Multiethnic machine learning brain signatures of ADRD

ADRD 的多种族机器学习大脑特征

• 批准号: 10524844
• 负责人: Mohamad Habes
• 金额: $ 72.07万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524844
• 关键词: Address; Adult; Affect; Age; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid beta-42; Atherosclerosis Risk in Communities; Atrophic; Attenuated; Base of the Brain; Biological; Biological Markers; Black American; Black Populations; Blood; Brain; Brain imaging; CHI3L1 gene; Caring; Cerebrovascular Trauma; Clinical; Cognition; Cohort Studies; Communities; Computer software; Data; Data Set; Dementia; Development; Diabetes Mellitus; Dimensions; Disease; Dyslipidemias; Ethnic Origin; Ethnic group; Exposure to; Framingham Heart Study; Functional Magnetic Resonance Imaging; Funding; Future; Genetic; Glial Fibrillary Acidic Protein; Health Care Costs; Heart; Heterogeneity; Hispanic Americans; Hispanic Populations; Hypertension; Impaired cognition; Incidence; Individual; Insulin Resistance; Intervention; Intervention Trial; Knowledge; Light; Longevity; Longitudinal cohort; Machine Learning; Magnetic Resonance Imaging; Medical; Metabolic syndrome; Microvascular Dysfunction; Morbidity - disease rate; Multi-Ethnic Study of Atherosclerosis; Neurodegenerative Disorders; Not Hispanic or Latino; Obesity; Onset of illness; Participant; Pathogenesis; Pathologic Processes; Pathway interactions; Pattern; Population; Preventive; Race; Research; Rest; Smoking; Stage at Diagnosis; Structure; Training; United States; United States National Institutes of Health; Validation; advanced analytics; aging brain; aging demographic; aging population; analytical method; base; blood-based biomarker; cardiovascular health; cardiovascular risk factor; clinical diagnosis; clinically relevant; cognitive function; cohort; comorbidity; dementia risk; disorder risk; ethnic difference; ethnic diversity; ethnic minority; functional decline; genomic epidemiology; health disparity; high dimensionality; lifestyle data; machine learning algorithm; machine learning method; machine learning model; mild cognitive impairment; mortality; multi-ethnic; multimodal neuroimaging; neurofilament; neuroimaging; neuroimaging marker; novel; pre-clinical; predictive marker; predictive signature; public health priorities; racial and ethnic; racial diversity; racial minority; sex; socioeconomics; tau-1; therapy development; trend; vascular cognitive impairment and dementia; vascular risk factor

PROJECT SUMMARY / ABSTRACT The underlying pathology of Alzheimer's disease and related dementias (ADRDs) accumulates gradually over decades, making the identification of non-invasive, sensitive biomarkers in the preclinical stage a critical public health priority. Harnessing advanced analytic methods, our team and others have established neuroimaging signatures of advanced brain aging (Spatial Pattern of Atrophy Recognition of Brain Aging, SPARE-BA) and functional decline (fSPARE-BA), and ADRDs (SPARE-AD and SPARE-Small vessel disease), which predict incident cognitive decline. Unfortunately, most research to date has been conducted in predominantly non- Hispanic white populations, which limits the ability to generalize results to the diverse ethnoracial makeup of the United States' growing aging demographic. If current trends continue, machine learning models will primarily be trained in ethnically imbalanced datasets, leading to biases that may affect clinical relevance. Thus, the primary aims of the current proposal are to: leverage an ethnically diverse neuroimaging consortium to build new machine learning models trained by data from ethnically well-balanced populations, derive sensitive and specific neuroimaging signatures of brain aging and ADRD, and evaluate whether they can be practical non-invasive biomarkers of incident cognitive decline, mild cognitive impairment (MCI), and dementia across ethnoracial groups. We propose to leverage the rich clinical and neuroimaging (structural MRI and resting-state functional MRI) data within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, including the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Genetics of Brain Structure and Function Study (GOBS), the Framingham Heart Study (FHS), the Vascular Contributions to Cognitive Impairment and Dementia consortium (MARK-VCID) and the Multi-Ethnic Study of Atherosclerosis (MESA). We will leverage a collaborative research framework across existing longitudinal cohorts to address unanswered questions contributing to disparities in ADRD burden. Machine learning algorithms will be applied to brain imaging data of over 7,200 non-Hispanic Whites, 1,400 Blacks, and 1,425 Hispanics to address our Specific Aims: 1) Generate and evaluate clinical utility of machine learning-based signatures of brain aging and ADRD for each race/ethnic group and uncover multidimensional heterogeneity in aging across groups; 2) Examine associations of vascular risk factors with the derived machine learning-based brain signatures of ADRD by race/ethnicity, and 3) Explore blood-based biomarker predictors of these machine learning-based brain signatures by ethnoracial group to elucidate underlying biological mechanisms. Further, we will share our robust machine learning models together with implementation software with the scientific community. This project will develop and validate neuroimaging markers with robust predictive utility for incident cognitive decline and to identify underlying pathophysiologic pathways, expanding opportunities for novel intervention development across diverse ethnoracial cohorts. ii

