1:
307-310,
1986[Medline].
6.
Clarys, J. P.,
A. D. Martin,
and
D. T. Drinkwater.
Gross tissue weights in the human body by cadaver dissection.
Hum. Biol.
56:
459-473,
1984[Medline].
7.
Daniels, S. R.,
T. R. Kimball,
J. A. Morrison,
P. Khoury,
and
R. A. Meyer.
Indexing left ventricular mass to account for differences in body size in children and adolescents without cardiovascular disease.
Am. J. Cardiol.
76:
699-701,
1995[Medline].
8.
Daniels, S. R.,
T. R. Kimball,
J. A. Morrison,
P. Khoury,
S. Witt,
and
R. A. Meyer.
Effect of lean body mass, fat mass, blood pressure, and sexual maturation on left ventricular mass in children and adolescents. Statistical, biological, and clinical significance.
Circulation
92:
3249-3254,
1995[Abstract/Free Full Text].
9.
De Simone, G.,
S. R. Daniels,
R. B. Devereux,
R. A. Meyer,
M. J. Roman,
O. de Divitiis,
and
M. H. Alderman.
Left ventricular mass and body size in normotensive children and adults: assessment of allometric relations and impact of overweight.
J. Am. Coll. Cardiol.
20:
1251-1260,
1992[Abstract].
10.
De Simone, G.,
R. B. Devereux,
S. R. Daniels,
M. J. Koren,
R. A. Meyer,
and
J. H. Laragh.
Effect of growth on variability of left ventricular mass: assessment of allometric signals in adults and children and their capacity to predict cardiovascular risk.
J. Am. Coll. Cardiol.
25:
1056-1062,
1995[Abstract].
11.
Devereux, R. B.,
E. M. Lutas,
P. N. Casale,
P. Kligfield,
R. R. Eisenberg,
I. W. Hammond,
D. H. Miller,
G. Reis,
M. H. Alderman,
and
J. H. Laragh.
Standardization of M-mode echocardiographic left ventricular anatomic measurements.
J. Am. Coll. Cardiol.
4:
1220-1230,
1984.
12.
Devereux, R. B.,
D. C. Wallerson,
G. de Simone,
A. Ganau,
and
M. J. Roman.
Evaluation of left ventricular hypertrophy by M-mode echocardiography in patients and experimental animals.
Am. J. Card. Imaging
8:
291-304,
1994[Medline].
13.
DuBois, D.,
and
E. F. DuBois.
Clinical calorimetry. A formula to estimate the approximate surface area if height and weight be known.
Arch. Intern. Med.
17:
863-871,
1916.
14.
Durnin, J. V. G. A.,
and
J. Womersley.
Body fat assessed from total body density and its estimation from skinfold thicknesses: measurement on 481 men and women aged 16-72 years.
Br. J. Nutr.
32:
77-97,
1974[Medline].
15.
Eckerson, J. M.,
J. R. Stout,
T. H. Housh,
and
G. O. Johnson.
Validity of bioelectrical impedance equations for estimating percent fat in males.
Med. Sci. Sports Exerc.
28:
523-530,
1996[Medline].
16.
George, K. P.,
L. A. Wolfe,
and
G. W. Burggraf.
The "athletic heart syndrome": a critical review.
Sports Med.
11:
300-331,
1991[Medline].
17.
Goble, M. M.,
M. Mosteller,
W. B. Moskowitz,
and
R. M. Schieken.
Sex differences in the determinants of left ventricular mass in childhood. The Medical College of Virginia twin study.
Circulation
85:
1661-1665,
1992[Abstract/Free Full Text].
18.
Guo, S.,
A. F. Roche,
W. C. Chumlea,
D. S. Miles,
and
R. L. Pohlman.
Body composition predictions from bioelectric impedance.
Hum. Biol.
59:
221-233,
1987[Medline].
19.
Gutsegell, H. P.,
and
C. M. Rembold.
Growth of the human heart relative to body surface area.
Am. J. Cardiol.
65:
662-668,
1990[Medline].
20.
Hammond, I. W.,
R. B. Devereux,
M. H. Alderman,
and
J. H. Laragh.
Relation of blood pressure and body build to left ventricular mass in normotensive and hypertensive employed adults.
J. Am. Coll. Cardiol.
12:
996-1004,
1988[Abstract].
21.
Hawes, M. R.
Human body composition.
In: Kinanthropometry and Exercise Physiology Laboratory Manual, edited by R. Eston,
and T. Reilly. London, UK: Spon, 1996, p. 5-33.
22.
Lauer, M. S.,
K. M. Anderson,
W. B. Kannel,
and
D. Levy.
The impact of obesity on left ventricular mass and geometry, the Framingham Heart Study.
JAMA
266:
231-236,
1991[Abstract/Free Full Text].
23.
Lauer, M. S.,
K. M. Anderson,
M. G. Larson,
and
D. Levy.
A new method for indexing left ventricular mass for differences in body size.
Am. J. Cardiol.
74:
487-491,
1994[Medline].
24.
Lauer, M. S.,
M. G. Larson,
and
D. Levy.
Gender-specific reference M-mode values in adults: population-derived values with consideration of the impact of height.
J. Am. Coll. Cardiol.
26:
1039-1046,
1995[Abstract].
25.
Levy, D.,
K. M. Anderson,
D. D. Savage,
W. B. Kannel,
and
J. C. Christiansen.
Echocardiographically detected left ventricular hypertrophy: prevalence and risk factors. The Framingham Heart Study.
Ann. Intern. Med.
108:
7-13,
1988.
