Abstract
Background
Adolescence is a critical period for bone mass accrual, and mechanical loading through sport may enhance bone mineral acquisition. Olympic weightlifting provides high-magnitude, weight-bearing stimuli, but evidence in pubertal male athletes remains limited, particularly regarding its associations with endocrine factors related to growth and maturation.
Methods
In this cross-sectional study, 47 pubertal boys aged 13 to 15 years were classified into three groups: controls (N=16), non-elite weightlifters (N=16), and elite weightlifters (N=15). Biological maturation was assessed using Tanner staging. Bone mineral density (BMD) and bone mineral content (BMC) were measured by dual energy X-ray absorptiometry (DXA). Serum growth hormone (GH), insulin-like growth factor-1 (IGF-1), IGF binding protein-3 (IGFBP-3), and testosterone concentrations were determined. Annual training exposure (h/year) was calculated as the total number of hours of structured physical activity performed during the preceding 12 months.
Results
Elite weightlifters were taller and had greater training exposure than non-elite weightlifters and controls. BMD and BMC at whole-body and regional skeletal sites were higher in the trained groups, with the highest values generally observed in elite weightlifters. Elite weightlifters also exhibited higher GH, IGF-1, IGFBP-3, and testosterone concentrations than controls. Total lean mass was strongly correlated with total and regional BMD and BMC, while IGF-1, IGFBP-3, and GH were positively associated with several bone outcomes.
Conclusions
Pubertal male weightlifters, particularly elite athletes, exhibited greater bone mineral status and a more favorable growth-related hormonal profile than age-matched controls. These findings support a positive association between training status, lean mass, and skeletal health during puberty; however, causal inferences are limited by the cross-sectional design.
Keywords: Athletic performance, Bone density, Male, Puberty, Weight lifting
GRAPHICAL ABSTRACT
INTRODUCTION
Peak bone mass acquired during childhood and adolescence is a major determinant of lifelong skeletal health and an important predictor of osteoporosis and fracture risk later in life.[1] Adolescence is a particularly critical period for bone mass accrual, as substantial gains in bone mineral content (BMC) and bone mineral density (BMD) occur during the pubertal years in parallel with rapid somatic growth and maturation.[1,2] Consequently, factors that influence skeletal development during this stage are of considerable clinical and practical importance.
Among these factors, physical activity is widely recognized as a key modulator of bone development. Mechanical loading induced by regular exercise stimulates bone modeling and remodeling, thereby promoting bone mineral acquisition during growth.[2–4] Exercise modalities characterized by high-impact loading, rapid force production, and substantial muscular strain appear to be especially osteogenic in youth.[5,6] In addition, nutritional factors, particularly calcium intake, may interact with physical activity to influence skeletal adaptation during childhood and adolescence.[4,7]
The close functional relationship between muscle and bone has also been emphasized in the concept of the “muscle-bone unit,” according to which bone adapts to the mechanical forces generated by muscle contractions.[8] During growth, increases in lean mass may therefore contribute directly to bone development by increasing skeletal loading.[8] This interaction appears particularly relevant during puberty, when lean mass and bone mass increase simultaneously and are both influenced by biological maturation and endocrine regulation.[2,9,10]
Olympic weightlifting is characterized by high-magnitude, weight-bearing mechanical loads, explosive multijoint movements, and repeated exposure to substantial muscle-generated forces. These characteristics suggest that it may provide a strong osteogenic stimulus during growth.[5,11,12] Previous studies have shown that athletes engaged in resistance- and power-oriented sports may exhibit higher bone mass than non-athletic peers, and adolescent weightlifters have been reported to present favorable bone mineral characteristics.[11,12] However, evidence in pubertal male weightlifters remains limited, particularly when considering different training levels.
In addition to mechanical loading, endocrine factors may contribute to skeletal adaptation during puberty. Growth hormone (GH), insulin-like growth factor-1 (IGF-1), IGF binding protein-3 (IGFBP-3), and testosterone are all involved in growth, tissue anabolism, and bone metabolism.[10,13,14] These hormones may partly mediate the relationship between training status, lean mass development, and bone mineral accrual. Nevertheless, few studies have simultaneously examined bone mineral variables, body composition, and growth-related hormonal markers in pubertal male weightlifters.