项目总结/摘要 阿尔茨海默病和相关痴呆症（ADRD）的潜在病理学逐渐累积， 几十年来，在临床前阶段识别非侵入性，敏感的生物标志物是一个关键的公共 健康优先。利用先进的分析方法，我们的团队和其他人建立了神经成像 高级脑老化的特征（脑老化萎缩识别的空间模式，SPARE-BA）和 功能下降（fSPARE-BA）和ADRD（SPARE-AD和SPARE-Small Vessel Disease），它们预测 偶发性认知衰退不幸的是，迄今为止，大多数研究都是在非主流的环境中进行的。 西班牙裔白色人群，这限制了将结果推广到不同民族构成的能力。 美国人口老龄化日益严重。如果目前的趋势继续下去，机器学习模型将主要 在种族不平衡的数据集中进行培训，导致可能影响临床相关性的偏见。因此，主要 当前提案的目的是：利用种族多样的神经成像联盟来构建新的机器 由来自种族平衡的人群的数据训练的学习模型， 脑老化和ADRD的神经影像学特征，并评估它们是否可以是实用的非侵入性的 跨民族的偶发性认知下降、轻度认知障碍（MCI）和痴呆的生物标志物 组我们建议利用丰富的临床和神经影像学（结构MRI和静息态功能 基因组流行病学中的心脏和衰老研究（CHARGE）联盟队列中的MRI）数据， 包括社区动脉粥样硬化风险研究（ARIC），心血管健康研究（CHS）， 脑结构和功能遗传学研究（GOBS）、心脏衰竭研究（FHS）、血管 对认知障碍和痴呆联盟（MARK-VCID）和多种族研究的贡献 动脉粥样硬化（梅萨）。我们将利用现有纵向研究框架， 队列，以解决导致ADRD负担差异的未回答问题。机器学习 算法将应用于超过7，200名非西班牙裔白人，1，400名黑人和1，425名黑人的大脑成像数据。 西班牙裔，以解决我们的具体目标：1）生成和评估基于机器学习的临床效用 每个种族/民族的脑老化和ADRD的特征，并揭示了 2）检查血管风险因素与基于机器学习的衍生模型之间的关联。 按种族/民族列出的ADRD的大脑特征，以及3）探索这些机器的基于血液的生物标志物预测因子 基于学习的大脑签名，由ethnoregion集团阐明潜在的生物机制。我们还 我们将与科学家分享我们强大的机器学习模型以及实施软件， 社区该项目将开发和验证神经影像学标记物，这些标记物对事件具有强大的预测效用。 认知功能下降，并确定潜在的病理生理途径，扩大新的机会， 在不同族裔群体中制定干预措施。 II