Human physical performance is determined by both genetic and environmental factors (23). Low physical performance is an independent risk factor for death and several diseases (40). The beneficial effects of exercise training on physical performance are well established, although the role of genetic factors in this process is less clear. Heritability on an athletic status has been estimated to be approximately 66% (5), and close to 200 physical fitness-associated genes have been identified (31). Twin studies have shown that up to 90% of the variance in muscle mass and up to 60% of the variance in muscle strength are heritable (17). Furthermore, skeletal muscle force production is dependent on the properties of the muscle tissue and the nervous system (7).
Insulin-like growth factor-1 (IGF-1) is produced by the liver in response to increased growth hormone secretion and mediates the actions of growth hormone in peripheral tissues. In addition, IGF-1 shows independent autocrine and paracrine actions on cell proliferation, differentiation, and survival (41). A role for IGF-1 as a regulator of human myocyte differentiation and proliferation has been demonstrated (28,41). Moreover, IGF-1 is important for the growth of various other tissues including the nervous system (26). In blood, IGF-1 is mainly bound to insulin-like growth factor binding proteins (IGFBPs) (19). Local production of IGF-1 after physical exercise has been suggested to play an important role in tissue remodeling and hypertrophy (36). On the other hand, physical training has also been reported to have decreasing or no effect on the circulating levels of IGF-1 and IGFBP-3 (24,29,37).
Genes encoding proteins associated with muscle hypertrophy are potential candidates for regulation of muscular performance. Among these, variations in the IGF1 and IGFBP3 gene may alter their expression or biological function. Previously, IGF1 variants have been associated with higher levels of plasma IGF-1 and improvements in muscular strength in response to resistance training (15,20) and with increased economy and aerobic performance of human locomotion (22). In addition, variants of the IGFBP3 gene have been reported to alter plasma IGFBP-3 levels (4) and also exercise-induced muscle damage (9) but not the phenotypic responses to strength training (15).
Interleukin-6 (IL-6) has been considered as a marker of inflammation and an immunomodulatory cytokine produced mainly by the immune system (10). IL-6 is also secreted by skeletal muscle, and plasma levels of IL-6 may rise up to 100-fold after a strenuous physical exercise (33). IL-6 may also improve skeletal muscle energy supply and assist in the maintenance of stable blood glucose levels by stimulating lipolysis in the adipose tissue and augmenting glycogenolysis by the liver (32,34). Moreover, infusion of recombinant IL-6 to human subjects resulted in decreased plasma IGF-1 levels similar to the effect of strenuous endurance training (27). As such, chronic IL-6 secretion may inhibit IGF-1–mediated anabolic actions in humans (1). However, acute efflux of IL-6 may have opposite effects on muscle growth (1). There are only few studies on the associations of IL6 gene variants with physical performance (16,30,38). Moreover, the IL6 receptor (IL6R) SNP rs4537545 was reported to be associated with circulating IL-6 levels (35), but no studies are available about its influence on physical performance.
Owing to the important role of IGF-1 on skeletal muscle homeostasis and growth, neuroprotection, and neurotrophy, we investigated the association of selected SNPs in the IGF1 and IGFBP3 gene with body composition, muscular and cardiorespiratory performance, and plasma biochemical parameters in 841 healthy male subjects. In addition, the association of selected variants of IL6 and IL6R gene with these parameters was studied because of the important role of IL-6 in the energy homeostasis during strenuous physical exercise (32,34), and IL-6 also may have an effect on IGF-1 regulation (1). Our hypothesis was that these SNPs are associated with human physical performance, body composition, and health-related risk factors. This information may be useful for the identification of the individuals having the risk for decreased physical performance and fitness and for better understanding of the effect of genetic factors on human physical performance.
MATERIALS AND METHODS
The subjects were 841 healthy Finnish male volunteers of Caucasian origin with mean ± SD age of 25 ± 5 yr. All subjects were informed on the purpose of the study and gave written informed consent before the tests. The research plan was approved by the ethics committee of Central Finland Health Care District.
Anthropometric data and blood samples were collected after an overnight fast. The subjects ate a light breakfast 1–2 h before the fitness tests.
Physical performance and body composition
Body height, weight, and waist circumference were recorded, and values of body mass index (BMI) were calculated. Body fat percentages and lean mass of legs were recorded using an eight-polar bioimpedance method with multifrequency current (InBody 720; Biospace Company, Seoul, Korea). Bioimpedance was recorded after an overnight fast and with at least 1 d off from any intensive physical activity. Lean body mass was calculated by subtracting body fat mass from total body mass.
