Biomedical Research

Research Article - Biomedical Research (2018) Volume 29, Issue 1

Investigation the effect of weightlifting training on apelin level and hematologic parameters of professional handball players

Aykut Dundar1*, Fatih Murathan1, Zait Burak Aktug2 and Tayfun Servi3

1Physical Education and Sports College, Adiyaman University, Adiyaman, Turkey

2Omer Halisdemir University, Sports College, Nigde, Turkey

3Faculty of Economics and Administrative Sciences, Adiyaman University, Adiyaman, Turkey

*Corresponding Author:
Aykut Dundar
Physical Education and Sports College
Altin Sehir Campus
Adiyaman University
Adiyaman, Turkey

Accepted date: October 24, 2017

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Objectives: Apelin is a member of myokines which has recently been characterized. The purpose of this study was to examine the effects of a weightlifting training (WT) program on some biochemical (glucose, insulin and apelin) and haematological parameters in professional male handball players. It was hypothesized that significant increase would be found in apelin levels of subjects.

Material and Methods: Ten professional handball players (Trained Handball Player Group, THG) completed 6-week WT. Ten sedentary subjects (Trained Sedentary Group, TSG) also completed WT to compare with THG and investigate the effect of being a handball player. Ten subjects have been considered as control group (Control Group, CG).

Results: The apelin levels of THG and TSG have been increased significantly (p<0.05) but the increase in THG (55%) is more than TSG (38%). CG subjects did not exhibit any significant changes during the 6- week period (p>0.05).

Conclusion: It has been concluded that the WT has an important role on apelin level, especially in professional handball players.


Apelin, Weightlifting training, Hematologic parameters, Professional team handball


Team handball has been one of the most popular team sports at both national and international levels during the last sixty years [1]. Competitive team handball requires muscular strength, speed and endurance but it is not very clear that how these parameters change during the different exercise programs and season [2]. The lack of experimental data (haematological, biochemical or hormonal parameters of blood samples) on professional team handball players is one of the most important reasons for this phenomenon.

However, the haematological parameters and biochemical characteristics can be crucial for predicting optimal physical performance [3,4] there is not enough study which concerns these parameters about professional team handball players.

There is a very strong relationship between the muscle activity (exercises) and different body properties (mood, physical performance, haematological parameters and cognitive function) [5-8]. Some of the effects of different exercises including lower blood pressure, improved glucose homeostasis and lipid profile, higher resting energy expenditure and reduced fat mass [6-8]. There are several explanations for these benefits of exercise like increasing nutrient metabolism in various tissues by regulating the expression and activity of key metabolic control genes, leading to enhanced insulin sensitivity and metabolic flexibility [9]. But there is one more interesting issue, which is still unclear, the skeletal muscle exhibits remarkable metabolic adaptations to exercise [10,11]. Myokines, which are secreted in response to exercise and can regulate the function of muscle and other organs, may be one of the most sensible explanations [12].

Myokines are cytokines produced by skeletal muscles, especially induced by exercise, modulating different metabolic processes [13]. There are many well documented myokines like interleukin (IL6) and myostatin (MSTN) [14-16]. Apelin is a newly described myokine [17,18]. Production of apelin is induced by exercise and can behave as an exercise regulated myokine with autocrine-paracrine action [6]. Apelin is also related with insulin that is why it contributes to glucose homeostasis [19] and highly implicated in cardiovascular functions [20].

Hematologic parameters and apelin are clearly involved in exercise-associated metabolic changes, but also can be potentially effective in performance improvements of professional team handball players during weightlifting training [7]. We aimed to analyse the fluctuations of some biochemical (glucose, insulin and apelin) and hematologic parameters in professional team handball players and sedentary individuals after WT.


This study has been approved by the “Ethical Committee of Biomedical Researches” of Adiyaman University (Date: 25/03/2015, No: 2015/02-20, Chair Person: Prof. Dr. Haydar Bagis). All subjects were informed of the benefits and risks of the investigation.