Furthermore, interpretation of BMD in pediatric populations requires caution because dual energy X-ray absorptiometry (DXA) provides areal rather than volumetric estimates, and the results may be influenced by body size and growth-related factors.[15,16] Therefore, studies in youth athletes should consider both maturity status and body composition when examining skeletal outcomes.
Accordingly, the aim of the present cross-sectional study was to compare bone mineral status, body composition, and growth-related hormonal profile among pubertal boys according to Olympic weightlifting training status, and to examine the associations of lean mass and hormonal variables with bone outcomes. We hypothesized that elite and non-elite weightlifters would exhibit higher BMD and BMC than controls, with the greatest values in elite athletes, and that lean mass and growth-related hormonal markers would be positively associated with skeletal parameters.
METHODS
1. Subjects
Forty-seven pubertal boys aged 13–15 years were recruited from secondary schools and weightlifting clubs in Tunis (Tunisia) and classified into three groups according to training status: controls (N=16), non-elite weightlifters (N=16), and elite weightlifters (N=15). Participants were similar in chronological age and pubertal maturation stage.
Control participants took part only in standard school physical education classes (2 sessions/week, 50 min/session). The non-elite weightlifters had practiced Olympic weightlifting for at least 3 years, training 11 months per year, with four sessions per week (2 h/session), and competed approximately every 3 months, in addition to school physical education classes. The elite weightlifters also had at least 3 years of weightlifting experience and trained 11 months per year, but completed eight sessions per week (2 h/session), with competitions approximately every 3 months, in addition to school physical education classes.
Annual training exposure was calculated as the total number of hours of structured physical activity performed over the preceding 12 months (sessions per week×session duration×weeks per year). For controls, this exposure reflected school physical education only, whereas for weightlifters it reflected organized Olympic weightlifting training in addition to school physical education.
All participants were in pubertal Tanner stages IV–V. Exclusion criteria were any current or previous major medical condition, neurological disorder, orthopedic or musculoskeletal pathology, chronic disease, or use of medication known to affect bone metabolism. Parents/legal guardians were informed about the study procedures and laboratory assessments and provided written informed consent. The study protocol was approved by the Ethics Committees of Charles Nicolle Hospital (Tunis) and Clinique Ibnou Zohr and was conducted in accordance with the Declaration of Helsinki.
2. Anthropometric parameters
Body height and body mass were measured using a portable stadiometer (Seca Model 225, Hanover, MD, USA) and a digital scale (Tanita, Tokyo, Japan), respectively. Body mass index (BMI) was calculated as body mass (kg) divided by height squared (m2).
3. Body composition and bone measurements
Whole-body and regional BMD, BMC, and total lean mass were assessed by DXA using a Lunar Prodigy system (version 3.6; GE Lunar, Madison, WI, USA). The device was calibrated daily according to the manufacturer’s recommendations, and all scans were performed by the same experienced operator. Because DXA provides areal rather than volumetric estimates in growing individuals, the results were interpreted cautiously with attention to body size and maturation status.
4. Daily calcium intake
Daily calcium intake (mg/day) was estimated from dietary intake recorded over 3 consecutive days. All foods and beverages consumed during this period were documented and analyzed by a nutritionist from the elite sports school. Calcium intake was calculated using Bilnut software (version 2.01; SCDAA Nutrisoft, Cerelles, France).
5. Weightlifting performance
Weightlifting performance was assessed using the snatch and the clean and jerk, and the sum of the best successful attempts was recorded as the Olympic total (kg). In addition, the Olympic total/body mass ratio was calculated. Sinclair score was determined according to the International Weightlifting Federation Sinclair coefficients.[17]
6. Vertical jump performance
Explosive lower-limb performance was assessed using squat jump (SJ) and countermovement jump (CMJ) tests with an Optojump system (Microgate, Bolzano, Italy), as previously described. Each participant performed three trials under each condition with 1 min of passive recovery, and the best performance was retained for analysis.
