Social Stressors, Epigenetics and Health Status in Underrepresented minorities

代表性不足的少数群体的社会压力源、表观遗传学和健康状况

• 批准号: 10842568
• 负责人: Jose M. Ordovas
• 金额: $ 30.25万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10842568
• 关键词: Address; Administrative Supplement; Adopted; Affect; Behavior; Biological Markers; Biomedical Research; Boston; Chronic Disease; Classification; Common Data Element; Complex; DNA Methylation; Data; Data Set; Detection; Development; Diabetes Mellitus; Disparity; Documentation; Ensure; Environment; Epigenetic Process; Ethics; Evaluation; FAIR principles; Funding; Funding Opportunities; Future; Genetic; Goals; Grant; Guidelines; Health; Health Care Costs; Health Status; Incidence; Investigation; Knowledge Discovery; Metabolic; Methods; Modality; Modeling; Modification; Non-Insulin-Dependent Diabetes Mellitus; Observational Study; Ontology; Outcome; Parents; Pathway interactions; Patients; Pattern; PhenX Toolkit; Prevalence; Prevention; Process; Productivity; Psychosocial Assessment and Care; Public Health; Puerto Rican; Puerto Rico; Qualifying; Quality Control; Readiness; Reproducibility; Research; Research Design; Research Methodology; Research Personnel; Risk Factors; Structure; Techniques; Training; Underrepresented Minority; United States National Institutes of Health; Vulnerable Populations; Work; analytical method; cohort; coping; data analysis pipeline; data cleaning; data modeling; data quality; design; diabetes management; diabetes risk; disability; environmental stressor; epigenomics; ethnic minority; health disparity; improved; innovation; insight; interdisciplinary collaboration; machine learning model; metabolomics; minority disparity; minority health; mortality; novel; prevent; protective factors; risk prediction; social; social determinants; social factors; social health determinants; social stressor; trend

Summary Type 2 Diabetes (T2D) is a rapidly growing chronic disease that disproportionately affects ethnic minorities, particularly Puerto Ricans, in the US. The Boston Puerto Rican Health Study (BPRHS) and Puerto Rico Observational Study of Psychosocial, Environmental, and Chronic Disease Trends (PROSPECT) are complementary cohorts designed to explore T2D-related disparities in different social and environmental contexts. The parent proposal aims to examine the relationship between DNA methylation, T2D prevalence and incidence, and various social stressors, protective factors, and behaviors in these cohorts. The supplement aims to expand the parent proposal by focusing on three main objectives. First, it will improve the AI/ML readiness of the BPRHS and PROSPECT datasets using FAIR guidelines and identifying and documenting potential biases and imbalances in the dataset. This process will ensure that the datasets are suitable for advanced analysis and knowledge discovery using AI/ML techniques. Second, the supplement will concentrate on identifying and structuring SDOH information for AI/ML applications. By using the NIMHD PhenX toolkit of Common Data Elements (CDEs) associated with SDOH, the research team will be able to perform a more comprehensive investigation into the relationships between social determinants and T2D risk and outcomes. This will also improve the AI/ML readiness of the SDOH-related data, allowing for a more sophisticated analysis of the complex interactions between social factors and T2D. Lastly, the supplement will involve developing a proof-of-concept machine learning model for T2D classification and risk factor identification. By applying AI/ML techniques to the transformed BPRHS and PROSPECT datasets, the research team will be able to uncover novel insights into T2D risk and management that were previously unattainable using traditional analytical methods. This model will serve as a practical example of how AI/ML can be used to advance biomedical research and improve health outcomes in the context of T2D. To ensure transparency and reproducibility in AI/ML research, the supplement will also involve documenting and sharing the pipeline for data processing and model training/evaluation. This will enable other researchers to build upon the work conducted in this study, further promoting the application of AI/ML techniques to NIH-funded biomedical data. By integrating AI/ML techniques into the original research plan, the supplement will contribute to a deeper understanding of the complex interplay between epigenetics, genetics, environment, and social factors in T2D. This additional layer of analysis will not only support the development of innovative public health strategies to prevent T2D and related health disparities in vulnerable populations but also showcase the benefits of interdisciplinary collaborations in harnessing AI/ML for scientific discovery and improved health outcomes.

总结 2型糖尿病（T2 D）是一种快速增长的慢性疾病，不成比例地影响少数民族， 尤其是波多黎各人波士顿波多黎各健康研究（BPRHS）和波多黎各 社会心理、环境和慢性病趋势的观察性研究（前景） 旨在探索不同社会和环境中T2 D相关差异的补充队列 contexts.父母的建议旨在研究DNA甲基化，T2 D患病率和 发病率和各种社会应激源，保护因素和这些队列中的行为。 该补编旨在通过侧重于三个主要目标来扩展母提案。第一，它将改善 BPRHS和前景数据集的AI/ML准备使用FAIR指南，并识别和 记录数据集中潜在的偏差和不平衡。此过程将确保数据集 适合使用AI/ML技术进行高级分析和知识发现。 其次，补充将集中于识别和构建AI/ML应用程序的SDOH信息。 通过使用与SDOH相关的公共数据元素（CDE）的NIMHD PhenX工具包，研究 团队将能够对社会之间的关系进行更全面的调查， 决定因素和T2 D风险和结果。这也将提高SDOH相关数据的AI/ML准备程度， 允许对社会因素和T2 D之间的复杂相互作用进行更复杂的分析。 最后，补充将涉及开发用于T2 D分类的概念验证机器学习模型 风险因素识别。通过将AI/ML技术应用于转换后的BPRHS和前景数据集， 研究小组将能够发现以前对T2 D风险和管理新见解， 用传统的分析方法是不可能的。该模型将作为AI/ML如何 用于推进生物医学研究和改善T2 D背景下的健康结果。确保 AI/ML研究的透明度和可重复性，补充还将涉及记录和共享 用于数据处理和模型训练/评估的管道。这将使其他研究人员能够建立在 本研究所做的工作，进一步推动了AI/ML技术在NIH资助的生物医学领域的应用。 数据通过将AI/ML技术集成到原始研究计划中，该补充将有助于更深入地研究 了解T2 D中表观遗传学，遗传学，环境和社会因素之间的复杂相互作用。 这一额外的分析层不仅将支持制定创新的公共卫生战略， 预防T2 D和弱势群体的相关健康差异，同时展示 跨学科合作，利用AI/ML进行科学发现和改善健康结果。