Experimental approach

There are three groups of subjects in this study. The first group includes professional team handball players (THG: Trained handball group), second is sedentary group (TSG: Trained sedentary group) (who are not sportsmen) and the third group is control group (CG) (who are not sportsmen and did not train). The members of THG have been chosen from a city handball team which is a professional handball team in best league of related country. Each group composes from 10 subjects (totally 30 subjects). First group (THG) and second group (TSG) have performed WT three days in a week for six weeks (totally 18 training days). In the first training day of each week the subjects performed 3 × 10 bench press, 3 × 10 incline bench press, 3 × 10 dumbbells flyes, 3 × 10 butterfly, 3 × 10 pullover for chest workout and 3 × 10 squad, 3 × 10 leg extension, 3 × 10 leg lying curl, 3 × 12 standing calf raises for leg workout. In second training day 3 × 10 shoulder press, 3 × 10 front raise, 3 × 10 dumbbell side lateral raises, 3 × 10 upright barbell rowing for shoulder workout and 3 × 10 lying triceps extension, 3 × 10 seated overhead, 3 × 10 standing cable push down for triceps workout performed by THG and TSG. In the third training day 3 × 10 lat machine pull down, 3 × 10 cable row seated, 3 × 10 T-bar rowing for back workout, 3 × 10 standing barbell curl, 3 × 10 preacher curl, 3x10 concentration curl for biceps workout and 3 × 12 wrist curl for wrist workout performed by all subjects.

Blood samples of all groups have been analysed before and after the training period. The blood samples of CG have been also analysed to exclude the external factors (temperature, etc.). The blood samples have been analysed by means of haematological parameters (WBC, RBC, etc) and biochemical parameters (glucose, insulin and apelin). The differences have been calculated between two analyses (before and after weightlifting training) for each group. Calculated differences were considered to analyse the effect of WT to THG and TSG. Calculated differences subtracted, and the results were analysed statistically to understand if the weightlifting training has a meaningful effect on haematological parameters, insulin, glucose or apelin levels of THG (Table 1).

WBC (BT) WBC (AT) Differences
THG a d a-d=dTHG
TSG b e b-e=dTSG
CG c f c-f=dCG

Table 1. A representative table to explain the statistical methodology (BT: before training, AT: after training, WBC: chosen hematologic parameter).

The averages of dTHG, dTSG and dCG were examined to understand the effect of WT to a single group, and averages of dTHG-dTSG was studied with a multi comparison (Benferroni) test to investigate if the WT has a special meaning on THG. dTSG-dCG parameter has also been considered to understand the effect of WT on non-sportsmen.

The age, height, weight and body/fat averages were reported with standard errors on Table 2 for each group.

Weight (kg) Age Body/fat %
THG 79.575 81.115 20.9 20.9 15.426 13.548
TSG 76.598 76.853 20.6 20.6 14.493 13.276
CG 78.391 77.286 20.4 20.4 15.353 15.142

Table 2. Weight, age, and body/fat % averages for each group, BT: before weightlifting training, AT: after weightlifting training.

Statistical analysis

Descriptive statistics were reported for all groups. A 3x2 (group x time) repeated measures analysed with ANOVA. Oneway analysis of variance (ANOVA) was used to examine if there is a significant correlation between any two of three groups (THG, TSG and CG). If significant F values were determined, paired comparisons combined with a Bonferroni post hoc adjustment to determine differences. Raw difference scores were analysed with the use of a 1-way ANOVA. Pearson’s product moment correlation coefficient was used to investigate the relationship between selected variables. Statistical significance was set at p˂0.05. All statistical analyses were performed using SPSS [21].

Blood collection

Blood samples were collected from participants 30 minutes prior to first WT and within 30 minutes after the last WT. Twenty milliliters of blood was collected during each blood draw using one heparinized 10 mL tube and one 10-mL ethylenediaminetetraacetic acid tube. Plasma was isolated from the heparinized blood by centrifugation.

THG represents the weightlifting trained professional handball players, TSG represents the weightlifting trained sedentary group (not sportsman) and the CG represents non-trained sedentary participants. So, the comparison of TSG and CG was provided information about the effect of WT on hematologic and biochemical parameters of sedentary group and the comparison of THG and TSG was provided information about the effect of being a professional handball player because both THG and TSG performed same WT.


Basically, two kinds of data were reported and discussed. The first one is the comparison of groups (table 1, between groups, dTHG-dTSG). The other is the differences between two blood sample analyses (dTHG and dTSG) in each group.

1st data type (between groups)

The statistical analysis of the groups (THG and TSG) provided useful information that how the weightlifting training effects the hematologic (WBC, RBC, PLT, HGB, HCT, MCH and PCT) and biochemical (glucose, insulin and apelin) parameters in professional handball players and sedentary individuals.

The one-way analyses, ANOVA test and multiple comparison Bonferroni post hoc adjustment have been reported on table 3. A “p” value<0.05 was considered significant in all analyses.

