Global Burden of Disease analysis dataset of BMI and CVD outcomes, risk factors, and SAS codes

Published: 17 August 2021| Version 6 | DOI: 10.17632/g6b39zxck4.6
David Cundiff,


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 body mass index (BMI) and cardiovascular disease (CVD) 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 BMI and CVD and other health outcomes with risk factors. Our research hypothesis is that we can successfully apply artificial intelligence to model BMI and CVD risk factors and health outcomes. We derived a BMI multiple regression risk factor formula that satisfied all nine Bradford Hill causality criteria for epidemiology research. We found that animal products and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths in high quantities. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats. For questions, please email Thanks.


Steps to reproduce

Download the above files for BMI and/or CVD analyses. Upload the files into SAS software Note the descriptions of the files. Check the Excel file of Tables to see if they correspond with the SAS codes. 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: The BMI paper preprint is at For questions, email


Health Sciences