Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM)
Abstract
Rationale Appendicular skeletal muscle mass (ASMM) is useful in the evaluation of nutritional status because it reflects the body muscle protein mass. The purpose of this study was to validate, against dual-energy X-ray absorptiometry (DEXA), a BIA equation to predict ASMM to be used in volunteers and patients. Method Healthy men (n = 246 men, BMI 25.3+/-2.9 kg/m(2)) and women (n =198, 24.1+/-3.6 kg/m(2)), and heart, lung and liver transplant patients (213 men, BMI of 24.6+/-4.4 kg/m(2); 113 women, BMI 23.0+/-5.2 kg/m(2)) were measured by BIA (Xitron Technologies) and DEXA (Hologic QDR 4500). A BIA equation to predict ASMM (kg) that included height(2)/resistance, weight, gender, age and reactance, was developed by means of multiple regressions. [table: see text] Mean difference (Bland-Altman) for volunteers was 0.1+/-1.1 kg, r =0.95, SEE 1.12 kg and for patients -0.4+/-1.5 kg, r =0.91, SEE 1.5 kg. Best fitted multiple regression equation was -4.211 + (0.267 x height2 / resistance) + (0.095 x weight)+(1.909 x sex (men = 1, women = 0)) + (-0.012 x age) + (0.058 x reactance). Conclusions BIA permits the prediction of ASMM in healthy volunteers and patients between 22 and 94 years of age. A slightly larger, though clinically not significant, error was noted in patients.
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No generalizable BIA-derived formulas exist for predicting appendicular lean mass (ALM) and fat mass (AFM) in sarcopenic older adults.
“No generalizable formulas exist that are derived from bioelectrical impedance analysis (BIA) for predicting appendicular lean mass (ALM) and fat mass (AFM) in sarcopenic older adults.”
From Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline – The PROVIDE study (2017) · cites this paper as [12]
The existing BIA models of Kyle et al. and Sergi et al. significantly overestimated appendicular lean mass measured by DXA.
“The existing models of Kyle et al. [12] and of Sergi et al. [13] overestimated significantly lean-DXA outcome.”
From Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline – The PROVIDE study (2017) · cites this paper as [12]
The newly proposed ALM equations are more reliable when applied to sarcopenic older adults than previously published equations, which were developed in adults without physical function decline.
“The proposed ALM equations seem to be more reliable when applied to sarcopenic older adults than the ones previously published in the literature [12,13], which were developed in adults without physical function decline.”
From Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline – The PROVIDE study (2017) · cites this paper as [12]
The equations predicting LSM in adults were developed for the appendicular and trunk body segments.
“Particularly, the equations predicting LSM were developed for the appendicular [61, 77] and trunk [61] body segments.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [77]
Ten equations for adults are suitable for estimating LSM in both sexes.
“Two equations are suitable for estimating TBW [42, 54], ten for estimating LSM [61, 77], and one for estimating SMM [82] in both sexes.”
From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [77]
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“Two equations are suitable for estimating TBW [42, 54], ten for estimating LSM [61, 77], and one for estimating SMM [82] in both sexes.”
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“Appendicular skeletal muscle mass (ASMMBIA) was calculated using the Kyle equation [19]:ASMMBIA kg ð Þ 1⁄4 −4:211þ Ht2=R 0:267 ð Þþ 0:095 BW ð Þþ 1:909 sex ð Þþ −0:012 age ð Þ þ 0:058 Xc ð Þ, where Ht is height in centimeters; for sex, men = 1 and women = 0; age is in years and Xc is reactance derived from BIA.”
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BackgroundScafoglieri, 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
“The proposed ALM equations seem to be more reliable when applied to sarcopenic older adults than the ones previously published in the literature [12,13], which were developed in adults without physical function decline.”
BackgroundMethodResultMarzetti, E., Lorenzi, M., Antocicco, M., Bonassi, S., Celi, M., Mastropaolo, S., Settanni, S., Valdiglesias, V., Landi, F., Bernabei, R., et al. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00233 · Cited by 62 · see citing works
“Moreover, although BIA is an established technique for the estimation of lean body mass (Kyle et al., 2003), it does not represent the gold standard for the quantification of muscle mass.”
