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Multiple biomarkers covering several pathways for the prediction of depression after ischemic stroke
Bizhong Che1, Zhengbao Zhu2, Xiaoqing Bu3
1Department 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.
Insights
Adding multiple biomarkers significantly improves depression risk prediction after stroke. Four key biomarkers enhance accuracy, aiding in better patient stratification and management strategies for post-stroke mood disorders.
Area of Science:
- Neurology
- Biomarker Research
- Psychiatry
Background:
- Post-stroke depression (PSD) is a common and serious complication.
- Identifying reliable risk predictors for PSD is crucial for timely intervention.
- Existing risk models may not fully capture the complex pathophysiology of PSD.
Purpose of the Study:
- To evaluate the incremental predictive utility of multiple biomarkers for depression risk following ischemic stroke.
- To assess if a panel of biomarkers reflecting diverse pathological pathways improves depression risk stratification.
Main Methods:
- Utilized data from the China Antihypertensive Trial in Acute Ischemic Stroke.
- Measured 13 circulating biomarkers in 631 ischemic stroke patients.
- Employed logistic regression and analyzed discrimination and risk reclassification for depression at 3 months.
Main Results:
- Elevated growth differentiation factor-15, anticardiolipin antibodies, antiphosphatidylserine antibodies, and matrix metalloproteinase-9 were linked to increased PSD risk.
- A clear risk gradient was observed with an increasing number of elevated biomarkers.
- The inclusion of these four biomarkers significantly improved model discrimination (C-statistic from 0.702 to 0.748) and risk reclassification (NRI 45.0%, IDI 6.2%).
Conclusions:
- Simultaneously incorporating multiple biomarkers from various pathophysiological pathways offers substantial incremental utility for stratifying depression risk after stroke.
- This multi-biomarker approach enhances the prediction of post-stroke depression beyond traditional risk factors.
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.
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