Characterizing the AD Metabolome in Brain Tissue Samples of Individuals from Diverse Populations

我们以前的工作和AD研究的一个关键限制是主要依赖于来自 高加索血统的参与者。虽然在高加索人群中进行的AD代谢组学研究为我们提供了 有了大量的发现和与药物发现和开发相关的线索，目前还没有大的- 在不同人群中进行脑组织代谢组学研究。这严重阻碍了我们的能力 以评估这些人群中发现的普遍性，或确定 共享的以及可能是特定人群的。然而，这是最重要的，作为贡献。 AD的遗传和环境危险因素以及与AD共病的类型在不同的人群中可能是不同的。 人口。例如，增加的心血管风险被认为在增加的 与白人相比，非裔美国人（AA）的AD患病率。考虑到人体代谢的强大基础 心血管和心脏代谢健康，可能有机会发现新的疾病途径， 基因x环境（GxE）的相互作用：社会经济环境可能是不同的， 与白人相比，不同的人群，从而提高了发现新的代谢变化的可能性 受到不同环境的影响。为了确保早期诊断的成功，临床试验的患者分层 (and最终的治疗），患者-靶标匹配，基于生物标志物分析的临床预测， 精确医学范式，我们需要对代谢，临床和生物标志物有完整的了解 不同人群以及高加索人的资料。本附录旨在克服这些数据-以及 通过使用来自AA和拉丁美洲的大脑样本生成大规模代谢组学数据， (LA)个人，利用多个群体的力量， AMP-AD倡议。这有望扩大AD代谢失败的生化知识， 包括与这些少数群体有关政策。通过与我们的AMP-AD合作伙伴合作，我们将系统地 解决种族差异对整个AD连续体大脑健康代谢方面的贡献， 在这些AD风险升高的人群中发现与AD发病机制相关的途径和分子。通过 将这些数据与我们的高加索人AD代谢组学特征的大型数据库进行比较和对比，我们 旨在提供有关常见和特异性疾病机制和亚类的生化见解 对某些人群来说。最后，通过将研究结果整合到我们的AD图谱中，我们进一步旨在注释AMP- 与人群相关性相关的AD候选目标。通过本补充文件生成的所有数据将 通过在AD知识门户中的沉积与研究社区进行真实的共享。成功 该项目的完成将产生一个途径和潜在的治疗目标，是相关的跨 人口，与那些更相关的单一人口相比。

A key limitation of our previous work and AD research in general is the predominant reliance of samples from participants of Caucasian ancestry. While metabolomics studies of AD in Caucasian cohorts have provided us with a large body of findings and leads relevant to drug discovery and development, there are currently no large- scale metabolomics studies of brain tissue available in diverse populations. This significantly hampers our ability to assess generalizability of findings across these populations or to identify pathways and molecules that are shared as well as those that may be population-specific. This is, however, of greatest importance, as contribution and types of genetic and environmental risk factors for and co-morbidities with AD may be distinct in diverse populations. For example, increased cardiovascular risk is thought to play an important role in the increased prevalence of AD in African Americans (AA) compared to whites. Given the strong metabolic underpinnings of cardiovascular and cardiometabolic health, there may be opportunities to uncover novel disease pathways and gene x environment (GxE) interactions in this population: the socioeconomic milieu is likely to be different in diverse populations compared to whites, thereby raising the possibility to discover novel metabolic changes influenced by distinct environments. To ensure success in early diagnosis, patient-stratification for clinical trials (and ultimately therapies), patient-target matching, clinical predictions based on biomarker profiling in a precision-medicine paradigm, we need to have complete knowledge on the metabolic, clinical and biomarker profiles of diverse populations, as well as Caucasians. This supplement aims to overcome this data- and knowledge-gap by generating large-scale metabolomics data using brain samples from AA and Latin American (LA) individuals, harnessing the power of multiple cohorts with ethnically diverse participants collected as part of the AMP-AD initiative. This is expected to expand the biochemical knowledge on metabolic failures in AD to include those relevant to these minority populations. By working with our AMP-AD partners, we will systematically address contributions of racial differences on metabolic aspects of brain health across the AD continuum to discover pathways and molecules linked to AD pathogenesis in these populations with elevated AD risk. Through comparing and contrasting this data to our large database of metabolomic signatures of AD in Caucasians, we aim to provide biochemical insights about mechanisms and sub-classes of disease that are common and specific to certain populations. Finally, via integrating study outcomes into our AD Atlas, we further aim to annotate AMP- AD candidate targets with respect to population relevance. All data generated through this supplement will be shared in real time with the research community through deposition in the AD Knowledge Portal. Successful completion of this project will generate a list of pathways and potential therapeutic targets that are relevant across populations, contrasted by those that are more relevant to single populations.