Sophia X Sui1, Kara L Holloway-Kew1, Natalie K Hyde1, Lana J Williams1, Monica C Tembo1, Sarah Leach2, Julie A Pasco1,3,4,5. 1. Deakin University, IMPACT-Institute for Mental and Physical Health and Clinical Translation, Geelong, VIC 3220, Australia. 2. GMHBA, Geelong, VIC 3220, Australia. 3. Department of Medicine-Western Health, The University of Melbourne, St Albans, VIC 3021, Australia. 4. Department of Epidemiology and Preventive Medicine, Monash University, Prahran, VIC 3181, Australia. 5. University Hospital Geelong, Barwon Health, Geelong, VIC 3220, Australia.
Abstract
BACKGROUND: Prevalence estimates for sarcopenia vary depending on the ascertainment criteria and thresholds applied. We aimed to estimate the prevalence of sarcopenia using two international definitions but employing Australian population-specific cut-points. METHODS: Participants (n = 665; 323 women) aged 60-96 years old were from the Geelong Osteoporosis Study. Handgrip strength (HGS) was measured by dynamometers and appendicular lean mass (ALM) by whole-body dual-energy X-ray absorptiometry. Physical performance was assessed using gait speed (GS, men only) and/or the timed up-and-go (TUG) test. Using cut-points equivalent to two standard deviations (SDs) below the mean young reference range from the same population and recommendations from the European Working Group on Sarcopenia in Older People (EWGSOP), sarcopenia was identified by low ALM/height2 (<5.30 kg for women; <6.94 kg for men) + low HGS (<16 kg women; <31 kg men); low ALM/height2 + slow TUG (>9.3 s); low ALM/height2 + slow GS (<0.8 m/s). For the Foundation for the National Institutes of Health (FNIH) equivalent, sarcopenia was identified as low ALM/BMI (<0.512 m2 women, <0.827 m2 men) + low HGS (<16 kg women, <31 kg men). Receiver Operating Characteristic curves were also applied to determine optimal cut-points for ALM/BMI (<0.579 m2 women, <0.913 m2 men) that discriminated poor physical performance. Prevalence estimates were standardized to the Australian population and compared to estimates using international thresholds. RESULTS: Using population-specific cut-points and low ALM/height2 + HGS, point-estimates for sarcopenia prevalence were 0.9% for women and 2.9% for men. Using ALM/height2 + TUG, prevalence was 2.5% for women and 4.1% for men, and using ALM/height2 + GS, sarcopenia was identified for 1.6% of men. Using ALM/BMI + HGS, prevalence estimates were 5.5-10.4% for women and 11.6-18.4% for men. CONCLUSIONS: This study highlights the range of prevalence estimates that result from employing different criteria for sarcopenia. While population-specific criteria could be pertinent for some populations, a consensus is needed to identify which deficits in skeletal muscle health are important for establishing an operational definition for sarcopenia.
BACKGROUND: Prevalence estimates for sarcopenia vary depending on the ascertainment criteria and thresholds applied. We aimed to estimate the prevalence of sarcopenia using two international definitions but employing Australian population-specific cut-points. METHODS:Participants (n = 665; 323 women) aged 60-96 years old were from the Geelong Osteoporosis Study. Handgrip strength (HGS) was measured by dynamometers and appendicular lean mass (ALM) by whole-body dual-energy X-ray absorptiometry. Physical performance was assessed using gait speed (GS, men only) and/or the timed up-and-go (TUG) test. Using cut-points equivalent to two standard deviations (SDs) below the mean young reference range from the same population and recommendations from the European Working Group on Sarcopenia in Older People (EWGSOP), sarcopenia was identified by low ALM/height2 (<5.30 kg for women; <6.94 kg for men) + low HGS (<16 kg women; <31 kg men); low ALM/height2 + slow TUG (>9.3 s); low ALM/height2 + slow GS (<0.8 m/s). For the Foundation for the National Institutes of Health (FNIH) equivalent, sarcopenia was identified as low ALM/BMI (<0.512 m2 women, <0.827 m2 men) + low HGS (<16 kg women, <31 kg men). Receiver Operating Characteristic curves were also applied to determine optimal cut-points for ALM/BMI (<0.579 m2 women, <0.913 m2 men) that discriminated poor physical performance. Prevalence estimates were standardized to the Australian population and compared to estimates using international thresholds. RESULTS: Using population-specific cut-points and low ALM/height2 + HGS, point-estimates for sarcopenia prevalence were 0.9% for women and 2.9% for men. Using ALM/height2 + TUG, prevalence was 2.5% for women and 4.1% for men, and using ALM/height2 + GS, sarcopenia was identified for 1.6% of men. Using ALM/BMI + HGS, prevalence estimates were 5.5-10.4% for women and 11.6-18.4% for men. CONCLUSIONS: This study highlights the range of prevalence estimates that result from employing different criteria for sarcopenia. While population-specific criteria could be pertinent for some populations, a consensus is needed to identify which deficits in skeletal muscle health are important for establishing an operational definition for sarcopenia.
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