Search PubMed⌕ Search

PubMed · 16303113

Update on bone density testing.

Abstract

Bone mineral density (BMD) testing is a noninvasive measurement to diagnose osteoporosis or low bone density, predict fracture risk, and monitor changes in bone density over time. The "gold-standard" technology for diagnosis and monitoring is dual-energy x-ray absorptiometry of the spine, hip, or forearm. Fracture risk can be predicted using a variety of technologies at many skeletal sites. BMD is usually reported as T-score, the standard deviation variance of the patient's BMD compared with a normal young-adult reference population. In untreated postmenopausal women, there is a strong correlation between T-score and fracture risk, with fracture risk increasing approximately two-fold for every standard deviation decrease in bone density. BMD in postmenopausal women is classified as normal, osteopenia, or osteoporosis according to criteria established by the World Health Organization. Standardized methodologies are being developed to establish intervention thresholds for pharmacologic therapy based on T-score combined with clinical risk factors for fracture.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E Michael Lewiecki. 2005. Update on bone density testing.. https://doi.org/10.1007/s11914-996-0016-3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Estimating the response rate in the presence of measurement error.

In clinical research, it is often of interest to estimate the response rate (i.e. the proportion of subjects who achieve a clinically meaningful threshold) for a particular variable. The standard estimator of the response rate is generally biased in the presence of measurement error. The estimation accounting for the measurement error utilizing fully nonparametric (NP) methods is complicated and may not be efficient. Therefore, we propose a model-based approach assuming a parametric model for the true value and only the first few moments for the measurement error. The estimator for the true response rate and the variance for the estimator are derived. An innovative method using bootstrap simulation is proposed to check the model assumption. Simulations show that the proposed estimator outperforms a fully NP estimator if the model assumption for X holds. This method is applied to address a commonly occurring question in osteoporosis regarding response to treatment in terms of longitudinal changes in bone mineral density (BMD). Bootstrap simulations showed that the model utilized is appropriate. The proposed method can also be applied in other fields of clinical research.

Absorptiometry, Photon↗

Quantitative bone mineral density assessment in malignant infantile osteopetrosis.

PURPOSE: To investigate the use of quantitative computed tomography (QCT) and dual energy absorptiometry (DXA) for assessing bone mineral density (BMD) in the evaluation of children with malignant infantile osteopetrosis (MIOP). METHODS AND PATIENTS: We retrospectively reviewed QCT- and DXA-determined BMD in six patients with infantile osteopetrosis and correlated BMD measured during the initial evaluation with patient characteristics. RESULTS: Five male and one female infant met the eligibility criteria. BMD was markedly elevated in all patients as determined by each modality, QCT or DXA. For QCT, in which age-specific normal values are known, the BMD was found to be 22.4-32.6 standard deviations above the mean. Using DXA, the estimated BMD of the lumbar spine ranged from 0.45 to 0.8 g/cm(2); children with similarly appearing bone radiographs had quite disparate BMD. While the qualitative trend of BMD among the patients was similar for both measures, the units and numerical values of BMD differed. We found no correlation between BMD results and hematopoiesis observed in the bone marrow or with visual evaluation of the radiographs. CONCLUSION: Both QCT and DXA are effective quantitative measures of BMD in children with MIOP. However, the same modality should be employed for longitudinal evaluation of a given patient. Each technique has unique advantages and disadvantages and may complement one another in the evaluation of MIOP or other bone disorders. Quantitative assessment of BMD in children with MIOP will likely serve to further characterize this disease.

Absorptiometry, Photon↗

Body composition in normal weight, overweight and obese children: matched case-control analyses of total and regional tissue masses, and body composition trends in relation to relative weight.

BACKGROUND: Childhood obesity is defined on the basis of weight and height, using body mass index (BMI). There is little detailed information on the body composition characteristic of overweight and obesity. OBJECTIVE: To evaluate total and regional body composition in overweight, obese and control children aged 7-14 years. DESIGN: Body composition was measured by the four-component model and dual X-ray absorptiometry in 38 age- and sex-matched pairs of obese and control children. Body composition trends were also evaluated by quintile of BMI standard deviation score (SDS) in these and 31 other children (n=107; BMI SDS range -1.0 to 4.3). RESULTS: Obese children were taller than controls (Delta=0.6 SDS; P=0.01) and had greater hydration of fat-free mass (FFM) (Delta=1.8 %, P<0.0001). After adjusting for these variables, obese children had greater FFM, fat mass (FM) and mineral (P<0.0001). Regional analyses showed that these differences were apparent in the arm, leg and trunk, but the three tissues had different proportional distributions of the excess. Fat was primarily in the trunk, but mineral in the leg. FM, FFM, hydration and mineral mass all increased across BMI SDS quintiles (P<0.0001), but the trend for FM was much the steepest. DISCUSSION: The greater weight of obese children is due to excess FFM including mineral as well as excess fatness. Increasing weight has a strong continuous relationship with increasing FM across the whole spectrum of weight.

Absorptiometry, Photon↗