Download Predicting Heart Growth During Puberty: The Muscatine

Predicting Heart Growth During Puberty: The Muscatine Study
Kathleen F. Janz, EdD*; Jeffrey D. Dawson, ScD‡; and Larry T. Mahoney, MD§
ABSTRACT. Objectives. During childhood, heart
growth is closely associated with somatic growth including increases in body weight, fat-free body mass (FFM),
and height. However, with age, greater variability in
heart size in relationship to body size is observed, presumably attributable to the increased effect of cardiac
workload. At this time, little is known as to what functional attributes (eg, aerobic fitness) contribute to cardiac
workload and the relative contribution of these attributes
to heart growth during childhood and adolescence. In
this article, we report cross-sectional and longitudinal
relationships among aerobic fitness, body size, blood
pressure (BP), and left ventricular mass (LVM) through
puberty including the predictors of heart growth during
puberty and the tracking of LVM from pre-puberty to
late and post-puberty. Describing the predictors of heart
size and heart growth and establishing the likelihood
that a large heart, relative to peers, may (or may not)
remain a large heart should aid pediatricians in discerning between normal developmental increases in LVM
and increases in LVM suggestive of excessive heart
growth (left ventricular hypertrophy).
Methodology. Using a repeated-measures design, we
assessed aerobic fitness, FFM, fatness, weight, height,
sexual maturation, resting BP, peak exercise BP, and
LVM in 125 healthy children (mean baseline age: 10.5
years) for a period of 5 years. All subjects were either in
prepuberty or early puberty at the beginning of the
study. At follow-up, 110 subjects attempted all research
procedures (87% of the initial cohort). Using anthropometry and bioelectrical impedance, we measured FFM, fatness, weight, and height quarterly (once every 3 months)
for a total of 20 examinations. Resting BP and LVM
(2-dimensional echocardiography) were also assessed
quarterly. Aerobic fitness, peak exercise BP, and sexual
maturation (staging of secondary sex characteristics and,
for boys, serum testosterone) were measured annually (5
examinations). The same field staff conducted all examinations.
Statistical methods included Spearman rank correlation coefficients (rs) calculated to estimate how well the
year 5 LVM was predicted by LVM at earlier years. We
also categorized the LVM data into tertiles and reported
the percentage who remained in the extreme tertiles in
year 5, given they began in that tertile in year 1. Genderspecific stepwise multivariate analysis was used to evaluate predictors of follow-up LVM and predictors of
changes in LVM. The latter model examined whether the
variability in the changes in LVM, as quantified by subFrom the Departments of *Sport, Health, and Leisure Studies; ‡Biostatistics;
and §Pediatrics, Division of Pediatric Cardiology, University of Iowa, Iowa
City, Iowa.
Received for publication Oct 15, 1999; accepted Dec 9, 1999.
Address correspondence to Kathleen F. Janz, EdD, Department of Sport,
Health, and Leisure Studies, 102 FH, University of Iowa, Iowa City, IA
52242. E-mail: [email protected]
PEDIATRICS (ISSN 0031 4005). Copyright © 2000 by the American Academy of Pediatrics.
http://www.pediatrics.org/cgi/content/full/105/5/e63
ject-specific slopes, could be explained by changes in
predictor variables, also quantified by subject-specific
slopes.
Results. At baseline and at follow-up, boys tended to
be taller, leaner, more aerobically fit, and had greater
LVM than girls. Rate of change for these variables was
also greater in boys than girls. For example, LVM increased 62% in boys and 48% in girls. At year 5, subjects
had advanced at least 1 stage in genital or breast development and over 80% of the subjects were in late- or
post-puberty. Significant and strong tracking of heart
size (rs ⴝ .65–.87) was observed. The likelihood that a
subject would be in an extreme tertile for heart size at
follow-up was approximately doubled if he or she started
there at baseline.
