Lara Bianchi1, Luigi Ferrucci2, Antonio Cherubini3, Marcello Maggio4, Stefania Bandinelli5, Elisabetta Savino1, Gloria Brombo1, Giovanni Zuliani1, Jack M Guralnik6, Francesco Landi7, Stefano Volpato8. 1. Department of Medical Sciences, University of Ferrara, Italy. 2. Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, NIH, Baltimore, Maryland. 3. Geriatrics, IRCCS-INRCA, Ancona, Italy. 4. Department of Clinical and Experimental Medicine, Section of Geriatrics, University of Parma, Italy. 5. Geriatric Unit, Azienda Sanitaria di Firenze, Italy. 6. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore. 7. Department of Gerontology and Geriatric Sciences, University Cattolica del Sacro Cuore, Rome, Italy. 8. Department of Medical Sciences, University of Ferrara, Italy. Center for Clinical Epidemiology, School of Medicine, University of Ferrara, Italy. vlt@unife.it.
Abstract
BACKGROUND: Sarcopenia is associated with increased risk of adverse outcomes in older people. Aim of the study was to explore the predictive value of the European Working Group on Sarcopenia in Older People (EWGSOP) diagnostic algorithm in terms of disability, hospitalization, and mortality and analyze the specific role of grip strength and walking speed as diagnostic criteria for sarcopenia. METHODS: Longitudinal analysis of 538 participants enrolled in the InCHIANTI study. Sarcopenia was defined as having low muscle mass plus low grip strength or low gait speed (EWGSOP criteria). Muscle mass was assessed using bioimpedance analysis. Cox proportional and logistic regression models were used to assess risk of death, hospitalization, and disability for sarcopenic people and to investigate the individual contributions of grip strength and walking speed to the predictive value of the EWGSOP's algorithm. RESULTS: Prevalence of EWGSOP-defined sarcopenia at baseline was 10.2%. After adjusting for potential confounders, sarcopenia was associated with disability (odds ratio 3.15; 95% confidence interval [CI] 1.41-7.05), hospitalization (hazard ratio [HR] 1.57; 95% CI 1.03-2.41), and mortality (HR 1.88; 95% CI 0.91-3.91). The association between an alternative sarcopenic phenotype, defined only by the presence of low muscle mass and low grip strength, and both disability and mortality were similar to the association with the phenotypes defined by low muscle mass and low walking speed or by the EWGSOP algorithm. CONCLUSIONS: The EWGSOP's phenotype is a good predictor of incident disability, hospitalization and death. Assessment of only muscle weakness, in addition to low muscle mass, provided similar predictive value as compared to the original algorithm.
BACKGROUND:Sarcopenia is associated with increased risk of adverse outcomes in older people. Aim of the study was to explore the predictive value of the European Working Group on Sarcopenia in Older People (EWGSOP) diagnostic algorithm in terms of disability, hospitalization, and mortality and analyze the specific role of grip strength and walking speed as diagnostic criteria for sarcopenia. METHODS: Longitudinal analysis of 538 participants enrolled in the InCHIANTI study. Sarcopenia was defined as having low muscle mass plus low grip strength or low gait speed (EWGSOP criteria). Muscle mass was assessed using bioimpedance analysis. Cox proportional and logistic regression models were used to assess risk of death, hospitalization, and disability for sarcopenic people and to investigate the individual contributions of grip strength and walking speed to the predictive value of the EWGSOP's algorithm. RESULTS: Prevalence of EWGSOP-defined sarcopenia at baseline was 10.2%. After adjusting for potential confounders, sarcopenia was associated with disability (odds ratio 3.15; 95% confidence interval [CI] 1.41-7.05), hospitalization (hazard ratio [HR] 1.57; 95% CI 1.03-2.41), and mortality (HR 1.88; 95% CI 0.91-3.91). The association between an alternative sarcopenic phenotype, defined only by the presence of low muscle mass and low grip strength, and both disability and mortality were similar to the association with the phenotypes defined by low muscle mass and low walking speed or by the EWGSOP algorithm. CONCLUSIONS: The EWGSOP's phenotype is a good predictor of incident disability, hospitalization and death. Assessment of only muscle weakness, in addition to low muscle mass, provided similar predictive value as compared to the original algorithm.
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