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The Journal of Clinical Endocrinology & Metabolism Vol. 82, No. 1 123-128
Copyright © 1997 by The Endocrine Society

Clinical Studies

Abdominal Fat Determines Growth Hormone-Binding Protein Levels in Healthy Nonobese Adults1

Sanne Fisker, Nina Vahl, Jens Otto Lunde Jørgensen, Jens Sandahl Christiansen and Hans Ørskov

Department of Endocrinology and Diabetes (S.F., N.V., J.O.L.J., J.S.C.) and Institute of Experimental Clinical Research (H.O.), University Hospital of Aarhus, Aarhus, Denmark

Address all correspondence and requests for reprints to: Sanne Fisker, M.D., Department of Endocrinology and Diabetes, Aarhus Kommunehospital, Nørrebrogade 44, DK-8000 Aarhus C, Denmark.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
The circulating high affinity GH-binding protein (GHBP), which derives from the extracellular domain of the hepatic GH receptor, correlates inversely to GH levels and directly to body mass index (BMI) in healthy adults. As GH secretion and adiposity are also interrelated, we tested the hypothesis that body composition, more than GH, determines GHBP levels in healthy adults. Forty-two healthy adults [21 females and 21 males; mean age, 39.4 yr range, 27–59 yr); mean BMI, 23.9 kg/m2 (range, 18.9–34.7 kg/m2)], underwent anthropometric measurements (BMI, W/H ratio, computed tomography scan, dual energy x-ray absortiometry (DEXA) scan, and bioimpedance) in addition to two GH stimulation tests (arginine and clonidine) and a 24-h GH profile. By simple linear regression, serum GHBP correlated positively to several indices of adiposity: intraabdominal fat (r = 0.537; P = 0.001), sc abdominal fat (r = 0.680; P < 0.001), BMI (r = 0.483; P = 0.001), W/H ratio (r = 0.452; P = 0.003), total body fat (DEXA scanning; r = 0.503; P = 0.002), and body fat (bioimpedance; r = 0.354; P = 0.023). Lean body mass estimated by DEXA scan was negatively associated with GHBP (r = 0.541; P < 0.001). GHBP was inversely proportional to arginine-stimulated GH release (r = -0.346; P = 0.027) and negatively associated with several measures of spontaneous GH release as estimated by deconvolution analysis (GH mass, GH production rate, and mean GH; r = -0.371; P = 0.017, r = -0.393; P = 0.011, and r = -0.343; P = 0.028, respectively)). With multiple linear regression analyses, indices of adiposity were significant determinants of GHBP levels, whereas GH status did not contribute independently to the prediction of GHBP. Neither insulin-like growth factor I nor fasting insulin levels correlated to GHBP levels.

In conclusion, GHBP levels in normal adults seem to be determined by abdominal fat mass rather than GH secretion.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
THE HIGH affinity GH-binding protein (GHBP) corresponds to the extracellular domain of the GH receptor (GHR), and in humans and rabbits, this GHBP is derived from the GHR by proteolytic cleavage (1, 2, 3, 4). The biological actions and the regulation of circulating GHBP are not clarified. The high affinity GHBP has been shown to bind, on the average, about 40–50% of circulating GH (5, 6, 7), and this complex formation seems to protect GH from elimination and degradation (8, 9). On the other hand, the high affinity GHBP may also modulate GH action by inhibiting binding of GH to tissue receptors (10, 11, 12). Finally, studies in experimental animals suggest that circulating GHBP mainly reflects hepatic GHR density (13). Thus, a parallel between GHBP levels and GHR status in humans has been documented in Laron-type dwarfism, which is due to mutations in the gene for the GHR and is associated with very low levels of GHBP (14, 15, 16, 17). There is also convincing evidence to suggest that a subgroup of children with so-called idiopathic short stature has low serum GHBP levels and exhibits partial insensitivity to GH due to mutations in the GHR gene (18, 19).

Somewhat surprisingly, conditions associated with distinctly altered GH secretion, such as GH deficiency and acromegaly, have not shown reproducible abnormalities in GHBP levels (18, 20, 21, 22, 23, 24, 25, 26, 27), whereas different catabolic conditions, such as long term fasting, type 1 diabetes mellitus, and liver cirrhosis, exhibit low GHBP levels (28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39).

