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In-vivo staging of regional amyloid deposition predicts functional conversion in the preclinical and prodromal phases of Alzheimer’s disease
Neurobiology of Aging ( IF 3.7 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.neurobiolaging.2020.03.011
Stefan J Teipel 1 , Martin Dyrba 2 , Patrizia A Chiesa 3 , Fatemah Sakr 4 , Irina Jelistratova 2 , Simone Lista 3 , Andrea Vergallo 5 , Pablo Lemercier 3 , Enrica Cavedo 6 , Marie Odile Habert 7 , Bruno Dubois 8 , Harald Hampel 5 , Michel J Grothe 9 ,
Affiliation  

We tested the usefulness of a regional amyloid staging based on amyloid sensitive positron emission tomography to predict conversion to cognitive impairment and dementia in preclinical and prodromal Alzheimer's disease (AD). We analyzed 884 cases, including normal controls, and people with subjective cognitive decline or mild cognitive impairment (MCI), from the Alzheimer's Disease Neuroimaging Initiative with a maximum follow-up of 6 years and 318 cases with subjective memory complaints with a maximum follow-up time of three years from the INveStIGation of AlzHeimer's PredicTors cohort (INSIGHT-preAD study). Cox regression showed a significant association of regional amyloid stages with time to conversion from a cognitively normal to an MCI, and from an MCI to a dementia status. The most advanced amyloid stages identified very-high-risk groups of conversion. All results were robustly replicated across the independent samples. These findings indicate the usefulness of regional amyloid staging for identifying preclinical and prodromal AD cases at very high risk of conversion for future amyloid targeted trials.

中文翻译:

区域淀粉样蛋白沉积的体内分期预测阿尔茨海默病临床前和前驱期的功能转换

我们测试了基于淀粉样蛋白敏感正电子发射断层扫描的区域淀粉样蛋白分期在预测临床前和前驱阿尔茨海默病 (AD) 向认知障碍和痴呆症的转化的有效性。我们分析了来自阿尔茨海默病神经影像学倡议的 884 例病例,包括正常对照和主观认知能力下降或轻度认知障碍 (MCI) 的患者,最长随访时间为 6 年,以及 318 例主观记忆投诉最长随访时间 -从 AlzHeimer 的 PredicTors 队列的调查(INSIGHT-preAD 研究)开始,三年的正常运行时间。Cox 回归显示区域淀粉样蛋白分期与从认知正常到 MCI 以及从 MCI 到痴呆状态的时间显着相关。最先进的淀粉样蛋白阶段确定了非常高风险的转化组。所有结果都在独立样本中稳健复制。这些发现表明,区域淀粉样蛋白分期对于识别未来淀粉样蛋白靶向试验中转化风险非常高的临床前和前驱 AD 病例非常有用。
更新日期:2020-09-01
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