Multiple biomarkers covering several pathways for the prediction of depression after ischemic stroke
Multiple biomarkers covering several pathways for the prediction of depression after ischemic stroke
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多种生物标志物涵盖预测缺血性中风后抑郁症的多种途径
DOI：
10.1016/j.jad.2020.10.075
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发表时间：
2021-02-01
影响因子：
6.6
通讯作者：
Zhong, Chongke
中科院分区：
文献类型：
--
作者：
Che, Bizhong;Zhu, Zhengbao;Zhong, Chongke
背景：为了评估反映多种病理途径的多种生物标志物在预测中风后抑郁症风险方面的潜在增量效用。方法：我们使用中国急性缺血性中风抗高血压试验的数据，并测量了一组 13 种循环生物标志物。研究结果为缺血性中风后 3 个月的抑郁症（24 项汉密尔顿抑郁评定量表评分=8）。采用逻辑回归模型来评估与多种生物标志物相关的抑郁风险。分析了抑郁症的歧视和风险重新分类。结果：在 631 名缺血性中风患者中，生长分化因子 15、抗心磷脂抗体、抗磷脂酰丝氨酸抗体和基质金属蛋白酶 9 的升高分别与中风后抑郁症风险增加相关。多重生物标志物分析显示，随着升高的生物标志物数量的增加，抑郁症的风险存在明显的梯度，与 4 种生物标志物中任何一种都没有升高的患者相比，具有 4 种升高的生物标志物的患者的多变量调整优势比（95% 置信区间）为 6.52（2.24-18.95）。将所有 4 种生物标志物同时纳入传统模型显着改善了区分度（C 统计量从 0.702 增加到 0.748，P=0.004）和风险重新分类（净重新分类改善 45.0%；综合区分度改善 6.2%；两者 P
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