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

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 [e-mail].
Thanks.

本格式化数据集源自健康计量评估研究所（Institute of Health Metrics and Evaluation）全球疾病负担（GBD2017）的原始数据文件。该数据集包含了全球范围内15-69岁男性和女性队列的人口加权数据，包括体质指数（BMI）、心血管疾病（CVD）及相关膳食、代谢和其他风险因素。
创建此格式化数据库的目的是探究BMI、CVD及其他健康结果与风险因素之间的单变量和多变量回归相关性。我们的研究假设是，我们可以成功地应用人工智能来建模BMI、CVD风险因素及健康结果。
我们推导出一种BMI多重回归风险因素公式，该公式满足流行病学研究的九项布拉德福德·希尔因果性标准。
我们发现，动物产品和添加的脂肪与全球CVD早期死亡率呈负相关，但与高量摄入的CVD早期死亡率呈正相关。我们解读这一现象，认为最佳的心血管健康状况伴随着对动物食品和添加脂肪的中等（而非低或高）摄入量。
如有疑问，请通过[e-mail]邮箱联系。
感谢。