Year: 2018
Type: dissertation
Study design: Observational Study
Abstract: The effects of skeletal muscle mass on depressive symptoms remain poorly understood, especially in the middle-aged population.I examined the relationship vii between skeletal muscle mass and depressive symptoms according to sex and menopausal status in the middle-aged Korean population. METHODS:Herein, 1,151 men and 2,176 women aged 30-64 years completed questionnaires and underwent health examinations in the Cardiovascular and Metabolic Disease Etiology Research Center study.Appendicular skeletal muscle mass (ASM) was measured via bioelectrical impedance analysis and adjusted for height squared (ASM/Ht 2 ).Tertile groups of ASM/Ht 2 were used for analysis.Depressive symptoms were assessed using the Beck Depressive Inventory-II (BDI), and the presence of depressive symptoms was defined as a BDI score ≥20 or participants with taking antidepressants. RESULTS:Multiple logistic regression analysis using a fully adjusted model showed that depressive symptoms was more frequently observed among men in the lower (OR: 3.91, 95% CI: 1.63-9.39)and middle (OR: 2.81, 95% CI: 1.31-6.01)ASM/Ht 2 tertile than among those in the higher ASM/Ht 2 tertile.In women, the risk for depressive symptoms was higher in the lower (OR: 1.38, CI: 0.90-2.01)and middle (OR: 1.31, viii CI: 0.91-1.88)ASM/Ht 2 tertile than those in the higher ASM/Ht 2 tertile; however, these associations did not demonstrate statistical significance.Stratification analysis according to the menopausal status showed that the risk for depressive symptoms was lower among premenopausal women in the lower and middle ASM/Ht 2 tertile, and higher among postmenopausal women in the lower and middle ASM/Ht 2 tertile than among those in the higher ASM/Ht 2 tertile; however, both of their associations did not demonstrate statistical significance. CONCLUSION:I conclude that the independent association between low skeletal muscle mass and depressive symptoms was observed in men, but not in women.
Author: 허지은
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2939067210