Relation of skeletal maturation to obesity was a point of interest to researchers; however, previous studies showed contradictory results about this relation [11].
This study was done to evaluate skeletal maturation and age in obese children and adolescents and correlate it with their chronological age, anthropometric data, and body fat content.
Our study shows that obese children and adolescents have accelerated skeletal maturation compared to control normal-weight healthy subjects (mean skeletal age difference of 0.123 ± 0.67 years versus − 0.175 ± 0.32 years). Also, bone age and BMI showed a significant correlation (r = 0.435, P value = 0.00).
This can be explained by leptin; made by the adipose tissue once, in high quantity, it will stimulate skeletal growth through the activation of assorted mediators, such as insulin-like growth factor 1 and sex hormones. So an obese subject has a high sensitivity to leptin at a peripheral level, leading to increased differentiation and proliferation of chondrocytes and resulting in precocious skeletal maturation [12].
A study done by Russell et al. 2001 investigated the connection between skeletal maturation and adiposity in American and Caucasian children. Hand-wrist radiographs were accustomed to assess skeletal maturity and age, whereas BMI standard deviation score (SDS) was the measure of adiposity. The findings were prompted that there was a significant correlation between skeletal age and BMI as American children were considerably heavier than Caucasians (BMI SDS 2.7 ± 3.4 vs 1.7 ± 2.4, P < 0.05). Both BA–CA (0.75 ± 1.46 vs 0.28 ± 1.38, P < 0.05), and BA/CA (1.09 ± 0.17 vs 1.03 ± 0.16, P < 0.05) were significantly higher in Americans than Caucasians. BA–CA and BA/CA were considerably related to lean body mass, BMI, BMI SDS, and X-ray Absorptiometry (DXA) fat mass (all r > 0.46, P < 0.001) [13].
The previous findings are in accordance with our study showing that there have been accelerated skeletal maturation and significant correlation with lean body mass, BMI, and fat content (r = 0.622, 0.435, 0.463; P 0.000, 0.001, 0.000, respectively).
The study done by Akridge et al. 2007 showed that the normal, overweight, and obese children had accelerated skeletal age as compared to their chronological age. However, the increase in skeletal age difference was non-significant in all three groups. This distinction is maybe as a result that the population was different in each study. Akridge et al. studied the American population comprising of the whites, whereas this study was done on the Egyptian population. Akridge used Fishman’s method for assessing bone age, whereas in our study Greulich and Pyle method was used (Figs. 1, 2, and 3) [5].
Deb et al. 2017 recruited 150 individuals belonging to age group 10 to 14 years to assess their skeletal maturation using hand-wrist radiographs in late childhood obesity and found that the mean skeletal age difference was more in obese (0.72) than in normal (0.6) and overweight (0.58); however, it had been statistically non-significant P = 0.61 and P = 0.598, respectively [14]. The main distinction from our study was that the age range was confined to the pubertal period of 10 to 14 years.
Cervical vertebral maturation (CVM) was used widely as another method to hand-wrist radiographs in the assessment of skeletal maturity. Costacurta et al. 2012 conducted a study in 107 individuals belonging to the age group 6 to 12 years; they assessed CVM and dental age, in normal weight, pre-obese and obese patients, using the BMI and the DXA to evaluate their skeletal maturation. They found that no statistically significant differences were determined among the groups regarding their chronological, dental, and skeletal age. Also, they observed no statistically significant differences in the obese subjects as for chronological and skeletal-dental age (P = 0.09) [15]. Mack et al. 2013 found that CVM and dental age were more advanced in subjects with increased BMI percentiles [16].
Duplessis et al. 2016 found weak significant correlations between BMI percentile and CVM (r = 0.157, P < 0.05); higher BMI percentiles correlated with higher CVM stages [17].
In the study by Giuca et al. 2012, 50 white subjects were selected both hand wrist and CVM were accustomed to assess skeletal age. In the normal group, they had a mean delayed skeletal maturation of 2.2 ± 3.1 months. This was similar to this study which showed a mean age difference is − 0.175 ± 0.32 years. Also, 25 obese individuals had a mean accelerated skeletal maturation of 11.8 ± 11.4 months [18]. This was similar to this study which also showed that there was accelerated skeletal maturation.
When skeletal age difference was correlated between the male and female, the females tended to have greater skeletal age differences (0.07) than males (− 0.13); however, it had been non-significant (P = 0.138). This is contrary to the study by Sadeghianrizi, 2005 who found that the skeletal age difference was lesser in females than in males which may ensue to a shorter peak of the pubertal spurt for females [19].