In boys, baseline FFM explained 54% of the variability
in follow-up LVM. Change in aerobic fitness and change
in FFM explained 55% of the variability in change in
LVM. In girls, baseline aerobic fitness and fatness explained 45% of the variability in follow-up LVM. Because FFM did not enter in this model, we constructed an
alternative model in which baseline aerobic fitness adjusted for FFM was entered. Using this approach, 43% of
the variability in follow-up LVM was explained by baseline FFM, fatness, and adjusted aerobic fitness. Change
in FFM explained 58% of the variability in change in
LVM. For both boys and girls, all statistically significant
variables entered as positive regression coefficients.
Conclusions. Our tracking results suggest predictability in LVM, most likely attributable to normal
growth and regulated by the genetic and hormonal influences that affect FFM as well as LVM. Our multiple
regression results indicate that during adolescence FFM
is an important determinant of heart size and heart
growth for both boys and girls but changes in aerobic
fitness, presumably attributable to improvements in cardiac function, also affect heart growth in boys. This latter
finding indicates that the known age-related decrease in
the ability of body size to fully predict heart size begins
sooner in males than females. It also suggests that
changes in cardiac functioning as well as morphologic
increases in lean tissue cause the “athletic heart
syndrome.” Pediatrics 2000;105(5). URL: http://www.
pediatrics.org/cgi/content/full/105/5/e63; adolescence, cardiac function, echocardiography, exercise, fat-free body
mass, left ventricular mass, physical fitness, sexual maturation, tracking.
ABBREVIATIONS. FFM, fat-free body mass; LVM, left ventricular
mass; BP, blood pressure; Vo2, volume of oxygen uptake; HR,
heart rate; SKF, skinfolds; SBP, systolic blood pressure; DBP, diastolic blood pressure.
D
uring childhood, heart growth is closely associated with somatic growth including increases in body weight, fat-free body mass
(FFM), and height.1,2 However, with age, greater
PEDIATRICS Vol. 105 No. 5 May 2000
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variability in heart size in relationship to body size is
observed, presumably attributable to the increased
effect of cardiac workload.3,4 For example, using a
sample of 766 subjects ranging from 0 to 85 years of
age, de Simone and colleagues5 have shown an agerelated decrease in the value of body size and an
age-related increase in the value of cardiac workload
for predicting heart size. At this time, little is known
as to what functional attributes (eg, aerobic fitness)
contribute to cardiac workload and the relative contribution of these attributes to heart growth during
childhood and adolescence.
Previously, we reported cross-sectional and 2-year
follow-up relationships among aerobic fitness, body
composition, and left ventricular mass (LVM) in prepubescent and early pubescent children (mean baseline age: 10.5 years) and found FFM to be the most
important predictor of LVM (heart size)6 and increased FFM to be the most important predictor of
increased LVM (heart growth).7 In these studies,
FFM explained 55% of the variability in LVM, while
increased FFM explained 18% of the variability in
increased LVM. In this article, using the same cohort,
we report cross-sectional and longitudinal relationships among aerobic fitness, body size, blood pressure (BP), and LVM through puberty including the
predictors of heart growth during puberty and the
tracking of LVM from prepuberty to late and postpuberty. Describing the predictors of heart size and
heart growth and establishing the likelihood that a
large heart, relative to peers, may (or may not) remain a large heart should aid pediatricians in discerning between normal developmental increases in
LVM and increases in LVM suggestive of excessive
heart growth (left ventricular hypertrophy).
METHODS
Subjects
The Muscatine Study is a longitudinal, population-based investigation of cardiovascular disease risk factors in children, young
adults, and selected family groups from Muscatine, Iowa. From a
large (N ⫽ 925) cross-sectional screening of Muscatine School
children, 150 subjects thought to be prepubertal based on age were
identified. Initial contact was made through information mailed to
the parents of each child and a phone call to the parents by a
Muscatine Study staff member. Nineteen parents reported that
their child had begun puberty, and 1 parent declined participation
for her child. Informed consent was secured from 130 subjects and
their parents. Based on a physician’s physical examination, 4 of the
130 subjects were judged to have begun significant pubertal development (greater than stage 2 using the criteria of Tanner) and
1 child withdrew from the study before data collection. One
hundred twenty-five subjects, all in prepuberty or early puberty,
were enrolled in this study. Sixty-one boys (mean age: 10.8 years;
age range: 8 –12 years) and 62 girls (mean age: 10.3 years; age
range: 7–11 years) attempted all baseline research procedures. At
year 5, 53 boys and 57 girls attempted all research procedures
(87% of the initial cohort). All enrolled children were within
expected ranges for weight, height, and resting blood pressure,
suggesting that the sample was representative of normal children
in this age range.8 All children were white. Protocol requirements
established by the University of Iowa’s Human Subject Review
Committee including written permission from subjects and their
parents were satisfied before data collection.