7. Biochemical analyses
Fasting venous blood samples were collected between 08:00 and 09:30 a.m., centrifuged at 2,100×g for 10 min, and the separated serum was stored at −20°C until analysis.
Serum GH was measured by chemiluminescence assay (Immulite; Diagnostic Products, Los Angeles, CA, USA). IGF-1 and IGFBP-3 were determined by immunoradiometric assay kits (Diagnostic Systems Laboratories, Webster, TX, USA), and total testosterone was measured by radioimmunoassay (DSL-4000; Diagnostic Systems Laboratories). All assays were performed according to the manufacturers’ instructions.
8. Statistical analysis
Data are presented as mean±standard deviation (SD). Normality was assessed using the Shapiro–Wilk test together with visual inspection of distribution plots, and homogeneity of variances was verified with Levene’s test. Between-group differences in anthropometric, performance, DXA-derived, and endocrine variables were analyzed using one-way analysis of variance (ANOVA) with group (control, non-elite weightlifters, elite weightlifters) as the fixed factor. When the omnibus F test was significant, pairwise comparisons were performed using Fisher’s protected least significant difference post-hoc procedure. Effect sizes for omnibus comparisons were expressed as eta-squared (η2), and standardized mean differences were considered when interpreting the magnitude of pairwise contrasts together with their 95% confidence intervals. Associations between total lean mass and selected bone, endocrine, and performance variables were examined using Pearson correlation coefficients and simple linear regression analyses. Statistical analyses were performed using StatView 5.0 (SAS Institute Inc., Cary, NC, USA), and statistical significance was set at a P value of less than 0.05.
RESULTS
1. Participant characteristics
Participant anthropometric, maturational, and training characteristics are presented in Table 1. No significant between-group differences were observed for age, body mass, BMI, Tanner stage, or calcium intake. Height differed across groups, with non-elite weightlifters showing lower stature than controls and elite weightlifters. As expected, training age was reported only in the two weightlifting groups and was numerically higher in elite than in non-elite weightlifters. Annual training exposure differed markedly between groups, with both trained groups showing higher exposure than controls and elite weightlifters showing the highest values overall (Table 1).
Table 1.
Anthropometric characteristics of study participants
| Variables | Controls (N=16) | Non-elite weightlifters (N=16) | Elite weightlifters (N=15) |
|---|---|---|---|
| Age (yr) | 14.63±0.57 | 14.22±0.56 | 14.80±0.57 |
| Height (m) | 1.66±0.06 | 1.59±0.08 | 1.67±0.09 |
| Weight (kg) | 55.34±9.69 | 55.49±6.67 | 55.38±7.90 |
| BMI (kg/m2) | 20.08±2.51 | 19.41±1.25 | 19.71±1.28 |
| Puberty stage | 4.56±0.51 | 4.53±0.41 | 4.56±0.51 |
| Training age (yr) | - | 4.20±0.77 | 4.94±0.77 |
| Annual training exposure (h/yr) | 59.13±1.83 | 268.10±12.96b) | 669.17±12.84a),c) |
| Calcium (mg/day) | 1,064.58±52.35 | 1,116.59±66.69 | 1,139.44±80.54 |
The data is presented as mean±standard deviation.
Significant difference between non-elite weightlifting group and elite weightlifting group.
Significant difference between control group and non-elite weightlifting group.
Significant difference between elite weightlifting group and control group.
BMI, body mass index.
2. Physical performance and weightlifting-specific variables
Physical performance and weightlifting-specific variables are shown in Table 2. CMJ and SJ performance were significantly higher in both trained groups than in controls, with the highest values observed in elite weightlifters. Weightlifting-specific variables, including snatch, clean and jerk, Olympic total, Olympic total/body mass ratio, and Sinclair score, were assessed only in the two trained groups. For all these variables, elite weightlifters demonstrated significantly higher values than non-elite weightlifters (Table 2).
