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.

概括 2型糖尿病（T2D）是一种快速发展的慢性疾病，对少数民族的影响不成比例 特别是在美国的波多黎各人。波士顿波多黎各人健康研究（BPRHS）和波多黎各 对社会心理，环境和慢性疾病趋势（前景）的观察性研究是 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 发病率以及各种社会压力源，保护因素和行为。 该补充剂旨在通过关注三个主要目标来扩大父母建议。首先，它将改善 BPRHS和Prospect数据集的AI/ML准备就绪，并使用公平的准则，确定和确定和 记录数据集中的潜在偏见和失衡。此过程将确保数据集为 适用于使用AI/ML技术的高级分析和知识发现。 其次，该补充剂将集中于为AI/ML应用识别和构建SDOH信息。 通过使用与SDOH相关的常见数据元素（CDE）的NIMHD苯苯克号工具包 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。这还将改善与SDOH相关数据的AI/ML准备情况， 允许对社会因素与T2D之间的复杂相互作用进行更复杂的分析。 最后，该补充剂将涉及为T2D分类开发概念验证机器学习模型 和风险因素识别。通过将AI/ML技术应用于转换后的BPRH和Prospect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 使用传统的分析方法无法实现。该模型将作为AI/ML如何可以的实用示例 在T2D的背景下，可用于推进生物医学研究并改善健康结果。确保 AI/ML研究中的透明度和可重复性，该补充剂还将涉及记录和共享 数据处理和模型培训/评估的管道。这将使其他研究人员能够基于 在这项研究中进行的工作进一步促进了AI/ML技术在NIH资助的生物医学上的应用 数据。通过将AI/ML技术集成到原始研究计划中，该补充剂将有助于更深入 理解T2D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。 这种额外的分析层不仅将支持创新的公共卫生策略的制定 防止T2D及其脆弱人群中相关的健康差异，但也展示了 利用AI/ML进行科学发现和改善健康成果的跨学科合作。