Procedures
Anthropometry, bioelectrical impedance, and echocardiography were measured quarterly (once every 3 months) for a total of
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20 examinations. Fitness and sexual maturation (staging of secondary sex characteristics and, for boys, serum testosterone) were
measured annually (5 examinations). The same field staff conducted all examinations.
Aerobic Fitness
We used peak volume of oxygen uptake (Vo2; mL䡠min⫺1) to
operationally define aerobic fitness and to serve as our functional
index of cardiac output.9 A Medgraphics System CPX metabolic
cart (Medical Graphics Corp, St Paul, MN) was used to measure
this variable. Before testing, the pneumotachograph was calibrated to within ⫾ 4 mL䡠s⫺1. Known reference gases were used to
calibrate the system analyzers within ⫾.03%. Heart rate (HR) was
measured from a Burdick electrocardiograph single channel recorder (Milton, WI). A Siemens Elema electromechanically braked
cycle ergometer (Siemens Systems, Coralville, IA) provided incremental workloads. The protocol for the graded exercise test consisted of a 1-minute warm-up, three 3-minute submaximal stages,
followed by a series of 30-second stages (“ramps”) until exhaustion. The highest VO2 was described as peak Vo2 (mL.min⫺1)
providing the subject could no longer maintain a pedal cadence of
at least 40 rev䡠min⫺1 and had a respiratory exchange ratio of ⬎1.0
or was within 95% of age predicted maximal HR. Using this
protocol, we previously reported laboratory reliability correlations
ranging from r ⫽ .83 to .97 for peak exercise variables. Our
reliability correlation for peak Vo2 was r ⫽ .96.10
Body Composition
Height was measured, without shoes, to the nearest .1 cm using
the Iowa anthropometric plane and square (University of Iowa
Department of Medical Engineering, Iowa City, IA). Weight was
measured to the nearest .1 kg using a Seca 770 digital metric scale
(Columbia, MD) calibrated daily with standardized weights. Skinfolds (SKF) were taken on the right side of the body at 6 sites
(tricep, bicep, subscapular, abdominal, suprailiac, and calf) using
a Lange caliper (Cambridge Scientific Industries, Cambridge,
MA). Two measurements were taken alternately at each site and
averaged.11 If the measurements varied by ⬎1 mm, additional
measurements were taken until the difference was ⬍1 mm. The
sum of the 6 SKF was used to define body fatness. FFM was
predicted using bioelectrical impedance and the equations of
Houtkooper et al12 and used to define body leanness. For white
children 10 to 19 years of age, the reported FFM standard error of
the estimate is 2.1 kg and the adjusted R2 is .95 when this method
is compared with underwater weighting using age-corrected density equations.12 All anthropometric measurements were obtained
by field staff members who attend yearly formal training sessions.
Staging of Secondary Sex Characteristics
Pediatricians conducted a general physical examination of each
subject including breast and pubic hair development in girls and
genital and pubic hair development in boys according to the
criteria of Tanner.13 Using these criteria, prepuberty was defined
as stage 1, early puberty as stage 2, midpuberty stage 3, late
puberty stage 4, and postpuberty stage 5. To ensure adequate cell
size, subjects in late and postpuberty were grouped together.
Testosterone
After a 12-hour fast, 15 mL of blood was drawn from the
antecubital vein using a venipuncture technique. From these samples, the plasma hormone level of testosterone was assessed and
used to index maturity in boys. Because of its cyclic nature,
estrogen was not measured. The specimens were prepared and
analyzed using the standardized laboratory procedures of the
University of Iowa Hospitals and Clinics. These procedures included testing each sample in duplicate and repeating the test for
any result with a coefficient of variation greater than 10%.
