Center for Alzheimer's and Related Dementias (CARD): Harmonized Data-Derived Resources for the Alzheimer's Disease and Related Dementias Community

阿尔茨海默病和相关痴呆症中心 (CARD)：阿尔茨海默病和相关痴呆症社区的统一数据衍生资源

• 批准号: 10913098
• 负责人: Andrew Singleton
• 金额: $ 1550.56万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10913098
• 关键词: Acceleration; Address; Adopted; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Area; Artificial Intelligence; Attention; Baltimore; Biological Markers; Biological Products; Body Composition; Brain; Brain Mapping; Cell Nucleus; Cells; Chromosome Mapping; Clinical; Clinical Data; Code; Cognitive aging; Collaborations; Common Data Element; Communities; Complex; Computerized Medical Record; Data; Data Analyses; Data Files; Data Science; Data Set; Data Sources; Data Storage and Retrieval; Dementia; Democracy; Development; Disease; Disease Progression; Disparate; Distal; Ensure; Environment; Equipment and supply inventories; Ethics; Evaluation; Extramural Activities; Feedback; GP2 gene; Genetic; Genetic Diseases; Genetic Risk; Genetic Variation; Genomics; Goals; Growth; Health; Heterogeneity; Home; Hour; Human; Image; In Situ; Infrastructure; Institution; Investigation; Language; Lewy Body Dementia; Libraries; Longitudinal Studies; Machine Learning; Manuscripts; Mediation; Medical center; Meta-Analysis; Metadata; Methods; Mississippi; Modeling; Molecular; Multiple Sclerosis; Nature; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Outcome; Parkinson Disease; Patients; Phenotype; Printing; Process; Public Domains; Publishing; Reporting; Research; Research Institute; Research Personnel; Resources; Risk; Sampling; Scientist; Sleep disturbances; Speed; Standardization; Time; Training; United States National Institutes of Health; United States National Library of Medicine; Universities; Viral; Virus Diseases; Vision; Work; advanced analytics; ancestry analysis; base; biobank; biomarker discovery; brain magnetic resonance imaging; cell type; cerebral atrophy; cognitive impairment in Parkinson' s; cohort; complex data; cost; data access; data analysis pipeline; data harmonization; data integration; data integrity; data management; data privacy; data sharing; data visualization; deep learning; depressive symptoms; design; druggable target; follow-up; frontotemporal lobar dementia amyotrophic lateral sclerosis; genome-wide; genomic data; hackathon; improved; insight; interest; invention; light weight; member; mortality; multimodality; multiple data types; multiple omics; neuroimaging; next generation; novel; online resource; repository; risk prediction; risk variant; serial imaging; systematic review; tool; unsupervised learning; usability