Maximal isometric force of the bilateral leg extensors was measured by a strain-gauge dynamometer developed in the Department of Biology of Physical Activity. This method has been described earlier in more detail (14), and its reproducibility has been reported to be high (r = 0.98, coefficient of variation = 4.1%) (43). The force tests were performed in the morning before the aerobic performance test. The test was performed in a sitting position with a knee angle of 107°. Subjects were instructed to exert maximal force as fast as possible and to maintain the force for 3 s. Data were analyzed with a 16-bit AD converter (CED power 1401; Cambridge Electronic Design Ltd., Cambridge, United Kingdom) and a Signal (2.16) program. A minimum of three trials was completed for each subject, and the best performance about maximal force was selected for the subsequent statistical analysis. The tested muscle groups were quadriceps femoris (knee extensor), hamstrings, and gluteus maximus (hip extensors).
Aerobic performance was measured by a maximal bicycle ergometer test (Ergoline 800S, Ergoselect 100K, Ergoselect 200K, Bitz, Germany) as previously described (13). The protocol involved increasing workload until exhaustion. The first load was 50 W with 25 W increase in 2-min intervals. HR was monitored throughout the test (Polar T-31; Polar Vantage, Kempele, Finland). The analyzed variables maximum HR, maximal workload, and maximal oxygen consumption (mL·kg−1·min−1) calculated by software (Milfit4/Fitware, Finland) [(11.016 × maximum workload) × (body weight−1) + 7.0]. The test was terminated when the subject could not maintain the required cycling speed at 60–90 rpm.
Blood samples and genotyping
The fasting blood samples were collected and analyzed immediately with a hemacytometer (Sysmex Co., Kobe, Japan). Plasma IL-6 concentrations were assayed by commercial high-sensitivity ELISA kit according to the manufacturer’s instructions (Quantikine HS; R&D Systems, Minneapolis, MN). Assay specifications for IL-6 were as follows: sensitivity limit was 0.16 pg·mL−1, maximum intra-assay and interassay coefficient of variation was 5.9% and 9.8%. Serum IGF-1 concentrations were measured on a Immulite 100 system (Siemens Healthcare Diagnostics Products Ltd., Gwynedd, UK) with assay sensitivity of 2.6 mmol·L−1, and the intra-assay and interassay variances were both <5%.
The SNP analysis was performed by using allele-specific polymerase chain reaction assays. For genotyping, no template controls and positive controls were used in every run, and all samples were run in duplicate. Briefly, genomic DNA was first isolated from the blood mononuclear cells using QIAamp DNA Blood kit (Qiagen, Hilden, Germany). Next, 50 ng of the DNA was amplified with Brilliant QPCR Master Mix (Stratagene, La Jolla, CA) and allele-specific SNP assays on a Mx3000P Real-time PCR System (Stratagene). For rs6220 and rs4537545, the commercially available TaqMan SNP assays were used (Applied Biosystems, Foster City, CA), and for rs7136446, rs2854744, and rs1800795, the following molecular beacons assays were used: rs7136446 forward primer (fw) AATTGGTTACCTGCTACATTGA, reverse primer (rev) GAGTTAACGCATCTCCTTACTG, and the fluorogenic beacons (FAM/HEX)- CGCTCGCTGCCCTAAGTGC(T/C)GCGTAGTCGAGCG-BHQ1; rs2854744 fw CACCTTGGTTCTTGTAGACGA, rev CGTGCAGCTCGAGACTC, and the beacons (FAM/HEX)-CCTCGCGTG(C/A)GCACGAGGAGCACGAGG-BHQ1; rs1800795 fw AAGAGTGGTTCTCGTTCTTACG, rev GTGAGGGTGGGCGCAGAG, and the beacons (FAM/HEX)-CCGGATCAGTTGTGTCTTCG(C/G)ATCGTAAAGGACGATCCGG-BHQ1.
Calculations were performed with SPSS software (Chicago, IL) by using one-way ANOVA or nonparametric statistics, when appropriate. Deviation from Hardy–Weinberg equation was tested by using χ 2 statistics. Linkage disequilibrium of the IGF1 SNPs was evaluated by using a MIDAS software package (11). Logistic regression was used to evaluate the genetic variation–related odds for continuous variables. Odds ratio for being in either upper or lower quartile was calculated. Age, smoking, physical activity, and BMI were used as covariates. Statistical power and sample sizes were estimated with a SISA Web calculator (46). Data are presented as mean ± SD unless otherwise stated. Statistical significance was set at P < 0.05.
