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Serum Leptin Levels Correlate with Growth Hormone Secretion and Body Fat in Children¹

International Pediatric Growth Research Center, Departments of Pediatrics (H.F., H.M., S.R., K.A.W., R.B.) and Clinical Nutrition (I.B.), Sahlgrenska University Hospital, Goteborg University, S-413 45 Goteborg; and the Department of Pediatrics, Northern Alusborg Hospital (H.F.), S-461 85 Trollhattan, Sweden

Address all correspondence and requests for reprints to: Dr. Hans Fors, International Pediatric Growth Research Center, Department of Pediatrics, Sahlgrenska University Hospital/East, S-416 85 Goteborg, Sweden. E-mail: hans.fors{at}mailbox.swipnet.se

	Abstract

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The aim of this study was to investigate the relationship among GH secretion, leptin concentrations, and body composition measured with x-ray absorptiometry (DXA) in children. In total, 71 children were investigated, 51 males and 20 females. Their mean chronological age was 10.8 yr (range, 6.2–17.7 ys), and their mean height (SD) was -2.1 (0.63) SD scores. Their mean weight for height SD scores (WH_SDS) was 0.2 (1.18). Body composition was investigated using DXA. Blood samples were taken for analysis of leptin, insulin-like growth factor I (IGF-I), IGF-binding protein-3, and 24-h GH secretion. A positive correlation was found between leptin and total body fat (r = 0.83; P < 0.0001) and when fat was expressed as a percentage of body weight (r = 0.86; P < 0.0001). There were significant (P < 0.0001) relationships between leptin and WH_SDS (r = 0.45) and between leptin and body mass index (r = 0.69). A significant gender difference in leptin levels was found, but this disappeared after adjustment for body fat, as measured by DXA. There were significant (P < 0.001) inverse correlations between leptin and the AUC_b for GH (r = -0.41), leptin, and GH_max (r = -0.38), where AUC_b is the area under the curve above the calculated baseline, and GH_max is the maximum peak during the 24-h GH profile (percent fat and AUC_b for GH, r = -0.43; percent fat and GH_max, r = -0.39). In a multiple stepwise forward regression analysis with leptin as the dependent variable, the percent trunk fat accounted for 77.7% of the leptin variation. With AUC_b for GH as the dependent variable, the percent trunk fat accounted for 20.3% of the variation. With GH_max as the dependent variable, the percent trunk fat accounted for 18.8% of the variation, IGF-binding protein-3 for another 8.5%, and the percentage of fat from arms and legs for another 4.4%.

We demonstrated a strong positive correlation between leptin levels and body fat, a significant negative correlation between leptin levels and GH secretion, and a significant negative correlation between body fat and GH secretion. We have also shown that specific regional fat depots have different relationships with leptin and particular markers of GH secretion.

	Introduction

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GH IS INVOLVED in the regulation of body composition through its lipolytic and anabolic effects (1). Obese individuals have low serum GH concentrations (2, 3, 4). Leptin, the product of the ob gene (5), is thought to play a key role in the regulation of body fat mass (6). Leptin concentrations in the blood correlate with measures of body fat in rodents (7), and similar correlations have been found in both lean and obese adults (8, 9, 10) and in children (11, 12). The correlation is better when specific measures of fat are used rather than body mass index (BMI) (10), as in children, BMI underestimates excess adiposity (13, 14). It is also reported that GH replacement therapy lowers plasma leptin and fat stores without affecting BMI in adults with GH deficiency (15). Gender differences in leptin levels have been reported, especially in late puberty and adolescence, with lower levels in males (16, 17). This has been explained at least in part by a suppressive effect of androgens on leptin levels (18). However, when differences in body composition were taken into account, gender had no significant influence on leptin levels (19).

Yu et al. (20) suggested that leptin plays an important role in hypothalamic-pituitary function. Thus, leptin can act as a metabolic signal to regulate GH secretion (21). Furthermore, a correlation has been found between leptin and GH-binding protein, suggesting that leptin may affect peripheral GH sensitivity (22).