A major collaborative project underway at CARD Advanced Analytics is being carried out in conjunction with Konica Minolta and its Invicro subdivision to standardize and harmonize longitudinal imaging data from the UK Biobank and multiple neurodegenerative disease specific resources such as ADNI and PPMI for hundreds of thousands of brain MRI images (https://invicro.com/invicro-dti-and-nih-bring-imaging-and-genomics-data-together/). Current early deliverables include machine learning derived maps of brain atrophy and the integration of these outcomes with genomics data at scale soon to result in publicly shared code, data and manuscripts (Dadu et al., 2023). CARD has curated all currently available public domain AD/ADRD genetics data, its relevant meta-data and is currently shifting focus to access more deep molecular data. To make all of this data easily discoverable across repositories such as local NIH (Biowulf cluster), commercial cloud resources (Terra.bio and the Alzheimers Disease Data Workbench), we have embarked on building a lightweight tool to locate data we have curated based on existing RedCap infrastructure in place at NIA. We are building an easy to use and low cost of entry tool called the Data file Inventory and Verification Environment for Research (or DIVER). DIVER interfaces with the National Library of Medicines common data elements (CDEs) library to aid in harmonization of AD/ADRD relevant data (https://cde.nlm.nih.gov/home). DIVER also aids in harmonization projects underway as part of collaborations with the University of Mississippi Medical Center on harmonizing extant studies of cognitive aging at NIA such as the Baltimore Longitudinal Study of Aging and the Health Aging and Body Composition Study. In particular the CDEs curated for DIVER have allowed us to accelerate the research of collaborators at the UK Dementias Research Institute to facilitate early work on automated metadata harmonization across global repositories. The goal of CARDs Advanced Analytics team is not to reinvent the wheel and build a new data sharing and analysis platform but leverage current gold standard tools after an internal systematic review of similar public offerings. Curated and harmonized data including deep molecular data from iNDI, clinical and genetic data from GP2 as well as tools have been shared appropriately to GitHub, Terra.bio and the Alzheimers Disease Data Workbench. We have been liaising with internal NIA as well as external/extramural teams to ensure we are following best practices and receive feedback on what we can improve. As part of this data harmonization strategy, we aimed to facilitate biobank scale collaborations by standardizing electronic medical record codes for both the UK Biobank, Finnish Biobank and AllOfUs Study with special attention paid to AD/ADRD relevant data. We are currently beginning collaborations to accomplish similar harmonization and analysis efforts with the Welsh Biobank in Cardiff (SAIL). One early deliverable of these efforts is an analysis of viral exposures associated with risk of neurodegeneration up to 15 years prior to disease manifestation. We identified and replicated twenty two novel pairs of viruses and neurodegenerative diseases in over 500,000 biobank samples as well as replicated the previous association between Epstein-Barr exposure and multiple sclerosis published recently in Neuron (Levine et al., 2022). The follow-up to this report includes an in depth analysis of sleep disturbances as a major contributor to risk of neurodegeneration using an expanded version of this data and codebase. Longitudinal data harmonization and analysis poses a unique set of challenges. We have built a democratized and easily deployable longitudinal data analysis pipeline tailored for genomics data. We are currently expanding its functionality and usability to identify AD/ADRD related imaging and CSF biomarker associations with genetics to provide insights into the genetics of disease progression (https://longitudinal-gwas-pipeline.readthedocs.io/en/latest/). Some proofs of concept for this pipeline include evaluations of cognitive decline in Parkinsons datasets, as well as mortality and depressive symptom studies (Tan et al., 2020). In parallel to our work on genetic clustering across diseases mentioned above, we have also utilized harmonized clinical and genomic data to identify progression phenotypes in ALS/FTD and Parkinsons, with Lewy body dementia and Alzheimers underway (Faghri et al., 2022). Work showcasing biomarker discovery that have identified potential new targets for CSF pTau (Ta et al., 2023, preprint) using these tools have recently been published as has the application of unsupervised learning within a longitudinal context on various types of biomedical data (Dadu et al., 2023). From data management and discoverability, to aggregation and harmonization, CARD Advanced Analytics' proof of concept for this aspect of our scope of work is our current multi-ancestry analysis of Alzheimers disease genetic risk. This project accurately quantifies risk heterogeneity across diverse continental ancestries, evaluates risk prediction generalizability and discovers two novel risk loci while leveraging genetic diversity to fine map genetic risk at nine loci (Lake et al., 2023). Finally, making harmonized datasets, tools and web resources is only useful if the research community can actually use them. CARD Advanced Analytics has been working with external collaborators and CARDs own newly formed Training Team to support hackathons, office hours and one-on-one interactions with members of the research community from a variety of backgrounds to not only show them the resources available to them but also to understand and use these resources efficiently. It is our goal to help democratize complex data science research in the biomedical space at CARD and understand the needs of the research community we are part of. Additional preprints that have resulted from this work: Alvarado CX, Makarious MB, Weller CA, Vitale D, Koretsky MJ, Bandres Ciga S, Iwaki H, Levine K, Singleton A, Faghri F, Nalls MA, Leonard H. omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases. medRxiv Preprint. 2023 Jul 14:2023.04.06.23288266. doi: 10.1101/2023.04.06.23288266. PMID: 37090611; PMCID: PMC10120805. Alvarado CX, Weller CA, Johnson N, Leonard HL, Singleton AB, Reed X, Blauwendraat C, Nalls MA. Human brain single nucleus cell type enrichments in neurodegenerative diseases. medRxiv Preprint 2023.06.30.23292084. Doi: https://doi.org/10.1101/2023.06.30.23292084 Ta M, Blauwendraat C, Antar T, Leonard HL, Singleton AB, Nalls MA, Iwaki H; Alzheimers Disease Neuroimaging Initiative (ADNI); Fox Investigation for New Discovery of Biomarkers. Genome-wide meta-analysis of CSF biomarkers in Alzheimer's disease and Parkinson's disease cohorts. medRxiv Preprint. 2023 Jun 19:2023.06.13.23291354. doi: 10.1101/2023.06.13.23291354. PMID: 37398091; PMCID: PMC10312859.