BackgroundZebrafish models of sarcopenia
Daya, A., Donaka, R., & Karasik, D. (2020) · Disease Models & Mechanisms · doi:10.1242/dmm.042689 · Cited by 55 · see citing works
“, 2018), and bioelectrical impedance (Kyle et al., 2003).”
Yu, S., Powell, A., Khow, K., & Visvanathan, R. (2016) · Nutrients · doi:10.3390/nu8040189 · Cited by 58 · see citing works
“More noticeable was that both the Sergi and Kyle equations were developed using single-frequency BIA machines [5,6] similar to the machine used in our study.”
MethodTakai, Y., Ohta, M., Akagi, R., Kato, E., Wakahara, T., Kawakami, Y., Fukunaga, T., & Kanehisa, H. (2013) · Journal of PHYSIOLOGICAL ANTHROPOLOGY · doi:10.1186/1880-6805-32-12 · Cited by 58 · see citing works
“These values were obtained from prediction equations derived from anthropometric measurements at multiple levels of limbs [20] or multiple BIA along the limb length [26].”
MethodInfluentialMacdonald, J. H., Marcora, S. M., Jibani, M., Roberts, G., Kumwenda, M. J., Glover, R., Barron, J., & Lemmey, A. B. (2006) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfl432 · Cited by 57 · see citing works
“[12] equation (ALMKyle) compared with our criterion measure of muscle mass (ALMDXA) equation is shown in Figure 1.”
InfluentialAlkahtani, S. A. (2017) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-017-1483-7 · Cited by 57 · see citing works
“However, different brands and models of BIA devices have been used to measure muscle mass and predict sarcopenia, including bioelectrical impedance spectroscopy models [10], Xitron Technologies [11], Valhalla Bio-Resistance Analyzer [5], Tanita BC [12], and Inbody Biospace [13].”
Benefits of home-based endurance training in lung transplant recipients
Vivodtzev, I., Pison, C. M., Guerrero, K., Mezin, P., Maclet, E., Borel, J., Chaffanjon, P., Hacini, R., Chavanon, O., Blin, D., et al. (2011) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2011.02.005 · Cited by 53 · see citing works
Calvani, R., Martone, A. M., Marzetti, E., Onder, G., Savera, G., Lorenzi, M., Serafini, E., Bernabei, R., & Landi, F. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00269 · Cited by 54 · see citing works
The LACE study group, Achison, M., Adamson, S., Akpan, A., Aspray, T., Avenell, A., Band, M. M., Bashir, T., Burton, L. A., Cvoro, V., et al. (2022) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12934 · Cited by 51 · see citing works
“These cut-offs were derived from normative bioimpedance data from healthy middle aged adults in the UK BioBank study 22 Total body fat-free mass index cut-offs for the fi fth percentile were used and converted to equivalent appendicular skeletal muscle mass values using values from Kyle et al .”
Perrot, L., Greil, A., Boirie, Y., Farigon, N., Mulliez, A., Costes, F., & Caillaud, D. M. (2020) · European Journal of Clinical Nutrition · doi:10.1038/s41430-020-0623-6 · Cited by 51 · see citing works
Rangel Peniche, D. B., Raya Giorguli, G., & Alemán-Mateo, H. (2015) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2015.03.007 · Cited by 50 · see citing works
Clinical Value of Muscle Mass Assessment in Clinical Conditions Associated with Malnutrition
Mareschal, J., Achamrah, N., Norman, K., & Genton, L. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8071040 · Cited by 50 · see citing works
Self-citationOhlsson, C., Gidestrand, E., Bellman, J., Larsson, C. L., Pálsdóttir, V., Hägg, D. A., Jansson, P., & Jansson, J. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100338 · Cited by 49 · see citing works
Marzi, C., Huth, C., Herder, C., Baumert, J. J., Thorand, B., Rathmann, W., Meisinger, C., Wichmann, H. ‐., Roden, M., Peters, A., et al. (2012) · Diabetes Care · doi:10.2337/dc12-1514 · Cited by 48 · see citing works
“(17,18) in model 2 of the association analysis.”
