Relationships between depression and anxiety symptoms and adipocyte-derived proteins in postmenopausal women.

IntroductionStudies on the association between adiponectin and leptin and anxiety and depression among postmenopausal women are limited. Therefore, the present study specifically evaluates the mutual relationships between adiponectin and leptin and anxiety and depression in postmenopausal women.Participants and designIn this cross-sectional study, a total of 190 women aged 40-65 years were enrolled. Depression symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), and anxiety symptoms were evaluated using the Hamilton Anxiety Rating Scale (HAM-A). Fasting specimens were collected to measure sex hormone, glucose, insulin, and adipokine levels. Multiple linear regression analysis was performed to evaluate the associations between depression and anxiety and adipocyte-derived hormones.SettingsThe study was performed in a hospital medical center.ResultsAmong 190 enrolled postmenopausal women, Spearman's rank correlation analysis revealed significant correlations between CES-D and HAM-A (r = 0.715, P < 0.0001), between CES-D and adiponectin (p = 0.009) and leptin (p = 0.015), and between HAM-A and adiponectin (p = 0.01) and leptin (p = 0.001). The subjects with CES-D ≥ 16 and with HAM-A ≥ 18 had higher adiponectin levels than those with CES-D < 16 and HAM-A < 18, respectively. After adjusting for age, body mass index, exercise, alanine amino transferase and parameters of lipid profiles, Log adiponectin levels were found to be significantly associated with both CES-D and HAM-A, and Log leptin levels were only significantly associated with HAM-A.ConclusionsThe data show that adiponectin and leptin levels are significantly associated with depression and anxiety symptoms. These results suggest that higher adiponectin and lower leptin levels may serve as potential markers related to anxiety and mood in postmenopausal women. More future research that is designed to deal with the important confounders (e.g., population heterogeneity) is needed to investigate comprehensively on these associations.