Whole Body Dual X-Ray Absorptiometry (DEXA) To Determine Body Composition - CAM 60140HB
Description:
Using low-dose X-rays of two different energy levels, whole-body dual-energy X-ray absorptiometry (DXA) measures lean tissue mass, total and regional body fat, as well as bone density. DXA scans have become a tool for research on body composition (e.g., as a more convenient replacement for underwater weighing). This evidence review addresses potential applications in clinical care rather than research use of the technology.
For individuals who have a clinical condition associated with abnormal body composition who receive DXA body composition studies, the evidence includes systematic reviews and several cross-sectional studies comparing DXA with other techniques. The relevant outcomes are symptoms and change in disease status. The available studies were primarily conducted in research settings and often used DXA body composition studies as a reference standard; these studies do not permit conclusions about the accuracy of DXA for measuring body composition. A systematic review exploring the clinical validity of DXA against reference methods for the quantification of intra-abdominal adipose tissue raised concerns regarding precision and reliability. More importantly, no studies were identified in which DXA body composition measurements were actively used in patient management. The evidence is insufficient to determine the effects of the technology on health outcomes.
For individuals who have a clinical condition managed by monitoring changes in body composition over time who receive serial DXA body composition studies, the evidence includes several prospective studies monitoring patients over time. The relevant outcomes are symptoms and change in disease status. The studies used DXA as a tool to measure body composition and were not designed to assess the accuracy of DXA. None of the studies used DXA findings to make patient management decisions or addressed how serial body composition assessment might improve health outcomes. The evidence is insufficient to determine the effects of the technology on health outcomes.
Background
Body Composition Measurement
Body composition measurements can be used to quantify and assess the relative proportions of specific body compartments such as fat and lean mass (e.g., bones, tissues, organs, muscles). These measurements may be more useful in informing diagnosis, prognosis, or therapy than standard assessments (e.g., body weight, body mass index) that do not identify the contributions of individual body compartments or their particular relationships with health and disease. While these body composition measurements have been most frequently utilized for research purposes, they may be useful in clinical settings to:
- Evaluate the health status of undernourished patients, those impacted by certain disease states (e.g., anorexia nervosa, cachexia), or those undergoing certain treatments (e.g., antiretroviral therapy, bariatric surgery).
- Evaluate the risk of heart disease or diabetes by measuring visceral fat vs total body fat.
- Assess body composition changes related to growth and development (e.g., infancy, childhood), aging (e.g., sarcopenia), and in certain disease states (e.g., HIV, diabetes).
- Evaluate patients in situations where body mass index is suspected to be discordant with total fat mass (e.g., body-building, edema).
A variety of techniques has been researched, including most commonly, anthropomorphic measures, bioelectrical impedance, and dual-energy X-ray absorptiometry (DXA). All of these techniques are based in part on assumptions about the distribution of different body compartments and their density, and all rely on formulas to convert the measured parameter into an estimate of body composition. Therefore, all techniques will introduce variation based on how the underlying assumptions and formulas apply to different populations of subjects (i.e., different age groups, ethnicities, or underlying conditions). Techniques using anthropomorphics, bioelectrical impedance, underwater weighing, and DXA are briefly reviewed below.
Anthropomorphic Techniques
Anthropomorphic techniques for the estimation of body composition include measurements of skinfold thickness at various sites, bone dimensions, and limb circumference. These measurements are used in various equations to predict body density and body fat. Due to its ease of use, measurement of skinfold thickness is one of the most common techniques. The technique is based on the assumption that the subcutaneous adipose layer reflects total body fat but this association may vary with age and sex.
Bioelectrical Impedance
Bioelectrical impedance analysis is based on the relation among the volume of the conductor (ie, human body), the conductor's length (i.e., height), the components of the conductor (i.e., fat and fat-free mass), and its impedance. Estimates of body composition are based on the assumption that the overall conductivity of the human body is closely related to lean tissue. The impedance value is then combined with anthropomorphic data to give body compartment measures. The technique involves attaching surface electrodes to various locations on the arm and foot. Alternatively, the patient can stand on the pad electrodes.
Underwater Weighing
Underwater weighing requires the use of a specially constructed tank in which the subject is seated on a suspended chair. The subject is then submerged in the water while exhaling. While valued as a research tool, weighing people underwater is typically not suitable for routine clinical use. This technique is based on the assumption the body can be divided into two compartments with constant densities: adipose tissue, with a density of 0.9 g/cm3, and lean body mass (i.e., muscle and bone), with a density of 1.1 g/cm3. One limitation of the underlying assumption is the variability in density between muscle and bone, e.g., bone has a higher density than muscle, and bone mineral density varies with age and other conditions. Also, the density of body fat may vary, depending on the relative components of its constituents (e.g., glycerides, sterols, glycolipids).
Dual-Energy X-Ray Absorptiometry
While the cited techniques assume two body compartments, DXA can estimate three body compartments consisting of fat mass, lean body mass, and bone mass. DXA systems use a source that generates X-rays at two energies. The differential attenuation of the two energies is used to estimate the bone mineral content and soft tissue composition. When two X-ray energies are used, only two tissue compartments can be measured; therefore, soft tissue measurements (i.e., fat and lean body mass) can only be measured in areas in which no bone is present. DXA can also determine body composition in defined regions (i.e., the arms, legs, and trunk). DXA measurements are based in part on the assumption that the hydration of fat-free mass remains constant at 73%. Hydration, however, can vary from 67% to 85% and can vary by disease state. Other assumptions used to derive body composition estimates are considered proprietary by DXA manufacturers. The use of DXA for bone mineral density assessment in patients diagnosed with or at risk of osteoporosis is addressed separately in evidence review 60101. Vertebral fracture assessment with densitometry by DXA is addressed separately in evidence review 60144.