High levels of GHBP have been reported in morbid obesity (29, 40), and even among clinically nonobese subjects a weak positive correlation between body mass index (BMI) and GHBP level has been reported (16, 41, 42, 43, 44). Furthermore, GHBP concentrations may vary considerably among apparently normal subjects in cross-sectional studies (24, 27, 45). Whether this intersubject variability in GHBP levels relates to differences in relative adiposity or the topographical distribution of fat has not been addressed to date. As BMI is an insensitive marker of adiposity among clinically nonobese subjects, it could be speculated that more specific methods might unravel hitherto unrecognized associations between GHBP levels and body composition.

To test this hypothesis we measured GHBP levels in a group of normal adults in whom detailed data on body composition were obtained by means of computed tomography (CT) scans, dual energy x-ray absortiometry (DEXA) scans, bioelectrical impedance, and conventional anthropometry. Furthermore, the same group was well characterized regarding GH secretion by means of GH stimulation tests and recordings of 24-h endogenous GH secretion. Our data strongly suggest that relative adiposity is the major predictor of serum GHBP concentrations in normal adults.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Forty-two healthy adults (21 females and 21 males; mean age, 39.4 yr; range, 27–59) participated in the study. They comprised 2 age groups with mean ages of 29.5 ± 1.8 yr (n = 23) and 50.8 ± 3.4 yr (n = 19), respectively. The study was approved by the regional ethics committee, and the subjects gave informed consent.

For determination of GH secretion, the participants underwent two GH stimulation tests, an L-arginine infusion (0.5 g/kg; maximum, 35 g) during 30 min and clonidine stimulation (150 µg/m2, orally). The two tests were performed on separate occasions and in random order. Furthermore, a 24-h GH profile was determined. Deconvolution analyses on spontaneous GH concentrations measured every 20 min for 24 h were performed by Veldhuis et al. as previously described (46).

The amount of intraabdominal (visceral) fat, abdominal sc fat, maximal anterior-posterior abdominal diameter, and the muscle/fat ratio of the midthigh region were evaluated by CT with a Somatom Plus-S scanner (Siemens, Erlangen, Holland). The areas scanned comprised 10-mm cross-sectional slices at the middle thigh and the umbilicus using 120 kV and 330 milliamperes. All scans were performed by the same technician, and the scans were analyzed blindly by the same radiologist. The percentage of body fat and lean body mass were measured by DEXA using a Hologic QDR-2000 densitometer (Waltham, MA). The same technician performed all scans. Whole body resistance was measured using the BIA 101 (RJL-Systems, Detroit, MI). The percentage of body fat and lean body mass were estimated by the software supplied with the BIA 101. Additional anthropometric measurements comprised BMI and waist/hip ratio (W/H).

Serum GHBP was analyzed in a newly developed in-house time-resolved fluoroimmunoassay for functional GHBP (27) In brief, the assay was performed in plates from the DELFIA GH kit (Wallac Oy, Turku, Finland), coated with a monoclonal anti-GH antibody. Calibrator/serum was dispensed in duplicate wells followed by a GHBP-saturating amount of recombinant human GH. Finally, Eu3+-labeled antibody against GHBP (Mab 263, Agen Biomedical, Queensland, Australia) was added. Plates were incubated for 20 h at 4 C and then washed six times with wash solution (Tris-HCl-buffered NaCl solution with sodium azide and polysorbate 20, pH 7.8). DELFIA enhancement solution (Wallac Oy) was added, and fluorometry was performed (fluorometer model 1232, Wallac Oy).

The calibration curve was prepared by dilution of recombinant GHBP (fully glycosylated GH receptor ectodomain, donated by Lars Skriver, Protein Technology, Novo Nordisk, Copenhagen, Denmark). The assay sensitivity was 0.044 nmol/L. The average intraassay coefficient of variation (CV) was 3.44%. The average interassay CVs at GHBP concentrations of 0.56 and 1.40 nmol/L were 12% and 6.3%, respectively. Insulin-like growth factor I (IGF-I) analyses were performed by an in-house time-resolved-immunofluorometric assay after extraction of serum to remove binding proteins as previously described (47). GH measurements were performed by a DELFIA assay (Wallac Oy). Intra- and interassay CVs ranged from 1.8–3.0% and 1.6–2.3% for 0.71–31.4 µg/L GH, respectively. Insulin analyses were performed by RIA as previously described (48).

Data on BMI, W/H, DEXA scans, CT scans, bioimpedance, and stimulated GH were reported previously (49).

