Protein Signatures of APOE2 and Cognitive Aging

APOE2 的蛋白质特征和认知衰老

• 批准号: 10408304
• 负责人: THOMAS T PERLS
• 金额: $ 32.28万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10408304
• 关键词: Administrative Supplement; Adopted; Age; Aging; Alleles; Alzheimer' s Disease; Area; Attention; Centenarian; Characteristics; Clinical; Cognitive aging; Communities; Data; Data Scientist; Data Set; Development; Electronic Health Record; Funding; General Population; Generations; Genetic; Genotype; Goals; Health Insurance Portability and Accountability Act; Impaired cognition; Individual; Longevity; Machine Learning; Medical History; Medical center; Methods; Modeling; New England; Parents; Participant; Patients; Pattern; Persons; Physical Function; Physical assessment; Process; Proteins; Proteomics; Protocols documentation; Readiness; Research; Research Personnel; Risk; Structure; Supervision; Techniques; Testing; Therapeutic; Training; Universities; Validation; Work; cognitive function; cohort; coronavirus disease; dashboard; data de-identification; data privacy; data sharing; electronic data; healthy aging; high risk; improved; learning strategy; machine learning method; metabolic profile; metabolomics; offspring; privacy preservation; proteomic signature; usability

Improving AI/ML readiness of data generated under the R01: Protein signatures of APOE2 and AG061844 “Protein signatures of APOE2 and cognitive aging”, we are generating proteomic and metabolomics data in a cohort of centenarians, their offspring, and unrelated controls from the New England Centenarian Study (NECS). Study participants have been characterized with detailed medical history, genetic profiles, and longitudinal assessment of physical and cognitive functions. The goal of the parent R01 is to validate a proteomic signature of APOE genotypes, and to evaluate its value together with metabolic profiles to predict patterns of cognitive function change in aging individuals. We plan to share data through the Alzheimer’s disease (AD) portal, and the new extreme longevity (EL) portal that is currently under development. Sharing the data in an unrestricted manner is not possible because they include HIPAA identifiers, particularly age >89. Unrestricted sharing of data would be an attractive option for AI/ML investigators, and the goal of this request for administrative supplement is to cognitive aging. Funded by the NIA: R01 use advanced machine learning techniques to generate high-fidelity, privacy-preserving, synthetic versions of the data obtained in the parent achine learning methods have emerged that can be used to generate synthetic data using a model that is trained in the real data. This model can be used to generate a synthetic data set in which no single data point corresponds to a real person in the original data set, but the synthetic data can be analyzed to produce results that are like those derived from the original data. This approach has received substantial attention in the past few years, and it has been adopted to compromise between data sharing and privacy, including generation of synthetic data for the National COVID Cohort Collaborative (N3C). We have put together a team of data scientists and partners from the company Syntegra R01 so they can be shared without restriction. M , to generate and validate a synthetic data set that matches the data generated with the parent R01. Our proposal is structured in three aims. In Aim 1, we will share with Syntegra real data from the NECS that include proteomics and metabolomics, genetic variables and patients’ characteristics including assessment of cognitive function. This real data will be used to train the data generation model and create synthetic data sets. In Aim 2 we will d evelop a protocol for validation of the synthetic data sets that includes fidelity to a variety of results of machine learning analyses and metrics to assess the deidentification of data. In Aim 3 we will conduct the analysis in the real and synthetic data sets and compare the results. Impact. This is a high risk, but potentially high return proposal. If the approach works, we will be able to generate data that can be widely shared with the community. The approach will also be applicable to several other studies of aging that struggle with the issues of data sharing to enhance scientific research while preserving privacy.

改善R01下产生的数据的AI/ML准备就绪：APOE2和APOE2的蛋白质特征 AG061844“ APOE2和认知衰老的蛋白质特征”，我们 在一群百岁老人中生成蛋白质组学和代谢组学数据，其后代和无关 来自新英格兰百年研究（NECS）的控制。研究参与者的特征是 详细的病史，遗传特征以及对身体和认知功能的纵向评估。 母体R01的目标是验证APOE基因型的蛋白质组学特征，并评估其值 以及代谢谱，以预测老龄化个体认知功能变化的模式。我们计划 通过阿尔茨海默氏病（AD）门户共享数据，以及新的极限寿命（EL）门户网站 目前正在开发。不可能以不受限制的方式共享数据，因为它们包括 HIPAA标识符，尤其是年龄> 89。无限制共享数据将是AI/ML的吸引人选择 调查人员，此要求进行管理补充的目的是 认知衰老。由NIA资助：R01 使用先进的机器学习 产生高保真性，保留隐私，合成版本的技术的技术 出现了可以用来用于的Achine学习方法 使用在真实数据中训练的模型生成合成数据。该模型可用于生成 合成数据集，其中没有一个数据点对应于原始数据集中的真实人员，但是 可以分析合成数据，以产生像原始数据衍生的结果一样。这 在过去的几年中，方法受到了很大的关注，并且已被采用以妥协 在数据共享和隐私之间，包括为国家共同研究的生成合成数据 协作（N3C）。我们已经组建了一组数据科学家和公司的合作伙伴 R01，因此可以在没有限制的情况下共享它们。 m ，，，， 为了生成和验证与父r01生成的数据匹配的合成数据集。我们的 提案以三个目标结构。在AIM 1中，我们将与NEC中的Syntegra共享实际数据，其中包括 蛋白质组学和代谢组学，遗传变量和患者特征，包括评估 认知功能。该真实数据将用于训练数据生成模型并创建合成数据集。 在目标2中，我们将D evelop A协议，用于验证包括的合成数据集 忠于多种多样的 机器学习分析的结果 和评估数据的去识别的指标。在目标3中，我们将 在真实和合成数据集中进行分析并比较结果。 影响。这是一个高风险， 但是潜在的高回报提案。如果该方法有效，我们将能够生成可以广泛的数据 与社区共享。该方法也将适用于其他几项衰老研究 随着数据共享的问题，可以在保护隐私的同时增强科学研究。