Milà-Alomà Marta, Tosun Duygu, Schindler Suzanne E, Hausle Zachary, Li Yan, Petersen Kellen K, Dage Jeffrey L, Du-Cuny Lei, Saad Ziad S, Saef Benjamin, Triana-Baltzer Gallen, Raunig David L, Coomaraswamy Janaky, Baratta Michael, Meyers Emily A, Mordashova Yulia, Rubel Carrie E, Ferber Kyle, Kolb Hartmuth, Ashton Nicholas J, Zetterberg Henrik, Rosenbaugh Erin G, Sabandal Martin, Shaw Leslie M, Bannon Anthony W, Potter William Z
medRxiv. 2024 Nov 14:2024.10.25.24316144. doi: 10.1101/2024.10.25.24316144.
Plasma biomarkers for Alzheimer's disease (AD) are increasingly being used to assist in making an etiological diagnosis for cognitively impaired (CI) individuals or to identify cognitively unimpaired (CU) individuals with AD pathology who may be eligible for prevention trials. However, a better understanding of the timing of plasma biomarker changes is needed to optimize their use in clinical and research settings. The aim of this study was to evaluate the timing of change of key AD plasma biomarkers (Aβ42/Aβ40, p-tau217, p-tau181, GFAP and NfL) from six different companies, along with established AD biomarkers, using AD progression timelines based on amyloid and tau PET. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 784 individuals with longitudinal F-florbetapir amyloid PET and 359 individuals with longitudinal F-flortaucipir tau PET, to estimate age at amyloid and tau positivity, defined as the age at the first positive PET scan. Of these, longitudinal plasma biomarker measures were available from 190 individuals with an estimated age at amyloid positivity and 70 individuals with an estimated age at tau positivity. Age at tau positivity was a stronger predictor of symptom onset than age at amyloid positivity in 17 individuals who progressed from CU to CI during their participation in the ADNI study (Adj R = 0.86 Adj R = 0.38), and therefore was used to estimate symptom onset age for all individuals with an estimated age at tau positivity. Generalized additive mixed models (GAMMs) were used to model biomarker trajectories across years since amyloid positivity, tau positivity, and symptom onset, and to identify the earliest timepoint of biomarker abnormality when compared to a reference group of amyloid- and tau-negative CU individuals, as well as time periods of significant change in biomarkers. All plasma biomarkers except NfL became abnormal prior to amyloid and tau positivity. Plasma Aβ42/Aβ40 was the first biomarker to reach abnormality consistently across timelines and plasma GFAP became abnormal early in the tau timeline. Plasma Aβ42/Aβ40 levels reached a plateau, while plasma p-tau217, p-tau181, GFAP and NfL increased throughout disease progression. Some differences in the timing of change were observed across biomarker assays. The primary utility of plasma Aβ42/Aβ40 may lie in early identification of individuals at high risk of AD. In contrast, p-tau217, p-tau181, GFAP and NfL increase throughout the estimated timelines, supporting their potential as biomarkers for staging and monitoring disease progression.
用于阿尔茨海默病(AD)的血浆生物标志物越来越多地被用于辅助对认知受损(CI)个体进行病因诊断,或识别患有AD病理但认知未受损(CU)的个体,这些个体可能符合预防试验的条件。然而,需要更好地了解血浆生物标志物变化的时间,以优化其在临床和研究环境中的应用。本研究的目的是使用基于淀粉样蛋白和tau PET的AD进展时间线,评估来自六家不同公司的关键AD血浆生物标志物(Aβ42/Aβ40、p-tau217、p-tau181、GFAP和NfL)以及已确立的AD生物标志物的变化时间。我们使用了来自阿尔茨海默病神经影像学倡议(ADNI)的数据,包括784名进行纵向18F-氟比他班淀粉样蛋白PET检查的个体和359名进行纵向18F-氟代tau蛋白PET检查的个体,以估计淀粉样蛋白和tau蛋白阳性的年龄,定义为首次PET扫描呈阳性的年龄。其中,有190名估计淀粉样蛋白阳性年龄的个体和70名估计tau蛋白阳性年龄的个体有纵向血浆生物标志物测量数据。在参与ADNI研究期间从CU进展为CI的17名个体中,tau蛋白阳性年龄比淀粉样蛋白阳性年龄更能预测症状发作(调整R² = 0.86,调整R² = 0.38),因此用于估计所有估计tau蛋白阳性年龄个体的症状发作年龄。使用广义相加混合模型(GAMMs)对自淀粉样蛋白阳性、tau蛋白阳性和症状发作以来各年份的生物标志物轨迹进行建模,并与淀粉样蛋白和tau蛋白阴性的CU个体参考组相比,确定生物标志物异常的最早时间点,以及生物标志物发生显著变化的时间段。除NfL外,所有血浆生物标志物在淀粉样蛋白和tau蛋白阳性之前就已异常。血浆Aβ42/Aβ40是第一个在各时间线中始终达到异常的生物标志物,血浆GFAP在tau时间线早期就已异常。血浆Aβ42/Aβ40水平达到平台期,而血浆p-tau217、p-tau181、GFAP和NfL在疾病进展过程中持续升高。在生物标志物检测方法之间观察到了一些变化时间上的差异。血浆Aβ42/Aβ40的主要用途可能在于早期识别AD高危个体。相比之下,p-tau217、p-tau181、GFAP和NfL在整个估计时间线中升高,支持它们作为疾病分期和监测疾病进展生物标志物的潜力。