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Original Studies |
University of Virginia Health Sciences Center, Department of Pediatrics, Division of Endocrinology (J.N.R., P.A.C., A.D.R.); Department of Radiology (V.M., S.S.B.); Department of Medicine, Division of Endocrinology and Metabolism (A.W., J.D.V.); Department of Pharmacology (A.D.R.); and University of Virginia, Curry School of Education (A.W.), Charlottesville, Virginia 22908
Address all correspondence and requests for reprints to: James N. Roemmich, University of Virginia Health Sciences Center, Department of Pediatrics, Division of Endocrinology, Box 386, Charlottesville, Virginia 22908. E-mail: jr5n{at}virginia.edu
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Abstract |
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GH peak heights), and the mean nadir GH concentration. GH release
was greater in the pubertal than prepubertal subjects due to an
increase in GH peak heights (43.8 ± 3.6 vs.
24.1 ± 3.5 ng·mL-1, P =
0.0002) and mean nadir (1.7 ± 0.2 vs. 0.7 ±
0.2 ng·mL-1, P = 0.0002),
but not peak number (4.3 ± 0.2 vs. 4.5 ±
0.2). The girls had a greater GH peak heights (39.0 ± 3.5
vs. 28.8 ± 3.6 ng·mL-1,
P = 0.05) and mean nadir concentration (1.4 ±
0.2 vs. 0.9 ± 0.2 ng·mL-1,
P = 0.05) than the boys. Significant inverse
relationships existed between GH peak heights (r = -0.35,
P = 0.06) or mean nadir (r = -0.39,
P = 0.04) and four-compartment percent body fat for
all boys but not for all girls or when combining all subjects. For all
girls, significant inverse relationships existed between GH peak
heights (r = -0.39, P = 0.03) or mean nadir
(r = -0.37, P = 0.04) and waist/hip ratio.
Similar inverse relationships in all boys or all subjects were not
significant. Forward stepwise regression analysis determined that bone
age (i.e. maturation, primary factor) and gender were the
significant predictors of AUC, GH peak heights, and mean nadir. The
influence of maturation reflects rising sex steroid concentrations, and
the gender differences appear to be because of differences in estradiol
concentrations rather than to body composition or body fat
distribution. |
Introduction |
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The relationship between total body composition and GH release in children and adolescents is unclear. Some investigations have found inverse relationships between the body mass index and GH release (10, 11), whereas others have not (12). Still others have found that the relationship is significant only in pubertal girls (13). The confusion may be due, in part, to the use of the body mass index, a crude marker of adiposity that is especially difficult to interpret during growth because weight changes relative to height consist of increases in fat and lean tissue (14). We have shown that valid estimates of body composition in children and adolescents require the use of a multicompartment model of body composition that corrects for the proportional water content of the fat-free mass (FFM) (15).
GH therapy increases energy expenditure in children (16, 17) and adults (18, 19, 20), but when the increase in energy expenditure was corrected for the increase in metabolically active FFM, accurate measures of body composition were not used (16, 20). We are unaware of published studies clarifying the relationship between energy expenditure and endogenous GH release in children and adolescents. Although aerobic fitness and exercise training enhance GH release in adults (6, 21, 22, 23), the same relationships have not been studied in children. Whereas gender differences in these and other attributes may be prominent in the adult (24), sex differences in body composition and in the GH axis are less well studied before and during adolescence. We hypothesized that the GH release of healthy prepubertal and pubertal boys and girls would be more highly related to the amount of AVF than to criterion estimates of percentage body fat and directly related to aerobic fitness and energy expenditure.
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Methods |
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Prepubertal boys (n = 18), pubertal boys (n = 11), prepubertal girls (n = 12), and pubertal girls (n = 18) were evaluated in a cross-sectional manner. Subjects were placed into pubertal groups based on the stage of secondary sex characteristics as assessed by the method of Tanner (25). Informed consent was obtained from a parent and assent from each child before entrance into the study. Using a triceps skinfold thickness of greater than the 85th percentile as a cutoff, three of the prepubertal boys and two of the pubertal girls were classified as obese. Bone age was determined by the Fels method (26) by an experienced assessor (J.N.R.).