Table 2.
Physical performance and weightlifting-related variables in controls, non-elite weightlifters, and elite weightlifters
| Variables | Controls | Non-elite weightlifters | Elite weightlifters |
|---|---|---|---|
| CMJ (cm) | 26.35±2.83 | 32.72±3.22b) | 37.28±2.10a),c) |
| SJ (cm) | 24.13±2.66 | 29.82±3.17b) | 34.86±1.55a),c) |
| Snatch (kg) | - | 67.46±9.59 | 96.23±10.11a) |
| Clean and jerk (kg) | - | 95.07±9.41 | 119.53±11.13a) |
| Olympic total (kg) | - | 162.53±17.12 | 215.76±20.42a) |
| Olympic total/body mass ratio | - | 2.93±0.31 | 3.90±0.26a) |
| Sinclair score | - | 4,931.96±1,660.63 | 13,161.96±4,913.06a) |
The data is presented as mean±standard deviation.
Significant difference between non-elite weightlifting group and elite weightlifting group.
Significant difference between control group and non-elite weightlifting group.
Significant difference between elite weightlifting group and control group.
CMJ, countermovement jump; SJ, squat jump.
3. BMD and BMC
BMD and BMC values at whole-body and regional sites are presented in Table 3. Non-elite weightlifters showed significantly higher BMD and BMC than controls at all measured sites, including the whole body, lower limbs, lumbar spine, upper limbs, and femoral neck. Elite weightlifters also showed significantly higher BMD and BMC than controls at all sites and, in addition, had significantly higher values than non-elite weightlifters. Overall, a clear training-status gradient was observed, with the highest bone mineral values consistently found in elite weightlifters (Table 3).
Table 3.
Bone mineral density and bone mineral content from whole-body and regional sites in controls, non-elite weightlifters, and elite weight-lifters
| Variables | Bone mineral density (g/cm2) | Bone mineral content (g) | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Controls | Non-elite weightlifters | Elite weightlifters | Controls | Non-elite weightlifters | Elite weightlifters | |
| Whole-body | 0.982±0.099 | 1.105±0.062b) | 1.270±0.155a),c) | 1,876.14±371.55 | 2,263.74±387.76b) | 2,772.74±500.59a),c) |
|
| ||||||
| Lower limbs | 1.067±0.064 | 1.163±0.106b) | 1.346±0.143a),c) | 658.68±143.96 | 927.32±220.91b) | 1,241.20±300.43a),c) |
|
| ||||||
| Lumbar spine | 0.804±0.064 | 0.932±0.048b) | 1.215±0.194a),c) | 160.40±46.86 | 219.41±22.60b) | 282.49±64.17a),c) |
|
| ||||||
| Upper limbs | 0.737±0.060 | 0.862±0.060b) | 1.086±0.218a),c) | 175.21±46.59 | 239.02±64.27b) | 321.49±86.64a),c) |
|
| ||||||
| Femoral neck | 0.994±0.093 | 1.120±0.032b) | 1.387±0.208a),c) | 232.43±45.64 | 323.56±67.72b) | 473.88±134.95a),c) |
The data is presented as mean±standard deviation.
Significant difference between non-elite weightlifting group and elite weightlifting group.
Significant difference between control group and non-elite weightlifting group.
Significant difference between elite weightlifting group and control group.
4. Growth-related hormonal concentrations
Growth-related hormonal concentrations are reported in Table 4. Compared with controls, non-elite weightlifters exhibited significantly higher IGF-1, IGFBP-3, GH, and testosterone concentrations. Elite weightlifters showed the highest hormonal values overall and differed significantly from both controls and non-elite weightlifters for all measured endocrine variables. These findings indicate a progressive increase in growth-related hormonal concentrations according to training status (Table 4).
Table 4.
