India ENIGMA Initiative for Global Aging & Mental Health

印度 ENIGMA 全球老龄化倡议

• 批准号: 10002026
• 负责人: VENKATASUBRAMANIAN GANESAN
• 金额: $ 56.89万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10002026
• 关键词: Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease risk; Anti-Cholinergics; Attention; Biometry; Blood; Brain; California; Caucasians; Clinical; Clinical Data; Clinical Research; DNA Methylation; Data; Dementia; Developing Countries; Diet; Diffusion Magnetic Resonance Imaging; Disease; Drug abuse; Drug usage; Education; Educational workshop; Endocrine; Equation; Estrogens; Ethnic group; Etiology; European; Evaluation; Functional Magnetic Resonance Imaging; Future; Genetic; Genetic Risk; Genetic study; Genome; Genotype; Goals; Grant; Health; High Density Lipoprotein Cholesterol; Hydrocortisone; Image; Impaired cognition; Incidence; India; Individual; Individual Differences; Inflammatory; Infrastructure; Interleukin-6; International; Journals; LDL Cholesterol Lipoproteins; Link; Longevity; Magnetic Resonance Imaging; Measures; Medical; Medical Research; Mental Depression; Mental Health; Modeling; Multimodal Imaging; National Institute of Mental Health; Pathway interactions; Peer Review; Persons; Pharmaceutical Preparations; Phenotype; Physical activity; Plasma; Population; Process; Proteomics; Psychosocial Factor; Publishing; Quality Control; Reproducibility; Research; Research Infrastructure; Research Personnel; Resources; Rest; Risk; Rural; Science; Scientist; Sleep; Structure; TNF gene; Techniques; Testing; Thyroid Gland; Training; Triglycerides; United States National Institutes of Health; Universities; aging brain; biobank; biological sex; brain research; cardiovascular risk factor; clinical predictors; cohort; cost effective; data harmonization; design; disorder risk; epigenetic marker; epigenome; ethnic diversity; family support; global health; imaging biomarker; innovation; insight; international partnership; lifestyle factors; literacy; low and middle-income countries; machine learning method; methylation pattern; multimodality; nervous system disorder; neuroimaging; novel; outcome forecast; predictive marker; predictive modeling; psychosocial; resilience; response; sex; specific biomarkers; sulfated glycoprotein 2; tau Proteins; tool; treatment trial

ABSTRACT Our revised proposal launches the India ENIGMA Initiative for Global Aging & Mental Health - a globally coordinated study of brain aging and Alzheimer's disease (AD), created response to the NIH FOA: Global Brain and Nervous System Disorders Research Across the Lifespan (R01; PAR-18-834; https://grants.nih.gov/grants/guide/pa-files/PAR-18-835.html). Our overall goal is to identify predictive markers in the blood, genome, and epigenome that influence brain aging in India, to better understand prognosis, and to support personalized risk evaluations on each continent. We plan to identify etiological pathways to resilience using the rich biobanking strategy developed by our partners at NIMHANS in India. To do this, we will leverage our global consortium, ENIGMA (http://enigma.ini.usc.edu), to partner with dementia research pioneers in India, creating new links between international biobanks, and building research capacity. By 2020, 70% of the world's population over age 60 will live in developing countries, with 14% in India (Mathuranath 2012). Recently, attention has been drawn to a “diversity” crisis in brain research, as most brain research is conducted in Caucasian populations from relatively wealthy backgrounds (LeWinn 2017). This lack of ethnic diversity means that: (1) we do not know if predictors of health (and disease) generalize to other ethnic groups, and (2) we fail to collect vital data that could teach us how AD progresses in populations with different genetic and environmental backgrounds. Our coordinated analyses in US/EU and Indian biobanks will help identify brain aging predictors specific to India and those that are universal. Specifically, we will: Aim 1. Create Lifespan Charts of brain aging Trajectories in India using MRI, DWI and Resting State Functional MRI. Aim 2. Identify Blood and Epigenetic Markers that Predict Brain Aging and AD in India. Aim 3. Using a combination of multimodal imaging, blood markers, and clinical data to predict clinical decline in India. We test structural equation models that hypothesize how brain aging depends on lifestyle and psychosocial factors (diet, family support, drug abuse, literacy, sleep, and depression), as well as sex, education, and AD genetic risk. With novel machine learning methods, will analyze blood markers and plasma proteomic analytes, to define processes that are harmful to brain aging. In Capacity Building Aims, we will leverage ENIGMA's successful strategies to train emerging and established scientists in India to analyze their data with high quality control and precision, with targeted biostatistical and imaging workshops to bolster capacity. This collaborative India-US initiative will enable future science initiatives, and equip the NIMHANS team with the necessary tools to train new scientists and independently conduct high impact research bridging efforts into numerous international partnerships.

摘要 我们修订后的提案启动了印度全球老龄化和心理健康的ENIGMA倡议-一个全球性的 脑老化和阿尔茨海默病（AD）的协调研究，创建响应NIH FOA：全球脑 和神经系统疾病的研究在整个生命周期（R 01; PAR-18-834; https://grants.nih.gov/grants/guide/pa-files/PAR-18-835.html).我们的总体目标是识别预测性 血液、基因组和表观基因组中影响印度大脑衰老的标志物，以更好地了解 预测，并支持每个大陆的个性化风险评估。我们计划找出 利用我们在NIMHANS的合作伙伴开发的丰富的生物库策略， 在印度为此，我们将利用我们的全球联盟ENIGMA（http：enigma.ini.usc.edu），与 印度的痴呆症研究先驱，在国际生物库和建筑研究之间建立新的联系， 容量到2020年，全球60岁以上人口的70%将生活在发展中国家，其中14%在印度。 （Mathuranath 2012）。最近，人们注意到大脑研究中的“多样性”危机，因为大多数大脑 研究在相对富裕背景的高加索人群中进行（LeWinn 2017）。这种缺乏 种族多样性意味着：（1）我们不知道健康（和疾病）的预测因素是否适用于其他种族 组，和（2）我们未能收集重要的数据，可以告诉我们如何AD的进展，在人群中不同 遗传和环境背景。我们在美国/欧盟和印度生物库的协调分析将有助于 确定印度特有的和普遍的大脑老化预测因素。具体而言，我们将：目标1。创建 使用MRI，DWI和静息状态功能MRI的印度脑老化轨迹的寿命图。目标2. 确定血液和表观遗传标记，预测印度的脑老化和AD。目标3。结合使用 多模式成像，血液标志物和临床数据，以预测印度的临床下降。我们测试结构 方程模型，假设大脑衰老如何取决于生活方式和心理社会因素（饮食，家庭 支持，药物滥用，识字，睡眠和抑郁），以及性别，教育和AD遗传风险。具有新型 机器学习方法，将分析血液标志物和血浆蛋白质组分析物，以定义 对大脑老化有害。在能力建设目标方面，我们将利用ENIGMA的成功策略， 印度新兴和成熟的科学家以高质量的控制和精确度分析他们的数据， 有针对性的生物统计和成像讲习班，以加强能力。印度-美国的这一合作计划将 实现未来的科学计划，并为NIMHANS团队提供培训新科学家的必要工具 并独立进行高影响力的研究，将努力与众多国际伙伴关系联系起来。