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

• 批准号: 10689301
• 负责人: CAROL A. BARNES
• 金额: $ 1193.66万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 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/10689301
• 关键词: Acceleration; Address; Age; Age-associated memory impairment; Aging; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' 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; Data; Dementia; Development; Disease; Educational process of instructing; Elderly; 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; Population; Predisposition; Prevention; Prevention strategy; Privatization; Process; Psychosocial Factor; Quality of life; Race; Research; Research Personnel; Resistance; Risk; Sampling; Science; Signal Pathway; Social Sciences; Sorting; Strategic Planning; Strategic vision; Symptoms; System; Technology; Translating; United States; Universities; aging brain; 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; information gathering; multidisciplinary; neuromechanism; outreach; physical conditioning; precision medicine; predictive modeling; prevent; process improvement; programs; sex; socioeconomics; success; therapy development; tool; web-based assessment

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.”

总结/摘要：总体项目 精准老龄化网络（PAN）的战略愿景是发展必要的科学知识， 理解认知健康寿命和人类寿命之间存在的差异。我们必须 揭示了神经机制，1）解释了老年人大脑的最佳表现， 认知功能，以及2）导致与年龄相关的认知功能下降的那些因素 损伤（ARCI）、阿尔茨海默病（AD）或阿尔茨海默病相关痴呆（ADRD）。最终 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，同时利用大数据方法， 延长最佳大脑功能的医学概念。为了达到维持最佳认知能力的目标 在老年时发挥作用，并延长ARCI、AD或ADRD风险水平人群的生活质量， 我们坚持认为，将需要在PAN中开发和实施的方法。 虽然“实际年龄”始终与残疾发生率的增加有关， 大脑疾病，如AD和ADRD，“生物学年龄”和 大脑功能下降的情况尚不清楚。现在患有某种形式痴呆症的人数是 据估计，全世界有5000万人，预计每20年翻一番。由于巨大的 在70、80和90岁的人群中，大脑和认知功能的异质性， 科学，医学和医疗保健提供者的任务是发现对个体有效的干预措施， 延迟或预防ARCI、AD或ADRD。 解开年龄和认知表现之间的复杂关系需要一种策略，包括 对非常大的、多样的、特征良好的和纵向抽样的人口的研究。这将需要 “大数据”也是将收集的大量信息转化为“智能数据”的手段， “知识”。这就需要完全不同的概念模型。目前，没有一种方法足以 确定了修改个人衰老轨迹以改善个人大脑健康的方法。的 PAN中提出的方法旨在克服早期方法的障碍。重点在于如何 区分年龄、性别、遗传、种族、健康、生活方式选择和 影响大脑驱动因素的环境因素增加了对功能障碍的易感性，以及 这些因素增加大脑保护和抵抗功能障碍。 精准医疗的基本原则是“个性化”。这将使强大的 以及对每个人的具体预测，以缩小认知健康跨度和人类健康之间的差距。 寿命这个概念的根源是希波克拉底的教导，他说：“更重要的是要知道， 比知道一个人得了什么病更重要。”

项目成果

Neuroimaging and verbal memory assessment in healthy aging adults using a portable low-field MRI scanner and a web-based platform: results from a proof-of-concept population-based cross-section study.

• DOI: 10.1007/s00429-022-02595-7
• 发表时间: 2023-03
• 期刊: Brain structure & function
• 影响因子: 3.1
• 作者: Deoni SCL;Burton P;Beauchemin J;Cano-Lorente R;De Both MD;Johnson M;Ryan L;Huentelman MJ
• 通讯作者: Huentelman MJ

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.

利用语音衍生的数字生物标记来检测和量化老年人的认知衰退严重程度。

• DOI: 10.1159/000536250
• 发表时间: 2024
• 期刊: Gerontology
• 影响因子: 3.5
• 作者: Cay,Gozde;Pfeifer,ValeriaA;Lee,Myeounggon;Rouzi,MohammadDehghan;Nunes,AdonayS;El-Refaei,Nesreen;Momin,AnmolSalim;Atique,MdMoinUddin;Mehl,MatthiasR;Vaziri,Ashkan;Najafi,Bijan
• 通讯作者: Najafi,Bijan

NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering.

• DOI: 10.3389/fgene.2022.847112
• 发表时间: 2022
• 期刊: Frontiers in genetics
• 影响因子: 3.7

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach.

• DOI: 10.2196/28333
• 发表时间: 2022-03-08
• 期刊: JMIR aging
• 影响因子: 4.9
• 作者: Ferrario A;Luo M;Polsinelli AJ;Moseley SA;Mehl MR;Yordanova K;Martin M;Demiray B
• 通讯作者: Demiray B

An independent regulator of global release pathways in astrocytes generates a subtype of extracellular vesicles required for postsynaptic function.

• DOI: 10.1126/sciadv.adg2067
• 发表时间: 2023-06-23
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Levy-Myers, Reuben;Daudelin, Daniel;Na, Chan Hyun;Sockanathan, Shanthini
• 通讯作者: Sockanathan, Shanthini