Growth-related hormonal concentrations in controls, non-elite weightlifters, and elite weightlifters
| Variables | Controls | Non-elite weightlifters | Elite weightlifters |
|---|---|---|---|
| IGF-1 (ng/mL) | 437.20±39.94 | 500.63±40.53b) | 568.08±47.19a),c) |
| IGFBP-3 (ng/mL) | 3,810.76±362.48 | 4,625.04±163.83b) | 5,210.73±172.28a),c) |
| GH (ng/mL) | 2.04±0.49 | 3.47±0.46b) | 4.04±0.51a),c) |
| Testosterone (nmol/L) | 2.27±0.39 | 2.82±0.32b) | 3.86±0.77a),c) |
The data is presented as mean±standard deviation.
Significant difference between non-elite weightlifting group and elite weightlifting group.
Significant difference between control group and non-elite weightlifting group.
Significant difference between elite weightlifting group and control group.
IGF-1, insulin-like growth factor-1; IGFBP-3, insulin-like growth factor binding protein-3; GH, growth hormone.
5. Correlations with total lean mass
Correlations between total lean mass and selected bone, hormonal, and performance variables are presented in Table 5. In controls, correlations between total lean mass and the measured variables were weak and not statistically significant. In non-elite weightlifters, total lean mass was positively associated with whole-body BMD (r=0.83, P<0.001), whole-body BMC (r=0.74, P=0.002), IGF-1 (r=0.75, P= 0.001), IGFBP-3 (r=0.85, P=0.001), testosterone (r=0.81, P=0.001), GH (r=0.86, P=0.001), SJ performance (r=0.85, P=0.001), and CMJ performance (r=0.65, P=0.008). In elite weightlifters, similarly positive associations were observed between total lean mass and whole-body BMD (r=0.80, P=0.001), whole-body BMC (r=0.73, P=0.001), IGF-1 (r=0.80, P=0.001), IGFBP-3 (r=0.82, P=0.001), testosterone (r=0.82, P=0.001), GH (r=0.82, P=0.001), SJ performance (r=0.67, P=0.006), and CMJ performance (r=0.79, P=0.001). Overall, these results indicate that total lean mass was strongly associated with bone mineral outcomes, endocrine variables, and jump performance in trained participants, but not in controls (Table 5).
Table 5.
Relationships between total lean mass and selected bone, endocrine, and performance variables in controls, non-elite weightlifters, and elite weightlifters
| Variables (correlated with total lean mass) | Controls | Non-elite weightlifters | Elite weightlifters | |||
|---|---|---|---|---|---|---|
|
|
|
|
||||
| r | P | r | P | r | P | |
| Whole-body BMD (g/cm2) | 0.30 | 0.20 | 0.83 | <0.001 | 0.80 | 0.001 |
|
| ||||||
| Whole-body BMC (g) | 0.10 | 0.60 | 0.74 | 0.002 | 0.73 | 0.001 |
|
| ||||||
| IGF-1 (ng/mL) | 0.40 | 0.10 | 0.75 | 0.001 | 0.80 | 0.001 |
|
| ||||||
| IGFBP-3 (ng/mL) | 0.40 | 0.20 | 0.85 | 0.001 | 0.82 | 0.001 |
|
| ||||||
| Testosterone (nmol/L) | 0.30 | 0.30 | 0.81 | 0.001 | 0.82 | 0.001 |
|
| ||||||
| GH (ng/mL) | 0.10 | 0.60 | 0.86 | 0.001 | 0.82 | 0.001 |
|
| ||||||
| SJ (cm) | 0.10 | 0.70 | 0.85 | 0.001 | 0.67 | 0.006 |
|
| ||||||
| CMJ (cm) | 0.10 | 0.90 | 0.65 | 0.008 | 0.79 | 0.001 |
|
| ||||||
| Snatch (kg) | 0.10 | 0.10 | 0.81 | 0.001 | 0.82 | 0.001 |
|
| ||||||
| Clean and jerk (kg) | 0.10 | 0.20 | 0.80 | 0.001 | 0.86 | 0.001 |
|
| ||||||
| Olympic total (kg) | 0.30 | 0.30 | 0.81 | 0.001 | 0.62 | 0.006 |
|
| ||||||
| Olympic total/body mass ratio | 0.10 | 0.20 | 0.65 | 0.008 | 0.79 | 0.001 |
|
| ||||||
| Sinclair score | 0.10 | 0.40 | 0.64 | 0.001 | 0.67 | 0.001 |
BMD, bone mineral density; BMC, bone mineral content; IGF-1, insulin-like growth factor-1; IGFBP-3, insulin-like growth factor binding protein-3; GH, growth hormone; SJ, squat jump; CMJ, countermovement jump.
