The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Impact of Recent Technological Advances on the Trueness and Precision of DXA to Assess Body Composition
Open accessAbstract
The introduction of dual‐energy X‐ray absorptiometry (DXA) in the 1980s for the assessment of areal bone mineral density (BMD) greatly benefited the field of bone imaging and the ability to diagnose and monitor osteoporosis. The additional capability of DXA to differentiate between bone mineral, fat tissue, and lean tissue has contributed to its emergence as a popular tool to assess body composition. Throughout the past 2 decades, technological advancements such as the transition from the original pencil‐beam densitometers to the most recent narrow fan‐beam densitometers have allowed for faster scan times and better resolution. The majority of reports that have compared DXA‐derived body composition measurements to the gold standard method of body composition appraisal, the four‐compartment model, have observed significant differences with this criterion method; however, the extent to which the technological advancements of the DXA have impacted its ability to accurately assess body composition remains unclear. Thus, this paper reviews the evidence regarding the trueness and precision of DXA body composition measurements from the pencil‐beam to the narrow fan‐beam densitometers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The metabolic syndrome—a new worldwide definition
Alberti, K. G. M., Zimmet, P., & Shaw, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67402-8 · Cited by 9,352
Lemieux, I., Pascot, A., Couillard, C., Lamarche, B., Tchernof, A., Alméras, N., Bergeron, J., Gaudet, D., Tremblay, G., Prud’homme, D., et al. (2000) · Circulation · doi:10.1161/01.cir.102.2.179 · Cited by 937
In vivo validation of whole body composition estimates from dual-energy X-ray absorptiometry
Prior, B. M., Cureton, K. J., Modlesky, C. M., Evans, E. M., Sloniger, M. A., Saunders, M., & Lewis, R. D. (1997) · Journal of Applied Physiology · doi:10.1152/jappl.1997.83.2.623 · Cited by 317
Bredella, M. A., Ghomi, R. H., Thomas, B. J., Torriani, M., Brick, D. J., Gerweck, A. V., Misra, M., Klibanski, A., & Miller, K. K. (2010) · Obesity · doi:10.1038/oby.2010.5 · Cited by 201
Pediatric body composition analysis with dual-energy X-ray absorptiometry.
Helba, M., & Binkovitz, L. A. (2009) · Pediatric Radiology · doi:10.1007/s00247-009-1247-0 · Cited by 127
Cited by
Pitfalls in the measurement of muscle mass: A need for a reference standard
Buckinx, F., Landi, F., Cesari, M., Fielding, R. A., Visser, M., Engelke, K., Maggi, S., Dennison, E., Al‐Daghri, N. M., Allepaerts, S., et al. (2018) · Journal of Cachexia, Sarcopenia and Muscle · doi:10.1002/jcsm.12268 · Cited by 765
De Souza, M. J., Nattiv, A., Joy, E., Misra, M., Williams, N. I., Mallinson, R. J., Gibbs, J. C., Olmsted, M., Goolsby, M., Matheson, G., et al. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2013-093218 · Cited by 661
Self-citationObesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association
Bays, H. E., Toth, P. P., Kris-Etherton, P. M., Abate, N., Aronne, L. J., Brown, W. V., Gonzalez-Campoy, J. M., Jones, S. R., Kumar, R., La Forge, R., et al. (2013) · Journal of Clinical Lipidology · doi:10.1016/j.jacl.2013.04.001 · Cited by 521
Nana, A., Slater, G. J., Stewart, A. D., & Burke, L. M. (2015) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2013-0228 · Cited by 372
DXA: Technical aspects and application
Bazzocchi, A., Ponti, F., Albisinni, U., Battista, G., & Guglielmi, G. (2016) · European Journal of Radiology · doi:10.1016/j.ejrad.2016.04.004 · Cited by 279
Reference Values for Body Composition and Anthropometric Measurements in Athletes
Santos, D. A., Dawson, J. A., Matias, C. N., Rocha, P. M., Minderico, C. S., Allison, D. B., Sardinha, L. B., & Silva, A. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097846 · Cited by 282
Kim, M., Shinkai, S., Murayama, H., & Mori, S. (2015) · Geriatrics & Gerontology International · doi:10.1111/ggi.12384 · Cited by 221
Kasper, A. M., Langan-Evans, C., Hudson, J. F., Brownlee, T. E., Harper, L. D., Naughton, R. J., Morton, J. P., & Close, G. L. (2021) · Nutrients · doi:10.3390/nu13041075 · Cited by 196
Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia
Guerri, S., Mercatelli, D., Aparisi Gómez, M. P., Napoli, A., Battista, G., Guglielmi, G., & Bazzocchi, A. (2018) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2018.01.05 · Cited by 169
Freedman, D. S., Horlick, M., & Berenson, G. S. (2013) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.065961 · Cited by 152
Can High-Intensity Interval Training Promote Skeletal Muscle Anabolism?
