title: Prediction models for evaluation of total-body bone mass with dual-energy X-ray absorptiometry among children and adolescents.
abstract: OBJECTIVE
  The performance of dual-energy x-ray absorptiometry (DXA) in identifying children with decreased bone mass is increasing, but there is no consensus regarding how to interpret the results. The World Health Organization diagnostic categories for normal, osteopenia, and osteoporosis, based on T scores, are not applicable to children and adolescents who have not yet reached peak bone mass. The pediatric reference standards provided by DXA manufacturers have been questioned. Bone mineral density determined with DXA is "areal" density (a 2-dimensional measurement of a 3-dimensional structure), and its misleading nature among growing and maturing children is well recognized. Few published pediatric reference values for bone mineral density measured with DXA include factors that are known to affect the results besides age and gender. Our objective was to develop an algorithm for the evaluation of bone mass among children that included known determinants of bone mass and of its measurement with DXA.
  
  
  METHODS
  Height, weight, pubertal status, and total-body bone mineral content, total-body bone area, and total-body bone mineral density measured with DXA were recorded for an ethnically diverse group of healthy pediatric subjects (n = 1218; age: 6-18 years). Prediction models for bone measurements were developed and validated with healthy pediatric subjects and then applied to children with medical disorders.
  
  
  RESULTS
  There was a significant gender effect, as well as an interaction between gender and ethnicity. Separate models were developed for log total-body bone mineral content, log total-body bone area, and 1/total-body bone mineral density for girls and boys. The variability explained for each measurement increased from level 1, including age and ethnicity (76-86%), to level 2, including age, ethnicity, height, and weight (84-97%), and to level 3, including age, ethnicity, height, weight, and bone area (89-99%). Pubertal stage was an additional significant predictor of bone measurements but increased the explained variability by only 0.1% with height and weight in the models. The values predicted with each model were not different from measured values for the validation group but were different for patients with medical disorders, with different patterns according to the diagnoses.
  
  
  CONCLUSIONS
  These models, including known determinants of bone mass and of bone measurements with DXA, provide an evaluation of pediatric bone mass that proceeds in steps from level 1 to level 3. The outcomes were different for patients at risk for compromised bone mass, compared with healthy children, with specific patterns for each medical disorder. We propose an algorithm for evaluation of bone measurements that follows levels 1 to 3. Our findings suggest that application of this algorithm to well-characterized groups of pediatric patients could identify disease-specific features of DXA results. We recommend this approach as a basis for consensus regarding the clinical evaluation of pediatric bone mass, and we suggest that it could lead to meaningful classification of pediatric bone disorders, investigation of pathophysiologic processes, and development of appropriate interventions.
authors.affiliation: Body Composition Unit, St Luke's-Roosevelt Hospital Center, New York, New York 10025, USA. mnh1@columbia.edu
authors.forename: Mary
authors.initials: M
authors.lastname: Horlick
authors.forename: Jack
authors.initials: J
authors.lastname: Wang
authors.forename: Richard N
authors.initials: RN
authors.lastname: Pierson
authors.forename: John C
authors.initials: JC
authors.lastname: Thornton
mesh_terms: D015502:Absorptiometry, Photon; D000293:Adolescent; D000465:Algorithms; D001827:Body Height / Q000208:ethnology; D001835:Body Weight / Q000208:ethnology; D015519:Bone Density; D002648:Child; D005006:Ethnicity; D005260:Female; D006801:Humans; D008297:Male; D012016:Reference Values; D012044:Regression Analysis; D012727:Sex Characteristics