REGULATORY STATUS
Body composition software for several bone densitometer systems have been approved by the U.S. Food and Drug Administration through the premarket approval process. They include the Lunar iDXA systems (GE Healthcare, Madison, WI), Hologic DXA systems (Hologic, Bedford, MA), and Norland DXA systems (Norland, at Swissray, Fort Atkinson, WI).
Policy:
Dual X-ray absorptiometry (DEXA) body composition studies are investigational and/or unproven and is therefore considered NOT MEDICALLY NECESSARY.
Policy Guidelines
Coding
Please see the Codes table for details.
Benefit Application
BlueCard/National Account Issues
State or federal mandates (e.g., FEP) may dictate that all devices approved by the U.S. Food and Drug Administration (FDA) may not be considered investigational, and thus these devices may be assessed only on the basis of their medical necessity.
Rationale
Evidence reviews assess whether a medical test is clinically useful. A useful test provides information to make a clinical management decision that improves the net health outcome. That is, the balance of benefits and harms is better when the test is used to manage the condition than when another test or no test is used to manage the condition.
The first step in assessing a medical test is to formulate the clinical context and purpose of the test. The test must be technically reliable, clinically valid, and clinically useful for that purpose. Evidence reviews assess the evidence on whether a test is clinically valid and clinically useful. Technical reliability is outside the scope of these reviews, and credible information on technical reliability is available from other sources.
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Dual-energy X-ray Absorptiometry as a Test to Detect Abnormal Body Composition
Clinical Context and Test Purpose
The purpose of dual-energy x-ray absorptiometry (DXA) body composition studies is to improve the diagnosis and management of patients who have a clinical condition associated with abnormal body composition.
The following PICO was used to select literature to inform this review.
Populations
The relevant population of interest is individuals with clinical conditions associated with abnormal body composition.
Interventions
The test being considered is DXA body composition studies.
Comparators
The following practices are currently being used to make decisions in this patient group: standard of care without DXA or an alternative method of body composition analysis.
Outcomes
The general outcomes of interest include symptom management and change in disease status. For patients with human immunodeficiency virus (HIV) who are treated with antiretroviral therapy, outcomes of interest would include lipodystrophy.
Study Selection Criteria
For the evaluation of clinical validity of DXA body composition testing, studies that meet the following eligibility criteria were considered:
- Reported on the accuracy of the marketed version of the technology (including any algorithms used to calculate scores)
- Included a suitable reference standard
- Patient/sample clinical characteristics were described
- Patient/sample selection criteria were described.
Clinically Valid
A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).
Review of Evidence
Systematic Reviews
A systematic review and meta-analysis comparing the accuracy of alternative comparators versus reference standard computed tomography (CT) and magnetic resonance imaging (MRI) methods for the quantification of intra-abdominal adipose tissue (IAAT) was published by Murphy et al (2019).3, This systematic review assessed the performance of DXA for IAAT volume quantification and compared the performance of both DXA and bioelectric impedance analysis (BIA) approaches for IAAT area quantification. The American Society for Parenteral and Enteral Nutrition (ASPEN) also conducted a systematic review to evaluate the validity of relevant body composition methods in various clinical populations.4, The use of DXA, ultrasound, and BIA for body composition analysis was investigated. Fifteen studies featuring comparisons of DXA to reference standard methods (eg, MRI and CT) were identified. Nine studies using CT or MRI to validate DXA measures of abdominal fat mass (FM) or total body FM were used for pooled analyses. Characteristics and results of these meta-analyses are summarized in Tables 1 and 2.
Table 1. Systematic Review & Meta-Analysis Characteristics
Study; Subgroup | Dates | Trials | Participants1 | N (Range) | Design | Duration |
Murphy et al (2019)3, | 1995-2018 | 23 | Studies:
|
6116 (29 to 2689) | Cross-sectional, diagnostic test accuracy studies Retrospective studies |
NR |
IAAT Area | ||||||
DXA | 2012-2014 | 3 | Included population groups:
|
381 (115 to 135) | Cross-sectional, diagnostic test accuracy studies Retrospective studies |
NR |
BIA | 2008-2018 | 9* | Included population groups:
|
2139 (100 to 1006) | Cross-sectional, diagnostic test accuracy studies Retrospective studies |
NR |
IAAT Volume | ||||||
DXA | 2012-2018 | 7** | Included population groups:
|
3410 (40 to 2689) | Cross-sectional, diagnostic test accuracy studies Retrospective studies |
NR |
IAAT Thickness | ||||||
US | 2010-2014 | 4 | Included population groups:
|
186 (29 to 74) | Cross-sectional, diagnostic test accuracy studies Retrospective studies |
NR |
Sheean et al (2019)4, (ASPEN) | 2001-2013 | 9 | Studies:
|
1660 (39 to 625) | Cross-sectional, diagnostic accuracy studies Retrospective studies |
NR |
Abdominal FM in any disease via DXA | 2004-2013 | 4 | Included population groups:
|
874 (39 to 625) | Cross-sectional, diagnostic accuracy studies Retrospective studies |
NR |
Total FM in any disease via DXA | 2001-2013 | 7 | Included population groups:
|
1473 (66 to 625) | Cross-sectional, diagnostic accuracy studies Retrospective studies |
NR |
Total FM in CVD via DXA | 2001-2013 | 5 | Included population groups:
|
521 (66 to 132) | Cross-sectional, diagnostic accuracy studies Retrospective studies |
NR |
ASPEN: American Society for Parenteral and Enteral Nutrition; BIA: bioelectrical impedance analysis; CT: computed tomography; CVD: cardiovascular disease; DXA: dual-energy x-ray absorptiometry; FM: fat mass; HIV: human immunodeficiency virus; IAAT: intra-abdominal adipose tissue; MRI: magnetic resonance imaging; NR: not reported; PCOS: polycystic ovarian syndrome; SD: standard deviation; US: ultrasound.