Neeley and Gonzales, 2007 expressed that “the orthodontic therapy can be affected by obesity”, given the probability for obese patients to show an irregular pubertal development, due to the hormonal changes associated with obesity, a different bone metabolism; leading to changes in growth and development, and specific craniofacial features; increased mandibular length, shorter upper face height [20]. However, knowing the stage is not sufficient to work out the timing of skeletal maturation accurately, particularly in girls, who show a more precocious maturation and a shorter developmental peak than do boys [19].
Several studies have explored the link between sexual maturity and increased body mass index. When the majority of evidence suggests that there is a significant correlation between the increased BMI and early sexual maturity in females; however, that relationship in boys is still questioned [21]. It has been suggested that those with higher childhood BMI tend to have an earlier onset of puberty [22]. While other investigators have indicated a reverse relationship exists between BMI and pubertal onset in boys [21].
In our study, no significant difference in the Tanner stage in females; this may be due to small sample size; however, in males, there is a significant difference between obese and control groups (P value 0.02).
Our results came contradictory to Kaplowitz et al. 2001 who reported that in white girls, BMI is significantly associated with early puberty [23]. Also, a correlation was found between age at adiposity rebound and age at menarche [24].
A study done by Silventoinen et al. 2008 showed that high childhood BMI was associated with an earlier pubertal growth spurt and a strong genetic factor has existed. Growth during puberty was therefore strictly genetically controlled, and these genetic factors also clarified why early maturing children had higher BMI through childhood in this study [25].
In line with our results, the latest research was done by Aksglaede et al. 2009 exploring the connection between pre-pubertal BMI and pubertal onset, evaluated by age at onset of pubertal growth spurt, height velocity peak, growth and puberty in obese children revealed that the heavier children, the earlier they reached puberty [26]. Also, Lee et al. 2016 conducted a study comprised of 3872 subjects to determine whether overweight and obesity were correlated with differences in the timing of puberty in the US boys and found that evidence of earlier puberty for overweight compared with normal boys [27].
Several studies have been done to evaluate the efficacy of using total body fat content as an alternative to BMI as it is the most frequently used index for the classification of overweight-obesity [28]. Our study demonstrates that there was a significant positive correlation between bone age and total body fat content (r = 0.463, P value = 0.00), a significant correlation between bone age and lean body mass (r = 0.622, P value 0.00); however, there is a significant negative correlation between bone age and percentage of lean body mass.
A multiple linear regression analysis was performed to detect the influence of clinical and biochemical variables on the degree of advanced skeletal maturation. It was revealed that there was one significant predictor for bone age and directly related to it, which is the lean body mass (with OR = 0.129; 95% CI 0.049–0.210, P = 0.002). For our knowledge, we are the first to identify this relation.
Whereas the other confounders (BMI, puberty, total body fat content, and lipid profile) showed non-significant relation to the outcome; this was agreed upon by Kim et al, 2017 who investigated these relations on fifty-three obese children and adolescents (age range, 7–15 years; 32 male and 21 female patients). They revealed that obesity and puberty were not associated with advanced skeletal maturation. BMI SDS was not associated with advanced skeletal maturation in multivariate regression analyses [29].
Although assessment of fat content % by bioelectrical impedance is easy, non-invasive, many studies reported that BIA-derived BF can be biased by multiple variables such as distinctions between study and reference population, skin temperature, skin blood flow, or nutritional status [30]. Limitations of bioelectrical impedance assessment are in morbidly obese patients who have an elevated quantity of extracellular water and total body water, which can overestimate fat-free mass and underestimate fat mass. Central body fat will lead to overestimate the percentage of fat-free mass and underestimate the percentage of fat mass in overweight and obese adults [31].
As the lipid levels were high among obese children; therefore, obesity is considered a risk factor for hypercholesterolemia, and screening obese children for hypercholesterolemia should be considered. Our study showed no statistical difference in the level of cholesterol, LDL or triglycerides between the obese and non-obese group (P > 0.05) (cholesterol 170.47 ± 49.22 vs 157.97 ± 16.85 mg/dl; TG 111.77 ± 45.15 vs 103.13 ± 15.11 mg/dl in obese and control children, respectively). However, it was found that mean serum HDL level 39.02 ± 8.69 in the patient group to 49.17 ± 10.68 in the healthy group which was found that those results were statistically significant (P value = 0.000).