BackgroundAcute Sarcopenia: Mechanisms and Management
Damanti, S., Senini, E., LORENZO, R. D., Merolla, A., Santoro, S., Festorazzi, C., Messina, M., Vitali, G., Sciorati, C., & Rovere, P. Q. (2024) · Nutrients · doi:10.3390/nu16203428 · Cited by 50 · see citing works
Clinical determinants and neural correlates of presbyphagia in community-dwelling older adults
Labeit, B., Muhle, P., von Itter, J., Slavik, J., Wollbrink, A., Sporns, P. B., Rusche, T., Ruck, T., Hüsing‐Kabar, A., Gellner, R., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.912691 · Cited by 46 · see citing works
“Assessment of nutritional status, muscle strength/volume and frailty: To characterize body composition, a bioimpedance analysis was done (Kyle et al., 2003) using a phase sensitive multifrequency analyzer (model BIA 2000-M, Data Input GmbH, Frankfurt, Germany) with the according analysis software (Nutri Plus, also Dat…”
MethodBrotelle, T., Lemal, R., Cabrespine, A., Combal, C., Hermet, É., Ravinet, A., Bay, J., & Bouteloup, C. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2017.03.016 · Cited by 46 · see citing works
Disentangling the genetics of lean mass
Karasik, D., Zillikens, M. C., Hsu, Y., Aghdassi, A. A., Åkesson, K., Amin, N., Barroso, I., Bennett, D. A. A., Bertram, L., Bochud, M., et al. (2018) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy272 · Cited by 48 · see citing works
“Nevertheless, the 2 methods are highly correlated (9), and by combining them, the power to detect GWS loci was greatly enhanced.”
BackgroundUse of Bioelectrical Impedance: General Principles and Overview
Stahn, A. C., Terblanche, E., & Gunga, H. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1788-1_3 · Cited by 49 · see citing works
Predicting sarcopenia from functional measures among community-dwelling older adults
Gray, M., Glenn, J. M., & Binns, A. (2016) · AGE · doi:10.1007/s11357-016-9887-0 · Cited by 41 · see citing works
Jeon, K. C., Kim, S.-Y., Jiang, F. L., Chung, S., Ambegaonkar, J. P., Park, J.-H., Kim, Y.-J., & Kim, C.-H. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17165847 · Cited by 38 · see citing works
“…and InBody S10 and the following eight equations including Vermeiren et al. (2019) [14], Scafoglieri et al. (2017) [15], Sergi et al. (2015) [16], Kyle et al. (2003) [17], Kim et al. (2014) [18], Peniche et al. (2015) [19], Yamada et al. (2017) [20], respectively: ASMVermeiren (kg) = 0.827 +…”
InfluentialPark, H., Lee, S., Lee, S., Kim, S.-Y., & Park, H. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12821 · Cited by 39 · see citing works
“Compared with the reference methods, such as dual-energy X-ray absorptiometry, segmental BIA is a reliable method to measure skeletal MM even in older populations.(6) To evaluate decline in lung function appropriately, long-term observation with no missing variables is essential.”
MethodIizuka, Y., Iizuka, H., Mieda, T., Tajika, T., Yamamoto, A., Ohsawa, T., Sasaki, T., & Takagishi, K. (2015) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-015-0759-z · Cited by 40 · see citing works
“Body composition measurements Regarding the body composition analysis, the Tanita MC-780 body composition analyzer, which analyzes the body composition using bioelectrical impedance analysis (BIA) technology [27–31], was used.”
