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Prevalence of Sarcopenia Among Slovenian Older Adults and Associated Risk Factors
Katarina Puš1,2,3, Saša Pišot1, Uroš Marušič1,2
1Science and Research Centre Koper, Institute for Kinesiology Research, Garibaldijeva ulica 1, Koper, Slovenia.
Introduction:
Sarcopenia is a multifaceted condition affecting between 10 and 16% of the global population, and although multiple classification algorithms exist, no prevalence has been reported for a representative sample of the Slovenian population. Furthermore, multiple behavioural factors, such as malnutrition, physical inactivity, sedentary lifestyle and lower cognitive function, can contribute to the risk of sarcopenia. This study aims to: a) determine sarcopenia prevalence among Slovenian older adults according to different classification algorithms, b) compare the agreement among the algorithms and c) evaluate the relationship between proposed risk factors and sarcopenia.
Methods:
654 participants (≥60 years, 30.4% males) have been classified into sarcopenia groups according to eight algorithms, and agreement (Fleiss K) between them was calculated. Additionally, age, sex, nutritional status, physical activity, sedentary levels and cognitive function were assessed as sarcopenia risk/protective factors.
Results:
The prevalence of sarcopenia according to EWGSOP2 was 4.1%, ranging from 2.1% to 15.3%, when classified by all eight algorithms. Overall agreement between algorithms was weak (K=.429; 95% CI .414 to .444) with 0.6% of participants classified as sarcopenic by all eight algorithms. Adequate nutrition and physical activity were identified as protective factors, while age, lower cognitive function and sedentary lifestyle were considered risk factors.
Conclusion:
Sarcopenia prevalence among the Slovenian general population was lower than in the global population. We can conclude that different sarcopenia algorithms lead to a different prevalence of sarcopenia. It is of great importance to be cautious when comparing prevalences among studies and to further validate the classification algorithms.
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