BP
We recorded 3 resting BP for each subject after a 5-minute
seated rest. A Random-Zero Sphygmomanometer (HawksleyGelman, Copiague, NY) was used for all measurements. The right
arm was positioned so that the heart and the BP cuff were at the
same level. The BP cuff encircled the entire circumference of the
upper arm and covered 75% of the arm between the top of the
shoulder and the olecranon.14 The mean of the 3 measures of the
first (systolic BP [SBP]) and fourth (diastolic BP [DBP]) Korotkoff
sounds were recorded as the resting BP. We recorded the fifth
Korotkoff sound in the very rare instances that the fourth sound
was not discernable (and in these instances set any value ⬍30 mm
Hg to missing).
associated with the changes in potential predictor variables. Finally, we used multiple linear regression to fit models to predict
follow-up LVM and changes in LVM. Predictor variables considered for these models included age, FFM, height, peak Vo2, peak
SBP, SBP, sum of SKF, and maturity (Tanner and, for boys, testosterone). Variables were allowed to enter the models if significant at the ⬍.05 probability level.
Echocardiographic Determination of LVM
RESULTS
Image Acquisition
Two-dimensional echocardiograms were obtained with a
Hewlett-Packard Ultrasound System (Andover, MA) with transducer selection determined by the child’s body size and image
quality. The transducer was positioned according to the standards
for transducer positions and imaging planes.15,16 A simultaneous
electrocardiogram was recorded for reference. A short-axis
parasternal view of the heart was obtained by rotating the transducer 90 degrees from the parasternal long-axis view. In this view,
the tips of the papillary muscles can be identified by a superior–
inferior sweep until the chordae disappear from the papillary
muscles. To assure acquisition of the maximum major axis, an
apical 4-chamber view was obtained with the left ventricle imaged
as parallel as possible to the ultrasound beam. End-systole was
defined by setting end-systole to trigger from the peak of the T
wave. Multiple gated images were recorded on videotape for later
measurement.
Measurements
All measurements were made off-line from videotape on a
Microsonics CAD 886 system (Indianapolis, IN). Accurate calibration of this system and the echocardiographic unit was confirmed
with an echocardiographic phantom. In the short-axis view, the
left ventricular epicardium at end-systole was traced using the
midinterface convention.17 The papillary muscles were excluded
from the tracing of the endocardium. The resultant planimetered
values were recorded as epicardial and endocardial areas. The
major axis, or length, of the left ventricle was measured in the
apical 4-chamber view at end-systole from the mitral valve annulus to both the endocardium and epicardium. If either interface
was not clearly visualized, the missing length was calculated by
measuring the left ventricular posterior wall at end-systole in the
short-axis view and the appropriate value was either added to the
endocardial measurement to equal the epicardial value or, conversely, subtracted from the epicardial measurement to equal the
endocardial value. Measurements were made on 3 cardiac cycles
and averaged. Epicardial and endocardial volumes and the derived LVM were calculated during systole as follows17:
Volume ⫽ .83 (area ⫻ length)
LVM ⫽ 1.05 (epicardial volume ⫺ endocardialvolume).
In our pediatric laboratory, intraclass correlations for 2-dimensional echocardiographic measures and derived variables range
from .89 to .96, with a precision of 7.8 g for LVM measured at
end-systole (95% confidence interval: 6.9 –9.6 g).18
Statistical Analysis
The 4 quarterly measures of body composition and LVM were
converted to yearly averages. Means and standard deviations
were then calculated for all variables. Variables were stratified by
gender and examined for distributional properties. Wilcoxon rank
sum tests were used to examine gender differences. Spearman
rank correlation coefficients (rs) were calculated to estimate how
well the year 5 LVM was predicted by LVM at earlier years. We
also categorized the LVM data into tertiles and reported the percentage who remained in the extreme tertiles in year 5, given they
began in that tertile in year 1. Under the null hypothesis of no
tracking, one would expect these percentages to be near 33%. The
statistical significance of the tracking within tertiles was assessed
using Kendall’s Tau-b.
Predictors of LVM and predictors of changes in LVM were
tested using the following approaches. First, we calculated the
Spearman correlation coefficients between follow-up LVM and
each potential baseline (year 1) and follow-up (year 5) predictor.
