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.

总结 6.1目前，美国有100万儿童患有哮喘，使其成为最常见的慢性疾病 童年经历的。非裔美国人（AA）儿童存在显著的种族和民族差异 死于哮喘的几率是非西班牙裔白色儿童的8倍。基因、环境和 心理社会因素被认为通过影响与哮喘相关的生物学途径共同引起疾病 病理生理学在我们的父R 01奖（5 R 01 MD 015409）-简称为“压力，表观基因组和 哮喘”（SEA）研究中，我们假设儿童期暴露于心理社会压力可能会在一个机械的 （生物）水平通过表观遗传修饰影响我们基因组的功能。为了验证我们的假设，我们 正在对哮喘性AA儿童/家庭进行前瞻性社会表观基因组学研究， 包括高分辨率表观遗传特征，健康的综合社会决定因素（SDOH），以及慢性 压力信息。虽然我们在母公司奖项中提议为下游做好“组学”数据集准备， AI/ML方法，我们认识到还需要准备我们的SDOH和慢性压力数据， 申请，但这是在父裁决的范围之外。具体来说，我们认为SEA研究 数据将极大地受益于AI/ML技术的使用，例如能够天真地 捕获跨特征组合的不同结果。然而，鉴于长期接触 压力源与儿童的社会环境有关，开发可靠的模型将需要大量的努力， 准备并将收集到的数据置于背景中。我们假设这可以通过连接 收集不同人群水平数据集的社会和临床数据。我们的补充将实现两个目标： 1)我们将开发新的定量指标来定义研究参与者数据的代表性。通过 利用公共可用的人口水平数据（例如，人口普查数据），我们会制定一个框架， 研究参与者的社会人口学概况与地理区域中个体的预期分布 参考区域并且，通过这样做，确定可能与结果所基于的社区不一致的子组 希望能概括一下。通过进一步将这种对齐与数据质量度量（例如，缺失），我们可以 创建一个标准化的工具来传达数据集对人群子集的内在偏见，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 考虑社会经济因素的方法。理解慢性压力与 儿童的社会环境和抽样不平衡的地理区域，目前的插补 缺失程度高的亚组中数据的估计值主要由关系驱动 在信息更完整的队列中发现。我们假设，人口水平的数据可以整合 为多重插补模型引入新的加权技术，以更好地解释 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。