MethodBennett, J. P., Cataldi, D., Liu, Y. E., Kelly, N. N., Quon, B. K., González, M. C., Heymsfield, S. B., & Shepherd, J. (2024) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2024.07.009 · Cited by 36 · see citing works
Cho, Y., Chang, Y., Ryu, S., Jung, H. S., Kim, C., Oh, H., Kim, M. K., Sohn, W., Shin, H. C., Wild, S. H., et al. (2022) · Hepatology Communications · doi:10.1002/hep4.1975 · Cited by 36 · see citing works
Vivodtzev, I., Flore, P., Levy, P. A., et al. (2007) · Muscle & Nerve · doi:10.1002/mus.20867 · Cited by 34 · see citing works
de Vasconcelos, F. D. A. G., Cordeiro, B. A., Rech, C. R., & Petróski, E. L. (2010) · Cadernos de Saúde Pública · doi:10.1590/s0102-311x2010000800006 · Cited by 34 · see citing works
Skeletal muscle estimation: A review of techniques and their applications
Rodriguez, C., Mota, J. A., Palmer, T. B., Heymsfield, S. B., & Tinsley, G. M. (2024) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12874 · Cited by 35 · see citing works
Evaluation of serum myostatin and sclerostin levels in chronic spinal cord injured patients
Invernizzi, M., Carda, S., Rizzi, M., Grana, E., Squarzanti, D. F., Cisari, C., Molinari, C., & Renò, F. (2015) · Spinal Cord · doi:10.1038/sc.2015.61 · Cited by 32 · see citing works
Knechtle, B., Wirth, A., Knechtle, P., Rosemann, T. J., Rüst, C. A., & Bescós, R. (2011) · PubMed · doi:10.1590/s0212-16112011000600032 · Cited by 31 · see citing works
Modeling upper and lower limb muscle volume by bioelectrical impedance analysis
Stahn, A. C., Terblanche, E., & Strobel, G. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01163.2006 · Cited by 32 · see citing works
Xu, H.-Q., Liu, J.-M., Zhang, X., Xue, Y.-T., Shi, J.-P., Chen, W., & Zheng, X.-Y. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2020.10.021 · Cited by 28 · see citing works
Nutritional status in post SARS-Cov2 rehabilitation patients
Gobbi, M., Brunani, A., Arreghini, M., Baccalaro, G., Dellepiane, D., Vela, V. L., Lucchetti, E., Barbaglia, M., Cova, A., Fornara, E., et al. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2021.04.013 · Cited by 30 · see citing works
Prevalence of Sarcopenia in Community-Dwelling Older Adults in Valencia, Spain
Guillamón-Escudero, C., Diago-Galmés, A., Tenías, J. M., Soriano, J. M., & Garrido, J. F. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17239130 · Cited by 28 · see citing works
Diago-Galmés, A., Guillamón-Escudero, C., Tenías, J. M., Soriano, J. M., & Garrido, J. F. (2021) · Biology · doi:10.3390/biology10020093 · Cited by 29 · see citing works
“This test is included in the test battery proposed by the EWGSOP [5] and consists of making 5 squats into the chair at maximum possible speed and without using any type of manual support [31].”
Methodda Silva, B. R., Mialich, M. S., Cruz, L. P., Rufato, S., Gozzo, T. D. O., & Jordão, A. A. (2021) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2021.02.007 · Cited by 29 · see citing works
Alizadehkhaiyat, O., Hawkes, D. H., Kemp, G. J., Howard, A., & Frostick, S. P. (2013) · European Journal of Applied Physiology · doi:10.1007/s00421-013-2764-y · Cited by 28 · see citing works
“In another study of 444 healthy volunteers (246 men and 198 women) between 22 and 94 years, Kyle et al. (2003) investigated SMM using BIA and dual-energy X-ray absorptiometry and reported BIA as a valid method for estimating SMM, with an error of 5 %.”
BackgroundMethodSteihaug, O. M., Gjesdal, C. G., Bogen, B. E., & Ranhoff, A. H. (2016) · The journal of nutrition health & aging · doi:10.1007/s12603-016-0686-1 · Cited by 28 · see citing works
“BIA has been validated in healthy volunteers and organ transplant patients (12), in community dwelling older persons (13) and in older hospitalised patients (14), compared to DXA and MRI.”
