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Relationship between subcutaneous fatness and leptin in male athletes


Medicine and Science in Sports and Exercise: August 2001 - Volume 33 - Issue 8 - p 1324-1329
BASIC SCIENCES: Original Investigations

SUDI, K., J. JÜRIMÄAE, D. PAYERL, E. PIHL, R. MÖLLER, E. TAFEIT, and T. JÜRIMÄE. Relationship between subcutaneous fatness and leptin in male athletes. Med. Sci. Sports Exerc., Vol. 33, No. 8, 2001, pp. 1324–1329. Purpose: Circulating leptin is low in trained subjects and closely related to body fat content. However, data are scarce as to whether differences exist in the relationship between different estimates of adiposity, metabolic parameters, and leptin in endurance- and resistance-trained male athletes. We investigated this relationship with special emphasis on subcutaneous fatness and its distribution.

Methods: 20 endurance (ET) and 17 resistance (RT) athletes recruited from different kind of sports were studied. Fat-free mass (FFM) was estimated by means of impedance and fat mass (FM) was calculated. Subcutaneous fat (SAT) and its distribution was measured by means of the optical device Lipometer at 15 body sites (SAT-layers; from 1-neck to 15-calf) on the right side of the body. Fifteen SAT-layers were summed to calculate SAT. Blood samples were obtained for determination for leptin, insulin, and glucose. Insulin resistance was calculated through the fasting insulin resistance index (FIRI; [insulin × glucose/25]).

Results: RT-athletes had a greater body mass and body fat content than ET-athletes, but no differences were found for leptin and metabolic parameters. In all athletes, estimates of adiposity were correlated to leptin. However, in ET-athletes FM (P < 0.05), FFM (P < 0.05), and SAT (P < 0.001) but not metabolic parameters were correlated to leptin. In RT-athletes, SAT (P < 0.0001), metabolic parameters (all P < 0.05), but not FM and FFM were in significant relationship with leptin. Stepwise regression revealed SAT as the main determinant for the variation in leptin in all athletes (adj. R2 = 0.52, P < 0.0001).

Conclusion: The results suggest that estimates of adiposity and metabolic parameters are associated with leptin in a sport-specific manner. Whereas leptin might be regulated by overall subcutaneous fatness in athletes, our study does not imply a main influence of fat patterning on leptin in this group of trained subjects.

Institute of Sport Pedagogy, Faculty of Exercise and Sport Sciences, University of Tartu, ESTONIA; and Institute for Sport Sciences and Institute for Medical Chemistry and Pregl-Laboratory, Karl-Franzens University, Graz, AUSTRIA

May 2000

November 2000

Leptin, the product of the ob gene (32), is involved in the regulation of body weight and energy expenditure (3). Besides total body fat content, which is an important determinant for circulating leptin in children (4,6) and adults (1), body fat distribution also exerts an independent influence on leptin in children (19,24) and adults (13,28).

In trained subjects, circulating leptin is low, and even at biological extreme low levels of body fat, leptin concentration is closely related to fat content in female (10) and male athletes (7,11). In resistance-trained subjects, no association between leptin and the body mass index (BMI) was reported (5), perhaps due to the suppressing effects of exogenous androgens on leptin (8,29). Recent findings have also shown that the fat-free mass (FFM) contributes to the variability of leptin in men (2), and experimental data have revealed that the administration of glucosamine activated ob gene expression in mouse skeletal muscle (31). Because resistance training increases FFM, it is possible that the relationship between estimates of adiposity and leptin depends on kind of sports athletes are engaged in.

Because leptin is influenced by body fat distribution, one might assume that any relationship between leptin and the FFM is also modulated by differences in subcutaneous fat distribution. However, a centralized distribution of subcutaneous fat has been associated with decreased aerobic capacity in men (18), but fatness might be more influenced by sport and, by inference, training than is fat patterning (12). Nonetheless, the relationship between subcutaneous fat patterning and leptin has not been investigated in athletes so far. We, therefore, sought to determine whether 1) differences in the relationship between leptin and body composition exist in resistance-trained athletes compared with endurance-trained athletes; and 2) whether body fat distribution influences leptin in trained male athletes after control for other determinants for leptin, i.e., adiposity and insulin sensitivity (9,30).

