Predictive equation for assessing appendicular lean soft tissue mass using bioelectric impedance analysis in older adults: Effect of body fat distribution
Open accessAbstract
Background Low muscle mass is associated with sarcopenia and increased mortality. Muscle mass, especially that of the limbs, is commonly estimated by dual-energy X-ray absorptiometry (DXA) or bioimpedance analysis (BIA). However, BIA-based predictive equations for estimating lean appendicular soft tissue mass (ALST) do not take into account body fat distribution, an important factor influencing DXA and BIA measurements.Objectives To develop and cross-validate a BIA-based equation for estimating ALST with DXA as criterion, and to compare our new formula to three previously published models.Methods One-hundred eighty-four older adults (140 women and 44 men) (age 71.5 ± 7.3 years, body mass index 27.9 ± 5.3 kg/m 2) were recruited. Participants were randomly split into validation (n = 118) and cross-validation groups (n = 66). Bioelectrical resistance was obtained with a phase-sensitive 50 kHz BIA device.Results A BIA-based model was developed for appendicular lean soft tissue mass [ALST (kg) = 5.982 + (0.188 × S 2 / resistance) + (0.014 × waist circumference) + (0.046 × Wt) + (3.881 × sex) - (0.053 × age), where sex is 0 if female or 1 if male, Wt is weight (kg), and S is stature (cm) (R 2 = 0.86, SEE = 1.35 kg)]. Cross validation revealed r 2 of 0.91 and no mean bias. Two of three previously published models showed a trend to significantly overestimate ALST in our sample (p < 0.01).Conclusions The new equation can be considered valid, with no observed bias and trend, thus affording practical means to quantify ALST mass in older adults.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Concerns with applicability were high in 19 studies.
“Concerns with applicability were high in 19 studies [37, 42–59] and unclear [41, 50, 60–69] in 12 studies due to the restricted sample size.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [46]
Out of the 26 available equations for elderly, 23 were developed with foot-to-hand technology.
“Out of the 26 available equations, 23 were developed with foot to hand technology [36, 38, 46, 49, 53, 63, 67, 89–93, 95, 97], while three equations were developed using segmental technology [68, 94].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [46]
Concerning foot-to-hand technology for elderly, thirteen equations are suitable for both sexes.
“Concerning foot-to-hand technology, three predictive equations were suitable for males [36, 49, 53] and four for females only [36, 49, 53, 97], while thirteen equations suitable for both sexes [38, 46, 63, 67, 89–93, 95].”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [46]
Four equations for elderly are for assessing appendicular LSM.
“There is one equation for estimating BCM [63], four equations for assessing appendicular LSM [46, 67, 92, 93], and one equation for assessing SMM [95] suitable for males and females.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Alberti, K. G. M. M., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., Fruchart, J.-C., James, W. P. T., Loria, C. M., & Smith, S. C. (2009) · Circulation · doi:10.1161/circulationaha.109.192644 · Cited by 14,860 · see citing works
Sarcopenia: revised European consensus on definition and diagnosis
Cruz-Jentoft, A. J., Bahat, G., Bauer, J., Boirie, Y., Bruyère, O., Cederholm, T., Cooper, C., Landi, F., Rolland, Y., Sayer, A. A., et al. (2019) · Age and Ageing · doi:10.1093/ageing/afy169 · Cited by 13,818 · see citing works
Sarcopenia: European consensus on definition and diagnosis
Cruz-Jentoft, A. J., Baeyens, J. P., Bauer, J. M., Boirie, Y., Cederholm, T., Landi, F., Martin, F. C., Michel, J.-P., Rolland, Y., Schneider, S. M., et al. (2010) · Age and Ageing · doi:10.1093/ageing/afq034 · Cited by 11,601 · see citing works
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · see citing works
General and abdominal adiposity and risk of death in Europe
Pischon, T., Boeing, H., Hoffmann, K., Bergmann, M., Schulze, M. B., Overvad, K., van der Schouw, Y. T., Spencer, E., Moons, K. G. M., Tjønneland, A., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801891 · Cited by 2,155 · see citing works
Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk
Després, J.-P., Lemieux, I., Bergeron, J., Pibarot, P., Mathieu, P., Larose, E., Rodés-Cabau, J., Bertrand, O. F., & Poirier, P. (2008) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.107.159228 · Cited by 1,589 · see citing works
Global aging : the challenge of success
Kinsella, K., & Phillips, D. R. (2005) · Digital Commons - Lingnan (Lingnan University) · Cited by 547 · see citing works
