Multiethnic machine learning brain signatures of ADRD

ADRD 的多种族机器学习大脑特征

• 批准号: 10693310
• 负责人: Mohamad Habes
• 金额: $ 70.62万
• 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/10693310
• 关键词: Acceleration; Address; Adult; Affect; Age; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid beta-42; Atherosclerosis Risk in Communities; Atrophic; Attenuated; 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; Disparity; Dyslipidemias; Ethnic Origin; Ethnic Population; 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; Neuroanatomy; 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 demography; aging population; analytical method; biomarker validation; blood-based biomarker; brain based; cardiovascular health; cardiovascular risk factor; clinical diagnosis; clinically relevant; cognitive function; cohort; comorbidity; dementia risk; disorder risk; ethnic difference; ethnic diversity; ethnic minority; ethnoracial; functional decline; genomic epidemiology; health disparity; high dimensionality; lifestyle data; machine learning algorithm; machine learning method; machine learning model; machine learning prediction; mild cognitive impairment; mortality; multi-ethnic; multimodal neuroimaging; neurofilament; neuroimaging; neuroimaging marker; novel; pre-clinical; predictive signature; public health priorities; racial difference; racial diversity; racial minority; racial population; 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

项目摘要/摘要