De Meyer Steffi, Schaeverbeke Jolien M, Luckett Emma S, Reinartz Mariska, Blujdea Elena R, Cleynen Isabelle, Dupont Patrick, Van Laere Koen, Vanbrabant Jeroen, Stoops Erik, Vanmechelen Eugeen, di Molfetta Guglielmo, Zetterberg Henrik, Ashton Nicholas J, Teunissen Charlotte E, Poesen Koen, Vandenberghe Rik
Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium.
Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium.
Brain Commun. 2024 May 23;6(4):fcae162. doi: 10.1093/braincomms/fcae162. eCollection 2024.
The dynamic phase of preclinical Alzheimer's disease, as characterized by accumulating cortical amyloid-β, is a window of opportunity for amyloid-β-lowering therapies to have greater efficacy. Biomarkers that accurately predict amyloid-β accumulation may be of critical importance for participant inclusion in secondary prevention trials and thus enhance development of early Alzheimer's disease therapies. We compared the abilities of baseline plasma pTau181, pTau217 and amyloid-β PET load to predict future amyloid-β accumulation in asymptomatic elderly. In this longitudinal cohort study, baseline plasma pTau181 and pTau217 were quantified using single molecule array assays in cognitively unimpaired elderly selected from the community-recruited F-PACK cohort based on the availability of baseline plasma samples and longitudinal amyloid-β PET data (median time interval = 5 years, range 2-10 years). The predictive abilities of pTau181, pTau217 and PET-based amyloid-β measures for PET-based amyloid-β accumulation were investigated using receiver operating characteristic analyses, correlations and stepwise regression analyses. We included 75 F-PACK subjects (mean age = 70 years, 48% female), of which 16 were classified as amyloid-β accumulators [median (interquartile range) Centiloid rate of change = 3.42 (1.60) Centiloids/year). Plasma pTau181 [area under the curve (95% confidence interval) = 0.72 (0.59-0.86)] distinguished amyloid-β accumulators from non-accumulators with similar accuracy as pTau217 [area under the curve (95% confidence interval) = 0.75 (0.62-0.88) and amyloid-β PET [area under the curve (95% confidence interval) = 0.72 (0.56-0.87)]. Plasma pTau181 and pTau217 strongly correlated with each other ( = 0.93, < 0.001) and, together with amyloid-β PET, similarly correlated with amyloid-β rate of change ( = 0.33, = 0.36, = 0.35, all ≤ 0.01). Addition of plasma pTau181, plasma pTau217 or amyloid-β PET to a linear demographic model including age, sex and carriership similarly improved the prediction of amyloid-β accumulation (ΔAkaike information criterion ≤ 4.1). In a multimodal biomarker model including all three biomarkers, each biomarker lost their individual predictive ability. These findings indicate that plasma pTau181, plasma pTau217 and amyloid-β PET convey overlapping information and therefore predict the dynamic phase of asymptomatic amyloid-β accumulation with comparable performances. In clinical trial recruitment, confirmatory PET scans following blood-based prescreening might thus not provide additional value for detecting participants in these early disease stages who are destined to accumulate cortical amyloid-β. Given the moderate performances, future studies should investigate whether integrating plasma pTau species with other factors can improve performance and thus enhance clinical and research utility.
临床前阿尔茨海默病的动态阶段，其特征是皮质淀粉样蛋白-β不断积累，这是降低淀粉样蛋白-β疗法获得更高疗效的机会窗口。准确预测淀粉样蛋白-β积累的生物标志物对于将参与者纳入二级预防试验可能至关重要，从而促进早期阿尔茨海默病疗法的开发。我们比较了基线血浆pTau181、pTau217和淀粉样蛋白-β PET负荷预测无症状老年人未来淀粉样蛋白-β积累的能力。在这项纵向队列研究中，基于基线血浆样本和纵向淀粉样蛋白-β PET数据（中位时间间隔=5年，范围2 - 10年）的可用性，从社区招募的F - PACK队列中选择认知未受损的老年人，使用单分子阵列分析对基线血浆pTau181和pTau217进行定量。使用受试者工作特征分析、相关性分析和逐步回归分析研究pTau181、pTau217和基于PET的淀粉样蛋白-β测量值对基于PET的淀粉样蛋白-β积累的预测能力。我们纳入了75名F - PACK受试者（平均年龄=70岁，48%为女性），其中16名被归类为淀粉样蛋白-β积累者[中位（四分位间距）Centiloid变化率=3.42（1.60）Centiloids/年]。血浆pTau181[曲线下面积（95%置信区间）=0.72（0.59 - 0.86）]区分淀粉样蛋白-β积累者和非积累者与pTau217[曲线下面积（95%置信区间）=0.75（0.62 - 0.88）]以及淀粉样蛋白-β PET[曲线下面积（95%置信区间）=0.72（0.56 - 0.87）]的准确性相似。血浆pTau181和pTau217彼此高度相关（=0.93，<0.001），并且与淀粉样蛋白-β PET一起，与淀粉样蛋白-β变化率同样相关（=0.33，=0.36，=0.35，均≤0.01）。将血浆pTau181、血浆pTau217或淀粉样蛋白-β PET添加到包括年龄、性别和载脂蛋白E携带者状态的线性人口统计学模型中同样改善了对淀粉样蛋白-β积累的预测（Δ赤池信息准则≤4.1）。在包括所有三种生物标志物的多模态生物标志物模型中，每种生物标志物都失去了其个体预测能力。这些发现表明血浆pTau181、血浆pTau217和淀粉样蛋白-β PET传达重叠信息，因此以可比的性能预测无症状淀粉样蛋白-β积累的动态阶段。在临床试验招募中，基于血液的预筛查后的验证性PET扫描可能因此对于检测这些注定会积累皮质淀粉样蛋白-β的早期疾病阶段的参与者没有额外价值。鉴于性能中等，未来研究应调查将血浆pTau种类与其他因素整合是否可以提高性能，从而增强临床和研究效用。