DISCUSSION
The main finding of the present study is that pubertal male weightlifters, particularly elite athletes, exhibited greater bone mineral status than age-matched controls, as reflected by higher BMD and BMC values at total and regional skeletal sites. In addition, trained participants, especially elite weightlifters, demonstrated more favorable bone mineral and endocrine profiles, while total lean mass was strongly associated with several skeletal outcomes. Significant positive associations were also observed between lean mass and bone variables, as well as between selected endocrine markers and skeletal outcomes. Taken together, these findings support a positive association between Olympic weightlifting training status and skeletal health during puberty.
These observations are biologically plausible given the known osteogenic effects of mechanical loading during growth.[2–4] Bone tissue is highly responsive to strain, and repeated exposure to dynamic, high-magnitude forces can stimulate modeling and remodeling processes that favor bone mineral accrual.[2,6] Olympic weightlifting involves explosive lifting actions, rapid force development, and substantial ground reaction and muscle-generated forces, all of which may provide an effective osteogenic stimulus. [5,11,12] The gradient observed across the three groups in the present study further suggests that a higher level of training exposure may be associated with greater skeletal adaptation.
Our results are also consistent with previous evidence indicating that resistance- and impact-based activities during youth may enhance bone mineral development.[3,5,6] In particular, adolescent weightlifters have previously been shown to present higher bone mass than less active peers. [11,12] The present findings extend this literature by showing that these differences are evident not only between trained and untrained boys, but also across a training-status gradient that distinguishes elite from non-elite weightlifters. This suggests that training level and cumulative exposure may be relevant determinants of skeletal adaptation during the pubertal years.
Another important finding of this study is the strong positive association between lean mass and bone mineral outcomes. This observation supports the concept of the muscle-bone unit, according to which skeletal adaptation is closely linked to muscle development and the forces generated by muscle contractions.[8] During puberty, lean mass increases rapidly and may exert a particularly strong influence on bone mineral accrual.[2,18] In the present study, the stronger relationships observed in the trained groups, especially among elite athletes, suggest that systematic weightlifting training may amplify this interaction by simultaneously enhancing muscle mass and skeletal loading.
The endocrine findings also deserve consideration. Elite weightlifters showed higher GH, IGF-1, IGFBP-3, and testosterone concentrations than controls, and several of these markers were positively associated with bone outcomes. The GH–IGF-1 axis plays an important role in skeletal growth, pubertal development, and tissue anabolism during adolescence.[9,13] Testosterone also contributes to musculoskeletal development by supporting protein synthesis, muscle hypertrophy, and bone formation during male puberty.[10] Therefore, the more favorable endocrine profile observed in elite lifters may represent a biological environment conducive to enhanced bone mineral accrual. These results support the idea that skeletal adaptation during puberty may reflect not only the direct effect of mechanical loading, but also the interaction between training and endocrine regulation.
At the same time, these endocrine findings should be interpreted cautiously. Hormone concentrations in adolescents are influenced by multiple factors, including pubertal timing, sleep, nutritional status, energy availability, and recent training load.[10,14] Although Tanner stage was considered in the present study, residual variability in maturation tempo and endocrine status cannot be excluded. Thus, the observed associations do not necessarily indicate that training alone explains the hormonal differences between groups.