When investigating the relationship between GH secretion, other hormones, and body composition in children, it is important to use methods that are as accurate and precise as possible. The anthropometric methods available, i.e. BMI, weight for height SD score (WH_SDS), and skinfold measurements of sc fat, are all relatively inaccurate. More accurate methods, such as computed tomography, magnetic resonance imaging, and various isotope measurements, are expensive. In addition, computed tomography and some isotope measurements expose children to considerable radiation. These methods are therefore not suitable for repeated studies in children. Dual energy x-ray absorptiometry (DXA) is a technique that measures body composition in a three-compartment model: bone mineral content, lean tissue, and fat mass. DXA measurements are precise, require only moderate cooperation from the subject, and deliver a very low radiation dose (23). These advantages make DXA an attractive tool for investigation of body composition and bone mineral density in children. Furthermore, in an animal model, DXA-derived body compartment values have been shown to compare well with those of chemical carcass analysis in the weight range of children (24).

In this study we analyzed various components of the GH-insulin-like growth factor (IGF) axis, serum leptin concentration and body composition, using DXA, and auxology in a group of children admitted for investigation of short stature or as normal controls to determine the relationship among GH secretion, leptin concentrations, and body composition.

	Subjects and Methods

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Study group

Ninety-eight children were originally enrolled. They were consecutively recruited from children admitted to the Children’s Hospital, Sahlgrenska University Hospital/East (Goteborg, Sweden), for investigation of short stature (89 children) or as normal controls (9 children). Information from 71 children was available for analysis after 27 were excluded because of disease, i.e. previous malignancies, chromosomal aberration, or malabsorption. Except for differences in GH secretory capacity, the children were healthy and had normal thyroid, liver, and kidney functions, and celiac disease was excluded.

Study protocol

In all children, body composition was investigated using DXA. Blood samples were taken for analysis of leptin, IGF-I, IGF-binding protein-3 (IGFBP-3), and 24-h GH secretion. Auxological/clinical characteristics of the study group are given in Table 1. Height was compared with Swedish reference values (25). WH_SDS was calculated according to Karlberg and Albertsson-Wikland (26) and BMI as standard practice (27). Pubertal development was estimated according to the method of Tanner et al. (28), and testicular volume was measured according to the method of Zachmann et al. (29). The study was approved by the ethical committee of the Medical Faculty, University of Goteborg. Informed consent was obtained from all children and their parents.

DXA was performed using a Lunar Corp. DPX-L scanner (Scanexport Medical, Helsingborg, Sweden). The system uses a constant potential x-ray source, and a K-edge filter to achieve a congruent beam of stable dual energy radiation. Whole body scans were performed in the fast scan mode, and body compartments were analyzed using the extended research mode of software version 1.31. The precision error of the scanner, as determined from double examinations in 10 healthy subjects, was 1.7% for fat mass.

Hormone measurements

From all 71 children, 24-h GH profiles were obtained by integrated 20-min blood sampling for determination of GH secretory patterns and GH secretion rate (30). Serum leptin, IGF-I, and IGFBP-3 concentrations were measured in single samples collected between 1000–1400 h during the 24-h sampling period. After withdrawal, blood samples were kept at room temperature and centrifuged within 24 h. Serum was stored at -20 C until assayed.

Leptin was measured by RIA (Linco Research, Inc., St. Charles, MO). The assay has a detection range of 0.22–100 µg/L and, in our hands, intraassay coefficients of variation of 7.0% at 2.4 µg/L and 4.9% at 14.0 µg/L. The corresponding interassay coefficients of variation were 9.6% and 6.7%, respectively (17).

GH concentrations were measured using a polyclonal antibody-based immunoradiometric assay (Pharmacia & Upjohn, Inc., Uppsala, Sweden) (31, 32) with the First International Reference Preparation 80/505 as standard. The intraassay coefficient of variation was between 3.2–3.5%, and the interassay coefficient of variation was between 2.7–5.0%.

Twenty-four-hour GH profiles were performed for pulse detection, Fourier time series analysis, and peak analysis (30). We calculated the area under the curve above the calculated baseline (AUC_b) and the maximal level of GH (GH_max). The rate of GH secretion per 24 h (GH_t) was estimated using a simplified deconvolution formula, as reported previously (33).