Next, we calculated subject-specific slopes for LVM and each
predictor, and then used the Spearman correlation to assess
whether the changes in LVM, as quantified by the slopes, were
Table 1 characterizes, by gender, the study subjects
and includes the yearly rate of change (slope) for
variables. At baseline and at follow-up, boys tended
to be taller, leaner, more aerobically fit, and had
greater LVM than girls. Rate of change for these
variables was also greater in boys than girls. For
example, LVM increased 62% in boys and 48% in
girls. At the beginning of the study, all boys and girls
were either in prepuberty or early puberty. At year 5,
subjects had advanced at least 1 stage in genital or
breast development and over 80% of the subjects
were in late- or post-puberty. The proportion of boys
and girls within specific stages for pubic hair development was similar to the proportion in specific
stages for genital and breast development.
Tracking of LVM
Table 2 presents the Spearman correlation coefficients between follow-up LVM and preceding years.
The table also shows the likelihood of remaining in
an extreme tertile at follow-up given the subject was
in the same extreme tertile at baseline. The tracking
correlation from baseline (year 1) to follow-up (year
5) was .66 for boys and .78 for girls. There was an
increased likelihood that a subject would be in an
extreme tertile at follow-up if he or she was in the
same tertile at baseline. The relative chance of a boy
or girl being in the upper tertile for LVM at the end
of the study was approximately doubled if he or she
started there at baseline. The relative chance of a
subject being in the lower tertile for LVM at follow-up was 2.6 times as great for a girl and 1.6 times
as great for a boy if he or she started there at baseline.
Bivariate Results
Relationships among potential predictor variables
and LVM were examined using Spearman correlation coefficients (Table 3). Baseline and follow-up
body size variables, SBP, peak SBP, and peak Vo2
were moderately to strongly associated with follow-up LVM. Overall, associations between baseline
variables and follow-up LVM were similar in direction and magnitude to associations between follow-up variables and follow-up LVM. However,
baseline Tanner index was not associated with follow-up LVM, but follow-up maturation was associated with follow-up LVM (rs ⫽ .40-.43). Baseline DBP
in girls was not associated with follow-up LVM (rs ⫽
.20), but follow-up DBP was associated with follow-up LVM (rs ⫽ .36).
Spearman correlation coefficients describing associations among change in potential predictor variables and increases in LVM are presented in Table 4.
In boys, change in testosterone, change in maturation, change in height, change in FFM, and change in
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TABLE 1.
Description of Subjects
Age (y)
Boys
Girls
Height (cm)
Boys
Girls
Weight (kg)
Boys
Girls
FFM (kg)
Boys
Girls
Sum of SKF (mm)
Boys
Girls
SBP (mm Hg)
Boys
Girls
DBP (mm Hg)
Boys
Girls
Peak SBP (mm Hg)
Boys
Girls
Peak Vo2 (mL/min)
Boys
Girls
LVM (g)
Boys
Girls
Testosterone (mg/dL)
Boys
Baseline
Follow-up
Slope (Change/Year)
10.8 (1.0)*
10.3 (1.0)
14.6 (1.0)*
14.2 (1.0)
1.0 (0.1)
1.0 (0.1)
143.9 (7.2)*
140.6 (8.4)
169.4 (8.3)*
160.0 (5.8)
6.3 (1.2)*
4.9 (1.5)
39.2 (10.6)
37.9 (9.5)
62.4 (16.0)
58.8 (13.5)
5.6 (1.0)
5.2 (2.0)
30.1 (5.1)*
27.8 (5.4)
49.3 (9.4)*
40.8 (6.4)
4.7 (1.6)*
3.3 (1.1)
72.9 (41.5)*
88.1 (34.2)
78.1 (44.5)*
111.0 (43.1)
1.6 (7.1)*
5.3 (6.7)
98.0 (7.5)
98.3 (6.8)
106.8 (11.2)
105.1 (8.1)
2.2 (2.4)
1.6 (1.6)
60.9 (6.8)*
63.3 (6.7)
63.2 (6.3)
63.1 (6.3)
.7 (2.3)
.1 (1.6)
166.8 (19.3)
163.0 (19.8)
187.9 (20.2)*
174.4 (14.8)
5.1 (5.0)*
2.9 (5.7)
1860 (299)*
1479 (345)
2729 (490)*
1907 (293)
223 (122)*
133 (80)
91.4 (19.8)*
83.1 (18.3)
148.4 (39.7)*
122.8 (22.3)
13.5 (8.1)*
10.5 (3.9)
15.8 (15.1)
484.9 (174.4)
112.5 (61.7)
* Boys significantly different than girls (P ⬍ .05).