Genotyping call rates were >99% with duplicate concordance rates of >99.5%. All investigated SNPs conformed to Hardy–Weinberg equilibrium, and the allele frequencies were found validated against CEU population. Schematic positions of the investigated SNPs in the genes are shown in Figure 1, and they did not show linkage disequilibria in the study population. Mean values (all subjects) for BMI, body weight, lean body mass, lean mass of legs, V˙O2max, maximal force of the leg extensors, plasma IL-6, and serum IGF-1 levels were as follows: 24.8 ± 3.8 kg·m−2, 80.5 ± 13.6 kg, 65.4 ± 7.4 kg, 10.1 ± 1.1 kg, 41.5 ± 8.0 mL·kg−1·min−1, 2939 ± 876 N, 1.1 ± 1.2 pg·mL−1, and 31.1 ± 7.6 mmol·L−1, respectively. Basic demography and allele frequencies are shown in Tables 1 and 2.
Plasma IL-6 levels were significantly lower in AA homozygotes of the IGFBP3 SNP rs2854744 compared with CC homozygotes (1.0 vs 1.2 pg·mL−1, respectively, P = 0.05; Table 1). In logistic regression analysis, the C allele decreased odds for lower body fat percentage (Table 3). No differences in lean mass were found in ANOVA or logistic regression.
Carriers of genotype GA of the IGF1 SNP rs6220 had significantly higher maximal force production compared with AA homozygotes (Table 1). However, in logistic regression analysis, no association of allele G with maximal force production was found. In addition, AA homozygotes had lower levels of serum IGF-1 compared with genotype GA carriers (Table 1). Again, no differences in lean mass were found in ANOVA or logistic regression.
CC homozygotes of the IGF1 SNP rs7136446 showed higher maximal force production (Table 1), higher body fat (absolute differences shown in Fig. 2), and BMI values compared with the other genotypes (Table 1). In logistic regression analysis, T allele decreased the odds for higher maximal force production and body fat (Table 3), but no differences in lean mass were found in ANOVA or logistic regression.
CC homozygotes of the IL6 SNP rs1800795 had lower IGF-1 levels compared with genotype CG carriers (Table 2). TT homozygotes of the IL6R SNP rs4537545 had the highest IL-6 levels of all genotypes (Table 2). No differences in lean mass were found in ANOVA or logistic regression.
The primary finding of this study was that IGF1 gene variant was associated with maximal force production. Our data also support the hypothesis that IGF1 and IGFBP3 gene variants may have an effect on body composition.
Maximal force production and body fat percentage were associated with the IGF1 SNP rs7136446. Indeed, CC homozygotes were able to generate approximately 9% greater maximal force compared withthe other genotypes. In addition, CC homozygotes of IGF1 SNP rs7136446 had higher body fat and BMI values compared with the other genotypes. This is a novel finding and suggests an association of the minor allele with maximal force production and body composition. However, the exact mechanism is unknown because this SNP is located in the intronic region of IGF1 gene and potentially alters its regulation or tags other SNPs that may result in structural change of the IGF-1 protein. CC homozygotes also tended to have highest lean mass of legs, but this difference was not statistically significant. It is possible that the sample size was insufficient for the chosen method, and therefore, minor-to-moderate changes in lean mass could not be accurately detected for statistical significance. Indeed, in addition to hypertrophy, muscle force production also depends on the level of voluntary neural drive to the muscle in question, in which IGF-1 shows protective effects on neurons, including myelination, antiapoptotic effects, stimulation of axonal sprouting, and repair of axonal damage (12). On the other hand, there are many other factors that ultimately affect muscle force, including skeletal muscle plasticity (2), myosin heavy chain content (21), proportion of fast-twitch fibers, and connective tissue properties (3,44). The CC homozygotes had also higher fat percent than the other genotypes, which may indicate that these subjects were more often in an anabolic state, and may suggest more favorable genotype for power performance rather than aerobic performance. In an earlier study, IGF1 promoter repeat polymorphism was associated with increased force without significant effect on muscle volume (20). Although we did not find any effect of IGF1 variants on aerobic performance, the reported IGF1 promoter repeat polymorphism has also been associated with increased economy of human locomotion and aerobic capacity (22).