Descriptive **Multiple Comparisons (Bonferroni)
N Mean p (I) Group (J) Group Mean Difference (I-J) Sig.
WBC THG 10 2.0 ± 0.1 0.000 THG CG 2.0 ± 0.2* 0.000
TSG 2.2 ± 0.2* 0.000
CG 10 0.1 ± 0.2 CG THG 2.0 ± 0.2* 0.000
TSG 0.2 ± 0.2 0.875
TSG 10 -0.1 ± 0.2 TSG THG -2.2 ± 0.2* 0.000
Total 30 0.7 ± 0.2 CG -0.2 ± 0.2 0.875
RBC THG 10 0.5 ± 0.1 0.036 THG CG 0.5 ± 0.2* 0.032
TSG 0.2 ± 0.2 0.793
CG 10 0.004 ± 0.1 CG THG -0.5 ± 0.2* 0.032
TSG -0.3 ± 0.2 0.365
TSG 10 0.3 ± 0.1 TSG THG -0.2 ± 0.2 0.793
Total 30 0.3 ± 0.1 CG 0.3 ± 0.2 0.365
PLT THG 10 50.0 ± 10.0 0.009 THG CG 48.1 ± 16.0* 0.017
TSG 45,0 ± 16.0* 0.026
CG 10 2.3 ± 8.1 CG THG -48.1 ± 16.0* 0.017
TSG -3.1 ± 16.0 1.000
TSG 10 5.4 ± 14.7 TSG THG -45.0 ± 16.0* 0.026
Total 30 19.4 ± 7.5 CG 3.1 ± 16.0 1.000
HGB THG 10 1.2 ± 0.1 0.001 THG CG 1.1 ± 0.3* 0.001
TSG 0.6 ± 0.3 0.105
CG 10 0.08 ± 0.2 CG THG -1.1 ± 0.3* 0.001
TSG -0.5 ± 0.3 0.216
TSG 10 0.6 ± 0.2 TSG THG -0.6 ± 0.3 0.105
Total 30 0.6 ± 0.1 CG 0.5 ± 0.3 0.216
HCT THG 10 0.3 ± 0.1 0.151 THG CG 0.5 ± 0.6 1.000
TSG -0.6 ± 0.6 0.819
CG 10 -0.2 ± 0.5 CG THG -0.5 ± 0.6 1.000
TSG -1.1 ± 0.6 0.163
TSG 10 0.9 ± 0.4 TSG THG 0.6 ± 0.6 0.819
Total 30 0.3 ± 0.2 CG 1.1 ± 0.6 0.163
MCH THG 10 0.3 ± 0.1 0.579 THG CG -0.1 ± 0.3 1.000
TSG -0.3 ± 0.3 0.939
CG 10 0.3 ± 0.2 CG THG 0.1 ± 0.3 1.000
TSG -0.2 ± 0.3 1.000
TSG 10 0.6 ± 0.3 TSG THG 0.3 ± 0.3 0.939
Total 30 0.4 ± 0.1 CG 0.2 ± 0.3 1.000
PCT THG 10 0.004 ± 0.002 0.650 THG CG -0.003 ± 0.007 1.000
TSG -0.007 ± 0.007 1.000
CG 10 0.007 ± 0.006 CG THG 0.003 ± 0.007 1.000
TSG -0.003 ± 0.007 1.000
TSG 10 0.01 ± 0.006 TSG THG 0.007 ± 0.007 1.000
Total 30 0.007 ± 0.02 CG 0.003 ± 0.007 1.000

Table 3. One way analyze, ANOVA test (p˂0.05) and multiple comparison Bonferroni post hoc adjustment of hematologic parameters (N=number of subjects, Mean= average of differences (AT-BT) with standard errors, p=statistical significance).

The first column on table 3 represents the analysed haematological parameters. “p” represents the statistical significance and tells that there is a meaningful correlation between groups, when smaller than 0.05. The other columns of table 3 gives chance to understand which groups are correlated, because there are three groups (THG, TSG and CG) and it should be clearly understood that which two of these three groups are correlated. The starred (*) numbers in “Mean Difference” column shows the correlated groups.

2nd data type (within groups)

A paired samples test has been performed for each group. The statistical analysis results let us to compare analysis of each blood parameter before and after WT. It has been concluded that a significant change observed at four hematologic parameters (WBC, p=0.000; RBC, p=0.002; PLT, p=0.001 and HGB, p=0.000) and three biochemical parameters (glucose, p=0.000; insulin; p=0.000 and apelin, p=0.000) for THG. None of these significant changes have been observed for hematologic parameters of TSG (all p values are higher than 0.05) but the biochemical parameters have changed significantly like THG (glucose, p=0.000; insulin; p=0.001 and apelin, p=0.000). And finally, none of the hematologic and biochemical parameters changed significantly for CG.


