Alex Gibson, Cytox Ltd., John Eccles House, Robert Robinson Avenue, Oxford Science Park, Oxford, OX4 4GP, United Kingdom. Email:
J Prev Alzheimers Dis. 2021;8(1):78-83. doi: 10.14283/jpad.2020.64.
There is a clear need for simple and effective tests to identify individuals who are most likely to develop Alzheimer's Disease (AD) both for the purposes of clinical trial recruitment but also for improved management of patients who may be experiencing early pre-clinical symptoms or who have clinical concerns.
To predict individuals at greatest risk of progression of cognitive impairment due to Alzheimer's Disease in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) using a polygenic risk scoring algorithm. To compare the performance of a PRS algorithm in predicting cognitive decline against that of using the pTau/Aß1-42 ratio CSF biomarker profile.
A longitudinal analysis of data from the Alzheimer's Disease Neuroimaging Initiative study conducted across over 50 sites in the US and Canada.
Multi-center genetics study.
515 subjects who upon entry to the study were diagnosed as cognitively normal or with mild cognitive impairment.
Use of genotyping and/or whole genome sequencing data to calculate polygenic risk scores and assess ability to predict subsequent cognitive decline as measured by CDR-SB and ADAS-Cog13 over 4 years.
The overall performance for predicting those individuals who would decline by at least 15 ADAS-Cog13 points from a baseline mild cognitive impairment in 4 years was 72.8% (CI:67.9-77.7) AUC increasing to 79.1% (CI: 75.6-82.6) when also including cognitively normal participants. Assessing mild cognitive impaired subjects only and using a threshold of greater than 0.6, the high genetic risk participant group declined, on average, by 1.4 points (CDR-SB) more than the low risk group over 4 years. The performance of the PRS algorithm tested was similar to that of the pTau/Aß1-42 ratio CSF biomarker profile in predicting cognitive decline.
Calculating polygenic risk scores offers a simple and effective way, using DNA extracted from a simple mouth swab, to select mild cognitively impaired patients who are most likely to decline cognitively over the next four years.
为了临床试验招募的目的,也为了更好地管理可能出现早期临床前症状或有临床担忧的患者,我们迫切需要简单有效的测试来识别最有可能患上阿尔茨海默病(AD)的个体。
使用多基因风险评分算法预测阿尔茨海默病神经影像学倡议(ADNI)个体中因阿尔茨海默病导致认知障碍进展风险最大的个体。比较 PRS 算法预测认知能力下降的性能与使用 pTau/Aß1-42 比值 CSF 生物标志物谱的性能。
在美国和加拿大的 50 多个地点进行的阿尔茨海默病神经影像学倡议研究的纵向分析。
多中心遗传学研究。
515 名参与者,在入组时被诊断为认知正常或轻度认知障碍。
使用基因分型和/或全基因组测序数据计算多基因风险评分,并评估在 4 年内使用 CDR-SB 和 ADAS-Cog13 预测随后认知下降的能力。
从基线轻度认知障碍 4 年内至少下降 15 分 ADAS-Cog13 的个体的整体预测性能为 72.8%(置信区间:67.9-77.7),当还包括认知正常参与者时,AUC 增加到 79.1%(置信区间:75.6-82.6)。仅评估轻度认知障碍患者,并使用大于 0.6 的阈值,高遗传风险组参与者在 4 年内的平均下降幅度比低风险组多 1.4 分(CDR-SB)。测试的 PRS 算法的性能与 pTau/Aß1-42 比值 CSF 生物标志物谱在预测认知下降方面相似。
使用从简单的口腔拭子中提取的 DNA 计算多基因风险评分,提供了一种简单有效的方法,可选择在接下来的四年中认知能力最有可能下降的轻度认知障碍患者。