The present findings should also be interpreted in light of methodological considerations related to DXA assessment in growing individuals. Although DXA is a well-established and widely used method for assessing bone mineral status in pediatric research, areal BMD is influenced by body size and skeletal dimensions.[15,16] This is particularly relevant in pubertal populations, in whom rapid growth may affect the interpretation of bone measures.
This study has several strengths. It included three distinct groups representing a gradient of training status, combined assessment of DXA-derived bone outcomes, body composition, and endocrine variables, and considered pubertal maturation stage. However, several limitations must also be acknowledged. First, the cross-sectional design precludes causal inference; therefore, it cannot be concluded whether Olympic weightlifting training directly caused the observed differences in bone and endocrine variables. Second, the sample size within each group was modest, which may have limited statistical power and external validity. Third, residual confounding related to nutrition, habitual physical activity outside formal training, and inter-individual differences in maturation timing cannot be excluded.[4,7] Fourth, as noted above, DXA-derived areal BMD in pediatric populations should be interpreted with caution because of the influence of body size and growth.[15,16]
Overall, the present results indicate that pubertal boys engaged in Olympic weightlifting, particularly at elite level, display more favorable bone mineral characteristics and growth-related endocrine profiles than age-matched controls. These findings reinforce the notion that appropriately practiced high-load athletic training during puberty may be associated with enhanced musculoskeletal development.[5,11,12] Nevertheless, prospective longitudinal studies are required to determine whether these differences reflect training-induced adaptations, pre-existing biological advantages, or a combination of both.
The present study has several strengths, notably the inclusion of three groups reflecting different levels of training status, the integrated assessment of bone mineral outcomes, body composition, endocrine markers, and performance measures, and the consideration of pubertal maturation stage. Nevertheless, some limitations must be acknowledged. First, the cross-sectional design does not allow causal inferences. Second, the relatively small sample size in each group may have reduced statistical power and limited the external validity of the findings. Third, potential residual confounding from nutritional factors, habitual physical activity, and variation in maturation timing cannot be ruled out. Fourth, areal BMD derived from DXA should be interpreted cautiously in pediatric populations because it is affected by body size and growth-related changes.
Pubertal male weightlifters, especially elite athletes, exhibited greater BMD and BMC, stronger lean-mass-related associations, and a more favorable growth-related hormonal profile than age-matched controls. These findings support a positive association between Olympic weightlifting training status and skeletal health during puberty. However, because of the cross-sectional design and the known limitations of DXA interpretation in growing populations, causal conclusions cannot be drawn. Future longitudinal studies should clarify the respective contributions of training load, maturation, nutrition, and endocrine regulation to skeletal development in young weightlifters.
Footnotes
Funding
The authors received no financial support for this article.
Ethics approval and consent to participate
This study conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Ethics Committees of Charles Nicolle Hospital (Tunis) and Clinique Ibnou Zohr.
Conflict of interest
The authors declare no potential conflict of interest relevant to this article.
