Multiethnic machine learning brain signatures of ADRD

ADRD 的多种族机器学习大脑特征

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease pathology Alzheimer&apos 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

项目摘要

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

项目概要/摘要阿尔茨海默氏病和相关痴呆症 (ADRD) 的潜在病理学在过去一段时间内逐渐积累几十年来，临床前阶段非侵入性、敏感生物标志物的鉴定成为公众关注的焦点健康优先。我们的团队和其他人利用先进的分析方法建立了神经影像学高级大脑老化的特征（大脑老化萎缩的空间模式识别，SPARE-BA）和功能衰退 (fSPARE-BA) 和 ADRD（SPARE-AD 和 SPARE-小血管疾病），可预测事件认知能力下降。不幸的是，迄今为止大多数研究主要是在非西班牙裔白人人口，这限制了将结果推广到不同种族构成的能力美国人口老龄化不断加剧。如果当前的趋势继续下去，机器学习模型将主要是在种族不平衡的数据集中进行训练，导致可能影响临床相关性的偏差。因此，初级当前提案的目标是：利用种族多元化的神经影像联盟来构建新机器由来自种族均衡人群的数据训练的学习模型，得出敏感且具体的数据大脑衰老和 ADRD 的神经影像特征，并评估它们是否可以实用的非侵入性跨种族认知能力下降、轻度认知障碍 (MCI) 和痴呆症的生物标志物组。我们建议利用丰富的临床和神经影像（结构 MRI 和静息态功能 MRI）基因组流行病学心脏和衰老研究队列（CHARGE）联盟内的数据，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)、弗雷明汉心脏研究 (FHS)、血管对认知障碍和痴呆症联盟 (MARK-VCID) 以及多种族研究的贡献动脉粥样硬化（MESA）。我们将利用跨现有纵向的合作研究框架队列来解决导致 ADRD 负担差异的未解答问题。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人要解决我们的具体目标：1）生成并评估基于机器学习的临床效用每个种族/民族的大脑衰老和ADRD特征，并揭示多维异质性跨群体老龄化； 2) 检查血管危险因素与基于机器学习的派生结果的关联按种族/民族划分的 ADRD 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种族群体基于学习的大脑特征，以阐明潜在的生物机制。此外，我们将与科学界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具有强大事件预测效用的神经影像标记认知能力下降并确定潜在的病理生理途径，扩大新的机会跨不同种族群体的干预发展。二