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Thirty-seven male national-class athletes from Estonia were investigated. Anthropometric and metabolic characteristics are shown in Table 1. Subjects’ stature (in cm) was measured using a Martin metal anthropometer to the nearest 0.1 cm, and body mass (in kg) was estimated using a medical scale to the nearest 0.5 kg. The body mass index (BMI) was calculated as kg·m−2. The whole study group included 20 endurance-trained (ET) male subjects (mean and SD, age: 21.1 ± 3.6 yr, BMI: 21.6 ± 2) and 17 resistance-trained (RT) male subjects (age: 23.2 ± 2.1 yr, BMI: 23.6 ± 1.4). The ET-group included distance runners and cross-country skiers. The RT-group included rowers, paddlers, handball players, and wrestlers. None of the athletes reported the use of anabolic steroids. We did not include body-builders and weight lifters because 1) these athletes often use anabolic steroids to increase strength and muscle size and 2) because it is likely that anabolic-androgenic steroids suppress leptin (8). However, it has to be mentioned that rowers, paddlers, and handball players cannot completely be characterized as resistance-trained athletes. Therefore, the selection criteria were not clearly defined and rather reflect a fluent transition range between resistance- and endurance-trained subjects.

Table 1

Table 1

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Assessment of hormones.

Venous blood samples were taken after an overnight fast. Leptin was determined by means of radioimmuno assay (RIA, Mediagnost, Tübingen, Germany). This assay has a detection limit of 0.01 ng·mL−1, and intra-assay and inter-assay coefficient of variation (CV) was <5% and <7.5%, respectively. Insulin was determined by means of an immunoradiometric assay (IRMA; Biosource Europe S.A., Nivelles, Belgium) with and intra- and inter-assay CV of 4.5% and 12.2% at an insulin concentration of 6.6 μlU·mL−1, respectively. Glucose (mmol·L−1) was measured by means of the hexokinase/glucose 6-phosphate-dehydrogenase method by using a commercial kit (Boehringer Mannheim, Mannheim, Germany).

Insulin resistance was calculated through the fasting insulin resistance index (FIRI): [fasting glucose (mmol·L−1) × fasting insulin (μIU·mL−1)]/25 (2). Written informed consent was given by the participants and the study was approved by the local ethical committee.

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Measurement of body composition.

Whole-body resistance was measured by means of a multiple-frequency impedance device (MULTISCAN-5000, Bodystat Ltd., Douglas, Isle of Man) with an applied current of 0.8 mA at 50 kHz. Measurements were performed in supine position with limbs slightly abduced. Skin current electrodes were placed on the right dorsal surface at the hand and feet at the metacarpals and metatarsals, respectively. FFM was calculated using the Segal equation for male subjects with an assumed % body fat of less than 20%(21). Fat mass (FM) was calculated as the difference between body mass and FFM.

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Measurement of subcutaneous adipose tissue-layers (SAT-layers).

Measurements were performed by means of the optical device Lipometer (15,16). The Lipometer uses light-emitting diodes, which illuminate the interesting subcutaneous fatty layer (SAT-layer), forming certain geometrical patterns varying in succession. A photodiode measures the corresponding light intensities back scattered in the subcutaneous adipose tissue. These light signals are amplified, digitized, and stored on computer. Measurement for the thickness of SAT-layers in mm were performed at 15 body sites, from 1-neck to 15-calf (Fig. 1) on the right side of the body in standing position. The coefficients of variation of SAT-layers are ranging between 1.9% for SAT-layer 5-front chest and 12.2% for SAT-layer 13-rear thigh (14). To give an estimate of overall subcutaneous fatness (SAT), linear addition was performed for all 15 SAT-layers (Table 1).



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Leptin, insulin, and FIRI were skewed and therefore log10 transformed. Analysis of variance was used to compare parameters between ET and RT. In case of a significant difference, post hoc analysis was employed. Kruskal-Wallis test was used if variances were not normally distributed. The relationship between parameters of interest was calculated by means of Spearman’s rank sum test and Person’s product moment correlation where appropriate. Partial correlation was performed to adjust for the influence of confounding variables. Univariate correlations were performed for all athletes combined and for ET and RT separately. Because of interrelationship of SAT-layers, and to reduce number of correlations, factor analysis was employed (17). To give an easy way to calculate the resulting factors (see Results), a linear addition was performed for those SAT-layers that belong to the extracted factors. The independence and significance of variables was tested by stepwise, multiple regression analysis based on results of the univariate correlations. A maximum of three independent variables was allowed to enter the equation. The significance level of P-values was set at 5%. Data are given as mean and standard deviation as otherwise indicated.

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Significant differences between groups of athletes were found for age, body mass, FM, and %FM, which were higher in RT-athletes (Table 1). However, FFM, log leptin, log insulin, glucose, and log FIRI were not different between ET- and RT-athletes. A greater thickness of SAT-layers 2-triceps, 4-upper back, and 6-lateral chest (all P < 0.05) were found in RT-athletes (Fig. 1). The differences in SAT-layers 7-upper abdomen (P = 0.08), 8-lower abdomen (P = 0.09), 9-lower back (P = 0.053), 12-lateral thigh (P = 0.08), and 14-inner thigh (P = 0.07) slightly failed to reach statistical significance (by means of Kruskal-Wallis test).

