当前位置: X-MOL 学术Brain › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings
Brain ( IF 14.5 ) Pub Date : 2024-04-24 , DOI: 10.1093/brain/awae118
Hannah Baumeister 1 , Jacob W Vogel 2 , Philip S Insel 3 , Luca Kleineidam 4, 5 , Steffen Wolfsgruber 4, 5 , Melina Stark 5 , Helena M Gellersen 1 , Renat Yakupov 1, 6 , Matthias C Schmid 4, 7 , Falk Lüsebrink 1 , Frederic Brosseron 4 , Gabriel Ziegler 6 , Silka D Freiesleben 8, 9 , Lukas Preis 9 , Luisa-Sophie Schneider 9 , Eike J Spruth 8, 9 , Slawek Altenstein 8, 9 , Andrea Lohse 9 , Klaus Fliessbach 4, 5 , Ina R Vogt 4 , Claudia Bartels 10 , Björn H Schott 10, 11, 12 , Ayda Rostamzadeh 13 , Wenzel Glanz 1 , Enise I Incesoy 1, 6, 14 , Michaela Butryn 1 , Daniel Janowitz 15 , Boris-Stephan Rauchmann 16, 17, 18 , Ingo Kilimann 19, 20 , Doreen Goerss 19, 20 , Matthias H Munk 21, 22 , Stefan Hetzer 23 , Peter Dechent 24 , Michael Ewers 15, 25 , Klaus Scheffler 26 , Anika Wuestefeld 2 , Olof Strandberg 2 , Danielle van Westen 27, 28 , Niklas Mattsson-Carlgren 2, 29, 30 , Shorena Janelidze 2 , Erik Stomrud 2, 31 , Sebastian Palmqvist 2, 31 , Annika Spottke 4, 32 , Christoph Laske 21, 22, 33 , Stefan Teipel 19, 20 , Robert Perneczky 16, 25, 34, 35 , Katharina Buerger 15, 25 , Anja Schneider 4, 5 , Josef Priller 8, 9, 36, 37 , Oliver Peters 8, 9 , Alfredo Ramirez 4, 5, 38, 39, 40 , Jens Wiltfang 10, 11, 41 , Michael T Heneka 42 , Michael Wagner 4, 5 , Emrah Düzel 1, 6, 43 , Frank Jessen 4, 13, 38, 43 , Oskar Hansson 2, 31 , David Berron 1, 2, 43
Affiliation  

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer’s disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± SD age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (CU; n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer’s type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5), as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test if baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and MCI conversion rates of CU and SCD participants. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological AD biomarker levels, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic-predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of MCI conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, both on the subject and group level, were excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for AD in applied settings. The implementation of atrophy subtype- and stage-specific end-points may increase the statistical power of pharmacological trials targeting early AD.

中文翻译:

记忆诊所环境中萎缩异质性和进展的通用数据驱动模型

记忆诊所患者是代表病理性衰老的各种病因的异质人群。目前尚不清楚脑萎缩的不同时空进展模式(如先前在阿尔茨海默病(AD)患者中所描述的那样)在这组老年人中是否普遍且具有临床意义。为了发现不同的萎缩亚型,我们应用亚型和阶段推断 (SuStaIn) 算法对 DELCODE 队列中 813 名参与者的基线结构 MRI 数据进行分析(平均 ± 标准差年龄 = 70.67 ± 6.07 岁,52% 为女性)。参与者认知未受损(CU;n = 285)或满足主观认知衰退(SCD;n = 342)、轻度认知障碍(MCI;n = 118)或阿尔茨海默氏症型痴呆(n = 68)的诊断标准。比较了萎缩亚型的基线人口统计、液体 AD 生物标志物水平、临床前阿尔茨海默氏症认知综合指标 (PACC-5) 以及情景记忆和执行功能。检查了长达 240 周的 PACC-5 轨迹。为了测试基线萎缩亚型和阶段是否可以预测出现认知障碍之前的临床轨迹,我们分析了 CU 和 SCD 参与者的 PACC-5 轨迹和 MCI 转换率。确定了边缘系统主导型和海马保留型萎缩亚型。以边缘系统为主的萎缩首先影响内侧颞叶,然后是进一步的颞叶,最后是其余的皮质区域。在基线时,这种亚型与年龄较大、更多病理性 AD 生物标志物水平、APOE ε4 携带和遗忘性认知障碍有关。保留海马的萎缩最初发生在颞叶外,而内侧颞叶则保留到晚期萎缩阶段。这种萎缩模式也影响了 AD 生物标志物呈阳性的个体,并与更广泛的认知障碍相关。与没有或有海马保留萎缩的参与者相比,在所有和仅未受损的参与者中,以边缘系统为主的萎缩与更多的负纵向 PACC-5 斜率相关,并增加了 MCI 转换的风险。 SuStaIn 模型在瑞典 BioFINDER-2 队列的样本中重复进行。确定了高度相似的萎缩进展模式和相关的认知特征。跨队列模型的普遍性,无论是在受试者水平还是在群体水平上,都非常出色,表明在以前未见过的数据中表现可靠。所提出的模型是一种很有前途的工具,可以在应用环境中捕获处于 AD 早期风险状态的老年人的异质性。萎缩亚型和阶段特异性终点的实施可能会提高针对早期 AD 的药理学试验的统计功效。
更新日期:2024-04-24
down
wechat
bug