Nirmala Rathnayake1, Gayani Alwis2, Janaka Lenora3, Sarath Lekamwasam4. 1. Department of Nursing, Faculty of Allied Health Sciences, University of Ruhuna, Galle, Sri Lanka. nirmala.priyanthi@gmail.com. 2. Department of Anatomy, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka. 3. Department of Physiology, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka. 4. Population Health Research Centre, Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka.
Abstract
BACKGROUND: Menopause associated low serum estradiol marks varieties of derangements in muscle mass and functions leading to sarcopenia. This cross-sectional study was carried out to examine the factors associated with measures of sarcopenia; skeletal muscle mass (SMM), muscle strength and physical performance (PP) in a group of premenopausal (PrMW) and postmenopausal women (PMW) selected from Sri Lanka. METHODS: Randomly selected 184 PrMW and 166 PMW from Galle district, Sri Lanka were studied. SMM was measured with duel energy X ray absorptiometry and relative appendicular SMM index (RSMI; kg/m2) was calculated. Other measurements made include handgrip strength (HGS; kg) and gait speed (GS; m/s), anthropometric indices, consumption of macro and micronutrients, and pattern of physical activities (PA). A serum sample was analyzed for fasting insulin, serum estradiol and vitamin D. Variables which significantly correlated with RSMI, HGS and GS of PrMW and PMW were separately entered into multiple linear regression models to extract the associated factors. RESULTS: Mean (SD) age of PrMW and PMW were 42.4 (6.0) and 55.8 (3.8) years respectively. In the regression analysis, RSMI in PrMW showed significant associations with body mass index (BMI), HGS, total-body-fat-mass (TBFM) and weight (adjusted R2 = 0.85) and in PMW with BMI, weight, TBFM, hip-circumference and fasting insulin (adjusted R2 = 0.80). BMI showed the strongest association with RSMI in both PrMW (r = 0.87, R2 = 0.76) and in PMW (r = 0.87, R2 = 0.76). HGS in PrMW showed significant associations with appendicular SMM (ASMM), total-body-bone-mineral-content, vigorous PA score, age and weight (adjusted R2 = 0.33) and in PMW with ASMM and height (adjusted R2 = 0.23). ASMM showed the strongest association with HGS in both PrMW (r = 0.44, R2 = 0.20) and PMW (r = 0.44, R2 = 0.20). GS in PrMW showed significant associations with height, BMI and energy consumption (adjusted R2 = 0.13) while in PMW, with carbohydrate consumption and total-body-bone-mineral-density (adjusted R2 = 0.09). While in PrMW, height showed the strongest association with GS (r = 0.28, R2 = 0.08) in PMW, it was carbohydrate consumption (r = 0.24, R2 = 0.06). CONCLUSIONS: Factors that are associated with different measures of sarcopenia are not uniform and vary widely from anthropometry to nutrient intake indicating that these measures are somewhat independent and are governed by different factors.
BACKGROUND: Menopause associated low serum estradiol marks varieties of derangements in muscle mass and functions leading to sarcopenia. This cross-sectional study was carried out to examine the factors associated with measures of sarcopenia; skeletal muscle mass (SMM), muscle strength and physical performance (PP) in a group of premenopausal (PrMW) and postmenopausal women (PMW) selected from Sri Lanka. METHODS: Randomly selected 184 PrMW and 166 PMW from Galle district, Sri Lanka were studied. SMM was measured with duel energy X ray absorptiometry and relative appendicular SMM index (RSMI; kg/m2) was calculated. Other measurements made include handgrip strength (HGS; kg) and gait speed (GS; m/s), anthropometric indices, consumption of macro and micronutrients, and pattern of physical activities (PA). A serum sample was analyzed for fasting insulin, serum estradiol and vitamin D. Variables which significantly correlated with RSMI, HGS and GS of PrMW and PMW were separately entered into multiple linear regression models to extract the associated factors. RESULTS: Mean (SD) age of PrMW and PMW were 42.4 (6.0) and 55.8 (3.8) years respectively. In the regression analysis, RSMI in PrMW showed significant associations with body mass index (BMI), HGS, total-body-fat-mass (TBFM) and weight (adjusted R2 = 0.85) and in PMW with BMI, weight, TBFM, hip-circumference and fasting insulin (adjusted R2 = 0.80). BMI showed the strongest association with RSMI in both PrMW (r = 0.87, R2 = 0.76) and in PMW (r = 0.87, R2 = 0.76). HGS in PrMW showed significant associations with appendicular SMM (ASMM), total-body-bone-mineral-content, vigorous PA score, age and weight (adjusted R2 = 0.33) and in PMW with ASMM and height (adjusted R2 = 0.23). ASMM showed the strongest association with HGS in both PrMW (r = 0.44, R2 = 0.20) and PMW (r = 0.44, R2 = 0.20). GS in PrMW showed significant associations with height, BMI and energy consumption (adjusted R2 = 0.13) while in PMW, with carbohydrate consumption and total-body-bone-mineral-density (adjusted R2 = 0.09). While in PrMW, height showed the strongest association with GS (r = 0.28, R2 = 0.08) in PMW, it was carbohydrate consumption (r = 0.24, R2 = 0.06). CONCLUSIONS: Factors that are associated with different measures of sarcopenia are not uniform and vary widely from anthropometry to nutrient intake indicating that these measures are somewhat independent and are governed by different factors.
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