Blood sampling and hormone assays
After the patients’ admission to the General Clinical Research Center at 0800 h, a catheter was inserted into a forearm vein at 1600 h and kept patent with a heparin lock. Serial blood sampling (every 10 min) was initiated at 1800 h and continued until 0600 h. Activity was limited to watching television, reading, and walking, and the subjects had to remain in bed with the lights out after 2200 h. Starting with breakfast, the subjects consumed meals and snacks constant for energy, fat (30% of calories), protein (15% of calories), and carbohydrate (55% of calories) at standard times. The Nichols Luma Tag human GH chemiluminescence assay (San Juan Capistrano, CA) was used to measure the serum GH concentrations. Use of the assay has been previously described (27). The sensitivity of the assay is 0.002 µg/L. The intraassay coefficients of variation (CVs) are 4.9% at 0.2 µg/L, 6.7% at 2 µg/L, and 6.4% at 4.9 µg/L whereas the interassay CVs were 7.2% at both 1.7 µg/L and 4.2 µg/L (28). GH pulse characteristics were assessed by the model-free Cluster algorithm version 6.01 (29).
Serum total testosterone concentration in the 0600-h blood sample was measured by RIA using kits from Diagnostic Products Corp. (Los Angeles, CA). The sensitivity of the testosterone assay was 10.0 ng·dL-1 with an intraassay CV of 5–6% within the range of 100–800 ng·dL-1. The interassay CV ranged from 9.2–12.9% within the range of 70–840 ng·dL-1. Insulin-like growth factor-I (IGF-I) and IGF-binding protein-3 (IGFBP-3) concentrations were measured by RIA (Nichols Institute, San Juan Capistrano, CA). IGF-I concentrations were measured after acid-ethanol extraction and had an intraassay CV of 2.4% and 3.0% at 0.53 ng·mL-1 and 0.92 ng·mL-1, respectively, and an interassay CV of 5.2% and 8.4% at 0.54 ng·mL-1 and 0.82 ng·mL-1, respectively. The sensitivity was 0.06 ng·mL-1. For the IGFBP-3 assay, the intraassay CV ranged from 7.3% at 0.17 µg·mL-1 to 3.8% at 3.08 µg·mL-1, and the interassay CV ranged from 5.3% at 0.60 µg·mL-1 to 6.3% at 31.69 µg·mL-1. The sensitivity was 0.038 µg·mL-1.
Body composition
Body composition was estimated using the four-compartment model of Lohman (14). We recently described and validated the use of this model in children and adolescents (15). In this model, body density is measured by underwater weighing and corrected for residual lung volume by nitrogen washout, total body water by deuterium oxide dilution, and bone mineral content by dual-energy X-ray absorptiometry (15).
Anthropometry
A trained anthropometrist (J.N.R.) completed all measures. Height, waist girth, hip girth, trunk skinfolds (subscapular, chest, midaxillary, suprailliac, and abdominal) and peripheral skinfolds (triceps, biceps, thigh, and medial calf) were measured as recommended by Lohman et al. (30).
Magnetic resonance imaging (MRI)
Subcutaneous and visceral fat areas at the level of the L4-L5 intervertebral space were measured with MRI using a Siemens Vision 1.5T scanner (Islan, NJ). A T1 weighted spin-echo saggital scout scan with a repetition of 500 msec, echo time (TE) of 20 msec, 10-mm slice thickness with a 10-mm gap, 128 x 256 matrix, and two signal averages was used to locate the L4-L5 disk space. Adipose tissue at the L4-L5 levels was assessed using a Dixon imaging sequence phase corrected for magnetic field inhomogeneities. A standard spin-echo pulse sequence was used for the in-phase image. The out-of-phase image was acquired by shifting the 180 degrees refocusing pulse by 1.12 ms. Images were acquired with a slice thickness of 6 mm, matrix of 256 x 256, and TR/TE of 575/15 ms. No oversampling or raw filters were used in the data acquisition, and two acquisitions were used per slice. The images were acquired with 100% gap, followed by a shift in slices that led to a set of contiguous two-dimensional images. For postacquisition processing, a magnetic field inhomogeneity map was calculated from the in-phase and out-of-phase images as previously described (31), which assumes that there are unequal amounts of fat and water in each pixel. This map was then used to unwrap phase shifts induced by the inhomogeneities using a region-growing technique, and used to correct for phase error in the opposed phase image (32). Adding or subtracting the in-phase and opposed phase images resulted in the water and fat images, respectively. The fat- and water-based tissue areas were determined using MedX Software (Sensor Systems, Sterling, VA).