1 Key study eligibility criteria and demographics of included subgroup participants.
* 3 of 9 trials were sampled twice for a total of 12 result sets due to use of multiple techniques for IAAT quantification via BIA.
** 1 of 8 trials was categorized as an outlier and excluded from pooled analysis.
Table 2. Systematic Review & Meta-Analysis Results
Study | Mean Difference in IAAT Volume | Mean Difference in IAAT Area | Mean Difference in IAAT Thickness | |||
Murphy et al (2019)3, | DXA* | DXA | BIA | US | ||
Total N | 3410 | 381 | 2139 | 186 | ||
Pooled mean difference (95% LoA) | -10 (-280 to 300) (cm3) | 8.09 (-98.88 to 115.07) (cm2) | -11.63 (-43.12 to 19.85) (cm2) | -0.32 (-3.82 to 3.17) (cm) | ||
Significance of mean difference (p) | .808 | .061 | .004 | .400 | ||
I2 (p) | 99% (<.001) | 98% (<.001) | 94% (<.001) | 93% (<.001) | ||
Q | Q(6) = 458 | Q(2) = 31 | Q(11) = 544 | Q(3) = 41 | ||
Range of N | 40 to 2689 | 115 to 135 | 100 to 1006 | 29 to 74 | ||
Range of pooled mean differences | (-451 to 262) (cm3) | (3.78 to 16.70) (cm2) | (-57.20 to 10.96) (cm2) | (-1.10 to 0.40) (cm) | ||
DXA Subgroup Analysis | Mean Difference in IAAT Volumeby DXA and Gender |
Mean Difference in IAAT Volumeby DXA and Reference Method | ||||
Subgroup | Men | Women | CT | MRI | ||
Subgroup N (Total N) | 1483 (3287) | 1804 (3287) | 377 (3410) | 3033 (3410) | ||
Pooled mean difference (95% LoA) (cm3) | 144.04 (-512.29 to 800.38) | 59.96 (-381.08 to 492.99) | -41.15 (-881.96 to 930.25) | 49.52 (-498.42 to 586.23) | ||
Significance for subgroup comparison (p) | .042 | .311 | ||||
I2 | 95% | 90% | 100% | 90% | ||
Range of Subgroup N | 20 to 1212 | 20 to 1477 | 109 to 145 | 40 to 2689 | ||
Range of pooled mean differences (cm3) | -43 to 379 | 4 to 143 | 451 to 262 | 4 to 104 | ||
Sheean et al (2019)4, (ASPEN) | DXA-derived Abdominal FM | DXA-derived Total FM | ||||
DXA vs. CT-derived VAT in any disease | DXA vs. CT/MRI-derived VAT in any disease | DXA vs. CT/MRI-derived VAT in CVD | ||||
Total N | 874 | 1473 | 521 | |||
Pooled random effects correlation (95% CI) | 0.74 (0.52 to 0.86) | 0.71 (0.45 to 0.86) | 0.71 (0.45 to 0.84) | |||
I2 (p) | 87% (<.01) | 98% (<.01) | 95% (<.01) | |||
Range of N | 39 to 625 | 66 to 625 | 66 to 132 | |||
Range of individual correlations | 0.52 to 0.86 | 0.49 to 0.80 | 0.49 to 0.87 |
ASPEN: American Society for Parenteral and Enteral Nutrition; BIA: bioelectrical impedance analysis; CI: confidence interval; CT: computed tomography; CVD: cardiovascular disease; DXA: dual-energy x-ray absorptiometry; FM: fat mass; IAAT: intra-abdominal adipose tissue; LoA: limits of agreement; MRI: magnetic resonance imaging; US: ultrasound; VAT: visceral adipose tissue.
* Results following the removal of a study due to identification as an outlier.
Because the analysis by Murphy et al (2019) aimed to evaluate agreement between DXA and CT or MRI, direct effects on key health outcomes were not explored and patient populations included for analysis displayed extensive heterogeneity and largely featured healthy populations. Measurements of IAAT volume via DXA were deemed comparable to the reference methods, however, 95% limits of agreement (LoA) were wide and these results were not seen until the removal of an outlying study. Performance of DXA for the measurement of IAAT volume also varied significantly between male and female subgroups. Furthermore, included studies did not pre-determine clinically meaningful LoA. The authors further caution that DXA measurement of IAAT volume has the capacity to differ from reference methods by more than 100%, however, the clinical significance of these margins of error are uncertain in individuals with obesity. While IAAT area cutoff points have been described for the determination of metabolic risk and visceral obesity based on single-slice CT, the authors do not recommend utilization of DXA IAAT area measurements for this purpose due to wide LoA. The clinical utility of existing IAAT area cutpoints is also uncertain as these parameters were found to have applicability for women and cannot necessarily be extrapolated to mixed populations.
