Social Stressors, Epigenetics and Health Status in Underrepresented minorities

摘要 2型糖尿病(T2D)是一种快速增长的慢性疾病，对少数民族的影响不成比例， 尤其是在美国的波多黎各人。波士顿波多黎各健康研究和波多黎各 心理社会、环境和慢性病趋势的观察性研究(展望) 旨在探索不同社会和环境中与T2D相关的差异的互补队列 上下文。父母的建议旨在检查DNA甲基化、T2D患病率和 发病率，以及这些队列中的各种社会应激源、保护因素和行为。 该补编旨在通过关注三个主要目标来扩大母公司的提案。首先，它将得到改善 BPRHS和Prospects数据集的AI/ML准备情况，使用公平的指南并确定和 记录数据集中潜在的偏差和不平衡。此过程将确保数据集 适用于使用AI/ML技术的高级分析和知识发现。 其次，本附录将集中于为AI/ML应用程序识别和组织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T2D风险和结果。这还将改善SDOH相关数据的AI/ML准备情况， 允许对社会因素和T2D之间的复杂交互作用进行更复杂的分析。 最后，补充内容将涉及开发用于T2D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， 研究团队将能够发现以前对T2D风险和管理的新见解 用传统的分析方法是无法达到的。该模型将作为AI/ML如何 在T2D的背景下，用于推进生物医学研究和改善健康结果。为了确保 AI/ML研究的透明度和重现性，补充资料还将涉及记录和共享 数据处理和模型培训/评估的渠道。这将使其他研究人员能够在 本研究开展的工作，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技术整合到原始研究计划中，补充将有助于更深层次的 理解表观遗传学、遗传学、环境和社会因素在T2D中的复杂相互作用。 这一额外的分析不仅将支持制定创新的公共卫生战略，以 在弱势人群中预防T2D和相关的健康差距，也展示了 在利用AI/ML进行科学发现和改善健康成果方面的跨学科合作。

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.