Ding Lingling, Mane Ravikiran, Wu Zhenzhou, Jiang Yong, Meng Xia, Jing Jing, Ou Weike, Wang Xueyun, Liu Yu, Lin Jinxi, Zhao Xingquan, Li Hao, Wang Yongjun, Li Zixiao
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
China National Clinical Research Center for Neurological Diseases, Beijing, China.
EClinicalMedicine. 2022 Sep 5;53:101639. doi: 10.1016/j.eclinm.2022.101639. eCollection 2022 Nov.
Acute ischaemic stroke (AIS) is a highly heterogeneous disorder and warrants further investigation to stratify patients with different outcomes and treatment responses. Using a large-scale stroke registry cohort, we applied data-driven approach to identify novel phenotypes based on multiple biomarkers.
In a nationwide, prospective, 201-hospital registry study taking place in China between August 01, 2015 and March 31, 2018, the patients with AIS who were over 18 years of age and admitted to the hospital within 7 days from symptom onset were included. 92 biomarkers were included in the analysis. In the derivation cohort (n=9539), an unsupervised Gaussian mixture model was applied to categorize patients into distinct phenotypes. A classifier was developed using the most important biomarkers and was applied to categorize patients into their corresponding phenotypes in an validation cohort (n=2496). The differences in biological features, clinical outcomes, and treatment response were compared across the phenotypes.
We identified four phenotypes with distinct characteristics in 9288 patients with non-cardioembolic ischaemic stroke. Phenotype 1 was associated with abnormal glucose and lipid metabolism. Phenotype 2 was characterized by inflammation and abnormal renal function. Phenotype 3 had the least laboratory abnormalities and small infarct lesions. Phenotype 4 was characterized by disturbance in homocysteine metabolism. Findings were replicated in the validation cohort. In comparison with phenotype 3, the risk of stroke recurrence (adjusted hazard ratio [aHR] 2.02, 95% confidence intervals [CI] 1.04-3.94), and mortality (aHR 18.14, 95%CI 6.62-49.71) at 3-month post-stroke were highest in phenotype 2, followed by phenotype 4 and phenotype 1, after adjustment for age, gender, smoking, drinking, history of stroke, hypertension, diabetes mellitus, dyslipidemia, and coronary heart disease. The Monte Carlo simulation showed that the patients with phenotype 2 could benefit from high-intensity statin therapy.
A data-driven approach could aid in the identification of patients at a higher risk of adverse clinical outcomes following non-cardioembolic ischaemic stroke. These phenotypes, based on different pathophysiology, can suggest individualized treatment plans.
Beijing Natural Science Foundation (grant number Z200016), Beijing Municipal Committee of Science and Technology (grant number Z201100005620010), National Natural Science Foundation of China (grant number 82101360, 92046016, 82171270), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant number 2019-I2M-5-029).
急性缺血性卒中（AIS）是一种高度异质性疾病，需要进一步研究以对具有不同预后和治疗反应的患者进行分层。我们使用大规模卒中登记队列，采用数据驱动方法，基于多种生物标志物识别新的表型。
在2015年8月1日至2018年3月31日期间在中国进行的一项全国性、前瞻性、201家医院的登记研究中，纳入年龄超过18岁且在症状发作后7天内入院的AIS患者。分析中纳入了92种生物标志物。在推导队列（n = 9539）中，应用无监督高斯混合模型将患者分类为不同的表型。使用最重要的生物标志物开发了一个分类器，并将其应用于在验证队列（n = 2496）中对患者进行相应表型分类。比较各表型之间的生物学特征、临床结局和治疗反应的差异。
我们在9288例非心源性缺血性卒中患者中识别出四种具有不同特征的表型。表型1与糖脂代谢异常有关。表型2的特征是炎症和肾功能异常。表型3的实验室异常最少且梗死灶较小。表型4的特征是同型半胱氨酸代谢紊乱。这些发现在验证队列中得到了重复。与表型3相比，在调整年龄、性别、吸烟、饮酒、卒中史、高血压、糖尿病、血脂异常和冠心病后，表型2在卒中后3个月时的卒中复发风险（调整后风险比[aHR] 2.02，95%置信区间[CI] 1.04 - 3.94）和死亡率（aHR 18.14，95%CI 6.62 - 49.71）最高，其次是表型4和表型1。蒙特卡罗模拟显示，表型2的患者可能从高强度他汀类药物治疗中获益。
数据驱动方法有助于识别非心源性缺血性卒中后不良临床结局风险较高的患者。这些基于不同病理生理学的表型可以提示个体化治疗方案。
北京市自然科学基金（项目编号Z200016）、北京市科学技术委员会（项目编号Z201100005620010）、国家自然科学基金（项目编号82101360、92046016、82171270）、中国医学科学院医学与健康科技创新工程（项目编号2019 - I2M - 5 - 029）。
