将阿尔茨海默氏症血浆生物标志物与核磁共振成像、心血管、遗传学和生活方式测量相结合,能否改善认知预测?

IF 4.1 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI:10.1093/braincomms/fcae300
Robel K Gebre, Jonathan Graff-Radford, Vijay K Ramanan, Sheelakumari Raghavan, Ekaterina I Hofrenning, Scott A Przybelski, Aivi T Nguyen, Timothy G Lesnick, Jeffrey L Gunter, Alicia Algeciras-Schimnich, David S Knopman, Mary M Machulda, Maria Vassilaki, Val J Lowe, Clifford R Jack, Ronald C Petersen, Prashanthi Vemuri
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引用次数: 0

摘要

由于与阿尔茨海默病相关的血浆生物标记物具有易获取性和可扩展性,人们对它们的兴趣与日俱增。我们假设,利用机器学习(ML)模型将血浆生物标志物与其他常用和可用的参与者数据(核磁共振成像、心血管因素、生活方式、遗传学)整合在一起,可以改善对认知结果的个体预测。此外,我们的目标是评估这些预测因素在不同年龄段的异质性。这项纵向研究包括来自梅奥诊所老龄化研究的 1185 名参与者,他们在基线时进行了完整的血浆分析工作。我们使用 Quanterix Simoa 免疫测定法测定了神经丝光、Aβ1-42 和 Aβ1-40(用作 Aβ42/Aβ40 比值)、胶质纤维酸性蛋白和磷酸化 tau 181(p-tau181)。参与者的大脑健康状况通过灰质和白质结构核磁共振成像进行评估。研究还考虑了心血管因素(高脂血症、高血压、中风、糖尿病、慢性肾病)、生活方式因素(地区剥夺指数、体重指数、认知和体力活动)以及遗传因素(APOE、单核苷酸多态性和多基因风险评分)。我们建立了一个 ML 模型来预测基线和下降(斜率)时的认知结果。建立了三个模型:以风险因素组为预测因子的基础模型、包含社会人口统计学因素的增强模型,以及将血浆和社会人口统计学因素纳入基础模型的最终增强模型。模型对三个年龄层进行了解释:65 岁以下、65-80 岁和 80 岁以上,并根据淀粉样蛋白阳性状态进一步划分。在预测认知能力下降时,无论淀粉样蛋白状态如何,血浆生物标志物的表现(R² = 0.15)与核磁共振成像(R² = 0.18)和心血管指标(R² = 0.10)相当。将心血管或核磁共振成像测量与血浆一起纳入社会-人口统计学,可提高认知功能衰退的预测能力(R² = 0.26 和 0.27)。对于淀粉样蛋白阳性者,Aβ42/Aβ40、神经胶质纤维酸性蛋白和p-tau181是预测认知能力下降的首要指标,而对于所有年龄组的淀粉样蛋白阴性者,Aβ42/Aβ40则是突出指标。社会人口统计学解释了淀粉样蛋白阴性个体的大部分变异,而血浆生物标志物则主要解释了淀粉样蛋白阳性个体的变异(从年轻到年长年龄组的21%到37%)。在预测认知结果时,血浆生物标志物的表现与核磁共振成像和心血管测量结果相似,将它们与其中任何一种测量结果相结合都能获得更好的结果。横断面和纵向认知模型之间、不同年龄组之间以及淀粉样蛋白状态之间的顶级预测因子存在差异。通过仔细考虑研究人群的社会人口统计学、大脑和心血管健康状况,多模式方法将提高血浆生物标志物的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can integration of Alzheimer's plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?

There is increasing interest in Alzheimer's disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ1-42 and Aβ1-40 (used as Aβ42/Aβ40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants' brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65-80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R² = 0.15) to MRI (R² = 0.18) and cardiovascular measures (R² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R² = 0.26 and 0.27). For amyloid positive individuals Aβ42/Aβ40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ42/Aβ40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population's socio-demographics, brain and cardiovascular health.

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