Forearm Fractures in Overweight-Obese Children and Adolescents: A Matter of Bone Density, Bone Geometry or Body Composition?

Predictive factors of bone strength variation in adolescent girls according to body composition

PURPOSE

This study examined correlations among anthropometric parameters, body composition, bone parameters and predictive factors of bone mass in adolescent girls with different body fat percentages (%fat).

METHODS

A total of 129 females aged 15-18 years were categorized into 3 groups using %fat-for-age at the 50th and 95th percentiles as cutoff points (normal, over, and excess %fat groups). We recorded anthropometric data and measured the speed of sound at the tibia and radius using quantitative ultrasound. Dual-energy x-ray absorptiometry (DXA) was used to measure body composition and bone parameters, including bone mineral density (BMD), bone mineral content (BMC), and the BMD-z-score (z-score) in the lumbar spine (LS) and whole body (WB). These parameters were compared among the 3 groups using bivariate and multivariate correlation analyses.

RESULTS

There were strong correlations among all anthropometric parameters, body composition, and DXA in the over %fat group. Lean parameters strongly correlated with LS and WB in the normal %fat group, whereas both lean mass (LM) and fat mass (FM) were positively correlated with BMC in the excess %fat group. The predictive factors of bone mass differed among the groups, as follows: lean body mass was predictive of BMD and BMC at both sites in the normal and over %fat groups; LM and body weight were predictive of LS-BMC and WB-bones, respectively, in the over %fat group; and FM was predictive of WB-bones in the excess %fat group. Body fat and waist circumference were negative predictors of bone mass.

CONCLUSION

Predictive factors of bone strength appear to depend on the amount of body fat in adolescent girls.

Not Only Metabolic Complications of Childhood Obesity

The increasing incidence of obesity in the pediatric population requires attention to its serious complications. It turns out that in addition to typical, well-known metabolic complications, obesity as a systemic disease carries the risk of equally serious, although less obvious, non-metabolic complications, such as cardiovascular diseases, polycystic ovary syndrome, chronic kidney disease, asthma, thyroid dysfunction, immunologic and dermatologic conditions, and mental health problems. They can affect almost all systems of the young body and also leave their mark in adulthood. In addition, obesity also contributes to the exacerbation of existing childhood diseases. As a result, children suffering from obesity may have a reduced quality of life, both physically and mentally, and their life expectancy may be shortened. It also turns out that, in the case of obese pregnant girls, the complications of obesity may also affect their unborn children. Therefore, it is extremely important to take all necessary actions to prevent the growing epidemic of obesity in the pediatric population, as well as to treat existing complications of obesity and detect them at an early stage. In summary, physicians treating a child with a systemic disease such as obesity must adopt a holistic approach to treatment.

Saturation Effect of Body Mass Index on Bone Mineral Density in Adolescents of Different Ages: A Population-Based Study

BACKGROUND

Adolescence is a critical period for bone development, and peak bone mass may be reached in late adolescence. Boosting bone accumulation at this time can help preserve adult bone health and avoid osteoporosis later in life. Body mass index (BMI) has been found to have a favorable impact on bone mineral density (BMD) in previous research. However, excessive obesity is harmful to health and may lead to various systemic diseases. Therefore, finding an appropriate BMI to maintain a balance between obesity and BMD is critical for adolescents.

METHODS

The datasets from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 were used in a cross-sectional investigation. Multivariate linear regression models were used to examine the linear connection between BMI and BMD. Fitted smoothing curves and threshold effect analysis were used to describe the nonlinear relationship. Subgroup analyses were then conducted based on gender and age.

RESULTS

This population-based study included a total of 6,143 adolescents aged 8-19 years. In a multivariate linear regression analysis, a good association between BMI and total BMD was shown [0.014 (0.013, 0.014)]. This positive association was maintained in all subgroup analyses grouped by sex and age. Furthermore, the association between BMI and BMD was nonlinear with a saturation point present, as evidenced by smoothed curve fitting. According to the threshold effect study, with an age group of two years, adolescents of different ages had different BMI saturation values with respect to BMD.

CONCLUSIONS

Our study showed a significant positive and saturated association between BMI and BMD in adolescents aged 8-19 years. Maintaining BMI at saturation values may reduce other adverse effects while achieving optimal BMD.