TABLE 2.
Spearman Correlation Coefficients Tracking LVM
Between Follow-up (Year 5) and Preceding Years (4 to 1) and
Percentage of Subjects Remaining in Lower and Upper Tertiles for
LVM at Follow-up Who Were in These Same Tertiles at Baseline
LVM (g)
Boys
Girls
Percentage of Subjects
Remaining in Tertiles*
LVM (g)
Boys
Girls
Year 4
Year 3
Year 2
Year 1
.87
.83
.81
.73
.66
.65
.66
.78
Lower
Upper
P
53
84
65
68
⬍.001
⬍.001
All correlations were highly significant (P ⬍ .001).
* Thirty-three percent would be expected by chance alone, while
100% would indicate perfect tracking.
peak Vo2 were strongly associated with change in
LVM (rs ⫽ .60 –.77), whereas, in girls, change in
height, change in weight, and change in FFM were
strongly associated with change in LVM (rs ⫽ .61–
.75). Moderate associations existed between change
in LVM and change in SBP (resting SBP and peak
SBP) for both boys and girls, with correlation coefficients ranging from .32 to .43. Change in DBP was
not significantly associated with change in LVM but
was barely significant in boys (rs ⫽ .26; P ⫽ .043).
Change in sum of SKF (our measure of body fatness)
was inversely associated with change in LVM (rs ⫽
⫺.28) in boys but was positively associated with
change in LVM in girls (rs ⫽ .38).
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HEART GROWTH DURING PUBERTY
Regression Results
Stepwise multivariate analysis was used to evaluate predictors of follow-up LVM and predictors of
changes in LVM. Three gender-specific models were
considered. The first model used predictor variables
measured at follow-up to examine variability in follow-up LVM (a cross-sectional approach). The second model examined predictors of follow-up LVM
using baseline variables (prospective follow-up approach). The third model examined whether the variability in the changes in LVM, as quantified by subject-specific slopes, could be explained by changes in
predictor variables, also quantified by subjectspecific slopes.
In boys, follow-up peak Vo2, sum of SKF, peak
SBP, and age explained 82% of the variability in
follow-up LVM (Fig 1). In this cross-sectional model,
FFM did not enter; therefore, we could not be sure if
peak Vo2 was independent of its effects, so we constructed an alternative model in which peak Vo2
adjusted for FFM was entered. Using this approach,
82% of the variability in follow-up LVM was explained by follow-up FFM, peak SBP, sum of SKF,
and adjusted peak Vo2 (data not shown). When baseline variables were entered (prospective follow-up
approach), baseline FFM explained 54% of the variability in follow-up LVM (Fig 2). Change in peak Vo2
and change in FFM explained 55% of the variability
in change in LVM (Fig 3).
In girls, follow-up FFM, peak Vo2, and sum of SKF
explained 61% of the variability in follow-up LVM
(Fig 1). Baseline peak Vo2 and sum of SKF explained
TABLE 3.
