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Updated: Jun 12, 2026
Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
Published on: January 29, 2018
Fitting of bone mineral density with consideration of anthropometric parameters
Insights
A new model for children's bone mineral density (BMD) incorporates weight and body fat, offering a more accurate standard than traditional age, gender, and race metrics. This enhanced model provides better comparative norms for individual children.
Area of Science:
- Pediatrics
- Orthopedics
- Biostatistics
Background:
- Previous bone mineral density (BMD) models for children often segmented populations by race and gender without accounting for anthropometric variables.
- Alternatively, some models included anthropometric effects but used relatively homogeneous populations, limiting generalizability.
Purpose of the Study:
- To evaluate a new model for normal bone mineral density (BMD) values in children.
- To assess the impact of anthropometric variables (weight, height, percent body fat, sexual maturity) alongside traditional factors (age, gender, race) on BMD.
- To develop a more accurate comparative norm for assessing individual children's BMD.
Main Methods:
- Utilized multivariate semi-metric smoothing (MS(2)) to model diverse populations with multiple interacting effects.
- Applied MS(2) to spine BMD data from the Bone Mineral Density in Childhood Study.
- Selected a final model balancing statistical performance (adjusted R-squared, mean square error) with clinical relevance, including age, gender, race, weight, and percent body fat.
Main Results:
- The proposed model demonstrated narrower BMD distributions and slight shifts compared to traditional models (age, gender, race only).
- The new model offers a potentially superior comparative standard for individual children, being less dependent on the specific cohort's anthropometric characteristics.
- Inclusion of multiple variables and smooth output curves make the MS(2) method suitable for practical data sets.
Conclusions:
- The developed model, incorporating key anthropometric variables, provides a refined standard for pediatric bone mineral density assessment.
- Further clinical validation is needed to fully establish the widespread use of this MS(2) model within the bone research community.
Unlabelled:
A new model describing normal values of bone mineral density in children has been evaluated, which includes not only the traditional parameters of age, gender, and race, but also weight, height, percent body fat, and sexual maturity. This model may constitute a better comparative norm for a specific child with given anthropometric values.
Introduction:
Previous descriptions of children's bone mineral density (BMD) by age have focused on segmenting diverse populations by race and gender without adjusting for anthropometric variables or have included the effects of anthropometric variables over a relatively homogeneous population.
Methods:
Multivariate semi-metric smoothing (MS(2)) provides a way to describe a diverse population using a model that includes multiple effects and their interactions while producing a result that can be smoothed with respect to age in order to provide connected percentiles. We applied MS(2) to spine BMD data from the Bone Mineral Density in Childhood Study to evaluate which of gender, race, age, height, weight, percent body fat, and sexual maturity explain variations in the population's BMD values. By balancing high adjusted R (2) values and low mean square errors with clinical needs, a model using age, gender, race, weight, and percent body fat is proposed and examined.
Results:
This model provides narrower distributions and slight shifts of BMD values compared to the traditional model, which includes only age, gender, and race. Thus, the proposed model might constitute a better comparative standard for a specific child with given anthropometric values and should be less dependent on the anthropometric characteristics of the cohort used to devise the model.
Conclusions:
The inclusion of multiple explanatory variables in the model, while creating smooth output curves, makes the MS(2) method attractive in modeling practically sized data sets. The clinical use of this model by the bone research community has yet to be fully established.
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