Tandon Raghav, Zhao Liping, Watson Caroline M, Elmor Morgan, Heilman Craig, Sanders Katherine, Hales Chadwick M, Yang Huiying, Loring David W, Goldstein Felicia C, Hanfelt John J, Duong Duc M, Johnson Erik C B, Wingo Aliza P, Wingo Thomas S, Roberts Blaine R, Seyfried Nicholas T, Levey Allan I, Mitchell Cassie S, Lah James J
Department of Biomedical Engineering, Georgia Institute of Technology.
Center for Machine Learning, Georgia Institute of Technology.
Res Sq. 2023 Feb 28:rs.3.rs-2577025. doi: 10.21203/rs.3.rs-2577025/v1.
Alzheimer's disease (AD) progresses through a lengthy asymptomatic period during which pathological changes accumulate prior to development of clinical symptoms. As disease-modifying treatments are developed, tools to stratify risk of clinical disease will be required to guide their use. In this study, we examine the relationship of AD biomarkers in healthy middle-aged individuals to health history, family history, and neuropsychological measures and identify cerebrospinal fluid (CSF) biomarkers to stratify risk of progression from asymptomatic to symptomatic AD. CSF from cognitively normal (CN) individuals (N=1149) in the Emory Healthy Brain Study were assayed for Aβ, total Tau (tTau), and phospho181-Tau (pTau), and a subset of 134 cognitively normal, but biomarker-positive, individuals were identified with asymptomatic AD (AsymAD) based on a locally-determined cutoff value for ratio of tTau to Aβ. These AsymAD cases were matched for demographic features with 134 biomarker-negative controls (CN/BM-) and compared for differences in medical comorbidities and family history. Dyslipidemia emerged as a distinguishing feature between AsymAD and CN/BM-groups with significant association with personal and family history of dyslipidemia. A weaker relationship was seen with diabetes, but there was no association with hypertension. Examination of the full cohort by median regression revealed a significant relationship of CSF Aβ (but not tTau or pTau) with dyslipidemia and diabetes. On neuropsychological tests, CSF Aβ was not correlated with performance on any measures, but tTau and pTau were strongly correlated with visuospatial perception and visual episodic memory. In addition to traditional CSF AD biomarkers, a panel of AD biomarker peptides derived from integrating brain and CSF proteomes were evaluated using machine learning strategies to identify a set of 8 peptides that accurately classified CN/BM- and symptomatic AD CSF samples with AUC of 0.982. Using these 8 peptides in a low dimensional t-distributed Stochastic Neighbor Embedding analysis and k-Nearest Neighbor (k=5) algorithm, AsymAD cases were stratified into "Control-like" and "AD-like" subgroups based on their proximity to CN/BM- or AD CSF profiles. Independent analysis of these cases using a Joint Mutual Information algorithm selected a set of 5 peptides with 81% accuracy in stratifying cases into AD-like and Control-like subgroups. Performance of both sets of peptides was evaluated and validated in an independent data set from the Alzheimer's Disease Neuroimaging Initiative. Based on our findings, we conclude that there is an important role of lipid metabolism in asymptomatic stages of AD. Visuospatial perception and visual episodic memory may be more sensitive than language-based abilities to earliest stages of cognitive decline in AD. Finally, candidate CSF peptides show promise as next generation biomarkers for predicting progression from asymptomatic to symptomatic stages of AD.
阿尔茨海默病(AD)会经历漫长的无症状期,在此期间,病理变化在临床症状出现之前就会不断累积。随着疾病修饰治疗方法的研发,需要有工具来对临床疾病风险进行分层,以指导这些治疗方法的使用。在本研究中,我们考察了健康中年个体中AD生物标志物与健康史、家族史及神经心理学指标之间的关系,并确定脑脊液(CSF)生物标志物,以对从无症状AD进展到有症状AD的风险进行分层。在埃默里健康大脑研究中,对认知正常(CN)个体(N = 1149)的脑脊液进行了Aβ、总tau蛋白(tTau)和磷酸化181 - tau蛋白(pTau)检测,并根据当地确定的tTau与Aβ比值临界值,从134名认知正常但生物标志物呈阳性的个体中识别出无症状AD(AsymAD)患者。将这些AsymAD病例与134名生物标志物阴性对照(CN/BM-)个体进行人口统计学特征匹配,并比较他们在合并症和家族史上的差异。血脂异常成为AsymAD组和CN/BM-组之间的一个显著特征,与血脂异常的个人史和家族史有显著关联。与糖尿病的关系较弱,但与高血压无关联。通过中位数回归对整个队列进行分析发现,脑脊液Aβ(而非tTau或pTau)与血脂异常和糖尿病存在显著关系。在神经心理学测试中,脑脊液Aβ与任何指标的表现均无相关性,但tTau和pTau与视觉空间感知和视觉情景记忆密切相关。除了传统的脑脊液AD生物标志物外,还使用机器学习策略对一组整合了脑和脑脊液蛋白质组的AD生物标志物肽进行了评估,以确定一组8种肽,这些肽能够以0.982的曲线下面积(AUC)准确分类CN/BM-和有症状AD的脑脊液样本。使用这8种肽进行低维t分布随机邻域嵌入分析和k近邻(k = 5)算法,根据AsymAD病例与CN/BM-或AD脑脊液谱的接近程度,将其分为“类似对照”和“类似AD”亚组。使用联合互信息算法对这些病例进行独立分析,选择了一组5种肽,将病例分为类似AD和类似对照亚组的准确率为81%。在来自阿尔茨海默病神经影像倡议组织的独立数据集中对这两组肽的性能进行了评估和验证。基于我们的研究结果,我们得出结论,脂质代谢在AD的无症状阶段起着重要作用。视觉空间感知和视觉情景记忆可能比基于语言的能力对AD认知衰退的早期阶段更敏感。最后,候选脑脊液肽有望成为预测AD从无症状阶段进展到有症状阶段的下一代生物标志物。