Spearman Correlation Coefficients of Associations
Among Baseline and Follow-up Age, Body Composition, BP, Aerobic Fitness, and Maturation With Follow-up LVM
Follow-up LVM (g) Follow-up LVM (g)
Boys
Girls
Age (y)
Baseline
Follow-up
Weight (kg)
Baseline
Follow-up
Height (cm)
Baseline
Follow-up
FFM (kg)
Baseline
Follow-up
Sum of SKF (mm)
Baseline
Follow-up
SBP (mm Hg)
Baseline
Follow-up
DBP (mm Hg)
Baseline
Follow-up
Peak SBP (mm Hg)
Baseline
Follow-up
Peak Vo2 (mL/min)
Baseline
Follow-up
Tanner index
Baseline
Follow-up
Testosterone (mg/dL)
Baseline
Follow-up
.52
.50
.04*
.02*
.65
.73
.65
.80
.58
.65
.24
.34
.70
.81
.56
.79
.48
.39
.62
.67
.51
.70
.32
.48
.00*
.24*
.20*
.36
.52
.57
.17*
.24*
.50
.82
.54
.66
.11*
.40
.15*
.43
.39
.29
—
—
* All correlations significant P ⬍ .05 except these.
TABLE 4.
Spearman Correlation Coefficients of Longitudinal
Associations Among Changing Age, Body Composition, Blood
Pressure, Aerobic Fitness, and Maturation With Changing LVM
Baseline age (y)
⌬ Weight (kg)
⌬ Height (cm)
⌬ FFM (kg)
⌬ Sum of SKF (mm)
⌬ SBP (mm Hg)
⌬ DBP (mm Hg)
⌬ Peak SBP (mm Hg)
⌬ Peak Vo2 (mL/min)
⌬ Tanner (index)
⌬ Testosterone (mg/dL)
⌬ LVM (g)
Boys
⌬ LVM (g)
Girls
.32
.68
.70
.77
⫺.28
.54
⫺.26
.43
.77
.60
.64
⫺.43
.67
.61
.75
.38
.39
⫺.10*
.30
.39
.32
—
* All correlations significant P ⬍ .05 except these.
⌬ ⫽ subject-specific slope from baseline through follow-up.
45% of the variability in follow-up LVM (Fig 2).
Again, because FFM did not enter in this model, we
constructed an alternative model in which baseline
peak Vo2 adjusted for FFM was entered. Using this
approach, 43% of the variability in follow-up LVM
was explained by baseline FFM, sum of SKF, and
adjusted peak Vo2 (data not shown). Change in FFM
explained 58% of the variability in change in LVM
(Fig 3). For both boys and girls, all statistically significant variables entered as positive regression coefficients.
DISCUSSION
Aerobic fitness, body size, BP, maturation, and
LVM were measured in a representative sample of
children in prepuberty and early puberty. At follow-up almost all study subjects were in late or postpuberty and heart size had increased 62% in boys
and 48% in girls. Throughout the study, we found
significant and strong tracking of heart size. Results
also indicate that during this period of rapid heart
growth, FFM is an important determinant of heart
growth for both boys and girls but changes in aerobic
fitness, presumably attributable to improvements in
cardiac function, also affect heart growth in boys.
Although, we detected several moderate bivariate
associations among BP and LVM, in our multivariate
analyses, only follow-up peak SBP predicted follow-up LVM (in boys) and its contribution was relatively weak, explaining 3.5% of the variability in
follow-up LVM.
There was a strong tendency for boys (rs ⫽ .66) and
an even stronger tendency for girls (rs ⫽ .78) to
maintain rank order LVM across the observational
period. Perhaps attributable to our ability to reduce
the imprecision associated with echocardiography
by using multiple (quarterly) measures for each observational year, these tracking results are greater in
magnitude than previous reports.1,19 For example,
within the Medical College of Virginia Twin Study,
Schieken and colleagues1 noted tracking correlation
coefficients of .48 for a 3-year interval and .46 for a
4.5-year interval for 231 subject, 11 years of age at
baseline. Gender differences in the magnitude of
tracking were not observed in their study. After adjusting for weight and height, Mahoney and colleagues19 reported a tracking correlation of .36 for a
3.4-year interval for 274 children and adolescents
with baseline ages ranging from 6 to 15 years. Gender differences in the magnitude of tracking were
also not reported in the study by Mahoney et al.19
Our results, when coupled with the work of others,
suggest predictability in LVM, most likely attributable to normal growth and regulated by the genetic
and hormonal influences that affect FFM as well as
LVM. In addition, our results indicate that LVM
tracks better in girls than boys. The poorer tracking
in boys may be associated with their wider variation
during pubertal growth spurts (compared with
girls), different patterns of growth between boys and
girls (20 –22), and possibly an earlier effect of cardiac
function to myocardial development in boys.
