Mortality, falls and slow walking speed are predicted by different muscle strength and physical performance measures in women and men.

Publisher:
Elsevier
Publication Type:
Journal Article
Citation:
Arch Gerontol Geriatr, 2023, 114, pp. 105084
Issue Date:
2023-11
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BACKGROUND: Different measures of muscle strength, physical performance and body size/composition are used in various sarcopenia definitions. This study investigated which baseline measures best predict incident mortality and falls, and prevalent slow walking speed in older women and men. MATERIALS AND METHODS: Data for 899 women (mean age±standard deviation, 68.7 ± 4.3 years) and 497 men (69.4 ± 3.9 years) from the Dubbo Osteoporosis Epidemiology Study 2, comprising sixty variables for muscle strength (quadriceps strength), physical performance (walking speed, timed up and go (TUG) test, sit to stand (STS) test), body size (weight, height, body mass index) and body composition (lean mass, body fat) were included. Sex-stratified Classification and Regression Tree (CART) analyses calculated baseline variable accuracy for predicting incident mortality and falls, and prevalent slow walking speed (<0.8 m/s). RESULTS: Over 14.5 years, 103/899 (11.5%) women and 96/497 (19.3%) men died, 345/899 (38.4%) women and 172/497 (34.6%) men had ≥1 fall, and 304/860 (35.3%) women and 172/461 (31.7%) had baseline slow walking speed (<0.8 m/s). CART models identified age and walking speed adjusted for height as the most important predictors for mortality in women, and quadriceps strength (with adjustments) as the most important predictor for mortality in men. In both sexes, STS (with adjustments) was the most important predictor for incident falls, and TUG test was the most important predictor for prevalent slow walking speed. Body composition measures were not important predictors for any outcome. CONCLUSIONS: Muscle strength and physical performance variables and cut points predict falls and mortality differently in women and men, suggesting targeted sex-specific application of selected measures may improve outcome prediction in older adults.
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