Global Burden of Disease analysis dataset of cardiovascular disease outcomes, risk factors, and SAS codes

Published: 23-06-2021| Version 5 | DOI: 10.17632/g6b39zxck4.5
Contributors:
David Cundiff,

Description

This formatted dataset originates from raw data files from the Institute of Health Metrics and Evaluation Global Burden of Disease (GBD2017). It is population weighted worldwide data on male and female cohorts ages 15-69 years including cardiovascular disease early death and associated dietary, metabolic and other risk factors. The purpose of creating this formatted database is to explore the univariate and multiple regression correlations of cardiovascular early deaths and other health outcomes with risk factors. Our research hypothesis is that we can successfully apply artificial intelligence to model cardiovascular disease outcomes with risk factors. We found that fat-soluble vitamin containing foods (animal products) and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths in high fat-soluble vitamin cohorts. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats. You are invited to download the dataset, the associated SAS code to access the dataset, and the tables that have resulted from the analysis. Please comment on the article by indicating what you found by exploring the dataset with the provided SAS codes. Please say whether or not you found the outputs from the SAS codes accurately reflected the tables provided and the tables in the published article. If you use our data to reproduce our findings and comment on your findings on the MedRxIV website (https://www.medrxiv.org/content/10.1101/2021.04.17.21255675v4) and would like to be recognized, we will be happy to list you as a contributor when the article is summited to JAMA. For questions, please email davidkcundiff@gmail.com. Thanks.

Files

Steps to reproduce

Download the above files. Upload the analysis dataset and SAS code files into SAS software. Run the import SAS code for the analysis dataset. Run the step 1 SAS code to format the data. Run the code for CVD versus risk factors to get the outputs as Tables 1-5. Check the about Excel file of Tables 1-5 to see if they correspond. The preprints of Global Burden of Disease worldwide cohort analysis of dietary and other risk factors for cardiovascular diseases: lipid hypothesis versus fat-soluble vitamin hypothesis is here: https://www.medrxiv.org/content/10.1101/2021.04.17.21255675v4