Neuroinflammation and Alzheimer's Disease Imaging Biomarkers in Midlife Obesity

总结/摘要： 中年高体脂肪，如超重或肥胖的身体质量指数（BMI）所证明的， 被认为是阿尔茨海默病的危险因素。然而， 这一风险的潜在原因仍然未知。通过这一R 01提案，我们请求资助创建一个新的队列， 认知正常的120名中年人，年龄40-60岁。我们建议将他们的超重或 使用代谢测试的肥胖状态，包括口服葡萄糖耐量测试、空腹血浆胰岛素、空腹 血浆葡萄糖和血红蛋白A1 c测量。这项测试将产生代谢类别， 异常超重或肥胖（MAOO），代谢正常超重或肥胖，和代谢正常 瘦人我们将用最新的神经影像学检查评估这些组之间的差异。 阿尔茨海默病生物标志物与淀粉样蛋白（A）、tau（T）和神经变性（N）或ATN的分类。 将通过FreeSurfer上的区域体积反映的脑MRI萎缩评估神经变性。 我们还将使用一种称为扩散的新方法来评估神经炎症的MR神经成像标记物 基础光谱成像（DBSI），由华盛顿大学的Mallinckrodt放射学研究所开发 在圣路易斯与查尔斯F. Joanne Knight阿尔茨海默病研究中心 （Knight ADRC）。本研究的参与者招募将与骑士 ADRC和华盛顿大学人类营养中心。在目标1中，我们假设萎缩增加 与代谢正常的超重、肥胖和偏瘦参与者相比， 对于AD病理学重要的是海马和亚区域。对于目标2，我们假设 与其他代谢性、超重或肥胖患者相比，MAOO患者的白色神经炎症对DBSI的负担 肥胖和瘦的群体。在目标3中，我们假设MAOO患者的脑PET上淀粉样蛋白和tau蛋白增加， 其他各组相关的子目标将检查萎缩和神经炎症的性别差异。我们还将 研究超重/肥胖和瘦人群中淀粉样蛋白和tau蛋白沉积的性别差异。这 这种方法不仅考虑到肥胖和AD风险的已知性别差异， NIA认为这对了解AD风险趋势很重要。我们还将研究 通过在脑部MRI扫描时获取单独的躯干MRI扫描，来检测异常高的体脂。这将 允许单独量化内脏，腹部皮下和肝脏脂肪体积，我们将 分别涉及目标1-3中的成像标记。通过了解代谢异常如何与，和 高体脂的解剖分布与ATN和神经炎症的脑成像指标有关，我们将 有助于了解肥胖如何增加阿尔茨海默氏症的风险。这些结果将对公众健康 影响，并为未来的干预研究提供信息，以通过识别重要的 用于优化超重和肥胖者的代谢和相关脑健康的指标。

Summary/Abstract: High body fat at midlife, as evidenced by overweight or obese body mass index (BMI), is increasingly understood as a risk factor for Alzheimer’s disease. However, the underlying processes and mechanisms that may underlie this risk remains unknown. With this R01 proposal, we request funding to create a new cohort of cognitively normal 120 midlife individuals, age 40-60 years. We propose to characterize their overweight or obese status using metabolic tests including, an oral glucose tolerance test, fasting plasma insulin, fasting plasma glucose, and hemoglobin A1c measurements. This testing will generate categories of metabolically abnormal overweight or obese (MAOO), metabolically normal overweight or obese, and metabolically normal lean persons. We will evaluate differences between these groups on neuroimaging with the newer classification of Alzheimer’s biomarkers with amyloid (A), tau (T), and neurodegeneration (N), or ATN. Neurodegeneration will be assessed by atrophy on brain MRI as reflected by regional volumes on FreeSurfer. We will also evaluate MR neuroimaging markers for neuroinflammation using a newer method called diffusion basis spectrum imaging (DBSI), developed at the Mallinckrodt Institute of Radiology at Washington University in St. Louis in collaboration with The Charles F. and Joanne Knight Alzheimer’s Disease Research Center (Knight ADRC). Recruitment of participants for this study will be done in conjunction with both the Knight ADRC and the Washington University Center for Human Nutrition. In Aim 1, we hypothesize increased atrophy in MAOO compared to metabolically normal overweight, obese, and lean participants particularly in regions important for AD pathology such as the hippocampus and subregions. With Aim 2, we hypothesize a higher burden of white matter neuroinflammation on DBSI in MAOO compared to other metabolic, overweight or obese and lean groups. In Aim 3, we hypothesize increased amyloid and tau on brain PET in MAOO versus other groups. Related sub-aims will examine sex differences in atrophy and neuroinflammation. We will also investigate sex differences in amyloid and tau deposition across the overweight/obese and lean groups. This approach is not only of interest given the known sex differences in obesity and AD risk but is also recognized by the NIA as important in understanding trends of risk in AD. We will also examine the anatomical distribution of abnormally high body fat by acquiring separate body torso MRI scans at the time of brain MRI scan. This will allow for separate quantification of visceral, abdominal subcutaneous, and liver fat volumes that we will separately relate to the imaging markers in Aims 1-3. By understanding how metabolic abnormalities with, and anatomical distribution of, high body fat relate to brain imaging metrics of ATN and neuroinflammation, we will contribute key understanding to how obesity increases Alzheimer’s risk. These results will have public health implications and inform future studies of interventions to reduce risk for Alzheimer’s by identifying important metrics to utilize for optimization of metabolic and related brain health in overweight and obese persons.