Hellström Santtu, Sajanti Antti, Srinath Abhinav, Bennett Carolyn, Girard Romuald, Jhaveri Aditya, Cao Ying, Falter Johannes, Frantzén Janek, Koskimäki Fredrika, Lyne Seán B, Rantamäki Tomi, Takala Riikka, Posti Jussi P, Roine Susanna, Kolehmainen Sulo, Nazir Kenneth, Jänkälä Miro, Puolitaival Jukka, Rahi Melissa, Rinne Jaakko, Nieminen Anni I, Castrén Eero, Koskimäki Janne
Neurocenter, Department of Neurosurgery, Turku University Hospital and University of Turku, P.O. Box 52, FI-20521 Turku, Finland.
Neurovascular Surgery Program, Section of Neurosurgery, The University of Chicago Medicine and Biological Sciences, Chicago, IL 60637, USA.
Neurobiol Dis. 2025 Jan;204:106762. doi: 10.1016/j.nbd.2024.106762. Epub 2024 Dec 9.
Lipidomic alterations have been associated with various neurological diseases. Examining temporal changes in serum lipidomic profiles, irrespective of injury type, reveals promising prognostic indicators. In this longitudinal prospective observational study, serum samples were collected early (46 ± 24 h) and late (142 ± 52 h) post-injury from 70 patients with ischemic stroke, aneurysmal subarachnoid hemorrhage, and traumatic brain injury that had outcomes dichotomized as favorable (modified Rankin Scores (mRS) 0-3) and unfavorable (mRS 4-6) three months post-injury. Lipidomic profiling of 1153 lipids, analyzed using statistical and machine learning methods, identified 153 lipids with late-stage significant outcome differences. Supervised machine learning pinpointed 12 key lipids, forming a combinatory prognostic equation with high discriminatory power (AUC 94.7 %, sensitivity 89 %, specificity 92 %; p < 0.0001). Enriched functions of the identified lipids were related to sphingolipid signaling, glycerophospholipid metabolism, and necroptosis (p < 0.05, FDR-corrected). The study underscores the dynamic nature of lipidomic profiles in acute brain injuries, emphasizing late-stage distinctions and proposing lipids as significant prognostic markers, transcending injury types. These findings advocate further exploration of lipidomic changes for a comprehensive understanding of pathobiological roles and enhanced prediction for recovery trajectories.
脂质组学改变与多种神经系统疾病有关。检查血清脂质组学谱的时间变化，无论损伤类型如何，都能发现有前景的预后指标。在这项纵向前瞻性观察研究中，从70例缺血性中风、动脉瘤性蛛网膜下腔出血和创伤性脑损伤患者中，在受伤后早期（46±24小时）和晚期（142±52小时）采集血清样本，这些患者在受伤后三个月的结果分为良好（改良Rankin评分（mRS）0-3）和不良（mRS 4-6）。使用统计和机器学习方法对1153种脂质进行脂质组学分析，确定了153种在晚期有显著结果差异的脂质。监督式机器学习确定了12种关键脂质，形成了一个具有高鉴别力的联合预后方程（AUC 94.7%，敏感性89%，特异性92%；p<0.0001）。所确定脂质的富集功能与鞘脂信号传导、甘油磷脂代谢和坏死性凋亡有关（p<0.05，经FDR校正）。该研究强调了急性脑损伤中脂质组学谱的动态性质，强调了晚期差异，并提出脂质作为重要的预后标志物，超越了损伤类型。这些发现主张进一步探索脂质组学变化，以全面了解病理生物学作用并增强对恢复轨迹的预测。
