Phenotyping and Environmental Modifiers Facility Core

项目摘要 表型和环境修饰设施核心（PEMFC）组织西奈山的广泛的 环境流行病学、临床、心理测量学、数据科学和技术专门知识， P30健康与环境中心（P30 Center on Health and Environment Across the Lifespan）自2014年中心成立以来，PEMFC 已经发展到指导选择适合年龄、有效、时效性和低负担的方法， 测量整个生命周期的健康和疾病，重点是特定的关键时期（怀孕，婴儿期， 幼儿期、青春期、中年、老年人）。这包括中心支持的空间和表型 设备覆盖越来越多的已确定的目标优先领域（肥胖，呼吸，过敏，睡眠， 心血管、内分泌、神经发育/心理表型）。核心还促进研究， 阐明化学毒物与环境改良剂之间关键相互作用的影响， 特别是社会决定因素（例如，心理压力、社交网络、性别）和营养。一个相关 重点是揭示在我们当地社区观察到的卫生不公平现象的潜在机制， 在全球范围内，链接到我们的社区参与核心。在这个融资周期中，我们将促进 大规模的基于人口的环境健康研究，表型组学，和分散 表型分析我们的P30中心位于一个由8家医院和400多个诊所组成的卫生系统内 使用统一的Epic电子健康记录因此，我们能够利用整合的临床数据 仓库与独特的生物库相连，为整合我们的医疗保健提供了前所未有的基础 交付和数据科学的努力，以产生新的环境研究。将此与我们中心的 研究环境暴露（环境污染、温度、犯罪、建筑环境）的专业知识， 捕捉时空变化，并将时间尺度从几天到几年，我们可以加速大- 规模基于地方的跨学科健康研究。我们还利用数字医疗的机构基础设施 (apps可穿戴设备）和数据科学（机器学习）来促进分散的表型分析。的范围 PEMFC的职责包括：1）开发、维护和提供儿童和成人 健康评估，包括自我报告、观察数据和基于表现的措施; 2） 根据具体的研究需要定制方案; 3）建议心理测量分析，包括使用多个 表型组学或真正的多变量分析中的表型;以及4）提供从 高分辨率和经过充分验证的地理空间数据集。质子交换膜燃料电池与 心理测量学总结以及数据收集、数据简化和评分的详细方案 程序，并纳入分析。促进数据共享、互操作性和协调， 中心成员参与协作多地点研究，PEMFC教师将强调使用 表型和效应修饰剂的措施，可以通过共同的标准化指标表征。

Project Summary The Phenotyping and Environmental Modifier Facility Core (PEMFC) organizes Mount Sinai's extensive environmental epidemiology, clinical, psychometric, data science and technical expertise as a key resource of the P30 Center on Health and Environment Across the LifeSpan. Since Center inception in 2014, the PEMFC has evolved to guide selection of age-appropriate, valid, time-effective, and low-burden approaches to measure health and disease across the lifespan with a focus on particular critical periods (pregnancy, infancy, early childhood, adolescence, midlife, older-adult). This includes Center-supported space and phenotyping equipment covering a growing list of identified target priority areas (obesity, respiratory, allergy, sleep, cardiovascular, endocrine, neurodevelopment/psychological phenotypes). The Core also facilitates research to elucidate the impacts of key interactions between chemical toxicants and environmental modifiers, specifically social determinants (e.g., psychological stress, social networks, gender) and nutrition. A related focus is to uncover mechanisms underlying health inequities observed in our local communities and more globally, linking to our Community Engagement Core. In this funding cycle, we will promote growth in large-scale population-based environmental health research, phenomics, and decentralized phenotyping. Our P30 Center sits within a health system comprising eight hospitals and over 400 practices that uses a unified Epic electronic health record. We are thus positioned to leverage an integrated clinical data warehouse linked with unique biobanks, providing an unprecedented foundation for integrating our healthcare delivery and data science efforts to generate novel environmental research. Coupling this with our Center's expertise in studying environmental exposures (ambient pollution, temperature, crime, built environment), capturing spatio-temporal variability and incorporating timescales from days to years, we can accelerate large- scale place-based transdisciplinary health research. We also harness institutional infrastructure in digital health (apps, wearables) and data science (machine learning) to facilitate decentralized phenotyping. The scope of responsibility for the PEMFC includes: 1) developing, maintaining, and providing access to pediatric and adult health assessments that include self-reports, observational data, and performance-based measures; 2) tailoring protocols to specific research needs; 3) advising psychometric analyses including the use of multiple phenotypes in a phenomic or true multivariate analysis; and 4) providing access to unique covariates from high-resolution and well-validated geospatial datasets. The PEMFC curates measures together with a summary of the psychometrics as well as detailed protocol(s) for data collection, data reduction and scoring procedures, and incorporation into analyses. To facilitate data sharing, interoperability, and harmonization for Center Member participation in collaborative multi-site studies, the PEMFC faculty will emphasize use of phenotype and effect modifier measures that can be characterized through common standardized metrics.