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Editorial |
Stanford University School of Medicine Stanford, California 94305
Address all correspondence and requests for reprints to: Dr. Laura K. Bachrach, Stanford University Medical Center, Department of Pediatrics, Room S302, 300 Pasteur Drive, Stanford, California 94305-5119. E-mail: lkbach{at}stanford.edu.
Myriad challenges confront clinicians caring for children at risk for bone fragility. Despite the consensus that chronic illness, undernutrition, immobilization, and genetic bone disorders can compromise bone health, there is little agreement about how best to identify and treat these conditions (1, 2, 3). For adult patients, dual energy x-ray absorptiometry (DXA) is the principal diagnostic tool to assess risk of fracture, select patients for therapy, and monitor the response to intervention. In fact, bone mineral density (BMD) is sufficiently predictive of fracture risk in the elderly that DXA measurements alone have been used to establish the diagnosis of osteoporosis. A postmenopausal woman with a BMD T-score (SD scores from the mean for healthy young adults) at or below –2.5 has been classified as "osteoporotic" by World Health Organization criteria (4). However, fractures occur in many adults with normal BMD values because bone fragility is modulated by age, gender, weight, alcohol consumption, smoking history, glucocorticoid therapy, and personal or family fracture history (4). Models of absolute fracture risk include both BMD values and these relevant clinical factors.
The role of densitometry in evaluating the skeletal status of children continues to evolve. DXA is considered the preferred method of assessing bone mineral status in clinical practice because of its speed, precision, and low radiation exposure (2, 3). Unfortunately, the acquisition and interpretation of DXA data in growing children is far more complex than in adults. Failure to recognize the myriad pitfalls in pediatric densitometry leads to misdiagnosis and potentially unwarranted intervention. A recent study by Gafni et al. (5) provides a clear cautionary note. Of the children referred for a drug treatment trial, 62% were mislabeled as "osteoporotic" because their BMD values had been compared with adult rather than pediatric norms. When DXA scans were reevaluated using appropriate controls, only 26% of the subjects had low BMD values.
A key barrier to interpretation of densitometry in children has been the lack of standardized pediatric reference data. Calculation of T-scores is not appropriate in younger patients who have not yet reached their adult (peak) bone mass. Instead, DXA results must be compared with normative data derived from healthy children of similar age and maturity to derive a Z-score. Until recently, the only available pediatric reference data came from relatively small, convenience samples of 200–400 subjects measured using older DXA equipment and software versions. Only lately have reference data emerged from larger cohorts collected on newer Hologic (6) and GE/Lunar (7) DXA equipment. The selection of a reference data set has important implications because the differing means and SD scores will alter the Z-score. In particular, failure to use gender-specific reference data can result in the misdiagnosis of low bone mass in boys during adolescence when they lag behind girls in growth and puberty (8).
The Bone Mineral Density in Childhood Study (BMDCS) addresses the need for improved pediatric DXA norms. The goal was to approximate the characteristics of an optimal reference data set (9). The cohort was comprised of healthy youth, representative of the general population, drawn from multiple regions (to address local differences in ethnicity and lifestyle), and sufficiently large to encompass the variability due to age, gender, race, and ethnicity. A total of 1554 healthy, ethnically diverse youth (ages 6–16 yr at entry) were recruited from five leading pediatric bone centers. The latest DXA equipment and software were used to reduce systematic differences between reference data and the DXA machines currently in clinical use. Results were analyzed using a statistical model that captures the nonlinear patterns of gains in bone size and mineral content and the increasing variability with age.
In this issue, Kalkwarf et al. (10) report the results from baseline and yr 1 and 2 of this 5-yr study. These data should be considered as the best available pediatric DXA norms for interpreting Hologic DXA scans for this cohort’s age group. The rigor that went into selection of subjects, cross-calibration of DXA scanners from multiple sites, assessment of bone age and pubertal stage, and statistical modeling are unparalleled. The authors acknowledge the relatively small numbers of Asians and Hispanics in the cohort. An earlier study in healthy 9- to 25-yr-old subjects found significant differences between Blacks and non-Blacks (non-Hispanic Caucasians, Asians, and Hispanics) but not between the three non-Black groups (2). For this reason, the BMDCS data are likely the best available reference source for Asian and Hispanic youth.
Despite the progress made by this important study, many challenges and controversies remain. Pediatric reference data from the BMDCS were collected only on Hologic DXA scanners. These values cannot be used to interpret data obtained from GE/Lunar or Norland densitometers because of systematic differences in software. Although formulae have been developed to convert DXA results from the differing machines into "universal" BMD values, these assumptions have not been adequately tested in children and adolescents. BMDCS reference data are available only for ages 7–17 yr, and norms for older and younger youth are needed. Until these become available, reference data for ages 17–20 yr can be found in the literature (6); standard adult reference data can be used after age 20. Perhaps most challenging is the dilemma of adjusting DXA data for variations in the tempo of growth and puberty.
