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The Journal of Clinical Endocrinology & Metabolism Vol. 83, No. 6 2006-2011
Copyright © 1998 by The Endocrine Society

Original Studies

Growth Hormone-Binding Protein Directly Depends on Serum Leptin Levels in Adults with Different Nutritional Status1

M. A. Llopis, M. L. Granada, G. Cuatrecasas, X. Formiguera, L. Sánchez-Planell, A. SanmartÍ, A. Alastrué, M. Rull, A. Corominas and M. Foz

Departments of Clinical Biochemistry (M.A.LL., M.L.G., A.C.), Endocrinology (G.C., A.S.), Eating Disorders Unit (X.F., M.F.), Psychiatry (L.S.-P.), and General Surgery (A.A., M.R.), Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain

Address all correspondence and requests for reprints to: Prof. M. Foz, Servei de Medicina Interna, Hospital Universitari Germans Trias i Pujol, C/Canyet s.n, Badalona 08916, Spain.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
The aim of this work was to assess the relationship between GH-binding protein (GHBP) and leptin. Both peptides are nutritionally regulated, but the recent implication of a role for leptin in the GH axis requires further study.

To avoid the sexual dimorphism in leptin values, we performed leptin standardization according to gender (SD score-leptin). The relationship between SD score-leptin and GHBP was studied in 128 adults with different nutritional status [8 groups according to body mass index (BMI)], ranging from severely underweight anorexia nervosa to highly morbid obesity.

Both GHBP and SD score-leptin significantly increased according to BMI within the range from 18–27 kg/m2, whereas no significant differences were found among underweight groups (BMI, <18 kg/m2) or among obesity grades (BMI, >27 kg/m2).

We found a strong correlation between GHBP and SD score-leptin (r = 0.8; P < 0.0001). Multiple regression analysis revealed SD score-leptin to be a significant determinant of GHBP, accounting for 64% of the variation, whereas BMI did not contribute further to explaining changes in GHBP. This suggests a physiological pathway involving both GHBP (the soluble fraction of GH receptor) and leptin. Thus, we might speculate that leptin could be the signal that induces the related nutritional changes observed in GHBP/GH receptor expression.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
NUTRITIONAL status plays an important role in the regulation of the GH axis, because GH secretion is markedly influenced by body composition (1). A component of this axis, the specific high affinity binding protein for GH (GHBP) in humans, derives from GH membrane receptors by proteolytic cleavage and is identical to the extracellular binding domain of this receptor (2, 3). Thus, serum GHBP activity provides an indirect measure of GH receptor status.

During adult life, serum GHBP levels remain relatively constant, but vary widely from individual to individual, related basically to their nourishment status. Thus, undernutrition situations such as anorexia nervosa are associated with elevated GH secretion and low GHBP activity and suggest GH resistance (4, 5, 6), whereas obesity is accompanied by low GH secretion and elevated GHBP levels (6, 7, 8, 9). Moreover, a direct relationship between body mass index (BMI) and GHBP activity has consistently been reported, even among normal weight subjects (10, 11). However, the mechanism underlying the strong link among nutritional status, GH secretion, and GHBP remains unknown.

Leptin, the protein of the adipocyte-specific (ob) gene produced in adipose tissue, has been implicated in the regulation of appetite and whole body energy balance (12, 13). In humans, a strong relationship between serum leptin levels and measurements of body fatness has been demonstrated (14, 15, 16, 17). Recent evidence suggests that leptin plays a role in GH secretion regulation and might be the signal connecting the GH axis with adipose tissue in rats (18). To date, only four studies analyzing the relationship between leptin and GHBP have been conducted in humans: normal weight adults (19), depressed adult patients (20), prepubertal children (21), and obese pubertal children and adolescents (22). The purpose of this paper was to study the connection between GHBP and leptin in adults with a wide range of BMI to ascertain whether serum leptin levels might be a factor linking nutrition with serum GHBP activity.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
One hundred and twenty-eight adults (95 women and 33 men; age range, 17–68 yr; BMI range, 12.6–77.4) were studied. Thirty-four were anorexia nervosa patients whose weight had remained unchanged within 3 months before the study. These patients with anorexia nervosa were divided into 3 groups: group I (BMI, <=14.5 kg/m2) consisted of 7 patients who were severely underweight, group II (BMI, >14.5 kg/m2 and <16 kg/m2) consisted of 8 underweight patients, and group III (BMI, >=16 kg/m2 and <18 kg/m2) consisted of 19 patients with partial weight recovery. Forty-eight were normal weight (group IV; BMI, >=18 kg/m2 and <27 kg/m2), and 12 were overweight (group V; BMI, >=27 kg/m2 and <30 kg/m2) healthy adults. Thirty-four patients with long lasting obesity were studied before entering a weight reduction program; 13 were simple obese (group VI; BMI, >=30 kg/m2 and <40 kg/m2), 11 were morbidly obese (group VII; BMI, >=40 kg/m2 and <50 kg/m2), and 10 were highly morbidly obese (group VIII; BMI, >=50 kg/m2; Table 1Go).


