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

随着美国人口老龄化，人们越来越担心与老龄化相关的智力敏锐度的丧失。即使是很小的缺陷也被认为是正常认知损耗和严重疾病(如阿尔茨海默病)之间的中间环节。轻度认知障碍在≥65岁的人群中影响约10%，在4年内有56%的人转化为痴呆症。由于痴呆症具有强烈的年龄依赖性，仅仅推迟发病可能会产生戏剧性的影响。阿尔茨海默病的发病延迟5年将导致美国的病例减少约400万例。缺乏反映不良暴露和临床前影响的生物标记物极大地限制了有效定向预防的机会。 环境暴露会加剧全身炎症，如环境空气污染和金属，已被证明会加速认知老化长达5年。我们最近还表明，长期暴露在空气污染中与阿尔茨海默病的发病率有关。我们的目标是识别新的生物标志物，反映环境影响并预测认知受损的风险。我们将利用关于细胞外小泡(EV)-即由组织细胞主动释放到血液中的微小的膜结合的小泡-及其生物活性货物的微小(MiRNAs)和长(LncRNAs)非编码RNA的实验和临床证据，这些非编码RNA可以通过其强大的基因调节能力发出炎症反应的信号。动物和人类的研究表明，环境暴露会诱导促炎EV释放到血液中。最近的临床数据也表明，血液EV生物标志物的变化在认知障碍之前和预测。特别是，一项小型但具有高度提示性的病例对照研究确定了阿尔茨海默病患者中相对于对照组的EV-miRNAs，这些EV-miRNAs被用来-通过机器学习模型(我们也将使用)-来确定阿尔茨海默病的早期临床前风险。 在拟议的研究中，我们将利用表型良好的纵向队列的资源。首先，在标准化的老龄化研究中，我们将在12年的跟踪调查中获得四次连续访问的随时可用的生物显微镜纵向收集、暴露数据和认知功能测量。我们假设，环境对空气污染和金属的暴露，无论是单独的还是混合的，都将与显著增加的电动汽车数量和不同的电动汽车规模相关(目标1)。我们进一步假设，EV包裹的miRNAs和lncRNAs的水平和纵向变化反映了当前和过去的环境暴露，并预测了随后的认知能力下降(目标2)。最后，我们将使用机器学习和中介分析来确定循环中的EV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路径上的作用(目标3)。我们将验证KORA队列中的所有发现，该队列具有类似的设计和数据。我们的工作可能会为认知受损和阿尔茨海默病以及其他与年龄相关的疾病的其他潜在可修改的风险因素提供一个模型。

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.