In our bivariate analysis, follow-up sexual maturation was associated with follow-up LVM and
change in sexual maturation was associated with
change in LVM in both boys and girls. Change in
testosterone, in boys, was the strongest maturationrelated association with change in LVM (rs ⫽ .64).
However, in multivariate analysis, neither Tanner
staging nor testosterone entered as an independent
predictor. Similar findings have been reported by
others23 and suggest that the effect of sexual maturation on LVM is mediated through hormonal
changes that also influence body size, particularly
acquisition of FFM.
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Fig 1. Explained variance (%) by gender for follow-up LVM using predictor variables measured at follow-up.
Fig 2. Explained variance (%) by gender for follow-up LVM using predictor variables measured at baseline.
In our regression analysis, changes in FFM explained approximately half of the observed heart
growth for boys and girls, ie, on average, the boys
and girls who were gaining the most lean tissue were
also the same children experiencing the greatest increases in heart size. This finding parallels the crosssectional work of Daniels and colleagues.22,23 Interestingly, changes in height and sum of SKF were not
independent predictors of heart growth in our study.
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HEART GROWTH DURING PUBERTY
These results indicate that during puberty, increased
FFM is the primary body size determinant of heart
growth, whereas increased fat mass is of substantially less importance. However, because lean tissue
is needed to support additional fat tissue, FFM may
increase in response to increased fat mass23 suggesting that for some of our heavier subjects, the observed link between changing FFM and changing
LVM was associated with their increasing obesity.
Fig 3. Explained variance (%) by gender for change in LVM (quantified by subject-specific slopes) using changes in predictor variables,
also quantified by subject-specific slopes.
Aerobic fitness (peak Vo2) was associated with
heart size and heart growth in both boys and girls.
However, improvements in aerobic physical fitness
independently predicted heart growth only in boys.
These data indicate that as boys mature, changes in
cardiac function associated with aerobic fitness influence myocardial development. Other investigators
have also observed gender-specific associations between cardiac function and LVM.3,5,24,25 Goble and
colleagues24 studied 11-year-old children and reported inverse associations between resting HR and
LVM. They attributed this relationship to physical
conditioning and speculated that the boys were more
physically fit than were girls and that this difference
might contribute to gender differences in LVM. De
Simone and colleagues5 have shown that while the
affect of body size to heart size decreases with age,
the influence of gender to heart size increases with
age, and the influence of cardiac workload increases
with age. In Native American adults, Bella and colleagues3 have reported that FFM, age, gender, stroke
volume, and cardiac midwall shortening (an index of
afterload) explain 72% of the variability in LVM.
Because the ability to increase stroke volume during
exercise is one of the most critical factors in determining aerobic fitness,26 their results support our
contention that changes in cardiac function associated with aerobic fitness influence myocardial development. Similarly, in healthy normotensive men,
Molina and colleagues27 have observed positive associations between physical activity and LVM and
no associations between physical activity and left
ventricular hypertrophy.
CONCLUSION
In summary, by considering aerobic fitness and
indices of body size, particularly FFM, we were able
to explain an appreciable proportion of the variability in heart growth in normal children and adolescents. We have also shown strong tracking of heart
size through puberty and gender-specific differences
in the effect of changes in aerobic fitness to heart
growth. The latter suggests that the known agerelated decrease in the ability of body size to fully
predict heart size4,5 may begin sooner in males than
in females. Finally, our work indicates that changes
in sexual maturation and changes in BP are not important in determining heart growth in normal children and adolescents. The biological mechanisms in
which FFM and aerobic fitness affect myocardial development and their potential value for indexing
normal, healthy heart growth warrant further inquiry.
ACKNOWLEDGMENTS
This work was supported by Grant HL44546 from the Specialized Center of Research in Hypertension of the National Institutes
of Health.
We thank the Muscatine Administrative Team—Betsy Fletcher,
Candy Hooten, Kathleen Schrieber, Cathy Rost, and John Witt.
We also thank the children of Muscatine, their parents, and
their teachers, without whom this study would not have been
possible.
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