Callahan, M. J., Parr, E. B., Hawley, J. A., & Camera, D. M. (2021) · Sports Medicine · doi:10.1007/s40279-020-01397-3 · Cited by 106
Measuring Muscle Mass and Strength in Obesity: a Review of Various Methods
Sizoo, D., de Heide, L. J. M., Emous, M., van Zutphen, T., Navis, G., & van Beek, A. P. (2021) · Obesity Surgery · doi:10.1007/s11695-020-05082-2 · Cited by 77
Smith, S., & Madden, A. M. (2016) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12372 · Cited by 67
Scafoglieri, A., Clarys, J. P., Bauer, J. M., Verlaan, S., Van Malderen, L., Vantieghem, S., Cederholm, T., Sieber, C. C., Mets, T., & Bautmans, I. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2016.04.026 · Cited by 65
Tinsley, G. M., Moore, M. L., Benavides, M. L., Dellinger, J. R., & Adamson, B. T. (2020) · Clinical Nutrition · doi:10.1016/j.clnu.2020.02.008 · Cited by 66
Silva, A. M., Fields, D. A., & Sardinha, L. B. (2013) · Journal of Obesity · doi:10.1155/2013/148696 · Cited by 52
Liao, Y.-S., Li, H.-C., Lu, H.-K., Lai, C.-L., Wang, Y.-S., & Hsieh, K.-C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17072595 · Cited by 48
Dubin, R. L., Heymsfield, S. B., Ravussin, E., & Greenway, F. L. (2024) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.15913 · Cited by 47
Are men at greater risk of lean mass deficits in rheumatoid arthritis?
Baker, J. F., Long, J., Ibrahim, S., Leonard, M. B., & Katz, P. (2015) · Arthritis Care & Research · doi:10.1002/acr.22396 · Cited by 44
Iversen, K. N., Carlsson, F., Andersson, A., Michaëlsson, K., Langton, M., Risérus, U., Hellström, P. M., & Landberg, R. (2021) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2021.07.007 · Cited by 40
Sylivris, A., Mesinovic, J., Scott, D., & Jansons, P. (2022) · Obesity Reviews · doi:10.1111/obr.13442 · Cited by 39
Masterson, T. D., Kirwan, C. B., Davidson, L. E., & LeCheminant, J. D. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9366-8 · Cited by 33
Tinsley, G. M. (2021) · British Journal of Nutrition · doi:10.1017/s0007114520003578 · Cited by 29
Paquin, J., Tremblay, R., Islam, H., Riesco, E., Marcotte-Chénard, A., & Dionne, I. J. (2024) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2024-0128 · Cited by 7
Metadata
- Authors
- Toombs, R. J., Ducher, G., Shepherd, J. A., & De Souza, M. J.
- Venue
- Obesity
- Publisher
- Wiley
- Published
- 2012-01
- Volume
- 20
- Issue
- 1
- Pages
- 30-39
- ISSN
- 1930-7381 (ISSN-L)
- 1930-739X
- OpenAlex
- W2013935353
- Cited by
- 337
- References
- 5
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.