Data Analysis Core for the Dietary Biomarkers Development Center at Harvard University

哈佛大学膳食生物标志物开发中心的数据分析核心

• 批准号: 10649591
• 负责人: Liming Liang
• 金额: $ 10.33万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-16 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10649591
• 关键词: Benchmarking; Bioinformatics; Biological Markers; Biometry; Calibration; Clinical Trials; Cohort Studies; Collaborations; Consultations; Data; Data Analyses; Data Coordinating Center; Data Set; Development; Diet; Dietary Intervention; Dietary Practices; Dietary intake; Discriminant Analysis; Disease; Disease Outcome; Dose; Drug Kinetics; Eating; Ensure; Epidemiology; Equation; Ethnic Population; Evaluation; Food; Food Analysis; Future; Health; High Performance Computing; Incidence; Individual; Intervention; Kinetics; Measurement; Mediation; Mendelian randomization; Metabolic; Methodology; Methods; Modeling; Modification; Multiomic Data; Performance; Phase; Plasma; Production; Protocols documentation; Quality Control; Sampling; Secure; Universities; Urine; Validation; Work; analytical method; biomarker development; biomarker discovery; biomarker performance; biomarker validation; clinical phenotype; cluster computing; cohort; data exchange; data harmonization; data management; data sharing; data standards; data submission; design; dietary; epidemiology study; feeding; genome wide association study; gut microbiota; high dimensionality; improved; interdisciplinary approach; interest; large datasets; machine learning method; metabolomics; multi-ethnic; multiple omics; novel; nutrition; nutritional epidemiology; power analysis; prospective; racial population; response; sex; statistics; synergism

ABSTRACT/SUMMARY – DATA ANALYSIS CORE The Data Analysis Core (DAC) aims to provide statistical expertise and programming support via a transdisciplinary approach during the design and implementation of the Biomarkers Project (BP). The DAC will actively participate in the BP and other Core activities, while maximizing the efficiency, avoiding duplication of efforts, and improving the synergy among the Dietary Biomarker Development Center (DBDC) at Harvard University. The DAC will participate in the consortium-wide planning activities and provide consultations in developing common strategies and protocols for the dietary intervention. The DAC will work with the other DBDCs and Data Coordinating Center (DCC) in determining the final statistical analytical strategies for the biomarker analysis and performance. The specific aims are: Aim 1: To develop and implement methods for biomarker discovery and validation across all stages of the Biomarkers Project. We will devise data analysis strategies for comparing the dietary biomarker performance against the dietary intake assessment data and benchmark biomarker data in an existing dietary feeding trial and several cohort studies with multi-ethnic samples. Aim 2: To develop common statistical analytical strategies for the biomarker analysis and performance applicable across different DBDCs. This Core will work with other DBDCs and the DCC in determining the final design and analytical strategies for the biomarker analysis and performance evaluation. Aim 3: To manage and maintain large datasets and ensure timely data sharing and submission to the DCC. This DAC will be responsible for managing the data entry, cleaning and analyzing the data generated by our DBDC. The DAC will work together with other DBDCs and DCC to harmonize data across platforms, standardize data management, QC, and analytic methods across DBDCs. Aim 4: To interface nutrition, epidemiology, bioinformatics/biostatistics, and metabolomics, and ensure that cutting-edge measurement error correction models and multi-omics integrations are incorporated into future nutritional epidemiologic studies of disease outcomes. Whenever appropriate, all analyses will assess specific effects by sex and across different racial/ethnic groups. As part of the transdisciplinary team of the DBDC at Harvard University, the DAC will develop and curate calibrated biomarkers, refined clinical phenotypes and summary statistics for use in future epidemiological analyses of food intake and prospective associations with disease incidence and other clinical phenotypes of interest, allowing measurement error corrections and further integration with multi-omics datasets, such as gut microbiota and genome-wide association studies in existing large cohort studies at Harvard.

摘要/总结 – 数据分析核心 数据分析核心 (DAC) 旨在通过 DAC 将在设计和实施生物标志物项目 (BP) 期间采用跨学科方法。 积极参与BP等核心活动，同时最大化效率，避免重复 ，并改善哈佛大学膳食生物标志物开发中心 (DBDC) 之间的协同努力 大学 DAC 将参与联盟范围内的规划活动并提供咨询。 DAC 将与其他机构合作制定饮食干预的共同策略和方案。 DBDC 和数据协调中心 (DCC) 确定最终的统计分析策略 生物标志物分析和性能的具体目标是： 目标 1：开发和实施方法。 我们将设计数据分析，涵盖生物标记项目各个阶段的生物标记发现和验证。 将膳食生物标志物性能与膳食摄入评估数据进行比较的策略 现有膳食喂养试验和多项多种族队列研究中的基准生物标志物数据 目标 2：开发用于生物标志物分析和分析的通用统计分析策略。 适用于不同 DBDC 的性能 该核心将与其他 DBDC 和 DCC 一起工作。 确定生物标志物分析和性能评估的最终设计和分析策略。 目标 3：管理和维护大型数据集并确保及时共享数据并提交给 DCC。 该 DAC 将负责管理数据输入、清理和分析我们生成的数据 DBDC 将与其他 DBDC 和 DCC 合作，协调跨平台的数据， 标准化 DBDC 的数据管理、质量控制和分析方法 目标 4：连接营养、 流行病学、生物信息学/生物统计学和代谢组学，并确保尖端的测量误差 校正模型和多组学整合被纳入未来的营养流行病学研究 在适当的情况下，所有分析都将评估不同性别和不同人群的具体影响。 作为哈佛大学 DBDC 跨学科团队的一部分，DAC 将 开发和管理校准的生物标志物、完善的临床表型和汇总统计数据以供将来使用 食物摄入量的流行病学分析以及与疾病发生率和其他临床的前瞻性关联 感兴趣的表型，允许测量误差校正并进一步与多组学集成 数据集，例如现有大型队列研究中的肠道微生物群和全基因组关联研究 哈佛。