Statistical analyses

Differences between groups were tested with Student’s t test. Simple linear and multiple linear regression analysis were used to correlate variables. P < 0.05 was considered significant. In multiple linear regression analyses, a protected P < 0.05/number of independent variables was considered significant. Results are expressed as the mean ± SEM, unless otherwise stated.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
There was no difference in GHBP levels between the two age groups (Fig. 1AGo). Furthermore, there were no gender-related differences in GHBP (Fig. 1BGo). Several features of spontaneous GH release were found to correlate negatively with GHBP. In a deconvolution analysis, GH mass, GH production rate, and mean GH correlated negatively with GHBP, whereas GH amplitude did not (Fig. 2Go). Furthermore, basal GH, half-life, bursts, amplitude, and intervals from the deconvolution analysis did not correlate with GHBP (data not shown). Peak GH values after arginine stimulation test also correlated negatively with GHBP (r = -0.346; P = 0.027), whereas the clonidine-stimulated GH did not (r = 0.081; P = 0.317). GHBP levels correlated positively with BMI and W/H ratio (r = 0.483; P = 0.001 and r = 0.452; P = 0.003, respectively). To evaluate a possible association between GHBP levels and GH sensitivity, we correlated serum GHBP to the ratio of mean 24-h GH levels over IGF-I, with the assumption that a low ratio denotes high GH sensitivity and vice versa. GHBP exhibited a significant inverse correlation with the mean 24-h GH/IGF-I ratio (r = 0.39; P = 0.014; Fig. 3Go). Body composition measurements determined by CT scanning revealed positive correlations between estimates for body fat and GHBP (Fig. 4Go, A–C). The muscle/fat ratio, which is a relative estimate of lean body mass, did not correlate to GHBP (Fig. 4DGo). Relative body fat, determined by DEXA scan and bioimpedance, correlated positively with GHBP (r = 0.320; P = 0.002 and r = 0.354; P = 0.023, respectively), whereas relative lean body mass correlated negatively (r = -0.541; P < 0.001). In a multiple linear regression indexes of adiposity (abdominal) were significant determinants of GHBP levels, whereas GH secretion (spontaneous and stimulated) did not contribute significantly to the prediction of GHBP. Selected results of multiple linear regression analyses with two independent factors representing GH secretion, stimulated or spontaneous, and estimates of body composition, respectively, are shown in Table 1Go. Introducing age and gender in a multiple linear regression did not add to the prediction of GHBP. IGF-I and fasting insulin levels did not correlate to GHBP levels in either simple linear or multiple linear analyses of regression (data not shown).



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  		Figure 1. A, Correlation between age and GHBP in 42 healthy adults (21 men and 21 females). B, GHBP in younger females (YF; n = 11), older females (OF; n = 10), younger males (YM; n = 12), and older males (OM; n = 9).

 


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  		Figure 2. Correlation between GH mass and GHBP (A), GH production rate and GHBP (B), amplitude and GHBP (C), and mean 24-h GH and GHBP (D).

 


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  		Figure 3. Correlation between mean 24-h GH/IGF-I and GHBP. •, BMI less than 24 kg/m2; {triangledown}, BMI more than 24 kg/m2.

 


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  		Figure 4. Correlation between abdominal sc fat and GHBP (A), intraabdominal fat and GHBP (B), maximal abdominal anterior-posterior diameter and GHBP (C), and muscle-fat ratio for the right femur and GHBP (D). The anthropometric measurements were performed by CT scan.

 

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  		Table 1. Multiple linear regression analyses

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
This study demonstrates that the high affinity GHBP in human serum is positively correlated to several estimates of body fat, in particular sc and visceral abdominal fat, whereas GH secretion seems of minor importance in the prediction of GHBP. Furthermore, we did not find correlations between GHBP and either age or gender.

Using simple linear regression analysis, serum GHBP levels were negatively associated with GH secretion, both stimulated and spontaneous. It has previously been found that spontaneously secreted GH correlates negatively to GHBP in children (50), but the observation that this also applies to stimulated GH release is new. It has been argued that serum GHBP mainly mirrors the amount of GH receptor, which, in turn, is regulated by the GH level, although this has only been demonstrated in animal models (13). Disturbances in GH secretion, e.g. GH deficiency, however, are associated with considerable changes in body composition (51, 52, 53, 54, 55, 56, 57, 58, 59, 60). Furthermore, multiple linear regression analysis suggests that the correlation between GH secretion and GHBP levels is secondary to changes in adiposity.