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  		Table 1. Characteristics of the eight groups of subjects

 
A baseline blood sample was drawn after an overnight fast in patients and controls for GH, GHBP, and leptin measurements (GHBP was not available in 19). After extraction, blood samples were maintained at room temperature and centrifuged within 4 h. Serum was stored at -20 C until assayed.

Hormone assays

Serum leptin concentrations were measured by a commercial RIA (Linco Research, St. Louis, MO). The intraassay coefficient of variations (CVs) ranged from 3.4–8.3%, and interassay CVs ranged from 3.6–6.2%. Serum GH concentrations were measured using a solid phase, two-site chemiluminescent enzyme immunometric assay for use with the Immulite Automated Analyzer (Immulite hGH, Diagnostic Products Corp., Los Angeles, CA), which uses standards calibrated against the International Standard 80/505. The assay has sensitivity of 0.05 ng/mL, with interassay CVs ranging from 5.5–6.5%.

GHBP measurement

GHBP activity was measured by the high performance liquid chromatography-gel filtration method of Tar et al. (23). Briefly, recombinant human (h) GH (Humatrope, Eli Lilly Co., Indianapolis, IN) was radiolabeled with [125I]Na using the chloramine-T method modified by Lesniak et al. (24), and purified weekly by gel filtration chromatography. Serum (100 µL) was incubated overnight at 4 C with 100 µL potassium phosphate buffer and 0.1% BSA containing monomeric [125I]hGH (0.5 ng). A parallel incubation was carried out in the presence of an excess of recombinant hGH (2 µg) to evaluate nonspecific binding. The entire incubation mixture was injected into an high performance liquid chromatograph (model series 2, Perkin-Elmer, Norwalk, CT) with a Protein Pack 300 sw column (Waters, Millipore, Milford, MA; 0.75 x 30 cm) to separate bound and free [125I]hGH. The eluate was collected in 30-s fractions, and radioactivity was counted in an automatic {gamma}-counter (GAMBYT, CR-20, Diagnostic Products Corp.). Binding of [125I]hGH was expressed as the radioactivity in individual peak II divided by the sum of the radioactivity in peaks I, II, and III. Binding to peak II-BP was given as a percentage of specific binding, calculated as the difference between total (incubation without unlabeled hGH) and nonspecific (incubation with excess hGH) binding. Interassay CVs were 8.4% and 9.8% at levels of 40.9% and 19.2% GHBP activity, respectively. GHBP activity was corrected for endogenous GH interference at serum GH concentrations above 7 ng/mL (25).

Calculations and statistical analysis

BMI was calculated dividing weight (kilograms) by height (meters)2.

All statistical analyses were made with the statistical software package SPSS, version 6.0.1 (SPSS, Chicago, IL). Data were first tested for normal distribution using the Kolmogorov-Smirnov test of normality. Leptin concentrations were not normally distributed, so they were logarithmically transformed before analysis (log leptin).

To overcome gender differences in leptin concentrations, individual leptin values were standardized according to sex-matched leptin levels observed in the normal weight group as follows: SD score-leptin = {[log (observed leptin) - log (geometric mean)]/SD}. For men: SD score-leptin = (log observed leptin - log 3.7)/0.49; for women: SD score-leptin = (log observed leptin - log 10.4)/0.65.

One-way ANOVA was used to assess potential differences among groups followed by Scheffe’s F test for multiple comparisons. Bivariate correlations and multiple linear regression analyses were performed to test the relationship among GHBP, log (leptin), SD score of leptin, and BMI. All data were expressed as the mean ± SEM. The level of significance was set at P < 0.05.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Hormone results

Hormone results obtained in all groups studied are shown in Table 1Go. In the normal weight group, no differences were found in GHBP activity according to gender, but serum leptin concentrations in men were significantly lower than those in women (P < 0.0001).

Figure 1AGo shows the mean ± SE GHBP activity in all groups studied. One-way ANOVA revealed significantly decreased GHBP activity in the underweight groups (groups I, II, and III) compared to the others, whereas no differences were found among them. GHBP activity was significantly lower in normal weight subjects (group IV) compared to obese patients (groups VI, VII, and VIII), but did not differ from that in overweight subjects (group V). No differences were observed in GHBP activity among groups with BMI above 27 kg/m2 (groups V, VI, VII, and VIII).