Calella et al (2019) performed a systematic review exploring various methods for body composition analysis in patients with cystic fibrosis (CF).5, A previous systematic review by Calella et al (2018) presented on differences in body composition between patients with CF and healthy controls evaluated by DXA and other methods.6, DXA was most frequently used to measure lean body or fat-free mass which was significantly reduced in CF patients. While several included studies showed a correlation between lower fat-free mass and impaired pulmonary function, application, and use of this measure in patient management and its impact on health outcomes was not explored and requires further clarification. Since these reviews featured qualitative analyses, data on clinical validity could not be extracted.
A systematic review by Bundred et al (2019) evaluated body composition assessment and sarcopenia in patients with pancreatic ductal adenocarcinoma.7, Meta-analyses revealed that sarcopenia was associated with lower overall survival in both operable (harms ratio, 1.95; 95% confidence interval [CI], 1.35 to 2.81; p<.001) and unresectable patients (harms ratio, 2.49; 95% CI, 1.38 to 4.48; p=.002). However, of the 42 included studies, only 1 utilized measurements obtained by DXA, limiting the relevance of the overall findings to this technology and preventing extraction of pertinent clinical validity data. Furthermore, the authors caution that many studies failed to account for variation introduced by gender, race, tumor stage, and other factors. Additionally, clear criteria for the diagnosis of sarcopenia or cachexia via body composition assessments with DXA are lacking.
Cross-Sectional Studies
Most of the literature on DXA as a diagnostic test to detect abnormal body composition involves the use of the technology in the research setting, often as a reference test; studies have been conducted in different populations of patients and underlying disorders.8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,In some cases, studies have compared other techniques with DXA to identify simpler methods of determining body composition. In general, these studies have shown that DXA is highly correlated to various methods of body composition assessment. For example, a study by Alves et al (2014) compared 2 bioelectrical impedance devices with DXA for the evaluation of body composition in heart failure.8, Ziai et al (2014) compared bioelectric impedance analysis with DXA for evaluating body composition in adults with CF.9, The literature on DXA in population-based cohorts (eg, National Health and Nutrition Examination Survey [NHANES], Prospective Epidemiological Risk Factor Study)24,25, involves the use of the technology to predict risk of overall mortality or cancer incidence. These studies often use DXA as a reference test to assess whether agreement with anthropometric measures (eg, body mass index [BMI], relative fat mass [RFM]) is present.24, or absent.25, Whether or not a DXA scan is considered the reference standard, the key consideration regarding its routine clinical use is whether the results of the scan can be used to manage patients and improve health outcomes.
Case-Control Studies
As a single diagnostic measure, it is important to establish diagnostic cutoff points for normal and abnormal values. This is problematic because normal values will require the development of normative databases for the different components of body composition (ie, bone, fat, lean mass) for different populations of patients at different ages. Regarding measuring bone mineral density (BMD), normative databases have largely focused on postmenopausal White women, and these values cannot necessarily be extrapolated to men or to different races. DXA determinations of BMD are primarily used for fracture risk assessment in postmenopausal women and to select candidates for various pharmacologic therapies to reduce fracture risk. In an example regarding lean mass, Reina et al (2019) conducted a case-control study to assess the correlation of BMI or serum albumin levels to DXA-derived parameters of nutritional status and sarcopenia in women (N=89) with rheumatoid arthritis.26, While 44% of cases met diagnostic criteria for sarcopenia based on quantification of the skeletal muscle index, a reference technique was not clearly identified in this study. Skeletal muscle index is calculated by dividing appendicular skeletal muscle mass by the square of the patient's height. A previously identified threshold of ≤5.75 kg/m2 in women was applied, however, this metric was established through the use of BIA in a slightly older patient population. Given that DXA provides measures of lean mass which may be influenced by body compartments other than skeletal muscle, the relevance of this diagnostic cutoff point is uncertain. Furthermore, the study utilized a control group composed of patients affected by non-inflammatory rheumatic disorders as opposed to healthy controls, further limiting the relevance of applied cutoff points. In addition to the aforementioned uncertainties of establishing and applying normal values for components of body composition, it also is unclear how a single measure of body composition would be used in patient management. Studies discussing appropriate use and determination of DXA-derived lean mass cutoffs for sarcopenia in various populations of patients and underlying disorders continue to be featured in the literature.27,28,
Clinically Useful
A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.
Direct Evidence
Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from randomized controlled trials (RCTs).
No RCTs were identified to support the utility of DXA for this indication.
Chain of Evidence
Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.
Pooled analyses indicate that there is generally strong correlation between estimates of FM as assessed by DXA versus CT or MRI, particularly in populations with clinical conditions for which risk of adverse outcomes associated with visceral adiposity may be of particular importance.4, In a broader population, including healthy individuals, while there remains a strong overall correlation between these methods of FM estimation, significant variability suggests that there are some subpopulations in whom DXA may perform poorly as an estimate of adiposity compared to CT or MRI.3, Additionally, there is a lack of evidence to indicate that evaluation of body composition via DXA changes clinical management.
