Contextualizing and Addressing Population-Level Bias in Social Epigenomics Study of Asthma in Childhood

儿童哮喘社会表观基因组学研究中的背景分析和解决人群水平偏差

• 批准号: 10593797
• 负责人: Elin Grundberg
• 金额: $ 30.19万
• 依托单位: CHILDREN'S MERCY HOSP (KANSAS CITY, MO)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-26 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593797
• 关键词: 10 year old; 15 year old; Accident and Emergency department; Accounting; Address; Affect; African American; African American population; Age; Area; Artificial Intelligence; Asthma; Award; Awareness; Biological; Black Populations; Censuses; Characteristics; Child; Childhood; Chronic; Chronic Disease; Chronic stress; Clinical; Clinical Data; Clinical Research; Cohort Studies; Communities; Complex; Data; Data Analyses; Data Collection; Data Set; Detection; Disease; Elements; Emergency department visit; Ensure; Environmental Risk Factor; Epigenetic Process; Exposure to; FAIR principles; Family; Female; Functional disorder; Funding; Future; Genetic; Genome; Genomics; Geographic Locations; Geography; Hispanic Populations; Hospitalization; Individual; Institution; Insurance; Label; Learning; Link; Location; Machine Learning; Measures; Methods; Modeling; Modification; Morbidity - disease rate; Nature; Not Hispanic or Latino; Outcome; Parents; Participant; Pathway interactions; Patients; Phenotype; Population; Prevalence; Privatization; Psychosocial Factor; Psychosocial Stress; Quality of life; Race; Readiness; Research; Research Personnel; Resolution; Rhinovirus; Rhinovirus infection; Risk; Sampling; Selection Bias; Severities; Social Environment; Socioeconomic Factors; Standardization; Stress; Subgroup; Surveys; Symptoms; Techniques; Testing; Time; United States; United States National Institutes of Health; Weight; adverse childhood events; asthma exacerbation; asthmatic; bench to bedside; blind; cohort; data quality; data reuse; design; epigenome; epigenomics; experience; health disparity populations; improved; inclusion criteria; insight; large scale data; machine learning model; marginalized community; mortality; novel; prospective; psychosocial; racial and ethnic disparities; recruit; response; social; social determinants; social health determinants; social influence; sociodemographics; socioeconomics; statistics; stressor; tool

SUMMARY 6.1 million children in the US currently suffer from asthma, making it the most common chronic disease experienced during childhood. Significant racial and ethnic disparities exist with African American (AA) children being 8 times more likely to die of asthma relative to non-Hispanic white children. Genetic, environmental, and psychosocial factors are believed to jointly cause the disease by affecting biological pathways related to asthma pathophysiology. Within our parent R01 award (5R01MD015409) – abbreviated as the “Stress, Epigenome and Asthma” (SEA) study, we hypothesize that exposure to psychosocial stress in childhood may act at a mechanistic (biological) level impacting the function of our genome by epigenetic modifications. To test our hypothesis, we are collecting large amounts of data in a prospective social epigenomics study of asthmatic AA children/families including high-resolution epigenetic profiles, comprehensive social determinants of health (SDOH), and chronic stress information. While we propose within the parent award to make the ‘omics’ dataset ready for downstream AI/ML approaches we recognize the need to also prepare our SDOH and chronic stress data for similar applications which is however outside of the scope of the parent award. Specifically, we argue the SEA study data will greatly benefit from use of AI/ML techniques such as ensemble models that are capable of naively capturing differential outcomes across combinations of features. However, given that exposure to chronic stressors is tied to a child’s social environment, to develop reliable models will require significant efforts to prepare and contextualize the collected data. We hypothesize this can be accomplished through the linking of collected social and clinical data with disparate population level datasets. Our supplement will address two aims: 1) We will develop novel quantitative measures to define the representativeness of study participant data. By utilizing publicly available population-level data (e.g., Census data) we will develop a framework to compare the sociodemographic profile of study participations against an expected distribution of individuals in a geographic reference area. And, by doing so, identify subgroups that may misaligned to the community on which results are expected to generalize. By further linking this alignment to data quality measures (e.g., missingness), we can create a standardized tool to convey the dataset’s intrinsic biases on population subsets to aid in designing analyses and interpreting AI/ML model results; and 2) We will extend traditional AI/ML imputation preprocessing methods to account for socioeconomic factors. Understanding that chronic stress is deeply interconnected with children’s social environment and that sampling is not balanced by geographic region, current imputation estimates for data in subgroups with a high degree of missingness, would be primarily driven by relationships found in cohorts with more complete information. We hypothesize, that population-level data can be integrated into novel weighting techniques for multiple imputation models to better account for socioeconomic similarity of patients. In turn, providing more precise estimates of missing data for smaller population subgroups.

概括 目前美国有 610 万儿童患有哮喘，使其成为最常见的慢性疾病 童年时经历过。非裔美国 (AA) 儿童存在显着的种族和民族差异 与非西班牙裔白人儿童相比，死于哮喘的可能性高出 8 倍。遗传、环境和 据信，心理社会因素通过影响与哮喘相关的生物途径共同导致该疾病 病理生理学。在我们的母 R01 奖项 (5R01MD015409) 中 – 缩写为“压力、表观基因组和 哮喘”（SEA）研究中，我们假设童年时期暴露于心理社会压力可能会以一种机械的方式起作用 （生物）水平通过表观遗传修饰影响我们基因组的功能。为了检验我们的假设，我们 正在对哮喘 AA 儿童/家庭进行前瞻性社会表观基因组学研究，收集大量数据 包括高分辨率表观遗传图谱、健康的综合社会决定因素 (SDOH) 和慢性病 压力信息。虽然我们在母奖中建议让“组学”数据集为下游做好准备 通过 AI/ML 方法，我们认识到还需要为类似的情况准备 SDOH 和慢性压力数据 然而，这超出了家长奖励的范围。具体来说，我们认为 SEA 研究 数据将极大地受益于人工智能/机器学习技术的使用，例如能够简单地预测的集成模型 捕获特征组合之间的差异结果。然而，鉴于接触慢性 压力源与儿童的社会环境有关，要开发可靠的模型需要付出巨大的努力 准备收集的数据并将其置于背景中。我们假设这可以通过链接来完成 使用不同的人口水平数据集收集社会和临床数据。我们的补充将解决两个目标： 1）我们将开发新颖的定量方法来定义研究参与者数据的代表性。经过 利用公开的人口数据（例如人口普查数据），我们将开发一个框架来比较 研究参与的社会人口学概况与地理区域中个人的预期分布 参考区域。并且，通过这样做，确定可能与结果所依据的社区不一致的子组 预计将得到普遍化。通过进一步将这种对齐与数据质量度量（例如缺失）联系起来，我们可以 创建一个标准化工具来传达数据集对人口子集的内在偏差，以帮助设计 分析和解释 AI/ML 模型结果； 2）我们将扩展传统的 AI/ML 插补预处理 考虑社会经济因素的方法。了解慢性压力与 儿童的社会环境以及抽样不按地理区域平衡，目前的估算 对高度缺失的子组中数据的估计将主要由关系驱动 在具有更完整信息的队列中发现。我们假设，人口层面的数据可以整合 多种插补模型的新颖加权技术，以更好地考虑社会经济相似性 患者。反过来，为较小的人口亚组提供更精确的缺失数据估计。