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

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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.