Energy expenditure
The basal metabolic rate (BMR) was measured for 30 min via indirect calorimetry (Deltatrac, SensorMedics, Yorba Linda, CA). Subjects were assessed on waking after the overnight blood sampling at the General Clinical Research Center. To measure the total energy expenditure (TEE) subjects consumed an oral dose of 2H2O (0.5 g•kg-1) and H218O (1.5 g•kg-1). Urine samples were collected at baseline, 4 h, and 5 h, and at 1, 6, and 12 days after dosing. All urine samples were collected between 0800 and 1200 h. The samples were kept frozen at -20 C in cryovials until analysis by isotope ratio mass spectroscopy (Metabolic Solutions, Merrimack, NH). Differences in 2H and 18O in the pre- and postdose urine samples were determined using the unprocessed mass spectrometric data as previously described (33). Linear regression was used to determine the slope and intercept of the relationship between baseline and the normalized isotope 2H and 18O data. The pool sizes for 2H2O (ND) and H218O (NO) were the reciprocals of the intercepts. The intercept of the regression line was the ND/NO ratio. The fractional turnover rates of 2H (kD) and 18O (kO) were determined from the slope of the regression line. The mean daily rate of CO2 production (rCO2, mol•day-1) was calculated by the revised equations of Speakman et al. (34). The mean daily energy expenditure was calculated by multiplying the rCO2 value by 127.5 kcal·mol-1 CO2, the energy equivalent of the typical Western diet.
Peak oxygen consumption (VO2 peak)
VO2 peak was measured using a treadmill (Quinton Q65, Seattle, WA) exercise test. After a walking warm-up, the subjects began at an initial velocity of 3.4–6 mph depending on the size of the child. The initial velocity was then held constant, and the grade was increased from 0 by 2.5% every 2 min until volitional exhaustion. Metabolic data were collected every 20 s during the exercise bout via standard indirect calorimetry procedures using a SensorMedics 2700-Z metabolic cart (Yorba Linda, CA). Heart rates were monitored by echocardiograph. Subjects were given verbal encouragement throughout the test.
Statistical analyses
Group differences in body composition, body fat distribution,
energy expenditure, aerobic fitness, serum hormone concentrations, and
nocturnal GH pulse characteristics were tested with two-way ANOVA [(2)
gender x (2) maturation]. Linear regression analysis was used to
examine the strength of the relationship between GH pulse attributes
and serum testosterone, body composition, body fat distribution,
VO2 peak, and energy expenditure variables. Multiple linear
regression was employed to correct the relationship between energy
expenditure and GH release for the FFM. Forward stepwise regression was
used to determine the combination of variables that most accurately
predicted GH area under the curve (AUC), sum of the GH peak heights
( GH peak heights), and mean nadir GH concentration with attention
to multicolinearity between variables. A maximum of two steps was
examined to maintain an adequate subject-to-predictor ratio (35).
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Results |
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).
The maturation effect for waist/hip circumference (W:H) was
P = 0.08. The males had a lower percentage body fat
(%BF, P = 0.0005), FM (P = 0.02), and
sum of skinfolds (P = 0.05) and a greater height
(P = 0.02) and BMR (P = 0.01) than the
females. There was an interaction for FFM (P = 0.05)
and VO2 peak (P = 0.05), because the
pubertal boys had a greater FFM and VO2 peak than the
pubertal girls did.
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. The pubertal subjects had a
greater AUC (P < 0.0001), mean nadir GH concentration
(P = 0.0002), GH peak heights (P =
0.0002), IGF-I (P < 0.0001), and IGFBP-3
(P = 0.007) concentrations. The girls had a greater
mean nadir GH concentration (P = 0.04) and GH peak
heights (P = 0.05) than the males. The pubertal boys
had greater testosterone concentrations than the other groups (gender
by maturation interaction: P < 0.0001).