Human Body Composition: Advances in Models and Methods
B. Heymsfield, S., Wang, Z., Baumgartner, R. N., & Ross, R. (1997) · Annual Review of Nutrition · doi:10.1146/annurev.nutr.17.1.527 · Cited by 527 · see citing works
Sergi, G., De Rui, M., Veronese, N., Bolzetta, F., Berton, L., Carraro, S., Bano, G., Coin, A., Manzato, E., & Perissinotto, E. (2015) · Clinical Nutrition · doi:10.1016/j.clnu.2014.07.010 · Cited by 448 · see citing works
A Pooled Analysis of Waist Circumference and Mortality in 650,000 Adults
Cerhan, J. R., Moore, S. C., Jacobs, E. J., Kitahara, C. M., Rosenberg, P. S., Adami, H.-O., Ebbert, J. O., English, D. R., Gapstur, S. M., Giles, G. G., et al. (2014) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2013.11.011 · Cited by 443 · see citing works
Leptin and Insulin Act on POMC Neurons to Promote the Browning of White Fat
Dodd, G. T., Decherf, S., Loh, K., Simonds, S. E., Wiede, F., Balland, E., Merry, T. L., Münzberg, H., Zhang, Z.-Y., Kahn, B. B., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.022 · Cited by 398 · see citing works
Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults
LI, R., XIA, J., ZHANG, X., GATHIRUA-MWANGI, W. G., GUO, J., LI, Y., MCKENZIE, S., & SONG, Y. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001448 · Cited by 398 · see citing works
Associations between gender, age and waist circumference
Stevens, J., Katz, E. G., & Huxley, R. R. (2010) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2009.101 · Cited by 306 · see citing works
Sergi, G., De Rui, M., Stubbs, B., Veronese, N., & Manzato, E. (2017) · Aging Clinical and Experimental Research · doi:10.1007/s40520-016-0622-6 · Cited by 291 · see citing works
Bland, M., Altman, D. G., & Warner, D. S. (2011) · Anesthesiology · doi:10.1097/aln.0b013e31823d7784 · Cited by 249 · see citing works
Kyle, U. G., Genton, L., Hans, D., & Pichard, C. (2003) · Clinical Nutrition · doi:10.1016/s0261-5614(03)00048-7 · Cited by 246 · see citing works
Intramyocellular lipid and impaired myofiber contraction in normal weight and obese older adults
Choi, S. J., Files, D. C., Zhang, T., Wang, Z.-M., Messi, M. L., Gregory, H., Stone, J., Lyles, M. F., Dhar, S., Marsh, A. P., et al. (2016) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glv169 · Cited by 171 · see citing works
Body Composition Remodeling and Mortality: The Health Aging and Body Composition Study
Santanasto, A. J., Goodpaster, B. H., Kritchevsky, S. B., Miljkovic, I., Satterfield, S., Schwartz, A. V., Cummings, S. R., Boudreau, R. M., Harris, T. B., & Newman, A. B. (2016) · The Journals of Gerontology Series A · doi:10.1093/gerona/glw163 · Cited by 162 · see citing works
Silva, A. M., Matias, C. N., Nunes, C. L., Santos, D. A., Marini, E., Lukaski, H. C., & Sardinha, L. B. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0355-z · Cited by 99 · see citing works
Self-citationExploring the Impact of Obesity on Skeletal Muscle Function in Older Age
Morgan, P., Smeuninx, B., & Breen, L. (2020) · Frontiers in Nutrition · doi:10.3389/fnut.2020.569904 · Cited by 90 · see citing works
Suspension Training HIIT Improves Gait Speed, Strength and Quality of Life in Older Adults
Jiménez-García, J. D., Martínez-Amat, A., de la Torre-Cruz, M. J., Fábrega-Cuadros, R., Cruz-Díaz, D., Aibar-Almazán, A., Achalandabaso-Ochoa, A., & Hita-Contreras, F. (2019) · International Journal of Sports Medicine · doi:10.1055/a-0787-1548 · Cited by 65 · see citing works
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 64 · see citing works
Lukaski, H. C., & Piccoli, A. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1788-1_16 · Cited by 62 · see citing works
Changes in Phase Angle and Handgrip Strength Induced by Suspension Training in Older Women
Campa, F., Silva, A., & Toselli, S. (2018) · International Journal of Sports Medicine · doi:10.1055/a-0574-3166 · Cited by 56 · see citing works
Self-citationMeyer, H. E., Willett, W. C., Flint, A. J., & Feskanich, D. (2016) · Osteoporosis International · doi:10.1007/s00198-016-3508-8 · Cited by 55 · see citing works
Cheng, Y.-H., Tsao, Y.-C., Tzeng, I., Chuang, H., Li, W.-C., Tung, T., & Chen, J. (2017) · Medicine · doi:10.1097/md.0000000000008126 · Cited by 55 · see citing works
Dellinger, J. R., Johnson, B. A., Benavides, M. L., Moore, M. L., Stratton, M. T., Harty, P. S., Siedler, M. R., & Tinsley, G. M. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abe6fa · Cited by 52 · see citing works
Tallis, J. A., Shelley, S. P., Degens, H., & Hill, C. (2021) · Biomolecules · doi:10.3390/biom11030372 · Cited by 44 · see citing works
BMI, Waist Circumference, and Risk of Incident Vertebral Fracture in Women