Section Summary: Dual-energy X-ray Absorptiometry as a Test to Detect Abnormal Body Composition
The available evidence was generated primarily in research settings and often used DXA body composition studies as a reference standard; these studies do not permit conclusions about the accuracy of DXA for measuring body composition. Systematic reviews with meta-analyses exploring the clinical validity of DXA measurements against reference methods for the quantification of FM indicate strong overall agreement between these modalities, but raise concerns regarding precision and reliability in some populations, particularly those without existing clinical conditions for which risk of adverse outcomes is influenced by abnormal visceral adiposity. Additionally, no studies were identified in which DXA body composition measurements were actively used in patient management.
Dual-energy X-ray Absorptiometry as a Test to Monitor Changes in Body Composition
Clinical Context and Test Purpose
The purpose of serial DXA body composition studies in patients who have a clinical condition managed by monitoring body composition changes over time is to improve disease management.
The following PICO was used to select literature to inform this review.
Populations
The relevant population of interest is individuals with clinical conditions managed by monitoring body composition changes over time.
Interventions
The test being considered is serial DXA body composition studies.
Comparators
The following practices are currently being used to make decisions in this patient group: standard of care without DXA or an alternative method of body composition analysis.
Outcomes
The general outcomes of interest include symptom management and change in disease status. For patients with anorexia nervosa, outcomes of interest would include disease-related morbidity, disease-related mortality, and rate of remission.
Study Selection Criteria
For the evaluation of clinical validity of DXA body composition testing, studies that meet the following eligibility criteria were considered:
- Reported on the accuracy of the marketed version of the technology (including any algorithms used to calculate scores)
- Included a suitable reference standard
- Patient/sample clinical characteristics were described
- Patient/sample selection criteria were described.
Clinically Valid
A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).
The ability to detect a change in body composition over time is related in part to the precision of the technique, defined as the degree to which repeated measurements of the same variable give the same value. For example, DXA measurements of bone mass are thought to have a precision error of 1% to 3% and, given the slow rate of change in BMD in postmenopausal women treated for osteoporosis, it is likely that DXA scans would only be able to detect a significant change in BMD in the typical patient after 2 years of therapy. Of course, changes in body composition are anticipated to be larger and more rapid than changes in BMD in postmenopausal women; therefore, precision errors in DXA scans become less critical in interpreting results. However, precision errors for other body compartments such as lean and fat mass may differ and impact clinical validity. Coefficients of variation as high as 42.2% have been reported for FM.29,
Review of Evidence
Prospective Studies
Several studies have reported on DXA measurement of body composition changes over time in clinical populations; none of these studies used DXA findings to make patient management decisions and few addressed how serial body composition assessment might improve health outcomes.30,31,29,32,33,34, A long-term prospective study assessing the association between body fat and breast cancer risk in postmenopausal women with a normal BMI was published by Iyengar et al (2019), featuring the ad hoc secondary analysis of results from the Women's Health Initiative RCT and observational study cohorts.32, Women (N=3460) were assessed at baseline and during years 1, 3, 6, and 9 for BMI and via DXA. Multivariable-adjusted hazard ratios (HR) for the association of various body fat measures with the risk of developing invasive or estrogen receptor positive (ER+) breast cancer were reported. Median follow-up duration was 16.9 years. Characteristics and results of clinical validity for breast cancer risk assessment are summarized in Tables 3 and 4.
Table 3. Study Characteristics of Clinical Validity of Risk Assessment
Study | Study Population | Designa | Reference Standard | Timing of Reference and Index Tests | Blinding of Assessors | Commentb |
Iyengar et al (2019)32, | Postmenopausal women aged 50 to 79 years enrolled in the Women's Health Initiative (WHI) RCT or observational study were considered for study. Women from 3 WHI trial centers were assessed longitudinally for body fat composition. Data from women with normal BMIs were assessed for correlations with breast cancer outcomes. | Prospective, sample selection NR | Clinical outcomes were confirmed via questionnaires. Breast cancer cases were confirmed via review of medical records and pathology reports. | NR | NR | Risk outcomes for women in the RCT and observational cohorts were not analyzed separately. Given that treatments utilized in the RCT group may have had an impact on breast cancer risk and outcomes, the relevance and utility of this study is uncertain. |
BMI: body mass index; NR: not reported; RCT: randomized controlled trial.
a Note 2 aspects of design: prospective, retrospective or nonconcurrent prospective and sample selection random or consecutive
b Note other characteristics that could cause bias or limit relevance such as timeframe or practice setting.