IGF-I concentrations were measured by an IGFBP-blocked RIA without extraction and in the presence of an approximately 250-fold excess of IGF-II (Mediagnost GmbH, Tubingen, Germany) (34). The intraassay coefficient of variation for the IGF-I assay was 4.4% at 219 µg/L.

IGFBP-3 concentrations were determined using an RIA method as reported previously (Mediagnost GmbH) (34). The intraassay coefficient of variation was 5.6% at 2927 µg/L (35).

Statistical methods

Correlations were tested using Pitman’s nonparametric permutation test (36), and Pearson’s correlation coefficient was calculated. To determine the independent effects of variables on the serum leptin concentration, GH_max, and AUC_b for GH, multiple stepwise forward regression analysis was performed. In this analysis, GH_max and AUC_b for GH were log transformed to normalize the distributions. Differences were considered significant P 0.05.

	Results

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Leptin and body composition

A positive correlation was found between leptin levels and total body fat (r = 0.83; P < 0.0001; Fig. 1a) and when fat was expressed as a percentage of body weight (body fat/weight; r = 0.86; P < 0.0001; Fig. 1b). There was no significant difference in the correlation between leptin and percentage of total body fat compared with regional fat percentages (Table 2). A relationship was found between leptin and WH_SDS (r = 0.45; P < 0.0001; Fig. 1c) and between leptin and BMI (r = 0.69; P < 0.0001; Fig. 1d).

View larger version (25K): [in this window] [in a new window]   Figure 1. Correlations between leptin and various estimations of body composition. a, Total body fat (grams; r = 0.83; P < 0.0001). b, Fat as a percentage of body weight (r = 0.86; P < 0.0001). c, WHSDS (r = 0.45; P < 0.0001). d, BMI (r = 0.69; P < 0.0001). , Boys; , girls.

Leptin and GH secretion

Table 3 summarizes various hormone measurements and their relationships with leptin levels. An inverse correlation was found between leptin and the plasma concentration of GH, estimated as AUC_b (r = -0.41; P < 0.001) and between leptin and GH_max (r = -0.38; P < 0.001). There was, however, no significant correlation between leptin and the estimated GH secretion rate, GH_t (r = 0.18), IGF-I (r = 0.24), or IGFBP-3 (r = 0.21).

There was a significant negative correlation between percent body fat and AUC_b for GH (r = -0.43; P < 0.001) and GH_max (r = -0.39; P < 0.001); however, there was no significant correlation between percent body fat and GH_t (r = -0.04).

There was a significant inverse correlation between WH_SDS and AUC_b for GH (r = -0.40; P < 0.01) and between WH_SDS and GH_max (r = -0.33; P < 0.01). No significant correlation was found between WH_SDS and GH_t (r = -0.11).

There was a significant negative correlation between BMI and AUC_b (r = -0.32; P < 0.01), and between BMI and GH_max (r = -0.29; P < 0.01). No significant correlation was found between BMI and GH_t (r = 0.15).

Multivariate analysis

When a multiple stepwise forward regression analysis was performed with leptin as the dependent variable, the percent body fat accounted for 71.3% of the leptin variation, with age, gender, percent body fat, GH_max, AUC_b for GH, GH_t, IGF-I, IGFBP-3, pubertal stage, WH_SDS, heightSD score, weight SD score, and BMI also as independent variables. When regional fat distribution was considered, and the percent trunk fat and the percentage of fat from arms and legs were added to the analysis, the percent trunk fat accounted for 77.7% of the leptin variation. With AUC_b for GH as the dependent variable, the percent trunk fat accounted for 20.3% of the variation, with no other variable contributing significantly. With GH_max as the dependent variable, the percent trunk fat accounted for 18.8% of the variation. In addition to the percent trunk fat, IGFBP-3 (8.5%) and the percentage of fat from arms and legs (4.4%) contributed significantly to this variation.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The results of the present study support the existing evidence for a close correlation between leptin levels and body fat. Furthermore, we have shown a significant correlation between leptin levels and GH secretion and between body fat and GH secretion, using DXA to measure body composition.