Factor analysis for measured SAT-layers extracted two factors. Factor 1 (Eigenvalue of 11.09) includes SAT-layers from the upper extremities (2-triceps and 3-biceps), lower extremities (from 11-front thigh to 15-calf) and SAT-layer 9-lower back. Factor 2 (Eigenvalue of 1.06) includes SAT-layers from the trunk (1-neck), from 4-upper back to 8-lower abdomen, and 10-hip (see also Fig. 1). Linear addition was performed for factor-1–related SAT-layers and factor-2–related SAT-layers (Table 1). All 15 SAT-layers were summed to calculate subcutaneous fatness (SAT) (Table 1). There was a trend for higher values of factor 1 (P = 0.07), factor 2 (P = 0.085), and SAT (P = 0.052) in RT-athletes, but these differences slightly failed to reach statistical significance (Table 1).

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Univariate correlations between log leptin and estimated parameters.

When all subjects were considered together, the expected relationship between adiposity and leptin was found. All SAT-layers were significantly correlated to log leptin after control for differences between ET- and RT-athletes. The coefficients of partial correlations ranged between 0.44 for SAT-layer 3-biceps (P = 0.003) and 0.695 for SAT-layer 6-lateral chest (P < 0.0001) (Table 2, Fig. 1). Factor 1, factor 2, and SAT (Fig. 2) showed the highest correlation to leptin (all P < 0.0001). Metabolic parameters were not significantly associated with leptin.

Table 2

Table 2



When groups of athletes were considered separately, age, body mass, FM, and FFM were significantly associated with leptin in ET (all P < 0.05) but not in RT-athletes. Factor 1, factor 2, and SAT were significantly correlated to leptin in both groups. The magnitude of these relationships were greater in RT-athletes. Metabolic parameters were correlated to leptin only in RT-athletes (all P < 0.05).

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Multiple, stepwise regression models with log leptin as dependent variable.

Several models based on results of Tables 2 and 3 were tested. Because of the group-differences in the relationship between log leptin and parameters of interest, we used group (with ET = 1 and RT = 2) as an independent variable in each model. Those variable(s) which significantly contributed to the variance of log leptin was (were) hold constant in the next model.

Table 3

Table 3

The first model included age and estimates of adiposity. However, only %FM contributed significantly to log leptin (adj. R2 = 0.195). The next model included %FM and metabolic parameters, because metabolic parameters were associated with leptin in RT-athletes. Nonetheless, %FM had the same slope as in the previous model and remained as the main determinant. In the final model, factor 1, factor 2 and SAT were added. All estimates of subcutaneous fat were significantly associated with log leptin but only SAT remained as an independent determinant for log leptin (adj. R2 = 0.52, P < 0.0001). Because each SAT-layer was significantly correlated to log leptin, we also tested whether certain SAT-layers contribute to log leptin. The best model explained almost 50% of the variance, and included SAT-layer 6-lateral chest (adj. R2 = 0.497, P < 0.0001, not shown).

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We investigated endurance- (ET) and resistance-trained (RT) athletes to study the relationship between leptin, measures of adiposity, and metabolic parameters. ET-athletes were long-distance runners and cross-country skiers, whereas RT-athletes came from different sports in which strength training is of fundamental importance. We did not rely on weight- and power-lifters or body-builders because we wanted to exclude possible downregulating effects of anabolic-steroids on leptin (8,29). Unfortunately, we did not measure testosterone, but there were no differences in leptin, glucose, insulin, and insulin resistance, indicating that the metabolic status did not differ between the two group of athletes (Table 1).

Body mass was higher in RT-athletes but not FFM. It is unlikely that differences in the water content of FFM between athletes (22) were responsible for this because total body water, extra-, and intra-cellular volume were not different between groups (data not shown). However, we measured FFM by means of impedance at a single frequency, and despite methodological concern it is also possible that the equation used (21) was not the most accurate one to predict FFM of Estonian male athletes. Another possible explanation for the finding that FFM was not greater in RT-athletes is that these subjects cannot be completely described as resistance-trained athletes because their training also included endurance exercises.

However, RT-athletes had a greater fat mass (FM), percentage FM (%FM), and overall subcutaneous fat (SAT;P = 0.052), which suggests that their greater adiposity might be due to greater SAT and (or) greater amount of visceral fat, which was not measured in the present study. We used an optical device (Lipometer) to measure the thickness of different subcutaneous fatty layers (SAT-layers) and calculated SAT by linear addition of these SAT-layers. Although this is a simple approach to calculate SAT, this might be applicable because 1) SAT-layers are distributed over the whole body and 2) because of the use of a larger number of measurement sites (N = 15) when compared with skinfolds to estimate SAT (19). However, the Lipometer measures a subcutaneous monolayer, and any comparison with skinfolds should be made with caution.