REFERENCES
- 1.Bonjour JP, Theintz G, Buchs B, et al. Critical years and stages of puberty for spinal and femoral bone mass accumulation during adolescence. J Clin Endocrinol Metab. 1991;73:555–63. doi: 10.1210/jcem-73-3-555. [DOI] [PubMed] [Google Scholar]
- 2.Bailey DA, McKay HA, Mirwald RL, et al. A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: The university of Saskatchewan bone mineral accrual study. J Bone Miner Res. 1999;14:1672–9. doi: 10.1359/jbmr.1999.14.10.1672. [DOI] [PubMed] [Google Scholar]
- 3.MacKelvie KJ, Khan KM, McKay HA. Is there a critical period for bone response to weight-bearing exercise in children and adolescents? A systematic review. Br J Sports Med. 2002;36:250–7. doi: 10.1136/bjsm.36.4.250. discussion 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Vicente-Rodriguez G. How does exercise affect bone development during growth? Sports Med. 2006;36:561–9. doi: 10.2165/00007256-200636070-00002. [DOI] [PubMed] [Google Scholar]
- 5.Weaver CM, Gordon CM, Janz KF, et al. The National Osteoporosis Foundation’s position statement on peak bone mass development and lifestyle factors: A systematic review and implementation recommendations. Osteoporos Int. 2016;27:1281–386. doi: 10.1007/s00198-015-3440-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chaari H, Zouch M, Zribi A, et al. Specific sites of bone expansion depend on the level of volleyball practice in prepubescent boys. Biol Sport. 2013;30:227–34. doi: 10.5604/20831862.1059297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Courteix D, Lespessailles E, Peres SL, et al. Effect of physical training on bone mineral density in prepubertal girls: A comparative study between impact-loading and non-impact-loading sports. Osteoporos Int. 1998;8:152–8. doi: 10.1007/bf02672512. [DOI] [PubMed] [Google Scholar]
- 8.Schoenau E, Frost HM. The “muscle-bone unit” in children and adolescents. Calcif Tissue Int. 2002;70:405–7. doi: 10.1007/s00223-001-0048-8. [DOI] [PubMed] [Google Scholar]
- 9.Vicente-Rodriguez G, Ara I, Perez-Gomez J, et al. Muscular development and physical activity as major determinants of femoral bone mass acquisition during growth. Br J Sports Med. 2005;39:611–6. doi: 10.1136/bjsm.2004.014431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pomerants T, Tillmann V, Jurimae J, et al. The influence of serum ghrelin, IGF axis and testosterone on bone mineral density in boys at different stages of sexual maturity. J Bone Miner Metab. 2007;25:193–7. doi: 10.1007/s00774-006-0744-6. [DOI] [PubMed] [Google Scholar]
- 11.Maimoun L, Sultan C. Effects of physical activity on bone remodeling. Metabolism. 2011;60:373–88. doi: 10.1016/j.metabol.2010.03.001. [DOI] [PubMed] [Google Scholar]
- 12.Lloyd RS, Faigenbaum AD, Stone MH, et al. Position statement on youth resistance training: The 2014 International Consensus. Br J Sports Med. 2014;48:498–505. doi: 10.1136/bjsports-2013-092952. [DOI] [PubMed] [Google Scholar]
- 13.Blum WF, Albertsson-Wikland K, Rosberg S, et al. Serum levels of insulin-like growth factor I (IGF-I) and IGF binding protein 3 reflect spontaneous growth hormone secretion. J Clin Endocrinol Metab. 1993;76:1610–6. doi: 10.1210/jcem.76.6.7684744. [DOI] [PubMed] [Google Scholar]
- 14.Schwarz AJ, Brasel JA, Hintz RL, et al. Acute effect of brief low- and high-intensity exercise on circulating insulin-like growth factor (IGF) I, II, and IGF-binding protein-3 and its proteolysis in young healthy men. J Clin Endocrinol Metab. 1996;81:3492–7. doi: 10.1210/jcem.81.10.8855791. [DOI] [PubMed] [Google Scholar]
- 15.Shuhart CR, Yeap SS, Anderson PA, et al. Executive summary of the 2019 ISCD position development conference on monitoring treatment, DXA cross-calibration and least significant change, spinal cord injury, peri-prosthetic and orthopedic bone health, transgender medicine, and pediatrics. J Clin Densitom. 2019;22:453–71. doi: 10.1016/j.jocd.2019.07.001. [DOI] [PubMed] [Google Scholar]
- 16.Guss CE, McAllister A, Gordon CM. DXA in children and adolescents. J Clin Densitom. 2021;24:28–35. doi: 10.1016/j.jocd.2020.01.006. [DOI] [PubMed] [Google Scholar]
- 17.International Weightlifting Federation . IWF technical and competition rules & regulations. Lausanne, CH: International Weightlifting Federation; 2025. [Google Scholar]
- 18.Faigenbaum AD, Kraemer WJ, Blimkie CJ, et al. Youth resistance training: Updated position statement paper from the national strength and conditioning association. J Strength Cond Res. 2009;23:S60–79. doi: 10.1519/JSC.0b013e31819df407. [DOI] [PubMed] [Google Scholar]