The most important result of this study was that the WT, which has been going on for six weeks, significantly changed the glucose, insulin and apelin levels of both THG and TSG but the apelin level of THG (55%) has been increased more than TSG (38%) (Figure 1). The different amount of muscle tissues of professional team handball players and sedentary group may be the main reason for this phenomenon. It is clear that apelin is produced by skeletal muscles and induced by exercise [6,13]. So, although the two-analysed group (THG and TSG) have been performed the same training the apelin level of THG, who have more muscle tissue because this group includes professional handball players, has increased more than TSG.


Figure 1: Schematically comparison of apelin level increase of groups (THG and TSG) after WT.

This is an important result because apelin is a newly described myokin and such findings to our knowledge are the first to demonstrate that a significant upregulation of serum apelin levels after WT in professional handball players. The result of this study supports a previous study on apelin levels of a diabetic population after exercise treatment [22]. A significant increase in levels of plasma IL-6, which is a well described myokine, has been reported [22] and this result is also supporting our results about apeline increase with training.

By means of group comparison (THG and TSG) it has been concluded that a significant increase in four haematological parameters of (WBC, RBC, PLT and HGB) have been observed (Table 3). WBC, RBC, PLT and HGB parameters of THG have been increased 34%, 9%, 22% and 8% respectively after the WT. There is no significant change in any hematologic parameter of TSG. These results confirm a previous study on the effect of exercise on haematological parameters [23].

In case of WBC while there is a meaningful change between THG and TSG, there is no such relation between TSG and CG. Thus, it can be concluded that WT has no effect on WBC level of sedentary participants but being a professional handball player is a significant factor. While same interpretation can be done for PLT level, there is no such relation between groups for RBC and HGB parameters (Table 3). P values of RBC and HGB are smaller than 0.05 but this result can’t be interpreted because the relations (“*” on table 3) are between THG and CG. It is not clear that what is the driving factor for the differences of related parameters (RBC and HGB) between THG and CG, WT or being a handball player? There are not enough data to interpret this phenomenon.

THG, TSG and CG have been compared by means of three biochemical parameters (glucose, insulin and apelin) on Table 4. It can be concluded that both WT and being a professional handball player are effective on glucose, insulin and apelin levels because all values are marked with a star (*) in “Mean Difference” column of multiple comparison Bonferroni test (Table 4).

Descriptive Multiple Comparisons (Bonferroni)
N Mean p (I) Group (J) Group Mean Difference (I-J) Sig.
Glucose THG 10 13.0 ± 1.0 0.000 THG CG 12.2 ± 1.1* 0.000
TSG 9.3 ± 1.1* 0.000
CG 10 0.8 ± 0.6 CG THG -12.2 ± 1.1* 0.000
TSG -3.0 ± 1.1* 0.045
TSG 10 3.7 ± 0.7 TSG THG -9.3 ± 1.1* 0.000
Total 30 5.8 ± 1.1 CG 2.9 ± 1.1* 0.045
Insulin THG 10 -4.0 ± 1.1 0.000 THG CG -3.7 ± 0.4* 0.000
TSG -2.0 ± 0.4* 0.000
CG 10 -0.3 ± 0.2 CG THG 3.7 ± 0.4* 0.000
TSG 1.7 ± 0.4* 0.003
TSG 10 -2.0 ± 0.4 TSG THG 2.0 ± 0.4* 0.000
Total 30 -2.1 ± 0.3 CG -1.7 ± 0.4* 0.003
Apelin THG 10 1289.3 ± 140.0 0.000 THG CG 1201.1 ± 143.6* 0.000
TSG 515.9 ± 143.6* 0.004
CG 10 88.2 ± 63.0 CG THG -1201.1 ± 143.6* 0.000
TSG -685,2 ± 143.6* 0.000
TSG 10 773.4 ± 86.0 TSG THG -515,9 ± 143.6* 0.004
Total 30 717.0 ± 107.4 CG 685.2 ± 143.6* 0.000

Table 4. One way analyze ANOVA test (p˂0.05) and multiple comparison Bonferroni post hoc adjustment of glucose, insulin and apeline.

Apelin is one of the responsible hormones for the occurrence and development of cardiovascular diseases [23] that is why the relationship between the WT and apelin should be well described.


The results of this study may help the professional handball coaches to associate the WT status and apelin levels of players. As a result of this study a strong relationship has been detected between the apelin levels and WT, especially in professional handball players.