There is more to the appropriate interpretation of pediatric densitometry data than deriving a BMD Z-score. The influence of bone size, skeletal maturity, and body composition on results must be considered as well. DXA provides a two-dimensional measurement of the three-dimensional skeleton. BMD adjusts for the area but not the thickness of bone; for this reason, BMD is influenced by bone size. Several methods have been developed to account for bone size by adjusting bone mineral content for estimated bone volume or for height, weight, or other anthropometric variables (2, 3). Other models adjust bone mineral content for lean body mass based on the strong functional relationship between bone strength and mechanical forces exerted by muscle (7, 11).
Rates of bone growth and mineral accrual are more closely linked to pubertal and skeletal maturation than to chronologic age (1, 2). Because the timing of growth and puberty is frequently delayed by chronic illness, failure to adjust for these factors may lead to an overestimation of low bone mass. DXA data can be interpreted in light of skeletal maturation (bone age) or pubertal stage to adjust for delays, but few normative data sets provide reference values for bone age or Tanner stage. The BMDCS appropriately examined both skeletal and pubertal maturity.
To date, there is no consensus regarding which of these adjustment methods is best. Proponents of each "correction" method have justified their approach, but what is the gold standard by which they should be judged? The adjustment models for DXA have been assessed by their concordance with using quantitative computed tomography (QCT), which directly measures bone size and volumetric bone density. A subset of the BMDHC cohort was studied using both DXA and QCT (12). Spine bone mineral content (BMC) results obtained with DXA and QCT were highly correlated, but areal BMD (by DXA) was only weakly related to the true volumetric BMD measured by QCT. Only when BMD was adjusted for Tanner stage, height, weight, and bone age did DXA results correlate significantly with volumetric BMD determined by QCT. DXA and QCT results were also discrepant in chronically ill youth (13). In a cohort of 200 healthy and 200 ill subjects, 19% were identified with low spine BMD (Z-score < –2) using DXA as compared with only 6% using volumetric BMD measurements from QCT (13). Those with low Z-scores by QCT were highly likely to have low Z-scores by DXA, but a DXA Z-score below –2 had only a 24% positive predictive value for abnormal QCT. The authors concluded that DXA frequently underestimates the amount of bone in children, which can lead to inaccuracies in the identification of children at risk for poor skeletal health.
A critical question is whether QCT should be considered the gold standard against which these varying adjustments of DXA data should be judged. From a clinical perspective, a more relevant criterion for selecting the best model for DXA adjustments is its ability to predict fractures. Three recent studies have examined the relationship between DXA measurements of bone mass and childhood fracture. This research has focused on healthy youth during the peri-puberty period when forearm fractures are common (14, 15, 16). Children with a history of fracture were found to have lower mean values than controls for BMC, BMD, and estimated volumetric bone mineral density [bone mineral apparent density (BMAD)] (14). In a larger study of youth with and without arm fractures, spine BMD and BMAD were the only DXA measures consistently associated with all upper limb fractures; spine and hip BMAD were the only measures consistently associated with wrist and forearm fractures (15). In the only prospective study to date, DXA measures that best predicted fractures were whole-body BMC and BMD adjusted for body size (16). These findings suggest that adjustments for bone volume or body size predict fracture risk in children better than adjustments of bone mass for lean body mass.
Although these data from healthy youth are informative, they establish neither a fracture threshold nor criteria for diagnosing osteoporosis based upon BMD. As in adults, it is likely that clinical variables will be identified that improve the prediction of fracture risk. One group found that milk avoidance, lower calcium intake, and increased body weight were more common in children with forearm fractures (14). Another important caveat is that findings related to fracture risk in healthy children may not be applicable to those with chronic illness. Clinical risk factors and skeletal sites of fracture will likely differ. For example, children with muscular dystrophy have greater deficits in bone mass and experience more fractures in the lower extremities, not the forearm as in healthy youth; vertebral fractures occur after initiation of glucocorticoid therapy (17). Fracture registries and other multicenter collaborations are needed to expand our knowledge of bone fragility in chronic disease.
Pediatric densitometry has come a long way in the past two decades, with improved reference data, faster scan times, and greater availability. Along with increased usage has come a greater appreciation for the pitfalls and complexities of interpreting the data. At present the diagnosis of osteoporosis in children cannot be made on the basis of bone densitometry findings alone (18). The armamentarium of drugs to treat bone fragility in children is limited and most have never been established as safe or effective in randomized controlled trials. Estrogen and progestin therapy alone has shown little to no benefit in treating low bone mass in anorexia nervosa and exercise-associated amenorrhea (1, 2). It has been suggested that bisphosphonate use be reserved for patients with clinical signs of bone fragility (recurrent fractures, vertebral fractures, bony deformities) and for those participating in clinical trials (19). For this reason, the diagnosis of osteoporosis cannot be pronounced lightly in children. The jury is still out—deliberating the best approach to interpreting DXA findings. For now, the doctor must be in—using good clinical judgment in the management of young patients at risk for bone fragility.
Footnotes
Abbreviations: BMAD, Bone mineral apparent density; BMC, bone mineral content; BMD, bone mineral density; DXA, dual energy x-ray absorptiometry; QCT, quantitative computed tomography.
Received April 12, 2007.
Accepted April 19, 2007.
References
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