Precision Aging Network: Closing the Gap Between Cognitive Healthspan andHuman Lifespan

精准老龄化网络：缩小认知健康寿命与人类寿命之间的差距

基本信息

批准号：
10270187
负责人：
CAROL A. BARNES
金额：
$ 1247.25万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10270187
关键词：
Address Age Age-associated memory impairment Aging Alzheimer disease prevention Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Arizona Assessment tool Baltimore Behavioral Sciences Big Data Biological Biological Markers Blood Brain Brain Diseases Categories Chronic Chronology Clinical assessments Cognitive Cognitive aging Communities Complex Critical Pathways Custom Data Dementia Development Disease Educational process of instructing Environmental Risk Factor Ethnic Origin Foundations Functional disorder Future Genetic Geography Goals Health Health Personnel Heterogeneity Human Impaired cognition Incidence Individual Individual Differences Intervention Knowledge Life Style Longevity Longitudinal cohort Medical Medicine Methodology Methods Modeling National Institute on Aging Neurologic Online Systems Outcome Performance Personal Satisfaction Persons Plant Roots Population Predisposition Prevention Prevention strategy Privatization Process Psychosocial Factor Quality of life Race Research Research Personnel Resistance Risk Sampling Science Signal Pathway Social Sciences Strategic Planning Symptoms System Technology Translating United States Universities Vision aging brain base brain health cognitive enhancement cognitive function cognitive performance cognitive testing cohort data integration data sharing design disability experience healthspan high risk human old age (65+)improved multidisciplinary neuromechanism outreach physical conditioning precision medicine predictive modeling prevent programs sex socioeconomics success therapy development tool web-based assessment

项目摘要

SUMMARY/ABSTRACT: Overall Project The strategic vision of the Precision Aging Network (PAN) is to develop the essential scientific knowledge to understand the discrepancy that currently exists between cognitive healthspan and human lifespan. We must reveal the neural mechanisms that 1) account for optimal brain performance in old age resulting in healthy cognitive function, and 2) those that underlie decline in brain function leading to age-related cognitive impairment (ARCI), Alzheimer’s disease (AD), or Alzheimer’s disease-related dementias (ADRD). The ultimate goal of the PAN is to develop not only a strong scientific foundation for the essential knowledge needed to match cognitive healthspan with human lifespan, but also to leverage big data approaches that apply precision medicine concepts to prolong optimal brain function. To achieve this goal of sustaining optimal cognitive function in old age, and to extend quality of life for people across levels of risk for ARCI, AD, or ADRD, we maintain that methodologies such as those developed and implemented in the PAN will be required. Although ‘chronological age’ is consistently associated with increasing incidence of disability, including chronic brain disorders such as AD and ADRD, the exact mechanistic relationships between ‘biological age’ and decline in brain function is not known. The number of people now living with some form of dementia is estimated to be 50 million worldwide, which is expected to double every 20 years. Because of the enormous heterogeneity in brain and cognitive function among individuals in their 70s, 80s and 90s, the urgent challenge for science, medicine and healthcare providers is to discover interventions that are individually effective in delaying or preventing ARCI, AD, or ADRD. Untangling the complex relationship between age and cognitive performance requires a strategy that includes the study of very large, diverse, well-characterized and longitudinally sampled populations. This will require ‘big data’ but also the means to translate the massive amounts of information gathered into ‘smart data’ or ‘knowledge’. This demands radically different conceptual models. Currently, no single approach adequately identifies the means to modify personal aging trajectories for improved brain health in individuals. The approach proposed in PAN is designed to overcome obstacles of earlier methods. The focus is on how to distinguish the various combinations of age, sex, genetics, race-ethnicity, health, lifestyle choices and environmental factors that influence brain drivers that increase susceptibility to dysfunction, as well as those factors that increase brain protection and resistance against dysfunction. The fundamental principle of the precision medicine approach is to ’individualize’. This will enable strong and specific predictions for each person to close the gap between cognitive healthspan and human lifespan. The root of this concept is in the teachings of Hippocrates, who said – “It is more important to know what sort of person has a disease than to know what sort of disease a person has.”

摘要/摘要：总体项目精准老龄化网络 (PAN) 的战略愿景是开发必要的科学知识，我们必须了解目前认知健康寿命和人类寿命之间存在的差异。揭示神经机制，1）解释老年时大脑的最佳表现，从而导致健康认知功能，以及 2) 那些导致大脑功能下降并导致与年龄相关的认知功能下降的因素损伤 (ARCI)、阿尔茨海默病 (AD) 或阿尔茨海默病相关痴呆 (ADRD)。 PAN 的目标不仅是为所需的基本知识奠定坚实的科学基础将认知健康寿命与人类寿命相匹配，同时也利用大数据方法来实现精确性延长最佳大脑功能的医学概念，以实现维持最佳认知的目标。老年功能，并延长不同 ARCI、AD 或 ADRD 风险水平的人们的生活质量，我们认为需要使用 PAN 中开发和实施的方法。尽管“实际年龄”始终与残疾发生率增加相关，包括慢性残疾 AD 和 ADRD 等脑部疾病，“生物年龄”和“ADRD”之间的确切机制关系目前尚不清楚有多少人患有某种形式的痴呆症。据估计，全球有 5000 万，预计每 20 年就会翻一番。 70、80、90 岁人群大脑和认知功能的异质性是紧迫的挑战对于科学、医学和医疗保健提供者来说，就是发现单独有效的干预措施延迟或预防 ARCI、AD 或 ADRD。理清年龄和认知表现之间的复杂关系需要采取一种策略，其中包括这需要对非常大的、多样化的、特征明确的纵向抽样人群进行研究。 “大数据”也是将收集到的大量信息转化为“智能数据”或 “知识”。这需要完全不同的概念模型。目前，没有一种方法可以充分满足这一需求。确定了改变个人衰老轨迹以改善个人大脑健康的方法。 PAN 中提出的方法旨在克服早期方法的障碍。区分年龄、性别、遗传、种族、健康、生活方式选择的各种组合影响大脑驱动因素的环境因素会增加功能障碍的易感性，以及这些因素可以增强大脑保护和抵抗功能障碍的能力。精准医疗方法的基本原则是“个体化”，这将带来强大的力量。以及针对每个人的具体预测，以缩小认知健康寿命与人类之间的差距这个概念的根源在于希波克拉底的教义，他说：“了解更重要。什么样的人得了病，不如知道一个人得了什么样的病。”