Sarcopenia Transitions and Influencing Factors Among Chinese Older Adults With Multistate Markov Model.
Innov Aging
; 7(8): igad105, 2023.
Article
em En
| MEDLINE
| ID: mdl-37954524
ABSTRACT
Background and Objectives:
Little is known about the sarcopenia transition process across different stages among Chinese community-dwelling older adults. We aimed to explore dynamic transitions of sarcopenia and its influencing factors in Chinese older adults. Research Design andMethods:
Data were derived from the China Health and Retirement Longitudinal Study. A total of 2856 older adults with complete data in the 2011, 2013, and 2015 waves were included in our study. Participants were categorized into 3 states no sarcopenia, possible sarcopenia, and sarcopenia according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. Continuous-time multistate Markov model was performed to estimate the 1-year transition probabilities and the associated factors of sarcopenia transitions. The association strength was expressed as hazard ratio and 95% confidence interval.Results:
The progression and reversion rates between no sarcopenia and sarcopenia state were 6.01 and 9.20 per 100 person-years, respectively. The 1-year progression probability to possible sarcopenia was higher compared with the likelihood of moving to the sarcopenia state (0.127 vs 0.034). The reversion probability to no sarcopenia was also higher among those with possible sarcopenia (0.281 vs 0.157). Older age, rural living, worse cognition status, higher chronic disease numbers, and lower nutrition status measured by body mass index accelerated the sarcopenia progression. Cognition status and body mass index level were related to higher chances of reverting. Discussion and Implications Possible sarcopenia might be a critical time window to promote sarcopenia reversion. Timely interventions aimed at delaying the progression and facilitating sarcopenia recovery should focus on improving cognitive function and nutrition levels.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article