C Beaudart1, J Y Reginster2, J Slomian3, F Buckinx4, N Dardenne5, A Quabron6, C Slangen7, S Gillain8, J Petermans9, O Bruyère10. 1. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium. Electronic address: c.beaudart@ulg.ac.be. 2. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium; Bone, Cartilage and Muscle Metabolism Unit, CHU Liège, Quai Godefroid Kurth 45, 4000 Liège, Belgium. Electronic address: jyreginster@ulg.ac.be. 3. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium. Electronic address: jslomian@ulg.ac.be. 4. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium. Electronic address: fanny.buckinx@ulg.ac.be. 5. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium. Electronic address: ndardenne@ulg.ac.be. 6. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium. Electronic address: a.quabron@student.ulg.ac.be. 7. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium. Electronic address: catherine.slan@hotmail.com. 8. Geriatric Department, CHU Liège, Rue de Gaillarmont 600, 4032 Chênée, Belgium. Electronic address: s.gillain@gmail.com. 9. Geriatric Department, CHU Liège, Rue de Gaillarmont 600, 4032 Chênée, Belgium. Electronic address: jean.petermans@chu.ulg.ac.be. 10. Department of Public Health, Epidemiology and Health Economics, University of Liège, Avenue de l'Hôpital 3, CHUB23, 4000 Liège, Belgium; Support Unit in Epidemiology and Biostatistics, University of Liège, Belgium; Department of Motricity Sciences, University of Liège, Liège, Belgium. Electronic address: olivier.bruyere@ulg.ac.be.
Abstract
BACKGROUND: Sarcopenia is defined as a progressive and generalized loss of muscle mass with either a loss of muscle strength or a loss of physical performance but there is no recommendation regarding the diagnostic tools that have to be used. In this study, we compared the prevalence of sarcopenia assessed using different diagnostic tools. METHODS: To measure muscle mass, muscle strength and physical performance, we used for each outcome two different diagnostic tools. For muscle mass, we used Dual Energy X-Ray Absorptiometry (DXA) and bio-electrical impedance analysis (BIA); for muscle strength, we used a hydraulic dynamometer and a pneumatic dynamometer; for physical performance we used the Short Physical Performance Battery test (SPPB test) and the walk speed. Eight diagnostic groups were hereby established. RESULTS: A total of 250 consecutive subjects were recruited in an outpatient clinic in Liège, Belgium. Estimated prevalence of sarcopenia varied from 8.4% to 27.6% depending on the method of diagnosis used. Regarding muscle mass, BIA systematically overestimated muscle mass compared to DXA (mean estimated prevalence with BIA=12.8%; mean prevalence with DXA=21%). For muscle strength, the pneumatic dynamometer diagnosed twice more sarcopenic subjects than the hydraulic dynamometer (mean estimated prevalence with PD=22.4%; mean estimated prevalence with HD=11.4%). Finally, no difference in prevalence was observed when the walking speed or the SPPB test was used. A weak overall kappa coefficient was observed (0.53), suggesting that the 8 methods of diagnosis are moderately concordant. CONCLUSION: Within the same definition of sarcopenia, prevalence of sarcopenia is highly dependent on the diagnostic tools used.
BACKGROUND:Sarcopenia is defined as a progressive and generalized loss of muscle mass with either a loss of muscle strength or a loss of physical performance but there is no recommendation regarding the diagnostic tools that have to be used. In this study, we compared the prevalence of sarcopenia assessed using different diagnostic tools. METHODS: To measure muscle mass, muscle strength and physical performance, we used for each outcome two different diagnostic tools. For muscle mass, we used Dual Energy X-Ray Absorptiometry (DXA) and bio-electrical impedance analysis (BIA); for muscle strength, we used a hydraulic dynamometer and a pneumatic dynamometer; for physical performance we used the Short Physical Performance Battery test (SPPB test) and the walk speed. Eight diagnostic groups were hereby established. RESULTS: A total of 250 consecutive subjects were recruited in an outpatient clinic in Liège, Belgium. Estimated prevalence of sarcopenia varied from 8.4% to 27.6% depending on the method of diagnosis used. Regarding muscle mass, BIA systematically overestimated muscle mass compared to DXA (mean estimated prevalence with BIA=12.8%; mean prevalence with DXA=21%). For muscle strength, the pneumatic dynamometer diagnosed twice more sarcopenic subjects than the hydraulic dynamometer (mean estimated prevalence with PD=22.4%; mean estimated prevalence with HD=11.4%). Finally, no difference in prevalence was observed when the walking speed or the SPPB test was used. A weak overall kappa coefficient was observed (0.53), suggesting that the 8 methods of diagnosis are moderately concordant. CONCLUSION: Within the same definition of sarcopenia, prevalence of sarcopenia is highly dependent on the diagnostic tools used.
Authors: D Beckwée; A Delaere; S Aelbrecht; V Baert; C Beaudart; O Bruyere; M de Saint-Hubert; I Bautmans Journal: J Nutr Health Aging Date: 2019 Impact factor: 4.075
Authors: Brandon M Roberts; Kaleen M Lavin; Gina M Many; Anna Thalacker-Mercer; Edward K Merritt; C Scott Bickel; David L Mayhew; S Craig Tuggle; James M Cross; David J Kosek; John K Petrella; Cynthia J Brown; Gary R Hunter; Samuel T Windham; Richard M Allman; Marcas M Bamman Journal: Exp Gerontol Date: 2018-02-24 Impact factor: 4.032