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GH peak
heights (r = 0.83, P < 0.0001), but not to the GH
peak number (r = -0.13, P = 0.34) or the peak
interval (r = 0.14, P = 0.29) (data not shown).
Serum testosterone concentration was directly related to GH pulse
parameters of the boys (Table 3).
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. The AUC
was not related to %BF for any subject group. The inverse
relationships between %BF and GH peak heights and mean nadir GH
concentration were stronger for the boys than for the girls or all
subjects combined. As shown in Fig. 2, there were only weak direct relationships between the AVF and GH pulse
characteristics. The waist circumference and VO2 peak were
not related to GH pulse characteristics. In the boys, modest
correlations were found between the sum of skinfolds and AUC (r =
-0.31, P = 0.11), GH peak heights (r =
-0.30, P = 0.11), and mean nadir (r = -0.30,
P = 0.11). The W:H was inversely related to all three
pulse characteristics for all subjects and for the GH peak heights
and mean nadir in the girls (Fig. 3).
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GH peak heights for all girls
(r = 0.37, P = 0.04). There was a modest
relationship between BMR and AUC (r = 0.32, P =
0.09) in the boys. The TEE was related to the AUC (r = 0.48,
P < 0.05) and GH peak heights (r = 0.43,
P < 0.05) for all girls, but not all boys. The only
relationship to be maintained after correcting the BMR and TEE for the
four-compartment estimated FFM was that between the AUC and BMR in the
girls (Table 4).
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)
showed that bone age (maturation) was the primary variable influencing
AUC, whereas gender was a secondary predictor. For GH peak heights,
bone age emerged as the strongest predictor and W:H as a weaker
predictor. The W:H (P = 0.006) and bone age
(P = 0.07) contributed in the stepwise regression to
predict the mean nadir. We also ran the forward stepwise regressions
without W:H as an independent variable because of the potential
spurious results caused by the use of ratios in statistical analyses
(36). When W:H was excluded, bone age and gender were the primary and
secondary predictors of all three pulse attributes, respectively (Table 6).
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Discussion |
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As shown previously (11, 12, 13), pubescents have a greater nocturnal GH
release (AUC) than prepubescents because of an increase in the GH
peak heights and mean nadir GH concentration, rather than number of GH
peaks (Table 2
). Our use of a sensitive chemiluminescent GH assay has
permitted detection of a pubertal elevation in nadir GH concentration
in boys as has been previously reported in girls (10). Pubertal
elevations in GH have been attributed, in part, to concurrent
elevations in sex steroid concentrations (37, 38, 39), particularly
estrogen (40), which in the male is primarily derived from testosterone
via aromatization (40, 41). We found that the testosterone
concentration was directly related to GH release in the boys (Table 3).
There are also gender differences in GH release, because girls have a
greater GH peak heights (P = 0.05) and mean nadir
(P = 0.04) than boys (Table 2). Women also have greater
GH release than men because of a greater GH pulse mass, which has been
attributed to estrogen (42, 43). Estradiol concentrations are not
reported here because several of the prepubertal girls had estradiol
concentrations below the sensitivity of the assay, and in the pubertal
girls, a single blood sample to determine estradiol concentrations is
of limited utility because it is influenced by the phase of the
menstrual cycle. The nonregular menstrual period length of pubescent
girls and the occurrence of menstrual cycles without menstrual bleeding
make it difficult to determine the girls’ menstrual phase when they
are studied.
Few data are available concerning the relationship between endogenous GH release and energy expenditure in children and adolescents. Both the quantity of metabolically active tissue (FFM) and release of GH increase during puberty, requiring correction for the FFM. After a regression-based correction technique, the AUC was still related to the BMR in girls. Thus, the FFM cannot totally account for the direct relationship between GH release and energy expenditure. GH is thought to increase the BMR, in part, by increasing the conversion of T4 to the more metabolically active T3 (20).
We found that there were modest inverse relationships between our
accurate four-compartment model-estimated %BF and pulsatile GH release
( GH peak heights) and basal GH release (mean nadir) in the boys
(Fig. 1). These data suggest that general adiposity affects GH release
(or vice versa) more in boys than in girls. That the basal GH
concentrations were more highly related to body composition than the
peak values agrees with the data of Hindmarsh et al. (44).