Table 4. Clinical Validity of Breast Cancer Risk Assessment with Dual-Energy X-ray Absorptiometry
Study; Subgroup; Body Fat DXA Measurement (Cutoff) | Initial N | Final N Cases/Person-Years | Excluded Samples | Prevalence of Condition | Clinical Validity Outcome: Multivariable Adjusted HR (95% CI) | |||||
Baseline Body Fat Measures | Serial Body Fat Measures | |||||||||
Iyengar et al (2019)32,Invasive Breast Cancer | 3464* | 3460 | 4* | 182 | Highest Quartile | p-value for trend | Per 5-unit increase | Cutoff | Time-Dependent | |
Whole-body fat mass, kg (>25.1) | NR | NR | NR | 57 | 1.89 (1.21 to 2.95) | .004 | 1.28 (1.10 to 1.49) | ≥22.1 | 1.43 (1.06 to 1.93) | |
Whole-body fat, % (>41.3) | NR | NR | NR | 52 | 1.79 (1.14 to 2.83) | .03 | 1.19 (1.03 to 1.37) | ≥38.0 | 1.45 (1.07 to 1.95) | |
Fat mass of trunk, kg (>11.4) | NR | NR | NR | 50 | 1.88 (1.18 to 2.98) | .002 | 1.46 (1.14 to 1.87) | ≥9.4 | 1.50 (1.12 to 2.03) | |
Ratio of trunk fat mass to mean of legs (>2.6) | NR | NR | NR | 43 | 1.30 (0.83 to 2.02) | .10 | NR | NR | NR | |
Iyengar et al (2019)32,ER+ Breast Cancer | 3464 | 3460 | 4* | 146 | Highest Quartile | p-value for trend | Per 5-unit increase | Cutoff | Time-Dependent | |
Whole-body fat mass, kg (>25.1) | NR | NR | NR | 48 | 2.21 (1.23 to 3.67) | .002 | 1.35 (1.14 to 1.60) | ≥22.1 | 1.41 (1.01 to 1.97) | |
Whole-body fat, % (>41.3) | NR | NR | NR | 44 | 2.17 (1.29 to 3.66) | .01 | 1.27 (1.08 to 1.48) | ≥38.0 | 1.50 (1.07 to 2.10) | |
Fat mass of trunk, kg (>11.4) | NR | NR | NR | 41 | 1.98 (1.18 to 3.31) | .003 | 1.56 (1.18 to 2.06) | ≥9.4 | 1.46 (1.05 to 2.04) | |
Ratio of trunk fat mass to mean of legs (>2.6) | NR | NR | NR | 34 | 1.28 (0.78 to 2.10) | .13 | NR | NR | NR |
CI: confidence interval; DXA: dual-energy x-ray absorptiometry; ER+: estrogen receptor-positive; HR: hazard ratio; NR: not reported.
* Excluded cases were lost to follow-up with ER+ status not reported.
These results suggest that standard BMI categorization may be inadequate for the risk assessment of invasive breast cancers in postmenopausal women. However, the clinical utility of DXA findings on patient management protocols and health outcomes requires further study.
Arthur et al (2020) published additional results from the Women's Health Initiative cohort of postmenopausal women (N=10,931), reporting additional associations between DXA-derived measures of body fat and breast cancer risk.35, The multivariable-adjusted HR for risk of invasive breast cancer per standard deviation (SD) increase in trunk fat mass was 1.21 (95% CI, 1.12 to 1.31) and whole body fat mass was 1.21 (95% CI, 1.12 to 1.30). The multivariable-adjusted HR for risk of ER+ breast cancer per SD increase in trunk fat mass was 1.21 (95% CI, 1.11 to 1.31) and whole body fat mass was 1.22 (95% CI, 1.11 to 1.33). Multivariable-adjusted HR for invasive breast cancer per SD increase in BMI was also significant, with an HR of 1.19 (95% CI, 1.10 to 1.28). Trends of time-dependent analyses of anthropometric measures and overall ER + incident breast cancer cases were significant for BMI (p <.001) and waist circumference (p<.001). Therefore, the added clinical utility of DXA-derived fat measures is unclear for this population.
Relevance and study design and conduct limitations are summarized in Tables 5 and 6.
Table 5. Study Relevance Limitations
Study | Populationa | Interventionb | Comparatorc | Outcomesd | Duration of Follow-Upe |
Arthur et al (2020)35, | 1. Study population is unclear. | 2. Version used unclear regarding both DXA and patient participation in RCT treatment or observational groups. | 3. Not compared to other tests used for same purpose. | 3, 5. Key clinical validity outcomes not reported; adverse events of the test not described. | |
Iyengar et al (2019)32, | 1, 4. Study population is unclear; study population not representative of intended use. | 2. Version used unclear regarding both DXA and patient participation in RCT treatment or observational groups. | 3. Not compared to other tests used for same purpose. | 3, 5. Key clinical validity outcomes not reported; adverse events of the test not described. |
DXA: dual-energy x-ray absorptiometry; RCT: randomized controlled trial.
The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment.
a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use.
b Intervention key: 1. Classification thresholds not defined; 2. Version used unclear; 3. Not intervention of interest.
c Comparator key: 1. Classification thresholds not defined; 2. Not compared to credible reference standard; 3. Not compared to other tests in use for same purpose.
d Outcomes key: 1. Study does not directly assess a key health outcome; 2. Evidence chain or decision model not explicated; 3. Key clinical validity outcomes not reported (sensitivity, specificity, and predictive values); 4. Reclassification of diagnostic or risk categories not reported; 5. Adverse events of the test not described (excluding minor discomforts and inconvenience of venipuncture or noninvasive tests).
e Follow-Up key: 1. Follow-up duration not sufficient with respect to natural history of disease (true-positives, true-negatives, false-positives, false-negatives cannot be determined).