It has previously been demonstrated that age is negatively correlated to GHBP (24, 45, 61), with one exception (20). Our data could not confirm a correlation of age to GHBP. An explanation for this might be that the age range in the present study was only from 27–59 yr and displayed a dichotomous distribution. Although we did not find any significant influence of age on GHBP, there was a tendency for increased GHBP with age, which could be due to the influence of significantly increased body fat percentage with age. It is well known that GH decreases with age (62). If we accept that GH and GHBP are inversely correlated, as suggested by our results of simple linear regression analyses between several estimates of GH secretion and GHBP, GHBP should increase with age, but this was not the case. This controversy suggests that the regulation of GHBP is complex and supports the idea that GH secretory status is not a predominant regulator of GHBP.

Disagreement exists about the possible influence of gender on GHBP levels (20, 24, 45, 63). We did not find different GHBP levels in age-matched males and females. A possible explanation for the ambiguous findings could be an unrecognized influence of the fat distribution. In the present study, introducing gender in a multiple linear regression analysis together with estimates of abdominal fat did not reveal any influence of gender on GHBP levels.

The relation between IGF-I and GHBP is also not fully clarified (32, 37, 61, 64). IGF-I is mainly determined by GH, nutritional status, and perhaps insulin. From our results it seems that IGF-I is not a significant determinant of GHBP, as IGF-I did not contribute to the prediction of GHBP in a multiple linear regression compared with estimates of body fat and GH secretion. By contrast, the ratio of mean 24-h GH over IGF-I showed a significant inverse correlation with GHBP. When accepting IGF-I as an index of GH action, our observation suggests that GHBP levels to some degree reflect GH sensitivity in subjects not suspected of having defects in the GHR gene. We also think that our finding extends and accords with the observation of high GHBP levels in obesity and low GHBP levels in catabolic conditions, inasmuch as obese subjects exhibit IGF-I levels that are inappropriately high compared to their GH secretory status, whereas catabolic conditions are characterized by low IGF-I levels and hypersomatropinemia.

It has previously been demonstrated that GHBP is positively associated with BMI (24, 65), but this anthropometric measurement does not provide information about the distribution of body fat. Furthermore, BMI is only indirectly associated with fat mass and is a weak marker of adiposity among clinically nonobese subjects. We have demonstrated that specific measurements of fat mass, in particular abdominal fat mass, correlate closely to the GHBP levels, suggesting that abdominal fat may play a key role in the regulation of GHBP. The liver has a very high density of GH receptors (66), and circulating GHBP is traditionally assumed to originate from the liver. Accepting the liver as the main source of circulating GHBP, the production might be regulated by portal levels of metabolites and hormones, such as insulin and fatty acids. In this study, we did not find any significance of fasting insulin in the prediction of GHBP levels. It could be speculated that metabolites from the visceral fat mass were conveyed to the portal vein and thereby influenced hepatic GHBP release. Adiposity is associated with decreased GH levels (67), and abdominal adiposity, especially, has been found to determine decreased GH secretion in healthy adults (49). It could serve as a compensatory mechanism by increasing GHBP and thereby protecting GH from elimination. In an attempt to detect the origin of circulating GHBP, it has recently been reported that GHBP levels are identical in venous and arterial plasma and considerably lower in lymph, suggesting that GHBP is not produced peripherally. Lymph was collected from the lower extremities (68). That particular study, however, does not exclude the possibility that GHBP could arise from abdominal tissues drained by the portal circulation.

In summary, in healthy, clinically nonobese adults, significant positive correlations were observed between GHBP and abdominal adiposity. In multiple linear regression, this correlation was stronger than the inverse correlation between GHBP and GH secretion. This observation raises the intriguing question of whether GHBP arises from GH receptors in visceral adipose tissues. Further investigation is needed to provide more information on the function, regulation, and origin of GHBP.


   Acknowledgments
 
The excellent technical assistance of Inga Bisgaard is appreciated. Dr. Kim Brixen, Aarhus Amtssygehus, is thanked for carrying out the DEXA scans. Technician Lisbeth Thingholm and Dr. Anne Grethe Jurik, Aarhus Kommunehospital, are thanked for performing and analyzing the CT scans.


   Footnotes
 
1 Presented as an abstract at the International Congress of Endocrinology, San Francisco, CA, 1996. Back

Received June 19, 1996.

Revised August 4, 1996.

Accepted September 4, 1996.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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