To our knowledge, this is the first study to report association of the IGF1 SNP rs6220 with muscular strength. This SNP is located in the 3′-untranslated region of IGF1 gene, which contains regulatory regions for mRNA expression, and may therefore alter the regulation of protein expression. We found that the AA homozygotes had significantly lower maximal force production values compared with carriers of the GA genotype. Also, GG homozygotes tended to have higher force output compared with AA homozygotes, but this difference was not statistically significant and may be attributed to the small number of GG homozygotes. In logistic regression analysis, no such association was found with either allele. Previously, the aforementioned IGF1 promoter repeat polymorphism has been reported to be associated with elevated IGF-1 levels and strength improvements after training (15,20). These reports support our findings on the effects of IGF1 variants on skeletal muscle force and which may have further affect the anabolic effects of IGF-1 in the skeletal muscle or the nervous system, resulting in altered muscle force production.
There is only one study on the association of the IGFBP3 SNP rs2854744 with muscle force production (15). In that study, the authors reported no effect on muscle force before or after an exercise training intervention. This baseline finding is confirmed by our results. The rs2854744 is located in the regulatory promoter region of the IGFBP3 gene, and allele A of this SNP has been reported to be associated with higher IGFBP-3 levels (6). Furthermore, increased levels of IGFBP-3 may decrease the proportion of active IGF-1 in blood.
Earlier studies have suggested that the IL6 promoter SNP rs1800795 may associate with maximal physical working capacity in smokers (30) and with increased muscle power production (38) and aerobic performance in response to physical training (16). This SNP is located in the promoter region of the IL6 gene, with reported effects on IL-6 levels in plasma (45). However, we could not confirm these findings in the present study.
Serum IGF-1 levels were lower in AA homozygotes of the IGF1 SNP rs6220 compared with carriers of genotype GA. This may be explained by a small statistical power as previously mentioned. On the other hand, higher IGF-1 levels have been reported in CC homozygotes of the IGF1 SNP rs7136446 (18,42), although this was not found in the present study. It may be that local IGF-1 production plays more significant role in exercise-induced muscle hypertrophy than circulating levels of IGF-1 (7), and therefore, increased levels of IGF-1 are not absolutely needed for the altered muscle phenotype. Finally, CC homozygotes of the IL6 SNP rs1800795 had lower levels of serum IGF-1 compared with the CG heterozygotes. This may indicate a favorable effect of the major G allele on muscle force and power output through circulating IGF-1. However, the difference between the CC and GG homozygotes was not statistically significant. Furthermore, the GG homozygotes were not associated with higher maximal leg extension force. We also found that the minor T allele of the IL6R SNP rs4537545 was associated with higher IL-6 levels, which is in agreement with a previous study (35). The rs4537545 tags the functional nonsynonymous Asp358Ala variant (rs8192284) in the IL6R gene and also affects circulating IL-6r levels (35).
There are some limitations to the present study. Although the number of subjects was estimated to be sufficient for this type of cross-sectional candidate gene study, the calculated statistical power was not reached with every parameter. Therefore, confirmatory studies are warranted using larger cohorts. Habitual physical activity was assessed with a retrospective questionnaire, whereas direct analysis methods and prospective study design would likely have improved the quality of the data. Body composition was estimated by eight-polar bioimpedance method with multifrequency current, which has been considered as a valid method for estimating the proportion of adipose tissue from other tissues (8,25,39). However, the method is not completely precise for estimation of muscle tissue from other tissues with high protein content, especially in the segmental areas of human body, such as lower limbs, and hence muscle mass was not analyzed in the present study. With respect to the genetic analysis, only few SNPs were directly analyzed instead of a genome-wide SNP genotyping due to a high financial cost of such technology.
In summary, our results suggest that variation in the IGF1 gene, but not in the IL6 or IL6R gene, associates with maximal muscle force production. Furthermore, IGFBP3 variants may associate with body composition. This information can be used for personal physical activity recommendations and for risk assessment for obesity and sarcopenia and other public health-related musculoskeletal disorders.
This study was supported by grants from the Research Council for Physical Education and Sports; Finnish Ministry of Education; TBGS Graduate School; Yrjö Jahnsson Foundation; the Scientific Advisory Board to Defense, Finland; and COST actions BM0602 and CM1001. Satu Marttila, Taina Vihavainen, and Taija Hukkanen provided skillful technical assistance. Elina Kokkonen is thanked for the statistical assistance.
The authors report no conflict of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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