Table 6. Study Design and Conduct Limitations
Study | Selectiona | Blindingb | Delivery of Testc | Selective Reportingd | Data Completenesse | Statisticalf |
Arthur et al (2020)35, | 1. Selection not described. | 1. Blinding not described. | 1, 4. Timing of delivery of index or reference tests not clear; expertise of evaluators not described. | 2. Evidence of selective reporting (covariates did not have to be pre-specified). | ||
Iyengar et al (2019)32, | 1. Selection not described. | 1. Blinding not described. | 1, 4. Timing of delivery of index or reference tests not clear; expertise of evaluators not described. | 2. Evidence of selective reporting (covariates did not have to be pre-specified). | 1. Inadequate description of indeterminate and missing samples. | 2. Comparison with other tests not reported. |
a Selection key: 1. Selection not described; 2. Selection not random or consecutive (ie, convenience).
b Blinding key: 1. Not blinded to results of reference or other comparator tests.
c Test Delivery key: 1. Timing of delivery of index or reference test not described; 2. Timing of index and comparator tests not same; 3. Procedure for interpreting tests not described; 4. Expertise of evaluators not described.
d Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication.
e Data Completeness key: 1. Inadequate description of indeterminate and missing samples; 2. High number of samples excluded; 3. High loss to follow-up or missing data.
f Statistical key: 1. Confidence intervals and/or p values not reported; 2. Comparison with other tests not reported.
Clinically Useful
A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.
Direct Evidence
Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from RCTs.
No RCTs were identified to support the utility of DXA for this indication.
Chain of Evidence
Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.
Because the clinical validity of DXA for this population cannot be established, a chain of evidence cannot be constructed.
Section Summary: Dual-Energy X-ray Absorptiometry as a Test to Monitor Changes in Body Composition
Studies assessing serial DXA used it as a tool to measure body composition and were not designed to assess the accuracy of DXA. None of the studies used DXA findings to make patient management decisions or addressed how serial body composition assessment might improve health outcomes.
The purpose of the following information is to provide reference material. Inclusion does not imply endorsement or alignment with the evidence review conclusions.
Practice Guidelines and Position Statements
Guidelines or position statements will be considered for inclusion in ‘Supplemental Information' if they were issued by, or jointly by, a US professional society, an international society with US representation, or National Institute for Health and Care Excellence (NICE). Priority will be given to guidelines that are informed by a systematic review, include strength of evidence ratings, and include a description of management of conflict of interest.
American Association of Clinical Endocrinology et al
The American Association of Clinical Endocrinology (AACE) and American College of Endocrinology (ACE) clinical practice guideline on obesity was updated in 2016.36, Table 7 describes relevant recommendations for the diagnosis of overweight and obesity from the AACE/ACE guideline. The authors also state that "The DEXA [dual x-ray absorptiometry] scan also allows for calculation of the fat mass index (total body fat mass [kg] divided by height [m2]), which is a physiologic relevant measure of adiposity. The clinical utility of these measures is limited by availability, cost, and lack of outcomes data, but they have been applied extensively in research settings. Body fat percentage cut points for obesity have been proposed by the World Health Organization (WHO) to be 25% for men and 35% for women."
Table 7. American Association of Clinical Endocrinology/American College of Endocrinology Recommendations for Diagnosis of Overweight and Obesity
Recommendation | Quality of evidencea | Grade of recommendationb |
All adults should be screened annually using a BMI measurement; in most populations a cutoff point of ≥25 kg/m2 shoudl be used to initiate further evaluation of overweight or obesity. | 2 (upgraded due to high relevance) | A |
BMI should be used to confirm an excessive degree of adiposity and to classify individuals as having overweight (BMI 25 to 29.9 kg/m2) or obesity (BMI ≥30 kg/m2), after taking into account age, gender, ethnicity, fluid status, and muscularity; therefore, clinical evaluation and judgment must be used when BMI is employed as the anthropometric indicator of excess adiposity, particularly in athleses and those with sarcopenia. | 2 (upgraded due to high relevance) | A |
When evaluating patients for adiposity-related disease risk, WC should be measure in all patients with BMI <35 kg/m2. | 2 (upgraded due to high relevance) | A |
In many populations, a WC cutoff point of ≥94 cm in mean and ≥80 cm in women should be considered at risk and consistent with abdominal obesity; in the U.S. and Canada, cutoff points that can be used to indicate increased risk are ≥102 cm for men and ≥88 cm for women. | 2 (upgraded due to high relevance) | A |
Other measurements of adiposity (e.g., bioelectric impedance, air/water displacement plethysmography, or dual-energy X-ray absorptiometry [DEXA]) may be considered at the clinician's discretion if BMI and physical examination results are equivocal or require further evaluation. | 2 (downgraded due to evidence gaps) | C |
However, the clinical utility of these measures [listed in the above recommendation] is limited by availability, cost, and lack of outcomes data for validated cutoff points. | 2 | B |
BMI: body mass index; WC: waist circumference.
aEvidence quality 2 indicates intermediate-level evidence, including meta-analyses of nonrandomized prospective or case-controlled trials, nonrandomized controlled trials, prospective cohort studies, and/or retrospective case-control studies.
bGrade A, B, and C indicate strong, intermediate, and weak recommendations, respectively.
