Department of Neurosurgery, Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Advanced Innovation Center for Human Brain Protection, Beijing Institute of Brain Disorders, The Capital Medical University, Beijing 100070, China; Department of Neurosurgery and Emergency Medicine, Jiangnan University Medical Center, Wuxi 214001, China.
Department of Neurosurgery, Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Advanced Innovation Center for Human Brain Protection, Beijing Institute of Brain Disorders, The Capital Medical University, Beijing 100070, China.
Sci Bull (Beijing). 2023 Jun 15;68(11):1162-1175. doi: 10.1016/j.scib.2023.05.001. Epub 2023 May 10.
Intracranial aneurysm is the leading cause of nontraumatic subarachnoid hemorrhage. Evaluating the unstable (rupture and growth) risk of aneurysms is helpful to guild decision-making for unruptured intracranial aneurysms (UIA). This study aimed to develop a model for risk stratification of UIA instability. The UIA patients from two prospective, longitudinal multicenter Chinese cohorts recruited from January 2017 to January 2022 were set as the derivation cohort and validation cohort. The primary endpoint was UIA instability, comprising aneurysm rupture, growth, or morphology change, during a 2-year follow-up. Intracranial aneurysm samples and corresponding serums from 20 patients were also collected. Metabolomics and cytokine profiling analysis were performed on the derivation cohort (758 single-UIA patients harboring 676 stable UIAs and 82 unstable UIAs). Oleic acid (OA), arachidonic acid (AA), interleukin 1β (IL-1β), and tumor necrosis factor-α (TNF-α) were significantly dysregulated between stable and unstable UIAs. OA and AA exhibited the same dysregulated trends in serums and aneurysm tissues. The feature selection process demonstrated size ratio, irregular shape, OA, AA, IL-1β, and TNF-α as features of UIA instability. A machine-learning stratification model (instability classifier) was constructed based on radiological features and biomarkers, with high accuracy to evaluate UIA instability risk (area under curve (AUC), 0.94). Within the validation cohort (492 single-UIA patients harboring 414 stable UIAs and 78 unstable UIAs), the instability classifier performed well to evaluate the risk of UIA instability (AUC, 0.89). Supplementation of OA and pharmacological inhibition of IL-1β and TNF-α could prevent intracranial aneurysms from rupturing in rat models. This study revealed the markers of UIA instability and provided a risk stratification model, which may guide treatment decision-making for UIAs.
颅内动脉瘤是创伤性蛛网膜下腔出血的主要原因。评估动脉瘤的不稳定（破裂和生长）风险有助于指导未破裂颅内动脉瘤（UIA）的决策。本研究旨在开发一种 UIA 不稳定风险分层模型。从 2017 年 1 月至 2022 年 1 月招募的两个前瞻性、纵向多中心中国队列的 UIA 患者被设置为推导队列和验证队列。主要终点是 UIA 在 2 年随访期间的不稳定，包括动脉瘤破裂、生长或形态变化。还从 20 名患者中收集了颅内动脉瘤样本和相应的血清。对推导队列（758 名单个 UIA 患者，其中 676 个稳定 UIA 和 82 个不稳定 UIA）进行代谢组学和细胞因子分析。稳定和不稳定 UIA 之间，油酸（OA）、花生四烯酸（AA）、白细胞介素 1β（IL-1β）和肿瘤坏死因子-α（TNF-α）显著失调。OA 和 AA 在血清和动脉瘤组织中表现出相同的失调趋势。特征选择过程证明，大小比、不规则形状、OA、AA、IL-1β 和 TNF-α 是 UIA 不稳定的特征。基于放射学特征和生物标志物构建了一种机器学习分层模型（不稳定分类器），具有评估 UIA 不稳定风险的高准确性（曲线下面积（AUC），0.94）。在验证队列（492 名单个 UIA 患者，其中 414 个稳定 UIA 和 78 个不稳定 UIA）中，不稳定分类器在评估 UIA 不稳定风险方面表现良好（AUC，0.89）。OA 的补充和 IL-1β 和 TNF-α 的药物抑制可以防止大鼠模型中的颅内动脉瘤破裂。本研究揭示了 UIA 不稳定的标志物，并提供了一种风险分层模型，这可能有助于指导 UIA 的治疗决策。