DXA is a relatively new method for assessing body composition. It provides measurements of body fat mass as well as lean tissue and bone mineral content. The main advantage of DXA is its precision compared with other clinically available methods. Although the accuracy of DXA is still not fully validated, it has been shown to provide accurate measurements of body composition in several studies (37, 38). The possibility of obtaining regional as well as total body measurements is another advantage of the technique. As the procedure is associated with a very low radiation dose (23), it can be used safely for repeated measurements. Although DXA cannot distinguish between sc and visceral fat deposits, we regard this technique as preferable for measurements of body fat compared with other methods that estimate body composition indirectly, such as BMI or WH_SDS.

As in previous reports, we found higher levels of leptin in girls than in boys. The difference remained after adjustment for age and BMI, but disappeared after adjustment for body fat, as measured by DXA. This is in agreement with the findings of Nagy et al. (19). We conclude that the reported gender difference in leptin levels in prepubertal children (16) and adults (10) may at least in part be explained by differences in body composition.

We have also shown, as in our previous report in children born small for gestational age (35), a negative correlation between spontaneous GH secretion and leptin. This negative correlation between spontaneous GH secretion and leptin is probably due to variation in body fat, as there is a negative correlation between spontaneous GH secretion and fat.

The relationship between GH and body composition has been known for a long time (39); however, we are not aware of reports on correlations between GH secretion and a more direct measure of body fat, i.e. DXA.

The limitations of WH_SDS and BMI as indexes of body fat are demonstrated by the weaker correlations between leptin and body fat than the correlation between leptin and body fat as measured by DXA.

In the multiple regression analysis with AUC_b for GH as the dependent variable, the percentage of trunk fat was the only variable that added significantly to the model. With GH_max as the dependent variable, the percentage of trunk fat accounted for 18.8% of the variation, IGFBP-3 contributed another 8.5%, and the percentage of fat from arms and legs made up another 4.4% of the variation in GH_max. This is interesting, as it is generally believed that IGF-I and IGFBP-3 concentrations are highly GH dependent. However, compared with measures of body fat, these factors explained very little of the variation in GH secretion.

Interestingly, it is the GH measures from the GH peaks, i.e. GH_max and AUC_b, that correlate with both the fat mass and leptin levels. These are the same parameters that correlate best with growth (43, 44). In contrast, the total GH secretion rate [GH_t; estimated from the AUC_t, with a simplified formula (45) using the total GH secretion from zero line and the weight of the child] does not correlate with either leptin levels or fat mass. Today we can only speculate if this is due to fact that the baseline GH secretion is of importance or if the weight of the child still interferes.

Previous studies have found that obesity depresses GH concentrations and endogenous GH release (1, 4), and loss of fat mass leads to an increase in GH secretion (40). These reports are indirectly supported by our findings of an inverse correlation between GH and fat mass. It is also reported that increased adiposity is associated with reduced GH secretion in normal growing children (2). We found that fat from different body compartments was not equally correlated with the variation in GH secretion, which is interesting in relation to the findings of Beshyah et al. (41), who reported an upper body fat deposition in adults with hypopituitarism and that GH treatment had beneficial effects on body fat distribution. Bengtsson et al. (42) also demonstrated that GH treatment of adults with GH deficiency leads to redistribution of fat from visceral to sc depots.

In conclusion, we have demonstrated, using DXA, a strong correlation between leptin levels and body fat, a significant negative correlation between leptin levels and GH secretion, and a significant negative correlation between body fat and GH secretion in a group of children investigated for short stature or as normal controls. We have also shown that specific regional fat depots have different relationships with leptin and particular markers of GH secretion.

	Acknowledgments

 
We are grateful to Birgitta Svensson, Carina Ankarberg, Ann-Sofie Petersson, and Irene Leonardsson for their technical support; to the DXA operators at the Clinical Metabolic Laboratory, Sahlgrens University Hospital; to the personnel at Ward 34T, the Children’s Hospital, Goteborg; and to all of the children who participated in this study.

	Footnotes

 
¹ This work was supported by grants from Western Sweden Foundation (no. 159), the Swedish Medical Research Council (no. 7509), the Wilhelm and Martina Lundgren’s Foundation, and Pharmacia & Upjohn, Inc.

Received October 29, 1998.

Revised February 3, 1999.

Revised June 3, 1999.

Accepted June 21, 1999.

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