AIM: Several operative definitions and screening methods for sarcopenia have been proposed in previous studies; however, the opinions of researchers still differ. We compared the prevalence of sarcopenia using two different algorithms: (i) the European working group on sarcopenia in older people (EWGSOP)-suggested algorithm using gait speed as the first step; and (ii) the muscle mass and strength algorithm. METHODS: A population-based, cross-sectional survey of adults aged over 65 years was carried out. Data on a total of 4811 participants were available for analysis. Gait speed, grip strength and appendicular skeletal muscle mass were assessed to determine sarcopenia. Appendicular skeletal muscle mass was estimated from bioimpedance analysis measurements and expressed as skeletal muscle mass index. Grip strength and skeletal muscle mass index were considered to be low if they fell below the threshold of the lowest 20% of values measured in a subset of healthy subjects. We compared the prevalence rates of sarcopenia determined by the two algorithms. RESULTS: The prevalence rate of sarcopenia in a representative sample of older Japanese adults was 8.2% for men and 6.8% for women based on the EWGSOP algorithm. The two algorithms identified the same participants as sarcopenic, the only difference being the EWGSOP algorithm classified an additional seven participants (0.15%) into sarcopenia compared with the muscle mass and strength algorithm. CONCLUSION: It is debatable whether inclusion of gait speed is necessary when screening for sarcopenia in community-dwelling older adults. Future research should examine the necessity of including gait speed in algorithms and the validity of cut-off values.
AIM: Several operative definitions and screening methods for sarcopenia have been proposed in previous studies; however, the opinions of researchers still differ. We compared the prevalence of sarcopenia using two different algorithms: (i) the European working group on sarcopenia in older people (EWGSOP)-suggested algorithm using gait speed as the first step; and (ii) the muscle mass and strength algorithm. METHODS: A population-based, cross-sectional survey of adults aged over 65 years was carried out. Data on a total of 4811 participants were available for analysis. Gait speed, grip strength and appendicular skeletal muscle mass were assessed to determine sarcopenia. Appendicular skeletal muscle mass was estimated from bioimpedance analysis measurements and expressed as skeletal muscle mass index. Grip strength and skeletal muscle mass index were considered to be low if they fell below the threshold of the lowest 20% of values measured in a subset of healthy subjects. We compared the prevalence rates of sarcopenia determined by the two algorithms. RESULTS: The prevalence rate of sarcopenia in a representative sample of older Japanese adults was 8.2% for men and 6.8% for women based on the EWGSOP algorithm. The two algorithms identified the same participants as sarcopenic, the only difference being the EWGSOP algorithm classified an additional seven participants (0.15%) into sarcopenia compared with the muscle mass and strength algorithm. CONCLUSION: It is debatable whether inclusion of gait speed is necessary when screening for sarcopenia in community-dwelling older adults. Future research should examine the necessity of including gait speed in algorithms and the validity of cut-off values.
Authors: E Dent; J E Morley; A J Cruz-Jentoft; H Arai; S B Kritchevsky; J Guralnik; J M Bauer; M Pahor; B C Clark; M Cesari; J Ruiz; C C Sieber; M Aubertin-Leheudre; D L Waters; R Visvanathan; F Landi; D T Villareal; R Fielding; C W Won; O Theou; F C Martin; B Dong; J Woo; L Flicker; L Ferrucci; R A Merchant; L Cao; T Cederholm; S M L Ribeiro; L Rodríguez-Mañas; S D Anker; J Lundy; L M Gutiérrez Robledo; I Bautmans; I Aprahamian; J M G A Schols; M Izquierdo; B Vellas Journal: J Nutr Health Aging Date: 2018 Impact factor: 4.075
Authors: N C Htun; K Ishikawa-Takata; A Kuroda; T Tanaka; T Kikutani; S P Obuchi; H Hirano; K Iijima Journal: J Nutr Health Aging Date: 2016-02 Impact factor: 4.075