Although we do not yet know the precise role of various pulse
attributes in the interaction with body composition, there is evidence
that the peak GH concentration and the nadir concentration may impart
different metabolic signals and gene responses to the target tissues in
humans (45, 46) and rodents (47, 48).
The W:H was also inversely related to GH release (Fig. 3) but the
relationships were much stronger in the girls than in the boys.
Currently, we do not know what the W:H is a marker of in children and
youth. Although others have shown that the W:H is highly related to AVF
in adults (49), we found that the W:H was not related to either the AVF
(r = 0.03, P = 0.84) or the %BF (r = 0.05,
P = 0.72) (data not shown). The W:H is generally
thought to be a marker of abdominal fat distribution (50). Our data
suggest that those girls with a more android distribution of abdominal
fat have lesser amounts of GH released during pulses and a lower nadir
GH concentration, which corresponds to our finding of a lower sum of
peak heights and mean nadir for GH release in boys than girls (Table 2).
Interestingly, other measures of fat distribution; abdominal visceral
adipose tissue (Fig. 2), abdominal subcutaneous adipose tissue, waist
girth, and sum of skinfolds (a marker of total subcutaneous fat) were
not so strongly related to the GH release parameters (Table 3), as was
the W:H. In adults, visceral adipose tissue is the primary negative
determinant affecting neruosecretory activity of the GH axis (6, 9, 51). The lack of relationship in youth is probably because of increases
in both the AVF and GH release during puberty (Tables 1 and 2). This
may explain why we found weak positive relationships between AVF and GH
release parameters, whereas others have found inverse relationships in
adults (6, 9, 51). We contend that because of the overriding effects of
puberty to increase sex steroid and GH release, despite concomitant
increases in FM and AVF, the relationship between AVF and GH release is
not apparent by the current criterion methods and may not become
apparent until early adulthood. In addition, a critical amount of
visceral fat (130 cm2), found in only one of our subjects,
may be necessary before the effects on the GH axis are evident
(49).
Although body composition and energy expenditure were related to GH
release, forward stepwise regression showed that maturation, gender,
and the W:H could best predict the various GH pulse attributes (Table 5). Bone age (maturation) was a common factor in
predicting all three pulse attributes, and the primary factor
influencing AUC and GH peak heights. The secondary factor for AUC
was gender, suggesting that the total nocturnal GH release depends most
on the pubertal status and gender. As discussed above, the greater
total nocturnal GH release during puberty and in girls compared with
boys is thought to be because of differences in sex steroid (mainly
estrogen) concentrations. The secondary factor influencing the GH
peak heights is W:H, because the more android the distribution of
abdominal fat, the smaller the GH peak heights. This would partially
account for the males having smaller GH peak heights than the
females (Table 2). However, the inverse relationship between W:H and
the size of the GH peak heights was strongest in the females. The W:H
was the primary predictor of the mean nadir GH concentration,
suggesting that nadir GH concentrations may influence body fat
distribution and metabolism (or vice versa). Other studies suggest that
low basal concentrations of GH have an important metabolic effect in
the human including increased lipolysis (45).
However, the use of ratios, such as the W:H, in statistical analyses
can produce spurious results and should be interpreted with caution
(36). When the forward stepwise regression analyses were completed and
W:H was omitted as an independent variable, bone age and gender were
the only variables that were related to the various GH pulse parameters
(Table 6). These results support our theory that during puberty, the
maturation process and gender override the influence of body
composition and body fat distribution on GH release.
In conclusion, girls had greater GH release than boys despite the
former’s greater FM and %BF. Thus, in children and adolescents
additional factors, such as the general state of maturation, gender,
and sex steroids are more important than the absolute body composition
or body fat distribution for modulating GH release. Forward stepwise
regression analysis showed that maturation (bone age) and gender were
the primary determinants of total nocturnal GH release, GH peak
heights, and mean nadir GH concentration.
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Acknowledgments |
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Footnotes |
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Received October 2, 1997.
Revised January 8, 1998.
Accepted January 15, 1998.
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
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, pubertal boys;
, prepubertal girls; 
pubertal girls.
GH peak heights, and mean nadir
GH concentration as the dependent variables. Only those variables that
accounted for a significant amount of variance in dependent variable
are shown.