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 美国目前有610万儿童患哮喘，使其成为最常见的慢性病 在童年时期。非裔美国人（AA）儿童存在重大的种族和种族差异 相对于非西班牙裔白人儿童，死于哮喘的可能性高出8倍。遗传，环境和 人们认为，通过影响与哮喘有关的生物学途径，可以共同引起疾病 病理生理学。在我们的父母R01奖（5R01MD015409）中 - 缩写为“压力，表观基因组和 哮喘”（SEA）研究，我们假设儿童时期暴露于社会心理压力可能会以机械作用 （生物学）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 正在对哮喘儿童/家庭的前瞻性社会表观基因组学研究中收集大量数据 包括高分辨率的表观遗传概况，健康的全面社会决定者（SDOH）和慢性 压力信息。当我们在父母奖励中提出建议，以使“ OMICS”数据集为下游准备 AI/ML方法我们认识到需要为类似的SDOH和慢性应力数据准备 但是，申请超出了父母奖的范围。具体来说，我们认为海洋研究 数据将通过使用AI/ML技术（例如能够天真的集合模型）大大受益 捕获跨特征组合的差异结果。但是，鉴于暴露于慢性 Stresors与儿童的社交环境相关，以开发可靠的模型将需要巨大的努力才能 准备并上下文化收集的数据。我们假设这可以通过链接来实现 通过不同的人口水平数据集收集了社交和临床数据。我们的补充将解决两个目标： 1）我们将制定新的定量措施来定义研究参与者数据的代表性。经过 利用公开可用的人群级数据（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统计学概况，反对个人在地理中的预期分布 参考区域。而且，通过这样做，请确定可能将结果错位的亚组 期望概括。通过进一步将这种对齐方式与数据质量度量（例如缺失）联系起来，我们可以 创建一个标准化工具，以传达数据集对人口子集的固有偏见，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 解决社会经济因素的方法。了解慢性压力与 儿童的社交环境，而采样不与地理区域，当前的插补平衡 高度缺失的亚组中数据的估计，主要由关系驱动 在队列中找到，提供更多完整的信息。我们假设可以集成人口级数据 进入多个插补模型的新型加权技术，以更好地说明社会经济的相似性 患者。反过来，为较小的人群亚组提供了更精确的估计数据。