CARD Advanced Analytics 正在与柯尼卡美能达及其 Invicro 部门联合开展一个重大合作项目，以标准化和协调来自英国生物库和多种神经退行性疾病特定资源（例如 ADNI 和 PPMI）的数十万张脑部 MRI 图像的纵向成像数据 （https://invicro.com/invicro-dti-and-nih-bring-imaging-and-genomics-data-together/）。目前的早期成果包括机器学习衍生的脑萎缩图，以及将这些结果与基因组学数据进行大规模整合，很快就会产生公开共享的代码、数据和手稿（Dadu 等人，2023）。 CARD 整理了所有当前可用的公共领域 AD/ADRD 遗传学数据及其相关元数据，目前正在将重点转向访问更深入的分子数据。为了使所有这些数据可以在本地 NIH（Biowulf 集群）、商业云资源（Terra.bio 和阿尔茨海默病数据工作台）等存储库中轻松发现，我们已着手构建一个轻量级工具来定位我们基于 NIA 现有 RedCap 基础设施整理的数据。我们正在构建一个易于使用且低成本的输入工具，称为研究数据文件清单和验证环境（或 DIVER）。 DIVER 与国家药物图书馆通用数据元素 (CDE) 库接口，以帮助协调 AD/ADRD 相关数据 (https://cde.nlm.nih.gov/home)。 DIVER 还协助正在进行的协调项目，作为与密西西比大学医学中心合作的一部分，协调 NIA 现有的认知衰老研究，例如巴尔的摩老龄化纵向研究和健康老龄化和身体成分研究。特别是为 DIVER 策划的 CDE 使我们能够加速英国痴呆症研究所合作者的研究，以促进跨全球存储库自动化元数据协调的早期工作。 CARD 高级分析团队的目标不是重新发明轮子并构建新的数据共享和分析平台，而是在对类似公开发行进行内部系统审查后利用当前的黄金标准工具。精心策划和协调的数据，包括来自 iNDI 的深层分子数据、来自 GP2 的临床和遗传数据以及工具，已适当共享到 GitHub、Terra.bio 和阿尔茨海默病数据工作台。我们一直与 NIA 内部以及外部/校外团队保持联系，以确保我们遵循最佳实践并收到有关我们可以改进的方面的反馈。 作为该数据协调战略的一部分，我们的目标是通过标准化英国生物银行、芬兰生物银行和 AllOfUs 研究的电子病历代码来促进生物银行规模合作，并特别关注 AD/ADRD 相关数据。我们目前正在开始与卡迪夫威尔士生物库 (SAIL) 合作，以完成类似的协调和分析工作。这些努力的早期成果之一是对疾病表现前 15 年内与神经退行性变风险相关的病毒暴露进行分析。我们在超过 500,000 个生物库样本中鉴定并复制了 22 对新型病毒和神经退行性疾病，并复制了最近在 Neuron 上发表的 Epstein-Barr 暴露与多发性硬化症之间的关联（Levine 等人，2022）。本报告的后续内容包括使用该数据和代码库的扩展版本，对睡眠障碍作为神经退行性变风险的主要影响因素进行了深入分析。 纵向数据协调和分析提出了一系列独特的挑战。我们建立了一个专为基因组数据量身定制的民主化且易于部署的纵向数据分析管道。我们目前正在扩展其功能和可用性，以识别 AD/ADRD 相关成像和 CSF 生物标志物与遗传学的关联，以提供对疾病进展遗传学的见解 (https://longitudinal-gwas-pipeline.readthedocs.io/en/latest/)。该管道的一些概念证明包括帕金森数据集中认知能力下降的评估，以及死亡率和抑郁症状研究（Tan 等人，2020）。在开展上述跨疾病遗传聚类工作的同时，我们还利用统一的临床和基因组数据来识别 ALS/FTD 和帕金森病、路易体痴呆和阿尔茨海默病的进展表型（Faghri 等人，2022）。最近发表的工作展示了使用这些工具识别 CSF pTau 潜在新靶标的生物标志物发现（Ta 等人，2023 年，预印本），以及在各种类型的生物医学数据的纵向背景下应用无监督学习（Dadu 等人，2023 年）。 从数据管理和可发现性，到聚合和协调，CARD Advanced Analytics 对我们工作范围这方面的概念证明是我们当前对阿尔茨海默病遗传风险的多祖先分析。