OPTION 1: MEAS TASK AREA A DIVERSITY SUPPLEMENT - SUPPLEMENTAL FUNDING, JANUARY 6, 2021-JANUARY 5, 2022.

选项 1：MEAS 任务领域 A 多样性补充 - 补充资金，2021 年 1 月 6 日至 2022 年 1 月 5 日。

• 批准号: 10470994
• 负责人: ROBYN MCCLELLAND
• 金额: $ 6.55万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-06 至 2022-01-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10470994
• 关键词: Address; Area; Biological; Blood Pressure; Body mass index; Cardiovascular Diseases; Cholesterol; Communication Research; Data; Diet; Event; Food Patterns; Funding; Goals; Heart Diseases; Lasso; Least-Squares Analysis; Methods; Nutritional; Nutritional Science; Outcome; Pathway interactions; Pattern; Performance; Principal Component Analysis; Research; Risk Factors; Science; Statistical Methods; Supervision; Techniques; Time; Training; cardiovascular disorder prevention; coronary artery calcium; coronary calcium scoring; experience; heart disease risk; improved; research data dissemination; simulation; skills

The first goal of this supplement is to determine a way to use nutritional information to identify understandable food patterns that protect against heart disease. Three established biological pathways that we will consider relating diet to CVD are blood pressure, BMI, and cholesterol. We will compare how well various supervised and unsupervised methods construct interpretable diet patterns to predict each of these heart disease risk factors. The analyses with MESA data will be supplemented with simulation studies to demonstrate performance on a broader range of data. We suspect that Lasso and Sparse Multi-Block Partial Least Squares (SMBPLS) will perform the best, as they both incorporate variable selection and use outcome information. We compare these to Principal Component Analysis (PCA), as it is a common technique used in nutritional science and can ground our comparison for that audience. The second goal is to improve upon current statistical methods, and we focus on extending SMBPLS specifically. While SMBPLS is useful to analyze the relationship between diet and our three CVD pathways (blood pressure, BMI, and cholesterol), this analysis method is limited to continuous linear endpoints. Other relevant endpoints exist that would be interesting to explore, such as coronary artery calcium (CAC) score and time to CVD event. In particular, the relationship between CAC and diet has not been well established, so this could be a valuable scientific contribution. Although CAC score is continuous, it is unusually distributed, so it has been analyzed as a binary outcome (none vs. any) in the past. Thus, the focus of the second goal would be to extend SMBPLS so that it is equipped to analyze more generalized endpoints.

该补充剂的第一个目标是确定一种使用营养信息来识别可理解食物的方法 预防心脏病的模式。三种已建立的生物学途径，我们将考虑将饮食与 CVD是血压，BMI和胆固醇。我们将比较各种监督和无监督的情况 方法构建了可解释的饮食模式，以预测这些心脏病的危险因素中的每一个。分析 MESA数据将补充模拟研究，以证明在更广泛的数据范围内的性能。我们 怀疑拉索和稀疏的多块部分最小二乘（SMBPL）会表现最好，因为它们俩都 结合变量选择并使用结果信息。我们将这些与主成分分析进行比较 （PCA），因为它是一种在营养科学中使用的常见技术，可以对我们的观众进行比较。 第二个目标是改进当前的统计方法，我们专注于扩展SMBPL。尽管 SMBPLs对于分析饮食与我们的三种CVD途径之间的关系很有用（血压，BMI和 胆固醇），这种分析方法仅限于连续线性终点。存在其他相关端点 探索很有趣，例如冠状动脉钙（CAC）得分和CVD事件的时间。特别是 CAC与饮食之间的关系尚未得到很好的确定，因此这可能是有价值的科学贡献。 尽管CAC得分是连续的，但分布异常，因此已被分析为二进制结果（无VS。 任何）过去。因此，第二个目标的重点是扩展SMBPLS，以便它可以分析更多 广义端点。