Anthropometric adjustments are helpful in the interpretation of BMD and BMC Z-scores of pediatric patients with Prader-Willi syndrome.

Anthropometric adjustments of bone measurements are necessary in Prader-Willi syndrome patients to correctly assess the bone status of these patients. This enables physicians to get a more accurate diagnosis of normal versus abnormal bone, allow for early and effective intervention, and achieve better therapeutic results. INTRODUCTION: Bone mineral density (BMD) is decreased in patients with Prader-Willi syndrome (PWS). Because of largely abnormal body height and weight, traditional BMD Z-scores may not provide accurate information in this patient group. The goal of the study was to assess a cohort of individuals with PWS and characterize the development of low bone density based on two adjustment models applied to a dataset of BMD and bone mineral content (BMC) from dual-energy X-ray absorptiometry (DXA) measurements. METHODS: Fifty-four individuals, aged 5-20 years with genetically confirmed PWS, underwent DXA scans of spine and hip. Thirty-one of them also underwent total body scans. Standard Z-scores were calculated for BMD and BMC of spine and total hip based on race, sex, and age for all patients, as well as of whole body and whole-body less head for those patients with total-body scans. Additional Z-scores were generated based on anthropometric adjustments using weight, height, and percentage body fat and a second model using only weight and height in addition to race, sex, and age. RESULTS: As many PWS patients have abnormal anthropometrics, addition of explanatory variables weight, height, and fat resulted in different bone classifications for many patients. Thus, 25-70 % of overweight patients, previously diagnosed as normal, were subsequently diagnosed as below normal, and 40-60 % of patients with below-normal body height changed from below normal to normal depending on bone parameter. CONCLUSIONS: This is the first study to include anthropometric adjustments into the interpretation of BMD and BMC in children and adolescents with PWS. This enables physicians to get a more accurate diagnosis of normal versus abnormal BMD and BMC and allows for early and effective intervention.

Anthropometric models of bone mineral content and areal bone mineral density based on the bone mineral density in childhood study.

UNLABELLED: New models describing anthropometrically adjusted normal values of bone mineral density and content in children have been created for the various measurement sites. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters. INTRODUCTION: Previous descriptions of children's bone mineral measurements by age have focused on segmenting diverse populations by race and sex without adjusting for anthropometric variables or have included the effects of a single anthropometric variable. METHODS: We applied multivariate semi-metric smoothing to the various pediatric bone-measurement sites using data from the Bone Mineral Density in Childhood Study to evaluate which of sex, race, age, height, weight, percent body fat, and sexual maturity explain variations in the population's bone mineral values. By balancing high adjusted R(2) values with clinical needs, two models are examined. RESULTS: At the spine, whole body, whole body sub head, total hip, hip neck, and forearm sites, models were created using sex, race, age, height, and weight as well as an additional set of models containing these anthropometric variables and percent body fat. For bone mineral density, weight is more important than percent body fat, which is more important than height. For bone mineral content, the order varied by site with body fat being the weakest component. Including more anthropometrics in the model reduces the overlap of the critical groups, identified as those individuals with a Z-score below -2, from the standard sex, race, and age model. CONCLUSIONS: If body fat is not available, the simpler model including height and weight should be used. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.

Fitting of bone mineral density with consideration of anthropometric parameters.

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.