Predictive Capability of 5 Sarcopenia Diagnostic Criteria for Fall Incidents in Older Japanese Women: The Otassha Study.

The association between sarcopenia diagnosis according to various diagnostic criteria and falls is unclear. We examined the association between sarcopenia defined by 5 different international criteria, and fall and injurious fall numbers among older community-dwelling Japanese women. Prospective observational study. Two population-based cohort studies in Itabashi, Japan, were integrated, including 552 community-dwelling Japanese women >75 years of age. Muscle mass, muscle strength, and physical performance were assessed by bioimpedance analysis, handgrip strength, and gait speed, respectively. We recorded the fall and injurious fall numbers for 1 year using a diary. The association between sarcopenia diagnosis defined by the Asian Working Group for Sarcopenia (AWGS) 2019, European Working Group on Sarcopenia in Older People 2, Foundation for the National Institutes of Health (FNIH), International Working Group on Sarcopenia, and Society on Sarcopenia, Cachexia, and Wasting Disorders, and fall numbers was assessed by Poisson regression analysis to estimate the incidence rate ratio (IRR) and 95% CI for the presence or absence of sarcopenia. Of 470 participants (mean age 77.1 ± 8.1 years), 144 reported a combined 352 falls. Overall, 80 individuals experienced a single fall, 64 experienced multiple falls, and 91 experienced injurious falls. Sarcopenia defined by the AWGS 2019 (IRR 1.39, 95% CI 1.10-1.76) and FNIH (IRR 2.55, 95% CI 1.99-3.26) was significantly associated with the number of falls. In addition, sarcopenia defined by the FNIH (IRR 2.72, 95% CI 1.84-4.03) was significantly associated also with the number of injurious falls. No significant association was observed between sarcopenia diagnosis based on the other definitions and fall outcomes. The AWGS 2019 and FNIH sarcopenia definitions were associated with the fall and injurious fall numbers in older Japanese women, indicating they can help in predicting falls in this population.

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