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  		Figure 1. Serum GHBP activity (A) and SD score-leptin (B) in all subjects studied: group I, severely underweight (BMI, <=14.5 kg/m2; n = 7); group II, underweight (BMI, >14.5 and <16 kg/m2) (n = 8); group III, partly weight recovered (BMI, >=16 and <18 kg/m2; n = 19); group IV, normal weight (BMI, >=18 and 27 kg/m2; n = 48); group V, overweight (BMI, >=27 and <30 kg/m2; n = 12); group VI, simple obese (BMI, >=30 and <40 kg/m2; n = 13); group VII, morbidly obese (BMI, >=40 and <50 kg/m2; n = 11); and group VIII, highly morbidly obese (BMI, >=50 kg/m2; n = 10). Results are expressed as the mean ± SEM. *, P < 0.05 vs. other groups and P = NS among them; **, P < 0.05 vs. groups VI, VII, and VIII and P = NS vs. group V; ***, P = NS among them.

 
Mean ± SE SD score-leptin results in all groups are shown in Fig. 1BGo. One-way ANOVA showed significantly decreased SD score-leptin in the underweight groups (groups I, II, and III) compared to the others, whereas no differences were found among them. The SD score-leptin was significantly lower in normal weight subjects (group IV) compared to those in obese patients (groups VI, VII, and VIII), but did not differ from that in overweight subjects (group V). No differences were observed in SD score-leptin among groups with BMI above 27 kg/m2 (groups V, VI, VII, and VIII).

Correlation analyses

Bivariate correlation analyses showed a positive correlation between serum GHBP activity and BMI (r = 0.71; P < 0.0001) and between SD score-leptin and BMI (r = 0.78; P < 0.0001; Fig. 2Go, A and B). Moreover, GHBP activity correlated significantly with log leptin concentrations (r = 0.77; P < 0.0001), but when sexual dimorphism was avoided (SD score-leptin), a stronger correlation was found (r = 0.8; P < 0.0001; Fig. 3Go).



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  		Figure 2. Correlation between GHBP and BMI (A; r = 0.71; P < 0.0001) and between SD score-leptin and BMI (B; r = 0.77; P < 0.0001) in all subjects studied (n = 109).

 


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  		Figure 3. Correlation between GHBP and SD score-leptin (r = 0.8; P < 0.0001) in all subjects studied (n = 109).

 
Multiple regression analysis revealed that changes in SD score-leptin were significant determinants of changes in GHBP, whereas the contribution of BMI was not significant (Table 2Go). The same observations were found when logarithmically transformed leptin concentrations were separately analyzed by gender.


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  		Table 2. Multiple regression analysis with GHBP as dependent variable

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Despite the increasing knowledge of leptin physiology, the relationship between the GH axis and leptin has only recently been studied (18, 19, 20, 21, 22, 26, 27, 28). Carro et al. found that leptin antiserum administration to rats leads to a decrease in spontaneous GH secretion, and that leptin administration reverses the inhibitory effect of fasting; thus, they propose that leptin might act as a metabolic signal connecting the somatotroph axis with adipose tissue in rats (18). Both GHBP and leptin have been proposed as good markers of nutritional status (4, 5, 6, 7, 8, 9, 29, 30). Both change acutely with feeding and fasting manipulation (4, 8, 16) and correlate strongly with body fatness measurements (10, 11, 14, 15, 16, 17), but to date only four reports have studied the relationship among BMI, GHBP activity, and leptin in humans (19, 20, 21, 22).

The relationship between body fatness and leptin concentrations is significantly influenced by gender in children and adults (14, 17, 27, 31, 32). In accordance with other studies, serum leptin levels in women in our normal weight group were almost 3-fold higher than those in men; thus, sex must be taken into account when analyzing leptin data (14, 17, 19, 27, 28, 31, 32). Previous reports have found that gender influence can be overcome by correcting leptin levels according to body fat (15, 16, 27), but others have reported that this sexual dimorphism persists after body composition and fat distribution have been adjusted (14, 17, 27, 32). To perform statistical comparisons with GHBP, which is not significantly affected by gender (23), we standardized leptin concentrations according to sex (SD score-leptin) after logarithmic transformation of leptin data. Standardization is a common procedure in age-dependent hormone parameters, but has not yet been used to express male and female leptin levels together.

To our knowledge, no studies on GHBP and leptin in individuals with such a wide range of BMI including different degrees of underweight (ranging from severe underweight to partly weight recovered) and overweight (from overweight to highly morbid obesity) have been reported. As expected, we found that GHBP activity and SD score-leptin increased according to BMI. Both GHBP and SD score-leptin were significantly higher in obese groups than in normal weight adults or anorexic patients and were significantly lower in anorexic compared with normal weight subjects (4, 5, 6, 7, 8, 9, 33). However, we found no further increase in GHBP or SD score-leptin above a BMI of 27 kg/m2 despite almost triplicate BMI. Furthermore, we found no significant decrease in either GHBP or SD score-leptin among the underweight groups (BMI, <18 kg/m2). The fact that leptin levels remained unchanged in the underweight and obese groups supports the hypothesis that leptin is not merely a ponderostat (34). If leptin only informed of the size of the adipose tissue depot, we would expect a progressive increase in its levels parallel to the degree of obesity. Recently, a number of experimental studies have suggested that the relationship between leptin and energy balance does not depend on body fatness alone, but is much more complex, as supported by the close relationship between leptin and thermogenesis (35), the influence of selective micronutrients on leptin expression, and the circadian rhythmicity of leptin levels (29, 34).

