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Quantile Regression of Depression and Body Mass Index in Patients with Cor-onary Artery Disease in China / 中国卫生统计
Chinese Journal of Health Statistics ; (6): 745-748, 2017.
Article in Chinese | WPRIM | ID: wpr-659858
ABSTRACT
Objective Depression and obesity are two major disorders associated with coronary heart disease ( CHD) , and both have a high prevalence. Different studies concluded different outcomes about the relation between body mass index ( BMI) and depression. The aim of this study was to assess the status of depression and investigate the relationship between de-pression and BMI in CHD patients using quantile regression. Methods 580 patients were enrolled. Depression was tested using the Zung Self-Rating Depression Scale ( SDS) . Demographic data and clinical data were recorded. Quantile regression was con-ducted to determine whether BMI was a predictor of depression. Results SDS scores were significant different in underweight, normal weight,overweight and obesity group (P<0. 001). Overweight and obesity patients were least depressed. Differences in SDS scores increased steeply as depression increased at 5th,10th,25th,50th,75th,90th and 95th percentiles,with the coefficients ranging from -0. 36 to -0. 88 in total population,-0. 26 to -1. 09 in male and -0. 46 to -0. 75 in female respectively. The effects of BMI on depression were significantly different in different quantiles of depression in total population ( P=0. 04 ) and in male (P=0. 006),but no difference in female. Conclusion There was also a phenomenon of "obesity paradox" between BMI and depression in CHD patients. The effects of BMI on depression were significantly different in different quantiles in total population and in male.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Health Statistics Year: 2017 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Health Statistics Year: 2017 Type: Article