Air Particulate, Metals, and Cognitive Performance in an Aging Cohort- Roles of Circulating Extracellular Vesicles and Non-coding RNAs

空气颗粒物、金属和衰老人群的认知表现——循环细胞外囊泡和非编码 RNA 的作用

基本信息

批准号：
10226996
负责人：
Joel D Schwartz
金额：
$ 72.57万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2023-07-31
项目状态：
已结题

项目摘要

As the U.S. population ages, there is growing concern about the loss of mental acuity associated with aging. Even small deficits are considered intermediates between normal cognitive attrition and severe conditions, such as Alzheimer’s disease. Mild cognitive impairment, which affects ~10% of those ≥65 years, has a 56% conversion rate to dementia over 4 years. Because of dementia’s strong age dependence, merely delaying its onset could have a dramatic impact. A 5-year delay in the onset of Alzheimer’s disease would lead to ~4 million fewer cases in the U.S. The lack of biomarkers that reflect adverse exposures and preclinical effects dramatically limits opportunities for effective targeted prevention. Environmental exposures that augment systemic inflammation, such as ambient air pollution and metals, have been shown to hasten cognitive aging by as much as 5 years. We also recently showed that long term exposure to air pollution is associated with Alzheimer's disease incidence. Our goal is to identify new biomarkers that reflect environmental influences and predict the risk of impaired cognition. We will leverage experimental and clinical evidence on extracellular vesicles (EVs)—i.e., tiny membrane-bound vesicles actively released by tissue cells into the bloodstream—and of their bioactive cargo of micro (miRNAs) and long (lncRNAs) noncoding RNAs, which can signal inflammatory responses via their potent capacity for gene regulation. Animal and human studies have shown that environmental exposures induce the release of pro-inflammatory EVs into the bloodstream. Recent clinical data also show that changes in blood EV biomarkers precede and predict cognitive impairment. In particular, a small, but highly suggestive, case-control study identified EV-miRNAs in patients with Alzheimer’s disease, relative to controls, which were used—by means of a machine learning model (which we will also employ)—to identify early preclinical risk of Alzheimer’s disease. In the proposed studies, we will leverage the resources of well-phenotyped longitudinal cohorts. First, in the Normative Aging Study, we will access ready-to-use longitudinal collections of biospecimens, exposure data, and cognitive function measurements from four serial visits over 12 years of follow up. We hypothesize that environmental exposures to air pollution and metals, individually and as mixtures, will be associated with significantly higher numbers of EVs and differential EV size (Aim 1). We further hypothesize that the levels of and longitudinal changes in EV-encapsulated miRNAs and lncRNAs reflect current and past environmental exposures and predict subsequent cognitive decline (Aim 2). Finally, we will use machine learning and mediation analysis to determine the roles of circulating EVs and their noncoding RNA cargo on the biological pathways linking environmental exposures to cognitive decline (Aim 3). We will validate all findings in the KORA cohort, which has similar design and data. Our work may yield a model for other potentially modifiable risk factors for impaired cognition and Alzheimer’s disease, as well as for additional age-related diseases.

随着美国人口的年龄，人们越来越关注与衰老相关的心理敏锐度丧失。甚至很小的定义也被认为是正常认知属性和严重疾病（例如阿尔茨海默氏病）之间的中间体。轻度认知障碍会影响≥65岁的约10％，在4年内转化为痴呆症的56％。由于痴呆症的强劲依赖性，仅仅延迟其发作可能会产生巨大影响。阿尔茨海默氏病发作的5年延迟将导致约400万病例在美国，缺乏反映不良暴露和临床前效应的生物标志物极大地限制了有效靶向预防的机会。增强全身感染的环境暴露，例如环境空气污染和金属，已显示出多达5年的认知衰老。我们最近还表明，长期暴露于空气污染与阿尔茨海默氏病的事件有关。我们的目标是确定反映环境影响并预测认知风险的新生物标志物。我们将利用有关细胞外蔬菜（EV）的实验和临床证据 - 即，由组织细胞积极释放到血液中的微小膜结合的蔬菜，以及它们的生物活性货物（miRNAS）和长（LNCRNA）的生物活性货物，并通过其不编码的RNA来通过炎症反应来通过其Patent intermations intermative RNAS，这可以通过其Patents partents进行炎症反应。动物和人类研究表明，环境暴露会诱导促炎性电动汽车释放到血液中。最近的临床数据还表明，血液EV生物标志物的变化先于并预测认知障碍。特别是，一项小但富有启发性的病例对照研究确定了阿尔茨海默氏病的患者的EV-MIRNA，相对于对照组，使用了机器学习模型（我们还将采用）来识别阿尔茨海默氏病的早期临床前风险。在拟议的研究中，我们将利用良好的纵向人群的资源。首先，在规范性衰老研究中，我们将访问生物测量，暴露数据和认知功能测量的现成的纵向收集，从四个串行访问中进行了12年的随访。我们假设，环境暴露于空气污染和金属，单独和作为混合物将与EVS和差异EV大小相关（AIM 1）。我们进一步假设，EV囊化miRNA和LNCRNA的水平和纵向变化水平反映了当前和过去的环境暴露，并预测了随后的认知下降（AIM 2）。最后，我们将使用机器学习和调解分析来确定循环电动汽车及其非编码RNA货物在将环境暴露与认知能力下降联系起来的生物学途径上的作用（AIM 3）。将验证具有类似设计和数据的Kora队列中的所有发现。我们的工作可能会为其他潜在可修改的危险因素产生模型，以减少认知和阿尔茨海默氏病，以及其他与年龄有关的疾病。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Intermediate and long-term exposure to air pollution and temperature and the extracellular microRNA profile of participants in the normative aging study (NAS).

规范衰老研究 (NAS) 参与者的中长期暴露于空气污染和温度以及细胞外 microRNA 谱。

DOI：
10.1016/j.envres.2023.115949
发表时间：
2023
期刊：
Environmental research
影响因子：
8.3
作者：
DaneshYazdi,Mahdieh;Nassan,FeibyL;Kosheleva,Anna;Wang,Cuicui;Xu,Zongli;Di,Qian;Requia,WeeberbJ;Comfort,NicoleT;Wu,Haotian;Laurent,LouiseC;DeHoff,Peter;Vokonas,Pantel;Baccarelli,AndreaA;Schwartz,JoelD
通讯作者：
Schwartz,JoelD

Short-term air pollution and temperature exposure and changes in the extracellular microRNA profile of Normative Aging Study (NAS) participants.

DOI：
10.1016/j.envint.2023.107735
发表时间：
2023-01
期刊：
Environment international
影响因子：
11.8
作者：
Mahdieh Danesh Yazdi;F. Nassan;A. Kosheleva;Cuicui Wang;Zongli Xu;Q. Di;W. Réquia;Nicole T. Comfort;Haotian Wu;L. Laurent;P. Dehoff;P. Vokonas;A. Baccarelli;J. Schwartz
通讯作者：
Mahdieh Danesh Yazdi;F. Nassan;A. Kosheleva;Cuicui Wang;Zongli Xu;Q. Di;W. Réquia;Nicole T. Comfort;Haotian Wu;L. Laurent;P. Dehoff;P. Vokonas;A. Baccarelli;J. Schwartz