InfluentialDe Rui, M., Veronese, N., Bolzetta, F., Berton, L., Carraro, S., Bano, G., Trevisan, C., Pizzato, S., Coin, A., Perissinotto, E., et al. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2016.04.011 · Cited by 26 · see citing works
“Our findings confirm the dominant role of RI in predicting the leanmass of single limbs in the elderly, as already seen for appendicular skeletal muscle mass estimates [9,11], irrespective of the abovementioned factors that might influence BIA parameters with aging.”
MethodResultInfluentialCoëffier, M., El Machkouri, M., L'Huillier, C., Folope, V., Lelandais, H., Grigioni, S., Déchelotte, P., & Achamrah, N. (2022) · Clinical Nutrition · doi:10.1016/j.clnu.2022.07.032 · Cited by 28 · see citing works
Evaluating skeletal muscle mass with ultrasound in patients with systemic sclerosis
Sarı, A., Eşme, M., Ayçiçek, G. Ş., Armağan, B., Kılıç, L., Ertenli, İ., Halil, M. G., & Akdoğan, A. (2020) · Nutrition · doi:10.1016/j.nut.2020.110999 · Cited by 29 · see citing works
Sex Differences in Age-Related Physical Changes among Community-Dwelling Adults
Okabe, T., Suzuki, M., Gotô, H., Iso, N., Cho, K., Hirata, K., & Shimizu, J. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10204800 · Cited by 25 · see citing works
“The bioelectrical impedance analysis (BIA) method is suitable for screening body composition, including muscle mass, because it is safe, simple, reliable, valid, and transportable, compared to computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry methods [18].”
MethodDupertuis, Y. M., Pereira, A. G., Karsegard, V. L., Hemmer, A., Biolley, E., Collet, T., & Genton, L. (2022) · Clinical Nutrition · doi:10.1016/j.clnu.2022.09.008 · Cited by 25 · see citing works
Self-citationvan den Helder, J., Verreijen, A. M., van Dronkelaar, C., Memelink, R. G., Engberink, M. F., Engelbert, R., Weijs, P. J. M., & Tieland, M. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.874980 · Cited by 26 · see citing works
Iwasaka, C., Yamada, Y., Nishida, Y., Hara, M., Yasukata, J., Miyoshi, N., Shimanoe, C., Nanri, H., Furukawa, T., Koga, K., et al. (2023) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2023.104931 · Cited by 25 · see citing works
Villani, A. M., Crotty, M., Cameron, I. D., Kurrle, S. E., Skuza, P., Cleland, L. G., Cobiac, L., & Miller, M. (2014) · Age and Ageing · doi:10.1093/ageing/afu106 · Cited by 23 · see citing works
Guida, B., Maro, M. D., Lauro, M. D., Lauro, T. D., Trio, R., Santillo, M., Belfiore, A., Memoli, A., & Cataldi, M. (2020) · Nutrition · doi:10.1016/j.nut.2020.110815 · Cited by 23 · see citing works
Vinke, J. S. J., Wouters, H. J. C. M., Stam, S. P., Douwes, R. M., Post, A., Neto, A. W. G., van der Klauw, M. M., Berger, S. P., Bakker, S. J. L., TransplantLines Investigators, et al. (2022) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12999 · Cited by 22 · see citing works
“ASMM was estimated from the equation described by Kyle et al.(24): ASMM (kg) = 4.”
MethodInfluentialOptimal Cutoffs for the Diagnosis of Sarcopenia in Older Chinese Adults
Ge, S., Du, Q., Feng, X., Liu, Y., Wang, H., Hai, S., Shi, X., Sun, W., Ma, A., Lv, T., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.853323 · Cited by 22 · see citing works
Vermeiren, S., Beckwée, D., Vella-Azzopardi, R., Beyer, I., Knoop, V., Jansen, B., Delaere, A., Antoine, A., Bautmans, I., Scafoglieri, A., et al. (2018) · Clinical Nutrition · doi:10.1016/j.clnu.2018.07.029 · Cited by 22 · see citing works
Anthropometric predictive equations for estimating body composition
Asgari, M., Salamat, M. R., Shanei, A., Salamat, A., & Khoshhali, M. (2015) · Advanced Biomedical Research · doi:10.4103/2277-9175.150429 · Cited by 21 · see citing works
“[10] Furthermore, DXA has been validated against MRI to predict the whole‑body lean mass. [11] Most of the previous studies have concentrated on the prediction of whole‑body fat mass with anthropometric indices.”