In our study, SD score-leptin and GHBP correlated significantly with BMI, in agreement with the findings of others (10, 11, 14, 15, 16, 17). Moreover, a stronger correlation was found between SD score-leptin and GHBP. Multiple regression analyses revealed that changes in SD score-leptin were significant determinants of GHBP variation, whereas BMI did not contribute further to accounting for changes in GHBP. Changes in SD score-leptin explained 64% of the variation in GHBP levels. Similar results were found when the relationship between GHBP and log leptin was analyzed separately in men and women. Our results concur with those of Bjarnason et al. (21), who reported a strong correlation between serum GHBP and leptin concentrations after adjustment for BMI in a group of normal prepubertal children. In the same report, in a group of short children born small for gestational age, serum GHBP correlated with serum leptin levels, whereas correlations between GHBP and BMI, and leptin and BMI were absent. Our results are also in agreement with those reported by Kratzsch et al. (22), who found an association between serum GHBP and leptin levels in a group of obese pubertal children and adolescents, which remained significant after controlling for percent body fat. In contrast, Fisker et al. (19) and Deuschle et al. (20) found no significant correlation between GHBP and leptin when controlling for estimates of body fatness. However, in these reports the adult population studied was smaller than ours.

To date, the role of nutrition-induced alterations in GHBP levels, as well as the precise nutritional factor responsible for the GHBP/GH receptor change, remain unknown. From our findings, we speculate that leptin may be the signal linking nutrition and GHBP/receptor changes, which suggests a physiological pathway involving both GHBP and leptin. We concur with Jorgensen et al. (36), who speculated that GHBP/GH receptor levels may influence the balance or partitioning between the direct effects, those related to lipid and carbohydrate metabolism, and the insulin-like growth factor I (IGF-I)-mediated effects of GH. Low leptin levels observed in undernourished states might, therefore, account for the decreased production of GHBP/receptor and IGF-I, which, through reduced negative feedback, results in increased GH production (4, 5, 6, 30, 33). Thus, in undernutrition, direct metabolic actions of GH in terms of increased lipolysis and glucose sparing are enhanced so that available nutrients may be used instead of growth for energy production and other metabolic functions (4, 30, 36). The opposite occurs in overnutrition and obesity when nutrients are plentiful and can be used largely to promote energy storage and somatic growth. In these situations increased leptin levels could induce GHBP/GH receptor expression. This may explain the normal to high IGF-I levels found in overnutrition despite blunted GH secretion (7, 30). This higher GHBP/GH receptor and IGF-I levels are consistent with the accelerated growth rate observed in well nourished children despite low GH secretion (7, 10, 30), whereas during adulthood, diminished lipolytic GH activity might contribute to perpetuating obesity (36).

Other similarities between these peptides also lead us to think that they are closely linked. Leptin, GH, and other nutrition-related peptides such as tumor necrosis factor-{alpha} share some biochemical and structural analogies. These peptides are involved in the regulation of energy expenditure and body weight (37). Moreover, their receptors belong to the same cytokine receptor superfamily (38, 39, 40). These receptors have been found to have soluble isoforms in addition to the membrane-bound isoforms (40). Thus, leptin, GH, tumor necrosis factor {alpha}, and other cytokines circulate bound to specific binding proteins that modulate the availability of free hormones for their biological actions (2, 3, 41, 42). In this way, GHBP represents the soluble form of the GH receptor, and recently, the presence of leptin-binding proteins in serum, one of which appears to be the soluble leptin receptor Ob-Re, has been demonstrated (3, 41, 42). Further studies are required to clarify these relationships.


   Acknowledgments
 
The authors are grateful to Christine O’Hara for reviewing the English manuscript. Our thanks to Leonardo Pardo of the Department of Statistics of the Faculty of Medicine, Universitat Autònoma de Barcelona, for statistical advice.


   Footnotes
 
1 This work was supported by Grants 94/0034-02 and 95/0735 from the Fondo de Investigaciones de la Seguridad Social, National Health Service, Spain. Back

Received November 14, 1997.

Revised March 5, 1998.

Accepted March 11, 1998.


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 Introduction
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 Results
 Discussion
 References
 

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