American College of Radiology et al
The American College of Radiology (ACR), the Society for Pediatric Radiology (SPR), and the Society of Skeletal Radiology (SRR) (2018) issued a collaborative practice parameter to assist practitioners in providing appropriate radiologic care for their patients.37, Dual-energy x-ray absorptiometry (DXA) was described as a "clinically proven, accurate and reproducible method of measuring bone mineral density (BMD) in the lumbar spine, proximal femur, forearm, and whole body," that "may also be used to measure whole-body composition, including nonbone lean mass (LM) and fat mass (FM)." DXA measurement of BMD, LM, or FM is indicated whenever a clinical decision is likely to be directly influenced by the test result. In particular, LM and FM may be useful in assessing conditions such as sarcopenia and cachexia. Specifically, DXA may be indicated as a tool for the measurement of regional and whole body FM and LM in patients afflicted with conditions such as malabsorption, cancer, or eating disorders.
American Society for Parenteral and Enteral Nutrition
The American Society for Parenteral and Enteral Nutrition (ASPEN) published clinical guidelines on the validity of body composition assessment in clinical populations in 2019, as a complement to the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition (described below).4, The systematic review with meta-analysis used to develop these guidelines is described above. The target population of the guideline was adults "with a potentially inflammatory condition or pathological end point associated with a specific disease or clinical condition such as cancer, cardiovascular disease (CVD), cardiac failure, diabetes, hepatic or renal disease, human immunodeficiency virus, or possessing a condition that requires surgical intervention." The target population did not include healthy individuals or those with obesity, except when "linked to a clinical condition such as metabolic syndrome, hypertension, etc." Studies evaluated for guideline development involved specific body composition assessment methodologies (DXA, bioelectrical impedance analysis, or ultrasound) and were required to use a more precise comparator; for studies evaluating DXA, these included computed tomography, magnetic resonance imaging, or multicompartment models. Anthropometric measurements "were not included since these are considered surrogate measures of body composition." Table 8 describes relevant recommendations from the ASPEN guideline.
Table 8. American Society for Parenteral and Enteral Nutrition Clinical Guideline Recommendations for Body Composition Assessment in Adult Clinical Populations
Recommendation | Quality of evidence | Strength of recommendation |
We recommend the use of DXA for assessing fat mass in patients with clinical conditions. | Low | Strong |
No recommendation can be made at this time to support the use of ultrasound in a clinical setting for assessing body composition. | Very low | Weak |
No recommendations can be made regarding the validity of using bioelectrical impedance analysis in clinical populations. | Low | Weak |
DXA: dual-energy x-ray absorptiometry.
International Society for Clinical Densitometry
The International Society for Clinical Densitometry (2019) updated its statements on the use of DXA for body composition.38, Use of DXA for measurement of body composition was suggested for use in the following clinical conditions:
-
To assess fat distribution in patients with human immunodeficiency virus (HIV) who are using antiretroviral agents known to increase the risk of lipoatrophy.
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To assess fat and lean mass changes in obese patients undergoing bariatric surgery (or medical, diet, or weight loss regimens with anticipated large weight loss) when weight loss exceeds approximately 10%. The statement noted that the impact of DXA studies on clinical outcomes in these patients is uncertain.
-
To assess fat and lean mass in patients with muscle weakness and poor physical functioning. The impact on clinical outcomes is uncertain.
Of note, pregnancy is a contraindication to use of DXA to measure body composition. The statement also adds that the clinical utility of DXA measurements of adiposity and lean mass (eg, visceral adipose tissue, lean mass index, fat mass index) is uncertain. Furthermore, while the use of DXA adiposity measures such as fat mass index may be useful in risk-stratifying patients for cardio-metabolic outcomes, specific thresholds to define obesity have not been established.
U.S. Preventive Services Task Force Recommendations
No U.S. Preventive Services Task Force recommendations for whole-body DXA have been identified.
Ongoing and Unpublished Clinical Trials
Some currently unpublished trials that might influence this review are listed in Table 9.
Table 9. Summary of Key Trials
NCT No. | Trial Name | Planned Enrollment | Completion Date |
Ongoing | |||
NCT03621306 | Precision and Reliability of Dual X-ray Absorptiometry (DXA) Testing | 400 | Aug 2028 |
NCT05639556 | Strength and Muscle Related Outcomes for Nutrition and Lung Function in CF | 300 | Dec 2028 |
NCT05879692 | Response of Irritable Bowel Syndrome to Abdominal Fat Reduction | 60 | Dec 2023 |
NCT05699863 | A Multidisciplinary Approach to Screening for Obesity Complications - The MULTISITE Study | 90 | Dec 2025 |
NCT05885672 | A Multi-Modal Approach to Improving the Early Detection of Cardiometabolic Disease Risk | 200 | Jul 2024 |
NCT: national clinical trial.
a Denotes industry-sponsored or cosponsored trial.
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Coding Section
Codes | Number | Description |
---|---|---|
CPT | 76499 | Unlisted diagnostic radiographic procedure |
ICD-10-CM | Investigational for all diagnoses | |
ICD-10-PCS | ICD-10-PCS codes are only used for inpatient services. There is no specific ICD-10-PCS code for this imaging. | |
BW0KZZZ, BW0LZZZ | Imaging, anatomical regions, plain radiography, codes for whole body or whole skeleton | |
Type of service | Radiology | |
Place of service | Outpatient/inpatient |
Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy. They may not be all-inclusive.
This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross Blue Shield Association technology assessment program (TEC) and other nonaffiliated technology evaluation centers, reference to federal regulations, other plan medical policies and accredited national guidelines.
"Current Procedural Terminology © American Medical Association. All Rights Reserved"
History From 2024 Forward
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