Campa, F., Bongiovanni, T., Trecroci, A., Rossi, A., Greco, G., Pasta, G., & Coratella, G. (2021) · Biology · doi:10.3390/biology10111175 · Cited by 20 · see citing works
“Body composition parameters were estimated using bioimpedance-derived equations [18,25] as follows: %FM = [(Body mass − fat-free mass)/body mass] × 100, with fat-free mass = −2.”
MethodJo, M. H., Lim, T. S., Jeon, M. Y., Lee, H. W., Kim, B. K., Park, J. Y., Kim, D. Y., Ahn, S. H., Han, K., & Kim, S. U. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030322 · Cited by 20 · see citing works
Evaluation of techniques used to assess skeletal muscle quantity in patients with cirrhosis
Woodward, A. J., Wallen, M. P., Ryan, J., Ward, L. C., Coombes, J. S., & Macdonald, G. A. (2021) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2021.05.029 · Cited by 20 · see citing works
Bellafronte, N. T., Vega‐Piris, L., Barril, G., & Chiarello, P. G. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.683393 · Cited by 19 · see citing works
“AFFMKyle (9) was chosen because it was the first widely evaluated predictive equation for AFFM developed with spectroscopy BIA.”
MethodGuillamón-Escudero, C., Diago-Galmés, A., Zuazua-Rico, D., Maestro-González, A., Tenías, J. M., Soriano, J. M., & Garrido, J. F. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19148473 · Cited by 18 · see citing works
“[34] to obtain necessary data to complete the equation; the electrical bioimpedance measurement was used with a TANITA DC430MA-S scale (Tokyo, Japan) with a precision of 0.”
MethodWang, P.-C., Yeh, W.-C., Tsai, Y.-W., & Chen, J. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1038491 · Cited by 18 · see citing works
Silva, M. I. B., Barreto, A. P. M. M., Pontes, K. S. D. S., da Costa, M. S., Rosina, K. T. D. C., Souza, E., Bregman, R., Prado, C. M. M., & Klein, M. R. S. T. (2020) · Clinical Nutrition · doi:10.1016/j.clnu.2020.05.021 · Cited by 20 · see citing works
Antioxidants in Sport Sarcopenia
Cesare, M. M., Felice, F., Santini, V., & Stefano, R. D. (2020) · Nutrients · doi:10.3390/nu12092869 · Cited by 20 · see citing works
- Authors
- Kyle, U. G., Genton, L., Hans, D., & Pichard, C.
- Affiliations
- University Hospital of Geneva (CH)
- Corresponding author
- Pichard, C
- Venue
- Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2003-12
- Volume
- 22
- Issue
- 6
- Pages
- 537-543
- ISSN
- 0261-5614 (ISSN-L)
- OpenAlex
- W2072278615
- Cited by
- 246
- References
- 35
- Citations by year
- 2026: 16 · 2025: 29 · 2024: 25 · 2023: 16 · 2022: 24 · 2021: 17 · 2020: 20 · 2019: 12 · 2018: 7 · 2017: 8 · 2016: 10 · 2015: 9 · 2014: 10 · 2013: 5 · 2012: 13
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Nutrition (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Muscle metabolism and nutrition
- Keywords
- appendicular skeletal muscle mass, bioelectrical impedance analysis, equation validation, dual-energy X-ray absorptiometry, nutritional assessment, transplant recipients, reactance, body composition
- MeSH terms
- Adult, Aged, Aged, 80 and over, Anthropometry, Body Composition, Female, Humans, Male, Middle Aged
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