Bizhong Che1, Zhengbao Zhu2, Xiaoqing Bu3, Jieyun Yin1, Liyuan Han4, Tan Xu1, Zhong Ju5, Jiale Liu6, Jintao Zhang7, Jing Chen8, Jiang He8, Yonghong Zhang9, Chongke Zhong10. 1. Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province, 215123, China. 2. Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province, 215123, China; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA. 3. Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province, 215123, China; Department of Epidemiology, School of Public Health, Chongqing Medical University, Chongqing, China. 4. Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, Zhejiang, PR China. 5. Department of Neurology, Kerqin District First People's Hospital of Tongliao City, Inner Mongolia, China. 6. Department of Neurology, Jilin Central Hospital, Jilin, China. 7. Department of Neurology, the 88th Hospital of PLA, Taian, Shandong, China. 8. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA. 9. Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province, 215123, China. Electronic address: yhzhang@suda.edu.cn. 10. Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province, 215123, China. Electronic address: ckzhong@suda.edu.cn.
Abstract
BACKGROUND: To assess the potential incremental utility of multiple biomarkers reflecting several pathological pathways for the risk prediction of depression after stroke. METHODS: We used data from the China Antihypertensive Trial in Acute Ischemic Stroke, and a panel of 13 circulating biomarkers were measured. The study outcome was depression (24-item Hamilton Depression Rating Scale score≥8) at 3 months after ischemic stroke. Logistic regression models were performed to evaluate the risk of depression associated with multiple biomarkers. Discrimination and risk reclassification for depression were analyzed. RESULTS: Among 631 included ischemic stroke patients, elevated growth differentiation factor-15, anticardiolipin antibodies, antiphosphatidylserine antibodies and matrix metalloproteinase-9 were individually associated with increased risks of depression after stroke. The multiple biomarker analysis showed a clear gradient in the risk of depression with increasing numbers of elevated biomarkers, and multivariate adjusted odds ratio (95% confidence interval) of patients with 4 elevated biomarkers was 6.52 (2.24-18.95) compared with those without elevation in any of 4 biomarkers. The simultaneous inclusion of all 4 biomarkers to the conventional model significantly improved discrimination (C statistic increased from 0.702 to 0.748, P=0.004) and risk reclassification (net reclassification improvement 45.0%; integrated discrimination improvement 6.2%; both P<0.001) for depression after stroke. LIMITATIONS: We selected biomarkers that had previously been reported to be promising predictors of depression after stroke, while other novel biomarkers not tested might have additional predictive value. CONCLUSIONS: Simultaneously adding multiple biomarkers from several pathophysiological pathways to traditional risk factors provided substantial incremental utility of the risk stratification for depression after stroke.
BACKGROUND: To assess the potential incremental utility of multiple biomarkers reflecting several pathological pathways for the risk prediction of depression after stroke. METHODS: We used data from the China Antihypertensive Trial in Acute Ischemic Stroke, and a panel of 13 circulating biomarkers were measured. The study outcome was depression (24-item Hamilton Depression Rating Scale score≥8) at 3 months after ischemic stroke. Logistic regression models were performed to evaluate the risk of depression associated with multiple biomarkers. Discrimination and risk reclassification for depression were analyzed. RESULTS: Among 631 included ischemic strokepatients, elevated growth differentiation factor-15, anticardiolipin antibodies, antiphosphatidylserine antibodies and matrix metalloproteinase-9 were individually associated with increased risks of depression after stroke. The multiple biomarker analysis showed a clear gradient in the risk of depression with increasing numbers of elevated biomarkers, and multivariate adjusted odds ratio (95% confidence interval) of patients with 4 elevated biomarkers was 6.52 (2.24-18.95) compared with those without elevation in any of 4 biomarkers. The simultaneous inclusion of all 4 biomarkers to the conventional model significantly improved discrimination (C statistic increased from 0.702 to 0.748, P=0.004) and risk reclassification (net reclassification improvement 45.0%; integrated discrimination improvement 6.2%; both P<0.001) for depression after stroke. LIMITATIONS: We selected biomarkers that had previously been reported to be promising predictors of depression after stroke, while other novel biomarkers not tested might have additional predictive value. CONCLUSIONS: Simultaneously adding multiple biomarkers from several pathophysiological pathways to traditional risk factors provided substantial incremental utility of the risk stratification for depression after stroke.