该项目准确量化了不同大陆血统之间的风险异质性，评估了风险预测的普遍性，并发现了两个新的风险位点，同时利用遗传多样性来精细绘制九个位点的遗传风险图（Lake 等人，2023）。 最后，只有当研究界能够真正使用它们时，制作统一的数据集、工具和网络资源才有用。 CARD Advanced Analytics 一直与外部合作者和 CARD 自己新成立的培训团队合作，支持黑客马拉松、办公时间以及与来自不同背景的研究社区成员的一对一互动，不仅向他们展示可用的资源，还帮助他们有效地理解和使用这些资源。我们的目标是帮助 CARD 生物医学领域的复杂数据科学研究民主化，并了解我们所属研究社区的需求。 这项工作产生的其他预印本： Alvarado CX、Makarious MB、Weller CA、Vitale D、Koretsky MJ、Bandres Ciga S、Iwaki H、Levine K、Singleton A、Faghri F、Nalls MA、Leonard H。 omicSynth：用于识别神经退行性疾病可药物靶标的开放多组学社区资源。 medRxiv 预印本。 2023 年 7 月 14:2023.04.06.23288266。号码：10.1101/2023.04.06.23288266。电话号码：37090611； PMCID：PMC10120805。 阿尔瓦拉多 CX、韦勒 CA、约翰逊 N、伦纳德 HL、辛格尔顿 AB、里德 X、布劳文德拉特 C、纳尔斯 MA。神经退行性疾病中人脑单核细胞类型的丰富。 medRxiv 预印本 2023.06.30.23292084。土井：https://doi.org/10.1101/2023.06.30.23292084 Ta M、Blauwendraat C、Antar T、Leonard HL、Singleton AB、Nalls MA、Iwaki H；阿尔茨海默病神经影像倡议（ADNI）；福克斯对生物标志物新发现的调查。阿尔茨海默病和帕金森病队列中脑脊液生物标志物的全基因组荟萃分析。 medRxiv 预印本。 2023 年 6 月 19 日：2023.06.13.23291354。 DOI：10.1101/2023.06.13.23291354。电话号码：37398091； PMCID：PMC10312859。

项目成果

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

• DOI: 10.1038/s41531-022-00439-z
• 发表时间: 2022-12-16
• 期刊: NPJ PARKINSONS DISEASE
• 影响因子: 8.7
• 作者: Dadu, Anant;Satone, Vipul;Kaur, Rachneet;Hashemi, Sayed Hadi;Leonard, Hampton;Iwaki, Hirotaka;Makarious, Mary B.;Billingsley, Kimberley J.;Bandres-Ciga, Sara;Sargent, Lana J.;Noyce, Alastair J.;Daneshmand, Ali;Blauwendraat, Cornelis;Marek, Ken;Scholz, Sonja W.;Singleton, Andrew B.;Nalls, Mike A.;Campbell, Roy H.;Faghri, Faraz
• 通讯作者: Faghri, Faraz

Longitudinal risk factors for developing depressive symptoms in Parkinson's disease.

• DOI: 10.1016/j.jns.2021.117615
• 发表时间: 2021-10-15
• 期刊: Journal of the neurological sciences
• 影响因子: 4.4
• 作者: Antar T;Morris HR;Faghri F;Leonard HL;Nalls MA;Singleton AB;Iwaki H
• 通讯作者: Iwaki H