26.
Liao, Y.,
R. S. Cooper,
R. Durazo-Arvizu,
G. Mensah,
and
J. K. Ghali.
Prediction of mortality risk by different methods of indexation of left ventricular mass.
J. Am. Coll. Cardiol.
29:
641-647,
1997[Abstract].
27.
Lohman, T. G.
Skinfolds and body density and their relation to body fatness: a review.
Hum. Biol.
53:
181-225,
1981[Medline].
28.
Lohman, T. G.
Dual energy X-ray absorptiometry.
In: Human Body Composition, edited by A. F. Roche,
S. B. Heymsfield,
and T. G. Lohman. Champaign, IL: Human Kinetics, 1996, p. 63-78.
29.
Longhurst, J. C.,
A. R. Kelly,
W. J. Gonyea,
and
J. H. Mitchell.
Echocardiographic left ventricular masses in distance runners and weight lifters.
Med. Sci. Sports Exerc.
48:
154-162,
1980.
30.
Malcolm, D. D.,
T. L. Burns,
L. T. Mahoney,
and
R. M. Lauer.
Factors affecting left ventricular mass in childhood: the Muscataine Study.
Pediatrics
92:
703-709,
1993[Abstract/Free Full Text].
31.
Martin, A. D.,
W. D. Ross,
D. T. Drinkwater,
and
J. P. Clarys.
Prediction of body fat by skinfold caliper: assumptions and cadaver evidence.
Int. J. Obes.
9:
31-39,
1985.
32.
Mensah, G. A.,
and
B. F. Byrd.
Heart size: one-, two- and now three-dimensional echocardiography.
J. Am. Coll. Cardiol.
24:
514-516,
1994[Medline].
33.
Nevill, A. M.,
and
R. L. Holder.
Body mass index: a measure of fatness or leanness?
Br. J. Nutr.
73:
507-516,
1995[Medline].
34.
Roche, A. F.
Anthropometry and ultrasound.
In: Human Body Composition, edited by A. F. Roche,
S. B. Heymsfield,
and T. G. Lohman. Champaign, IL: Human Kinetics, 1996, p. 167-189.
35.
Roman, M. J.
How best to identify prognostically important left ventricular hypertrophy: a cut to the chase.
J. Am. Coll. Cardiol.
29:
648-650,
1997[Medline].
36.
Ross, W. D.,
S. M. Crawford,
D. A. Kerr,
R. Ward,
D. A. Bailey,
and
R. L. Mirwald.
The relation of the BMI with skinfolds, girths, and bone breadths in Canadian men and women age 20 to 70 years.
Am. J. Phys. Anthropol.
77:
253-260,
1988.
37.
Royal College of Physicians.
Obesity.
J. R. Coll. Physicians Lond.
17:
5-65,
1983[Medline].
38.
Schmidt-Nielsen, K.
Scaling: Why Is Animal Size so Important? Cambridge, UK: Cambridge Univ. Press, 1984, p. 7-32.
39.
Segal, K. R.,
M. Van Loan,
P. I. Fitzgerald,
J. A. Hodgdon,
and
T. B. Van Itallie.
Lean body mass estimation by bioelectrical impedance analysis: a four site cross-validation study.
Am. J. Clin. Nutr.
47:
7-14,
1988[Abstract/Free Full Text].
40.
Shephard, R. J.
The athlete's heart: is big beautiful?
Br. J. Sports Med.
30:
5-10,
1996[Abstract/Free Full Text].
41.
Siri, W. E.
The gross composition of the body.
In: Advances in Biological and Medical Physics, edited by C. A. Tobias,
and J. H. Lawrence. New York, NY: Academic, 1956, vol. 4, p. 239-280.
42.
Slaughter, M.,
and
T. Lohman.
An objective method for the measurement of the musculoskeletal size to characterize body physique with application to the athletic population.
Med. Sci. Sports Exerc.
12:
170-174,
1980[Medline].
43.
Snedecor, G. W.,
and
W. G. Cochran.
Statistical Methods (8th ed.). Ames: Iowa State Univ. Press, 1989, p. 333-397.
44.
Tanner, J. M.
Fallacy of per-weight and per-surface area standards and their relation to spurious correlation.
J. Appl. Physiol.
2:
1-15,
1949[Free Full Text].
45.
Urbina, E. M.,
S. S. Gidding,
W. Bao,
A. S. Pickoff,
K. Berdusis,
and
G. S. Berenson.
Effect of body size, ponderosity, and blood pressure on left ventricular growth in children and young adults in the Bogalusa Heart Study.
Circulation
91:
2400-2406,
1995[Abstract/Free Full Text].
46.
Wang, Z.,
M. Visser,
R. Ma,
R. N. Baumgartner,
D. Kotler,
D. Gallagher,
and
S. B. Heymsfield.
Skeletal muscle mass: evaluation of neutron activation and dual-energy X-ray absorptiometry.
J. Appl. Physiol.
80:
824-831,
1996[Abstract/Free Full Text].
47.
Yoshinaga, M.,
Y. Yuasa,
H. Hatano,
Y. Kono,
Y. Nomura,
S. Oku,
M. Nakamura,
S. Kanekura,
K. Otsubo,
S. Akiba,
and
K. Miyata.
Effect of total adipose weight and systemic hypertension on left ventricular mass in children.
Am. J. Cardiol.
76:
785-787,
1995[Medline].
AJP Heart Circ Physiol 274(2):H701-H708
0363-6135/98 $5.00
Copyright © 1998 the American Physiological Society
Top
Abstract
Introduction