In ET-athletes, FM, FFM, but not %FM were correlated to leptin (Table 2). The relationship between FM and leptin (r = 0.43) was of minor magnitude when compared with available data in male distance runners (7). By using the sum of seven skinfolds to calculate body fat content, it was shown that fat mass (r = 0.91) and %FM (r = 0.90) were highly related to leptin in 13 athletes (7). Leptin (mean of 2.19 ng·mL−1) and %FM (mean of 9.7%) were nearly identical in that study and in the present one (Table 1). Insulin was not related to leptin in the former study, and insulin was also not related to leptin in our ET-athletes. Hence, the discrepancy between these two studies might be due to age- and (or) ethnic differences in investigated athletes. Alternatively, the conversion of summed skinfolds into body fat content (7) could reflect subcutaneous fatness rather than body composition. If so, then the relationship between leptin and fatness would be approximately the same in both studies.

FFM was related to leptin in ET- but not RT-athletes. A greater amount of FFM contributes to leptin in male subjects (2) and in obese boys (23). The role of FFM in the regulation of leptin in humans is unclear, but regular training increases insulin sensitivity in skeletal muscle in endurance- but not resistance-athletes (26). We found a relationship between insulin, insulin resistance, and leptin in RT-athletes, perhaps reflecting some specific adaptations at the level of the FFM in response to training, which in turn could influence circulating leptin. However, we calculated insulin resistance by the fasting insulin resistance index, which is not a state of the art approach to estimating insulin resistance and sensitivity. Notwithstanding this, body mass, BMI, FM, and %FM were not correlated to leptin in RT-athletes. Because of this and, due to the significant relationship between leptin and metabolic parameters in RT-athletes, the regulation of leptin might also depend on factors at the metabolic level associated with certain training regimens.

In both groups of athletes, SAT was significantly correlated to leptin (Table 2), as was every single SAT-layer per se (Fig. 1). This suggests that each measure of subcutaneous fatness reflects leptin to some extent. Although there were significant differences in the thickness of three measured SAT-layers between groups (Fig. 1), those factors that were extracted by factor analysis (see Results) were not different between athletes (Table 1). However, both factors slightly failed to reach significance, perhaps due to great interindividual differences in RT-athletes. These factors can be described as a model of fat patterning that differentiates in extremities fatness (factor 1) and trunk fatness (factor 2). To quantitate subcutaneous fat distribution, linear addition was performed for those SAT-layers that belong to these factors. Both factors were correlated to leptin in all athletes and in each group to a similar extent (Table 2), but the relationship was of greater magnitude in RT-athletes. Combined, these findings indicate that the relationship between leptin and measures of subcutaneous fatness is not the result of a main influence of fat patterning and that SAT has a somewhat greater influence on leptin in RT-athletes.

The main influence of SAT on leptin was further confirmed by stepwise regression (Table 3). We adjusted for group differences between athletes, but SAT remained as the strongest predictor of leptin regardless of other independent determinants. A similar finding was obtained in women, in particular due to a certain mass effect of subcutaneous adipocytes on the expression rate of leptin (28). However, this is the first study which reports that SAT is an independent determinant for leptin in male athletes. Because SAT requires the measurement of 15 SAT-layers, we also tested the possibility that certain SAT-layers contribute to leptin (24,25). When SAT-layers were considered in different stepwise regression models (not shown), SAT-layer 6-lateral chest explained almost the same variance of leptin (∼50%) as SAT (52%) did. Whether this suggests that this SAT-layer from the trunk reflects SAT and (or) some kind of central fat patterning, needs to be studied in detail.

It is also possible that leptin is acutely regulated in adipocytes by surrounding hormones (20). Catecholamines were shown to suppress leptin release from differentiated human subcutaneous adipocytes, mediated via beta (1) - and beta (2) -adrenergic receptors (20). In athletes during long-term training, the definitive signal for the reduction in leptin may be linked to prolonged beta-3-adrenoceptor stimulation (27). Therefore, we cannot rule out the possibility that our findings of an independent influence of SAT on leptin in athletes is secondarily and most probably indirectly via beta-3-stimulation to SAT and (or) leptin is also acutely regulated independently from alterations in SAT. It clearly remains to be shown whether leptin is under a strong influence of SAT in ET- and RT-athletes regardless of their metabolic and hormonal status.

In conclusion, the present study shows that metabolic parameters and estimates of adiposity are associated with leptin in a sport-specific manner, and overall subcutaneous fatness was found to be the main determinant for leptin in endurance- and resistance-trained athletes.

Address for correspondence: Dr. Karl Michael Sudi, Institute for Sport Sciences, Karl-Franzens University, Mozartgasse 14, 8010 Graz, Austria; E-mail:

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