From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom.
Neurology. 2024 Jul 9;103(1):e209419. doi: 10.1212/WNL.0000000000209419. Epub 2024 Jun 11.
Discordance between CSF and PET biomarkers of β-amyloid (Aβ) might reflect an imbalance between soluble and aggregated species, possibly reflecting disease heterogeneity. Previous studies generally used binary cutoffs to assess discrepancies in CSF/PET biomarkers, resulting in a loss of information on the extent of discordance. In this study, we (1) jointly modeled Aβ-CSF/PET data to derive a continuous measure of the imbalance between soluble and fibrillar pools of Aβ, (2) investigated factors contributing to this imbalance, and (3) examined associations with cognitive trajectories.
Across 822 cognitively unimpaired (n = 261) and cognitively impaired (n = 561) Alzheimer's Disease Neuroimaging Initiative individuals (384 [46.7%] females, mean age 73.0 ± 7.4 years), we fitted baseline CSF-Aβ and global Aβ-PET to a hyperbolic regression model, deriving a participant-specific Aβ-aggregation score (standardized residuals); negative values represent more soluble relative to aggregated Aβ and positive values more aggregated relative to soluble Aβ. Using linear models, we investigated whether methodological factors, demographics, CSF biomarkers, and vascular burden contributed to Aβ-aggregation scores. With linear mixed models, we assessed whether Aβ-aggregation scores were predictive of cognitive functioning. Analyses were repeated in an early independent validation cohort of 383 Amyloid Imaging to Prevent Alzheimer's Disease Prognostic and Natural History Study individuals (224 [58.5%] females, mean age 65.2 ± 6.9 years).
The imbalance model could be fit (pseudo- = 0.94) in both cohorts, across CSF kits and PET tracers. Although no associations were observed with the main methodological factors, lower Aβ-aggregation scores were associated with larger ventricular volume (β = 0.13, < 0.001), male sex (β = -0.18, = 0.019), and homozygous -ε4 carriership (β = -0.56, < 0.001), whereas higher scores were associated with increased uncorrected CSF p-tau (β = 0.17, < 0.001) and t-tau (β = 0.16, < 0.001), better baseline executive functioning (β = 0.12, < 0.001), and slower global cognitive decline (β = 0.14, = 0.006). In the validation cohort, we replicated the associations with -ε4, CSF t-tau, and, although modestly, with cognition.
We propose a novel continuous model of Aβ CSF/PET biomarker imbalance, accurately describing heterogeneity in soluble vs aggregated Aβ pools in 2 independent cohorts across the full Aβ continuum. Aβ-aggregation scores were consistently associated with genetic and AD-associated CSF biomarkers, possibly reflecting disease heterogeneity beyond methodological influences.
CSF 和 PET 生物标志物β-淀粉样蛋白（Aβ）之间的不一致可能反映了可溶性和聚集物之间的不平衡，这可能反映了疾病异质性。先前的研究通常使用二进制截止值来评估 CSF/PET 生物标志物的差异，从而导致对不平衡程度的信息丢失。在这项研究中，我们（1）联合建模 Aβ-CSF/PET 数据，以得出可溶性和纤维状 Aβ池之间不平衡的连续度量，（2）研究导致这种不平衡的因素，以及（3）检查与认知轨迹的关联。
在 822 名认知正常（n = 261）和认知障碍（n = 561）阿尔茨海默病神经影像学倡议（Alzheimer's Disease Neuroimaging Initiative，ADNI）个体（384 [46.7%] 名女性，平均年龄 73.0 ± 7.4 岁）中，我们将基线 CSF-Aβ 和全球 Aβ-PET 拟合到双曲回归模型中，得出参与者特异性 Aβ 聚集评分（标准化残差）；负值表示与聚集的 Aβ 相比具有更多的可溶性，正值表示与可溶性 Aβ 相比具有更多的聚集性。使用线性模型，我们研究了方法因素、人口统计学、CSF 生物标志物和血管负担是否有助于 Aβ 聚集评分。使用线性混合模型，我们评估了 Aβ 聚集评分是否可预测认知功能。分析在 383 名淀粉样蛋白成像预防阿尔茨海默病预后和自然史研究（Amyloid Imaging to Prevent Alzheimer's Disease Prognostic and Natural History Study，AIPAD）个体的早期独立验证队列中重复进行（224 [58.5%] 名女性，平均年龄 65.2 ± 6.9 岁）。
该不平衡模型可以拟合（伪= 0.94）在两个队列中，涵盖了 CSF 试剂盒和 PET 示踪剂。尽管与主要方法因素没有观察到关联，但较低的 Aβ 聚集评分与更大的脑室体积（β = 0.13，<0.001）、男性（β = -0.18，= 0.019）和纯合子 -ε4 携带（β = -0.56，<0.001）有关，而较高的评分与未校正的 CSF p-tau（β = 0.17，<0.001）和 t-tau（β = 0.16，<0.001）增加、基线执行功能更好（β = 0.12，<0.001）和全球认知下降较慢（β = 0.14，= 0.006）有关。在验证队列中，我们复制了与 -ε4、CSF t-tau 的关联，尽管与认知的关联适度。
我们提出了一种新的 Aβ CSF/PET 生物标志物不平衡的连续模型，该模型在两个独立的队列中准确地描述了整个 Aβ 连续体中可溶性与聚集物 Aβ 池之间的异质性。Aβ 聚集评分与遗传和 AD 相关的 CSF 生物标志物一致相关，这可能反映了方法影响之外的疾病异质性。