Paik, J. M., Rosen, H. N., Katz, J. N., ROSNER, B. A., Rimm, E. B., Gordon, C. M., & Curhan, G. C. (2019) · Obesity · doi:10.1002/oby.22555 · Cited by 42 · see citing works
Premanath, M., Basavanagowdappa, H., Mahesh, M., & Suresh, M. (2014) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/2230-8210.139231 · Cited by 28 · see citing works
Location of body fat and body size impacts DXA soft tissue measures: a simulation study
Valentine, R. J., Misic, M. M., Kessinger, R. B., Mojtahedi, M. C., & Evans, E. M. (2007) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602770 · Cited by 23 · see citing works
Cavedon, V., Milanese, C., & Zancanaro, C. (2020) · International Journal of Medical Sciences · doi:10.7150/ijms.41713 · Cited by 22 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Campa, F., Coratella, G., Cerullo, G., Stagi, S., Paoli, S., Marini, S., Grigoletto, A., Moroni, A., Petri, C., Andreoli, A., et al. (2023) · Clinical Nutrition · doi:10.1016/j.clnu.2023.07.025 · Cited by 69 · see citing works
Self-citationCampa, F., Coratella, G., Cerullo, G., Noriega, Z., Francisco, R., Charrier, D., Irurtia, A., Lukaski, H., Silva, A. M., & Paoli, A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05272-x · Cited by 80 · see citing works
“Out of the 26 available equations, 23 were developed with foot to hand technology [36, 38, 46, 49, 53, 63, 67, 89–93, 95, 97], while three equations were developed using segmental technology [68, 94].”
BackgroundMethodInfluentialSelf-citationLi, L., Xia, Z., Zeng, X., Tang, A., Wang, L., & Su, Y. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1089 · Cited by 24 · see citing works
Paoli, A., & Campa, F. (2024) · Current Obesity Reports · doi:10.1007/s13679-024-00573-0 · Cited by 24 · see citing works
Self-citationTwo-megahertz impedance index prediction equation for appendicular lean mass in Korean older people
Kim, H., Song, K.-H., Ambegaonkar, J. P., Chung, S., Jeon, K., Jiang, F. L., Eom, J. J., & Kim, C.-H. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-02997-6 · Cited by 12 · see citing works
Sardinha, L. B., Rosa, G. B., Hetherington-Rauth, M., Correia, I. R., Magalhães, J. P., Silva, A. M., & Lukaski, H. (2023) · European Journal of Clinical Nutrition · doi:10.1038/s41430-022-01224-0 · Cited by 10 · see citing works
Wang, L., Lu, S., Feng, J., Ma, K., Duan, F., Wang, R., Cheng, X., Liu, Y., Imai, Y., Chen, H., et al. (2025) · Calcified Tissue International · doi:10.1007/s00223-025-01447-w · Cited by 9 · see citing works
Cáñez-Ríos, M., Esparza‐Romero, J., González-Arellanes, R., Ramírez-Torres, M., Figueroa-Pesqueira, G., Urquídez-Romero, R., Peniche, D. B. R., & Alemán-Mateo, H. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.951346 · Cited by 9 · see citing works
Quaresma, M. V. L. D. S., de Melo, C. M., & Ribeiro, S. M. L. (2022) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2022.07.001 · Cited by 1 · see citing works
Campa, F., Coratella, G., Cerullo, G., Noriega, Z., Francisco, R., Charrier, D., Irurtia, A., Lukaski, H. C., Silva, A. M., & Paoli, A. (2024) · Figshare · doi:10.6084/m9.figshare.26727262
Self-citationSales, W. B., Ribeiro-Samora, G. A., Barbosa, P. E. E. S., Vieira, E. R., Souza, G. F., & Maciel, Á. C. C. (2026) · European Review of Aging and Physical Activity · doi:10.1186/s11556-025-00400-y
Romero, R. P., Yabushita, F. T., Teixeira, D. C., Ribeiro, A. S., & de Oliveira, M. R. (2026) · Nutrition · doi:10.1016/j.nut.2026.113403
- Authors
- Toselli, S., Campa, F., Matias, C. N., de Alencar Silva, B. S., dos Santos, V. R., Maietta Latessa, P., & Gobbo, L. A.
- Affiliations
- University of Bologna (IT)
- Universidade Lusófona (PT)
- Universidade Estadual Paulista (Unesp) (BR)
- Corresponding author
- Campa, F.
- Venue
- Experimental Gerontology
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 150
- Pages
- 111393
- ISSN
- 0531-5565 (ISSN-L)
- OpenAlex
- W3161601511
- Cited by
- 12
- References
- 32
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 4
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Experimental Gerontology (publishedVersion)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc-nd)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-nd)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
- Keywords
- bioelectrical impedance analysis, prediction equations, body fat distribution, waist circumference, older adults, muscle mass assessment, sarcopenia
- MeSH terms
- Aged, Body Composition, Female, Humans, Male, Reproducibility of Results, Absorptiometry, Photon, Body Mass Index, Electric Impedance, Body Fat Distribution
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
