Linking Complex Disease and Exposure Data to Established Data Standards

将复杂疾病和暴露数据与既定数据标准联系起来

• 批准号: 9766828
• 负责人: Carol Marie Hamilton
• 金额: $ 32.94万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766828
• 关键词: Address; Adopted; Affect; Area; Aromatic Hydrocarbons; Aromatic Polycyclic Hydrocarbons; Atherosclerosis; Big Data to Knowledge; Biomedical Research; Cardiovascular Diseases; Child; Child Welfare; Clinical; Clinical Informatics; Cohort Studies; Communities; Complex; Consensus; Coronary; Data; Data Element; Data Linkages; Disasters; Disease; Environment; Environmental Exposure; Environmental Health; Evaluation; Exposure to; Female of child bearing age; Follow-Up Studies; Funding Opportunities; Future; Genes; Goals; Health; Human; Individual; Laboratories; Link; Logical Observation Identifiers Names and Codes; Longterm Follow-up; Measurement; Measures; Metadata; Minority; Modeling; Multi-Ethnic Study of Atherosclerosis; Natural Disasters; Oils; Online Systems; Ontology; Outcome; Personal Satisfaction; Phenotype; Population; Predictive Factor; Process; Psychosocial Factor; Public Health; Published Comment; Research; Research Personnel; Risk Factors; Scientific Inquiry; Semantics; Seminal; Structure; Study Section; System; United States National Institutes of Health; Well in self; Woman; Women' s Health; Work; aged; base; cardiovascular disorder risk; child bearing; cohort; cost; data integration; database of Genotypes and Phenotypes; exposure route; genome wide association study; gulf coast; lifestyle factors; research study; resilience; sociodemographics; standard measure; stressor; study characteristics; tool; working group

PROJECT SUMMARY/ABSTRACT The goals of this proposed study, “Linking Complex Disease and Exposure Data to Established Data Standards,” are to use the community-based data standard consensus measures for Phenotypes and eXposures (PhenX) to link existing studies and, through that linkage, to extend semantic standards to environmental contributors to complex diseases and conditions. Our approach will address a documented standards gap, perform a pilot analysis, increase the reusability of seminal studies, and serve as a model for future data-driven standards efforts. To accomplish this, we offer a unique cross-disciplinary team of standards developers and researchers. The proposed 3-year research plan will meet the evolving needs of the environmental and human health research communities and address the need for Big Data to Knowledge (BD2K) Community-Based Data and Metadata Standards. We propose extending the application of existing standards to identify multifaceted interactions between environmental exposures and complex diseases through the following specific aims. 1: Use PhenX measures to link variables and identify gaps within our collaborative analysis of the four seminal studies: the Gulf Resilience on Women's Health Study, Gulf Long-term Follow-up Study, Multi-Ethnic Study of Atherosclerosis, and Columbia Center for Children's Environmental Health cohort. We will (a) identify study variable linkages to the PhenX data standard, common variables among the four studies, and potential cross-cutting research questions and (b) perform a pilot cross-study analysis to demonstrate that data harmonization revealed new results. 2: Extend existing formal ontologies in exposures and complex diseases to further support variable linkage and data harmonization efforts and to begin modeling exposure-disease interactions. (a) Use identified linkages to evaluate and extend the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) to include concepts relevant to environmental contributors to complex disease, such as route of exposure and health outcomes. (b) Use the complex hierarchical structure provided by the ontology to facilitate further harmonization and evaluation of mappings to standard data elements. (c) Build and make available a community-based ontology of concepts relevant to environmental exposures and complex diseases. 3: Create Logical Observation Identifiers Names and Codes (LOINC) to simplify the electronic exchange of results. LOINC, a widely adopted standard in laboratory and clinical observation systems, will facilitate the integration of identified, linked data elements into existing clinical informatics pipelines. Through these aims, the proposed work has the potential to have a dramatic impact on the scientific inquiries by advancing tools for data integration in an area of current public health concern and active research: environmental contributors to complex disease occurrences and outcomes.

项目摘要/摘要 这项拟议的研究的目标是“将复杂疾病和暴露数据联系起来 标准，“将使用基于社区的数据标准共识措施进行表型和 暴露（苯克斯）将现有研究以及通过这种联系将语义标准扩展到 为复杂疾病和条件的环境贡献者。我们的方法将解决已记录的 标准差距，进行试验分析，增加第二项研究的可重复性，并作为模型 未来数据驱动的标准工作。为此，我们提供了一个独特的跨学科团队 标准开发人员和研究人员。拟议的三年研究计划将满足 环境和人类健康研究社区，并满足对知识的大数据的需求 （BD2K）基于社区的数据和元数据标准。我们建议扩展现有的应用 确定环境暴露与复杂疾病之间的多方面相互作用的标准 通过以下特定目标。 1：使用苯克斯措施链接变量并识别差距 我们对四项第二项研究的合作分析：海湾对妇女健康研究的韧性， 海湾长期随访研究，动脉粥样硬化的多种族研究和哥伦比亚儿童中心 环境健康队列。我们将（a）确定研究变量链接到苯克斯数据标准， 这四个研究之间的常见变量以及潜在的跨切研究问题，并且（b）执行 试点跨研究分析证明数据协调显示了新的结果。 2：扩展现有 暴露和复杂疾病中的正式本体论，以进一步支持可变链接和数据 协调努力并开始建模暴露 - 疾病相互作用。 （a）使用已确定的链接 评估和扩展环境条件，治疗和暴露本体（ECTO） 包括与环境疾病有关的概念，例如暴露途径和 健康结果。 （b）使用本体提供的复杂层次结构来进一步促进 对标准数据元素映射的统一和评估。 （c）建造并提供 基于社区的概念本体与环境暴露和复杂疾病有关。 3： 创建逻辑观察标识符名称和代码（LOINC）来简化电子交换 结果。 Loinc是实验室和临床观察系统中广泛采用的标准，将有助于 已确定的，链接的数据元素集成到现有的临床信息管道中。通过这些 目的，拟议的工作有可能通过前进对科学询问产生巨大影响 在当前公共卫生关注和积极研究领域的数据集成工具：环境 复杂疾病发生和结果的贡献者。

项目成果

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.

• DOI: 10.3389/fphar.2022.883433
• 发表时间: 2022
• 期刊: FRONTIERS IN PHARMACOLOGY
• 影响因子: 5.6
• 作者: Mandal, Meisha;Levy, Josh;Ives, Cataia;Hwang, Stephen;Zhou, Yi-Hui;Motsinger-Reif, Alison;Pan, Huaqin;Huggins, Wayne;Hamilton, Carol;Wright, Fred;Edwards, Stephen
• 通讯作者: Edwards, Stephen