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Effects of metabolic phenotype on functional connectivity in aging and Alzheimer’s Disease

代谢表型对衰老和阿尔茨海默病功能连接的影响

基本信息

批准号：
10389416
负责人：
Zachary D Green
金额：
$ 3.52万
依托单位：
UNIVERSITY OF KANSAS MEDICAL 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：
2022-09-19 至 2027-02-18
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10389416
关键词：
Affect Aging Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease diagnosis Alzheimer&apos s disease diagnostic Alzheimer&apos s disease pathology Alzheimer&apos s disease risk Alzheimer’s disease biomarker American Amyloid Amyloid beta-Protein Award Biological Markers Blood Glucose Blood Vessels Brain Brain Pathology Brain imaging Brain region Cerebrovascular Disorders Cerebrum Clinical Cognitive Complex Data Data Set Dementia Diabetes Mellitus Diagnosis Diagnostic Disease Doctor of Philosophy Early Diagnosis Elderly Evaluation Fasting Functional Magnetic Resonance Imaging Functional disorder Genetic Genetic Risk Genotype Glucose Goals Health Heterogeneity Hippocampus (Brain)Hypertension Impaired cognition Impairment Individual Inflammatory Latino Link Measures Mediating Metabolic Metabolic dysfunction Metabolism Monitor Nerve Degeneration Neurobehavioral Manifestations Neurosciences Non-Insulin-Dependent Diabetes Mellitus Obesity Outcome Pathogenesis Pathology Patients Persons Pharmaceutical Preparations Phenotype Physicians Population Positioning Attribute Prediabetes syndrome Rest Risk Risk Factors Role Sample Size Scientist Signal Transduction Site Structure Subgroup Training Vascular Diseases Work aging brain amyloid pathology apolipoprotein E-4 base blood oxygen level dependent brain health cardiometabolic risk cardiometabolism career carrier status cohort dementia risk disorder risk epidemiologic data epidemiology study genetic risk factor glucose metabolism health data hyperphosphorylated tau imaging biomarker improved in vivo lipid metabolism metabolic phenotype mild cognitive impairment neural correlate neuroimaging neuropathology non-demented potential biomarker precision medicine recruit skills socioeconomics

项目摘要

Summary/Abstract Alzheimer’s Disease (AD), the most common cause of dementia, affects over 6.2 million Americans and 50 million individuals worldwide. Ten to fifteen years prior to the onset of cognitive symptoms, AD pathology begins to appear in the brain. While the primary pathology associated with AD include amyloid beta and hyperphosphorylated tau, the pathogenesis of AD remains elusive. Other changes in the AD brain include derangements in cerebral glucose metabolism. Recent epidemiological studies have highlighted an association between AD and systemic metabolic impairment (i.e. diabetes). This indicates a major role of altered metabolism in AD, and warrants examination of how metabolic risk may contribute to brain health and dementia risk. Biomarker studies of brain structure and function offer potential opportunities for early detection of AD, and for improving our understanding of factors relating to disease risk. Functional connectivity, a measure of correlated neural activity in two brain regions can be measured by resting-state functional magnetic resonance imaging (rsfMRI). This functional neuroimaging approach has emerged as a potential biomarker for AD diagnosis and disease monitoring. Importantly, metabolic risk has previously been associated with altered functional connectivity. However, data regarding early forms of metabolic risk, like prediabetes, is lacking. Additionally, functional connectivity studies often suffer from limited sample size and a lack of diversity. We intend to analyze the existing rsfMRI data of individuals recruited into the Health and Aging Brain Among Latino Elders (HABLE) Study, a diverse cohort of approximately 2,000 individuals. In our first aim, we will characterize the effect of metabolic risk (prediabetes or Type 2 Diabetes status assessed by blood glucose, diagnosis, or medication use) on functional connectivity in cognitively healthy individuals. As part of this aim, we will also examine the role of APOE4, a foremost genetic risk factor of AD that is closely related to cerebrovascular dysfunction. In our second aim, we will examine functional networks across the AD diagnostic spectrum, and examine to what degree metabolic risk mediates changes in these networks. We will then assess the relationship between cardiometabolic risk score and functional connectivity across the entire cohort.

摘要/文摘